diff --git a/.github/conda-env/doctest-env.yml b/.github/conda-env/doctest-env.yml index ab7965b7b..4c0d36728 100644 --- a/.github/conda-env/doctest-env.yml +++ b/.github/conda-env/doctest-env.yml @@ -7,9 +7,10 @@ dependencies: - numpy - matplotlib - scipy - - sphinx + - sphinx<8.2 - sphinx_rtd_theme - ipykernel - nbsphinx - docutils - numpydoc + - sphinx-copybutton diff --git a/.github/conda-env/test-env.yml b/.github/conda-env/test-env.yml index 6731443ab..b0e6c3cea 100644 --- a/.github/conda-env/test-env.yml +++ b/.github/conda-env/test-env.yml @@ -9,3 +9,4 @@ dependencies: - numpy - matplotlib - scipy + - numpydoc diff --git a/.github/scripts/set-conda-test-matrix.py b/.github/scripts/set-conda-test-matrix.py index 954480cb0..6bcd0fa6f 100644 --- a/.github/scripts/set-conda-test-matrix.py +++ b/.github/scripts/set-conda-test-matrix.py @@ -1,19 +1,16 @@ -""" set-conda-test-matrix.py +"""Create test matrix for conda packages in OS/BLAS test matrix workflow.""" -Create test matrix for conda packages -""" -import json, re +import json from pathlib import Path +import re osmap = {'linux': 'ubuntu', 'osx': 'macos', 'win': 'windows', } -blas_implementations = ['unset', 'Generic', 'OpenBLAS', 'Intel10_64lp'] - -combinations = {'ubuntu': blas_implementations, - 'macos': blas_implementations, +combinations = {'ubuntu': ['unset', 'Generic', 'OpenBLAS', 'Intel10_64lp'], + 'macos': ['unset', 'Generic', 'OpenBLAS'], 'windows': ['unset', 'Intel10_64lp'], } diff --git a/.github/scripts/set-pip-test-matrix.py b/.github/scripts/set-pip-test-matrix.py index ed18239d0..a28a63240 100644 --- a/.github/scripts/set-pip-test-matrix.py +++ b/.github/scripts/set-pip-test-matrix.py @@ -1,7 +1,5 @@ -""" set-pip-test-matrix.py +"""Create test matrix for pip wheels in OS/BLAS test matrix workflow.""" -Create test matrix for pip wheels -""" import json from pathlib import Path diff --git a/.github/workflows/doctest.yml b/.github/workflows/doctest.yml index edf1f163f..590d4a97f 100644 --- a/.github/workflows/doctest.yml +++ b/.github/workflows/doctest.yml @@ -13,14 +13,13 @@ jobs: uses: actions/checkout@v3 - name: Setup Conda - uses: conda-incubator/setup-miniconda@v2 + uses: conda-incubator/setup-miniconda@v3 with: python-version: 3.12 activate-environment: doctest-env environment-file: .github/conda-env/doctest-env.yml miniforge-version: latest - miniforge-variant: Mambaforge - channels: conda-forge + channels: conda-forge,defaults channel-priority: strict auto-update-conda: false auto-activate-base: false @@ -37,8 +36,15 @@ jobs: make html make doctest + - name: Run pytest + shell: bash -l {0} + working-directory: doc + run: | + make html + PYTHONPATH=../ pytest + - name: Archive results - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: name: doctest-output path: doc/_build/doctest/output.txt diff --git a/.github/workflows/install_examples.yml b/.github/workflows/install_examples.yml index cfbf40fe7..6893a99fb 100644 --- a/.github/workflows/install_examples.yml +++ b/.github/workflows/install_examples.yml @@ -20,7 +20,8 @@ jobs: --quiet --yes \ python=3.12 pip \ numpy matplotlib scipy \ - slycot pmw jupyter + slycot pmw jupyter \ + ipython!=9.0 - name: Install from source run: | diff --git a/.github/workflows/os-blas-test-matrix.yml b/.github/workflows/os-blas-test-matrix.yml index 0e5fd25fc..263afb7a4 100644 --- a/.github/workflows/os-blas-test-matrix.yml +++ b/.github/workflows/os-blas-test-matrix.yml @@ -9,7 +9,7 @@ on: - .github/scripts/set-conda-pip-matrix.py - .github/conda-env/build-env.yml - .github/conda-env/test-env.yml - + jobs: build-pip: name: Build pip Py${{ matrix.python }}, ${{ matrix.os }}, ${{ matrix.bla_vendor}} BLA_VENDOR @@ -71,14 +71,14 @@ jobs: unset | Generic | Apple ) ;; # Found in system OpenBLAS ) brew install openblas - echo "BLAS_ROOT=/usr/local/opt/openblas/" >> $GITHUB_ENV - echo "LAPACK_ROOT=/usr/local/opt/openblas/" >> $GITHUB_ENV + echo "LDFLAGS=-L/opt/homebrew/opt/openblas/lib" >> $GITHUB_ENV + echo "CPPFLAGS=-I/opt/homebrew/opt/openblas/include" >> $GITHUB_ENV ;; *) echo "bla_vendor option ${{ matrix.bla_vendor }} not supported" exit 1 ;; esac - echo "FC=gfortran-11" >> $GITHUB_ENV + echo "FC=gfortran-14" >> $GITHUB_ENV - name: Build wheel env: BLA_VENDOR: ${{ matrix.bla_vendor }} @@ -91,10 +91,11 @@ jobs: mkdir -p ${wheeldir} cp ./slycot*.whl ${wheeldir}/ - name: Save wheel - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - name: slycot-wheels + name: slycot-wheels-${{ matrix.os }}-${{ matrix.python }}-${{ matrix.bla_vendor }} path: slycot-wheels + retention-days: 5 build-conda: @@ -119,18 +120,18 @@ jobs: fetch-depth: 0 submodules: 'recursive' - name: Setup Conda - uses: conda-incubator/setup-miniconda@v2 + uses: conda-incubator/setup-miniconda@v3 with: python-version: ${{ matrix.python }} activate-environment: build-env environment-file: .github/conda-env/build-env.yml miniforge-version: latest - miniforge-variant: Mambaforge + channels: conda-forge,defaults channel-priority: strict auto-update-conda: false auto-activate-base: false - name: Conda build - shell: bash -l {0} + shell: bash -el {0} run: | set -e conda mambabuild conda-recipe @@ -142,10 +143,11 @@ jobs: done python -m conda_index ./slycot-conda-pkgs - name: Save to local conda pkg channel - uses: actions/upload-artifact@v3 + uses: actions/upload-artifact@v4 with: - name: slycot-conda-pkgs + name: slycot-conda-pkgs-${{ matrix.os }}-${{ matrix.python }} path: slycot-conda-pkgs + retention-days: 5 create-wheel-test-matrix: @@ -156,15 +158,23 @@ jobs: outputs: matrix: ${{ steps.set-matrix.outputs.matrix }} steps: + - name: Merge artifacts + uses: actions/upload-artifact/merge@v4 + with: + name: slycot-wheels + pattern: slycot-wheels-* - name: Checkout python-control uses: actions/checkout@v3 - name: Download wheels (if any) - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: name: slycot-wheels path: slycot-wheels - id: set-matrix - run: echo "matrix=$(python3 .github/scripts/set-pip-test-matrix.py)" >> $GITHUB_OUTPUT + run: | + TEMPFILE="$(mktemp)" + python3 .github/scripts/set-pip-test-matrix.py | tee $TEMPFILE + echo "matrix=$(cat $TEMPFILE)" >> $GITHUB_OUTPUT create-conda-test-matrix: @@ -175,15 +185,23 @@ jobs: outputs: matrix: ${{ steps.set-matrix.outputs.matrix }} steps: + - name: Merge artifacts + uses: actions/upload-artifact/merge@v4 + with: + name: slycot-conda-pkgs + pattern: slycot-conda-pkgs-* - name: Checkout python-control uses: actions/checkout@v3 - name: Download conda packages - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: name: slycot-conda-pkgs path: slycot-conda-pkgs - id: set-matrix - run: echo "matrix=$(python3 .github/scripts/set-conda-test-matrix.py)" >> $GITHUB_OUTPUT + run: | + TEMPFILE="$(mktemp)" + python3 .github/scripts/set-conda-test-matrix.py | tee $TEMPFILE + echo "matrix=$(cat $TEMPFILE)" >> $GITHUB_OUTPUT test-wheel: @@ -204,8 +222,6 @@ jobs: path: slycot-src - name: Checkout python-control uses: actions/checkout@v3 - with: - repository: 'python-control/python-control' - name: Setup Python uses: actions/setup-python@v4 with: @@ -217,7 +233,7 @@ jobs: sudo apt-get -y update case ${{ matrix.blas_lib }} in Generic ) sudo apt-get -y install libblas3 liblapack3 ;; - unset | OpenBLAS ) sudo apt-get -y install libopenblas-base ;; + unset | OpenBLAS ) sudo apt-get -y install libopenblas0 ;; *) echo "BLAS ${{ matrix.blas_lib }} not supported for wheels on Ubuntu" exit 1 ;; @@ -240,14 +256,14 @@ jobs: exit 1 ;; esac - name: Download wheels - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: name: slycot-wheels path: slycot-wheels - name: Install Wheel run: | python -m pip install --upgrade pip - pip install matplotlib scipy pytest pytest-cov pytest-timeout coverage + pip install matplotlib scipy pytest pytest-cov pytest-timeout coverage numpydoc pip install slycot-wheels/${{ matrix.packagekey }}/slycot*.whl pip show slycot - name: Test with pytest @@ -268,7 +284,7 @@ jobs: defaults: run: - shell: bash -l {0} + shell: bash -el {0} steps: - name: Checkout Slycot @@ -282,17 +298,17 @@ jobs: if: matrix.os == 'macos' run: brew install coreutils - name: Setup Conda - uses: conda-incubator/setup-miniconda@v2 + uses: conda-incubator/setup-miniconda@v3 with: python-version: ${{ matrix.python }} miniforge-version: latest - miniforge-variant: Mambaforge activate-environment: test-env environment-file: .github/conda-env/test-env.yml + channels: conda-forge,defaults channel-priority: strict auto-activate-base: false - name: Download conda packages - uses: actions/download-artifact@v3 + uses: actions/download-artifact@v4 with: name: slycot-conda-pkgs path: slycot-conda-pkgs @@ -301,22 +317,22 @@ jobs: set -e case ${{ matrix.blas_lib }} in unset ) # the conda-forge default (os dependent) - mamba install libblas libcblas liblapack + conda install libblas libcblas liblapack ;; Generic ) - mamba install 'libblas=*=*netlib' 'libcblas=*=*netlib' 'liblapack=*=*netlib' + conda install 'libblas=*=*netlib' 'libcblas=*=*netlib' 'liblapack=*=*netlib' echo "libblas * *netlib" >> $CONDA_PREFIX/conda-meta/pinned ;; OpenBLAS ) - mamba install 'libblas=*=*openblas' openblas + conda install 'libblas=*=*openblas' openblas echo "libblas * *openblas" >> $CONDA_PREFIX/conda-meta/pinned ;; Intel10_64lp ) - mamba install 'libblas=*=*mkl' mkl + conda install 'libblas=*=*mkl' mkl echo "libblas * *mkl" >> $CONDA_PREFIX/conda-meta/pinned ;; esac - mamba install -c ./slycot-conda-pkgs slycot + conda install -c ./slycot-conda-pkgs slycot conda list - name: Test with pytest run: JOBNAME="$JOBNAME" pytest control/tests diff --git a/.github/workflows/python-package-conda.yml b/.github/workflows/python-package-conda.yml index aac8ab054..0aabf33bf 100644 --- a/.github/workflows/python-package-conda.yml +++ b/.github/workflows/python-package-conda.yml @@ -32,14 +32,13 @@ jobs: - uses: actions/checkout@v3 - name: Setup Conda - uses: conda-incubator/setup-miniconda@v2 + uses: conda-incubator/setup-miniconda@v3 with: python-version: ${{ matrix.python-version }} activate-environment: test-env environment-file: .github/conda-env/test-env.yml miniforge-version: latest - miniforge-variant: Mambaforge - channels: conda-forge + channels: conda-forge,defaults channel-priority: strict auto-update-conda: false auto-activate-base: false @@ -56,6 +55,9 @@ jobs: if [[ '${{matrix.pandas}}' == 'conda' ]]; then mamba install pandas fi + if [[ '${{matrix.mplbackend}}' == 'QtAgg' ]]; then + mamba install pyqt + fi - name: Test with pytest shell: bash -l {0} diff --git a/.github/workflows/ruff-check.yml b/.github/workflows/ruff-check.yml new file mode 100644 index 000000000..e056204bf --- /dev/null +++ b/.github/workflows/ruff-check.yml @@ -0,0 +1,29 @@ +# run ruff check on library source +# TODO: extend to tests, examples, benchmarks + +name: ruff-check + +on: [push, pull_request] + +jobs: + ruff-check-linux: + # ruff *shouldn't* be sensitive to platform + runs-on: ubuntu-latest + + steps: + - name: Checkout python-control + uses: actions/checkout@v3 + + - name: Setup environment + uses: actions/setup-python@v4 + with: + python-version: 3.13 # todo: latest? + + - name: Install ruff + run: | + python -m pip install --upgrade pip + python -m pip install ruff + + - name: Run ruff check + run: | + ruff check diff --git a/.gitignore b/.gitignore index 4a6aa3cc0..9359defa9 100644 --- a/.gitignore +++ b/.gitignore @@ -14,7 +14,7 @@ record.txt .coverage doc/_build doc/generated -examples/.ipynb_checkpoints/ +.ipynb_checkpoints/ .settings/org.eclipse.core.resources.prefs .pydevproject .project @@ -42,3 +42,6 @@ venv/ ENV/ env.bak/ venv.bak/ + +# Files for MacOS +.DS_Store diff --git a/Pending b/Pending deleted file mode 100644 index a1b5bda09..000000000 --- a/Pending +++ /dev/null @@ -1,63 +0,0 @@ -List of Pending changes for control-python -RMM, 5 Sep 09 - -This file contains brief notes on features that need to be added to -the python control library. Mainly intended to keep track of "bigger -picture" things that need to be done. - ---> See src/matlab.py for a list of MATLAB functions that eventually need - to be implemented. - -OPEN BUGS - * matlab.step() doesn't handle systems with a pole at the origin (use lsim2) - * TF <-> SS transformations are buggy; see tests/convert_test.py - * hsvd returns different value than MATLAB (2010a); see modelsimp_test.py - * lsim doesn't work for StateSpace systems (signal.lsim2 bug??) - -Transfer code from Roberto Bucher's yottalab to python-control - acker - pole placement using Ackermann method - c2d - contimous to discrete time conversion - full_obs - full order observer - red_obs - reduced order observer - comp_form - state feedback controller+observer in compact form - comp_form_i - state feedback controller+observer+integ in compact form - dsimul - simulate discrete time systems - dstep - step response (plot) of discrete time systems - dimpulse - imoulse response (plot) of discrete time systems - bb_step - step response (plot) of continous time systems - sysctr - system+controller+observer+feedback - care - Solve Riccati equation for contimous time systems - dare - Solve Riccati equation for discrete time systems - dlqr - discrete linear quadratic regulator - minreal - minimal state space representation - -Transfer code from Ryan Krauss's control.py to python-control - * phase margin computations (as part of margin command) - * step reponse - * c2d, c2d_tustin (compare to Bucher version first) - -Examples and test cases - * Put together unit tests for all functions (after deciding on framework) - * Figure out how to import 'figure' command properly (version issue?) - * Figure out source of BadCoefficients warning messages (pvtol-lqr and others) - * tests/test_all.py should report on failed tests - * tests/freqresp.py needs to be converted to unit test - * Convert examples/test-{response,statefbk}.py to unit tests - -Root locus plot improvements - * Make sure that scipy.signal.lti objects still work - * Update calling syntax to be consistent with other plotting commands - -State space class fixes - * Implement pzmap for state space systems - -Basic functions to be added - * margin - compute gain and phase margin (no plot) - * lyap - solve Lyapunov equation (use SLICOT SB03MD.f) - * See http://www.slicot.org/shared/libindex.html for list of functions - ----- -Instructions for building python package - * python setup.py build - * python setup.py install - * python setup.py sdist diff --git a/README.rst b/README.rst index ebcf77c43..825693c91 100644 --- a/README.rst +++ b/README.rst @@ -31,7 +31,7 @@ Try out the examples in the examples folder using the binder service. The package can also be installed on Google Colab using the commands:: - !pip install control + %pip install control import control as ct Features @@ -49,11 +49,11 @@ Features Links ----- -- Project home page: http://python-control.org +- Project home page: https://python-control.org - Source code repository: https://github.com/python-control/python-control -- Documentation: http://python-control.readthedocs.org/ +- Documentation: https://python-control.readthedocs.io/ - Issue tracker: https://github.com/python-control/python-control/issues -- Mailing list: http://sourceforge.net/p/python-control/mailman/ +- Mailing list: https://sourceforge.net/p/python-control/mailman/ Dependencies ------------ @@ -110,7 +110,7 @@ from the github repository or archive, unpack, and run from within the toplevel `python-control` directory:: pip install . - + Article and Citation Information ================================ @@ -129,7 +129,6 @@ the library is available on IEEE Explore. If the Python Control Systems Library or the GitHub site: https://github.com/python-control/python-control - Development =========== @@ -158,7 +157,7 @@ License ------- This is free software released under the terms of `the BSD 3-Clause -License `_. There is no +License `_. There is no warranty; not even for merchantability or fitness for a particular purpose. Consult LICENSE for copying conditions. @@ -178,4 +177,3 @@ Your contributions are welcome! Simply fork the GitHub repository and send a Please see the `Developer's Wiki`_ for detailed instructions. .. _Developer's Wiki: https://github.com/python-control/python-control/wiki - diff --git a/benchmarks/flatsys_bench.py b/benchmarks/flatsys_bench.py index 05a2e7066..a2f8ae1d2 100644 --- a/benchmarks/flatsys_bench.py +++ b/benchmarks/flatsys_bench.py @@ -7,7 +7,6 @@ import numpy as np import math -import control as ct import control.flatsys as flat import control.optimal as opt diff --git a/benchmarks/optestim_bench.py b/benchmarks/optestim_bench.py index fdc4dc824..534d1024d 100644 --- a/benchmarks/optestim_bench.py +++ b/benchmarks/optestim_bench.py @@ -6,7 +6,6 @@ # used for optimization-based estimation. import numpy as np -import math import control as ct import control.optimal as opt @@ -64,7 +63,6 @@ def time_oep_minimizer_methods(minimizer_name, noise_name, initial_guess): initial_guess = (res.states, V) else: initial_guess = None - # Set up optimal estimation function using Gaussian likelihoods for cost traj_cost = opt.gaussian_likelihood_cost(sys, Rv, Rw) diff --git a/benchmarks/optimal_bench.py b/benchmarks/optimal_bench.py index 997b5a241..bd0c0cd6b 100644 --- a/benchmarks/optimal_bench.py +++ b/benchmarks/optimal_bench.py @@ -6,7 +6,6 @@ # performance of the functions used for optimization-base control. import numpy as np -import math import control as ct import control.flatsys as fs import control.optimal as opt @@ -21,7 +20,6 @@ 'RK23': ('RK23', {}), 'RK23_sloppy': ('RK23', {'atol': 1e-4, 'rtol': 1e-2}), 'RK45': ('RK45', {}), - 'RK45': ('RK45', {}), 'RK45_sloppy': ('RK45', {'atol': 1e-4, 'rtol': 1e-2}), 'LSODA': ('LSODA', {}), } @@ -129,9 +127,6 @@ def time_optimal_lq_methods(integrator_name, minimizer_name, method): Tf = 10 timepts = np.linspace(0, Tf, 20) - # Create the basis function to use - basis = get_basis('poly', 12, Tf) - res = opt.solve_ocp( sys, timepts, x0, traj_cost, constraints, terminal_cost=term_cost, solve_ivp_method=integrator[0], solve_ivp_kwargs=integrator[1], @@ -223,8 +218,6 @@ def time_discrete_aircraft_mpc(minimizer_name): # compute the steady state values for a particular value of the input ud = np.array([0.8, -0.3]) xd = np.linalg.inv(np.eye(5) - A) @ B @ ud - yd = C @ xd - # provide constraints on the system signals constraints = [opt.input_range_constraint(sys, [-5, -6], [5, 6])] @@ -234,7 +227,6 @@ def time_discrete_aircraft_mpc(minimizer_name): cost = opt.quadratic_cost(model, Q, R, x0=xd, u0=ud) # Set the time horizon and time points - Tf = 3 timepts = np.arange(0, 6) * 0.2 # Get the minimizer parameters to use diff --git a/control/__init__.py b/control/__init__.py index 40f3a783b..d2929c799 100644 --- a/control/__init__.py +++ b/control/__init__.py @@ -1,53 +1,27 @@ # __init__.py - initialization for control systems toolbox # -# Author: Richard M. Murray -# Date: 24 May 09 -# -# This file contains the initialization information from the control package. -# -# Copyright (c) 2009 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ - -""" -The Python Control Systems Library :mod:`control` provides common functions -for analyzing and designing feedback control systems. +# Initial author: Richard M. Murray +# Creation date: 24 May 2009 +# Use `git shortlog -n -s` for full list of contributors + +"""The Python Control Systems Library (python-control) provides common +functions for analyzing and designing feedback control systems. + +The initial goal for the package is to implement all of the +functionality required to work through the examples in the textbook +`Feedback Systems `_ by Astrom and Murray. In +addition to standard techniques available for linear control systems, +support for nonlinear systems (including trajectory generation, gain +scheduling, phase plane diagrams, and describing functions) is +included. A :ref:`matlab-module` is available that provides many of +the common functions corresponding to commands available in the MATLAB +Control Systems Toolbox. Documentation is available in two forms: docstrings provided with the code, -and the python-control users guide, available from `the python-control +and the python-control User Guide, available from the `python-control homepage `_. -The docstring examples assume that the following import commands:: +The docstring examples assume the following import commands:: >>> import numpy as np >>> import control as ct @@ -57,19 +31,27 @@ The main control package includes the most common functions used in analysis, design, and simulation of feedback control systems. Several -additional subpackages are available that provide more specialized -functionality: +additional subpackages and modules are available that provide more +specialized functionality: * :mod:`~control.flatsys`: Differentially flat systems * :mod:`~control.matlab`: MATLAB compatibility module * :mod:`~control.optimal`: Optimization-based control * :mod:`~control.phaseplot`: 2D phase plane diagrams +These subpackages and modules are described in more detail in the +subpackage and module docstrings and in the User Guide. + """ # Import functions from within the control system library # Note: the functions we use are specified as __all__ variables in the modules +# don't warn about `import *` +# ruff: noqa: F403 +# don't warn about unknown names; they come via `import *` +# ruff: noqa: F405 + # Input/output system modules from .iosys import * from .nlsys import * @@ -106,8 +88,8 @@ from .sysnorm import * # Allow access to phase_plane functions as ct.phaseplot.fcn or ct.pp.fcn -from . import phaseplot -from . import phaseplot as pp +from . import phaseplot as phaseplot +pp = phaseplot # Exceptions from .exception import * diff --git a/control/bdalg.py b/control/bdalg.py index 7bfd327eb..0ed490084 100644 --- a/control/bdalg.py +++ b/control/bdalg.py @@ -1,55 +1,14 @@ -"""bdalg.py +# bdalg.py - block diagram algebra +# +# Initial author: Richard M. Murray +# Creation date: 24 May 09 +# Pre-2014 revisions: Kevin K. Chen, Dec 2010 +# Use `git shortlog -n -s bdalg.py` for full list of contributors -This file contains some standard block diagram algebra. +"""Block diagram algebra. -Routines in this module: - -append -series -parallel -negate -feedback -connect - -""" - -"""Copyright (c) 2010 by California Institute of Technology -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Author: Richard M. Murray -Date: 24 May 09 -Revised: Kevin K. Chen, Dec 10 - -$Id$ +This module contains some standard block diagram algebra, including +series, parallel, and feedback functions. """ @@ -63,43 +22,46 @@ from . import xferfcn as tf from .iosys import InputOutputSystem -__all__ = ['series', 'parallel', 'negate', 'feedback', 'append', 'connect'] +__all__ = ['series', 'parallel', 'negate', 'feedback', 'append', 'connect', + 'combine_tf', 'split_tf'] + +def series(*sys, **kwargs): + """series(sys1, sys2[, ..., sysn]) -def series(sys1, *sysn, **kwargs): - r"""series(sys1, sys2, [..., sysn]) + Series connection of I/O systems. - Return the series connection (`sysn` \* ...\ \*) `sys2` \* `sys1`. + Generates a new system ``[sysn * ... *] sys2 * sys1``. Parameters ---------- - sys1, sys2, ..., sysn: scalar, array, or :class:`InputOutputSystem` + sys1, sys2, ..., sysn : scalar, array, or `InputOutputSystem` I/O systems to combine. Returns ------- - out : scalar, array, or :class:`InputOutputSystem` + out : `InputOutputSystem` Series interconnection of the systems. Other Parameters ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, - signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See :class:`InputOutputSystem` for more information. + signal names will be of the form 's[i]' (where 's' is one of 'u, + or 'y'). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be - of of the form `x[i]` for interconnections of linear systems or + of the form 'x[i]' for interconnections of linear systems or '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Raises ------ ValueError - if `sys2.ninputs` does not equal `sys1.noutputs` - if `sys1.dt` is not compatible with `sys2.dt` + If `sys2.ninputs` does not equal `sys1.noutputs` or if `sys1.dt` is + not compatible with `sys2.dt`. See Also -------- @@ -108,13 +70,12 @@ def series(sys1, *sysn, **kwargs): Notes ----- This function is a wrapper for the __mul__ function in the appropriate - :class:`NonlinearIOSystem`, :class:`StateSpace`, - :class:`TransferFunction`, or other I/O system class. The output type - is the type of `sys1` unless a more general type is required based on - type type of `sys2`. + `NonlinearIOSystem`, `StateSpace`, `TransferFunction`, or other I/O + system class. The output type is the type of `sys1` unless a more + general type is required based on type type of `sys2`. - If both systems have a defined timebase (dt = 0 for continuous time, - dt > 0 for discrete time), then the timebase for both systems must + If both systems have a defined timebase (`dt` = 0 for continuous time, + `dt` > 0 for discrete time), then the timebase for both systems must match. If only one of the system has a timebase, the return timebase will be set to match it. @@ -133,44 +94,47 @@ def series(sys1, *sysn, **kwargs): (2, 1, 5) """ - sys = reduce(lambda x, y: y * x, sysn, sys1) + sys = reduce(lambda x, y: y * x, sys[1:], sys[0]) sys.update_names(**kwargs) return sys -def parallel(sys1, *sysn, **kwargs): - r"""parallel(sys1, sys2, [..., sysn]) +def parallel(*sys, **kwargs): + r"""parallel(sys1, sys2[, ..., sysn]) + + Parallel connection of I/O systems. - Return the parallel connection `sys1` + `sys2` (+ ...\ + `sysn`). + Generates a parallel connection ``sys1 + sys2 [+ ... + sysn]``. Parameters ---------- - sys1, sys2, ..., sysn: scalar, array, or :class:`InputOutputSystem` + sys1, sys2, ..., sysn : scalar, array, or `InputOutputSystem` I/O systems to combine. Returns ------- - out : scalar, array, or :class:`InputOutputSystem` + out : `InputOutputSystem` Parallel interconnection of the systems. Other Parameters ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, - signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See :class:`InputOutputSystem` for more information. + signal names will be of the form 's[i'` (where 's' is one of 'u', + or 'y'). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be - of of the form `x[i]` for interconnections of linear systems or + of the form 'x[i]' for interconnections of linear systems or '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Raises ------ ValueError - if `sys1` and `sys2` do not have the same numbers of inputs and outputs + If `sys1` and `sys2` do not have the same numbers of inputs and + outputs. See Also -------- @@ -179,12 +143,12 @@ def parallel(sys1, *sysn, **kwargs): Notes ----- This function is a wrapper for the __add__ function in the - StateSpace and TransferFunction classes. The output type is usually + `StateSpace` and `TransferFunction` classes. The output type is usually the type of `sys1`. If `sys1` is a scalar, then the output type is the type of `sys2`. - If both systems have a defined timebase (dt = 0 for continuous time, - dt > 0 for discrete time), then the timebase for both systems must + If both systems have a defined timebase (`dt` = 0 for continuous time, + `dt` > 0 for discrete time), then the timebase for both systems must match. If only one of the system has a timebase, the return timebase will be set to match it. @@ -203,37 +167,36 @@ def parallel(sys1, *sysn, **kwargs): (3, 4, 7) """ - sys = reduce(lambda x, y: x + y, sysn, sys1) + sys = reduce(lambda x, y: x + y, sys[1:], sys[0]) sys.update_names(**kwargs) return sys def negate(sys, **kwargs): - """ - Return the negative of a system. + """Return the negative of a system. Parameters ---------- - sys: scalar, array, or :class:`InputOutputSystem` + sys : scalar, array, or `InputOutputSystem` I/O systems to negate. Returns ------- - out : scalar, array, or :class:`InputOutputSystem` + out : `InputOutputSystem` Negated system. Other Parameters ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, - signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See :class:`InputOutputSystem` for more information. + signal names will be of the form 's[i]' (where 's' is one of 'u', + or 'y'). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be - of of the form `x[i]` for interconnections of linear systems or + of of the form 'x[i]' for interconnections of linear systems or '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. See Also -------- @@ -241,8 +204,9 @@ def negate(sys, **kwargs): Notes ----- - This function is a wrapper for the __neg__ function in the StateSpace and - TransferFunction classes. The output type is the same as the input type. + This function is a wrapper for the __neg__ function in the `StateSpace` + and `TransferFunction` classes. The output type is the same as the + input type. Examples -------- @@ -265,40 +229,39 @@ def feedback(sys1, sys2=1, sign=-1, **kwargs): Parameters ---------- - sys1, sys2: scalar, array, or :class:`InputOutputSystem` + sys1, sys2 : scalar, array, or `InputOutputSystem` I/O systems to combine. - sign: scalar - The sign of feedback. `sign` = -1 indicates negative feedback, and - `sign` = 1 indicates positive feedback. `sign` is an optional - argument; it assumes a value of -1 if not specified. + sign : scalar, optional + The sign of feedback. `sign=-1` indicates negative feedback + (default), and `sign=1` indicates positive feedback. Returns ------- - out : scalar, array, or :class:`InputOutputSystem` + out : `InputOutputSystem` Feedback interconnection of the systems. Other Parameters ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, - signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See :class:`InputOutputSystem` for more information. + signal names will be of the form 's[i]' (where 's' is one of 'u', + or 'y'). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be - of of the form `x[i]` for interconnections of linear systems or + of of the form 'x[i]' for interconnections of linear systems or '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Raises ------ ValueError - if `sys1` does not have as many inputs as `sys2` has outputs, or if - `sys2` does not have as many inputs as `sys1` has outputs + If `sys1` does not have as many inputs as `sys2` has outputs, or if + `sys2` does not have as many inputs as `sys1` has outputs. NotImplementedError - if an attempt is made to perform a feedback on a MIMO TransferFunction - object + If an attempt is made to perform a feedback on a MIMO `TransferFunction` + object. See Also -------- @@ -351,37 +314,38 @@ def feedback(sys1, sys2=1, sign=-1, **kwargs): return sys def append(*sys, **kwargs): - """append(sys1, sys2, [..., sysn]) + """append(sys1, sys2[, ..., sysn]) - Group LTI state space models by appending their inputs and outputs. + Group LTI models by appending their inputs and outputs. Forms an augmented system model, and appends the inputs and outputs together. Parameters ---------- - sys1, sys2, ..., sysn: scalar, array, or :class:`StateSpace` + sys1, sys2, ..., sysn : scalar, array, or `LTI` I/O systems to combine. + Returns + ------- + out : `LTI` + Combined system, with input/output vectors consisting of all + input/output vectors appended. Specific type returned is the type of + the first argument. + Other Parameters ---------------- inputs, outputs : str, or list of str, optional List of strings that name the individual signals. If not given, - signal names will be of the form `s[i]` (where `s` is one of `u`, - or `y`). See :class:`InputOutputSystem` for more information. + signal names will be of the form 's[i]' (where 's' is one of 'u', + or 'y'). See `InputOutputSystem` for more information. states : str, or list of str, optional List of names for system states. If not given, state names will be - of of the form `x[i]` for interconnections of linear systems or + of of the form 'x[i]' for interconnections of linear systems or '.' for interconnected nonlinear systems. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. - - Returns - ------- - out: :class:`StateSpace` - Combined system, with input/output vectors consisting of all - input/output vectors appended. + name 'sys[id]' is generated with a unique integer id. See Also -------- @@ -402,7 +366,7 @@ def append(*sys, **kwargs): (3, 8, 7) """ - s1 = ss._convert_to_statespace(sys[0]) + s1 = sys[0] for s in sys[1:]: s1 = s1.append(s) s1.update_names(**kwargs) @@ -412,8 +376,8 @@ def connect(sys, Q, inputv, outputv): """Index-based interconnection of an LTI system. .. deprecated:: 0.10.0 - `connect` will be removed in a future version of python-control in - favor of `interconnect`, which works with named signals. + `connect` will be removed in a future version of python-control. + Use `interconnect` instead, which works with named signals. The system `sys` is a system typically constructed with `append`, with multiple inputs and outputs. The inputs and outputs are connected @@ -426,7 +390,7 @@ def connect(sys, Q, inputv, outputv): Parameters ---------- - sys : :class:`InputOutputSystem` + sys : `InputOutputSystem` System to be connected. Q : 2D array Interconnection matrix. First column gives the input to be connected. @@ -436,13 +400,13 @@ def connect(sys, Q, inputv, outputv): values mean the feedback is negative. A zero value is ignored. Inputs and outputs are indexed starting at 1 to communicate sign information. inputv : 1D array - list of final external inputs, indexed starting at 1 + List of final external inputs, indexed starting at 1. outputv : 1D array - list of final external outputs, indexed starting at 1 + List of final external outputs, indexed starting at 1. Returns ------- - out : :class:`InputOutputSystem` + out : `InputOutputSystem` Connected and trimmed I/O system. See Also @@ -451,8 +415,7 @@ def connect(sys, Q, inputv, outputv): Notes ----- - The :func:`~control.interconnect` function in the :ref:`input/output - systems ` module allows the use of named signals and + The `interconnect` function allows the use of named signals and provides an alternative method for interconnecting multiple systems. Examples @@ -465,7 +428,7 @@ def connect(sys, Q, inputv, outputv): """ # TODO: maintain `connect` for use in MATLAB submodule (?) - warn("`connect` is deprecated; use `interconnect`", DeprecationWarning) + warn("connect() is deprecated; use interconnect()", FutureWarning) inputv, outputv, Q = \ np.atleast_1d(inputv), np.atleast_1d(outputv), np.atleast_1d(Q) @@ -507,3 +470,249 @@ def connect(sys, Q, inputv, outputv): Ytrim[i,y-1] = 1. return Ytrim * sys * Utrim + +def combine_tf(tf_array, **kwargs): + """Combine array of transfer functions into MIMO transfer function. + + Parameters + ---------- + tf_array : list of list of `TransferFunction` or array_like + Transfer matrix represented as a two-dimensional array or + list-of-lists containing `TransferFunction` objects. The + `TransferFunction` objects can have multiple outputs and inputs, as + long as the dimensions are compatible. + + Returns + ------- + `TransferFunction` + Transfer matrix represented as a single MIMO `TransferFunction` object. + + Other Parameters + ---------------- + inputs, outputs : str, or list of str, optional + List of strings that name the individual signals. If not given, + signal names will be of the form 's[i]' (where 's' is one of 'u', + or 'y'). See `InputOutputSystem` for more information. + name : string, optional + System name (used for specifying signals). If unspecified, a generic + name 'sys[id]' is generated with a unique integer id. + + Raises + ------ + ValueError + If timebase of transfer functions do not match. + ValueError + If `tf_array` has incorrect dimensions. + ValueError + If the transfer functions in a row have mismatched output or input + dimensions. + + Examples + -------- + Combine two transfer functions: + + >>> s = ct.tf('s') + >>> ct.combine_tf( + ... [[1 / (s + 1)], + ... [s / (s + 2)]], + ... name='G' + ... ) + TransferFunction( + [[array([1])], + [array([1, 0])]], + [[array([1, 1])], + [array([1, 2])]], + name='G', outputs=2, inputs=1) + + Combine NumPy arrays with transfer functions: + + >>> ct.combine_tf( + ... [[np.eye(2), np.zeros((2, 1))], + ... [np.zeros((1, 2)), ct.tf([1], [1, 0])]], + ... name='G' + ... ) + TransferFunction( + [[array([1.]), array([0.]), array([0.])], + [array([0.]), array([1.]), array([0.])], + [array([0.]), array([0.]), array([1])]], + [[array([1.]), array([1.]), array([1.])], + [array([1.]), array([1.]), array([1.])], + [array([1.]), array([1.]), array([1, 0])]], + name='G', outputs=3, inputs=3) + + """ + # Find common timebase or raise error + dt_list = [] + try: + for row in tf_array: + for tfn in row: + dt_list.append(getattr(tfn, "dt", None)) + except OSError: + raise ValueError("`tf_array` has too few dimensions.") + dt_set = set(dt_list) + dt_set.discard(None) + if len(dt_set) > 1: + raise ValueError("Time steps of transfer functions are " + f"mismatched: {dt_set}") + elif len(dt_set) == 0: + dt = None + else: + dt = dt_set.pop() + # Convert all entries to transfer function objects + ensured_tf_array = [] + for row in tf_array: + ensured_row = [] + for tfn in row: + ensured_row.append(_ensure_tf(tfn, dt)) + ensured_tf_array.append(ensured_row) + # Iterate over + num = [] + den = [] + for row_index, row in enumerate(ensured_tf_array): + for j_out in range(row[0].noutputs): + num_row = [] + den_row = [] + for col in row: + if col.noutputs != row[0].noutputs: + raise ValueError( + "Mismatched number of transfer function outputs in " + f"row {row_index}." + ) + for j_in in range(col.ninputs): + num_row.append(col.num_array[j_out, j_in]) + den_row.append(col.den_array[j_out, j_in]) + num.append(num_row) + den.append(den_row) + for row_index, row in enumerate(num): + if len(row) != len(num[0]): + raise ValueError( + "Mismatched number transfer function inputs in row " + f"{row_index} of numerator." + ) + for row_index, row in enumerate(den): + if len(row) != len(den[0]): + raise ValueError( + "Mismatched number transfer function inputs in row " + f"{row_index} of denominator." + ) + return tf.TransferFunction(num, den, dt=dt, **kwargs) + + + +def split_tf(transfer_function): + """Split MIMO transfer function into SISO transfer functions. + + System and signal names for the array of SISO transfer functions are + copied from the MIMO system. + + Parameters + ---------- + transfer_function : `TransferFunction` + MIMO transfer function to split. + + Returns + ------- + ndarray + NumPy array of SISO transfer functions. + + Examples + -------- + Split a MIMO transfer function: + + >>> G = ct.tf( + ... [ [[87.8], [-86.4]], + ... [[108.2], [-109.6]] ], + ... [ [[1, 1], [1, 1]], + ... [[1, 1], [1, 1]], ], + ... name='G' + ... ) + >>> ct.split_tf(G) + array([[TransferFunction( + array([87.8]), + array([1, 1]), + name='G', outputs=1, inputs=1), TransferFunction( + array([-86.4]), + array([1, 1]), + name='G', outputs=1, inputs=1)], + [TransferFunction( + array([108.2]), + array([1, 1]), + name='G', outputs=1, inputs=1), TransferFunction( + array([-109.6]), + array([1, 1]), + name='G', outputs=1, inputs=1)]], + dtype=object) + + """ + tf_split_lst = [] + for i_out in range(transfer_function.noutputs): + row = [] + for i_in in range(transfer_function.ninputs): + row.append( + tf.TransferFunction( + transfer_function.num_array[i_out, i_in], + transfer_function.den_array[i_out, i_in], + dt=transfer_function.dt, + inputs=transfer_function.input_labels[i_in], + outputs=transfer_function.output_labels[i_out], + name=transfer_function.name + ) + ) + tf_split_lst.append(row) + return np.array(tf_split_lst, dtype=object) + +def _ensure_tf(arraylike_or_tf, dt=None): + """Convert an array_like to a transfer function. + + Parameters + ---------- + arraylike_or_tf : `TransferFunction` or array_like + Array-like or transfer function. + dt : None, True or float, optional + System timebase. 0 (default) indicates continuous time, True + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, None + indicates unspecified timebase (either continuous or discrete + time). If None, timebase is not validated. + + Returns + ------- + `TransferFunction` + Transfer function. + + Raises + ------ + ValueError + If input cannot be converted to a transfer function. + ValueError + If the timebases do not match. + + """ + # If the input is already a transfer function, return it right away + if isinstance(arraylike_or_tf, tf.TransferFunction): + # If timebases don't match, raise an exception + if (dt is not None) and (arraylike_or_tf.dt != dt): + raise ValueError( + f"`arraylike_or_tf.dt={arraylike_or_tf.dt}` does not match " + f"argument `dt={dt}`." + ) + return arraylike_or_tf + if np.ndim(arraylike_or_tf) > 2: + raise ValueError( + "Array-like must have less than two dimensions to be converted " + "into a transfer function." + ) + # If it's not, then convert it to a transfer function + arraylike_3d = np.atleast_3d(arraylike_or_tf) + try: + tfn = tf.TransferFunction( + arraylike_3d, + np.ones_like(arraylike_3d), + dt, + ) + except TypeError: + raise ValueError( + "`arraylike_or_tf` must only contain array_likes or transfer " + "functions." + ) + return tfn diff --git a/control/canonical.py b/control/canonical.py index 7d091b22f..48fda7f5a 100644 --- a/control/canonical.py +++ b/control/canonical.py @@ -1,21 +1,22 @@ # canonical.py - functions for converting systems to canonical forms # RMM, 10 Nov 2012 -from .exception import ControlNotImplemented, ControlSlycot -from .iosys import issiso -from .statesp import StateSpace, _convert_to_statespace -from .statefbk import ctrb, obsv +"""Functions for converting systems to canonical forms. -import numpy as np - -from numpy import zeros, zeros_like, shape, poly, iscomplex, vstack, hstack, \ - transpose, empty, finfo, float64 -from numpy.linalg import solve, matrix_rank, eig +""" +import numpy as np +from numpy import poly, transpose, zeros_like +from numpy.linalg import matrix_rank, solve from scipy.linalg import schur -__all__ = ['canonical_form', 'reachable_form', 'observable_form', 'modal_form', - 'similarity_transform', 'bdschur'] +from .exception import ControlNotImplemented, ControlSlycot +from .iosys import issiso +from .statefbk import ctrb, obsv +from .statesp import StateSpace, _convert_to_statespace + +__all__ = ['canonical_form', 'reachable_form', 'observable_form', + 'modal_form', 'similarity_transform', 'bdschur'] def canonical_form(xsys, form='reachable'): @@ -23,8 +24,8 @@ def canonical_form(xsys, form='reachable'): Parameters ---------- - xsys : StateSpace object - System to be transformed, with state 'x' + xsys : `StateSpace` object + System to be transformed, with state 'x'. form : str Canonical form for transformation. Chosen from: * 'reachable' - reachable canonical form @@ -33,10 +34,10 @@ def canonical_form(xsys, form='reachable'): Returns ------- - zsys : StateSpace object - System in desired canonical form, with state 'z' + zsys : `StateSpace` object + System in desired canonical form, with state 'z'. T : (M, M) real ndarray - Coordinate transformation matrix, z = T * x + Coordinate transformation matrix, z = T * x. Examples -------- @@ -57,7 +58,7 @@ def canonical_form(xsys, form='reachable'): """ - # Call the appropriate tranformation function + # Call the appropriate transformation function if form == 'reachable': return reachable_form(xsys) elif form == 'observable': @@ -75,15 +76,15 @@ def reachable_form(xsys): Parameters ---------- - xsys : StateSpace object - System to be transformed, with state `x` + xsys : `StateSpace` object + System to be transformed, with state `x`. Returns ------- - zsys : StateSpace object - System in reachable canonical form, with state `z` + zsys : `StateSpace` object + System in reachable canonical form, with state `z`. T : (M, M) real ndarray - Coordinate transformation: z = T * x + Coordinate transformation: z = T * x. Examples -------- @@ -125,10 +126,12 @@ def reachable_form(xsys): # Check to make sure inversion was OK. Note that since we are inverting # Wrx and we already checked its rank, this exception should never occur if matrix_rank(Tzx) != xsys.nstates: # pragma: no cover - raise ValueError("Transformation matrix singular to working precision.") + raise ValueError( + "Transformation matrix singular to working precision.") # Finally, compute the output matrix - zsys.C = solve(Tzx.T, xsys.C.T).T # matrix right division, zsys.C = xsys.C * inv(Tzx) + # matrix right division, zsys.C = xsys.C * inv(Tzx) + zsys.C = solve(Tzx.T, xsys.C.T).T return zsys, Tzx @@ -138,15 +141,15 @@ def observable_form(xsys): Parameters ---------- - xsys : StateSpace object - System to be transformed, with state `x` + xsys : `StateSpace` object + System to be transformed, with state `x`. Returns ------- - zsys : StateSpace object - System in observable canonical form, with state `z` + zsys : `StateSpace` object + System in observable canonical form, with state `z`. T : (M, M) real ndarray - Coordinate transformation: z = T * x + Coordinate transformation: z = T * x. Examples -------- @@ -182,7 +185,8 @@ def observable_form(xsys): Tzx = solve(Wrz, Wrx) # matrix left division, Tzx = inv(Wrz) * Wrx if matrix_rank(Tzx) != xsys.nstates: - raise ValueError("Transformation matrix singular to working precision.") + raise ValueError( + "Transformation matrix singular to working precision.") # Finally, compute the output matrix zsys.B = Tzx @ xsys.B @@ -191,28 +195,31 @@ def observable_form(xsys): def similarity_transform(xsys, T, timescale=1, inverse=False): - """Perform a similarity transformation, with option time rescaling. + """Similarity transformation, with optional time rescaling. Transform a linear state space system to a new state space representation z = T x, or x = T z, where T is an invertible matrix. Parameters ---------- - xsys : StateSpace object - System to transform + xsys : `StateSpace` object + System to transform. T : (M, M) array_like The matrix `T` defines the new set of coordinates z = T x. timescale : float, optional - If present, also rescale the time unit to tau = timescale * t - inverse: boolean, optional - If True (default), transform so z = T x. If False, transform + If present, also rescale the time unit to tau = timescale * t. + inverse : bool, optional + If False (default), transform so z = T x. If True, transform so x = T z. Returns ------- - zsys : StateSpace object - System in transformed coordinates, with state 'z' + zsys : `StateSpace` object + System in transformed coordinates, with state 'z'. + See Also + -------- + canonical_form Examples -------- @@ -268,7 +275,10 @@ def _bdschur_defective(blksizes, eigvals): ------- True iff Schur blocks are defective. - blksizes, eigvals are the 3rd and 4th results returned by mb03rd. + Notes + ----- + `blksizes`, `eigvals` are the 3rd and 4th results returned by mb03rd. + """ if any(blksizes > 2): return True @@ -320,9 +330,10 @@ def _bdschur_condmax_search(aschur, tschur, condmax): Notes ----- - Outputs as for slycot.mb03rd + Outputs as for slycot.mb03rd. + + `aschur`, `tschur` are as returned by scipy.linalg.schur. - aschur, tschur are as returned by scipy.linalg.schur. """ try: from slycot import mb03rd @@ -389,7 +400,9 @@ def _bdschur_condmax_search(aschur, tschur, condmax): # hit search limit return reslower else: - raise ValueError('bisection failed to converge; pmaxlower={}, pmaxupper={}'.format(pmaxlower, pmaxupper)) + raise ValueError( + "bisection failed to converge; " + "pmaxlower={}, pmaxupper={}".format(pmaxlower, pmaxupper)) def bdschur(a, condmax=None, sort=None): @@ -397,21 +410,21 @@ def bdschur(a, condmax=None, sort=None): Parameters ---------- - a : (M, M) array_like - Real matrix to decompose - condmax : None or float, optional - If None (default), use 1/sqrt(eps), which is approximately 1e8 - sort : {None, 'continuous', 'discrete'} - Block sorting; see below. + a : (M, M) array_like + Real matrix to decompose. + condmax : None or float, optional + If None (default), use 1/sqrt(eps), which is approximately 1e8. + sort : {None, 'continuous', 'discrete'} + Block sorting; see below. Returns ------- - amodal : (M, M) real ndarray - Block-diagonal Schur decomposition of `a` - tmodal : (M, M) real ndarray - Similarity transform relating `a` and `amodal` - blksizes : (N,) int ndarray - Array of Schur block sizes + amodal : (M, M) real ndarray + Block-diagonal Schur decomposition of `a`. + tmodal : (M, M) real ndarray + Similarity transform relating `a` and `amodal`. + blksizes : (N,) int ndarray + Array of Schur block sizes. Notes ----- @@ -419,12 +432,11 @@ def bdschur(a, condmax=None, sort=None): If `sort` is 'continuous', the blocks are sorted according to associated eigenvalues. The ordering is first by real part of - eigenvalue, in descending order, then by absolute value of - imaginary part of eigenvalue, also in decreasing order. + eigenvalue, in descending order, then by absolute value of imaginary + part of eigenvalue, also in decreasing order. - If `sort` is 'discrete', the blocks are sorted as for - 'continuous', but applied to log of eigenvalues - (i.e., continuous-equivalent eigenvalues). + If `sort` is 'discrete', the blocks are sorted as for 'continuous', but + applied to log of eigenvalues (i.e., continuous-equivalent eigenvalues). Examples -------- @@ -439,7 +451,8 @@ def bdschur(a, condmax=None, sort=None): condmax = np.finfo(np.float64).eps ** -0.5 if not (np.isscalar(condmax) and condmax >= 1.0): - raise ValueError('condmax="{}" must be a scalar >= 1.0'.format(condmax)) + raise ValueError( + 'condmax="{}" must be a scalar >= 1.0'.format(condmax)) a = np.atleast_2d(a) if a.shape[0] == 0 or a.shape[1] == 0: @@ -486,8 +499,8 @@ def modal_form(xsys, condmax=None, sort=False): Parameters ---------- - xsys : StateSpace object - System to be transformed, with state `x` + xsys : `StateSpace` object + System to be transformed, with state x. condmax : None or float, optional An upper bound on individual transformations. If None, use `bdschur` default. @@ -497,10 +510,10 @@ def modal_form(xsys, condmax=None, sort=False): Returns ------- - zsys : StateSpace object - System in modal canonical form, with state `z` + zsys : `StateSpace` object + System in modal canonical form, with state z. T : (M, M) ndarray - Coordinate transformation: z = T * x + Coordinate transformation: z = T * x. Examples -------- diff --git a/control/config.py b/control/config.py index b6d5385d4..8da7e2fc2 100644 --- a/control/config.py +++ b/control/config.py @@ -1,15 +1,18 @@ # config.py - package defaults # RMM, 4 Nov 2012 # -# This file contains default values and utility functions for setting -# variables that control the behavior of the control package. -# Eventually it will be possible to read and write configuration -# files. For now, you can just choose between MATLAB and FBS default -# values + tweak a few other things. +# TODO: add ability to read/write configuration files (a la matplotlib) +"""Functions to access default parameter values. + +This module contains default values and utility functions for setting +parameters that control the behavior of the control package. + +""" import collections import warnings + from .exception import ControlArgument __all__ = ['defaults', 'set_defaults', 'reset_defaults', @@ -26,7 +29,7 @@ class DefaultDict(collections.UserDict): - """Map names for settings from older version to their renamed ones. + """Default parameters dictionary, with legacy warnings. If a user wants to write to an old setting, issue a warning and write to the renamed setting instead. Accessing the old setting returns the value @@ -72,6 +75,28 @@ def _check_deprecation(self, key): else: return key + # + # Context manager functionality + # + + def __call__(self, mapping): + self.saved_mapping = dict() + self.temp_mapping = mapping.copy() + return self + + def __enter__(self): + for key, val in self.temp_mapping.items(): + if not key in self: + raise ValueError(f"unknown parameter '{key}'") + self.saved_mapping[key] = self[key] + self[key] = val + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + for key, val in self.saved_mapping.items(): + self[key] = val + del self.saved_mapping, self.temp_mapping + return None defaults = DefaultDict(_control_defaults) @@ -82,6 +107,13 @@ def set_defaults(module, **keywords): The set_defaults() function can be used to modify multiple parameter values for a module at the same time, using keyword arguments. + Parameters + ---------- + module : str + Name of the module for which the defaults are being given. + **keywords : keyword arguments + Parameter value assignments. + Examples -------- >>> ct.defaults['freqplot.number_of_samples'] @@ -101,6 +133,7 @@ def set_defaults(module, **keywords): defaults[module + '.' + key] = val +# TODO: allow individual modules and individual parameters to be reset def reset_defaults(): """Reset configuration values to their default (initial) values. @@ -121,6 +154,10 @@ def reset_defaults(): # System level defaults defaults.update(_control_defaults) + from .ctrlplot import _ctrlplot_defaults, reset_rcParams + reset_rcParams() + defaults.update(_ctrlplot_defaults) + from .freqplot import _freqplot_defaults, _nyquist_defaults defaults.update(_freqplot_defaults) defaults.update(_nyquist_defaults) @@ -163,14 +200,14 @@ def _get_param(module, param, argval=None, defval=None, pop=False, last=False): parameter for a module based on the default parameter settings and any arguments passed to the function. The precedence order for parameters is the value passed to the function (as a keyword), the value from the - config.defaults dictionary, and the default value `defval`. + `config.defaults` dictionary, and the default value `defval`. Parameters ---------- module : str Name of the module whose parameters are being requested. param : str - Name of the parameter value to be determeind. + Name of the parameter value to be determined. argval : object or dict Value of the parameter as passed to the function. This can either be an object or a dictionary (i.e. the keyword list from the function @@ -178,15 +215,15 @@ def _get_param(module, param, argval=None, defval=None, pop=False, last=False): defval : object Default value of the parameter to use, if it is not located in the `config.defaults` dictionary. If a dictionary is provided, then - `module.param` is used to determine the default value. Defaults to + 'module.param' is used to determine the default value. Defaults to None. pop : bool, optional If True and if argval is a dict, then pop the remove the parameter - entry from the argval dict after retreiving it. This allows the use + entry from the argval dict after retrieving it. This allows the use of a keyword argument list to be passed through to other functions internal to the function being called. last : bool, optional - If True, check to make sure dictionary is empy after processing. + If True, check to make sure dictionary is empty after processing. """ @@ -219,6 +256,7 @@ def use_matlab_defaults(): The following conventions are used: * Bode plots plot gain in dB, phase in degrees, frequency in rad/sec, with grids + * Frequency plots use the label "Magnitude" for the system gain. Examples -------- @@ -227,15 +265,19 @@ def use_matlab_defaults(): """ set_defaults('freqplot', dB=True, deg=True, Hz=False, grid=True) + set_defaults('freqplot', magnitude_label="Magnitude") # Set defaults to match FBS (Astrom and Murray) def use_fbs_defaults(): - """Use `Feedback Systems `_ (FBS) compatible settings. + """Use Feedback Systems (FBS) compatible settings. + + The following conventions from `Feedback Systems `_ + are used: - The following conventions are used: * Bode plots plot gain in powers of ten, phase in degrees, frequency in rad/sec, no grid + * Frequency plots use the label "Gain" for the system gain. * Nyquist plots use dashed lines for mirror image of Nyquist curve Examples @@ -245,6 +287,7 @@ def use_fbs_defaults(): """ set_defaults('freqplot', dB=False, deg=True, Hz=False, grid=False) + set_defaults('freqplot', magnitude_label="Gain") set_defaults('nyquist', mirror_style='--') @@ -254,7 +297,7 @@ def use_legacy_defaults(version): Parameters ---------- version : string - Version number of the defaults desired. Ranges from '0.1' to '0.8.4'. + Version number of the defaults desired. Ranges from '0.1' to '0.10.1'. Examples -------- @@ -267,26 +310,26 @@ def use_legacy_defaults(version): (major, minor, patch) = (None, None, None) # default values # Early release tag format: REL-0.N - match = re.match("REL-0.([12])", version) + match = re.match(r"^REL-0.([12])$", version) if match: (major, minor, patch) = (0, int(match.group(1)), 0) # Early release tag format: control-0.Np - match = re.match("control-0.([3-6])([a-d])", version) + match = re.match(r"^control-0.([3-6])([a-d])$", version) if match: (major, minor, patch) = \ (0, int(match.group(1)), ord(match.group(2)) - ord('a') + 1) # Early release tag format: v0.Np - match = re.match("[vV]?0.([3-6])([a-d])", version) + match = re.match(r"^[vV]?0\.([3-6])([a-d])$", version) if match: (major, minor, patch) = \ (0, int(match.group(1)), ord(match.group(2)) - ord('a') + 1) # Abbreviated version format: vM.N or M.N - match = re.match("([vV]?[0-9]).([0-9])", version) + match = re.match(r"^[vV]?([0-9]*)\.([0-9]*)$", version) if match: (major, minor, patch) = \ (int(match.group(1)), int(match.group(2)), 0) # Standard version format: vM.N.P or M.N.P - match = re.match("[vV]?([0-9]).([0-9]).([0-9])", version) + match = re.match(r"^[vV]?([0-9]*)\.([0-9]*)\.([0-9]*)$", version) if match: (major, minor, patch) = \ (int(match.group(1)), int(match.group(2)), int(match.group(3))) @@ -313,7 +356,7 @@ def use_legacy_defaults(version): # switched to 'array' as default for state space objects warnings.warn("NumPy matrix class no longer supported") - # switched to 0 (=continuous) as default timestep + # switched to 0 (=continuous) as default timebase set_defaults('control', default_dt=None) # changed iosys naming conventions @@ -338,19 +381,49 @@ def use_legacy_defaults(version): return (major, minor, patch) -# -# Utility function for processing legacy keywords -# -# Use this function to handle a legacy keyword that has been renamed. This -# function pops the old keyword off of the kwargs dictionary and issues a -# warning. If both the old and new keyword are present, a ControlArgument -# exception is raised. -# -def _process_legacy_keyword(kwargs, oldkey, newkey, newval): - if kwargs.get(oldkey) is not None: - warnings.warn( - f"keyword '{oldkey}' is deprecated; use '{newkey}'", - DeprecationWarning) +def _process_legacy_keyword(kwargs, oldkey, newkey, newval, warn_oldkey=True): + """Utility function for processing legacy keywords. + + .. deprecated:: 0.10.2 + Replace with `_process_param` or `_process_kwargs`. + + Use this function to handle a legacy keyword that has been renamed. + This function pops the old keyword off of the kwargs dictionary and + issues a warning. If both the old and new keyword are present, a + `ControlArgument` exception is raised. + + Parameters + ---------- + kwargs : dict + Dictionary of keyword arguments (from function call). + oldkey : str + Old (legacy) parameter name. + newkey : str + Current name of the parameter. + newval : object + Value of the current parameter (from the function signature). + warn_oldkey : bool + If set to False, suppress generation of a warning about using a + legacy keyword. This is useful if you have two versions of a + keyword and you want to allow either to be used (see the `cost` and + `trajectory_cost` keywords in `flatsys.point_to_point` for an + example of this). + + Returns + ------- + val : object + Value of the (new) keyword. + + """ + # TODO: turn on this warning when ready to deprecate + # warnings.warn( + # "replace `_process_legacy_keyword` with `_process_param` " + # "or `_process_kwargs`", PendingDeprecationWarning) + if oldkey in kwargs: + if warn_oldkey: + warnings.warn( + f"keyword '{oldkey}' is deprecated; use '{newkey}'", + FutureWarning, stacklevel=3) if newval is not None: raise ControlArgument( f"duplicate keywords '{oldkey}' and '{newkey}'") @@ -358,3 +431,143 @@ def _process_legacy_keyword(kwargs, oldkey, newkey, newval): return kwargs.pop(oldkey) else: return newval + + +def _process_param(name, defval, kwargs, alias_mapping, sigval=None): + """Process named parameter, checking aliases and legacy usage. + + Helper function to process function arguments by mapping aliases to + either their default keywords or to a named argument. The alias + mapping is a dictionary that returns a tuple consisting of valid + aliases and legacy aliases:: + + alias_mapping = { + 'argument_name_1': (['alias', ...], ['legacy', ...]), + ...} + + If `param` is a named keyword in the function signature with default + value `defval`, a typical calling sequence at the start of a function + is:: + + param = _process_param('param', defval, kwargs, function_aliases) + + If `param` is a variable keyword argument (in `kwargs`), `defval` can + be passed as either None or the default value to use if `param` is not + present in `kwargs`. + + Parameters + ---------- + name : str + Name of the parameter to be checked. + defval : object or dict + Default value for the parameter. + kwargs : dict + Dictionary of variable keyword arguments. + alias_mapping : dict + Dictionary providing aliases and legacy names. + sigval : object, optional + Default value specified in the function signature (default = None). + If specified, an error will be generated if `defval` is different + than `sigval` and an alias or legacy keyword is given. + + Returns + ------- + newval : object + New value of the named parameter. + + Raises + ------ + TypeError + If multiple keyword aliases are used for the same parameter. + + Warns + ----- + PendingDeprecationWarning + If legacy name is used to set the value for the variable. + + """ + # Check to see if the parameter is in the keyword list + if name in kwargs: + if defval != sigval: + raise TypeError(f"multiple values for parameter {name}") + newval = kwargs.pop(name) + else: + newval = defval + + # Get the list of aliases and legacy names + aliases, legacy = alias_mapping[name] + + for kw in legacy: + if kw in kwargs: + warnings.warn( + f"alias `{kw}` is legacy name; use `{name}` instead", + PendingDeprecationWarning) + kwval = kwargs.pop(kw) + if newval != defval and kwval != newval: + raise TypeError( + f"multiple values for parameter `{name}` (via {kw})") + newval = kwval + + for kw in aliases: + if kw in kwargs: + kwval = kwargs.pop(kw) + if newval != defval and kwval != newval: + raise TypeError( + f"multiple values for parameter `{name}` (via {kw})") + newval = kwval + + return newval + + +def _process_kwargs(kwargs, alias_mapping): + """Process aliases and legacy keywords. + + Helper function to process function arguments by mapping aliases to + their default keywords. The alias mapping is a dictionary that returns + a tuple consisting of valid aliases and legacy aliases:: + + alias_mapping = { + 'argument_name_1': (['alias', ...], ['legacy', ...]), + ...} + + If an alias is present in the dictionary of keywords, it will be used + to set the value of the argument. If a legacy keyword is used, a + warning is issued. + + Parameters + ---------- + kwargs : dict + Dictionary of variable keyword arguments. + alias_mapping : dict + Dictionary providing aliases and legacy names. + + Raises + ------ + TypeError + If multiple keyword aliased are used for the same parameter. + + Warns + ----- + PendingDeprecationWarning + If legacy name is used to set the value for the variable. + + """ + for name in alias_mapping or []: + aliases, legacy = alias_mapping[name] + + for kw in legacy: + if kw in kwargs: + warnings.warn( + f"alias `{kw}` is legacy name; use `{name}` instead", + PendingDeprecationWarning) + if name in kwargs: + raise TypeError( + f"multiple values for parameter `{name}` (via {kw})") + kwargs[name] = kwargs.pop(kw) + + for kw in aliases: + if kw in kwargs: + if name in kwargs: + raise TypeError( + f"multiple values for parameter `{name}` (via {kw})") + kwargs[name] = kwargs.pop(kw) diff --git a/control/ctrlplot.py b/control/ctrlplot.py index c8c30880d..b1a989ce5 100644 --- a/control/ctrlplot.py +++ b/control/ctrlplot.py @@ -1,81 +1,252 @@ # ctrlplot.py - utility functions for plotting -# Richard M. Murray, 14 Jun 2024 +# RMM, 14 Jun 2024 # -# Collection of functions that are used by various plotting functions. +"""Utility functions for plotting. + +This module contains a collection of functions that are used by +various plotting functions. + +""" + +# Code pattern for control system plotting functions: +# +# def name_plot(sysdata, *fmt, plot=None, **kwargs): +# # Process keywords and set defaults +# ax = kwargs.pop('ax', None) +# color = kwargs.pop('color', None) +# label = kwargs.pop('label', None) +# rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) +# +# # Make sure all keyword arguments were processed (if not checked later) +# if kwargs: +# raise TypeError("unrecognized keywords: ", str(kwargs)) +# +# # Process the data (including generating responses for systems) +# sysdata = list(sysdata) +# if any([isinstance(sys, InputOutputSystem) for sys in sysdata]): +# data = name_response(sysdata) +# nrows = max([data.noutputs for data in sysdata]) +# ncols = max([data.ninputs for data in sysdata]) +# +# # Legacy processing of plot keyword +# if plot is False: +# return data.x, data.y +# +# # Figure out the shape of the plot and find/create axes +# fig, ax_array = _process_ax_keyword(ax, (nrows, ncols), rcParams) +# legend_loc, legend_map, show_legend = _process_legend_keywords( +# kwargs, (nrows, ncols), 'center right') +# +# # Customize axes (curvilinear grids, shared axes, etc) +# +# # Plot the data +# lines = np.full(ax_array.shape, []) +# line_labels = _process_line_labels(label, ntraces, nrows, ncols) +# color_offset, color_cycle = _get_color_offset(ax) +# for i, j in itertools.product(range(nrows), range(ncols)): +# ax = ax_array[i, j] +# for k in range(ntraces): +# if color is None: +# color = _get_color( +# color, fmt=fmt, offset=k, color_cycle=color_cycle) +# label = line_labels[k, i, j] +# lines[i, j] += ax.plot(data.x, data.y, color=color, label=label) +# +# # Customize and label the axes +# for i, j in itertools.product(range(nrows), range(ncols)): +# ax_array[i, j].set_xlabel("x label") +# ax_array[i, j].set_ylabel("y label") +# +# # Create legends +# if show_legend != False: +# legend_array = np.full(ax_array.shape, None, dtype=object) +# for i, j in itertools.product(range(nrows), range(ncols)): +# if legend_map[i, j] is not None: +# lines = ax_array[i, j].get_lines() +# labels = _make_legend_labels(lines) +# if len(labels) > 1: +# legend_array[i, j] = ax.legend( +# lines, labels, loc=legend_map[i, j]) +# else: +# legend_array = None +# +# # Update the plot title (only if ax was not given) +# sysnames = [response.sysname for response in data] +# if ax is None and title is None: +# title = "Name plot for " + ", ".join(sysnames) +# _update_plot_title(title, fig, rcParams=rcParams) +# elif ax == None: +# _update_plot_title(title, fig, rcParams=rcParams, use_existing=False) +# +# # Legacy processing of plot keyword +# if plot is True: +# return data +# +# return ControlPlot(lines, ax_array, fig, legend=legend_map) + +import itertools +import warnings from os.path import commonprefix +import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np from . import config -__all__ = ['suptitle', 'get_plot_axes'] +__all__ = [ + 'ControlPlot', 'suptitle', 'get_plot_axes', 'pole_zero_subplots', + 'rcParams', 'reset_rcParams'] +# +# Style parameters +# -def suptitle( - title, fig=None, frame='axes', **kwargs): - """Add a centered title to a figure. +rcParams_default = { + 'axes.labelsize': 'small', + 'axes.titlesize': 'small', + 'figure.titlesize': 'medium', + 'legend.fontsize': 'x-small', + 'xtick.labelsize': 'small', + 'ytick.labelsize': 'small', +} +_ctrlplot_rcParams = rcParams_default.copy() # provide access inside module +rcParams = _ctrlplot_rcParams # provide access outside module + +_ctrlplot_defaults = {'ctrlplot.rcParams': _ctrlplot_rcParams} + + +# +# Control figure +# + +class ControlPlot(): + """Return class for control platting functions. - This is a wrapper for the matplotlib `suptitle` function, but by - setting ``frame`` to 'axes' (default) then the title is centered on the - midpoint of the axes in the figure, rather than the center of the - figure. This usually looks better (particularly with multi-panel - plots), though it takes longer to render. + This class is used as the return type for control plotting functions. + It contains the information required to access portions of the plot + that the user might want to adjust, as well as providing methods to + modify some of the properties of the plot. + + A control figure consists of a `matplotlib.figure.Figure` with + an array of `matplotlib.axes.Axes`. Each axes in the figure has + a number of lines that represent the data for the plot. There may also + be a legend present in one or more of the axes. Parameters ---------- - title : str - Title text. - fig : Figure, optional - Matplotlib figure. Defaults to current figure. - frame : str, optional - Coordinate frame to use for centering: 'axes' (default) or 'figure'. - **kwargs : :func:`matplotlib.pyplot.suptitle` keywords, optional - Additional keywords (passed to matplotlib). + lines : array of list of `matplotlib.lines.Line2D` + Array of Line2D objects for each line in the plot. Generally, the + shape of the array matches the subplots shape and the value of the + array is a list of Line2D objects in that subplot. Some plotting + functions will return variants of this structure, as described in + the individual documentation for the functions. + axes : 2D array of `matplotlib.axes.Axes` + Array of Axes objects for each subplot in the plot. + figure : `matplotlib.figure.Figure` + Figure on which the Axes are drawn. + legend : `matplotlib.legend.Legend` (instance or ndarray) + Legend object(s) for the plot. If more than one legend is + included, this will be an array with each entry being either None + (for no legend) or a legend object. """ - rcParams = config._get_param('freqplot', 'rcParams', kwargs, pop=True) - - if fig is None: - fig = plt.gcf() + def __init__(self, lines, axes=None, figure=None, legend=None): + self.lines = lines + if axes is None: + _get_axes = np.vectorize(lambda lines: lines[0].axes) + axes = _get_axes(lines) + self.axes = np.atleast_2d(axes) + if figure is None: + figure = self.axes[0, 0].figure + self.figure = figure + self.legend = legend + + # Implement methods and properties to allow legacy interface (np.array) + __iter__ = lambda self: self.lines + __len__ = lambda self: len(self.lines) + def __getitem__(self, item): + warnings.warn( + "return of Line2D objects from plot function is deprecated in " + "favor of ControlPlot; use out.lines to access Line2D objects", + category=FutureWarning) + return self.lines[item] + def __setitem__(self, item, val): + self.lines[item] = val + shape = property(lambda self: self.lines.shape, None) + def reshape(self, *args): + """Reshape lines array (legacy).""" + return self.lines.reshape(*args) + + def set_plot_title(self, title, frame='axes'): + """Set the title for a control plot. + + This is a wrapper for the matplotlib `suptitle` function, but by + setting `frame` to 'axes' (default) then the title is centered on + the midpoint of the axes in the figure, rather than the center of + the figure. This usually looks better (particularly with + multi-panel plots), though it takes longer to render. + + Parameters + ---------- + title : str + Title text. + fig : Figure, optional + Matplotlib figure. Defaults to current figure. + frame : str, optional + Coordinate frame for centering: 'axes' (default) or 'figure'. + **kwargs : `matplotlib.pyplot.suptitle` keywords, optional + Additional keywords (passed to matplotlib). + + """ + _update_plot_title( + title, fig=self.figure, frame=frame, use_existing=False) - if frame == 'figure': - with plt.rc_context(rcParams): - fig.suptitle(title, **kwargs) +# +# User functions +# +# The functions below can be used by users to modify control plots or get +# information about them. +# - elif frame == 'axes': - # TODO: move common plotting params to 'ctrlplot' - with plt.rc_context(rcParams): - plt.tight_layout() # Put the figure into proper layout - xc, _ = _find_axes_center(fig, fig.get_axes()) +def suptitle( + title, fig=None, frame='axes', **kwargs): + """Add a centered title to a figure. - fig.suptitle(title, x=xc, **kwargs) - plt.tight_layout() # Update the layout + .. deprecated:: 0.10.1 + Use `ControlPlot.set_plot_title`. - else: - raise ValueError(f"unknown frame '{frame}'") + """ + warnings.warn( + "suptitle() is deprecated; use cplt.set_plot_title()", FutureWarning) + _update_plot_title( + title, fig=fig, frame=frame, use_existing=False, **kwargs) # Create vectorized function to find axes from lines def get_plot_axes(line_array): """Get a list of axes from an array of lines. - This function can be used to return the set of axes corresponding to - the line array that is returned by `time_response_plot`. This is useful for - generating an axes array that can be passed to subsequent plotting - calls. + .. deprecated:: 0.10.1 + This function will be removed in a future version of python-control. + Use `cplt.axes` to obtain axes for an instance of `ControlPlot`. + + This function can be used to return the set of axes corresponding + to the line array that is returned by `time_response_plot`. This + is useful for generating an axes array that can be passed to + subsequent plotting calls. Parameters ---------- - line_array : array of list of Line2D + line_array : array of list of `matplotlib.lines.Line2D` A 2D array with elements corresponding to a list of lines appearing in an axes, matching the return type of a time response data plot. Returns ------- - axes_array : array of list of Axes - A 2D array with elements corresponding to the Axes assocated with + axes_array : array of list of `matplotlib.axes.Axes` + A 2D array with elements corresponding to the Axes associated with the lines in `line_array`. Notes @@ -83,16 +254,260 @@ def get_plot_axes(line_array): Only the first element of each array entry is used to determine the axes. """ + warnings.warn( + "get_plot_axes() is deprecated; use cplt.axes()", FutureWarning) _get_axes = np.vectorize(lambda lines: lines[0].axes) - return _get_axes(line_array) + if isinstance(line_array, ControlPlot): + return _get_axes(line_array.lines) + else: + return _get_axes(line_array) + + +def pole_zero_subplots( + nrows, ncols, grid=None, dt=None, fig=None, scaling=None, + rcParams=None): + """Create axes for pole/zero plot. + + Parameters + ---------- + nrows, ncols : int + Number of rows and columns. + grid : True, False, or 'empty', optional + Grid style to use. Can also be a list, in which case each subplot + will have a different style (columns then rows). + dt : timebase, option + Timebase for each subplot (or a list of timebases). + scaling : 'auto', 'equal', or None + Scaling to apply to the subplots. + fig : `matplotlib.figure.Figure` + Figure to use for creating subplots. + rcParams : dict + Override the default parameters used for generating plots. + Default is set by `config.defaults['ctrlplot.rcParams']`. + + Returns + ------- + ax_array : ndarray + 2D array of axes. + + """ + from .grid import nogrid, sgrid, zgrid + from .iosys import isctime + + if fig is None: + fig = plt.gcf() + rcParams = config._get_param('ctrlplot', 'rcParams', rcParams) + + if not isinstance(grid, list): + grid = [grid] * nrows * ncols + if not isinstance(dt, list): + dt = [dt] * nrows * ncols + + ax_array = np.full((nrows, ncols), None) + index = 0 + with plt.rc_context(rcParams): + for row, col in itertools.product(range(nrows), range(ncols)): + match grid[index], isctime(dt=dt[index]): + case 'empty', _: # empty grid + ax_array[row, col] = fig.add_subplot(nrows, ncols, index+1) + + case True, True: # continuous-time grid + ax_array[row, col], _ = sgrid( + (nrows, ncols, index+1), scaling=scaling) + + case True, False: # discrete-time grid + ax_array[row, col] = fig.add_subplot(nrows, ncols, index+1) + zgrid(ax=ax_array[row, col], scaling=scaling) + + case False | None, _: # no grid (just stability boundaries) + ax_array[row, col] = fig.add_subplot(nrows, ncols, index+1) + nogrid( + ax=ax_array[row, col], dt=dt[index], scaling=scaling) + index += 1 + return ax_array + + +def reset_rcParams(): + """Reset rcParams to default values for control plots.""" + _ctrlplot_rcParams.update(rcParams_default) + # # Utility functions # +# These functions are used by plotting routines to provide a consistent way +# of processing and displaying information. +# + +def _process_ax_keyword( + axs, shape=(1, 1), rcParams=None, squeeze=False, clear_text=False, + create_axes=True, sharex=False, sharey=False): + """Process ax keyword to plotting commands. + + This function processes the `ax` keyword to plotting commands. If no + ax keyword is passed, the current figure is checked to see if it has + the correct shape. If the shape matches the desired shape, then the + current figure and axes are returned. Otherwise a new figure is + created with axes of the desired shape. + + If `create_axes` is False and a new/empty figure is returned, then `axs` + is an array of the proper shape but None for each element. This allows + the calling function to do the actual axis creation (needed for + curvilinear grids that use the AxisArtist module). + + Legacy behavior: some of the older plotting commands use an axes label + to identify the proper axes for plotting. This behavior is supported + through the use of the label keyword, but will only work if shape == + (1, 1) and squeeze == True. + + """ + if axs is None: + fig = plt.gcf() # get current figure (or create new one) + axs = fig.get_axes() + + # Check to see if axes are the right shape; if not, create new figure + # Note: can't actually check the shape, just the total number of axes + if len(axs) != np.prod(shape): + with plt.rc_context(rcParams): + if len(axs) != 0 and create_axes: + # Create a new figure + fig, axs = plt.subplots( + *shape, sharex=sharex, sharey=sharey, squeeze=False) + elif create_axes: + # Create new axes on (empty) figure + axs = fig.subplots( + *shape, sharex=sharex, sharey=sharey, squeeze=False) + else: + # Create an empty array and let user create axes + axs = np.full(shape, None) + if create_axes: # if not creating axes, leave these to caller + fig.set_layout_engine('tight') + fig.align_labels() + + else: + # Use the existing axes, properly reshaped + axs = np.asarray(axs).reshape(*shape) + + if clear_text: + # Clear out any old text from the current figure + for text in fig.texts: + text.set_visible(False) # turn off the text + del text # get rid of it completely + else: + axs = np.atleast_1d(axs) + try: + axs = axs.reshape(shape) + except ValueError: + raise ValueError( + "specified axes are not the right shape; " + f"got {axs.shape} but expecting {shape}") + fig = axs[0, 0].figure + + # Process the squeeze keyword + if squeeze and shape == (1, 1): + axs = axs[0, 0] # Just return the single axes object + elif squeeze: + axs = axs.squeeze() + + return fig, axs + + +# Turn label keyword into array indexed by trace, output, input +# TODO: move to ctrlutil.py and update parameter names to reflect general use +def _process_line_labels(label, ntraces=1, ninputs=0, noutputs=0): + if label is None: + return None + + if isinstance(label, str): + label = [label] * ntraces # single label for all traces + + # Convert to an ndarray, if not done already + try: + line_labels = np.asarray(label) + except ValueError: + raise ValueError("label must be a string or array_like") + + # Turn the data into a 3D array of appropriate shape + # TODO: allow more sophisticated broadcasting (and error checking) + try: + if ninputs > 0 and noutputs > 0: + if line_labels.ndim == 1 and line_labels.size == ntraces: + line_labels = line_labels.reshape(ntraces, 1, 1) + line_labels = np.broadcast_to( + line_labels, (ntraces, ninputs, noutputs)) + else: + line_labels = line_labels.reshape(ntraces, ninputs, noutputs) + except ValueError: + if line_labels.shape[0] != ntraces: + raise ValueError("number of labels must match number of traces") + else: + raise ValueError("labels must be given for each input/output pair") + + return line_labels + + +# Get labels for all lines in an axes +def _get_line_labels(ax, use_color=True): + labels_colors, lines = [], [] + last_color, counter = None, 0 # label unknown systems + for i, line in enumerate(ax.get_lines()): + label = line.get_label() + color = line.get_color() + if use_color and label.startswith("Unknown"): + label = f"Unknown-{counter}" + if last_color != color: + counter += 1 + last_color = color + elif label[0] == '_': + continue + + if (label, color) not in labels_colors: + lines.append(line) + labels_colors.append((label, color)) + + return lines, [label for label, color in labels_colors] + + +def _process_legend_keywords( + kwargs, shape=None, default_loc='center right'): + legend_loc = kwargs.pop('legend_loc', None) + if shape is None and 'legend_map' in kwargs: + raise TypeError("unexpected keyword argument 'legend_map'") + else: + legend_map = kwargs.pop('legend_map', None) + show_legend = kwargs.pop('show_legend', None) + + # If legend_loc or legend_map were given, always show the legend + if legend_loc is False or legend_map is False: + if show_legend is True: + warnings.warn( + "show_legend ignored; legend_loc or legend_map was given") + show_legend = False + legend_loc = legend_map = None + elif legend_loc is not None or legend_map is not None: + if show_legend is False: + warnings.warn( + "show_legend ignored; legend_loc or legend_map was given") + show_legend = True + + if legend_loc is None: + legend_loc = default_loc + elif not isinstance(legend_loc, (int, str)): + raise ValueError("legend_loc must be string or int") + + # Make sure the legend map is the right size + if legend_map is not None: + legend_map = np.atleast_2d(legend_map) + if legend_map.shape != shape: + raise ValueError("legend_map shape just match axes shape") + + return legend_loc, legend_map, show_legend # Utility function to make legend labels def _make_legend_labels(labels, ignore_common=False): + if len(labels) == 1: + return labels # Look for a common prefix (up to a space) common_prefix = commonprefix(labels) @@ -100,7 +515,7 @@ def _make_legend_labels(labels, ignore_common=False): if last_space < 0 or ignore_common: common_prefix = '' elif last_space > 0: - common_prefix = common_prefix[:last_space] + common_prefix = common_prefix[:last_space + 2] prefix_len = len(common_prefix) # Look for a common suffix (up to a space) @@ -120,8 +535,15 @@ def _make_legend_labels(labels, ignore_common=False): return labels -def _update_suptitle(fig, title, rcParams=None, frame='axes'): - if fig is not None and isinstance(title, str): +def _update_plot_title( + title, fig=None, frame='axes', use_existing=True, **kwargs): + if title is False or title is None: + return + if fig is None: + fig = plt.gcf() + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) + + if use_existing: # Get the current title, if it exists old_title = None if fig._suptitle is None else fig._suptitle._text @@ -140,8 +562,19 @@ def _update_suptitle(fig, title, rcParams=None, frame='axes'): separator = ',' if len(common_prefix) > 0 else ';' title = old_title + separator + title[common_len:] - # Add the title - suptitle(title, fig=fig, rcParams=rcParams, frame=frame) + if frame == 'figure': + with plt.rc_context(rcParams): + fig.suptitle(title, **kwargs) + + elif frame == 'axes': + with plt.rc_context(rcParams): + fig.suptitle(title, **kwargs) # Place title in center + plt.tight_layout() # Put everything into place + xc, _ = _find_axes_center(fig, fig.get_axes()) + fig.suptitle(title, x=xc, **kwargs) # Redraw title, centered + + else: + raise ValueError(f"unknown frame '{frame}'") def _find_axes_center(fig, axs): @@ -160,3 +593,183 @@ def _find_axes_center(fig, axs): ylim = [min(ll[1], ylim[0]), max(ur[1], ylim[1])] return (np.sum(xlim)/2, np.sum(ylim)/2) + + +# Internal function to add arrows to a curve +def _add_arrows_to_line2D( + axes, line, arrow_locs=[0.2, 0.4, 0.6, 0.8], + arrowstyle='-|>', arrowsize=1, dir=1): + """ + Add arrows to a matplotlib.lines.Line2D at selected locations. + + Parameters + ---------- + axes: Axes object as returned by axes command (or gca) + line: Line2D object as returned by plot command + arrow_locs: list of locations where to insert arrows, % of total length + arrowstyle: style of the arrow + arrowsize: size of the arrow + + Returns + ------- + arrows : list of arrows + + Notes + ----- + Based on https://stackoverflow.com/questions/26911898/ + + """ + # Get the coordinates of the line, in plot coordinates + if not isinstance(line, mpl.lines.Line2D): + raise ValueError("expected a matplotlib.lines.Line2D object") + x, y = line.get_xdata(), line.get_ydata() + + # Determine the arrow properties + arrow_kw = {"arrowstyle": arrowstyle} + + color = line.get_color() + use_multicolor_lines = isinstance(color, np.ndarray) + if use_multicolor_lines: + raise NotImplementedError("multi-color lines not supported") + else: + arrow_kw['color'] = color + + linewidth = line.get_linewidth() + if isinstance(linewidth, np.ndarray): + raise NotImplementedError("multi-width lines not supported") + else: + arrow_kw['linewidth'] = linewidth + + # Figure out the size of the axes (length of diagonal) + xlim, ylim = axes.get_xlim(), axes.get_ylim() + ul, lr = np.array([xlim[0], ylim[0]]), np.array([xlim[1], ylim[1]]) + diag = np.linalg.norm(ul - lr) + + # Compute the arc length along the curve + s = np.cumsum(np.sqrt(np.diff(x) ** 2 + np.diff(y) ** 2)) + + # Truncate the number of arrows if the curve is short + # TODO: figure out a smarter way to do this + frac = min(s[-1] / diag, 1) + if len(arrow_locs) and frac < 0.05: + arrow_locs = [] # too short; no arrows at all + elif len(arrow_locs) and frac < 0.2: + arrow_locs = [0.5] # single arrow in the middle + + # Plot the arrows (and return list if patches) + arrows = [] + for loc in arrow_locs: + n = np.searchsorted(s, s[-1] * loc) + + if dir == 1 and n == 0: + # Move the arrow forward by one if it is at start of a segment + n = 1 + + # Place the head of the arrow at the desired location + arrow_head = [x[n], y[n]] + arrow_tail = [x[n - dir], y[n - dir]] + + p = mpl.patches.FancyArrowPatch( + arrow_tail, arrow_head, transform=axes.transData, lw=0, + **arrow_kw) + axes.add_patch(p) + arrows.append(p) + return arrows + + +def _get_color_offset(ax, color_cycle=None): + """Get color offset based on current lines. + + This function determines that the current offset is for the next color + to use based on current colors in a plot. + + Parameters + ---------- + ax : `matplotlib.axes.Axes` + Axes containing already plotted lines. + color_cycle : list of matplotlib color specs, optional + Colors to use in plotting lines. Defaults to matplotlib rcParams + color cycle. + + Returns + ------- + color_offset : matplotlib color spec + Starting color for next line to be drawn. + color_cycle : list of matplotlib color specs + Color cycle used to determine colors. + + """ + if color_cycle is None: + color_cycle = plt.rcParams['axes.prop_cycle'].by_key()['color'] + + color_offset = 0 + if len(ax.lines) > 0: + last_color = ax.lines[-1].get_color() + if last_color in color_cycle: + color_offset = color_cycle.index(last_color) + 1 + + return color_offset % len(color_cycle), color_cycle + + +def _get_color( + colorspec, offset=None, fmt=None, ax=None, lines=None, + color_cycle=None): + """Get color to use for plotting line. + + This function returns the color to be used for the line to be drawn (or + None if the default color cycle for the axes should be used). + + Parameters + ---------- + colorspec : matplotlib color specification + User-specified color (or None). + offset : int, optional + Offset into the color cycle (for multi-trace plots). + fmt : str, optional + Format string passed to plotting command. + ax : `matplotlib.axes.Axes`, optional + Axes containing already plotted lines. + lines : list of matplotlib.lines.Line2D, optional + List of plotted lines. If not given, use ax.get_lines(). + color_cycle : list of matplotlib color specs, optional + Colors to use in plotting lines. Defaults to matplotlib rcParams + color cycle. + + Returns + ------- + color : matplotlib color spec + Color to use for this line (or None for matplotlib default). + + """ + # See if the color was explicitly specified by the user + if isinstance(colorspec, dict): + if 'color' in colorspec: + return colorspec.pop('color') + elif fmt is not None and \ + [isinstance(arg, str) and + any([c in arg for c in "bgrcmykw#"]) for arg in fmt]: + return None # *fmt will set the color + elif colorspec != None: + return colorspec + + # Figure out what color cycle to use, if not given by caller + if color_cycle == None: + color_cycle = plt.rcParams['axes.prop_cycle'].by_key()['color'] + + # Find the lines that we should pay attention to + if lines is None and ax is not None: + lines = ax.lines + + # If we were passed a set of lines, try to increment color from previous + if offset is not None: + return color_cycle[offset] + elif lines is not None: + color_offset = 0 + if len(ax.lines) > 0: + last_color = ax.lines[-1].get_color() + if last_color in color_cycle: + color_offset = color_cycle.index(last_color) + 1 + color_offset = color_offset % len(color_cycle) + return color_cycle[color_offset] + else: + return None diff --git a/control/ctrlutil.py b/control/ctrlutil.py index 6cd32593b..4db22f9c6 100644 --- a/control/ctrlutil.py +++ b/control/ctrlutil.py @@ -1,51 +1,18 @@ # ctrlutil.py - control system utility functions # -# Author: Richard M. Murray -# Date: 24 May 09 -# -# These are some basic utility functions that are used in the control -# systems library and that didn't naturally fit anyplace else. -# -# Copyright (c) 2009 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ +# Initial author: Richard M. Murray +# Creation date: 24 May 2009 +# Use `git shortlog -n -s ctrlutil.py` for full list of contributors + +"""Control system utility functions.""" -# Packages that we need access to -from . import lti -import numpy as np import math import warnings +import numpy as np + +from .lti import LTI + __all__ = ['unwrap', 'issys', 'db2mag', 'mag2db'] # Utility function to unwrap an angle measurement @@ -55,14 +22,14 @@ def unwrap(angle, period=2*math.pi): Parameters ---------- angle : array_like - Array of angles to be unwrapped + Array of angles to be unwrapped. period : float, optional - Period (defaults to `2*pi`) + Period (defaults to 2 pi). Returns ------- - angle_out : array_like - Output array, with jumps of period/2 eliminated + angle_out : ndarray + Output array, with jumps of period/2 eliminated. Examples -------- @@ -88,12 +55,13 @@ def unwrap(angle, period=2*math.pi): def issys(obj): """Deprecated function to check if an object is an LTI system. - Use isinstance(obj, ct.LTI) + .. deprecated:: 0.10.0 + Use isinstance(obj, ct.LTI) """ warnings.warn("issys() is deprecated; use isinstance(obj, ct.LTI)", FutureWarning, stacklevel=2) - return isinstance(obj, lti.LTI) + return isinstance(obj, LTI) def db2mag(db): """Convert a gain in decibels (dB) to a magnitude. @@ -105,12 +73,12 @@ def db2mag(db): Parameters ---------- db : float or ndarray - input value or array of values, given in decibels + Input value or array of values, given in decibels. Returns ------- mag : float or ndarray - corresponding magnitudes + Corresponding magnitudes. Examples -------- @@ -133,12 +101,12 @@ def mag2db(mag): Parameters ---------- mag : float or ndarray - input magnitude or array of magnitudes + Input magnitude or array of magnitudes. Returns ------- db : float or ndarray - corresponding values in decibels + Corresponding values in decibels. Examples -------- diff --git a/control/delay.py b/control/delay.py index d22e44107..550a779af 100644 --- a/control/delay.py +++ b/control/delay.py @@ -1,79 +1,45 @@ -# -*-coding: utf-8-*- -#! TODO: add module docstring # delay.py - functions involving time delays # -# Author: Sawyer Fuller -# Date: 26 Aug 2010 -# -# This file contains functions for implementing time delays (currently -# only the pade() function). -# -# Copyright (c) 2010 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ +# Initial author: Sawyer Fuller +# Creation date: 26 Aug 2010 +"""Functions to implement time delays (pade).""" __all__ = ['pade'] def pade(T, n=1, numdeg=None): - """ - Create a linear system that approximates a delay. + """Create a linear system that approximates a delay. - Return the numerator and denominator coefficients of the Pade approximation. + Return the numerator and denominator coefficients of the Pade + approximation of the given order. Parameters ---------- T : number - time delay + Time. delay n : positive integer - degree of denominator of approximation - numdeg: integer, or None (the default) - If None, numerator degree equals denominator degree - If >= 0, specifies degree of numerator - If < 0, numerator degree is n+numdeg + Degree of denominator of approximation. + numdeg : integer, or None (the default) + If numdeg is None, numerator degree equals denominator degree. + If numdeg >= 0, specifies degree of numerator. + If numdeg < 0, numerator degree is n+numdeg. Returns ------- - num, den : array + num, den : ndarray Polynomial coefficients of the delay model, in descending powers of s. Notes ----- - Based on: - 1. Algorithm 11.3.1 in Golub and van Loan, "Matrix Computation" 3rd. - Ed. pp. 572-574 - 2. M. Vajta, "Some remarks on Padé-approximations", - 3rd TEMPUS-INTCOM Symposium + Based on [1]_ and [2]_. + + References + ---------- + .. [1] Algorithm 11.3.1 in Golub and van Loan, "Matrix Computation" 3rd. + Ed. pp. 572-574. + + .. [2] M. Vajta, "Some remarks on Padé-approximations", + 3rd TEMPUS-INTCOM Symposium. Examples -------- @@ -107,7 +73,7 @@ def pade(T, n=1, numdeg=None): num[-1] = 1. cn = 1. for k in range(1, numdeg+1): - # derived from Gloub and van Loan eq. for Dpq(z) on p. 572 + # derived from Golub and van Loan eq. for Dpq(z) on p. 572 # this accumulative style follows Alg 11.3.1 cn *= -T * (numdeg - k + 1)/(numdeg + n - k + 1)/k num[numdeg-k] = cn diff --git a/control/descfcn.py b/control/descfcn.py index f52b43a2c..22d83d9fc 100644 --- a/control/descfcn.py +++ b/control/descfcn.py @@ -1,25 +1,20 @@ # descfcn.py - describing function analysis -# # RMM, 23 Jan 2021 -# -# This module adds functions for carrying out analysis of systems with -# memoryless nonlinear feedback functions using describing functions. -# -"""The :mod:~control.descfcn` module contains function for performing -closed loop analysis of systems with memoryless nonlinearities using -describing function analysis. +"""This module contains functions for performing closed loop analysis of +systems with memoryless nonlinearities using describing function analysis. """ import math +from warnings import warn + import numpy as np -import matplotlib.pyplot as plt import scipy -from warnings import warn -from .freqplot import nyquist_response from . import config +from .ctrlplot import ControlPlot +from .freqplot import nyquist_response __all__ = ['describing_function', 'describing_function_plot', 'describing_function_response', 'DescribingFunctionResponse', @@ -33,12 +28,12 @@ class DescribingFunctionNonlinearity(): This class is intended to be used as a base class for nonlinear functions that have an analytically defined describing function. Subclasses should override the `__call__` and `describing_function` methods and (optionally) - the `_isstatic` method (should be `False` if `__call__` updates the + the `_isstatic` method (should be False if `__call__` updates the instance state). """ def __init__(self): - """Initailize a describing function nonlinearity (optional).""" + """Initialize a describing function nonlinearity (optional).""" pass def __call__(self, A): @@ -53,6 +48,16 @@ def describing_function(self, A): describing function for a nonlinearity. It turns the (complex) value of the describing function for sinusoidal input of amplitude `A`. + Parameters + ---------- + A : float + Amplitude of the sinusoidal input to the nonlinearity. + + Returns + ------- + float + Value of the describing function at the given amplitude. + """ raise NotImplementedError( "describing function not implemented for this function") @@ -61,7 +66,7 @@ def _isstatic(self): """Return True if the function has no internal state (memoryless). This internal function is used to optimize numerical computation of - the describing function. It can be set to `True` if the instance + the describing function. It can be set to True if the instance maintains no internal memory of the instance state. Assumed False by default. @@ -76,7 +81,7 @@ def _f(self, x): def describing_function( F, A, num_points=100, zero_check=True, try_method=True): - """Numerically compute the describing function of a nonlinear function. + """Numerically compute describing function of a nonlinear function. The describing function of a nonlinearity is given by magnitude and phase of the first harmonic of the function when evaluated along a sinusoidal @@ -94,27 +99,31 @@ def describing_function( If the function is an object with a method `describing_function` then this method will be used to computing the describing function instead of a nonlinear computation. Some common nonlinearities - use the :class:`~control.DescribingFunctionNonlinearity` class, + use the `DescribingFunctionNonlinearity` class, which provides this functionality. A : array_like The amplitude(s) at which the describing function should be calculated. + num_points : int, optional + Number of points to use in computing describing function (default = + 100). + zero_check : bool, optional - If `True` (default) then `A` is zero, the function will be evaluated + If True (default) then `A` is zero, the function will be evaluated and checked to make sure it is zero. If not, a `TypeError` exception - is raised. If zero_check is `False`, no check is made on the value of + is raised. If zero_check is False, no check is made on the value of the function at zero. try_method : bool, optional - If `True` (default), check the `F` argument to see if it is an object + If True (default), check the `F` argument to see if it is an object with a `describing_function` method and use this to compute the describing function. More information in the `describing_function` - method for the :class:`~control.DescribingFunctionNonlinearity` class. + method for the `DescribingFunctionNonlinearity` class. Returns ------- - df : array of complex + df : ndarray of complex The (complex) value of the describing function at the given amplitudes. Raises @@ -142,7 +151,7 @@ def describing_function( # # The describing function of a nonlinear function F() can be computed by # evaluating the nonlinearity over a sinusoid. The Fourier series for a - # static nonlinear function evaluated on a sinusoid can be written as + # nonlinear function evaluated on a sinusoid can be written as # # F(A\sin\omega t) = \sum_{k=1}^\infty M_k(A) \sin(k\omega t + \phi_k(A)) # @@ -198,7 +207,7 @@ def describing_function( # Evaluate the function along a sinusoid F_eval = np.array([F(x) for x in a*sin_theta]).squeeze() - # Compute the prjections onto sine and cosine + # Compute the projections onto sine and cosine df_real = (F_eval @ sin_theta) * scale # = M_1 \cos\phi / a df_imag = (F_eval @ cos_theta) * scale # = M_1 \sin\phi / a @@ -216,27 +225,27 @@ class DescribingFunctionResponse: """Results of describing function analysis. Describing functions allow analysis of a linear I/O systems with a - static nonlinear feedback function. The DescribingFunctionResponse - class is used by the :func:`~control.describing_function_response` - function to return the results of a describing function analysis. The - response object can be used to obtain information about the describing + nonlinear feedback function. The DescribingFunctionResponse class + is used by the `describing_function_response` function to return + the results of a describing function analysis. The response + object can be used to obtain information about the describing function analysis or generate a Nyquist plot showing the frequency response of the linear systems and the describing function for the nonlinear element. - Attributes + Parameters ---------- - response : :class:`~control.FrequencyResponseData` + response : `FrequencyResponseData` Frequency response of the linear system component of the system. intersections : 1D array of 2-tuples or None A list of all amplitudes and frequencies in which - :math:`H(j\\omega) N(a) = -1`, where :math:`N(a)` is the describing - function associated with `F`, or `None` if there are no such + :math:`H(j\\omega) N(A) = -1`, where :math:`N(A)` is the describing + function associated with `F`, or None if there are no such points. Each pair represents a potential limit cycle for the closed loop system with amplitude given by the first value of the tuple and frequency given by the second value. N_vals : complex array - Complex value of the describing function. + Complex value of the describing function, indexed by amplitude. positions : list of complex Location of the intersections in the complex plane. @@ -251,7 +260,7 @@ def __init__(self, response, N_vals, positions, intersections): def plot(self, **kwargs): """Plot the results of a describing function analysis. - See :func:`~control.describing_function_plot` for details. + See `describing_function_plot` for details. """ return describing_function_plot(self, **kwargs) @@ -269,43 +278,54 @@ def __len__(self): # Compute the describing function response + intersections def describing_function_response( H, F, A, omega=None, refine=True, warn_nyquist=None, - plot=False, check_kwargs=True, **kwargs): + _check_kwargs=True, **kwargs): """Compute the describing function response of a system. This function uses describing function analysis to analyze a closed - loop system consisting of a linear system with a static nonlinear - function in the feedback path. + loop system consisting of a linear system with a nonlinear function in + the feedback path. Parameters ---------- H : LTI system - Linear time-invariant (LTI) system (state space, transfer function, or - FRD) - F : static nonlinear function - A static nonlinearity, either a scalar function or a single-input, + Linear time-invariant (LTI) system (state space, transfer function, + or FRD). + F : nonlinear function + Feedback nonlinearity, either a scalar function or a single-input, single-output, static input/output system. A : list List of amplitudes to be used for the describing function plot. omega : list, optional List of frequencies to be used for the linear system Nyquist curve. warn_nyquist : bool, optional - Set to True to turn on warnings generated by `nyquist_plot` or False - to turn off warnings. If not set (or set to None), warnings are - turned off if omega is specified, otherwise they are turned on. + Set to True to turn on warnings generated by `nyquist_plot` or + False to turn off warnings. If not set (or set to None), + warnings are turned off if omega is specified, otherwise they are + turned on. + refine : bool, optional + If True, `scipy.optimize.minimize` to refine the estimate + of the intersection of the frequency response and the describing + function. Returns ------- - response : :class:`~control.DescribingFunctionResponse` object + response : `DescribingFunctionResponse` object Response object that contains the result of the describing function - analysis. The following information can be retrieved from this - object: - response.intersections : 1D array of 2-tuples or None + analysis. The results can plotted using the + `~DescribingFunctionResponse.plot` method. + response.intersections : 1D ndarray of 2-tuples or None A list of all amplitudes and frequencies in which :math:`H(j\\omega) N(a) = -1`, where :math:`N(a)` is the describing - function associated with `F`, or `None` if there are no such + function associated with `F`, or None if there are no such points. Each pair represents a potential limit cycle for the closed loop system with amplitude given by the first value of the tuple and frequency given by the second value. + response.Nvals : complex ndarray + Complex value of the describing function, indexed by amplitude. + + See Also + -------- + DescribingFunctionResponse, describing_function_plot Examples -------- @@ -315,7 +335,7 @@ def describing_function_response( >>> response = ct.describing_function_response(H_simple, F_saturation, amp) >>> response.intersections # doctest: +SKIP [(3.343844998258643, 1.4142293090899216)] - >>> lines = response.plot() + >>> cplt = response.plot() """ # Decide whether to turn on warnings or not @@ -326,7 +346,7 @@ def describing_function_response( # Start by drawing a Nyquist curve response = nyquist_response( H, omega, warn_encirclements=warn_nyquist, warn_nyquist=warn_nyquist, - check_kwargs=check_kwargs, **kwargs) + _check_kwargs=_check_kwargs, **kwargs) H_omega, H_vals = response.contour.imag, H(response.contour) # Compute the describing function @@ -378,14 +398,13 @@ def _cost(x): def describing_function_plot( - *sysdata, label="%5.2g @ %-5.2g", **kwargs): + *sysdata, point_label="%5.2g @ %-5.2g", label=None, **kwargs): """describing_function_plot(data, *args, **kwargs) - Plot a Nyquist plot with a describing function for a nonlinear system. + Nyquist plot with describing function for a nonlinear system. This function generates a Nyquist plot for a closed loop system - consisting of a linear system with a static nonlinear function in the - feedback path. + consisting of a linear system with a nonlinearity in the feedback path. The function may be called in one of two forms: @@ -394,20 +413,20 @@ def describing_function_plot( describing_function_plot(H, F, A[, omega[, options]]) In the first form, the response should be generated using the - :func:`~control.describing_function_response` function. In the second + `describing_function_response` function. In the second form, that function is called internally, with the listed arguments. Parameters ---------- - data : :class:`~control.DescribingFunctionData` + data : `DescribingFunctionResponse` A describing function response data object created by - :func:`~control.describing_function_response`. + `describing_function_response`. H : LTI system - Linear time-invariant (LTI) system (state space, transfer function, or - FRD) - F : static nonlinear function - A static nonlinearity, either a scalar function or a single-input, - single-output, static input/output system. + Linear time-invariant (LTI) system (state space, transfer function, + or FRD). + F : nonlinear function + Nonlinearity in the feedback path, either a scalar function or a + single-input, single-output, static input/output system. A : list List of amplitudes to be used for the describing function plot. omega : list, optional @@ -417,32 +436,62 @@ def describing_function_plot( refine : bool, optional If True (default), refine the location of the intersection of the Nyquist curve for the linear system and the describing function to - determine the intersection point - label : str, optional + determine the intersection point. + label : str or array_like of str, optional + If present, replace automatically generated label with the given label. + point_label : str, optional Formatting string used to label intersection points on the Nyquist - plot. Defaults to "%5.2g @ %-5.2g". Set to `None` to omit labels. + plot. Defaults to "%5.2g @ %-5.2g". Set to None to omit labels. + ax : `matplotlib.axes.Axes`, optional + The matplotlib axes to draw the figure on. If not specified and + the current figure has a single axes, that axes is used. + Otherwise, a new figure is created. + title : str, optional + Set the title of the plot. Defaults to plot type and system name(s). + warn_nyquist : bool, optional + Set to True to turn on warnings generated by `nyquist_plot` or + False to turn off warnings. If not set (or set to None), + warnings are turned off if omega is specified, otherwise they are + turned on. + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional + Additional keywords passed to `matplotlib` to specify line properties + for Nyquist curve. Returns ------- - lines : 1D array of Line2D - Arrray of Line2D objects for each line in the plot. The first + cplt : `ControlPlot` object + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : array of `matplotlib.lines.Line2D` + Array containing information on each line in the plot. The first element of the array is a list of lines (typically only one) for - the Nyquist plot of the linear I/O styem. The second element of + the Nyquist plot of the linear I/O system. The second element of the array is a list of lines (typically only one) for the describing function curve. + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + + See Also + -------- + DescribingFunctionResponse, describing_function_response Examples -------- >>> H_simple = ct.tf([8], [1, 2, 2, 1]) >>> F_saturation = ct.saturation_nonlinearity(1) >>> amp = np.linspace(1, 4, 10) - >>> lines = ct.describing_function_plot(H_simple, F_saturation, amp) + >>> cplt = ct.describing_function_plot(H_simple, F_saturation, amp) """ # Process keywords warn_nyquist = config._process_legacy_keyword( kwargs, 'warn', 'warn_nyquist', kwargs.pop('warn_nyquist', None)) + point_label = config._process_legacy_keyword( + kwargs, 'label', 'point_label', point_label) + # TODO: update to be consistent with ctrlplot use of `label` if label not in (False, None) and not isinstance(label, str): raise ValueError("label must be formatting string, False, or None") @@ -454,27 +503,36 @@ def describing_function_plot( *sysdata, refine=kwargs.pop('refine', True), warn_nyquist=warn_nyquist) elif len(sysdata) == 1: - dfresp = sysdata[0] + if not isinstance(sysdata[0], DescribingFunctionResponse): + raise TypeError("data must be DescribingFunctionResponse") + else: + dfresp = sysdata[0] else: raise TypeError("1, 3, or 4 position arguments required") + # Don't allow legend keyword arguments + for kw in ['legend_loc', 'legend_map', 'show_legend']: + if kw in kwargs: + raise TypeError(f"unexpected keyword argument '{kw}'") + # Create a list of lines for the output - out = np.empty(2, dtype=object) + lines = np.empty(2, dtype=object) # Plot the Nyquist response - out[0] = dfresp.response.plot(**kwargs)[0] + cplt = dfresp.response.plot(**kwargs) + ax = cplt.axes[0, 0] # Get the axes where the plot was made + lines[0] = cplt.lines[0] # Return Nyquist lines for first system # Add the describing function curve to the plot - lines = plt.plot(dfresp.N_vals.real, dfresp.N_vals.imag) - out[1] = lines + lines[1] = ax.plot(dfresp.N_vals.real, dfresp.N_vals.imag) # Label the intersection points - if label: + if point_label: for pos, (a, omega) in zip(dfresp.positions, dfresp.intersections): # Add labels to the intersection points - plt.text(pos.real, pos.imag, label % (a, omega)) + ax.text(pos.real, pos.imag, point_label % (a, omega)) - return out + return ControlPlot(lines, cplt.axes, cplt.figure) # Utility function to figure out whether two line segments intersection @@ -507,7 +565,7 @@ def _find_intersection(L1a, L1b, L2a, L2b): # Saturation nonlinearity class saturation_nonlinearity(DescribingFunctionNonlinearity): - """Create saturation nonlinearity for use in describing function analysis. + """Saturation nonlinearity for describing function analysis. This class creates a nonlinear function representing a saturation with given upper and lower bounds, including the describing function for the @@ -521,6 +579,11 @@ class saturation_nonlinearity(DescribingFunctionNonlinearity): functions will not have zero bias and hence care must be taken in using the nonlinearity for analysis. + Parameters + ---------- + lb, ub : float + Upper and lower saturation bounds. + Examples -------- >>> nl = ct.saturation_nonlinearity(5) @@ -555,6 +618,19 @@ def _isstatic(self): return True def describing_function(self, A): + """Return the describing function for a saturation nonlinearity. + + Parameters + ---------- + A : float + Amplitude of the sinusoidal input to the nonlinearity. + + Returns + ------- + float + Value of the describing function at the given amplitude. + + """ # Check to make sure the amplitude is positive if A < 0: raise ValueError("cannot evaluate describing function for A < 0") @@ -569,21 +645,28 @@ def describing_function(self, A): # Relay with hysteresis (FBS2e, Example 10.12) class relay_hysteresis_nonlinearity(DescribingFunctionNonlinearity): - """Relay w/ hysteresis nonlinearity for describing function analysis. + """Relay w/ hysteresis for describing function analysis. This class creates a nonlinear function representing a a relay with symmetric upper and lower bounds of magnitude `b` and a hysteretic region of width `c` (using the notation from [FBS2e](https://fbsbook.org), Example 10.12, including the describing function for the nonlinearity. The following call creates a nonlinear function suitable for describing - function analysis: + function analysis:: F = relay_hysteresis_nonlinearity(b, c) - The output of this function is `b` if `x > c` and `-b` if `x < -c`. For - `-c <= x <= c`, the value depends on the branch of the hysteresis loop (as + The output of this function is b if x > c and -b if x < -c. For -c <= + x <= c, the value depends on the branch of the hysteresis loop (as illustrated in Figure 10.20 of FBS2e). + Parameters + ---------- + b : float + Hysteresis bound. + c : float + Width of hysteresis region. + Examples -------- >>> nl = ct.relay_hysteresis_nonlinearity(1, 2) @@ -625,6 +708,19 @@ def _isstatic(self): return False def describing_function(self, A): + """Return the describing function for a hysteresis nonlinearity. + + Parameters + ---------- + A : float + Amplitude of the sinusoidal input to the nonlinearity. + + Returns + ------- + float + Value of the describing function at the given amplitude. + + """ # Check to make sure the amplitude is positive if A < 0: raise ValueError("cannot evaluate describing function for A < 0") @@ -644,14 +740,19 @@ class friction_backlash_nonlinearity(DescribingFunctionNonlinearity): This class creates a nonlinear function representing a friction-dominated backlash nonlinearity ,including the describing function for the nonlinearity. The following call creates a nonlinear function suitable - for describing function analysis: + for describing function analysis:: F = friction_backlash_nonlinearity(b) - This function maintains an internal state representing the 'center' of a - mechanism with backlash. If the new input is within `b/2` of the current - center, the output is unchanged. Otherwise, the output is given by the - input shifted by `b/2`. + This function maintains an internal state representing the 'center' of + a mechanism with backlash. If the new input is within b/2 of the + current center, the output is unchanged. Otherwise, the output is + given by the input shifted by b/2. + + Parameters + ---------- + b : float + Backlash amount. Examples -------- @@ -690,6 +791,19 @@ def _isstatic(self): return False def describing_function(self, A): + """Return the describing function for a backlash nonlinearity. + + Parameters + ---------- + A : float + Amplitude of the sinusoidal input to the nonlinearity. + + Returns + ------- + float + Value of the describing function at the given amplitude. + + """ # Check to make sure the amplitude is positive if A < 0: raise ValueError("cannot evaluate describing function for A < 0") diff --git a/control/dtime.py b/control/dtime.py index 9b91eabd3..11d2d90d3 100644 --- a/control/dtime.py +++ b/control/dtime.py @@ -1,111 +1,69 @@ -"""dtime.py +# dtime.py - functions for manipulating discrete-time systems +# +# Initial author: Richard M. Murray +# Creation date: 6 October 2012 -Functions for manipulating discrete time systems. - -Routines in this module: - -sample_system() -c2d() -""" - -"""Copyright (c) 2012 by California Institute of Technology -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Author: Richard M. Murray -Date: 6 October 2012 - -$Id: dtime.py 185 2012-08-30 05:44:32Z murrayrm $ - -""" +"""Functions for manipulating discrete-time systems.""" from .iosys import isctime -from .statesp import StateSpace __all__ = ['sample_system', 'c2d'] -# Sample a continuous time system +# Sample a continuous-time system def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, name=None, copy_names=True, **kwargs): - """Convert a continuous time system to discrete time by sampling. + """Convert a continuous-time system to discrete time by sampling. Parameters ---------- - sysc : LTI (:class:`StateSpace` or :class:`TransferFunction`) - Continuous time system to be converted + sysc : `StateSpace` or `TransferFunction` + Continuous time system to be converted. Ts : float > 0 - Sampling period + Sampling period. method : string - Method to use for conversion, e.g. 'bilinear', 'zoh' (default) + Method to use for conversion, e.g. 'bilinear', 'zoh' (default). alpha : float within [0, 1] The generalized bilinear transformation weighting parameter, which should only be specified with method="gbt", and is ignored - otherwise. See :func:`scipy.signal.cont2discrete`. + otherwise. See `scipy.signal.cont2discrete`. prewarp_frequency : float within [0, infinity) The frequency [rad/s] at which to match with the input continuous- time system's magnitude and phase (only valid for method='bilinear', - 'tustin', or 'gbt' with alpha=0.5) + 'tustin', or 'gbt' with alpha=0.5). Returns ------- - sysd : LTI of the same class (:class:`StateSpace` or :class:`TransferFunction`) - Discrete time system, with sampling rate Ts + sysd : LTI of the same class (`StateSpace` or `TransferFunction`) + Discrete time system, with sampling rate `Ts`. Other Parameters ---------------- inputs : int, list of str or None, optional - Description of the system inputs. If not specified, the origional - system inputs are used. See :class:`InputOutputSystem` for more + Description of the system inputs. If not specified, the original + system inputs are used. See `InputOutputSystem` for more information. outputs : int, list of str or None, optional Description of the system outputs. Same format as `inputs`. states : int, list of str, or None, optional Description of the system states. Same format as `inputs`. Only - available if the system is :class:`StateSpace`. + available if the system is `StateSpace`. name : string, optional Set the name of the sampled system. If not specified and - if `copy_names` is `False`, a generic name is generated - with a unique integer id. If `copy_names` is `True`, the new system + if `copy_names` is False, a generic name 'sys[id]' is generated + with a unique integer id. If `copy_names` is True, the new system name is determined by adding the prefix and suffix strings in - config.defaults['iosys.sampled_system_name_prefix'] and - config.defaults['iosys.sampled_system_name_suffix'], with the + `config.defaults['iosys.sampled_system_name_prefix']` and + `config.defaults['iosys.sampled_system_name_suffix']`, with the default being to add the suffix '$sampled'. - copy_names : bool, Optional + copy_names : bool, optional If True, copy the names of the input signals, output signals, and states to the sampled system. Notes ----- - See :meth:`StateSpace.sample` or :meth:`TransferFunction.sample` for - further details. + See `StateSpace.sample` or `TransferFunction.sample` for further + details on implementation for state space and transfer function + systems, including available methods. Examples -------- @@ -118,12 +76,14 @@ def sample_system(sysc, Ts, method='zoh', alpha=None, prewarp_frequency=None, """ - # Make sure we have a continuous time system + # Make sure we have a continuous-time system if not isctime(sysc): - raise ValueError("First argument must be continuous time system") + raise ValueError("First argument must be continuous-time system") return sysc.sample(Ts, method=method, alpha=alpha, prewarp_frequency=prewarp_frequency, name=name, copy_names=copy_names, **kwargs) + +# Convenience aliases c2d = sample_system diff --git a/control/exception.py b/control/exception.py index e4758cc49..69b140203 100644 --- a/control/exception.py +++ b/control/exception.py @@ -1,73 +1,42 @@ # exception.py - exception definitions for the control package # -# Author: Richard M. Murray -# Date: 31 May 2010 -# -# This file contains definitions of standard exceptions for the control package -# -# Copyright (c) 2010 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ +# Initial author: Richard M. Murray +# Creation date: 31 May 2010 + +"""Exception definitions for the control package.""" class ControlSlycot(ImportError): - """Exception for Slycot import. Used when we can't import a function - from the slycot package""" + """Slycot import failed.""" pass class ControlDimension(ValueError): - """Raised when dimensions of system objects are not correct""" + """Raised when dimensions of system objects are not correct.""" pass class ControlArgument(TypeError): - """Raised when arguments to a function are not correct""" + """Raised when arguments to a function are not correct.""" + pass + +class ControlIndexError(IndexError): + """Raised when arguments to an indexed object are not correct.""" pass class ControlMIMONotImplemented(NotImplementedError): - """Function is not currently implemented for MIMO systems""" + """Function is not currently implemented for MIMO systems.""" pass class ControlNotImplemented(NotImplementedError): - """Functionality is not yet implemented""" + """Functionality is not yet implemented.""" pass -# Utility function to see if slycot is installed +# Utility function to see if Slycot is installed slycot_installed = None def slycot_check(): - """Return True if slycot is installed, otherwise False.""" + """Return True if Slycot is installed, otherwise False.""" global slycot_installed if slycot_installed is None: try: - import slycot + import slycot # noqa: F401 slycot_installed = True except: slycot_installed = False @@ -81,7 +50,7 @@ def pandas_check(): global pandas_installed if pandas_installed is None: try: - import pandas + import pandas # noqa: F401 pandas_installed = True except: pandas_installed = False @@ -94,7 +63,7 @@ def cvxopt_check(): global cvxopt_installed if cvxopt_installed is None: try: - import cvxopt + import cvxopt # noqa: F401 cvxopt_installed = True except: cvxopt_installed = False diff --git a/control/flatsys/__init__.py b/control/flatsys/__init__.py index c6934d825..ce9650e9a 100644 --- a/control/flatsys/__init__.py +++ b/control/flatsys/__init__.py @@ -1,56 +1,23 @@ # flatsys/__init__.py: flat systems package initialization file # -# Copyright (c) 2019 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# # Author: Richard M. Murray # Date: 1 Jul 2019 -r"""Differentially flat systems sub-package. +r"""Flat systems subpackage. -The :mod:`control.flatsys` sub-package contains a set of classes and -functions to compute trajectories for differentially flat systems. +This subpackage contains a set of classes and functions to compute +trajectories for differentially flat systems. A differentially flat system is defined by creating an object using the -:class:`~control.flatsys.FlatSystem` class, which has member functions for -mapping the system state and input into and out of flat coordinates. The -:func:`~control.flatsys.point_to_point` function can be used to create a -trajectory between two endpoints, written in terms of a set of basis functions -defined using the :class:`~control.flatsys.BasisFamily` class. The resulting -trajectory is return as a :class:`~control.flatsys.SystemTrajectory` object -and can be evaluated using the :func:`~control.flatsys.SystemTrajectory.eval` -member function. Alternatively, the :func:`~control.flatsys.solve_flat_ocp` -function can be used to solve an optimal control problem with trajectory and -final costs or constraints. +`FlatSystem` class, which has member functions for mapping the +system state and input into and out of flat coordinates. The +`point_to_point` function can be used to create a trajectory +between two endpoints, written in terms of a set of basis functions defined +using the `BasisFamily` class. The resulting trajectory is return +as a `SystemTrajectory` object and can be evaluated using the +`SystemTrajectory.eval` member function. Alternatively, the +`solve_flat_optimal` function can be used to solve an optimal control +problem with trajectory and final costs or constraints. The docstring examples assume that the following import commands:: @@ -61,15 +28,18 @@ """ # Basis function families -from .basis import BasisFamily -from .poly import PolyFamily -from .bezier import BezierFamily -from .bspline import BSplineFamily +from .basis import BasisFamily as BasisFamily +from .bezier import BezierFamily as BezierFamily +from .bspline import BSplineFamily as BSplineFamily +from .poly import PolyFamily as PolyFamily # Classes -from .systraj import SystemTrajectory -from .flatsys import FlatSystem, flatsys -from .linflat import LinearFlatSystem +from .systraj import SystemTrajectory as SystemTrajectory +from .flatsys import FlatSystem as FlatSystem +from .flatsys import flatsys as flatsys +from .linflat import LinearFlatSystem as LinearFlatSystem # Package functions -from .flatsys import point_to_point, solve_flat_ocp +from .flatsys import point_to_point as point_to_point +from .flatsys import solve_flat_optimal as solve_flat_optimal +from .flatsys import solve_flat_ocp as solve_flat_ocp diff --git a/control/flatsys/basis.py b/control/flatsys/basis.py index 04abce88a..c1d295577 100644 --- a/control/flatsys/basis.py +++ b/control/flatsys/basis.py @@ -1,47 +1,19 @@ # basis.py - BasisFamily class # RMM, 10 Nov 2012 -# -# The BasisFamily class is used to specify a set of basis functions for -# implementing differential flatness computations. -# -# Copyright (c) 2012 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. + +"""Define base class for implementing basis functions. + +This module defines the `BasisFamily` class that used to specify a set +of basis functions for implementing differential flatness computations. + +""" import numpy as np # Basis family class (for use as a base class) class BasisFamily: - """Base class for implementing basis functions for flat systems. + """Base class for basis functions for flat systems. A BasisFamily object is used to construct trajectories for a flat system. The class must implement a single function that computes the jth @@ -53,11 +25,23 @@ class BasisFamily: each flat output (nvars = None) or a different variable for different flat outputs (nvars > 0). - Attributes + Parameters ---------- N : int Order of the basis set. + Attributes + ---------- + nvars : int or None + Number of variables represented by the basis (possibly of different + order/length). Default is None (single variable). + + coef_offset : list + Coefficient offset for each variable. + + coef_length : list + Coefficient length for each variable. + """ def __init__(self, N): """Create a basis family of order N.""" @@ -71,15 +55,43 @@ def __repr__(self): f'N={self.N}>' def __call__(self, i, t, var=None): - """Evaluate the ith basis function at a point in time""" + """Evaluate the ith basis function at a point in time.""" return self.eval_deriv(i, 0, t, var=var) def var_ncoefs(self, var): - """Get the number of coefficients for a variable""" + """Get the number of coefficients for a variable. + + Parameters + ---------- + var : int + Variable offset. + + Returns + ------- + int + + """ return self.N if self.nvars is None else self.coef_length[var] def eval(self, coeffs, tlist, var=None): - """Compute function values given the coefficients and time points.""" + """Compute function values given the coefficients and time points. + + Parameters + ---------- + coeffs : array + Basis function coefficient values. + tlist : array + List of times at which to evaluate the function. + var : int or None, optional + Number of independent variables represented using the basis. + If None, then basis represents a single variable. + + Returns + ------- + array + Values of the variable(s) at the times in `tlist`. + + """ if self.nvars is None and var != None: raise SystemError("multi-variable call to a scalar basis") @@ -108,6 +120,23 @@ def eval(self, coeffs, tlist, var=None): for i in range(self.var_ncoefs(var))]) for t in tlist]) - def eval_deriv(self, i, j, t, var=None): - """Evaluate the kth derivative of the ith basis function at time t.""" + def eval_deriv(self, i, k, t, var=None): + """Evaluate kth derivative of ith basis function at time t. + + Parameters + ---------- + i : int + Basis function offset. + k : int + Derivative order. + t : float + Time at which to evaluating the derivative. + var : int or None, optional + Variable offset. + + Returns + ------- + float + + """ raise NotImplementedError("Internal error; improper basis functions") diff --git a/control/flatsys/bezier.py b/control/flatsys/bezier.py index fcf6201e9..41b8d1cb3 100644 --- a/control/flatsys/bezier.py +++ b/control/flatsys/bezier.py @@ -1,47 +1,21 @@ # bezier.m - 1D Bezier curve basis functions # RMM, 24 Feb 2021 -# -# This class implements a set of basis functions based on Bezier curves: -# -# \phi_i(t) = \sum_{i=0}^n {n \choose i} (T - t)^{n-i} t^i -# - -# Copyright (c) 2012 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. + +r"""1D Bezier curve basis functions. + +This module defines the `BezierFamily` class, which implements a set of +basis functions based on Bezier curves: + +.. math:: \phi_i(t) = \sum_{i=0}^n {n \choose i} (T - t)^{n-i} t^i + +""" import numpy as np from scipy.special import binom, factorial + from .basis import BasisFamily + class BezierFamily(BasisFamily): r"""Bezier curve basis functions. @@ -58,7 +32,7 @@ class BezierFamily(BasisFamily): Degree of the Bezier curve. T : float - Final time (used for rescaling). + Final time (used for rescaling). Default value is 1. """ def __init__(self, N, T=1): @@ -68,7 +42,11 @@ def __init__(self, N, T=1): # Compute the kth derivative of the ith basis function at time t def eval_deriv(self, i, k, t, var=None): - """Evaluate the kth derivative of the ith basis function at time t.""" + """Evaluate kth derivative of ith basis function at time t. + + See `BasisFamily.eval_deriv` for more information. + + """ if i >= self.N: raise ValueError("Basis function index too high") elif k >= self.N: diff --git a/control/flatsys/bspline.py b/control/flatsys/bspline.py index c771beb59..f8247f04e 100644 --- a/control/flatsys/bspline.py +++ b/control/flatsys/bspline.py @@ -1,14 +1,19 @@ # bspline.py - B-spline basis functions # RMM, 2 Aug 2022 -# -# This class implements a set of B-spline basis functions that implement a -# piecewise polynomial at a set of breakpoints t0, ..., tn with given orders -# and smoothness. -# + +"""B-spline basis functions. + +This module implements a set of B-spline basis functions that +implement a piecewise polynomial at a set of breakpoints t0, ..., tn +with given orders and smoothness. + +""" import numpy as np +from scipy.interpolate import BSpline + from .basis import BasisFamily -from scipy.interpolate import BSpline, splev + class BSplineFamily(BasisFamily): """B-spline basis functions. @@ -38,7 +43,7 @@ class BSplineFamily(BasisFamily): The number of spline variables. If specified as None (default), then the spline basis describes a single variable, with no indexing. If the number of spine variables is > 0, then the spline basis is - index using the `var` keyword. + indexed using the `var` keyword. """ def __init__(self, breakpoints, degree, smoothness=None, vars=None): @@ -120,7 +125,7 @@ def process_spline_parameters( smoothness, nvars, (int), name='smoothness', minimum=0, default=[d - 1 for d in degree]) - # Make sure degree is sufficent for the level of smoothness + # Make sure degree is sufficient for the level of smoothness if any([degree[i] - smoothness[i] < 1 for i in range(nvars)]): raise ValueError("degree must be greater than smoothness") @@ -180,7 +185,11 @@ def __repr__(self): # Compute the kth derivative of the ith basis function at time t def eval_deriv(self, i, k, t, var=None): - """Evaluate the kth derivative of the ith basis function at time t.""" + """Evaluate kth derivative of ith basis function at time t. + + See `BasisFamily.eval_deriv` for more information. + + """ if self.nvars is None or (self.nvars == 1 and var is None): # Use same variable for all requests var = 0 diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index 0101d126b..92d32d01d 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -1,52 +1,24 @@ # flatsys.py - trajectory generation for differentially flat systems # RMM, 10 Nov 2012 -# -# This file contains routines for computing trajectories for differentially -# flat nonlinear systems. It is (very) loosely based on the NTG software -# package developed by Mark Milam and Kudah Mushambi, but rewritten from -# scratch in python. -# -# Copyright (c) 2012 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. + +"""Trajectory generation for differentially flat systems. + +""" import itertools +import warnings + import numpy as np import scipy as sp import scipy.optimize -import warnings -from .poly import PolyFamily -from .systraj import SystemTrajectory + +from ..config import _process_kwargs, _process_param +from ..exception import ControlArgument from ..nlsys import NonlinearIOSystem +from ..optimal import _optimal_aliases from ..timeresp import _check_convert_array +from .poly import PolyFamily +from .systraj import SystemTrajectory # Flat system class (for use as a base class) @@ -54,22 +26,49 @@ class FlatSystem(NonlinearIOSystem): """Base class for representing a differentially flat system. The FlatSystem class is used as a base class to describe differentially - flat systems for trajectory generation. The output of the system does not - need to be the differentially flat output. + flat systems for trajectory generation. The output of the system does + not need to be the differentially flat output. Flat systems are + usually created with the `flatsys` factory function. + + Parameters + ---------- + forward : callable + A function to compute the flat flag given the states and input. + reverse : callable + A function to compute the states and input given the flat flag. + dt : None, True or float, optional + System timebase. + + Attributes + ---------- + ninputs, noutputs, nstates : int + Number of input, output and state variables. + shape : tuple + 2-tuple of I/O system dimension, (noutputs, ninputs). + input_labels, output_labels, state_labels : list of str + Names for the input, output, and state variables. + name : string, optional + System name. + + See Also + -------- + flatsys Notes ----- The class must implement two functions: - zflag = flatsys.foward(x, u, params) + ``zflag = flatsys.forward(x, u, params)`` + This function computes the flag (derivatives) of the flat output. - The inputs to this function are the state 'x' and inputs 'u' (both + The inputs to this function are the state `x` and inputs `u` (both 1D arrays). The output should be a 2D array with the first dimension equal to the number of system inputs and the second dimension of the length required to represent the full system dynamics (typically the number of states) - x, u = flatsys.reverse(zflag, params) + ``x, u = flatsys.reverse(zflag, params)`` + This function system state and inputs give the the flag (derivatives) of the flat output. The input to this function is an 2D array whose first dimension is equal to the number of system inputs and whose @@ -78,17 +77,19 @@ class FlatSystem(NonlinearIOSystem): `x` and inputs `u` (both 1D arrays). A flat system is also an input/output system supporting simulation, - composition, and linearization. If the update and output methods are - given, they are used in place of the flat coordinates. + composition, and linearization. In the current implementation, the + update function must be given explicitly, but the output function + defaults to the flat outputs. If the output method is given, it is + used in place of the flat outputs. """ def __init__(self, forward, reverse, # flat system - updfcn=None, outfcn=None, # nonlinar I/O system + updfcn=None, outfcn=None, # nonlinear I/O system **kwargs): # I/O system """Create a differentially flat I/O system. - The FlatIOSystem constructor is used to create an input/output system + The `FlatSystem` constructor is used to create an input/output system object that also represents a differentially flat system. """ @@ -113,7 +114,6 @@ def __str__(self): + f"Reverse: {self.reverse}" def forward(self, x, u, params=None): - """Compute the flat flag given the states and input. Given the states and inputs for a system, compute the flat @@ -134,7 +134,7 @@ def forward(self, x, u, params=None): Returns ------- zflag : list of 1D arrays - For each flat output :math:`z_i`, zflag[i] should be an + For each flat output :math:`z_i`, `zflag[i]` should be an ndarray of length :math:`q_i` that contains the flat output and its first :math:`q_i` derivatives. @@ -176,20 +176,26 @@ def _flat_outfcn(self, t, x, u, params=None): def flatsys(*args, updfcn=None, outfcn=None, **kwargs): - """Create a differentially flat I/O system. + """flatsys(forward, reverse[, updfcn, outfcn]) \ + flatsys(linsys) + + Create a differentially flat I/O system. The flatsys() function is used to create an input/output system object that also represents a differentially flat system. It can be used in a variety of forms: ``fs.flatsys(forward, reverse)`` - Create a flat system with mapings to/from flat flag. + + Create a flat system with mappings to/from flat flag. ``fs.flatsys(forward, reverse, updfcn[, outfcn])`` + Create a flat system that is also a nonlinear I/O system. ``fs.flatsys(linsys)`` - Create a flat system from a linear (StateSpace) system. + + Create a flat system from a linear (`StateSpace`) system. Parameters ---------- @@ -202,28 +208,28 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): updfcn : callable, optional Function returning the state update function - `updfcn(t, x, u[, param]) -> array` + ``updfcn(t, x, u[, params]) -> array`` where `x` is a 1-D array with shape (nstates,), `u` is a 1-D array - with shape (ninputs,), `t` is a float representing the currrent - time, and `param` is an optional dict containing the values of + with shape (ninputs,), `t` is a float representing the current + time, and `params` is an optional dict containing the values of parameters used by the function. If not specified, the state space update will be computed using the flat system coordinates. outfcn : callable, optional Function returning the output at the given state - `outfcn(t, x, u[, param]) -> array` + ``outfcn(t, x, u[, params]) -> array`` - where the arguments are the same as for `upfcn`. If not + where the arguments are the same as for `updfcn`. If not specified, the output will be the flat outputs. inputs : int, list of str, or None Description of the system inputs. This can be given as an integer count or as a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be - of the form `s[i]` (where `s` is one of `u`, `y`, or `x`). If - this parameter is not given or given as `None`, the relevant + of the form 's[i]' (where 's' is one of 'u', 'y', or 'x'). If + this parameter is not given or given as None, the relevant quantity will be determined when possible based on other information provided to functions using the system. @@ -234,10 +240,9 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): Description of the system states. Same format as `inputs`. dt : None, True or float, optional - System timebase. None (default) indicates continuous - time, True indicates discrete time with undefined sampling - time, positive number is discrete time with specified - sampling time. + System timebase. None (default) indicates continuous time, True + indicates discrete time with undefined sampling time, positive + number is discrete time with specified sampling time. params : dict, optional Parameter values for the systems. Passed to the evaluation @@ -245,17 +250,22 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): defaults. name : string, optional - System name (used for specifying signals) + System name (used for specifying signals). Returns ------- - sys: :class:`FlatSystem` + sys : `FlatSystem` Flat system. + Other Parameters + ---------------- + input_prefix, output_prefix, state_prefix : string, optional + Set the prefix for input, output, and state signals. Defaults = + 'u', 'y', 'x'. + """ - from .linflat import LinearFlatSystem from ..statesp import StateSpace - from ..iosys import _process_iosys_keywords + from .linflat import LinearFlatSystem if len(args) == 1 and isinstance(args[0], StateSpace): # We were passed a linear system, so call linflat @@ -293,7 +303,7 @@ def flatsys(*args, updfcn=None, outfcn=None, **kwargs): def _basis_flag_matrix(sys, basis, flag, t): """Compute the matrix of basis functions and their derivatives - This function computes the matrix ``M`` that is used to solve for the + This function computes the matrix `M` that is used to solve for the coefficients of the basis functions given the state and input. Each column of the matrix corresponds to a basis function and each row is a derivative, with the derivatives (flag) for each output stacked on top @@ -316,7 +326,8 @@ def _basis_flag_matrix(sys, basis, flag, t): # Solve a point to point trajectory generation problem for a flat system def point_to_point( - sys, timepts, x0=0, u0=0, xf=0, uf=0, T0=0, cost=None, basis=None, + sys, timepts, initial_state=0, initial_input=0, final_state=0, + final_input=0, initial_time=0, integral_cost=None, basis=None, trajectory_constraints=None, initial_guess=None, params=None, **kwargs): """Compute trajectory between an initial and final conditions. @@ -325,72 +336,100 @@ def point_to_point( Parameters ---------- - flatsys : FlatSystem object + sys : `FlatSystem` object Description of the differentially flat system. This object must - define a function `flatsys.forward()` that takes the system state and - produceds the flag of flat outputs and a system `flatsys.reverse()` - that takes the flag of the flat output and prodes the state and - input. - + define a function `~FlatSystem.forward` that takes the system state + and produces the flag of flat outputs and a function + `~FlatSystem.reverse` that takes the flag of the flat output and + produces the state and input. timepts : float or 1D array_like The list of points for evaluating cost and constraints, as well as the time horizon. If given as a float, indicates the final time for the trajectory (corresponding to xf) - - x0, u0, xf, uf : 1D arrays - Define the desired initial and final conditions for the system. If - any of the values are given as None, they are replaced by a vector of - zeros of the appropriate dimension. - - T0 : float, optional + initial_state (or x0) : 1D array_like + Initial state for the system. Defaults to zero. + initial_input (or u0) : 1D array_like + Initial input for the system. Defaults to zero. + final_state (or xf) : 1D array_like + Final state for the system. Defaults to zero. + final_input (or uf) : 1D array_like + Final input for the system. Defaults to zero. + initial_time (or T0) : float, optional The initial time for the trajectory (corresponding to x0). If not specified, its value is taken to be zero. - - basis : :class:`~control.flatsys.BasisFamily` object, optional + basis : `BasisFamily` object, optional The basis functions to use for generating the trajectory. If not - specified, the :class:`~control.flatsys.PolyFamily` basis family + specified, the `PolyFamily` basis family will be used, with the minimal number of elements required to find a feasible trajectory (twice the number of system states) - - cost : callable + integral_cost (or cost) : callable Function that returns the integral cost given the current state - and input. Called as `cost(x, u)`. - - trajectory_constraints : list of tuples, optional - List of constraints that should hold at each point in the time vector. - Each element of the list should consist of a tuple with first element - given by :class:`scipy.optimize.LinearConstraint` or - :class:`scipy.optimize.NonlinearConstraint` and the remaining - elements of the tuple are the arguments that would be passed to those + and input. Called as ``integral_cost(x, u)``. + trajectory_constraints (or constraints) : list of tuples, optional + List of constraints that should hold at each point in the time + vector. Each element of the list should consist of a tuple with + first element given by `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` and the remaining elements of + the tuple are the arguments that would be passed to those functions. The following tuples are supported: - * (LinearConstraint, A, lb, ub): The matrix A is multiplied by stacked - vector of the state and input at each point on the trajectory for - comparison against the upper and lower bounds. + * (LinearConstraint, A, lb, ub): The matrix A is multiplied by + stacked vector of the state and input at each point on the + trajectory for comparison against the upper and lower bounds. * (NonlinearConstraint, fun, lb, ub): a user-specific constraint - function `fun(x, u)` is called at each point along the trajectory - and compared against the upper and lower bounds. + function ``fun(x, u)`` is called at each point along the + trajectory and compared against the upper and lower bounds. The constraints are applied at each time point along the trajectory. - - minimize_kwargs : str, optional - Pass additional keywords to :func:`scipy.optimize.minimize`. + initial_guess : 2D array_like, optional + Initial guess for the trajectory coefficients (not implemented). + params : dict, optional + Parameter values for the system. Passed to the evaluation + functions for the system as default values, overriding internal + defaults. Returns ------- - traj : :class:`~control.flatsys.SystemTrajectory` object + traj : `SystemTrajectory` object The system trajectory is returned as an object that implements the - `eval()` function, we can be used to compute the value of the state - and input and a given time t. + `~SystemTrajectory.eval` function, we can be used to + compute the value of the state and input and a given time t. + + Other Parameters + ---------------- + minimize_method : str, optional + Set the method used by `scipy.optimize.minimize`. + minimize_options : str, optional + Set the options keyword used by `scipy.optimize.minimize`. + minimize_kwargs : str, optional + Pass additional keywords to `scipy.optimize.minimize`. Notes ----- Additional keyword parameters can be used to fine tune the behavior of the underlying optimization function. See `minimize_*` keywords in - :func:`OptimalControlProblem` for more information. + `OptimalControlProblem` for more information. """ + # Process parameter and keyword arguments + _process_kwargs(kwargs, _optimal_aliases) + x0 = _process_param( + 'initial_state', initial_state, kwargs, _optimal_aliases, sigval=0) + u0 = _process_param( + 'initial_input', initial_input, kwargs, _optimal_aliases, sigval=0) + xf = _process_param( + 'final_state', final_state, kwargs, _optimal_aliases, sigval=0) + uf = _process_param( + 'final_input', final_input, kwargs, _optimal_aliases, sigval=0) + T0 = _process_param( + 'initial_time', initial_time, kwargs, _optimal_aliases, sigval=0) + cost = _process_param( + 'integral_cost', integral_cost, kwargs, _optimal_aliases) + trajectory_constraints = _process_param( + 'trajectory_constraints', trajectory_constraints, kwargs, + _optimal_aliases) + # # Make sure the problem is one that we can handle # @@ -408,11 +447,6 @@ def point_to_point( Tf = timepts[-1] T0 = timepts[0] if len(timepts) > 1 else T0 - # Process keyword arguments - if trajectory_constraints is None: - # Backwards compatibility - trajectory_constraints = kwargs.pop('constraints', None) - minimize_kwargs = {} minimize_kwargs['method'] = kwargs.pop('minimize_method', None) minimize_kwargs['options'] = kwargs.pop('minimize_options', {}) @@ -491,7 +525,7 @@ def point_to_point( warnings.warn("basis too small; solution may not exist") if cost is not None or trajectory_constraints is not None: - # Make sure that we have enough timepoints to evaluate + # Make sure that we have enough time points to evaluate if timepts.size < 3: raise ControlArgument( "There must be at least three time points if trajectory" @@ -631,96 +665,114 @@ def traj_const(null_coeffs): # Solve a point to point trajectory generation problem for a flat system -def solve_flat_ocp( - sys, timepts, x0=0, u0=0, trajectory_cost=None, basis=None, - terminal_cost=None, trajectory_constraints=None, +def solve_flat_optimal( + sys, timepts, initial_state=0, initial_input=0, integral_cost=None, + basis=None, terminal_cost=None, trajectory_constraints=None, initial_guess=None, params=None, **kwargs): """Compute trajectory between an initial and final conditions. - Compute an optimial trajectory for a differentially flat system starting + Compute an optimal trajectory for a differentially flat system starting from an initial state and input value. Parameters ---------- - flatsys : FlatSystem object + sys : `FlatSystem` object Description of the differentially flat system. This object must - define a function `flatsys.forward()` that takes the system state and - produceds the flag of flat outputs and a system `flatsys.reverse()` - that takes the flag of the flat output and prodes the state and - input. - + define a function `~FlatSystem.forward` that takes the system state + and produces the flag of flat outputs and a function + `~FlatSystem.reverse` that takes the flag of the flat output and + produces the state and input. timepts : float or 1D array_like The list of points for evaluating cost and constraints, as well as the time horizon. If given as a float, indicates the final time for the trajectory (corresponding to xf) - - x0, u0 : 1D arrays - Define the initial conditions for the system. If either of the - values are given as None, they are replaced by a vector of zeros of - the appropriate dimension. - - basis : :class:`~control.flatsys.BasisFamily` object, optional + initial_state (or x0), input_input (or u0) : 1D arrays + Define the initial conditions for the system (default = 0). + initial_input (or u0) : 1D array_like + Initial input for the system. Defaults to zero. + basis : `BasisFamily` object, optional The basis functions to use for generating the trajectory. If not - specified, the :class:`~control.flatsys.PolyFamily` basis family + specified, the `PolyFamily` basis family will be used, with the minimal number of elements required to find a feasible trajectory (twice the number of system states) - - trajectory_cost : callable + integral_cost : callable Function that returns the integral cost given the current state - and input. Called as `cost(x, u)`. - + and input. Called as ``cost(x, u)``. terminal_cost : callable Function that returns the terminal cost given the state and input. - Called as `cost(x, u)`. - + Called as ``cost(x, u)``. trajectory_constraints : list of tuples, optional - List of constraints that should hold at each point in the time vector. - Each element of the list should consist of a tuple with first element - given by :class:`scipy.optimize.LinearConstraint` or - :class:`scipy.optimize.NonlinearConstraint` and the remaining - elements of the tuple are the arguments that would be passed to those + List of constraints that should hold at each point in the time + vector. Each element of the list should consist of a tuple with + first element given by `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` and the remaining elements of + the tuple are the arguments that would be passed to those functions. The following tuples are supported: - * (LinearConstraint, A, lb, ub): The matrix A is multiplied by stacked - vector of the state and input at each point on the trajectory for - comparison against the upper and lower bounds. + * (LinearConstraint, A, lb, ub): The matrix A is multiplied by + stacked vector of the state and input at each point on the + trajectory for comparison against the upper and lower bounds. * (NonlinearConstraint, fun, lb, ub): a user-specific constraint - function `fun(x, u)` is called at each point along the trajectory - and compared against the upper and lower bounds. + function ``fun(x, u)`` is called at each point along the + trajectory and compared against the upper and lower bounds. The constraints are applied at each time point along the trajectory. - initial_guess : 2D array_like, optional Initial guess for the optimal trajectory of the flat outputs. - - minimize_kwargs : str, optional - Pass additional keywords to :func:`scipy.optimize.minimize`. + params : dict, optional + Parameter values for the system. Passed to the evaluation + functions for the system as default values, overriding internal + defaults. Returns ------- - traj : :class:`~control.flatsys.SystemTrajectory` object + traj : `SystemTrajectory` The system trajectory is returned as an object that implements the - `eval()` function, we can be used to compute the value of the state - and input and a given time t. + `SystemTrajectory.eval` function, we can be used to + compute the value of the state and input and a given time `t`. + + Other Parameters + ---------------- + minimize_method : str, optional + Set the method used by `scipy.optimize.minimize`. + + minimize_options : str, optional + Set the options keyword used by `scipy.optimize.minimize`. + + minimize_kwargs : str, optional + Pass additional keywords to `scipy.optimize.minimize`. Notes ----- - 1. Additional keyword parameters can be used to fine tune the behavior - of the underlying optimization function. See `minimize_*` keywords - in :func:`~control.optimal.OptimalControlProblem` for more information. + Additional keyword parameters can be used to fine tune the behavior of + the underlying optimization function. See `minimize_*` keywords in + `control.optimal.OptimalControlProblem` for more information. + + The return data structure includes the following additional attributes: - 2. The return data structure includes the following additional attributes: - * success : bool indicating whether the optimization succeeded - * cost : computed cost of the returned trajectory - * message : message returned by optimization if success if False + * `success` : bool indicating whether the optimization succeeded + * `cost` : computed cost of the returned trajectory + * `message` : message returned by optimization if success if False - 3. A common failure in solving optimal control problem is that the - default initial guess violates the constraints and the optimizer - can't find a feasible solution. Using the `initial_guess` parameter - can often be used to overcome these errors. + A common failure in solving optimal control problem is that the default + initial guess violates the constraints and the optimizer can't find a + feasible solution. Using the `initial_guess` parameter can often be + used to overcome these errors. """ + # Process parameter and keyword arguments + _process_kwargs(kwargs, _optimal_aliases) + x0 = _process_param( + 'initial_state', initial_state, kwargs, _optimal_aliases, sigval=0) + u0 = _process_param( + 'initial_input', initial_input, kwargs, _optimal_aliases, sigval=0) + trajectory_cost = _process_param( + 'integral_cost', integral_cost, kwargs, _optimal_aliases) + trajectory_constraints = _process_param( + 'trajectory_constraints', trajectory_constraints, kwargs, + _optimal_aliases) + # # Make sure the problem is one that we can handle # @@ -731,16 +783,7 @@ def solve_flat_ocp( # Process final time timepts = np.atleast_1d(timepts) - Tf = timepts[-1] - T0 = timepts[0] if len(timepts) > 1 else T0 - - # Process keyword arguments - if trajectory_constraints is None: - # Backwards compatibility - trajectory_constraints = kwargs.pop('constraints', None) - if trajectory_cost is None: - # Compatibility with point_to_point - trajectory_cost = kwargs.pop('cost', None) + T0 = timepts[0] if len(timepts) > 1 else 0 minimize_kwargs = {} minimize_kwargs['method'] = kwargs.pop('minimize_method', None) @@ -962,3 +1005,7 @@ def traj_const(null_coeffs): # Return a function that computes inputs and states as a function of time return systraj + + +# Convenience aliases +solve_flat_ocp = solve_flat_optimal diff --git a/control/flatsys/linflat.py b/control/flatsys/linflat.py index e03df514d..724586db6 100644 --- a/control/flatsys/linflat.py +++ b/control/flatsys/linflat.py @@ -1,44 +1,16 @@ # linflat.py - FlatSystem subclass for linear systems # RMM, 10 November 2012 -# -# This file defines a FlatSystem class for a linear system. -# -# Copyright (c) 2012 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. + +"""FlatSystem class for a linear system. + +""" import numpy as np + import control -from .flatsys import FlatSystem + from ..statesp import StateSpace +from .flatsys import FlatSystem class LinearFlatSystem(FlatSystem, StateSpace): @@ -49,14 +21,14 @@ class LinearFlatSystem(FlatSystem, StateSpace): Parameters ---------- - linsys : StateSpace - LTI StateSpace system to be converted + linsys : `StateSpace` + LTI `StateSpace` system to be converted. inputs : int, list of str or None, optional Description of the system inputs. This can be given as an integer count or as a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be - of the form `s[i]` (where `s` is one of `u`, `y`, or `x`). If - this parameter is not given or given as `None`, the relevant + of the form 's[i]' (where 's' is one of 'u', 'y', or 'x'). If + this parameter is not given or given as None, the relevant quantity will be determined when possible based on other information provided to functions using the system. outputs : int, list of str or None, optional @@ -73,7 +45,7 @@ class LinearFlatSystem(FlatSystem, StateSpace): functions for the system as default values, overriding internal defaults. name : string, optional - System name (used for specifying signals) + System name (used for specifying signals). """ @@ -87,7 +59,7 @@ def __init__(self, linsys, **kwargs): # Make sure we can handle the system if (not control.isctime(linsys)): raise control.ControlNotImplemented( - "requires continuous time, linear control system") + "requires continuous-time, linear control system") elif (not control.issiso(linsys)): raise control.ControlNotImplemented( "only single input, single output systems are supported") @@ -113,7 +85,7 @@ def __init__(self, linsys, **kwargs): def forward(self, x, u, params): """Compute the flat flag given the states and input. - See :func:`control.flatsys.FlatSystem.forward` for more info. + See `FlatSystem.forward` for more info. """ x = np.reshape(x, (-1, 1)) @@ -130,7 +102,7 @@ def forward(self, x, u, params): def reverse(self, zflag, params): """Compute the states and input given the flat flag. - See :func:`control.flatsys.FlatSystem.reverse` for more info. + See `FlatSystem.reverse` for more info. """ z = zflag[0][0:-1] diff --git a/control/flatsys/poly.py b/control/flatsys/poly.py index f315091aa..8902bc795 100644 --- a/control/flatsys/poly.py +++ b/control/flatsys/poly.py @@ -1,46 +1,21 @@ # poly.m - simple set of polynomial basis functions -# TODO: rename this as taylor.m # RMM, 10 Nov 2012 # -# This class implements a set of simple basis functions consisting of powers -# of t: 1, t, t^2, ... -# -# Copyright (c) 2012 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. +# TODO: rename this as taylor.m? + +"""Simple set of polynomial basis functions. + +This class implements a set of simple basis functions consisting of +powers of t: 1, t, t^2, ... + +""" import numpy as np from scipy.special import factorial + from .basis import BasisFamily + class PolyFamily(BasisFamily): r"""Polynomial basis functions. @@ -52,10 +27,10 @@ class PolyFamily(BasisFamily): Parameters ---------- N : int - Degree of the Bezier curve. + Degree of the polynomial. T : float - Final time (used for rescaling). + Final time (used for rescaling). Default value is 1. """ def __init__(self, N, T=1): @@ -65,7 +40,11 @@ def __init__(self, N, T=1): # Compute the kth derivative of the ith basis function at time t def eval_deriv(self, i, k, t, var=None): - """Evaluate the kth derivative of the ith basis function at time t.""" + """Evaluate kth derivative of ith basis function at time t. + + See `BasisFamily.eval_deriv` for more information. + + """ if (i < k): return 0 * t # higher derivative than power return factorial(i)/factorial(i-k) * \ np.power(t/self.T, i-k) / np.power(self.T, k) diff --git a/control/flatsys/systraj.py b/control/flatsys/systraj.py index 0fbd4e982..2de778d88 100644 --- a/control/flatsys/systraj.py +++ b/control/flatsys/systraj.py @@ -1,70 +1,46 @@ # systraj.py - SystemTrajectory class # RMM, 10 November 2012 -# -# The SystemTrajetory class is used to store a feasible trajectory for -# the state and input of a (nonlinear) control system. -# -# Copyright (c) 2012 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. + +"""SystemTrajectory class. + +The SystemTrajectory class is used to store a feasible trajectory for +the state and input of a (nonlinear) control system. + +""" import numpy as np + from ..timeresp import TimeResponseData + class SystemTrajectory: - """Class representing a trajectory for a flat system. + """Trajectory for a differentially flat system. - The `SystemTrajectory` class is used to represent the - trajectory of a (differentially flat) system. Used by the - :func:`~control.trajsys.point_to_point` function to return a trajectory. + The `SystemTrajectory` class is used to represent the trajectory + of a (differentially flat) system. Used by the `point_to_point` + and `solve_flat_optimal` functions to return a trajectory. Parameters ---------- - sys : FlatSystem + sys : `FlatSystem` Flat system object associated with this trajectory. - basis : BasisFamily + basis : `BasisFamily` Family of basis vectors to use to represent the trajectory. coeffs : list of 1D arrays, optional For each flat output, define the coefficients of the basis functions used to represent the trajectory. Defaults to an empty list. - flaglen : list of ints, optional + flaglen : list of int, optional For each flat output, the number of derivatives of the flat output used to define the trajectory. Defaults to an empty list. + params : dict, optional + Parameter values used for the trajectory. """ def __init__(self, sys, basis, coeffs=[], flaglen=[], params=None): - """Initilize a system trajectory object.""" + """Initialize a system trajectory object.""" self.nstates = sys.nstates self.ninputs = sys.ninputs self.system = sys @@ -75,7 +51,7 @@ def __init__(self, sys, basis, coeffs=[], flaglen=[], params=None): # Evaluate the trajectory over a list of time points def eval(self, tlist): - """Return the state and input for a trajectory at a list of times. + """Compute state and input for a trajectory at a list of times. Evaluate the trajectory at a list of time points, returning the state and input vectors for the trajectory: @@ -120,73 +96,73 @@ def eval(self, tlist): return xd, ud # Return the system trajectory as a TimeResponseData object - def response(self, tlist, transpose=False, return_x=False, squeeze=None): - """Return the trajectory of a system as a TimeResponseData object + def response(self, timepts, transpose=False, return_x=False, squeeze=None): + """Compute trajectory of a system as a TimeResponseData object. Evaluate the trajectory at a list of time points, returning the state and input vectors for the trajectory: - response = traj.response(tlist) + response = traj.response(timepts) time, yd, ud = response.time, response.outputs, response.inputs Parameters ---------- - tlist : 1D array + timepts : 1D array List of times to evaluate the trajectory. transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional If True, return the state vector when assigning to a tuple - (default = False). See :func:`forced_response` for more details. + (default = False). See `forced_response` for more details. squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) then - the output response is returned as a 1D array (indexed by time). - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, - keep the output as a 3D array (indexed by the output, input, and - time) even if the system is SISO. The default value can be set - using config.defaults['control.squeeze_time_response']. + By default, if a system is single-input, single-output (SISO) + then the output response is returned as a 1D array (indexed by + time). If `squeeze` = True, remove single-dimensional entries + from the shape of the output even if the system is not SISO. If + `squeeze` = False, keep the output as a 3D array (indexed by + the output, input, and time) even if the system is SISO. The + default value can be set using + `config.defaults['control.squeeze_time_response']`. Returns ------- - results : TimeResponseData - Time response represented as a :class:`TimeResponseData` object - containing the following properties: - - * time (array): Time values of the output. - - * outputs (array): Response of the system. If the system is SISO - and squeeze is not True, the array is 1D (indexed by time). If - the system is not SISO or ``squeeze`` is False, the array is 3D - (indexed by the output, trace, and time). - - * states (array): Time evolution of the state vector, represented - as either a 2D array indexed by state and time (if SISO) or a 3D - array indexed by state, trace, and time. Not affected by - ``squeeze``. - - * inputs (array): Input(s) to the system, indexed in the same - manner as ``outputs``. - - The return value of the system can also be accessed by assigning - the function to a tuple of length 2 (time, output) or of length 3 - (time, output, state) if ``return_x`` is ``True``. + response : `TimeResponseData` + Time response data object representing the input/output response. + When accessed as a tuple, returns ``(time, outputs)`` or ``(time, + outputs, states`` if `return_x` is True. If the input/output + system signals are named, these names will be used as labels for + the time response. If `sys` is a list of systems, returns a + `TimeResponseList` object. Results can be plotted using the + `~TimeResponseData.plot` method. See `TimeResponseData` for more + detailed information. + response.time : array + Time values of the output. + response.outputs : array + Response of the system. If the system is SISO and `squeeze` is + not True, the array is 1D (indexed by time). If the system is not + SISO or `squeeze` is False, the array is 2D (indexed by output and + time). + response.states : array + Time evolution of the state vector, represented as a 2D array + indexed by state and time. + response.inputs : array + Input(s) to the system, indexed by input and time. """ # Compute the state and input response using the eval function sys = self.system - xout, uout = self.eval(tlist) + xout, uout = self.eval(timepts) yout = np.array([ - sys.output(tlist[i], xout[:, i], uout[:, i]) - for i in range(len(tlist))]).transpose() + sys.output(timepts[i], xout[:, i], uout[:, i]) + for i in range(len(timepts))]).transpose() return TimeResponseData( - tlist, yout, xout, uout, issiso=sys.issiso(), + timepts, yout, xout, uout, issiso=sys.issiso(), input_labels=sys.input_labels, output_labels=sys.output_labels, - state_labels=sys.state_labels, + state_labels=sys.state_labels, sysname=sys.name, transpose=transpose, return_x=return_x, squeeze=squeeze) diff --git a/control/frdata.py b/control/frdata.py index 1b35c6b20..96d2cd5b6 100644 --- a/control/frdata.py +++ b/control/frdata.py @@ -1,82 +1,130 @@ # frdata.py - frequency response data representation and functions # -# Author: M.M. (Rene) van Paassen (using xferfcn.py as basis) -# Date: 02 Oct 12 +# Initial author: M.M. (Rene) van Paassen (using xferfcn.py as basis) +# Creation date: 02 Oct 2012 -""" -Frequency response data representation and functions. +"""Frequency response data representation and functions. + +This module contains the `FrequencyResponseData` (FRD) class and also +functions that operate on FRD data. -This module contains the FRD class and also functions that operate on -FRD data. """ +from collections.abc import Iterable from copy import copy from warnings import warn import numpy as np -from numpy import absolute, angle, array, empty, eye, imag, linalg, ones, \ - real, sort, where +from numpy import absolute, array, empty, eye, imag, linalg, ones, real, sort from scipy.interpolate import splev, splprep -from . import config +from . import bdalg, config from .exception import pandas_check -from .iosys import InputOutputSystem, _process_iosys_keywords, common_timebase +from .iosys import InputOutputSystem, NamedSignal, _extended_system_name, \ + _process_iosys_keywords, _process_subsys_index, common_timebase from .lti import LTI, _process_frequency_response __all__ = ['FrequencyResponseData', 'FRD', 'frd'] class FrequencyResponseData(LTI): - """FrequencyResponseData(d, w[, smooth]) + """FrequencyResponseData(frdata, omega[, smooth]) - A class for models defined by frequency response data (FRD). + Input/output model defined by frequency response data (FRD). The FrequencyResponseData (FRD) class is used to represent systems in frequency response data form. It can be created manually using the - class constructor, using the :func:~~control.frd` factory function - (preferred), or via the :func:`~control.frequency_response` function. + class constructor, using the `frd` factory function, or + via the `frequency_response` function. Parameters ---------- - d : 1D or 3D complex array_like + frdata : 1D or 3D complex array_like The frequency response at each frequency point. If 1D, the system is assumed to be SISO. If 3D, the system is MIMO, with the first dimension corresponding to the output index of the FRD, the second dimension corresponding to the input index, and the 3rd dimension - corresponding to the frequency points in omega - w : iterable of real frequencies - List of frequency points for which data are available. + corresponding to the frequency points in `omega`. When accessed as an + attribute, `frdata` is always stored as a 3D array. + omega : iterable of real frequencies + List of monotonically increasing frequency points for the response. + smooth : bool, optional + If True, create an interpolation function that allows the frequency + response to be computed at any frequency within the range of + frequencies give in `omega`. If False (default), frequency response + can only be obtained at the frequencies specified in `omega`. + dt : None, True or float, optional + System timebase. 0 (default) indicates continuous time, True + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, None + indicates unspecified timebase (either continuous or discrete time). + squeeze : bool + By default, if a system is single-input, single-output (SISO) then + the outputs (and inputs) are returned as a 1D array (indexed by + frequency) and if a system is multi-input or multi-output, then the + outputs are returned as a 2D array (indexed by output and + frequency) or a 3D array (indexed by output, trace, and frequency). + If `squeeze` = True, access to the output response will remove + single-dimensional entries from the shape of the inputs and outputs + even if the system is not SISO. If `squeeze` = False, the output is + returned as a 3D array (indexed by the output, input, and + frequency) even if the system is SISO. The default value can be set + using `config.defaults['control.squeeze_frequency_response']`. sysname : str or None Name of the system that generated the data. - smooth : bool, optional - If ``True``, create an interpolation function that allows the - frequency response to be computed at any frequency within the range of - frequencies give in ``w``. If ``False`` (default), frequency response - can only be obtained at the frequencies specified in ``w``. Attributes ---------- + complex : array + Complex frequency response, indexed by output index, input index, and + frequency point, with squeeze processing. + magnitude : array + Magnitude of the frequency response, indexed by output index, input + index, and frequency point, with squeeze processing. + phase : array + Phase of the frequency response, indexed by output index, input index, + and frequency point, with squeeze processing. + frequency : 1D array + Array of frequency points for which data are available. ninputs, noutputs : int - Number of input and output variables. - omega : 1D array - Frequency points of the response. - fresp : 3D array - Frequency response, indexed by output index, input index, and - frequency point. - dt : float, True, or None - System timebase. + Number of input and output signals. + shape : tuple + 2-tuple of I/O system dimension, (noutputs, ninputs). + input_labels, output_labels : array of str + Names for the input and output signals. + name : str + System name. For data generated using + `frequency_response`, stores the name of the + system that created the data. + + Other Parameters + ---------------- + plot_type : str, optional + Set the type of plot to generate with `~FrequencyResponseData.plot` + ('bode', 'nichols'). + title : str, optional + Set the title to use when plotting. + plot_magnitude, plot_phase : bool, optional + If set to False, don't plot the magnitude or phase, respectively. + return_magphase : bool, optional + If True, then a frequency response data object will enumerate + as a tuple of the form ``(mag, phase, omega)`` where where `mag` + is the magnitude (absolute value, not dB or log10) of the system + frequency response, `phase` is the wrapped phase in radians of the + system frequency response, and `omega` is the (sorted) frequencies + at which the response was evaluated. See Also -------- - frd + frd, frequency_response, InputOutputSystem, TransferFunction Notes ----- - The main data members are 'omega' and 'fresp', where 'omega' is a 1D array - of frequency points and and 'fresp' is a 3D array of frequency responses, - with the first dimension corresponding to the output index of the FRD, the - second dimension corresponding to the input index, and the 3rd dimension - corresponding to the frequency points in omega. For example, + The main data members are `omega` and `frdata`, where `omega` is a 1D + array of frequency points and and `frdata` is a 3D array of frequency + responses, with the first dimension corresponding to the output index of + the FRD, the second dimension corresponding to the input index, and the + 3rd dimension corresponding to the frequency points in omega. For example, >>> frdata[2,5,:] = numpy.array([1., 0.8-0.2j, 0.2-0.8j]) # doctest: +SKIP @@ -86,9 +134,19 @@ class constructor, using the :func:~~control.frd` factory function A frequency response data object is callable and returns the value of the transfer function evaluated at a point in the complex plane (must be on - the imaginary access). See :meth:`~control.FrequencyResponseData.__call__` + the imaginary axis). See `FrequencyResponseData.__call__` for a more detailed description. + Subsystem response corresponding to selected input/output pairs can be + created by indexing the frequency response data object:: + + subsys = sys[output_spec, input_spec] + + The input and output specifications can be single integers, lists of + integers, or slices. In addition, the strings representing the names + of the signals can be used and will be replaced with the equivalent + signal offsets. + """ # # Class attributes @@ -107,53 +165,95 @@ class constructor, using the :func:~~control.frd` factory function #: :meta hide-value: noutputs = 1 + #: Squeeze processing parameter. + #: + #: By default, if a system is single-input, single-output (SISO) then + #: the outputs (and inputs) are returned as a 1D array (indexed by + #: frequency) and if a system is multi-input or multi-output, then the + #: outputs are returned as a 2D array (indexed by output and frequency) + #: or a 3D array (indexed by output, trace, and frequency). If + #: `squeeze` = True, access to the output response will remove + #: single-dimensional entries from the shape of the inputs and outputs + #: even if the system is not SISO. If `squeeze` = False, the output is + #: returned as a 3D array (indexed by the output, input, and frequency) + #: even if the system is SISO. The default value can be set using + #: config.defaults['control.squeeze_frequency_response']. + #: + #: :meta hide-value: + squeeze = None + _epsw = 1e-8 #: Bound for exact frequency match def __init__(self, *args, **kwargs): - """Construct an FRD object. + """FrequencyResponseData(response, omega[, dt]) - The default constructor is FRD(d, w), where w is an iterable of - frequency points, and d is the matching frequency data. + Construct a frequency response data (FRD) object. - If d is a single list, 1D array, or tuple, a SISO system description - is assumed. d can also be + The default constructor is `FrequencyResponseData(response, omega)`, + where `omega` is an iterable of frequency points and `response` is + the matching frequency data. If `response` is a single list, 1D + array, or tuple, a SISO system description is assumed. `response` + can also be a 2D array, in which case a MIMO response is created. + To call the copy constructor, call `FrequencyResponseData(sys)`, + where `sys` is a FRD object. The timebase for the frequency + response can be provided using an optional third argument or the + `dt` keyword. - To call the copy constructor, call FRD(sys), where sys is a - FRD object. + To construct frequency response data for an existing LTI object, + other than an FRD, call `FrequencyResponseData(sys, omega)`. This + functionality can also be obtained using `frequency_response` + (which has additional options available). - To construct frequency response data for an existing LTI - object, other than an FRD, call FRD(sys, omega). + See `FrequencyResponseData` and `frd` for more + information. """ - # TODO: discrete-time FRD systems? smooth = kwargs.pop('smooth', False) # # Process positional arguments # + if len(args) == 3: + # Discrete time transfer function + dt = args[-1] + if 'dt' in kwargs: + warn("received multiple dt arguments, " + "using positional arg dt = %s" % dt) + kwargs['dt'] = dt + args = args[:-1] + if len(args) == 2: if not isinstance(args[0], FRD) and isinstance(args[0], LTI): - # not an FRD, but still a system, second argument should be - # the frequency range + # not an FRD, but still an LTI system, second argument + # should be the frequency range otherlti = args[0] self.omega = sort(np.asarray(args[1], dtype=float)) - # calculate frequency response at my points + + # calculate frequency response at specified points if otherlti.isctime(): s = 1j * self.omega - self.fresp = otherlti(s, squeeze=False) + self.frdata = otherlti(s, squeeze=False) else: z = np.exp(1j * self.omega * otherlti.dt) - self.fresp = otherlti(z, squeeze=False) + self.frdata = otherlti(z, squeeze=False) arg_dt = otherlti.dt + # Copy over signal and system names, if not specified + kwargs['inputs'] = kwargs.get('inputs', otherlti.input_labels) + kwargs['outputs'] = kwargs.get( + 'outputs', otherlti.output_labels) + if not otherlti._generic_name_check(): + kwargs['name'] = kwargs.get('name', _extended_system_name( + otherlti.name, prefix_suffix_name='sampled')) + else: # The user provided a response and a freq vector - self.fresp = array(args[0], dtype=complex, ndmin=1) - if self.fresp.ndim == 1: - self.fresp = self.fresp.reshape(1, 1, -1) + self.frdata = array(args[0], dtype=complex, ndmin=1) + if self.frdata.ndim == 1: + self.frdata = self.frdata.reshape(1, 1, -1) self.omega = array(args[1], dtype=float, ndmin=1) - if self.fresp.ndim != 3 or self.omega.ndim != 1 or \ - self.fresp.shape[-1] != self.omega.shape[-1]: + if self.frdata.ndim != 3 or self.omega.ndim != 1 or \ + self.frdata.shape[-1] != self.omega.shape[-1]: raise TypeError( "The frequency data constructor needs a 1-d or 3-d" " response data array and a matching frequency vector" @@ -167,9 +267,13 @@ def __init__(self, *args, **kwargs): "The one-argument constructor can only take in" " an FRD object. Received %s." % type(args[0])) self.omega = args[0].omega - self.fresp = args[0].fresp + self.frdata = args[0].frdata arg_dt = args[0].dt + # Copy over signal and system names, if not specified + kwargs['inputs'] = kwargs.get('inputs', args[0].input_labels) + kwargs['outputs'] = kwargs.get('outputs', args[0].output_labels) + else: raise ValueError( "Needs 1 or 2 arguments; received %i." % len(args)) @@ -198,30 +302,41 @@ def __init__(self, *args, **kwargs): if self.squeeze not in (None, True, False): raise ValueError("unknown squeeze value") - # Process iosys keywords defaults = { - 'inputs': self.fresp.shape[1], 'outputs': self.fresp.shape[0], - 'dt': None} - name, inputs, outputs, states, dt = _process_iosys_keywords( - kwargs, defaults, end=True) - dt = common_timebase(dt, arg_dt) # choose compatible timebase + 'inputs': self.frdata.shape[1] if not getattr( + self, 'input_index', None) else self.input_labels, + 'outputs': self.frdata.shape[0] if not getattr( + self, 'output_index', None) else self.output_labels, + 'name': getattr(self, 'name', None)} + if arg_dt is not None: + if isinstance(args[0], LTI): + arg_dt = common_timebase(args[0].dt, arg_dt) + kwargs['dt'] = arg_dt # Process signal names + name, inputs, outputs, states, dt = _process_iosys_keywords( + kwargs, defaults) InputOutputSystem.__init__( - self, name=name, inputs=inputs, outputs=outputs, dt=dt) + self, name=name, inputs=inputs, outputs=outputs, dt=dt, **kwargs) # create interpolation functions if smooth: - self.ifunc = empty((self.fresp.shape[0], self.fresp.shape[1]), + # Set the order of the fit + if self.omega.size < 2: + raise ValueError("can't smooth with only 1 frequency") + degree = 3 if self.omega.size > 3 else self.omega.size - 1 + + self._ifunc = empty((self.frdata.shape[0], self.frdata.shape[1]), dtype=tuple) - for i in range(self.fresp.shape[0]): - for j in range(self.fresp.shape[1]): - self.ifunc[i, j], u = splprep( - u=self.omega, x=[real(self.fresp[i, j, :]), - imag(self.fresp[i, j, :])], - w=1.0/(absolute(self.fresp[i, j, :]) + 0.001), s=0.0) + for i in range(self.frdata.shape[0]): + for j in range(self.frdata.shape[1]): + self._ifunc[i, j], u = splprep( + u=self.omega, x=[real(self.frdata[i, j, :]), + imag(self.frdata[i, j, :])], + w=1.0/(absolute(self.frdata[i, j, :]) + 0.001), + s=0.0, k=degree) else: - self.ifunc = None + self._ifunc = None # # Frequency response properties @@ -232,53 +347,127 @@ def __init__(self, *args, **kwargs): @property def magnitude(self): - return np.abs(self.fresp) + """Magnitude of the frequency response. + + Magnitude of the frequency response, indexed by either the output + and frequency (if only a single input is given) or the output, + input, and frequency (for multi-input systems). See + `FrequencyResponseData.squeeze` for a description of how this + can be modified using the `squeeze` keyword. + + Input and output signal names can be used to index the data in + place of integer offsets. + + :type: 1D, 2D, or 3D array + + """ + frdata = _process_frequency_response( + self, self.omega, self.frdata, squeeze=self.squeeze) + return NamedSignal( + np.abs(frdata), self.output_labels, self.input_labels) @property def phase(self): - return np.angle(self.fresp) + """Phase of the frequency response. + + Phase of the frequency response in radians/sec, indexed by either + the output and frequency (if only a single input is given) or the + output, input, and frequency (for multi-input systems). See + `FrequencyResponseData.squeeze` for a description of how this + can be modified using the `squeeze` keyword. + + Input and output signal names can be used to index the data in + place of integer offsets. + + :type: 1D, 2D, or 3D array + + """ + frdata = _process_frequency_response( + self, self.omega, self.frdata, squeeze=self.squeeze) + return NamedSignal( + np.angle(frdata), self.output_labels, self.input_labels) @property def frequency(self): + """Frequencies at which the response is evaluated. + + :type: 1D array + + """ return self.omega + @property + def complex(self): + """Complex value of the frequency response. + + Value of the frequency response as a complex number, indexed by + either the output and frequency (if only a single input is given) + or the output, input, and frequency (for multi-input systems). See + `FrequencyResponseData.squeeze` for a description of how this + can be modified using the `squeeze` keyword. + + Input and output signal names can be used to index the data in + place of integer offsets. + + :type: 1D, 2D, or 3D array + + """ + frdata = _process_frequency_response( + self, self.omega, self.frdata, squeeze=self.squeeze) + return NamedSignal( + frdata, self.output_labels, self.input_labels) + @property def response(self): - return self.fresp + warn("response property is deprecated; use complex", FutureWarning) + return self.complex + + @property + def fresp(self): + warn("fresp attribute is deprecated; use frdata", FutureWarning) + return self.frdata def __str__(self): + """String representation of the transfer function.""" mimo = self.ninputs > 1 or self.noutputs > 1 outstr = [f"{InputOutputSystem.__str__(self)}"] + nl = "\n " if mimo else "\n" + sp = " " if mimo else "" for i in range(self.ninputs): for j in range(self.noutputs): if mimo: - outstr.append("Input %i to output %i:" % (i + 1, j + 1)) - outstr.append('Freq [rad/s] Response') - outstr.append('------------ ---------------------') + outstr.append( + "\nInput %i to output %i:" % (i + 1, j + 1)) + outstr.append(nl + 'Freq [rad/s] Response') + outstr.append(sp + '------------ ---------------------') outstr.extend( - ['%12.3f %10.4g%+10.4gj' % (w, re, im) + [sp + '%12.3f %10.4g%+10.4gj' % (w, re, im) for w, re, im in zip(self.omega, - real(self.fresp[j, i, :]), - imag(self.fresp[j, i, :]))]) + real(self.frdata[j, i, :]), + imag(self.frdata[j, i, :]))]) return '\n'.join(outstr) - def __repr__(self): - """Loadable string representation, + def _repr_eval_(self): + # Loadable format + out = "FrequencyResponseData(\n{d},\n{w}{smooth}".format( + d=repr(self.frdata), w=repr(self.omega), + smooth=(self._ifunc and ", smooth=True") or "") - limited for number of data points. - """ - return "FrequencyResponseData({d}, {w}{smooth})".format( - d=repr(self.fresp), w=repr(self.omega), - smooth=(self.ifunc and ", smooth=True") or "") + out += self._dt_repr() + if len(labels := self._label_repr()) > 0: + out += ",\n" + labels + + out += ")" + return out def __neg__(self): """Negate a transfer function.""" - return FRD(-self.fresp, self.omega) + return FRD(-self.frdata, self.omega) def __add__(self, other): """Add two LTI objects (parallel connection).""" @@ -292,7 +481,18 @@ def __add__(self, other): # Convert the second argument to a frequency response function. # or re-base the frd to the current omega (if needed) - other = _convert_to_frd(other, omega=self.omega) + if isinstance(other, (int, float, complex, np.number)): + other = _convert_to_frd( + other, omega=self.omega, + inputs=self.ninputs, outputs=self.noutputs) + else: + other = _convert_to_frd(other, omega=self.omega) + + # Promote SISO object to compatible dimension + if self.issiso() and not other.issiso(): + self = np.ones((other.noutputs, other.ninputs)) * self + elif not self.issiso() and other.issiso(): + other = np.ones((self.noutputs, self.ninputs)) * other # Check that the input-output sizes are consistent. if self.ninputs != other.ninputs: @@ -304,7 +504,7 @@ def __add__(self, other): "The first summand has %i output(s), but the " \ "second has %i." % (self.noutputs, other.noutputs)) - return FRD(self.fresp + other.fresp, other.omega) + return FRD(self.frdata + other.frdata, other.omega) def __radd__(self, other): """Right add two LTI objects (parallel connection).""" @@ -326,11 +526,17 @@ def __mul__(self, other): # Convert the second argument to a transfer function. if isinstance(other, (int, float, complex, np.number)): - return FRD(self.fresp * other, self.omega, - smooth=(self.ifunc is not None)) + return FRD(self.frdata * other, self.omega, + smooth=(self._ifunc is not None)) else: other = _convert_to_frd(other, omega=self.omega) + # Promote SISO object to compatible dimension + if self.issiso() and not other.issiso(): + self = bdalg.append(*([self] * other.noutputs)) + elif not self.issiso() and other.issiso(): + other = bdalg.append(*([other] * self.ninputs)) + # Check that the input-output sizes are consistent. if self.ninputs != other.noutputs: raise ValueError( @@ -340,24 +546,30 @@ def __mul__(self, other): inputs = other.ninputs outputs = self.noutputs - fresp = empty((outputs, inputs, len(self.omega)), - dtype=self.fresp.dtype) + frdata = empty((outputs, inputs, len(self.omega)), + dtype=self.frdata.dtype) for i in range(len(self.omega)): - fresp[:, :, i] = self.fresp[:, :, i] @ other.fresp[:, :, i] - return FRD(fresp, self.omega, - smooth=(self.ifunc is not None) and - (other.ifunc is not None)) + frdata[:, :, i] = self.frdata[:, :, i] @ other.frdata[:, :, i] + return FRD(frdata, self.omega, + smooth=(self._ifunc is not None) and + (other._ifunc is not None)) def __rmul__(self, other): """Right Multiply two LTI objects (serial connection).""" # Convert the second argument to an frd function. if isinstance(other, (int, float, complex, np.number)): - return FRD(self.fresp * other, self.omega, - smooth=(self.ifunc is not None)) + return FRD(self.frdata * other, self.omega, + smooth=(self._ifunc is not None)) else: other = _convert_to_frd(other, omega=self.omega) + # Promote SISO object to compatible dimension + if self.issiso() and not other.issiso(): + self = bdalg.append(*([self] * other.ninputs)) + elif not self.issiso() and other.issiso(): + other = bdalg.append(*([other] * self.noutputs)) + # Check that the input-output sizes are consistent. if self.noutputs != other.ninputs: raise ValueError( @@ -368,48 +580,44 @@ def __rmul__(self, other): inputs = self.ninputs outputs = other.noutputs - fresp = empty((outputs, inputs, len(self.omega)), - dtype=self.fresp.dtype) + frdata = empty((outputs, inputs, len(self.omega)), + dtype=self.frdata.dtype) for i in range(len(self.omega)): - fresp[:, :, i] = other.fresp[:, :, i] @ self.fresp[:, :, i] - return FRD(fresp, self.omega, - smooth=(self.ifunc is not None) and - (other.ifunc is not None)) + frdata[:, :, i] = other.frdata[:, :, i] @ self.frdata[:, :, i] + return FRD(frdata, self.omega, + smooth=(self._ifunc is not None) and + (other._ifunc is not None)) # TODO: Division of MIMO transfer function objects is not written yet. def __truediv__(self, other): """Divide two LTI objects.""" if isinstance(other, (int, float, complex, np.number)): - return FRD(self.fresp * (1/other), self.omega, - smooth=(self.ifunc is not None)) + return FRD(self.frdata * (1/other), self.omega, + smooth=(self._ifunc is not None)) else: other = _convert_to_frd(other, omega=self.omega) - if (self.ninputs > 1 or self.noutputs > 1 or - other.ninputs > 1 or other.noutputs > 1): - raise NotImplementedError( - "FRD.__truediv__ is currently only implemented for SISO " - "systems.") + if (other.ninputs > 1 or other.noutputs > 1): + # FRD.__truediv__ is currently only implemented for SISO systems + return NotImplemented - return FRD(self.fresp/other.fresp, self.omega, - smooth=(self.ifunc is not None) and - (other.ifunc is not None)) + return FRD(self.frdata/other.frdata, self.omega, + smooth=(self._ifunc is not None) and + (other._ifunc is not None)) # TODO: Division of MIMO transfer function objects is not written yet. def __rtruediv__(self, other): """Right divide two LTI objects.""" if isinstance(other, (int, float, complex, np.number)): - return FRD(other / self.fresp, self.omega, - smooth=(self.ifunc is not None)) + return FRD(other / self.frdata, self.omega, + smooth=(self._ifunc is not None)) else: other = _convert_to_frd(other, omega=self.omega) - if (self.ninputs > 1 or self.noutputs > 1 or - other.ninputs > 1 or other.noutputs > 1): - raise NotImplementedError( - "FRD.__rtruediv__ is currently only implemented for " - "SISO systems.") + if (self.ninputs > 1 or self.noutputs > 1): + # FRD.__rtruediv__ is currently only implemented for SISO systems + return NotImplemented return other / self @@ -417,51 +625,52 @@ def __pow__(self, other): if not type(other) == int: raise ValueError("Exponent must be an integer") if other == 0: - return FRD(ones(self.fresp.shape), self.omega, - smooth=(self.ifunc is not None)) # unity + return FRD(ones(self.frdata.shape), self.omega, + smooth=(self._ifunc is not None)) # unity if other > 0: return self * (self**(other-1)) if other < 0: - return (FRD(ones(self.fresp.shape), self.omega) / self) * \ + return (FRD(ones(self.frdata.shape), self.omega) / self) * \ (self**(other+1)) # Define the `eval` function to evaluate an FRD at a given (real) # frequency. Note that we choose to use `eval` instead of `evalfr` to - # avoid confusion with :func:`evalfr`, which takes a complex number as its + # avoid confusion with `evalfr`, which takes a complex number as its # argument. Similarly, we don't use `__call__` to avoid confusion between # G(s) for a transfer function and G(omega) for an FRD object. # update Sawyer B. Fuller 2020.08.14: __call__ added to provide a uniform # interface to systems in general and the lti.frequency_response method def eval(self, omega, squeeze=None): - """Evaluate a transfer function at angular frequency omega. + """Evaluate a transfer function at a frequency point. Note that a "normal" FRD only returns values for which there is an - entry in the omega vector. An interpolating FRD can return + entry in the `omega` vector. An interpolating FRD can return intermediate values. Parameters ---------- omega : float or 1D array_like - Frequencies in radians per second + Frequency(s) for evaluation, in radians per second. squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, - keep all indices (output, input and, if omega is array_like, - frequency) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_frequency_response']. + If `squeeze` = True, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + `squeeze` = False, keep all indices (output, input and, if + `omega` is array_like, frequency) even if the system is + SISO. The default value can be set using + `config.defaults['control.squeeze_frequency_response']`. Returns ------- - fresp : complex ndarray - The frequency response of the system. If the system is SISO and - squeeze is not True, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is False, the first - two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If ``squeeze`` is True - then single-dimensional axes are removed. + frdata : complex ndarray + The frequency response of the system. If the system is SISO + and `squeeze` is not True, the shape of the array matches the + shape of `omega`. If the system is not SISO or `squeeze` is + False, the first two dimensions of the array are indices for + the output and input and the remaining dimensions match `omega`. + If `squeeze` is True then single-dimensional axes are removed. """ - omega_array = np.array(omega, ndmin=1) # array-like version of omega + omega_array = np.array(omega, ndmin=1) # array of frequencies # Make sure that we are operating on a simple list if len(omega_array.shape) > 1: @@ -469,84 +678,86 @@ def eval(self, omega, squeeze=None): # Make sure that frequencies are all real-valued if any(omega_array.imag > 0): - raise ValueError("FRD.eval can only accept real-valued omega") + raise ValueError("eval can only accept real-valued frequencies") - if self.ifunc is None: + if self._ifunc is None: elements = np.isin(self.omega, omega) # binary array if sum(elements) < len(omega_array): raise ValueError( - "not all frequencies omega are in frequency list of FRD " + "not all frequencies are in frequency list of FRD " "system. Try an interpolating FRD for additional points.") else: - out = self.fresp[:, :, elements] + out = self.frdata[:, :, elements] else: out = empty((self.noutputs, self.ninputs, len(omega_array)), dtype=complex) for i in range(self.noutputs): for j in range(self.ninputs): for k, w in enumerate(omega_array): - frraw = splev(w, self.ifunc[i, j], der=0) + frraw = splev(w, self._ifunc[i, j], der=0) out[i, j, k] = frraw[0] + 1.0j * frraw[1] return _process_frequency_response(self, omega, out, squeeze=squeeze) - def __call__(self, s=None, squeeze=None, return_magphase=None): - """Evaluate system's transfer function at complex frequencies. + def __call__(self, x=None, squeeze=None, return_magphase=None): + """Evaluate system transfer function at point in complex plane. - Returns the complex frequency response `sys(s)` of system `sys` with - `m = sys.ninputs` number of inputs and `p = sys.noutputs` number of - outputs. + Returns the value of the system's transfer function at a point `x` + in the complex plane, where `x` is `s` for continuous-time systems + and `z` for discrete-time systems. For a frequency response data + object, the argument should be an imaginary number (since only the + frequency response is defined) and only the imaginary component of + `x` will be used. - To evaluate at a frequency omega in radians per second, enter - ``s = omega * 1j`` or use ``sys.eval(omega)`` + By default, a (complex) scalar will be returned for SISO systems + and a p x m array will be return for MIMO systems with m inputs and + p outputs. This can be changed using the `squeeze` keyword. - For a frequency response data object, the argument must be an - imaginary number (since only the frequency response is defined). + To evaluate at a frequency `omega` in radians per second, enter ``x + = omega * 1j`` for continuous-time systems, ``x = exp(1j * omega * + dt)`` for discrete-time systems, or use the + `~LTI.frequency_response` method. - If ``s`` is not given, this function creates a copy of a frequency + If `x` is not given, this function creates a copy of a frequency response data object with a different set of output settings. Parameters ---------- - s : complex scalar or 1D array_like - Complex frequencies. If not specified, return a copy of the - frequency response data object with updated settings for output - processing (``squeeze``, ``return_magphase``). - + x : complex scalar or 1D array_like + Imaginary value(s) at which frequency response will be evaluated. + The real component of `x` is ignored. If not specified, return + a copy of the frequency response data object with updated + settings for output processing (`squeeze`, `return_magphase`). squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, - keep all indices (output, input and, if omega is array_like, - frequency) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_frequency_response']. - + Squeeze output, as described below. Default value can be set + using `config.defaults['control.squeeze_frequency_response']`. return_magphase : bool, optional - If True, then a frequency response data object will enumerate as a - tuple of the form (mag, phase, omega) where where ``mag`` is the - magnitude (absolute value, not dB or log10) of the system - frequency response, ``phase`` is the wrapped phase in radians of - the system frequency response, and ``omega`` is the (sorted) - frequencies at which the response was evaluated. + (`x` = None only) If True, then a frequency response data object + will enumerate as a tuple of the form ``(mag, phase, omega)`` + where where `mag` is the magnitude (absolute value, not dB or + log10) of the system frequency response, `phase` is the wrapped + phase in radians of the system frequency response, and `omega` is + the (sorted) frequencies at which the response was evaluated. Returns ------- - fresp : complex ndarray - The frequency response of the system. If the system is SISO and - squeeze is not True, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is False, the first - two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If ``squeeze`` is True - then single-dimensional axes are removed. + frdata : complex ndarray + The value of the system transfer function at `x`. If the system + is SISO and `squeeze` is not True, the shape of the array matches + the shape of `x`. If the system is not SISO or `squeeze` is + False, the first two dimensions of the array are indices for the + output and input and the remaining dimensions match `x`. If + `squeeze` is True then single-dimensional axes are removed. Raises ------ ValueError - If `s` is not purely imaginary, because - :class:`FrequencyResponseData` systems are only defined at - imaginary values (corresponding to real frequencies). + If `s` is not purely imaginary, because `FrequencyResponseData` + systems are only defined at imaginary values (corresponding to + real frequencies). """ - if s is None: + if x is None: # Create a copy of the response with new keywords response = copy(self) @@ -557,34 +768,53 @@ def __call__(self, s=None, squeeze=None, return_magphase=None): return response + if return_magphase is not None: + raise ValueError("return_magphase not allowed when x != None") + # Make sure that we are operating on a simple list - if len(np.atleast_1d(s).shape) > 1: + if len(np.atleast_1d(x).shape) > 1: raise ValueError("input list must be 1D") - if any(abs(np.atleast_1d(s).real) > 0): + if any(abs(np.atleast_1d(x).real) > 0): raise ValueError("__call__: FRD systems can only accept " "purely imaginary frequencies") # need to preserve array or scalar status - if hasattr(s, '__len__'): - return self.eval(np.asarray(s).imag, squeeze=squeeze) + if hasattr(x, '__len__'): + return self.eval(np.asarray(x).imag, squeeze=squeeze) else: - return self.eval(complex(s).imag, squeeze=squeeze) + return self.eval(complex(x).imag, squeeze=squeeze) # Implement iter to allow assigning to a tuple def __iter__(self): - fresp = _process_frequency_response( - self, self.omega, self.fresp, squeeze=self.squeeze) + frdata = _process_frequency_response( + self, self.omega, self.frdata, squeeze=self.squeeze) if self._return_singvals: # Legacy processing for singular values - return iter((self.fresp[:, 0, :], self.omega)) + return iter((self.frdata[:, 0, :], self.omega)) elif not self.return_magphase: - return iter((self.omega, fresp)) - return iter((np.abs(fresp), np.angle(fresp), self.omega)) + return iter((self.omega, frdata)) + return iter((np.abs(frdata), np.angle(frdata), self.omega)) - # Implement (thin) getitem to allow access via legacy indexing - def __getitem__(self, index): - return list(self.__iter__())[index] + def __getitem__(self, key): + if not isinstance(key, Iterable) or len(key) != 2: + # Implement (thin) getitem to allow access via legacy indexing + return list(self.__iter__())[key] + + # Convert signal names to integer offsets (via NamedSignal object) + iomap = NamedSignal( + self.frdata[:, :, 0], self.output_labels, self.input_labels) + indices = iomap._parse_key(key, level=1) # ignore index checks + outdx, outputs = _process_subsys_index(indices[0], self.output_labels) + inpdx, inputs = _process_subsys_index(indices[1], self.input_labels) + + # Create the system name + sysname = config.defaults['iosys.indexed_system_name_prefix'] + \ + self.name + config.defaults['iosys.indexed_system_name_suffix'] + + return FrequencyResponseData( + self.frdata[outdx, :][:, inpdx], self.omega, self.dt, + inputs=inputs, outputs=outputs, name=sysname) # Implement (thin) len to emulate legacy testing interface def __len__(self): @@ -594,20 +824,30 @@ def freqresp(self, omega): """(deprecated) Evaluate transfer function at complex frequencies. .. deprecated::0.9.0 - Method has been given the more pythonic name - :meth:`FrequencyResponseData.frequency_response`. Or use - :func:`freqresp` in the MATLAB compatibility module. + Method has been given the more Pythonic name + `FrequencyResponseData.frequency_response`. Or use + `freqresp` in the MATLAB compatibility module. + """ warn("FrequencyResponseData.freqresp(omega) will be removed in a " "future release of python-control; use " "FrequencyResponseData.frequency_response(omega), or " "freqresp(sys, omega) in the MATLAB compatibility module " - "instead", DeprecationWarning) + "instead", FutureWarning) return self.frequency_response(omega) def feedback(self, other=1, sign=-1): - """Feedback interconnection between two FRD objects.""" + """Feedback interconnection between two FRD objects. + Parameters + ---------- + other : `LTI` + System in the feedback path. + + sign : float, optional + Gain to use in feedback path. Defaults to -1. + + """ other = _convert_to_frd(other, omega=self.omega) if (self.noutputs != other.ninputs or self.ninputs != other.noutputs): @@ -617,23 +857,54 @@ def feedback(self, other=1, sign=-1): # TODO: handle omega re-mapping # reorder array axes in order to leverage numpy broadcasting - myfresp = np.moveaxis(self.fresp, 2, 0) - otherfresp = np.moveaxis(other.fresp, 2, 0) - I_AB = eye(self.ninputs)[np.newaxis, :, :] + otherfresp @ myfresp - resfresp = (myfresp @ linalg.inv(I_AB)) - fresp = np.moveaxis(resfresp, 0, 2) + myfrdata = np.moveaxis(self.frdata, 2, 0) + otherfrdata = np.moveaxis(other.frdata, 2, 0) + I_AB = eye(self.ninputs)[np.newaxis, :, :] + otherfrdata @ myfrdata + resfrdata = (myfrdata @ linalg.inv(I_AB)) + frdata = np.moveaxis(resfrdata, 0, 2) + + return FRD(frdata, other.omega, smooth=(self._ifunc is not None)) + + def append(self, other): + """Append a second model to the present model. + + The second model is converted to FRD if necessary, inputs and + outputs are appended and their order is preserved. + + Parameters + ---------- + other : `LTI` + System to be appended. - return FRD(fresp, other.omega, smooth=(self.ifunc is not None)) + Returns + ------- + sys : `FrequencyResponseData` + System model with `other` appended to `self`. + + """ + other = _convert_to_frd(other, omega=self.omega, inputs=other.ninputs, + outputs=other.noutputs) + + # TODO: handle omega re-mapping + + new_frdata = np.zeros( + (self.noutputs + other.noutputs, self.ninputs + other.ninputs, + self.omega.shape[-1]), dtype=complex) + new_frdata[:self.noutputs, :self.ninputs, :] = np.reshape( + self.frdata, (self.noutputs, self.ninputs, -1)) + new_frdata[self.noutputs:, self.ninputs:, :] = np.reshape( + other.frdata, (other.noutputs, other.ninputs, -1)) + + return FRD(new_frdata, self.omega, smooth=(self._ifunc is not None)) # Plotting interface def plot(self, plot_type=None, *args, **kwargs): - """Plot the frequency response using a Bode plot. + """Plot the frequency response using Bode or singular values plot. Plot the frequency response using either a standard Bode plot - (default) or using a singular values plot (by setting `plot_type` - to 'svplot'). See :func:`~control.bode_plot` and - :func:`~control.singular_values_plot` for more detailed - descriptions. + (plot_type='bode', default) or a singular values plot + (plot_type='svplot'). See `bode_plot` and `singular_values_plot` + for more detailed descriptions. """ from .freqplot import bode_plot, singular_values_plot @@ -668,7 +939,7 @@ def to_pandas(self): # Create a dict for setting up the data frame data = {'omega': self.omega} data.update( - {'H_{%s, %s}' % (out, inp): self.fresp[i, j] \ + {'H_{%s, %s}' % (out, inp): self.frdata[i, j] \ for i, out in enumerate(self.output_labels) \ for j, inp in enumerate(self.input_labels)}) @@ -681,8 +952,8 @@ def to_pandas(self): # Note: This class was initially given the name "FRD", but this caused # problems with documentation on MacOS platforms, since files were generated # for control.frd and control.FRD, which are not differentiated on most MacOS -# filesystems, which are case insensitive. Renaming the FRD class to be -# FrequenceResponseData and then assigning FRD to point to the same object +# file systems, which are case insensitive. Renaming the FRD class to be +# FrequencyResponseData and then assigning FRD to point to the same object # fixes this problem. # FRD = FrequencyResponseData @@ -691,12 +962,12 @@ def to_pandas(self): def _convert_to_frd(sys, omega, inputs=1, outputs=1): """Convert a system to frequency response data form (if needed). - If sys is already an frd, and its frequency range matches or - overlaps the range given in omega then it is returned. If sys is - another LTI object or a transfer function, then it is converted to - a frequency response data at the specified omega. If sys is a - scalar, then the number of inputs and outputs can be specified - manually, as in: + If `sys` is already a frequency response data object, and its frequency + range matches or overlaps the range given in `omega` then it is + returned. If `sys` is another LTI object or a transfer function, then + it is converted to a frequency response data system at the specified + values in `omega`. If `sys` is a scalar, then the number of inputs and + outputs can be specified manually, as in: >>> import numpy as np >>> from control.frdata import _convert_to_frd @@ -728,68 +999,72 @@ def _convert_to_frd(sys, omega, inputs=1, outputs=1): elif isinstance(sys, LTI): omega = np.sort(omega) if sys.isctime(): - fresp = sys(1j * omega) + frdata = sys(1j * omega) else: - fresp = sys(np.exp(1j * omega * sys.dt)) - if len(fresp.shape) == 1: - fresp = fresp[np.newaxis, np.newaxis, :] - return FRD(fresp, omega, smooth=True) + frdata = sys(np.exp(1j * omega * sys.dt)) + if len(frdata.shape) == 1: + frdata = frdata[np.newaxis, np.newaxis, :] + return FRD(frdata, omega, smooth=True) elif isinstance(sys, (int, float, complex, np.number)): - fresp = ones((outputs, inputs, len(omega)), dtype=float)*sys - return FRD(fresp, omega, smooth=True) + frdata = ones((outputs, inputs, len(omega)), dtype=float)*sys + return FRD(frdata, omega, smooth=True) # try converting constant matrices try: sys = array(sys) outputs, inputs = sys.shape - fresp = empty((outputs, inputs, len(omega)), dtype=float) + frdata = empty((outputs, inputs, len(omega)), dtype=float) for i in range(outputs): for j in range(inputs): - fresp[i, j, :] = sys[i, j] - return FRD(fresp, omega, smooth=True) + frdata[i, j, :] = sys[i, j] + return FRD(frdata, omega, smooth=True) except Exception: pass - raise TypeError('''Can't convert given type "%s" to FRD system.''' % + raise TypeError("Can't convert given type '%s' to FRD system." % sys.__class__) def frd(*args, **kwargs): - """frd(response, omega[, dt]) + """frd(frdata, omega[, dt]) Construct a frequency response data (FRD) model. A frequency response data model stores the (measured) frequency response of a system. This factory function can be called in different ways: - ``frd(response, omega)`` + ``frd(frdata, omega)`` + Create an frd model with the given response data, in the form of - complex response vector, at matching frequencies ``omega`` [in rad/s]. + complex response vector, at matching frequencies `omega` [in rad/s]. ``frd(sys, omega)`` + Convert an LTI system into an frd model with data at frequencies - ``omega``. + `omega`. Parameters ---------- - response : array_like or LTI system + frdata : array_like or LTI system Complex vector with the system response or an LTI system that can - be used to copmute the frequency response at a list of frequencies. + be used to compute the frequency response at a list of frequencies. + sys : `StateSpace` or `TransferFunction` + A linear system that will be evaluated for frequency response data. omega : array_like Vector of frequencies at which the response is evaluated. dt : float, True, or None System timebase. smooth : bool, optional - If ``True``, create an interpolation function that allows the + If True, create an interpolation function that allows the frequency response to be computed at any frequency within the range - of frequencies give in ``omega``. If ``False`` (default), + of frequencies give in `omega`. If False (default), frequency response can only be obtained at the frequencies - specified in ``omega``. + specified in `omega`. Returns ------- - sys : :class:`FrequencyResponseData` + sys : `FrequencyResponseData` New frequency response data system. Other Parameters @@ -798,9 +1073,16 @@ def frd(*args, **kwargs): List of strings that name the individual signals of the transformed system. If not given, the inputs and outputs are the same as the original system. + input_prefix, output_prefix : string, optional + Set the prefix for input and output signals. Defaults = 'u', 'y'. name : string, optional - System name. If unspecified, a generic name is generated - with a unique integer id. + Set the name of the system. If unspecified and the system is + sampled from an existing system, the new system name is determined + by adding the prefix and suffix strings in + `config.defaults['iosys.sampled_system_name_prefix']` and + `config.defaults['iosys.sampled_system_name_suffix']`, with the + default being to add the suffix '$sampled'. Otherwise, a generic + name 'sys[id]' is generated with a unique integer id See Also -------- diff --git a/control/freqplot.py b/control/freqplot.py index 5ff690450..cba975e77 100644 --- a/control/freqplot.py +++ b/control/freqplot.py @@ -1,17 +1,20 @@ # freqplot.py - frequency domain plots for control systems # # Initial author: Richard M. Murray -# Date: 24 May 09 -# -# This file contains some standard control system plots: Bode plots, -# Nyquist plots and other frequency response plots. The code for Nichols -# charts is in nichols.py. The code for pole-zero diagrams is in pzmap.py -# and rlocus.py. +# Creation date: 24 May 2009 + +"""Frequency domain plots for control systems. + +This module contains some standard control system plots: Bode plots, +Nyquist plots and other frequency response plots. The code for +Nichols charts is in nichols.py. The code for pole-zero diagrams is +in pzmap.py and rlocus.py. + +""" import itertools import math import warnings -from os.path import commonprefix import matplotlib as mpl import matplotlib.pyplot as plt @@ -19,8 +22,10 @@ from . import config from .bdalg import feedback -from .ctrlplot import suptitle, _find_axes_center, _make_legend_labels, \ - _update_suptitle +from .ctrlplot import ControlPlot, _add_arrows_to_line2D, _find_axes_center, \ + _get_color, _get_color_offset, _get_line_labels, _make_legend_labels, \ + _process_ax_keyword, _process_legend_keywords, _process_line_labels, \ + _update_plot_title from .ctrlutil import unwrap from .exception import ControlMIMONotImplemented from .frdata import FrequencyResponseData @@ -32,23 +37,11 @@ __all__ = ['bode_plot', 'NyquistResponseData', 'nyquist_response', 'nyquist_plot', 'singular_values_response', 'singular_values_plot', 'gangof4_plot', 'gangof4_response', - 'bode', 'nyquist', 'gangof4'] - -# Default font dictionary -# TODO: move common plotting params to 'ctrlplot' -_freqplot_rcParams = mpl.rcParams.copy() -_freqplot_rcParams.update({ - 'axes.labelsize': 'small', - 'axes.titlesize': 'small', - 'figure.titlesize': 'medium', - 'legend.fontsize': 'x-small', - 'xtick.labelsize': 'small', - 'ytick.labelsize': 'small', -}) + 'bode', 'nyquist', 'gangof4', 'FrequencyResponseList', + 'NyquistResponseList'] # Default values for module parameter variables _freqplot_defaults = { - 'freqplot.rcParams': _freqplot_rcParams, 'freqplot.feature_periphery_decades': 1, 'freqplot.number_of_samples': 1000, 'freqplot.dB': False, # Plot gain in dB @@ -57,10 +50,11 @@ 'freqplot.grid': True, # Turn on grid for gain and phase 'freqplot.wrap_phase': False, # Wrap the phase plot at a given value 'freqplot.freq_label': "Frequency [{units}]", + 'freqplot.magnitude_label': "Magnitude", 'freqplot.share_magnitude': 'row', 'freqplot.share_phase': 'row', 'freqplot.share_frequency': 'col', - 'freqplot.suptitle_frame': 'axes', + 'freqplot.title_frame': 'axes', } # @@ -72,7 +66,19 @@ # class FrequencyResponseList(list): + """List of FrequencyResponseData objects with plotting capability. + + This class consists of a list of `FrequencyResponseData` objects. + It is a subclass of the Python `list` class, with a `plot` method that + plots the individual `FrequencyResponseData` objects. + + """ def plot(self, *args, plot_type=None, **kwargs): + """Plot a list of frequency responses. + + See `FrequencyResponseData.plot` for details. + + """ if plot_type == None: for response in self: if plot_type is not None and response.plot_type != plot_type: @@ -98,8 +104,7 @@ def bode_plot( plot=None, plot_magnitude=True, plot_phase=None, overlay_outputs=None, overlay_inputs=None, phase_label=None, magnitude_label=None, label=None, display_margins=None, - margins_method='best', legend_map=None, legend_loc=None, - sharex=None, sharey=None, title=None, **kwargs): + margins_method='best', title=None, sharex=None, sharey=None, **kwargs): """Bode plot for a system. Plot the magnitude and phase of the frequency response over a @@ -108,82 +113,133 @@ def bode_plot( Parameters ---------- data : list of `FrequencyResponseData` or `LTI` - List of LTI systems or :class:`FrequencyResponseData` objects. A + List of LTI systems or `FrequencyResponseData` objects. A single system or frequency response can also be passed. - omega : array_like, optoinal + omega : array_like, optional Set of frequencies in rad/sec to plot over. If not specified, this - will be determined from the proporties of the systems. Ignored if + will be determined from the properties of the systems. Ignored if `data` is not a list of systems. - *fmt : :func:`matplotlib.pyplot.plot` format string, optional + *fmt : `matplotlib.pyplot.plot` format string, optional Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). dB : bool If True, plot result in dB. Default is False. Hz : bool If True, plot frequency in Hz (omega must be provided in rad/sec). - Default value (False) set by config.defaults['freqplot.Hz']. + Default value (False) set by `config.defaults['freqplot.Hz']`. deg : bool - If True, plot phase in degrees (else radians). Default value (True) - set by config.defaults['freqplot.deg']. + If True, plot phase in degrees (else radians). Default + value (True) set by `config.defaults['freqplot.deg']`. display_margins : bool or str If True, draw gain and phase margin lines on the magnitude and phase graphs and display the margins at the top of the graph. If set to 'overlay', the values for the gain and phase margin are placed on - the graph. Setting display_margins turns off the axes grid. - **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional + the graph. Setting `display_margins` turns off the axes grid, unless + `grid` is explicitly set to True. + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. Returns ------- - lines : array of Line2D - Array of Line2D objects for each line in the plot. The shape of - the array matches the subplots shape and the value of the array is a - list of Line2D objects in that subplot. + cplt : `ControlPlot` object + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : Array of `matplotlib.lines.Line2D` objects + Array containing information on each line in the plot. The shape + of the array matches the subplots shape and the value of the array + is a list of Line2D objects in that subplot. + cplt.axes : 2D ndarray of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. Other Parameters ---------------- - grid : bool + ax : array of `matplotlib.axes.Axes`, optional + The matplotlib axes to draw the figure on. If not specified, the + axes for the current figure are used or, if there is no current + figure with the correct number and shape of axes, a new figure is + created. The shape of the array must match the shape of the + plotted data. + freq_label, magnitude_label, phase_label : str, optional + Labels to use for the frequency, magnitude, and phase axes. + Defaults are set by `config.defaults['freqplot.']`. + grid : bool, optional If True, plot grid lines on gain and phase plots. Default is set by `config.defaults['freqplot.grid']`. - initial_phase : float + initial_phase : float, optional Set the reference phase to use for the lowest frequency. If set, the initial phase of the Bode plot will be set to the value closest to the value specified. Units are in either degrees or radians, depending on the `deg` parameter. Default is -180 if wrap_phase is False, 0 if wrap_phase is True. - label : str or array-like of str + label : str or array_like of str, optional If present, replace automatically generated label(s) with the given label(s). If sysdata is a list, strings should be specified for each system. If MIMO, strings required for each system, output, and input. + legend_map : array of str, optional + Location of the legend for multi-axes plots. Specifies an array + of legend location strings matching the shape of the subplots, with + each entry being either None (for no legend) or a legend location + string (see `~matplotlib.pyplot.legend`). + legend_loc : int or str, optional + Include a legend in the given location. Default is 'center right', + with no legend for a single response. Use False to suppress legend. margins_method : str, optional - Method to use in computing margins (see :func:`stability_margins`). + Method to use in computing margins (see `stability_margins`). omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. Ignored if + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. Ignored if data is not a list of systems. omega_num : int - Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. Ignored if data is + Number of samples to use for the frequency range. Defaults to + `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. + overlay_inputs, overlay_outputs : bool, optional + If set to True, combine input and/or output signals onto a single + plot and use line colors, labels, and a legend to distinguish them. plot : bool, optional (legacy) If given, `bode_plot` returns the legacy return values of magnitude, phase, and frequency. If False, just return the values with no plot. + plot_magnitude, plot_phase : bool, optional + If set to False, do not plot the magnitude or phase, respectively. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['freqplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. + share_frequency, share_magnitude, share_phase : str or bool, optional + Determine whether and how axis limits are shared between the + indicated variables. Can be set set to 'row' to share across all + subplots in a row, 'col' to set across all subplots in a column, or + False to allow independent limits. Note: if `sharex` is given, + it sets the value of `share_frequency`; if `sharey` is given, it + sets the value of both `share_magnitude` and `share_phase`. + Default values are 'row' for `share_magnitude` and `share_phase`, + 'col', for `share_frequency`, and can be set using + `config.defaults['freqplot.share_']`. + show_legend : bool, optional + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on an + axis or `legend_loc` or `legend_map` has been specified. + title : str, optional + Set the title of the plot. Defaults to plot type and system name(s). + title_frame : str, optional + Set the frame of reference used to center the plot title. If set to + 'axes' (default), the horizontal position of the title will be + centered relative to the axes. If set to 'figure', it will be + centered with respect to the figure (faster execution). The default + value can be set using `config.defaults['freqplot.title_frame']`. wrap_phase : bool or float - If wrap_phase is `False` (default), then the phase will be unwrapped + If wrap_phase is False (default), then the phase will be unwrapped so that it is continuously increasing or decreasing. If wrap_phase is - `True` the phase will be restricted to the range [-180, 180) (or + True the phase will be restricted to the range [-180, 180) (or [:math:`-\\pi`, :math:`\\pi`) radians). If `wrap_phase` is specified as a float, the phase will be offset by 360 degrees if it falls below - the specified value. Default value is `False` and can be set using - config.defaults['freqplot.wrap_phase']. - - The default values for Bode plot configuration parameters can be reset - using the `config.defaults` dictionary, with module name 'bode'. + the specified value. Default value is False and can be set using + `config.defaults['freqplot.wrap_phase']`. See Also -------- @@ -191,19 +247,22 @@ def bode_plot( Notes ----- - 1. Starting with python-control version 0.10, `bode_plot`returns an - array of lines instead of magnitude, phase, and frequency. To - recover the old behavior, call `bode_plot` with `plot=True`, which - will force the legacy values (mag, phase, omega) to be returned - (with a warning). To obtain just the frequency response of a system - (or list of systems) without plotting, use the - :func:`~control.frequency_response` command. - - 2. If a discrete time model is given, the frequency response is plotted - along the upper branch of the unit circle, using the mapping ``z = - exp(1j * omega * dt)`` where `omega` ranges from 0 to `pi/dt` and `dt` - is the discrete timebase. If timebase not specified (``dt=True``), - `dt` is set to 1. + Starting with python-control version 0.10, `bode_plot` returns a + `ControlPlot` object instead of magnitude, phase, and + frequency. To recover the old behavior, call `bode_plot` with + `plot` = True, which will force the legacy values (mag, phase, omega) to + be returned (with a warning). To obtain just the frequency response of + a system (or list of systems) without plotting, use the + `frequency_response` command. + + If a discrete-time model is given, the frequency response is plotted + along the upper branch of the unit circle, using the mapping ``z = + exp(1j * omega * dt)`` where `omega` ranges from 0 to pi/`dt` and `dt` + is the discrete timebase. If timebase not specified (`dt` = True), + `dt` is set to 1. + + The default values for Bode plot configuration parameters can be reset + using the `config.defaults` dictionary, with module name 'bode'. Examples -------- @@ -218,6 +277,24 @@ def bode_plot( # Make a copy of the kwargs dictionary since we will modify it kwargs = dict(kwargs) + # Legacy keywords for margins + display_margins = config._process_legacy_keyword( + kwargs, 'margins', 'display_margins', display_margins) + if kwargs.pop('margin_info', False): + warnings.warn( + "keyword 'margin_info' is deprecated; " + "use 'display_margins='overlay'") + if display_margins is False: + raise ValueError( + "conflicting_keywords: `display_margins` and `margin_info`") + + # Turn off grid if display margins, unless explicitly overridden + if display_margins and 'grid' not in kwargs: + kwargs['grid'] = False + + margins_method = config._process_legacy_keyword( + kwargs, 'method', 'margins_method', margins_method) + # Get values for params (and pop from list to allow keyword use in plot) dB = config._get_param( 'freqplot', 'dB', kwargs, _freqplot_defaults, pop=True) @@ -231,16 +308,17 @@ def bode_plot( 'freqplot', 'wrap_phase', kwargs, _freqplot_defaults, pop=True) initial_phase = config._get_param( 'freqplot', 'initial_phase', kwargs, None, pop=True) - rcParams = config._get_param( - 'freqplot', 'rcParams', kwargs, _freqplot_defaults, pop=True) - suptitle_frame = config._get_param( - 'freqplot', 'suptitle_frame', kwargs, _freqplot_defaults, pop=True) + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) + title_frame = config._get_param( + 'freqplot', 'title_frame', kwargs, _freqplot_defaults, pop=True) # Set the default labels freq_label = config._get_param( 'freqplot', 'freq_label', kwargs, _freqplot_defaults, pop=True) if magnitude_label is None: - magnitude_label = "Magnitude [dB]" if dB else "Magnitude" + magnitude_label = config._get_param( + 'freqplot', 'magnitude_label', kwargs, + _freqplot_defaults, pop=True) + (" [dB]" if dB else "") if phase_label is None: phase_label = "Phase [deg]" if deg else "Phase [rad]" @@ -257,19 +335,6 @@ def bode_plot( "sharex cannot be present with share_frequency") kwargs['share_frequency'] = sharex - # Legacy keywords for margins - display_margins = config._process_legacy_keyword( - kwargs, 'margins', 'display_margins', display_margins) - if kwargs.pop('margin_info', False): - warnings.warn( - "keyword 'margin_info' is deprecated; " - "use 'display_margins='overlay'") - if display_margins is False: - raise ValueError( - "conflicting_keywords: `display_margins` and `margin_info`") - margins_method = config._process_legacy_keyword( - kwargs, 'method', 'margins_method', margins_method) - if not isinstance(data, (list, tuple)): data = [data] @@ -327,10 +392,8 @@ def bode_plot( else: raise ValueError("initial_phase must be a number.") - # Reshape the phase to allow standard indexing - phase = response.phase.copy().reshape((noutputs, ninputs, -1)) - # Shift and wrap the phase + phase = np.angle(response.frdata) # 3D array for i, j in itertools.product(range(noutputs), range(ninputs)): # Shift the phase if needed if abs(phase[i, j, 0] - initial_phase_value) > math.pi: @@ -353,11 +416,8 @@ def bode_plot( else: raise ValueError("wrap_phase must be bool or float.") - # Put the phase back into the original shape - phase = phase.reshape(response.magnitude.shape) - - # Save the data for later use (legacy return values) - mag_data.append(response.magnitude) + # Save the data for later use + mag_data.append(np.abs(response.frdata)) phase_data.append(phase) omega_data.append(response.omega) @@ -392,8 +452,8 @@ def bode_plot( if plot is not None: warnings.warn( - "`bode_plot` return values of mag, phase, omega is deprecated; " - "use frequency_response()", DeprecationWarning) + "bode_plot() return value of mag, phase, omega is deprecated; " + "use frequency_response()", FutureWarning) if plot is False: # Process the data to match what we were sent @@ -470,8 +530,10 @@ def bode_plot( if kw not in kwargs or kwargs[kw] is None: kwargs[kw] = config.defaults['freqplot.' + kw] - fig, ax_array = _process_ax_keyword(ax, ( - nrows, ncols), squeeze=False, rcParams=rcParams, clear_text=True) + fig, ax_array = _process_ax_keyword( + ax, (nrows, ncols), squeeze=False, rcParams=rcParams, clear_text=True) + legend_loc, legend_map, show_legend = _process_legend_keywords( + kwargs, (nrows,ncols), 'center right') # Get the values for sharing axes limits share_magnitude = kwargs.pop('share_magnitude', None) @@ -545,7 +607,7 @@ def bode_plot( # axes are available and no updates should be made. # - # Utility function to turn off sharing + # Utility function to turn on sharing def _share_axes(ref, share_map, axis): ref_ax = ax_array[ref] for index in np.nditer(share_map, flags=["refs_ok"]): @@ -631,8 +693,8 @@ def _make_line_label(response, output_index, input_index): for index, response in enumerate(data): # Get the (pre-processed) data in fully indexed form - mag = mag_data[index].reshape((noutputs, ninputs, -1)) - phase = phase_data[index].reshape((noutputs, ninputs, -1)) + mag = mag_data[index] + phase = phase_data[index] omega_sys, sysname = omega_data[index], response.sysname for i, j in itertools.product(range(noutputs), range(ninputs)): @@ -671,7 +733,7 @@ def _make_line_label(response, output_index, input_index): label='_nyq_mag_' + sysname) # Add a grid to the plot - ax_mag.grid(grid and not display_margins, which='both') + ax_mag.grid(grid, which='both') # Phase if plot_phase: @@ -686,7 +748,7 @@ def _make_line_label(response, output_index, input_index): label='_nyq_phase_' + sysname) # Add a grid to the plot - ax_phase.grid(grid and not display_margins, which='both') + ax_phase.grid(grid, which='both') # # Display gain and phase margins (SISO only) @@ -697,6 +759,10 @@ def _make_line_label(response, output_index, input_index): raise NotImplementedError( "margins are not available for MIMO systems") + if display_margins == 'overlay' and len(data) > 1: + raise NotImplementedError( + f"{display_margins=} not supported for multi-trace plots") + # Compute stability margins for the system margins = stability_margins(response, method=margins_method) gm, pm, Wcg, Wcp = (margins[i] for i in [0, 1, 3, 4]) @@ -717,13 +783,11 @@ def _make_line_label(response, output_index, input_index): if plot_magnitude: ax_mag.axhline(y=0 if dB else 1, color='k', linestyle=':', zorder=-20) - mag_ylim = ax_mag.get_ylim() if plot_phase: ax_phase.axhline(y=phase_limit if deg else math.radians(phase_limit), color='k', linestyle=':', zorder=-20) - phase_ylim = ax_phase.get_ylim() # Annotate the phase margin (if it exists) if plot_phase and pm != float('inf') and Wcp != float('nan'): @@ -790,12 +854,12 @@ def _make_line_label(response, output_index, input_index): else: # Put the title underneath the suptitle (one line per system) - ax = ax_mag if ax_mag else ax_phase - axes_title = ax.get_title() + ax_ = ax_mag if ax_mag else ax_phase + axes_title = ax_.get_title() if axes_title is not None and axes_title != "": axes_title += "\n" with plt.rc_context(rcParams): - ax.set_title( + ax_.set_title( axes_title + f"{sysname}: " "Gm = %.2f %s(at %.2f %s), " "Pm = %.2f %s (at %.2f %s)" % @@ -920,7 +984,7 @@ def gen_zero_centered_series(val_min, val_max, period): mag_bbox = inv_transform.transform( ax_mag.get_tightbbox(fig.canvas.get_renderer())) - # Figure out location for the text (center left in figure frame) + # Figure out location for text (center left in figure frame) xpos = mag_bbox[0, 0] # left edge # Put a centered label as text outside the box @@ -944,23 +1008,28 @@ def gen_zero_centered_series(val_min, val_max, period): # list of systems (e.g., "Step response for sys[1], sys[2]"). # - # Set the initial title for the data (unique system names, preserving order) + # Set initial title for the data (unique system names, preserving order) seen = set() - sysnames = [response.sysname for response in data \ - if not (response.sysname in seen or seen.add(response.sysname))] - if title is None: + sysnames = [response.sysname for response in data if not + (response.sysname in seen or seen.add(response.sysname))] + + if ax is None and title is None: if data[0].title is None: title = "Bode plot for " + ", ".join(sysnames) else: + # Allow data to set the title (used by gangof4) title = data[0].title - - _update_suptitle(fig, title, rcParams=rcParams, frame=suptitle_frame) + _update_plot_title(title, fig, rcParams=rcParams, frame=title_frame) + elif ax is None: + _update_plot_title( + title, fig=fig, rcParams=rcParams, frame=title_frame, + use_existing=False) # # Create legends # # Legends can be placed manually by passing a legend_map array that - # matches the shape of the suplots, with each item being a string + # matches the shape of the sublots, with each item being a string # indicating the location of the legend for that axes (or None for no # legend). # @@ -971,26 +1040,24 @@ def gen_zero_centered_series(val_min, val_max, period): # # Because plots can be built up by multiple calls to plot(), the legend # strings are created from the line labels manually. Thus an initial - # call to plot() may not generate any legends (eg, if no signals are + # call to plot() may not generate any legends (e.g., if no signals are # overlaid), but subsequent calls to plot() will need a legend for each # different response (system). # - # Figure out where to put legends - if legend_map is None: - legend_map = np.full(ax_array.shape, None, dtype=object) - if legend_loc == None: - legend_loc = 'center right' - - # TODO: add in additional processing later - - # Put legend in the upper right - legend_map[0, -1] = legend_loc - # Create axis legends - for i in range(nrows): - for j in range(ncols): + if show_legend != False: + # Figure out where to put legends + if legend_map is None: + legend_map = np.full(ax_array.shape, None, dtype=object) + legend_map[0, -1] = legend_loc + + legend_array = np.full(ax_array.shape, None, dtype=object) + for i, j in itertools.product(range(nrows), range(ncols)): + if legend_map[i, j] is None: + continue ax = ax_array[i, j] + # Get the labels to use, removing common strings lines = [line for line in ax.get_lines() if line.get_label()[0] != '_'] @@ -999,12 +1066,15 @@ def gen_zero_centered_series(val_min, val_max, period): ignore_common=line_labels is not None) # Generate the label, if needed - if len(labels) > 1 and legend_map[i, j] != None: + if show_legend == True or len(labels) > 1: with plt.rc_context(rcParams): - ax.legend(lines, labels, loc=legend_map[i, j]) + legend_array[i, j] = ax.legend( + lines, labels, loc=legend_map[i, j]) + else: + legend_array = None # - # Legacy return pocessing + # Legacy return processing # if plot is True: # legacy usage; remove in future release # Process the data to match what we were sent @@ -1019,7 +1089,7 @@ def gen_zero_centered_series(val_min, val_max, period): else: return mag_data, phase_data, omega_data - return out + return ControlPlot(out, ax_array, fig, legend=legend_array) # @@ -1051,18 +1121,18 @@ class NyquistResponseData: Nyquist contour analysis allows the stability and robustness of a closed loop linear system to be evaluated using the open loop response of the loop transfer function. The NyquistResponseData class is used - by the :func:`~control.nyquist_response` function to return the + by the `nyquist_response` function to return the response of a linear system along the Nyquist 'D' contour. The response object can be used to obtain information about the Nyquist response or to generate a Nyquist plot. - Attributes + Parameters ---------- count : integer Number of encirclements of the -1 point by the Nyquist curve for a system evaluated along the Nyquist contour. contour : complex array - The Nyquist 'D' contour, with appropriate indendtations to avoid + The Nyquist 'D' contour, with appropriate indentations to avoid open loop poles and zeros near/on the imaginary axis. response : complex array The value of the linear system under study along the Nyquist contour. @@ -1070,10 +1140,10 @@ class NyquistResponseData: The system timebase. sysname : str The name of the system being analyzed. - return_contour: bool - If true, when the object is accessed as an iterable return two - elements": `count` (number of encirlements) and `contour`. If - false (default), then return only `count`. + return_contour : bool + If True, when the object is accessed as an iterable return two + elements: `count` (number of encirclements) and `contour`. If + False (default), then return only `count`. """ def __init__( @@ -1102,23 +1172,40 @@ def __len__(self): return 2 if self.return_contour else 1 def plot(self, *args, **kwargs): + """Plot a list of Nyquist responses. + + See `nyquist_plot` for details. + + """ return nyquist_plot(self, *args, **kwargs) class NyquistResponseList(list): + """List of NyquistResponseData objects with plotting capability. + + This class consists of a list of `NyquistResponseData` objects. + It is a subclass of the Python `list` class, with a `plot` method that + plots the individual `NyquistResponseData` objects. + + """ def plot(self, *args, **kwargs): + """Plot a list of Nyquist responses. + + See `nyquist_plot` for details. + + """ return nyquist_plot(self, *args, **kwargs) def nyquist_response( - sysdata, omega=None, plot=None, omega_limits=None, omega_num=None, + sysdata, omega=None, omega_limits=None, omega_num=None, return_contour=False, warn_encirclements=True, warn_nyquist=True, - check_kwargs=True, **kwargs): + _kwargs=None, _check_kwargs=True, **kwargs): """Nyquist response for a system. Computes a Nyquist contour for the system over a (optional) frequency range and evaluates the number of net encirclements. The curve is - computed by evaluating the Nyqist segment along the positive imaginary + computed by evaluating the Nyquist segment along the positive imaginary axis, with a mirror image generated to reflect the negative imaginary axis. Poles on or near the imaginary axis are avoided using a small indentation. The portion of the Nyquist contour at infinity is not @@ -1135,10 +1222,10 @@ def nyquist_response( Returns ------- - responses : list of :class:`~control.NyquistResponseData` + responses : list of `NyquistResponseData` For each system, a Nyquist response data object is returned. If - `sysdata` is a single system, a single elemeent is returned (not a - list). For each response, the following information is available: + `sysdata` is a single system, a single element is returned (not a + list). response.count : int Number of encirclements of the point -1 by the Nyquist curve. If multiple systems are given, an array of counts is returned. @@ -1151,57 +1238,59 @@ def nyquist_response( encirclement_threshold : float, optional Define the threshold for generating a warning if the number of net encirclements is a non-integer value. Default value is 0.05 and can - be set using config.defaults['nyquist.encirclement_threshold']. + be set using `config.defaults['nyquist.encirclement_threshold']`. indent_direction : str, optional For poles on the imaginary axis, set the direction of indentation to - be 'right' (default), 'left', or 'none'. + be 'right' (default), 'left', or 'none'. The default value can + be set using `config.defaults['nyquist.indent_direction']`. indent_points : int, optional Number of points to insert in the Nyquist contour around poles that are at or near the imaginary axis. indent_radius : float, optional Amount to indent the Nyquist contour around poles on or near the - imaginary axis. Portions of the Nyquist plot corresponding to indented - portions of the contour are plotted using a different line style. + imaginary axis. Portions of the Nyquist plot corresponding to + indented portions of the contour are plotted using a different line + style. The default value can be set using + `config.defaults['nyquist.indent_radius']`. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. omega_num : int, optional - Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. + Number of samples to use for the frequency range. Defaults to + `config.defaults['freqplot.number_of_samples']`. warn_nyquist : bool, optional - If set to 'False', turn off warnings about frequencies above Nyquist. + If set to False, turn off warnings about frequencies above Nyquist. warn_encirclements : bool, optional - If set to 'False', turn off warnings about number of encirclements not + If set to False, turn off warnings about number of encirclements not meeting the Nyquist criterion. Notes ----- - 1. If a discrete time model is given, the frequency response is computed - along the upper branch of the unit circle, using the mapping ``z = - exp(1j * omega * dt)`` where `omega` ranges from 0 to `pi/dt` and `dt` - is the discrete timebase. If timebase not specified (``dt=True``), - `dt` is set to 1. - - 2. If a continuous-time system contains poles on or near the imaginary - axis, a small indentation will be used to avoid the pole. The radius - of the indentation is given by `indent_radius` and it is taken to the - right of stable poles and the left of unstable poles. If a pole is - exactly on the imaginary axis, the `indent_direction` parameter can be - used to set the direction of indentation. Setting `indent_direction` - to `none` will turn off indentation. If `return_contour` is True, the - exact contour used for evaluation is returned. - - 3. For those portions of the Nyquist plot in which the contour is - indented to avoid poles, resuling in a scaling of the Nyquist plot, - the line styles are according to the settings of the `primary_style` - and `mirror_style` keywords. By default the scaled portions of the - primary curve use a dotted line style and the scaled portion of the - mirror image use a dashdot line style. - - 4. If the legacy keyword `return_contour` is specified as True, the - response object can be iterated over to return `count, contour`. - This behavior is deprecated and will be removed in a future release. + If a discrete-time model is given, the frequency response is computed + along the upper branch of the unit circle, using the mapping ``z = + exp(1j * omega * dt)`` where `omega` ranges from 0 to pi/`dt` and + `dt` is the discrete timebase. If timebase not specified + (`dt` = True), `dt` is set to 1. + + If a continuous-time system contains poles on or near the imaginary + axis, a small indentation will be used to avoid the pole. The radius + of the indentation is given by `indent_radius` and it is taken to the + right of stable poles and the left of unstable poles. If a pole is + exactly on the imaginary axis, the `indent_direction` parameter can be + used to set the direction of indentation. Setting `indent_direction` + to 'none' will turn off indentation. + + For those portions of the Nyquist plot in which the contour is indented + to avoid poles, resulting in a scaling of the Nyquist plot, the line + styles are according to the settings of the `primary_style` and + `mirror_style` keywords. By default the scaled portions of the primary + curve use a dotted line style and the scaled portion of the mirror + image use a dashdot line style. + + If the legacy keyword `return_contour` is specified as True, the + response object can be iterated over to return ``(count, contour)``. + This behavior is deprecated and will be removed in a future release. See Also -------- @@ -1212,24 +1301,31 @@ def nyquist_response( >>> G = ct.zpk([], [-1, -2, -3], gain=100) >>> response = ct.nyquist_response(G) >>> count = response.count - >>> lines = response.plot() + >>> cplt = response.plot() """ + # Create unified list of keyword arguments + if _kwargs is None: + _kwargs = kwargs + else: + # Use existing dictionary, to keep track of processed keywords + _kwargs |= kwargs + # Get values for params omega_num_given = omega_num is not None omega_num = config._get_param('freqplot', 'number_of_samples', omega_num) indent_radius = config._get_param( - 'nyquist', 'indent_radius', kwargs, _nyquist_defaults, pop=True) + 'nyquist', 'indent_radius', _kwargs, _nyquist_defaults, pop=True) encirclement_threshold = config._get_param( - 'nyquist', 'encirclement_threshold', kwargs, + 'nyquist', 'encirclement_threshold', _kwargs, _nyquist_defaults, pop=True) indent_direction = config._get_param( - 'nyquist', 'indent_direction', kwargs, _nyquist_defaults, pop=True) + 'nyquist', 'indent_direction', _kwargs, _nyquist_defaults, pop=True) indent_points = config._get_param( - 'nyquist', 'indent_points', kwargs, _nyquist_defaults, pop=True) + 'nyquist', 'indent_points', _kwargs, _nyquist_defaults, pop=True) - if check_kwargs and kwargs: - raise TypeError("unrecognized keywords: ", str(kwargs)) + if _check_kwargs and _kwargs: + raise TypeError("unrecognized keywords: ", str(_kwargs)) # Convert the first argument to a list syslist = sysdata if isinstance(sysdata, (list, tuple)) else [sysdata] @@ -1257,7 +1353,7 @@ def nyquist_response( "Nyquist plot currently only supports SISO systems.") # Figure out the frequency range - if isinstance(sys, FrequencyResponseData) and sys.ifunc is None \ + if isinstance(sys, FrequencyResponseData) and sys._ifunc is None \ and not omega_range_given: omega_sys = sys.omega # use system frequencies else: @@ -1359,7 +1455,7 @@ def nyquist_response( splane_contour[last_point:])) # Indent points that are too close to a pole - if len(splane_poles) > 0: # accomodate no splane poles if dtime sys + if len(splane_poles) > 0: # accommodate no splane poles if dtime sys for i, s in enumerate(splane_contour): # Find the nearest pole p = splane_poles[(np.abs(splane_poles - s)).argmin()] @@ -1392,6 +1488,12 @@ def nyquist_response( else: contour = np.exp(splane_contour * sys.dt) + # Make sure we don't try to evaluate at a pole + if isinstance(sys, (StateSpace, TransferFunction)): + if any([pole in contour for pole in sys.poles()]): + raise RuntimeError( + "attempt to evaluate at a pole; indent required") + # Compute the primary curve resp = sys(contour) @@ -1409,10 +1511,10 @@ def nyquist_response( " frequency range that does not include zero.") # - # Make sure that the enciriclements match the Nyquist criterion + # Make sure that the encirclements match the Nyquist criterion # # If the user specifies the frequency points to use, it is possible - # to miss enciriclements, so we check here to make sure that the + # to miss encirclements, so we check here to make sure that the # Nyquist criterion is actually satisfied. # if isinstance(sys, (StateSpace, TransferFunction)): @@ -1436,8 +1538,8 @@ def nyquist_response( "number of encirclements does not match Nyquist criterion;" " check frequency range and indent radius/direction", UserWarning, stacklevel=2) - elif indent_direction == 'none' and any(sys.poles().real == 0) and \ - warn_encirclements: + elif indent_direction == 'none' and any(sys.poles().real == 0) \ + and warn_encirclements: warnings.warn( "system has pure imaginary poles but indentation is" " turned off; results may be meaningless", @@ -1458,12 +1560,12 @@ def nyquist_response( def nyquist_plot( data, omega=None, plot=None, label_freq=0, color=None, label=None, - return_contour=None, title=None, legend_loc='upper right', ax=None, + return_contour=None, title=None, ax=None, unit_circle=False, mt_circles=None, ms_circles=None, **kwargs): """Nyquist plot for a system. Generates a Nyquist plot for the system over a (optional) frequency - range. The curve is computed by evaluating the Nyqist segment along + range. The curve is computed by evaluating the Nyquist segment along the positive imaginary axis, with a mirror image generated to reflect the negative imaginary axis. Poles on or near the imaginary axis are avoided using a small indentation. The portion of the Nyquist contour @@ -1472,59 +1574,74 @@ def nyquist_plot( Parameters ---------- - data : list of LTI or NyquistResponseData + data : list of `LTI` or `NyquistResponseData` List of linear input/output systems (single system is OK) or - Nyquist ersponses (computed using :func:`~control.nyquist_response`). + Nyquist responses (computed using `nyquist_response`). Nyquist curves for each system are plotted on the same graph. omega : array_like, optional Set of frequencies to be evaluated, in rad/sec. Specifying - ``omega`` as a list of two elements is equivalent to providing - ``omega_limits``. - color : string, optional - Used to specify the color of the line and arrowhead. + `omega` as a list of two elements is equivalent to providing + `omega_limits`. unit_circle : bool, optional - If ``True``, display the unit circle, to read gain crossover frequency. + If True, display the unit circle, to read gain crossover + frequency. The circle style is determined by + `config.defaults['nyquist.circle_style']`. mt_circles : array_like, optional Draw circles corresponding to the given magnitudes of sensitivity. ms_circles : array_like, optional Draw circles corresponding to the given magnitudes of complementary sensitivity. - **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional - Additional keywords (passed to `matplotlib`) + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional + Additional keywords passed to `matplotlib` to specify line properties. Returns ------- - lines : array of Line2D - 2D array of Line2D objects for each line in the plot. The shape of - the array is given by (nsys, 4) where nsys is the number of systems - or Nyquist responses passed to the function. The second index - specifies the segment type: - - * lines[idx, 0]: unscaled portion of the primary curve - * lines[idx, 1]: scaled portion of the primary curve - * lines[idx, 2]: unscaled portion of the mirror curve - * lines[idx, 3]: scaled portion of the mirror curve + cplt : `ControlPlot` object + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : 2D array of `matplotlib.lines.Line2D` + Array containing information on each line in the plot. The shape + of the array is given by (nsys, 4) where nsys is the number of + systems or Nyquist responses passed to the function. The second + index specifies the segment type: + + - lines[idx, 0]: unscaled portion of the primary curve + - lines[idx, 1]: scaled portion of the primary curve + - lines[idx, 2]: unscaled portion of the mirror curve + - lines[idx, 3]: scaled portion of the mirror curve + + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. Other Parameters ---------------- arrows : int or 1D/2D array of floats, optional Specify the number of arrows to plot on the Nyquist curve. If an integer is passed. that number of equally spaced arrows will be - plotted on each of the primary segment and the mirror image. If a 1D - array is passed, it should consist of a sorted list of floats between - 0 and 1, indicating the location along the curve to plot an arrow. If - a 2D array is passed, the first row will be used to specify arrow - locations for the primary curve and the second row will be used for - the mirror image. + plotted on each of the primary segment and the mirror image. If a + 1D array is passed, it should consist of a sorted list of floats + between 0 and 1, indicating the location along the curve to plot an + arrow. If a 2D array is passed, the first row will be used to + specify arrow locations for the primary curve and the second row + will be used for the mirror image. Default value is 2 and can be + set using `config.defaults['nyquist.arrows']`. arrow_size : float, optional Arrowhead width and length (in display coordinates). Default value is - 8 and can be set using config.defaults['nyquist.arrow_size']. + 8 and can be set using `config.defaults['nyquist.arrow_size']`. arrow_style : matplotlib.patches.ArrowStyle, optional Define style used for Nyquist curve arrows (overrides `arrow_size`). + ax : `matplotlib.axes.Axes`, optional + The matplotlib axes to draw the figure on. If not specified and + the current figure has a single axes, that axes is used. + Otherwise, a new figure is created. encirclement_threshold : float, optional Define the threshold for generating a warning if the number of net encirclements is a non-integer value. Default value is 0.05 and can - be set using config.defaults['nyquist.encirclement_threshold']. + be set using `config.defaults['nyquist.encirclement_threshold']`. indent_direction : str, optional For poles on the imaginary axis, set the direction of indentation to be 'right' (default), 'left', or 'none'. @@ -1535,62 +1652,78 @@ def nyquist_plot( Amount to indent the Nyquist contour around poles on or near the imaginary axis. Portions of the Nyquist plot corresponding to indented portions of the contour are plotted using a different line style. - label : str or array-like of str + label : str or array_like of str, optional If present, replace automatically generated label(s) with the given label(s). If sysdata is a list, strings should be specified for each system. - label_freq : int, optiona + label_freq : int, optional Label every nth frequency on the plot. If not specified, no labels are generated. + legend_loc : int or str, optional + Include a legend in the given location. Default is 'upper right', + with no legend for a single response. Use False to suppress legend. max_curve_magnitude : float, optional Restrict the maximum magnitude of the Nyquist plot to this value. Portions of the Nyquist plot whose magnitude is restricted are - plotted using a different line style. + plotted using a different line style. The default value is 20 and + can be set using `config.defaults['nyquist.max_curve_magnitude']`. max_curve_offset : float, optional When plotting scaled portion of the Nyquist plot, increase/decrease the magnitude by this fraction of the max_curve_magnitude to allow any overlaps between the primary and mirror curves to be avoided. + The default value is 0.02 and can be set using + `config.defaults['nyquist.max_curve_magnitude']`. mirror_style : [str, str] or False Linestyles for mirror image of the Nyquist curve. The first element is used for unscaled portions of the Nyquist curve, the second element is used for portions that are scaled (using max_curve_magnitude). If - `False` then omit completely. Default linestyle (['--', ':']) is - determined by config.defaults['nyquist.mirror_style']. + False then omit completely. Default linestyle (['--', ':']) is + determined by `config.defaults['nyquist.mirror_style']`. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. omega_num : int, optional - Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. Ignored if data is + Number of samples to use for the frequency range. Defaults to + `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. plot : bool, optional - (legacy) If given, `bode_plot` returns the legacy return values - of magnitude, phase, and frequency. If False, just return the - values with no plot. + (legacy) If given, `nyquist_plot` returns the legacy return values + of (counts, contours). If False, return the values with no plot. primary_style : [str, str], optional Linestyles for primary image of the Nyquist curve. The first element is used for unscaled portions of the Nyquist curve, the second element is used for portions that are scaled (using max_curve_magnitude). Default linestyle (['-', '-.']) is - determined by config.defaults['nyquist.mirror_style']. + determined by `config.defaults['nyquist.mirror_style']`. rcParams : dict Override the default parameters used for generating plots. - Default is set by config.default['freqplot.rcParams']. + Default is set by `config.defaults['ctrlplot.rcParams']`. return_contour : bool, optional - (legacy) If 'True', return the encirclement count and Nyquist + (legacy) If True, return the encirclement count and Nyquist contour used to generate the Nyquist plot. + show_legend : bool, optional + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on the + plot or `legend_loc` has been specified. start_marker : str, optional Matplotlib marker to use to mark the starting point of the Nyquist plot. Defaults value is 'o' and can be set using - config.defaults['nyquist.start_marker']. + `config.defaults['nyquist.start_marker']`. start_marker_size : float, optional Start marker size (in display coordinates). Default value is - 4 and can be set using config.defaults['nyquist.start_marker_size']. + 4 and can be set using `config.defaults['nyquist.start_marker_size']`. + title : str, optional + Set the title of the plot. Defaults to plot type and system name(s). + title_frame : str, optional + Set the frame of reference used to center the plot title. If set to + 'axes' (default), the horizontal position of the title will + centered relative to the axes. If set to 'figure', it will be + centered with respect to the figure (faster execution). warn_nyquist : bool, optional - If set to 'False', turn off warnings about frequencies above Nyquist. + If set to False, turn off warnings about frequencies above Nyquist. warn_encirclements : bool, optional - If set to 'False', turn off warnings about number of encirclements not + If set to False, turn off warnings about number of encirclements not meeting the Nyquist criterion. See Also @@ -1599,27 +1732,27 @@ def nyquist_plot( Notes ----- - 1. If a discrete time model is given, the frequency response is computed - along the upper branch of the unit circle, using the mapping ``z = - exp(1j * omega * dt)`` where `omega` ranges from 0 to `pi/dt` and `dt` - is the discrete timebase. If timebase not specified (``dt=True``), - `dt` is set to 1. - - 2. If a continuous-time system contains poles on or near the imaginary - axis, a small indentation will be used to avoid the pole. The radius - of the indentation is given by `indent_radius` and it is taken to the - right of stable poles and the left of unstable poles. If a pole is - exactly on the imaginary axis, the `indent_direction` parameter can be - used to set the direction of indentation. Setting `indent_direction` - to `none` will turn off indentation. If `return_contour` is True, the - exact contour used for evaluation is returned. - - 3. For those portions of the Nyquist plot in which the contour is - indented to avoid poles, resuling in a scaling of the Nyquist plot, - the line styles are according to the settings of the `primary_style` - and `mirror_style` keywords. By default the scaled portions of the - primary curve use a dotted line style and the scaled portion of the - mirror image use a dashdot line style. + If a discrete-time model is given, the frequency response is computed + along the upper branch of the unit circle, using the mapping ``z = + exp(1j * omega * dt)`` where `omega` ranges from 0 to pi/`dt` and + `dt` is the discrete timebase. If timebase not specified + (`dt` = True), `dt` is set to 1. + + If a continuous-time system contains poles on or near the imaginary + axis, a small indentation will be used to avoid the pole. The radius + of the indentation is given by `indent_radius` and it is taken to the + right of stable poles and the left of unstable poles. If a pole is + exactly on the imaginary axis, the `indent_direction` parameter can be + used to set the direction of indentation. Setting `indent_direction` + to 'none' will turn off indentation. If `return_contour` is True, + the exact contour used for evaluation is returned. + + For those portions of the Nyquist plot in which the contour is indented + to avoid poles, resulting in a scaling of the Nyquist plot, the line + styles are according to the settings of the `primary_style` and + `mirror_style` keywords. By default the scaled portions of the primary + curve use a dotted line style and the scaled portion of the mirror + image use a dashdot line style. Examples -------- @@ -1648,18 +1781,18 @@ def nyquist_plot( arrow_size = config._get_param( 'nyquist', 'arrow_size', kwargs, _nyquist_defaults, pop=True) arrow_style = config._get_param('nyquist', 'arrow_style', kwargs, None) + ax_user = ax max_curve_magnitude = config._get_param( 'nyquist', 'max_curve_magnitude', kwargs, _nyquist_defaults, pop=True) max_curve_offset = config._get_param( 'nyquist', 'max_curve_offset', kwargs, _nyquist_defaults, pop=True) - rcParams = config._get_param( - 'freqplot', 'rcParams', kwargs, _freqplot_defaults, pop=True) + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) start_marker = config._get_param( 'nyquist', 'start_marker', kwargs, _nyquist_defaults, pop=True) start_marker_size = config._get_param( 'nyquist', 'start_marker_size', kwargs, _nyquist_defaults, pop=True) - suptitle_frame = config._get_param( - 'freqplot', 'suptitle_frame', kwargs, _freqplot_defaults, pop=True) + title_frame = config._get_param( + 'freqplot', 'title_frame', kwargs, _freqplot_defaults, pop=True) # Set line styles for the curves def _parse_linestyle(style_name, allow_false=False): @@ -1708,30 +1841,29 @@ def _parse_linestyle(style_name, allow_false=False): if all([isinstance( sys, (StateSpace, TransferFunction, FrequencyResponseData)) for sys in data]): - # Get the response, popping off keywords used there + # Get the response; pop explicit keywords here, kwargs in _response() nyquist_responses = nyquist_response( data, omega=omega, return_contour=return_contour, omega_limits=kwargs.pop('omega_limits', None), omega_num=kwargs.pop('omega_num', None), warn_encirclements=kwargs.pop('warn_encirclements', True), warn_nyquist=kwargs.pop('warn_nyquist', True), - indent_radius=kwargs.pop('indent_radius', None), - check_kwargs=False, **kwargs) + _kwargs=kwargs, _check_kwargs=False) else: nyquist_responses = data # Legacy return value processing if plot is not None or return_contour is not None: warnings.warn( - "`nyquist_plot` return values of count[, contour] is deprecated; " - "use nyquist_response()", DeprecationWarning) + "nyquist_plot() return value of count[, contour] is deprecated; " + "use nyquist_response()", FutureWarning) # Extract out the values that we will eventually return counts = [response.count for response in nyquist_responses] contours = [response.contour for response in nyquist_responses] if plot is False: - # Make sure we used all of the keywrods + # Make sure we used all of the keywords if kwargs: raise TypeError("unrecognized keywords: ", str(kwargs)) @@ -1742,7 +1874,9 @@ def _parse_linestyle(style_name, allow_false=False): return (counts, contours) if return_contour else counts fig, ax = _process_ax_keyword( - ax, shape=(1, 1), squeeze=True, rcParams=rcParams) + ax_user, shape=(1, 1), squeeze=True, rcParams=rcParams) + legend_loc, _, show_legend = _process_legend_keywords( + kwargs, None, 'upper right') # Create a list of lines for the output out = np.empty(len(nyquist_responses), dtype=object) @@ -1779,7 +1913,7 @@ def _parse_linestyle(style_name, allow_false=False): # Plot the regular portions of the curve (and grab the color) x_reg = np.ma.masked_where(reg_mask, resp.real) y_reg = np.ma.masked_where(reg_mask, resp.imag) - p = plt.plot( + p = ax.plot( x_reg, y_reg, primary_style[0], color=color, label=label, **kwargs) c = p[0].get_color() out[idx] += p @@ -1794,7 +1928,7 @@ def _parse_linestyle(style_name, allow_false=False): x_scl = np.ma.masked_where(scale_mask, resp.real) y_scl = np.ma.masked_where(scale_mask, resp.imag) if x_scl.count() >= 1 and y_scl.count() >= 1: - out[idx] += plt.plot( + out[idx] += ax.plot( x_scl * (1 + curve_offset), y_scl * (1 + curve_offset), primary_style[1], color=c, **kwargs) @@ -1805,20 +1939,19 @@ def _parse_linestyle(style_name, allow_false=False): x, y = resp.real.copy(), resp.imag.copy() x[reg_mask] *= (1 + curve_offset[reg_mask]) y[reg_mask] *= (1 + curve_offset[reg_mask]) - p = plt.plot(x, y, linestyle='None', color=c) + p = ax.plot(x, y, linestyle='None', color=c) # Add arrows - ax = plt.gca() _add_arrows_to_line2D( ax, p[0], arrow_pos, arrowstyle=arrow_style, dir=1) # Plot the mirror image if mirror_style is not False: # Plot the regular and scaled segments - out[idx] += plt.plot( + out[idx] += ax.plot( x_reg, -y_reg, mirror_style[0], color=c, **kwargs) if x_scl.count() >= 1 and y_scl.count() >= 1: - out[idx] += plt.plot( + out[idx] += ax.plot( x_scl * (1 - curve_offset), -y_scl * (1 - curve_offset), mirror_style[1], color=c, **kwargs) @@ -1829,7 +1962,7 @@ def _parse_linestyle(style_name, allow_false=False): x, y = resp.real.copy(), resp.imag.copy() x[reg_mask] *= (1 - curve_offset[reg_mask]) y[reg_mask] *= (1 - curve_offset[reg_mask]) - p = plt.plot(x, -y, linestyle='None', color=c, **kwargs) + p = ax.plot(x, -y, linestyle='None', color=c, **kwargs) _add_arrows_to_line2D( ax, p[0], arrow_pos, arrowstyle=arrow_style, dir=-1) else: @@ -1837,11 +1970,11 @@ def _parse_linestyle(style_name, allow_false=False): # Mark the start of the curve if start_marker: - plt.plot(resp[0].real, resp[0].imag, start_marker, + ax.plot(resp[0].real, resp[0].imag, start_marker, color=c, markersize=start_marker_size) # Mark the -1 point - plt.plot([-1], [0], 'r+') + ax.plot([-1], [0], 'r+') # # Draw circles for gain crossover and sensitivity functions @@ -1853,16 +1986,16 @@ def _parse_linestyle(style_name, allow_false=False): # Display the unit circle, to read gain crossover frequency if unit_circle: - plt.plot(cos, sin, **config.defaults['nyquist.circle_style']) - + ax.plot(cos, sin, **config.defaults['nyquist.circle_style']) + # Draw circles for given magnitudes of sensitivity if ms_circles is not None: for ms in ms_circles: pos_x = -1 + (1/ms)*cos pos_y = (1/ms)*sin - plt.plot( + ax.plot( pos_x, pos_y, **config.defaults['nyquist.circle_style']) - plt.text(pos_x[label_pos], pos_y[label_pos], ms) + ax.text(pos_x[label_pos], pos_y[label_pos], ms) # Draw circles for given magnitudes of complementary sensitivity if mt_circles is not None: @@ -1872,17 +2005,17 @@ def _parse_linestyle(style_name, allow_false=False): rt = mt/(mt**2-1) # Mt radius pos_x = ct+rt*cos pos_y = rt*sin - plt.plot( + ax.plot( pos_x, pos_y, **config.defaults['nyquist.circle_style']) - plt.text(pos_x[label_pos], pos_y[label_pos], mt) + ax.text(pos_x[label_pos], pos_y[label_pos], mt) else: - _, _, ymin, ymax = plt.axis() + _, _, ymin, ymax = ax.axis() pos_y = np.linspace(ymin, ymax, 100) - plt.vlines( + ax.vlines( -0.5, ymin=ymin, ymax=ymax, **config.defaults['nyquist.circle_style']) - plt.text(-0.5, pos_y[label_pos], 1) + ax.text(-0.5, pos_y[label_pos], 1) # Label the frequencies of the points on the Nyquist curve if label_freq: @@ -1905,7 +2038,7 @@ def _parse_linestyle(style_name, allow_false=False): # np.round() is used because 0.99... appears # instead of 1.0, and this would otherwise be # truncated to 0. - plt.text(xpt, ypt, ' ' + + ax.text(xpt, ypt, ' ' + str(int(np.round(f / 1000 ** pow1000, 0))) + ' ' + prefix + 'Hz') @@ -1918,15 +2051,24 @@ def _parse_linestyle(style_name, allow_false=False): lines, labels = _get_line_labels(ax) # Add legend if there is more than one system plotted - if len(labels) > 1: - ax.legend(lines, labels, loc=legend_loc) + if show_legend == True or (show_legend != False and len(labels) > 1): + with plt.rc_context(rcParams): + legend = ax.legend(lines, labels, loc=legend_loc) + else: + legend = None # Add the title - if title is None: - title = "Nyquist plot for " + ", ".join(labels) - suptitle(title, fig=fig, rcParams=rcParams, frame=suptitle_frame) + sysnames = [response.sysname for response in nyquist_responses] + if ax_user is None and title is None: + title = "Nyquist plot for " + ", ".join(sysnames) + _update_plot_title( + title, fig=fig, rcParams=rcParams, frame=title_frame) + elif ax_user is None: + _update_plot_title( + title, fig=fig, rcParams=rcParams, frame=title_frame, + use_existing=False) - # Legacy return pocessing + # Legacy return processing if plot is True or return_contour is not None: if len(data) == 1: counts, contours = counts[0], contours[0] @@ -1934,87 +2076,7 @@ def _parse_linestyle(style_name, allow_false=False): # Return counts and (optionally) the contour we used return (counts, contours) if return_contour else counts - return out - - -# Internal function to add arrows to a curve -def _add_arrows_to_line2D( - axes, line, arrow_locs=[0.2, 0.4, 0.6, 0.8], - arrowstyle='-|>', arrowsize=1, dir=1): - """ - Add arrows to a matplotlib.lines.Line2D at selected locations. - - Parameters: - ----------- - axes: Axes object as returned by axes command (or gca) - line: Line2D object as returned by plot command - arrow_locs: list of locations where to insert arrows, % of total length - arrowstyle: style of the arrow - arrowsize: size of the arrow - - Returns: - -------- - arrows: list of arrows - - Based on https://stackoverflow.com/questions/26911898/ - - """ - # Get the coordinates of the line, in plot coordinates - if not isinstance(line, mpl.lines.Line2D): - raise ValueError("expected a matplotlib.lines.Line2D object") - x, y = line.get_xdata(), line.get_ydata() - - # Determine the arrow properties - arrow_kw = {"arrowstyle": arrowstyle} - - color = line.get_color() - use_multicolor_lines = isinstance(color, np.ndarray) - if use_multicolor_lines: - raise NotImplementedError("multicolor lines not supported") - else: - arrow_kw['color'] = color - - linewidth = line.get_linewidth() - if isinstance(linewidth, np.ndarray): - raise NotImplementedError("multiwidth lines not supported") - else: - arrow_kw['linewidth'] = linewidth - - # Figure out the size of the axes (length of diagonal) - xlim, ylim = axes.get_xlim(), axes.get_ylim() - ul, lr = np.array([xlim[0], ylim[0]]), np.array([xlim[1], ylim[1]]) - diag = np.linalg.norm(ul - lr) - - # Compute the arc length along the curve - s = np.cumsum(np.sqrt(np.diff(x) ** 2 + np.diff(y) ** 2)) - - # Truncate the number of arrows if the curve is short - # TODO: figure out a smarter way to do this - frac = min(s[-1] / diag, 1) - if len(arrow_locs) and frac < 0.05: - arrow_locs = [] # too short; no arrows at all - elif len(arrow_locs) and frac < 0.2: - arrow_locs = [0.5] # single arrow in the middle - - # Plot the arrows (and return list if patches) - arrows = [] - for loc in arrow_locs: - n = np.searchsorted(s, s[-1] * loc) - - if dir == 1 and n == 0: - # Move the arrow forward by one if it is at start of a segment - n = 1 - - # Place the head of the arrow at the desired location - arrow_head = [x[n], y[n]] - arrow_tail = [x[n - dir], y[n - dir]] - - p = mpl.patches.FancyArrowPatch( - arrow_tail, arrow_head, transform=axes.transData, lw=0, - **arrow_kw) - axes.add_patch(p) - arrows.append(p) - return arrows + return ControlPlot(out, ax, fig, legend=legend) # @@ -2032,7 +2094,7 @@ def _compute_curve_offset(resp, mask, max_offset): offset = np.zeros(resp.size) arclen = np.zeros(resp.size) - # Walk through the response and keep track of each continous component + # Walk through the response and keep track of each continuous component i, nsegs = 0, 0 while i < resp.size: # Skip the regular segment @@ -2079,7 +2141,7 @@ def _compute_curve_offset(resp, mask, max_offset): # def gangof4_response( P, C, omega=None, omega_limits=None, omega_num=None, Hz=False): - """Compute the response of the "Gang of 4" transfer functions for a system. + """Compute response of "Gang of 4" transfer functions. Generates a 2x2 frequency response for the "Gang of 4" sensitivity functions [T, PS; CS, S]. @@ -2090,10 +2152,22 @@ def gangof4_response( Linear input/output systems (process and control). omega : array Range of frequencies (list or bounds) in rad/sec. + omega_limits : array_like of two values + Set limits for plotted frequency range. If Hz=True the limits are + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. Ignored if + data is not a list of systems. + omega_num : int + Number of samples to use for the frequency range. Defaults to + `config.defaults['freqplot.number_of_samples']`. Ignored if data is + not a list of systems. + Hz : bool, optional + If True, when computing frequency limits automatically set + limits to full decades in Hz instead of rad/s. Returns ------- - response : :class:`~control.FrequencyResponseData` + response : `FrequencyResponseData` Frequency response with inputs 'r' and 'd' and outputs 'y', and 'u' representing the 2x2 matrix of transfer functions in the Gang of 4. @@ -2102,7 +2176,7 @@ def gangof4_response( >>> P = ct.tf([1], [1, 1]) >>> C = ct.tf([2], [1]) >>> response = ct.gangof4_response(P, C) - >>> lines = response.plot() + >>> cplt = response.plot() """ if not P.issiso() or not C.issiso(): @@ -2110,7 +2184,7 @@ def gangof4_response( raise ControlMIMONotImplemented( "Gang of four is currently only implemented for SISO systems.") - # Compute the senstivity functions + # Compute the sensitivity functions L = P * C S = feedback(1, L) T = L * S @@ -2139,15 +2213,78 @@ def gangof4_response( return FrequencyResponseData( data, omega, outputs=['y', 'u'], inputs=['r', 'd'], - title=f"Gang of Four for P={P.name}, C={C.name}", plot_phase=False) + title=f"Gang of Four for P={P.name}, C={C.name}", + sysname=f"P={P.name}, C={C.name}", plot_phase=False) def gangof4_plot( - P, C, omega=None, omega_limits=None, omega_num=None, **kwargs): - """Legacy Gang of 4 plot; use gangof4_response().plot() instead.""" - return gangof4_response( - P, C, omega=omega, omega_limits=omega_limits, - omega_num=omega_num).plot(**kwargs) + *args, omega=None, omega_limits=None, omega_num=None, + Hz=False, **kwargs): + """gangof4_plot(response) \ + gangof4_plot(P, C, omega) + + Plot response of "Gang of 4" transfer functions. + + Plots a 2x2 frequency response for the "Gang of 4" sensitivity + functions [T, PS; CS, S]. Can be called in one of two ways: + + gangof4_plot(response[, ...]) + gangof4_plot(P, C[, ...]) + + Parameters + ---------- + response : FrequencyPlotData + Gang of 4 frequency response from `gangof4_response`. + P, C : LTI + Linear input/output systems (process and control). + omega : array + Range of frequencies (list or bounds) in rad/sec. + omega_limits : array_like of two values + Set limits for plotted frequency range. If Hz=True the limits are + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. Ignored if + data is not a list of systems. + omega_num : int + Number of samples to use for the frequency range. Defaults to + `config.defaults['freqplot.number_of_samples']`. Ignored if data is + not a list of systems. + Hz : bool, optional + If True, when computing frequency limits automatically set + limits to full decades in Hz instead of rad/s. + + Returns + ------- + cplt : `ControlPlot` object + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : 2x2 array of `matplotlib.lines.Line2D` + Array containing information on each line in the plot. The value + of each array entry is a list of Line2D objects in that subplot. + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. + + """ + if len(args) == 1 and isinstance(args[0], FrequencyResponseData): + if any([kw is not None + for kw in [omega, omega_limits, omega_num, Hz]]): + raise ValueError( + "omega, omega_limits, omega_num, Hz not allowed when " + "given a Gang of 4 response as first argument") + return args[0].plot(kwargs) + else: + if len(args) > 3: + raise TypeError( + f"expecting 2 or 3 positional arguments; received {len(args)}") + omega = omega if len(args) < 3 else args[2] + args = args[0:2] + return gangof4_response( + *args, omega=omega, omega_limits=omega_limits, + omega_num=omega_num, Hz=Hz).plot(**kwargs) + # # Singular values plot @@ -2171,7 +2308,7 @@ def singular_values_response( Returns ------- - response : FrequencyResponseData + response : `FrequencyResponseData` Frequency response with the number of outputs equal to the number of singular values in the response, and a single input. @@ -2179,11 +2316,11 @@ def singular_values_response( ---------------- omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. omega_num : int, optional - Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. + Number of samples to use for the frequency range. Defaults to + `config.defaults['freqplot.number_of_samples']`. See Also -------- @@ -2213,7 +2350,7 @@ def singular_values_response( svd_responses = [] for response in responses: # Compute the singular values (permute indices to make things work) - fresp_permuted = response.fresp.transpose((2, 0, 1)) + fresp_permuted = response.frdata.transpose((2, 0, 1)) sigma = np.linalg.svd(fresp_permuted, compute_uv=False).transpose() sigma_fresp = sigma.reshape(sigma.shape[0], 1, sigma.shape[1]) @@ -2234,7 +2371,7 @@ def singular_values_response( def singular_values_plot( data, omega=None, *fmt, plot=None, omega_limits=None, omega_num=None, - ax=None, label=None, title=None, legend_loc='center right', **kwargs): + ax=None, label=None, title=None, **kwargs): """Plot the singular values for a system. Plot the singular values as a function of frequency for a system or @@ -2244,53 +2381,62 @@ def singular_values_plot( Parameters ---------- data : list of `FrequencyResponseData` - List of :class:`FrequencyResponseData` objects. For backward + List of `FrequencyResponseData` objects. For backward compatibility, a list of LTI systems can also be given. omega : array_like List of frequencies in rad/sec over to plot over. - *fmt : :func:`matplotlib.pyplot.plot` format string, optional + *fmt : `matplotlib.pyplot.plot` format string, optional Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). dB : bool If True, plot result in dB. Default is False. Hz : bool If True, plot frequency in Hz (omega must be provided in rad/sec). - Default value (False) set by config.defaults['freqplot.Hz']. - **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional + Default value (False) set by `config.defaults['freqplot.Hz']`. + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. Returns ------- - legend_loc : str, optional - For plots with multiple lines, a legend will be included in the - given location. Default is 'center right'. Use False to suppress. - lines : array of Line2D - 1-D array of Line2D objects. The size of the array matches - the number of systems and the value of the array is a list of - Line2D objects for that system. - mag : ndarray (or list of ndarray if len(data) > 1)) - If plot=False, magnitude of the response (deprecated). - phase : ndarray (or list of ndarray if len(data) > 1)) - If plot=False, phase in radians of the response (deprecated). - omega : ndarray (or list of ndarray if len(data) > 1)) - If plot=False, frequency in rad/sec (deprecated). + cplt : `ControlPlot` object + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : array of `matplotlib.lines.Line2D` + Array containing information on each line in the plot. The size of + the array matches the number of systems and the value of the array + is a list of Line2D objects for that system. + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. Other Parameters ---------------- + ax : `matplotlib.axes.Axes`, optional + The matplotlib axes to draw the figure on. If not specified and + the current figure has a single axes, that axes is used. + Otherwise, a new figure is created. + color : matplotlib color spec + Color to use for singular values (or None for matplotlib default). grid : bool - If True, plot grid lines on gain and phase plots. Default is set by - `config.defaults['freqplot.grid']`. - label : str or array-like of str + If True, plot grid lines on gain and phase plots. Default is + set by `config.defaults['freqplot.grid']`. + label : str or array_like of str, optional If present, replace automatically generated label(s) with the given label(s). If sysdata is a list, strings should be specified for each system. + legend_loc : int or str, optional + Include a legend in the given location. Default is 'center right', + with no legend for a single response. Use False to suppress legend. omega_limits : array_like of two values Set limits for plotted frequency range. If Hz=True the limits are - in Hz otherwise in rad/s. Specifying ``omega`` as a list of two - elements is equivalent to providing ``omega_limits``. + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. omega_num : int, optional - Number of samples to use for the frequeny range. Defaults to - config.defaults['freqplot.number_of_samples']. Ignored if data is + Number of samples to use for the frequency range. Defaults to + `config.defaults['freqplot.number_of_samples']`. Ignored if data is not a list of systems. plot : bool, optional (legacy) If given, `singular_values_plot` returns the legacy return @@ -2298,24 +2444,45 @@ def singular_values_plot( the values with no plot. rcParams : dict Override the default parameters used for generating plots. - Default is set up config.default['freqplot.rcParams']. + Default is set up `config.defaults['ctrlplot.rcParams']`. + show_legend : bool, optional + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on an + axis or `legend_loc` or `legend_map` has been specified. + title : str, optional + Set the title of the plot. Defaults to plot type and system name(s). + title_frame : str, optional + Set the frame of reference used to center the plot title. If set to + 'axes' (default), the horizontal position of the title will + centered relative to the axes. If set to 'figure', it will be + centered with respect to the figure (faster execution). See Also -------- singular_values_response + Notes + ----- + If `plot` = False, the following legacy values are returned: + * `mag` : ndarray (or list of ndarray if len(data) > 1)) + Magnitude of the response (deprecated). + * `phase` : ndarray (or list of ndarray if len(data) > 1)) + Phase in radians of the response (deprecated). + * `omega` : ndarray (or list of ndarray if len(data) > 1)) + Frequency in rad/sec (deprecated). + """ # Keyword processing + color = kwargs.pop('color', None) dB = config._get_param( 'freqplot', 'dB', kwargs, _freqplot_defaults, pop=True) Hz = config._get_param( 'freqplot', 'Hz', kwargs, _freqplot_defaults, pop=True) grid = config._get_param( 'freqplot', 'grid', kwargs, _freqplot_defaults, pop=True) - rcParams = config._get_param( - 'freqplot', 'rcParams', kwargs, _freqplot_defaults, pop=True) - suptitle_frame = config._get_param( - 'freqplot', 'suptitle_frame', kwargs, _freqplot_defaults, pop=True) + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) + title_frame = config._get_param( + 'freqplot', 'title_frame', kwargs, _freqplot_defaults, pop=True) # If argument was a singleton, turn it into a tuple data = data if isinstance(data, (list, tuple)) else (data,) @@ -2347,15 +2514,15 @@ def singular_values_plot( if plot is not None: warnings.warn( "`singular_values_plot` return values of sigma, omega is " - "deprecated; use singular_values_response()", DeprecationWarning) + "deprecated; use singular_values_response()", FutureWarning) # Warn the user if we got past something that is not real-valued - if any([not np.allclose(np.imag(response.fresp[:, 0, :]), 0) + if any([not np.allclose(np.imag(response.frdata[:, 0, :]), 0) for response in responses]): warnings.warn("data has non-zero imaginary component") # Extract the data we need for plotting - sigmas = [np.real(response.fresp[:, 0, :]) for response in responses] + sigmas = [np.real(response.frdata[:, 0, :]) for response in responses] omegas = [response.omega for response in responses] # Legacy processing for no plotting case @@ -2368,15 +2535,11 @@ def singular_values_plot( fig, ax_sigma = _process_ax_keyword( ax, shape=(1, 1), squeeze=True, rcParams=rcParams) ax_sigma.set_label('control-sigma') # TODO: deprecate? + legend_loc, _, show_legend = _process_legend_keywords( + kwargs, None, 'center right') - # Handle color cycle manually as all singular values - # of the same systems are expected to be of the same color - color_cycle = plt.rcParams['axes.prop_cycle'].by_key()['color'] - color_offset = 0 - if len(ax_sigma.lines) > 0: - last_color = ax_sigma.lines[-1].get_color() - if last_color in color_cycle: - color_offset = color_cycle.index(last_color) + 1 + # Get color offset for first (new) line to be drawn + color_offset, color_cycle = _get_color_offset(ax_sigma) # Create a list of lines for the output out = np.empty(len(data), dtype=object) @@ -2391,14 +2554,13 @@ def singular_values_plot( else: nyq_freq = None - # See if the color was specified, otherwise rotate - if kwargs.get('color', None) or any( - [isinstance(arg, str) and - any([c in arg for c in "bgrcmykw#"]) for arg in fmt]): - color_arg = {} # color set by *fmt, **kwargs - else: - color_arg = {'color': color_cycle[ - (idx_sys + color_offset) % len(color_cycle)]} + # Determine the color to use for this response + current_color = _get_color( + color, fmt=fmt, offset=color_offset + idx_sys, + color_cycle=color_cycle) + + # To avoid conflict with *fmt, only pass color kw if non-None + color_arg = {} if current_color is None else {'color': current_color} # Decide on the system name sysname = response.sysname if response.sysname is not None \ @@ -2438,14 +2600,19 @@ def singular_values_plot( lines, labels = _get_line_labels(ax_sigma) # Add legend if there is more than one system plotted - if len(labels) > 1 and legend_loc is not False: + if show_legend == True or (show_legend != False and len(labels) > 1): with plt.rc_context(rcParams): - ax_sigma.legend(lines, labels, loc=legend_loc) + legend = ax_sigma.legend(lines, labels, loc=legend_loc) + else: + legend = None # Add the title - if title is None: - title = "Singular values for " + ", ".join(labels) - suptitle(title, fig=fig, rcParams=rcParams, frame=suptitle_frame) + if ax is None: + if title is None: + title = "Singular values for " + ", ".join(labels) + _update_plot_title( + title, fig=fig, rcParams=rcParams, frame=title_frame, + use_existing=False) # Legacy return processing if plot is not None: @@ -2454,7 +2621,7 @@ def singular_values_plot( else: return sigmas, omegas - return out + return ControlPlot(out, ax_sigma, fig, legend=legend) # # Utility functions @@ -2470,24 +2637,24 @@ def _determine_omega_vector(syslist, omega_in, omega_limits, omega_num, """Determine the frequency range for a frequency-domain plot according to a standard logic. - If omega_in and omega_limits are both None, then omega_out is computed - on omega_num points according to a default logic defined by - _default_frequency_range and tailored for the list of systems syslist, and - omega_range_given is set to False. + If `omega_in` and `omega_limits` are both None, then `omega_out` is + computed on `omega_num` points according to a default logic defined by + `_default_frequency_range` and tailored for the list of systems + syslist, and `omega_range_given` is set to False. - If omega_in is None but omega_limits is an array-like of 2 elements, then - omega_out is computed with the function np.logspace on omega_num points - within the interval [min, max] = [omega_limits[0], omega_limits[1]], and - omega_range_given is set to True. + If `omega_in` is None but `omega_limits` is a tuple of 2 elements, then + `omega_out` is computed with the function `numpy.logspace` on + `omega_num` points within the interval ``[min, max] = [omega_limits[0], + omega_limits[1]]``, and `omega_range_given` is set to True. - If omega_in is a list or tuple of length 2, it is interpreted as a - range and handled like omega_limits. If omega_in is a list or tuple of - length 3, it is interpreted a range plus number of points and handled - like omega_limits and omega_num. + If `omega_in` is a tuple of length 2, it is interpreted as a range and + handled like `omega_limits`. If `omega_in` is a tuple of length 3, it + is interpreted a range plus number of points and handled like + `omega_limits` and `omega_num`. - If omega_in is an array or a list/tuple of length greater than - two, then omega_out is set to omega_in (as an array), and - omega_range_given is set to True + If `omega_in` is an array or a list/tuple of length greater than two, + then `omega_out` is set to `omega_in` (as an array), and + `omega_range_given` is set to True Parameters ---------- @@ -2564,12 +2731,12 @@ def _default_frequency_range(syslist, Hz=None, number_of_samples=None, scale in Hz otherwise in rad/s. Omega is always returned in rad/sec. number_of_samples : int, optional Number of samples to generate. The default value is read from - ``config.defaults['freqplot.number_of_samples']. If None, then the - default from `numpy.logspace` is used. + `config.defaults['freqplot.number_of_samples']`. If None, + then the default from `numpy.logspace` is used. feature_periphery_decades : float, optional Defines how many decades shall be included in the frequency range on both sides of features (poles, zeros). The default value is read from - ``config.defaults['freqplot.feature_periphery_decades']``. + `config.defaults['freqplot.feature_periphery_decades']`. Returns ------- @@ -2626,7 +2793,7 @@ def _default_frequency_range(syslist, Hz=None, number_of_samples=None, (np.abs(sys.poles()), np.abs(sys.zeros()))) # Get rid of poles and zeros on the real axis (imag==0) # * origin and real < 0 - # * at 1.: would result in omega=0. (logaritmic plot!) + # * at 1.: would result in omega=0. (logarithmic plot!) toreplace = np.isclose(features_.imag, 0.0) & ( (features_.real <= 0.) | (np.abs(features_.real - 1.0) < 1.e-10)) @@ -2672,128 +2839,12 @@ def _default_frequency_range(syslist, Hz=None, number_of_samples=None, return omega -# Get labels for all lines in an axes -def _get_line_labels(ax, use_color=True): - labels, lines = [], [] - last_color, counter = None, 0 # label unknown systems - for i, line in enumerate(ax.get_lines()): - label = line.get_label() - if use_color and label.startswith("Unknown"): - label = f"Unknown-{counter}" - if last_color is None: - last_color = line.get_color() - elif last_color != line.get_color(): - counter += 1 - last_color = line.get_color() - elif label[0] == '_': - continue - - if label not in labels: - lines.append(line) - labels.append(label) - - return lines, labels - - -# Turn label keyword into array indexed by trace, output, input -# TODO: move to ctrlutil.py and update parameter names to reflect general use -def _process_line_labels(label, ntraces, ninputs=0, noutputs=0): - if label is None: - return None - - if isinstance(label, str): - label = [label] * ntraces # single label for all traces - - # Convert to an ndarray, if not done aleady - try: - line_labels = np.asarray(label) - except: - raise ValueError("label must be a string or array_like") - - # Turn the data into a 3D array of appropriate shape - # TODO: allow more sophisticated broadcasting (and error checking) - try: - if ninputs > 0 and noutputs > 0: - if line_labels.ndim == 1 and line_labels.size == ntraces: - line_labels = line_labels.reshape(ntraces, 1, 1) - line_labels = np.broadcast_to( - line_labels, (ntraces, ninputs, noutputs)) - else: - line_labels = line_labels.reshape(ntraces, ninputs, noutputs) - except: - if line_labels.shape[0] != ntraces: - raise ValueError("number of labels must match number of traces") - else: - raise ValueError("labels must be given for each input/output pair") - - return line_labels - - -def _process_ax_keyword( - axs, shape=(1, 1), rcParams=None, squeeze=False, clear_text=False): - """Utility function to process ax keyword to plotting commands. - - This function processes the `ax` keyword to plotting commands. If no - ax keyword is passed, the current figure is checked to see if it has - the correct shape. If the shape matches the desired shape, then the - current figure and axes are returned. Otherwise a new figure is - created with axes of the desired shape. - - Legacy behavior: some of the older plotting commands use a axes label - to identify the proper axes for plotting. This behavior is supported - through the use of the label keyword, but will only work if shape == - (1, 1) and squeeze == True. - - """ - if axs is None: - fig = plt.gcf() # get current figure (or create new one) - axs = fig.get_axes() - - # Check to see if axes are the right shape; if not, create new figure - # Note: can't actually check the shape, just the total number of axes - if len(axs) != np.prod(shape): - with plt.rc_context(rcParams): - if len(axs) != 0: - # Create a new figure - fig, axs = plt.subplots(*shape, squeeze=False) - else: - # Create new axes on (empty) figure - axs = fig.subplots(*shape, squeeze=False) - fig.set_layout_engine('tight') - fig.align_labels() - else: - # Use the existing axes, properly reshaped - axs = np.asarray(axs).reshape(*shape) - - if clear_text: - # Clear out any old text from the current figure - for text in fig.texts: - text.set_visible(False) # turn off the text - del text # get rid of it completely - else: - try: - axs = np.asarray(axs).reshape(shape) - except ValueError: - raise ValueError( - "specified axes are not the right shape; " - f"got {axs.shape} but expecting {shape}") - fig = axs[0, 0].figure - - # Process the squeeze keyword - if squeeze and shape == (1, 1): - axs = axs[0, 0] # Just return the single axes object - elif squeeze: - axs = axs.squeeze() - - return fig, axs - - # # Utility functions to create nice looking labels (KLD 5/23/11) # def get_pow1000(num): - """Determine exponent for which significand of a number is within the + """Determine exponent for which significance of a number is within the range [1, 1000). """ # Based on algorithm from http://www.mail-archive.com/ diff --git a/control/grid.py b/control/grid.py index ef9995947..a3e7f36e5 100644 --- a/control/grid.py +++ b/control/grid.py @@ -1,9 +1,13 @@ # grid.py - code to add gridlines to root locus and pole-zero diagrams -# -# This code generates grids for pole-zero diagrams (including root locus -# diagrams). Rather than just draw a grid in place, it uses the AxisArtist -# package to generate a custom grid that will scale with the figure. -# + +"""Functions to add gridlines to root locus and pole-zero diagrams. + +This code generates grids for pole-zero diagrams (including root locus +diagrams). Rather than just draw a grid in place, it uses the +AxisArtist package to generate a custom grid that will scale with the +figure. + +""" import matplotlib.pyplot as plt import mpl_toolkits.axisartist.angle_helper as angle_helper @@ -18,8 +22,8 @@ from .iosys import isdtime -class FormatterDMS(object): - '''Transforms angle ticks to damping ratios''' +class FormatterDMS(): + """Transforms angle ticks to damping ratios.""" def __call__(self, direction, factor, values): angles_deg = np.asarray(values)/factor damping_ratios = np.cos((180-angles_deg) * np.pi/180) @@ -28,10 +32,10 @@ def __call__(self, direction, factor, values): class ModifiedExtremeFinderCycle(angle_helper.ExtremeFinderCycle): - '''Changed to allow only left hand-side polar grid + """Changed to allow only left hand-side polar grid. https://matplotlib.org/_modules/mpl_toolkits/axisartist/angle_helper.html#ExtremeFinderCycle.__call__ - ''' + """ def __call__(self, transform_xy, x1, y1, x2, y2): x, y = np.meshgrid( np.linspace(x1, x2, self.nx), np.linspace(y1, y2, self.ny)) @@ -74,7 +78,7 @@ def __call__(self, transform_xy, x1, y1, x2, y2): return lon_min, lon_max, lat_min, lat_max -def sgrid(scaling=None): +def sgrid(subplot=(1, 1, 1), scaling=None): # From matplotlib demos: # https://matplotlib.org/gallery/axisartist/demo_curvelinear_grid.html # https://matplotlib.org/gallery/axisartist/demo_floating_axis.html @@ -101,11 +105,10 @@ def sgrid(scaling=None): # Set up an axes with a specialized grid helper fig = plt.gcf() - ax = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper) + ax = SubplotHost(fig, *subplot, grid_helper=grid_helper) # make ticklabels of right invisible, and top axis visible. - visible = True - ax.axis[:].major_ticklabels.set_visible(visible) + ax.axis[:].major_ticklabels.set_visible(True) ax.axis[:].major_ticks.set_visible(False) ax.axis[:].invert_ticklabel_direction() ax.axis[:].major_ticklabels.set_color('gray') @@ -141,25 +144,13 @@ def sgrid(scaling=None): return ax, fig -# Utility function used by all grid code -def _final_setup(ax, scaling=None): - ax.set_xlabel('Real') - ax.set_ylabel('Imaginary') - ax.axhline(y=0, color='black', lw=0.25) - ax.axvline(x=0, color='black', lw=0.25) - - # Set up the scaling for the axes - scaling = 'equal' if scaling is None else scaling - plt.axis(scaling) - - # If not grid is given, at least separate stable/unstable regions def nogrid(dt=None, ax=None, scaling=None): fig = plt.gcf() if ax is None: ax = fig.gca() - # Draw the unit circle for discrete time systems + # Draw the unit circle for discrete-time systems if isdtime(dt=dt, strict=True): s = np.linspace(0, 2*pi, 100) ax.plot(np.cos(s), np.sin(s), 'k--', lw=0.5, dashes=(5, 5)) @@ -167,7 +158,7 @@ def nogrid(dt=None, ax=None, scaling=None): _final_setup(ax, scaling=scaling) return ax, fig -# Grid for discrete time system (drawn, not rendered by AxisArtist) +# Grid for discrete-time system (drawn, not rendered by AxisArtist) # TODO (at some point): think about using customized grid generator? def zgrid(zetas=None, wns=None, ax=None, scaling=None): """Draws discrete damping and frequency grid""" @@ -185,11 +176,11 @@ def zgrid(zetas=None, wns=None, ax=None, scaling=None): x = linspace(0, sqrt(1-zeta**2), 200) ang = pi*x mag = exp(-pi*factor*x) - # Draw upper part in retangular coordinates + # Draw upper part in rectangular coordinates xret = mag*cos(ang) yret = mag*sin(ang) ax.plot(xret, yret, ':', color='grey', lw=0.75) - # Draw lower part in retangular coordinates + # Draw lower part in rectangular coordinates xret = mag*cos(-ang) yret = mag*sin(-ang) ax.plot(xret, yret, ':', color='grey', lw=0.75) @@ -208,7 +199,7 @@ def zgrid(zetas=None, wns=None, ax=None, scaling=None): x = linspace(-pi/2, pi/2, 200) ang = pi*a*sin(x) mag = exp(-pi*a*cos(x)) - # Draw in retangular coordinates + # Draw in rectangular coordinates xret = mag*cos(ang) yret = mag*sin(ang) ax.plot(xret, yret, ':', color='grey', lw=0.75) @@ -226,3 +217,15 @@ def zgrid(zetas=None, wns=None, ax=None, scaling=None): _final_setup(ax, scaling=scaling) return ax, fig + + +# Utility function used by all grid code +def _final_setup(ax, scaling=None): + ax.set_xlabel('Real') + ax.set_ylabel('Imaginary') + ax.axhline(y=0, color='black', lw=0.25) + ax.axvline(x=0, color='black', lw=0.25) + + # Set up the scaling for the axes + scaling = 'equal' if scaling is None else scaling + plt.axis(scaling) diff --git a/control/iosys.py b/control/iosys.py index d00dade65..29f5bfefb 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -1,21 +1,25 @@ # iosys.py - I/O system class and helper functions # RMM, 13 Mar 2022 -# -# This file implements the InputOutputSystem class, which is used as a -# parent class for StateSpace, TransferFunction, NonlinearIOSystem, LTI, -# FrequencyResponseData, InterconnectedSystem and other similar classes -# that allow naming of signals. + +"""I/O system class and helper functions. + +This module implements the `InputOutputSystem` class, which is used as a +parent class for `LTI`, `StateSpace`, `TransferFunction`, +`NonlinearIOSystem`, class:`FrequencyResponseData`, `InterconnectedSystem` +and other similar classes that allow naming of signals. + +""" import re from copy import deepcopy -from warnings import warn import numpy as np from . import config +from .exception import ControlIndexError -__all__ = ['InputOutputSystem', 'issiso', 'timebase', 'common_timebase', - 'isdtime', 'isctime'] +__all__ = ['InputOutputSystem', 'NamedSignal', 'issiso', 'timebase', + 'common_timebase', 'isdtime', 'isctime', 'iosys_repr'] # Define module default parameter values _iosys_defaults = { @@ -30,25 +34,107 @@ 'iosys.indexed_system_name_suffix': '$indexed', 'iosys.converted_system_name_prefix': '', 'iosys.converted_system_name_suffix': '$converted', + 'iosys.repr_format': 'eval', + 'iosys.repr_show_count': True, } -class InputOutputSystem(object): - """A class for representing input/output systems. +# Named signal class +class NamedSignal(np.ndarray): + """Named signal with label-based access. - The InputOutputSystem class allows (possibly nonlinear) input/output + This class modifies the `numpy.ndarray` class and allows signals to + be accessed using the signal name in addition to indices and slices. + + """ + def __new__(cls, input_array, signal_labels=None, trace_labels=None): + # See https://numpy.org/doc/stable/user/basics.subclassing.html + obj = np.asarray(input_array).view(cls) # Cast to our class type + obj.signal_labels = signal_labels # Save signal labels + obj.trace_labels = trace_labels # Save trace labels + obj.data_shape = input_array.shape # Save data shape + return obj # Return new object + + def __array_finalize__(self, obj): + # See https://numpy.org/doc/stable/user/basics.subclassing.html + if obj is None: + return + self.signal_labels = getattr(obj, 'signal_labels', None) + self.trace_labels = getattr(obj, 'trace_labels', None) + self.data_shape = getattr(obj, 'data_shape', None) + + def _parse_key(self, key, labels=None, level=0): + if labels is None: + labels = self.signal_labels + try: + if isinstance(key, str): + key = labels.index(item := key) + if level == 0 and len(self.data_shape) < 2: + # This is the only signal => use it + return () + elif isinstance(key, list): + keylist = [] + for item in key: # use for loop to save item for error + keylist.append( + self._parse_key(item, labels=labels, level=level+1)) + if level == 0 and key != keylist and len(self.data_shape) < 2: + raise ControlIndexError + key = keylist + elif isinstance(key, tuple) and len(key) > 0: + keylist = [] + keylist.append( + self._parse_key( + item := key[0], labels=self.signal_labels, + level=level+1)) + if len(key) > 1: + keylist.append( + self._parse_key( + item := key[1], labels=self.trace_labels, + level=level+1)) + if level == 0 and key[:len(keylist)] != tuple(keylist) \ + and len(keylist) > len(self.data_shape) - 1: + raise ControlIndexError + for i in range(2, len(key)): + keylist.append(key[i]) # pass on remaining elements + key = tuple(keylist) + except ValueError: + raise ValueError(f"unknown signal name '{item}'") + except ControlIndexError: + raise ControlIndexError( + "signal name(s) not valid for squeezed data") + + return key + + def __getitem__(self, key): + return super().__getitem__(self._parse_key(key)) + + def __repr__(self): + out = "NamedSignal(\n" + out += repr(np.array(self)) # NamedSignal -> array + if self.signal_labels is not None: + out += f",\nsignal_labels={self.signal_labels}" + if self.trace_labels is not None: + out += f",\ntrace_labels={self.trace_labels}" + out += ")" + return out + + +class InputOutputSystem(): + """Base class for input/output systems. + + The `InputOutputSystem` class allows (possibly nonlinear) input/output systems to be represented in Python. It is used as a parent class for a set of subclasses that are used to implement specific structures and operations for different types of input/output dynamical systems. - The timebase for the system, dt, is used to specify whether the system + The timebase for the system, `dt`, is used to specify whether the system is operating in continuous or discrete time. It can have the following values: - * dt = None No timebase specified - * dt = 0 Continuous time system - * dt > 0 Discrete time system with sampling time dt - * dt = True Discrete time system with unspecified sampling time + * `dt` = None: No timebase specified + * `dt` = 0: Continuous time system + * `dt` > 0: Discrete time system with sampling time dt + * `dt` = True: Discrete time system with unspecified sampling time Parameters ---------- @@ -56,16 +142,16 @@ class InputOutputSystem(object): Description of the system inputs. This can be given as an integer count or a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be of the - form `s[i]` (where `s` is given by the `input_prefix` parameter and + form 's[i]' (where 's' is given by the `input_prefix` parameter and has default value 'u'). If this parameter is not given or given as - `None`, the relevant quantity will be determined when possible + None, the relevant quantity will be determined when possible based on other information provided to functions using the system. outputs : int, list of str, or None Description of the system outputs. Same format as `inputs`, with - the prefix given by output_prefix (defaults to 'y'). + the prefix given by `output_prefix` (defaults to 'y'). states : int, list of str, or None Description of the system states. Same format as `inputs`, with - the prefix given by state_prefix (defaults to 'x'). + the prefix given by `state_prefix` (defaults to 'x'). dt : None, True or float, optional System timebase. 0 (default) indicates continuous time, True indicates discrete time with unspecified sampling time, positive @@ -73,7 +159,7 @@ class InputOutputSystem(object): unspecified timebase (either continuous or discrete time). name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. params : dict, optional Parameter values for the system. Passed to the evaluation functions for the system as default values, overriding internal defaults. @@ -81,20 +167,14 @@ class InputOutputSystem(object): Attributes ---------- ninputs, noutputs, nstates : int - Number of input, output and state variables + Number of input, output, and state variables. input_index, output_index, state_index : dict - Dictionary of signal names for the inputs, outputs and states and the - index of the corresponding array - dt : None, True or float - System timebase. 0 (default) indicates continuous time, True indicates - discrete time with unspecified sampling time, positive number is - discrete time with specified sampling time, None indicates unspecified - timebase (either continuous or discrete time). - params : dict, optional - Parameter values for the systems. Passed to the evaluation functions - for the system as default values, overriding internal defaults. - name : string, optional - System name (used for specifying signals) + Dictionary of signal names for the inputs, outputs, and states and + the index of the corresponding array. + input_labels, output_labels, state_labels : list of str + List of signal names for inputs, outputs, and states. + shape : tuple + 2-tuple of I/O system dimension, (noutputs, ninputs). Other Parameters ---------------- @@ -104,9 +184,11 @@ class InputOutputSystem(object): Set the prefix for output signals. Default = 'y'. state_prefix : string, optional Set the prefix for state signals. Default = 'x'. + repr_format : str + String representation format. See `control.iosys_repr`. """ - # Allow NDarray * IOSystem to give IOSystem._rmul_() priority + # Allow ndarray * IOSystem to give IOSystem._rmul_() priority # https://docs.scipy.org/doc/numpy/reference/arrays.classes.html __array_priority__ = 20 @@ -125,12 +207,14 @@ def __init__( # Process timebase: if not given use default, but allow None as value self.dt = _process_dt_keyword(kwargs) + self._repr_format = kwargs.pop('repr_format', None) + # Make sure there were no other keywords if kwargs: raise TypeError("unrecognized keywords: ", str(kwargs)) # Keep track of the keywords that we recognize - kwargs_list = [ + _kwargs_list = [ 'name', 'inputs', 'outputs', 'states', 'input_prefix', 'output_prefix', 'state_prefix', 'dt'] @@ -179,18 +263,135 @@ def _generic_name_check(self): #: :meta hide-value: nstates = None - def __repr__(self): - return f'<{self.__class__.__name__}:{self.name}:' + \ - f'{list(self.input_labels)}->{list(self.output_labels)}>' + #: System timebase. + #: + #: :meta hide-value: + dt = None + + # + # System representation + # def __str__(self): """String representation of an input/output object""" - str = f"<{self.__class__.__name__}>: {self.name}\n" - str += f"Inputs ({self.ninputs}): {self.input_labels}\n" - str += f"Outputs ({self.noutputs}): {self.output_labels}\n" + out = f"<{self.__class__.__name__}>: {self.name}" + out += f"\nInputs ({self.ninputs}): {self.input_labels}" + out += f"\nOutputs ({self.noutputs}): {self.output_labels}" if self.nstates is not None: - str += f"States ({self.nstates}): {self.state_labels}" - return str + out += f"\nStates ({self.nstates}): {self.state_labels}" + out += self._dt_repr(separator="\n", space=" ") + return out + + def __repr__(self): + return iosys_repr(self, format=self.repr_format) + + def _repr_info_(self, html=False): + out = f"<{self.__class__.__name__} {self.name}: " + \ + f"{list(self.input_labels)} -> {list(self.output_labels)}" + out += self._dt_repr(separator=", ", space="") + ">" + + if html: + # Replace symbols that might be interpreted by HTML processing + # TODO: replace -> with right arrow (later) + escape_chars = { + '$': r'\$', + '<': '<', + '>': '>', + } + return "".join([c if c not in escape_chars else + escape_chars[c] for c in out]) + else: + return out + + def _repr_eval_(self): + # Defaults to _repr_info_; override in subclasses + return self._repr_info_() + + def _repr_latex_(self): + # Defaults to using __repr__; override in subclasses + return None + + def _repr_html_(self): + # Defaults to using __repr__; override in subclasses + return None + + def _repr_markdown_(self): + return self._repr_html_() + + @property + def repr_format(self): + """String representation format. + + Format used in creating the representation for the system: + + * 'info' : [outputs]> + * 'eval' : system specific, loadable representation + * 'latex' : HTML/LaTeX representation of the object + + The default representation for an input/output is set to 'eval'. + This value can be changed for an individual system by setting the + `repr_format` parameter when the system is created or by setting + the `repr_format` property after system creation. Set + `config.defaults['iosys.repr_format']` to change for all I/O systems + or use the `repr_format` parameter/attribute for a single system. + + """ + return self._repr_format if self._repr_format is not None \ + else config.defaults['iosys.repr_format'] + + @repr_format.setter + def repr_format(self, value): + self._repr_format = value + + def _label_repr(self, show_count=None): + show_count = config._get_param( + 'iosys', 'repr_show_count', show_count, True) + out, count = "", 0 + + # Include the system name if not generic + if not self._generic_name_check(): + name_spec = f"name='{self.name}'" + count += len(name_spec) + out += name_spec + + # Include the state, output, and input names if not generic + for sig_name, sig_default, sig_labels in zip( + ['states', 'outputs', 'inputs'], + ['x', 'y', 'u'], # TODO: replace with defaults + [self.state_labels, self.output_labels, self.input_labels]): + if sig_name == 'states' and self.nstates is None: + continue + + # Check if the signal labels are generic + if any([re.match(r'^' + sig_default + r'\[\d*\]$', label) is None + for label in sig_labels]): + spec = f"{sig_name}={sig_labels}" + elif show_count: + spec = f"{sig_name}={len(sig_labels)}" + else: + spec = "" + + # Append the specification string to the output, with wrapping + if count == 0: + count = len(spec) # no system name => suppress comma + elif count + len(spec) > 72: + # TODO: check to make sure a single line is enough (minor) + out += ",\n" + count = len(spec) + elif len(spec) > 0: + out += ", " + count += len(spec) + 2 + out += spec + + return out + + def _dt_repr(self, separator="\n", space=""): + if config.defaults['control.default_dt'] != self.dt: + return "{separator}dt{space}={space}{dt}".format( + separator=separator, space=space, + dt='None' if self.dt is None else self.dt) + else: + return "" # Find a list of signals by name, index, or pattern def _find_signals(self, name_list, sigdict): @@ -229,13 +430,8 @@ def _copy_names(self, sys, prefix="", suffix="", prefix_suffix_name=None): """copy the signal and system name of sys. Name is given as a keyword in case a specific name (e.g. append 'linearized') is desired. """ # Figure out the system name and assign it - if prefix == "" and prefix_suffix_name is not None: - prefix = config.defaults[ - 'iosys.' + prefix_suffix_name + '_system_name_prefix'] - if suffix == "" and prefix_suffix_name is not None: - suffix = config.defaults[ - 'iosys.' + prefix_suffix_name + '_system_name_suffix'] - self.name = prefix + sys.name + suffix + self.name = _extended_system_name( + sys.name, prefix, suffix, prefix_suffix_name) # Name the inputs, outputs, and states self.input_index = sys.input_index.copy() @@ -245,15 +441,30 @@ def _copy_names(self, sys, prefix="", suffix="", prefix_suffix_name=None): self.state_index = sys.state_index.copy() def copy(self, name=None, use_prefix_suffix=True): - """Make a copy of an input/output system + """Make a copy of an input/output system. A copy of the system is made, with a new name. The `name` keyword can be used to specify a specific name for the system. If no name is given and `use_prefix_suffix` is True, the name is constructed - by prepending config.defaults['iosys.duplicate_system_name_prefix'] - and appending config.defaults['iosys.duplicate_system_name_suffix']. - Otherwise, a generic system name of the form `sys[]` is used, - where `` is based on an internal counter. + by prepending `config.defaults['iosys.duplicate_system_name_prefix']` + and appending `config.defaults['iosys.duplicate_system_name_suffix']`. + Otherwise, a generic system name of the form 'sys[]' is used, + where '' is based on an internal counter. + + Parameters + ---------- + name : str, optional + Name of the newly created system. + + use_prefix_suffix : bool, optional + If True and `name` is None, set the name of the new system + to the name of the original system with prefix + `config.defaults['duplicate_system_name_prefix']` and + suffix `config.defaults['duplicate_system_name_suffix']`. + + Returns + ------- + `InputOutputSystem` """ # Create a copy of the system @@ -278,29 +489,58 @@ def set_inputs(self, inputs, prefix='u'): Description of the system inputs. This can be given as an integer count or as a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be - of the form `u[i]` (where the prefix `u` can be changed using the + of the form 'u[i]' (where the prefix 'u' can be changed using the optional prefix parameter). prefix : string, optional If `inputs` is an integer, create the names of the states using the given prefix (default = 'u'). The names of the input will be - of the form `prefix[i]`. + of the form 'prefix[i]'. """ self.ninputs, self.input_index = \ _process_signal_list(inputs, prefix=prefix) def find_input(self, name): - """Find the index for an input given its name (`None` if not found)""" + """Find the index for an input given its name (None if not found). + + Parameters + ---------- + name : str + Signal name for the desired input. + + Returns + ------- + int + Index of the named input. + + """ return self.input_index.get(name, None) def find_inputs(self, name_list): - """Return list of indices matching input spec (`None` if not found)""" + """Return list of indices matching input spec (None if not found). + + Parameters + ---------- + name_list : str or list of str + List of signal specifications for the desired inputs. A + signal can be described by its name or by a slice-like + description of the form 'start:end` where 'start' and + 'end' are signal names. If either is omitted, it is taken + as the first or last signal, respectively. + + Returns + ------- + list of int + List of indices for the specified inputs. + + """ return self._find_signals(name_list, self.input_index) # Property for getting and setting list of input signals input_labels = property( lambda self: list(self.input_index.keys()), # getter - set_inputs) # setter + set_inputs, # setter + doc="List of labels for the input signals.") def set_outputs(self, outputs, prefix='y'): """Set the number/names of the system outputs. @@ -308,32 +548,61 @@ def set_outputs(self, outputs, prefix='y'): Parameters ---------- outputs : int, list of str, or None - Description of the system outputs. This can be given as an integer - count or as a list of strings that name the individual signals. - If an integer count is specified, the names of the signal will be - of the form `u[i]` (where the prefix `u` can be changed using the - optional prefix parameter). + Description of the system outputs. This can be given as an + integer count or as a list of strings that name the individual + signals. If an integer count is specified, the names of the + signal will be of the form 'y[i]' (where the prefix 'y' can be + changed using the optional prefix parameter). prefix : string, optional If `outputs` is an integer, create the names of the states using the given prefix (default = 'y'). The names of the input will be - of the form `prefix[i]`. + of the form 'prefix[i]'. """ self.noutputs, self.output_index = \ _process_signal_list(outputs, prefix=prefix) def find_output(self, name): - """Find the index for an output given its name (`None` if not found)""" + """Find the index for a output given its name (None if not found). + + Parameters + ---------- + name : str + Signal name for the desired output. + + Returns + ------- + int + Index of the named output. + + """ return self.output_index.get(name, None) def find_outputs(self, name_list): - """Return list of indices matching output spec (`None` if not found)""" + """Return list of indices matching output spec (None if not found). + + Parameters + ---------- + name_list : str or list of str + List of signal specifications for the desired outputs. A + signal can be described by its name or by a slice-like + description of the form 'start:end` where 'start' and + 'end' are signal names. If either is omitted, it is taken + as the first or last signal, respectively. + + Returns + ------- + list of int + List of indices for the specified outputs. + + """ return self._find_signals(name_list, self.output_index) # Property for getting and setting list of output signals output_labels = property( lambda self: list(self.output_index.keys()), # getter - set_outputs) # setter + set_outputs, # setter + doc="List of labels for the output signals.") def set_states(self, states, prefix='x'): """Set the number/names of the system states. @@ -344,29 +613,63 @@ def set_states(self, states, prefix='x'): Description of the system states. This can be given as an integer count or as a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be - of the form `u[i]` (where the prefix `u` can be changed using the + of the form 'x[i]' (where the prefix 'x' can be changed using the optional prefix parameter). prefix : string, optional If `states` is an integer, create the names of the states using the given prefix (default = 'x'). The names of the input will be - of the form `prefix[i]`. + of the form 'prefix[i]'. """ self.nstates, self.state_index = \ _process_signal_list(states, prefix=prefix, allow_dot=True) def find_state(self, name): - """Find the index for a state given its name (`None` if not found)""" + """Find the index for a state given its name (None if not found). + + Parameters + ---------- + name : str + Signal name for the desired state. + + Returns + ------- + int + Index of the named state. + + """ return self.state_index.get(name, None) def find_states(self, name_list): - """Return list of indices matching state spec (`None` if not found)""" + """Return list of indices matching state spec (None if not found). + + Parameters + ---------- + name_list : str or list of str + List of signal specifications for the desired states. A + signal can be described by its name or by a slice-like + description of the form 'start:end` where 'start' and + 'end' are signal names. If either is omitted, it is taken + as the first or last signal, respectively. + + Returns + ------- + list of int + List of indices for the specified states.. + + """ return self._find_signals(name_list, self.state_index) # Property for getting and setting list of state signals state_labels = property( lambda self: list(self.state_index.keys()), # getter - set_states) # setter + set_states, # setter + doc="List of labels for the state signals.") + + @property + def shape(self): + """2-tuple of I/O system dimension, (noutputs, ninputs).""" + return (self.noutputs, self.ninputs) # TODO: add dict as a means to selective change names? [GH #1019] def update_names(self, **kwargs): @@ -381,11 +684,17 @@ def update_names(self, **kwargs): inputs : list of str, int, or None, optional List of strings that name the individual input signals. If given as an integer or None, signal names default to the form - `u[i]`. See :class:`InputOutputSystem` for more information. + 'u[i]'. See `InputOutputSystem` for more information. outputs : list of str, int, or None, optional - Description of output signals; defaults to `y[i]`. + Description of output signals; defaults to 'y[i]'. states : int, list of str, int, or None, optional - Description of system states; defaults to `x[i]`. + Description of system states; defaults to 'x[i]'. + input_prefix : string, optional + Set the prefix for input signals. Default = 'u'. + output_prefix : string, optional + Set the prefix for output signals. Default = 'y'. + state_prefix : string, optional + Set the prefix for state signals. Default = 'x'. """ self.name = kwargs.pop('name', self.name) @@ -419,11 +728,10 @@ def isctime(self, strict=False): Parameters ---------- - sys : Named I/O system - System to be checked - strict: bool, optional + strict : bool, optional If strict is True, make sure that timebase is not None. Default is False. + """ # If no timebase is given, answer depends on strict flag if self.dt is None: @@ -432,11 +740,11 @@ def isctime(self, strict=False): def isdtime(self, strict=False): """ - Check to see if a system is a discrete-time system + Check to see if a system is a discrete-time system. Parameters ---------- - strict: bool, optional + strict : bool, optional If strict is True, make sure that timebase is not None. Default is False. """ @@ -452,10 +760,6 @@ def issiso(self): """Check to see if a system is single input, single output.""" return self.ninputs == 1 and self.noutputs == 1 - def _isstatic(self): - """Check to see if a system is a static system (no states)""" - return self.nstates == 0 - # Test to see if a system is SISO def issiso(sys, strict=False): @@ -465,9 +769,9 @@ def issiso(sys, strict=False): Parameters ---------- sys : I/O or LTI system - System to be checked - strict: bool (default = False) - If strict is True, do not treat scalars as SISO + System to be checked. + strict : bool (default = False) + If strict is True, do not treat scalars as SISO. """ if isinstance(sys, (int, float, complex, np.number)) and not strict: return True @@ -484,7 +788,21 @@ def timebase(sys, strict=True): dt = timebase(sys) returns the timebase for a system 'sys'. If the strict option is - set to False, dt = True will be returned as 1. + set to True, `dt` = True will be returned as 1. + + Parameters + ---------- + sys : `InputOutputSystem` or float + System whose timebase is to be determined. + strict : bool, optional + Whether to implement strict checking. If set to True (default), + a float will always be returned (`dt` = True will be returned as 1). + + Returns + ------- + dt : timebase + Timebase for the system (0 = continuous time, None = unspecified). + """ # System needs to be either a constant or an I/O or LTI system if isinstance(sys, (int, float, complex, np.number)): @@ -492,39 +810,41 @@ def timebase(sys, strict=True): elif not isinstance(sys, InputOutputSystem): raise ValueError("Timebase not defined") - # Return the sample time, with converstion to float if strict is false - if (sys.dt == None): + # Return the sample time, with conversion to float if strict is false + if sys.dt == None: return None - elif (strict): + elif strict: return float(sys.dt) return sys.dt def common_timebase(dt1, dt2): """ - Find the common timebase when interconnecting systems + Find the common timebase when interconnecting systems. Parameters ---------- - dt1, dt2: number or system with a 'dt' attribute (e.g. TransferFunction - or StateSpace system) + dt1, dt2 : `InputOutputSystem` or float + Number or system with a 'dt' attribute (e.g. `TransferFunction` + or `StateSpace` system). Returns ------- - dt: number + dt : number The common timebase of dt1 and dt2, as specified in :ref:`conventions-ref`. Raises ------ ValueError - when no compatible time base can be found + When no compatible time base can be found. + """ # explanation: # if either dt is None, they are compatible with anything # if either dt is True (discrete with unspecified time base), # use the timebase of the other, if it is also discrete - # otherwise both dts must be equal + # otherwise both dt's must be equal if hasattr(dt1, 'dt'): dt1 = dt1.dt if hasattr(dt2, 'dt'): @@ -549,10 +869,10 @@ def common_timebase(dt1, dt2): else: raise ValueError("Systems have incompatible timebases") -# Check to see if a system is a discrete time system +# Check to see if a system is a discrete-time system def isdtime(sys=None, strict=False, dt=None): """ - Check to see if a system is a discrete time system. + Check to see if a system is a discrete-time system. Parameters ---------- @@ -560,7 +880,7 @@ def isdtime(sys=None, strict=False, dt=None): System to be checked. dt : None or number, optional Timebase to be checked. - strict: bool, default=False + strict : bool, default=False If strict is True, make sure that timebase is not None. """ @@ -581,7 +901,7 @@ def isdtime(sys=None, strict=False, dt=None): return sys.isdtime(strict) -# Check to see if a system is a continuous time system +# Check to see if a system is a continuous-time system def isctime(sys=None, dt=None, strict=False): """ Check to see if a system is a continuous-time system. @@ -592,7 +912,7 @@ def isctime(sys=None, dt=None, strict=False): System to be checked. dt : None or number, optional Timebase to be checked. - strict: bool (default = False) + strict : bool (default = False) If strict is True, make sure that timebase is not None. """ @@ -613,15 +933,56 @@ def isctime(sys=None, dt=None, strict=False): return sys.isctime(strict) +def iosys_repr(sys, format=None): + """Return representation of an I/O system. + + Parameters + ---------- + sys : `InputOutputSystem` + System for which the representation is generated. + format : str + Format to use in creating the representation: + + * 'info' : [outputs]> + * 'eval' : system specific, loadable representation + * 'latex' : HTML/LaTeX representation of the object + + Returns + ------- + str + String representing the input/output system. + + Notes + ----- + By default, the representation for an input/output is set to 'eval'. + Set `config.defaults['iosys.repr_format']` to change for all I/O systems + or use the `repr_format` parameter for a single system. + + Jupyter will automatically use the 'latex' representation for I/O + systems, when available. + + """ + format = config.defaults['iosys.repr_format'] if format is None else format + match format: + case 'info': + return sys._repr_info_() + case 'eval': + return sys._repr_eval_() + case 'latex': + return sys._repr_html_() + case _: + raise ValueError(f"format '{format}' unknown") + + # Utility function to parse iosys keywords def _process_iosys_keywords( keywords={}, defaults={}, static=False, end=False): """Process iosys specification. This function processes the standard keywords used in initializing an - I/O system. It first looks in the `keyword` dictionary to see if a - value is specified. If not, the `default` dictionary is used. The - `default` dictionary can also be set to an InputOutputSystem object, + I/O system. It first looks in the `keywords` dictionary to see if a + value is specified. If not, the `defaults` dictionary is used. The + `defaults` dictionary can also be set to an `InputOutputSystem` object, which is useful for copy constructors that change system/signal names. If `end` is True, then generate an error if there are any remaining @@ -951,3 +1312,42 @@ def _parse_spec(syslist, spec, signame, dictname=None): ValueError(f"signal index '{index}' is out of range") return system_index, signal_indices, gain + + +# +# Utility function for processing subsystem indices +# +# This function processes an index specification (int, list, or slice) and +# returns a index specification that can be used to create a subsystem +# +def _process_subsys_index(idx, sys_labels, slice_to_list=False): + if not isinstance(idx, (slice, list, int)): + raise TypeError("system indices must be integers, slices, or lists") + + # Convert singleton lists to integers for proper slicing (below) + if isinstance(idx, (list, tuple)) and len(idx) == 1: + idx = idx[0] + + # Convert int to slice so that numpy doesn't drop dimension + if isinstance(idx, int): + idx = slice(idx, idx+1, 1) + + # Get label names (taking care of possibility that we were passed a list) + labels = [sys_labels[i] for i in idx] if isinstance(idx, list) \ + else sys_labels[idx] + + if slice_to_list and isinstance(idx, slice): + idx = range(len(sys_labels))[idx] + + return idx, labels + + +# Create an extended system name +def _extended_system_name(name, prefix="", suffix="", prefix_suffix_name=None): + if prefix == "" and prefix_suffix_name is not None: + prefix = config.defaults[ + 'iosys.' + prefix_suffix_name + '_system_name_prefix'] + if suffix == "" and prefix_suffix_name is not None: + suffix = config.defaults[ + 'iosys.' + prefix_suffix_name + '_system_name_suffix'] + return prefix + name + suffix diff --git a/control/lti.py b/control/lti.py index 2d69f6b91..e4c9b2f4e 100644 --- a/control/lti.py +++ b/control/lti.py @@ -1,14 +1,18 @@ -"""lti.py +# lti.py - LTI class and functions for linear systems + +"""LTI class and functions for linear systems. + +This module contains the LTI parent class to the child classes +StateSpace and TransferFunction. -The lti module contains the LTI parent class to the child classes StateSpace -and TransferFunction. It is designed for use in the python-control library. """ -import numpy as np import math - -from numpy import real, angle, abs from warnings import warn + +import numpy as np +from numpy import abs, real + from . import config from .iosys import InputOutputSystem @@ -17,36 +21,79 @@ class LTI(InputOutputSystem): - """LTI is a parent class to linear time-invariant (LTI) system objects. + """Parent class for linear time-invariant system objects. - LTI is the parent to the StateSpace and TransferFunction child classes. It - contains the number of inputs and outputs, and the timebase (dt) for the - system. This function is not generally called directly by the user. + LTI is the parent to the `FrequencyResponseData`, `StateSpace`, and + `TransferFunction` child classes. It contains the number of inputs and + outputs, and the timebase (dt) for the system. This class is not + generally accessed directly by the user. - When two LTI systems are combined, their timebases much match. A system - with timebase None can be combined with a system having a specified - timebase, and the result will have the timebase of the latter system. - - Note: dt processing has been moved to the InputOutputSystem class. + See Also + -------- + InputOutputSystem, StateSpace, TransferFunction, FrequencyResponseData """ def __init__(self, inputs=1, outputs=1, states=None, name=None, **kwargs): - """Assign the LTI object's numbers of inputs and ouputs.""" + """Assign the LTI object's numbers of inputs and outputs.""" super().__init__( name=name, inputs=inputs, outputs=outputs, states=states, **kwargs) + def __call__(self, x, squeeze=None, warn_infinite=True): + """Evaluate system transfer function at point in complex plane. + + Returns the value of the system's transfer function at a point `x` + in the complex plane, where `x` is `s` for continuous-time systems + and `z` for discrete-time systems. + + By default, a (complex) scalar will be returned for SISO systems + and a p x m array will be return for MIMO systems with m inputs and + p outputs. This can be changed using the `squeeze` keyword. + + To evaluate at a frequency `omega` in radians per second, + enter ``x = omega * 1j`` for continuous-time systems, + ``x = exp(1j * omega * dt)`` for discrete-time systems, or + use the `~LTI.frequency_response` method. + + Parameters + ---------- + x : complex or complex 1D array_like + Complex value(s) at which transfer function will be evaluated. + squeeze : bool, optional + Squeeze output, as described below. Default value can be set + using `config.defaults['control.squeeze_frequency_response']`. + warn_infinite : bool, optional + If set to False, turn off divide by zero warning. + + Returns + ------- + fresp : complex ndarray + The value of the system transfer function at `x`. If the system + is SISO and `squeeze` is not True, the shape of the array matches + the shape of `x`. If the system is not SISO or `squeeze` is + False, the first two dimensions of the array are indices for the + output and input and the remaining dimensions match `x`. If + `squeeze` is True then single-dimensional axes are removed. + + Notes + ----- + See `FrequencyResponseData.__call__`, `StateSpace.__call__`, + `TransferFunction.__call__` for class-specific details. + + """ + raise NotImplementedError("not implemented in subclass") + def damp(self): - '''Natural frequency, damping ratio of system poles + """Natural frequency, damping ratio of system poles. Returns ------- wn : array - Natural frequency for each system pole + Natural frequency for each system pole. zeta : array - Damping ratio for each system pole + Damping ratio for each system pole. poles : array - System pole locations - ''' + System pole locations. + """ poles = self.poles() if self.isdtime(strict=True): @@ -57,56 +104,33 @@ def damp(self): zeta = -real(splane_poles)/wn return wn, zeta, poles - def frequency_response(self, omega=None, squeeze=None): - """Evaluate the linear time-invariant system at an array of angular - frequencies. - - For continuous time systems, computes the frequency response as + def feedback(self, other=1, sign=-1): + """Feedback interconnection between two input/output systems. - G(j*omega) = mag * exp(j*phase) - - For discrete time systems, the response is evaluated around the - unit circle such that + Parameters + ---------- + other : `InputOutputSystem` + System in the feedback path. - G(exp(j*omega*dt)) = mag * exp(j*phase). + sign : float, optional + Gain to use in feedback path. Defaults to -1. - In general the system may be multiple input, multiple output (MIMO), - where `m = self.ninputs` number of inputs and `p = self.noutputs` - number of outputs. + """ + raise NotImplementedError("feedback not implemented in subclass") - Parameters - ---------- - omega : float or 1D array_like - A list, tuple, array, or scalar value of frequencies in - radians/sec at which the system will be evaluated. - squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, - keep all indices (output, input and, if omega is array_like, - frequency) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_frequency_response']. + def frequency_response(self, omega=None, squeeze=None): + """Evaluate LTI system response at an array of frequencies. - Returns - ------- - response : :class:`FrequencyResponseData` - Frequency response data object representing the frequency - response. This object can be assigned to a tuple using - - mag, phase, omega = response - - where ``mag`` is the magnitude (absolute value, not dB or log10) - of the system frequency response, ``phase`` is the wrapped phase - in radians of the system frequency response, and ``omega`` is - the (sorted) frequencies at which the response was evaluated. - If the system is SISO and squeeze is not True, ``magnitude`` and - ``phase`` are 1D, indexed by frequency. If the system is not - SISO or squeeze is False, the array is 3D, indexed by the - output, input, and, if omega is array_like, frequency. If - ``squeeze`` is True then single-dimensional axes are removed. + See `frequency_response` for more detailed information. """ from .frdata import FrequencyResponseData + if omega is None: + # Use default frequency range + from .freqplot import _default_frequency_range + omega = _default_frequency_range(self) + omega = np.sort(np.array(omega, ndmin=1)) if self.isdtime(strict=True): # Convert the frequency to discrete time @@ -124,9 +148,8 @@ def frequency_response(self, omega=None, squeeze=None): outputs=self.output_labels, plot_type='bode') def dcgain(self): - """Return the zero-frequency gain""" - raise NotImplementedError("dcgain not implemented for %s objects" % - str(self.__class__)) + """Return the zero-frequency (DC) gain.""" + raise NotImplementedError("dcgain not defined for subclass") def _dcgain(self, warn_infinite): zeroresp = self(0 if self.isctime() else 1, @@ -137,14 +160,14 @@ def _dcgain(self, warn_infinite): return zeroresp def bandwidth(self, dbdrop=-3): - """Evaluate the bandwidth of the LTI system for a given dB drop. + """Evaluate bandwidth of an LTI system for a given dB drop. Evaluate the first frequency that the response magnitude is lower than - DC gain by dbdrop dB. + DC gain by `dbdrop` dB. Parameters ---------- - dpdrop : float, optional + dbdrop : float, optional A strictly negative scalar in dB (default = -3) defines the amount of gain drop for deciding bandwidth. @@ -152,15 +175,16 @@ def bandwidth(self, dbdrop=-3): ------- bandwidth : ndarray The first frequency (rad/time-unit) where the gain drops below - dbdrop of the dc gain of the system, or nan if the system has - infinite dc gain, inf if the gain does not drop for all frequency + `dbdrop` of the dc gain of the system, or nan if the system has + infinite dc gain, inf if the gain does not drop for all frequency. Raises ------ TypeError - if 'sys' is not an SISO LTI instance + If `sys` is not an SISO LTI instance. ValueError - if 'dbdrop' is not a negative scalar + If `dbdrop` is not a negative scalar. + """ # check if system is SISO and dbdrop is a negative scalar if not self.issiso(): @@ -199,10 +223,110 @@ def bandwidth(self, dbdrop=-3): raise Exception(result.message) def ispassive(self): - # importing here prevents circular dependancy + r"""Indicate if a linear time invariant (LTI) system is passive. + + See `ispassive` for details. + + """ + # importing here prevents circular dependency from control.passivity import ispassive return ispassive(self) + # + # Convenience aliases for conversion functions + # + # Allow conversion between state space and transfer function types + # as methods. These are just pass throughs to factory functions. + # + # Note: in order for docstrings to created, these have to set these up + # as independent methods, not just assigned to ss() and tf(). + # + # Imports are done within the function to avoid circular imports. + # + def to_ss(self, *args, **kwargs): + """Convert to state space representation. + + See `ss` for details. + """ + from .statesp import ss + return ss(self, *args, **kwargs) + + def to_tf(self, *args, **kwargs): + """Convert to transfer function representation. + + See `tf` for details. + """ + from .xferfcn import tf + return tf(self, *args, **kwargs) + + # + # Convenience aliases for plotting and response functions + # + # Allow standard plots to be generated directly from the system object + # in addition to standalone plotting and response functions. + # + # Note: in order for docstrings to created, these have to set these up as + # independent methods, not just assigned to plotting/response functions. + # + # Imports are done within the function to avoid circular imports. + # + + def bode_plot(self, *args, **kwargs): + """Generate a Bode plot for the system. + + See `bode_plot` for more information. + """ + from .freqplot import bode_plot + return bode_plot(self, *args, **kwargs) + + def nichols_plot(self, *args, **kwargs): + """Generate a Nichols plot for the system. + + See `nichols_plot` for more information. + """ + from .nichols import nichols_plot + return nichols_plot(self, *args, **kwargs) + + def nyquist_plot(self, *args, **kwargs): + """Generate a Nyquist plot for the system. + + See `nyquist_plot` for more information. + """ + from .freqplot import nyquist_plot + return nyquist_plot(self, *args, **kwargs) + + def forced_response(self, *args, **kwargs): + """Generate the forced response for the system. + + See `forced_response` for more information. + """ + from .timeresp import forced_response + return forced_response(self, *args, **kwargs) + + def impulse_response(self, *args, **kwargs): + """Generate the impulse response for the system. + + See `impulse_response` for more information. + """ + from .timeresp import impulse_response + return impulse_response(self, *args, **kwargs) + + def initial_response(self, *args, **kwargs): + """Generate the initial response for the system. + + See `initial_response` for more information. + """ + from .timeresp import initial_response + return initial_response(self, *args, **kwargs) + + def step_response(self, *args, **kwargs): + """Generate the step response for the system. + + See `step_response` for more information. + """ + from .timeresp import step_response + return step_response(self, *args, **kwargs) + def poles(sys): """ @@ -210,19 +334,17 @@ def poles(sys): Parameters ---------- - sys: StateSpace or TransferFunction - Linear system + sys : `StateSpace` or `TransferFunction` + Linear system. Returns ------- - poles: ndarray + poles : ndarray Array that contains the system's poles. See Also -------- - zeros - TransferFunction.poles - StateSpace.poles + zeros, StateSpace.poles, TransferFunction.poles """ @@ -235,19 +357,17 @@ def zeros(sys): Parameters ---------- - sys: StateSpace or TransferFunction - Linear system + sys : `StateSpace` or `TransferFunction` + Linear system. Returns ------- - zeros: ndarray + zeros : ndarray Array that contains the system's zeros. See Also -------- - poles - StateSpace.zeros - TransferFunction.zeros + poles, StateSpace.zeros, TransferFunction.zeros """ @@ -255,44 +375,44 @@ def zeros(sys): def damp(sys, doprint=True): - """ - Compute natural frequencies, damping ratios, and poles of a system. + """Compute system's natural frequencies, damping ratios, and poles. Parameters ---------- - sys : LTI (StateSpace or TransferFunction) - A linear system object + sys : `StateSpace` or `TransferFunction` + A linear system object. doprint : bool (optional) - if True, print table with values + If True, print table with values. Returns ------- wn : array - Natural frequency for each system pole + Natural frequency for each system pole. zeta : array - Damping ratio for each system pole + Damping ratio for each system pole. poles : array - System pole locations + System pole locations. See Also -------- - pole + poles Notes ----- - If the system is continuous, - wn = abs(poles) - zeta = -real(poles)/poles + If the system is continuous + + | ``wn = abs(poles)`` + | ``zeta = -real(poles)/poles`` If the system is discrete, the discrete poles are mapped to their equivalent location in the s-plane via - s = log(poles)/dt + | ``s = log(poles)/dt`` and - wn = abs(s) - zeta = -real(s)/wn. + | ``wn = abs(s)`` + | ``zeta = -real(s)/wn`` Examples -------- @@ -315,12 +435,13 @@ def damp(sys, doprint=True): return wn, zeta, poles +# TODO: deprecate this function def evalfr(sys, x, squeeze=None): - """Evaluate the transfer function of an LTI system for complex frequency x. + """Evaluate transfer function of LTI system at complex frequency. - Returns the complex frequency response `sys(x)` where `x` is `s` for + Returns the complex frequency response ``sys(x)`` where `x` is `s` for continuous-time systems and `z` for discrete-time systems, with - `m = sys.ninputs` number of inputs and `p = sys.noutputs` number of + ``m = sys.ninputs`` number of inputs and ``p = sys.noutputs`` number of outputs. To evaluate at a frequency omega in radians per second, enter @@ -330,16 +451,17 @@ def evalfr(sys, x, squeeze=None): Parameters ---------- - sys: StateSpace or TransferFunction - Linear system + sys : `StateSpace` or `TransferFunction` + Linear system. x : complex scalar or 1D array_like - Complex frequency(s) + Complex frequency(s). squeeze : bool, optional (default=True) - If squeeze=True, remove single-dimensional entries from the shape of - the output even if the system is not SISO. If squeeze=False, keep all - indices (output, input and, if omega is array_like, frequency) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_frequency_response']. + If `squeeze` = True, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + `squeeze` = False, keep all indices (output, input and, if omega is + array_like, frequency) even if the system is SISO. The default + value can be set using + `config.defaults['control.squeeze_frequency_response']`. Returns ------- @@ -348,26 +470,23 @@ def evalfr(sys, x, squeeze=None): squeeze is not True, the shape of the array matches the shape of omega. If the system is not SISO or squeeze is False, the first two dimensions of the array are indices for the output and input and the - remaining dimensions match omega. If ``squeeze`` is True then + remaining dimensions match omega. If `squeeze` is True then single-dimensional axes are removed. See Also -------- - freqresp - bode + LTI.__call__, frequency_response, bode_plot Notes ----- - This function is a wrapper for :meth:`StateSpace.__call__` and - :meth:`TransferFunction.__call__`. + This function is a wrapper for `StateSpace.__call__` and + `TransferFunction.__call__`. Examples -------- >>> G = ct.ss([[-1, -2], [3, -4]], [[5], [7]], [[6, 8]], [[9]]) >>> fresp = ct.evalfr(G, 1j) # evaluate at s = 1j - .. todo:: Add example with MIMO system - """ return sys(x, squeeze=squeeze) @@ -375,50 +494,61 @@ def evalfr(sys, x, squeeze=None): def frequency_response( sysdata, omega=None, omega_limits=None, omega_num=None, Hz=None, squeeze=None): - """Frequency response of an LTI system at multiple angular frequencies. + """Frequency response of an LTI system. - In general the system may be multiple input, multiple output (MIMO), where - `m = sys.ninputs` number of inputs and `p = sys.noutputs` number of - outputs. + For continuous-time systems with transfer function G, computes the + frequency response as + + G(j*omega) = mag * exp(j*phase) + + For discrete-time systems, the response is evaluated around the unit + circle such that + + G(exp(j*omega*dt)) = mag * exp(j*phase). + + In general the system may be multiple input, multiple output (MIMO), + where ``m = self.ninputs`` number of inputs and ``p = self.noutputs`` + number of outputs. Parameters ---------- sysdata : LTI system or list of LTI systems Linear system(s) for which frequency response is computed. omega : float or 1D array_like, optional - Frequencies in radians/sec at which the system should be - evaluated. Can be a single frequency or array of frequencies, which - will be sorted before evaluation. If None (default), a common set - of frequencies that works across all given systems is computed. + A list, tuple, array, or scalar value of frequencies in radians/sec + at which the system will be evaluated. Can be a single frequency + or array of frequencies, which will be sorted before evaluation. + If None (default), a common set of frequencies that works across + all given systems is computed. omega_limits : array_like of two values, optional Limits to the range of frequencies, in rad/sec. Specifying - ``omega`` as a list of two elements is equivalent to providing - ``omega_limits``. Ignored if omega is provided. + `omega` as a list of two elements is equivalent to providing + `omega_limits`. Ignored if omega is provided. omega_num : int, optional Number of frequency samples at which to compute the response. - Defaults to config.defaults['freqplot.number_of_samples']. Ignored + Defaults to `config.defaults['freqplot.number_of_samples']`. Ignored if omega is provided. Returns ------- - response : :class:`FrequencyResponseData` - Frequency response data object representing the frequency response. - This object can be assigned to a tuple using - - mag, phase, omega = response - - where ``mag`` is the magnitude (absolute value, not dB or log10) of - the system frequency response, ``phase`` is the wrapped phase in - radians of the system frequency response, and ``omega`` is the - (sorted) frequencies at which the response was evaluated. If the - system is SISO and squeeze is not False, ``magnitude`` and ``phase`` - are 1D, indexed by frequency. If the system is not SISO or squeeze - is False, the array is 3D, indexed by the output, input, and - frequency. If ``squeeze`` is True then single-dimensional axes are - removed. - - Returns a list of :class:`FrequencyResponseData` objects if sys is - a list of systems. + response : `FrequencyResponseData` + Frequency response data object representing the frequency + response. When accessed as a tuple, returns ``(magnitude, + phase, omega)``. If `sysdata` is a list of systems, returns a + `FrequencyResponseList` object. Results can be plotted using + the `~FrequencyResponseData.plot` method. See + `FrequencyResponseData` for more detailed information. + response.magnitude : array + Magnitude of the frequency response (absolute value, not dB or + log10). If the system is SISO and squeeze is not True, the + array is 1D, indexed by frequency. If the system is not SISO + or squeeze is False, the array is 3D, indexed by the output, + input, and, if omega is array_like, frequency. If `squeeze` is + True then single-dimensional axes are removed. + response.phase : array + Wrapped phase, in radians, with same shape as `magnitude`. + response.omega : array + Sorted list of frequencies at which response was evaluated. Other Parameters ---------------- @@ -427,52 +557,44 @@ def frequency_response( limits to full decades in Hz instead of rad/s. Omega is always returned in rad/sec. squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape of - the output even if the system is not SISO. If squeeze=False, keep all - indices (output, input and, if omega is array_like, frequency) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_frequency_response']. + If `squeeze` = True, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + `squeeze` = False, keep all indices (output, input and, if omega is + array_like, frequency) even if the system is SISO. The default + value can be set using + `config.defaults['control.squeeze_frequency_response']`. See Also -------- - evalfr - bode_plot + LTI.__call__, bode_plot Notes ----- - 1. This function is a wrapper for :meth:`StateSpace.frequency_response` - and :meth:`TransferFunction.frequency_response`. - - 2. You can also use the lower-level methods ``sys(s)`` or ``sys(z)`` to - generate the frequency response for a single system. + This function is a wrapper for `StateSpace.frequency_response` and + `TransferFunction.frequency_response`. You can also use the + lower-level methods ``sys(s)`` or ``sys(z)`` to generate the frequency + response for a single system. - 3. All frequency data should be given in rad/sec. If frequency limits - are computed automatically, the `Hz` keyword can be used to ensure - that limits are in factors of decades in Hz, so that Bode plots with - `Hz=True` look better. + All frequency data should be given in rad/sec. If frequency limits are + computed automatically, the `Hz` keyword can be used to ensure that + limits are in factors of decades in Hz, so that Bode plots with + `Hz` = True look better. - 4. The frequency response data can be plotted by calling the - :func:`~control_bode_plot` function or using the `plot` method of - the :class:`~control.FrequencyResponseData` class. + The frequency response data can be plotted by calling the `bode_plot` + function or using the `plot` method of the `FrequencyResponseData` + class. Examples -------- >>> G = ct.ss([[-1, -2], [3, -4]], [[5], [7]], [[6, 8]], [[9]]) - >>> mag, phase, omega = ct.freqresp(G, [0.1, 1., 10.]) - - .. todo:: - Add example with MIMO system - - #>>> sys = rss(3, 2, 2) - #>>> mag, phase, omega = freqresp(sys, [0.1, 1., 10.]) - #>>> mag[0, 1, :] - #array([ 55.43747231, 42.47766549, 1.97225895]) - #>>> phase[1, 0, :] - #array([-0.12611087, -1.14294316, 2.5764547 ]) - #>>> # This is the magnitude of the frequency response from the 2nd - #>>> # input to the 1st output, and the phase (in radians) of the - #>>> # frequency response from the 1st input to the 2nd output, for - #>>> # s = 0.1i, i, 10i. + >>> mag, phase, omega = ct.frequency_response(G, [0.1, 1., 10.]) + + >>> sys = ct.rss(3, 2, 2) + >>> mag, phase, omega = ct.frequency_response(sys, [0.1, 1., 10.]) + >>> mag[0, 1, :] # Magnitude of second input to first output + array([..., ..., ...]) + >>> phase[1, 0, :] # Phase of first input to second output + array([..., ..., ...]) """ from .frdata import FrequencyResponseData @@ -490,13 +612,13 @@ def frequency_response( responses = [] for sys_ in syslist: - if isinstance(sys_, FrequencyResponseData) and sys_.ifunc is None and \ - not omega_range_given: + if isinstance(sys_, FrequencyResponseData) and sys_._ifunc is None \ + and not omega_range_given: omega_sys = sys_.omega # use system properties else: omega_sys = omega_syslist.copy() # use common omega vector - # Add the Nyquist frequency for discrete time systems + # Add the Nyquist frequency for discrete-time systems if sys_.isdtime(strict=True): nyquistfrq = math.pi / sys_.dt if not omega_range_given: @@ -514,14 +636,25 @@ def frequency_response( # Alternative name (legacy) def freqresp(sys, omega): - """Legacy version of frequency_response.""" - warn("freqresp is deprecated; use frequency_response", DeprecationWarning) + """Legacy version of frequency_response. + + .. deprecated:: 0.9.0 + This function will be removed in a future version of python-control. + Use `frequency_response` instead. + + """ + warn("freqresp() is deprecated; use frequency_response()", FutureWarning) return frequency_response(sys, omega) def dcgain(sys): """Return the zero-frequency (or DC) gain of the given system. + Parameters + ---------- + sys : LTI + System for which the zero-frequency gain is computed. + Returns ------- gain : ndarray @@ -540,42 +673,42 @@ def dcgain(sys): def bandwidth(sys, dbdrop=-3): - """Return the first freqency where the gain drop by dbdrop of the system. + """Find first frequency where gain drops by 3 dB. Parameters ---------- - sys: StateSpace or TransferFunction - Linear system + sys : `StateSpace` or `TransferFunction` + Linear system for which the bandwidth should be computed. dbdrop : float, optional By how much the gain drop in dB (default = -3) that defines the - bandwidth. Should be a negative scalar + bandwidth. Should be a negative scalar. Returns ------- bandwidth : ndarray - The first frequency (rad/time-unit) where the gain drops below dbdrop - of the dc gain of the system, or nan if the system has infinite dc - gain, inf if the gain does not drop for all frequency + The first frequency where the gain drops below `dbdrop` of the zero + frequency (DC) gain of the system, or nan if the system has infinite + zero frequency gain, inf if the gain does not drop for any frequency. Raises ------ TypeError - if 'sys' is not an SISO LTI instance + If `sys` is not an SISO LTI instance. ValueError - if 'dbdrop' is not a negative scalar + If `dbdrop` is not a negative scalar. - Example - ------- + Examples + -------- >>> G = ct.tf([1], [1, 1]) >>> ct.bandwidth(G) - 0.9976 + np.float64(0.9976283451102316) >>> G1 = ct.tf(0.1, [1, 0.1]) >>> wn2 = 1 >>> zeta2 = 0.001 >>> G2 = ct.tf(wn2**2, [1, 2*zeta2*wn2, wn2**2]) >>> ct.bandwidth(G1*G2) - 0.1018 + np.float64(0.10184838823897456) """ if not isinstance(sys, LTI): diff --git a/control/margins.py b/control/margins.py index 301baaf57..d7c7992be 100644 --- a/control/margins.py +++ b/control/margins.py @@ -1,62 +1,19 @@ -"""margins.py - -Functions for computing stability margins and related functions. - -Routines in this module: - -margins.stability_margins -margins.phase_crossover_frequencies -margins.margin -""" - -"""Copyright (c) 2011 by California Institute of Technology -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Author: Richard M. Murray -Date: 14 July 2011 +# margins.py - functions for computing stability margins +# +# Initial author: Richard M. Murray +# Creation date: 14 July 2011 -$Id$ -""" +"""Functions for computing stability margins and related functions.""" import math from warnings import warn + import numpy as np import scipy as sp -from . import xferfcn -from .lti import evalfr -from .iosys import issiso -from . import frdata -from . import freqplot + +from . import frdata, freqplot, xferfcn from .exception import ControlMIMONotImplemented +from .iosys import issiso __all__ = ['stability_margins', 'phase_crossover_frequencies', 'margin'] @@ -81,11 +38,16 @@ def _poly_iw_sqr(pol_iw): def _poly_iw_real_crossing(num_iw, den_iw, epsw): # Return w where imag(H(iw)) == 0 + + # Compute the imaginary part of H = (num.r + j num.i)/(den.r + j den.i) test_w = np.polysub(np.polymul(num_iw.imag, den_iw.real), np.polymul(num_iw.real, den_iw.imag)) + + # Find the real-valued w > 0 where imag(H(iw)) = 0 w = np.roots(test_w) w = np.real(w[np.isreal(w)]) w = w[w >= epsw] + return w @@ -248,18 +210,16 @@ def _likely_numerical_inaccuracy(sys): # systems +# TODO: consider handling sysdata similar to margin (via *sysdata?) def stability_margins(sysdata, returnall=False, epsw=0.0, method='best'): - """Calculate stability margins and associated crossover frequencies. + """Stability margins and associated crossover frequencies. Parameters ---------- - sysdata : LTI system or (mag, phase, omega) sequence - sys : LTI system - Linear SISO system representing the loop transfer function - mag, phase, omega : sequence of array_like - Arrays of magnitudes (absolute values, not dB), phases (degrees), - and corresponding frequencies. Crossover frequencies returned are - in the same units as those in `omega` (e.g., rad/sec or Hz). + sysdata : LTI system or 3-tuple of array_like + Linear SISO system representing the loop transfer function. + Alternatively, a three tuple of the form (mag, phase, omega) + providing the frequency response can be passed. returnall : bool, optional If true, return all margins found. If False (default), return only the minimum stability margins. For frequency data or FRD systems, only @@ -269,22 +229,23 @@ def stability_margins(sysdata, returnall=False, epsw=0.0, method='best'): and not returned as margin. method : string, optional Method to use (default is 'best'): - 'poly': use polynomial method if passed a :class:`LTI` system. - 'frd': calculate crossover frequencies using numerical interpolation - of a :class:`FrequencyResponseData` representation of the system if - passed a :class:`LTI` system. - 'best': use the 'poly' method if possible, reverting to 'frd' if it is - detected that numerical inaccuracy is likey to arise in the 'poly' - method for for discrete-time systems. + + * 'poly': use polynomial method if passed a `LTI` system. + * 'frd': calculate crossover frequencies using numerical + interpolation of a `FrequencyResponseData` representation + of the system if passed a `LTI` system. + * 'best': use the 'poly' method if possible, reverting to 'frd' if + it is detected that numerical inaccuracy is likely to arise in the + 'poly' method for for discrete-time systems. Returns ------- gm : float or array_like - Gain margin + Gain margin. pm : float or array_like - Phase margin + Phase margin. sm : float or array_like - Stability margin, the minimum distance from the Nyquist plot to -1 + Stability margin, the minimum distance from the Nyquist plot to -1. wpc : float or array_like Phase crossover frequency (where phase crosses -180 degrees), which is associated with the gain margin. @@ -292,14 +253,16 @@ def stability_margins(sysdata, returnall=False, epsw=0.0, method='best'): Gain crossover frequency (where gain crosses 1), which is associated with the phase margin. wms : float or array_like - Stability margin frequency (where Nyquist plot is closest to -1) - - Note that the gain margin is determined by the gain of the loop - transfer function at the phase crossover frequency(s), the phase - margin is determined by the phase of the loop transfer function at - the gain crossover frequency(s), and the stability margin is - determined by the frequency of maximum sensitivity (given by the - magnitude of 1/(1+L)). + Stability margin frequency (where Nyquist plot is closest to -1). + + Notes + ----- + The gain margin is determined by the gain of the loop transfer function + at the phase crossover frequency(s), the phase margin is determined by + the phase of the loop transfer function at the gain crossover + frequency(s), and the stability margin is determined by the frequency + of maximum sensitivity (given by the magnitude of 1/(1+L)). + """ # TODO: FRD method for cont-time systems doesn't work try: @@ -424,9 +387,8 @@ def _dstab(w): # find all stab margins? widx, = np.where(np.diff(np.sign(np.diff(_dstab(sys.omega)))) > 0) wstab = np.array( - [sp.optimize.minimize_scalar(_dstab, - bracket=(sys.omega[i], sys.omega[i+1]) - ).x + [sp.optimize.minimize_scalar( + _dstab, bracket=(sys.omega[i], sys.omega[i+1])).x for i in widx]) wstab = wstab[(wstab >= sys.omega[0]) * (wstab <= sys.omega[-1])] ws_resp = sys(1j * wstab) @@ -459,20 +421,20 @@ def _dstab(w): # Contributed by Steffen Waldherr def phase_crossover_frequencies(sys): - """Compute frequencies and gains at intersections with real axis - in Nyquist plot. + """Compute Nyquist plot real-axis crossover frequencies and gains. Parameters ---------- - sys : SISO LTI system + sys : LTI + SISO LTI system. Returns ------- omega : ndarray 1d array of (non-negative) frequencies where Nyquist plot - intersects the real axis - gain : ndarray - 1d array of corresponding gains + intersects the real axis. + gains : ndarray + 1d array of corresponding gains. Examples -------- @@ -493,35 +455,39 @@ def phase_crossover_frequencies(sys): omega = _poly_iw_real_crossing(num_iw, den_iw, 0.) # using real() to avoid rounding errors and results like 1+0j - gain = np.real(evalfr(sys, 1J * omega)) + gains = np.real(sys(omega * 1j, warn_infinite=False)) else: zargs = _poly_z_invz(sys) z, omega = _poly_z_real_crossing(*zargs, epsw=0.) - gain = np.real(evalfr(sys, z)) + gains = np.real(sys(z, warn_infinite=False)) - return omega, gain + return omega, gains def margin(*args): - """margin(sysdata) + """ + margin(sys) \ + margin(mag, phase, omega) + + Gain and phase margins and associated crossover frequencies. - Calculate gain and phase margins and associated crossover frequencies. + Can be called as ``margin(sys)`` where `sys` is a SISO LTI system or + ``margin(mag, phase, omega)``. Parameters ---------- - sysdata : LTI system or (mag, phase, omega) sequence - sys : StateSpace or TransferFunction - Linear SISO system representing the loop transfer function - mag, phase, omega : sequence of array_like - Input magnitude, phase (in deg.), and frequencies (rad/sec) from - bode frequency response data + sys : `StateSpace` or `TransferFunction` + Linear SISO system representing the loop transfer function. + mag, phase, omega : sequence of array_like + Input magnitude, phase (in deg.), and frequencies (rad/sec) from + bode frequency response data. Returns ------- gm : float - Gain margin + Gain margin. pm : float - Phase margin (in degrees) + Phase margin (in degrees). wcg : float or array_like Crossover frequency associated with gain margin (phase crossover frequency), where phase crosses below -180 degrees. diff --git a/control/mateqn.py b/control/mateqn.py index 05b47ffae..9d1349b0c 100644 --- a/control/mateqn.py +++ b/control/mateqn.py @@ -1,52 +1,26 @@ -# mateqn.py - Matrix equation solvers (Lyapunov, Riccati) +# mateqn.py - matrix equation solvers (Lyapunov, Riccati) # -# Implementation of the functions lyap, dlyap, care and dare -# for solution of Lyapunov and Riccati equations. -# -# Original author: Bjorn Olofsson - -# Copyright (c) 2011, All rights reserved. - -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: - -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. - -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. - -# 3. Neither the name of the project author nor the names of its -# contributors may be used to endorse or promote products derived -# from this software without specific prior written permission. - -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. +# Initial author: Bjorn Olofsson +# Creation date: 2011 + +"""Matrix equation solvers (Lyapunov, Riccati). + +This module contains implementation of the functions lyap, dlyap, care +and dare for solution of Lyapunov and Riccati equations. + +""" import warnings -import numpy as np -from numpy import copy, eye, dot, finfo, inexact, atleast_2d +import numpy as np import scipy as sp +from numpy import eye, finfo, inexact from scipy.linalg import eigvals, solve -from .exception import ControlSlycot, ControlArgument, ControlDimension, \ +from .exception import ControlArgument, ControlDimension, ControlSlycot, \ slycot_check -from .statesp import _ssmatrix -# Make sure we have access to the right slycot routines +# Make sure we have access to the right Slycot routines try: from slycot.exceptions import SlycotResultWarning except ImportError: @@ -114,11 +88,11 @@ def lyap(A, Q, C=None, E=None, method=None): Parameters ---------- A, Q : 2D array_like - Input matrices for the Lyapunov or Sylvestor equation + Input matrices for the Lyapunov or Sylvestor equation. C : 2D array_like, optional - If present, solve the Sylvester equation + If present, solve the Sylvester equation. E : 2D array_like, optional - If present, solve the generalized Lyapunov equation + If present, solve the generalized Lyapunov equation. method : str, optional Set the method used for computing the result. Current methods are 'slycot' and 'scipy'. If set to None (default), try 'slycot' first @@ -127,7 +101,7 @@ def lyap(A, Q, C=None, E=None, method=None): Returns ------- X : 2D array - Solution to the Lyapunov or Sylvester equation + Solution to the Lyapunov or Sylvester equation. """ # Decide what method to use @@ -151,12 +125,12 @@ def lyap(A, Q, C=None, E=None, method=None): m = Q.shape[0] # Check to make sure input matrices are the right shape and type - _check_shape("A", A, n, n, square=True) + _check_shape(A, n, n, square=True, name="A") # Solve standard Lyapunov equation if C is None and E is None: # Check to make sure input matrices are the right shape and type - _check_shape("Q", Q, n, n, square=True, symmetric=True) + _check_shape(Q, n, n, square=True, symmetric=True, name="Q") if method == 'scipy': # Solve the Lyapunov equation using SciPy @@ -171,8 +145,8 @@ def lyap(A, Q, C=None, E=None, method=None): # Solve the Sylvester equation elif C is not None and E is None: # Check to make sure input matrices are the right shape and type - _check_shape("Q", Q, m, m, square=True) - _check_shape("C", C, n, m) + _check_shape(Q, m, m, square=True, name="Q") + _check_shape(C, n, m, name="C") if method == 'scipy': # Solve the Sylvester equation using SciPy @@ -184,14 +158,14 @@ def lyap(A, Q, C=None, E=None, method=None): # Solve the generalized Lyapunov equation elif C is None and E is not None: # Check to make sure input matrices are the right shape and type - _check_shape("Q", Q, n, n, square=True, symmetric=True) - _check_shape("E", E, n, n, square=True) + _check_shape(Q, n, n, square=True, symmetric=True, name="Q") + _check_shape(E, n, n, square=True, name="E") if method == 'scipy': raise ControlArgument( "method='scipy' not valid for generalized Lyapunov equation") - # Make sure we have access to the write slicot routine + # Make sure we have access to the write Slycot routine try: from slycot import sg03ad @@ -210,7 +184,7 @@ def lyap(A, Q, C=None, E=None, method=None): else: raise ControlArgument("Invalid set of input parameters") - return _ssmatrix(X) + return X def dlyap(A, Q, C=None, E=None, method=None): @@ -240,11 +214,11 @@ def dlyap(A, Q, C=None, E=None, method=None): Parameters ---------- A, Q : 2D array_like - Input matrices for the Lyapunov or Sylvestor equation + Input matrices for the Lyapunov or Sylvestor equation. C : 2D array_like, optional - If present, solve the Sylvester equation + If present, solve the Sylvester equation. E : 2D array_like, optional - If present, solve the generalized Lyapunov equation + If present, solve the generalized Lyapunov equation. method : str, optional Set the method used for computing the result. Current methods are 'slycot' and 'scipy'. If set to None (default), try 'slycot' first @@ -253,7 +227,7 @@ def dlyap(A, Q, C=None, E=None, method=None): Returns ------- X : 2D array (or matrix) - Solution to the Lyapunov or Sylvester equation + Solution to the Lyapunov or Sylvester equation. """ # Decide what method to use @@ -281,12 +255,12 @@ def dlyap(A, Q, C=None, E=None, method=None): m = Q.shape[0] # Check to make sure input matrices are the right shape and type - _check_shape("A", A, n, n, square=True) + _check_shape(A, n, n, square=True, name="A") # Solve standard Lyapunov equation if C is None and E is None: # Check to make sure input matrices are the right shape and type - _check_shape("Q", Q, n, n, square=True, symmetric=True) + _check_shape(Q, n, n, square=True, symmetric=True, name="Q") if method == 'scipy': # Solve the Lyapunov equation using SciPy @@ -301,8 +275,8 @@ def dlyap(A, Q, C=None, E=None, method=None): # Solve the Sylvester equation elif C is not None and E is None: # Check to make sure input matrices are the right shape and type - _check_shape("Q", Q, m, m, square=True) - _check_shape("C", C, n, m) + _check_shape(Q, m, m, square=True, name="Q") + _check_shape(C, n, m, name="C") if method == 'scipy': raise ControlArgument( @@ -314,8 +288,8 @@ def dlyap(A, Q, C=None, E=None, method=None): # Solve the generalized Lyapunov equation elif C is None and E is not None: # Check to make sure input matrices are the right shape and type - _check_shape("Q", Q, n, n, square=True, symmetric=True) - _check_shape("E", E, n, n, square=True) + _check_shape(Q, n, n, square=True, symmetric=True, name="Q") + _check_shape(E, n, n, square=True, name="E") if method == 'scipy': raise ControlArgument( @@ -333,7 +307,7 @@ def dlyap(A, Q, C=None, E=None, method=None): else: raise ControlArgument("Invalid set of input parameters") - return _ssmatrix(X) + return X # @@ -341,7 +315,7 @@ def dlyap(A, Q, C=None, E=None, method=None): # def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, - A_s="A", B_s="B", Q_s="Q", R_s="R", S_s="S", E_s="E"): + _As="A", _Bs="B", _Qs="Q", _Rs="R", _Ss="S", _Es="E"): """Solves the continuous-time algebraic Riccati equation. X, L, G = care(A, B, Q, R=None) solves @@ -368,22 +342,25 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, Parameters ---------- A, B, Q : 2D array_like - Input matrices for the Riccati equation + Input matrices for the Riccati equation. R, S, E : 2D array_like, optional - Input matrices for generalized Riccati equation + Input matrices for generalized Riccati equation. method : str, optional Set the method used for computing the result. Current methods are 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy'. + stabilizing : bool, optional + If `method` is 'slycot', unstabilized eigenvalues will be returned + in the initial elements of `L`. Not supported for 'scipy'. Returns ------- X : 2D array (or matrix) - Solution to the Ricatti equation + Solution to the Riccati equation. L : 1D array - Closed loop eigenvalues + Closed loop eigenvalues. G : 2D array (or matrix) - Gain matrix + Gain matrix. """ # Decide what method to use @@ -404,10 +381,10 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, m = B.shape[1] # Check to make sure input matrices are the right shape and type - _check_shape(A_s, A, n, n, square=True) - _check_shape(B_s, B, n, m) - _check_shape(Q_s, Q, n, n, square=True, symmetric=True) - _check_shape(R_s, R, m, m, square=True, symmetric=True) + _check_shape(A, n, n, square=True, name=_As) + _check_shape(B, n, m, name=_Bs) + _check_shape(Q, n, n, square=True, symmetric=True, name=_Qs) + _check_shape(R, m, m, square=True, symmetric=True, name=_Rs) # Solve the standard algebraic Riccati equation if S is None and E is None: @@ -420,9 +397,9 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, X = sp.linalg.solve_continuous_are(A, B, Q, R) K = np.linalg.solve(R, B.T @ X) E, _ = np.linalg.eig(A - B @ K) - return _ssmatrix(X), E, _ssmatrix(K) + return X, E, K - # Make sure we can import required slycot routines + # Make sure we can import required Slycot routines try: from slycot import sb02md except ImportError: @@ -445,7 +422,7 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, # Return the solution X, the closed-loop eigenvalues L and # the gain matrix G - return _ssmatrix(X), w[:n], _ssmatrix(G) + return X, w[:n], G # Solve the generalized algebraic Riccati equation else: @@ -454,8 +431,8 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, E = np.eye(A.shape[0]) if E is None else np.array(E, ndmin=2) # Check to make sure input matrices are the right shape and type - _check_shape(E_s, E, n, n, square=True) - _check_shape(S_s, S, n, m) + _check_shape(E, n, n, square=True, name=_Es) + _check_shape(S, n, m, name=_Ss) # See if we should solve this using SciPy if method == 'scipy': @@ -466,13 +443,13 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, X = sp.linalg.solve_continuous_are(A, B, Q, R, s=S, e=E) K = np.linalg.solve(R, B.T @ X @ E + S.T) eigs, _ = sp.linalg.eig(A - B @ K, E) - return _ssmatrix(X), eigs, _ssmatrix(K) + return X, eigs, K - # Make sure we can find the required slycot routine + # Make sure we can find the required Slycot routine try: from slycot import sg02ad except ImportError: - raise ControlSlycot("Can't find slycot module 'sg02ad'") + raise ControlSlycot("Can't find slycot module sg02ad") # Solve the generalized algebraic Riccati equation by calling the # Slycot function sg02ad @@ -491,12 +468,11 @@ def care(A, B, Q, R=None, S=None, E=None, stabilizing=True, method=None, # Return the solution X, the closed-loop eigenvalues L and # the gain matrix G - return _ssmatrix(X), L, _ssmatrix(G) + return X, L, G def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, - A_s="A", B_s="B", Q_s="Q", R_s="R", S_s="S", E_s="E"): - """Solves the discrete-time algebraic Riccati - equation. + _As="A", _Bs="B", _Qs="Q", _Rs="R", _Ss="S", _Es="E"): + """Solves the discrete-time algebraic Riccati equation. X, L, G = dare(A, B, Q, R) solves @@ -522,22 +498,25 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, Parameters ---------- A, B, Q : 2D arrays - Input matrices for the Riccati equation + Input matrices for the Riccati equation. R, S, E : 2D arrays, optional - Input matrices for generalized Riccati equation + Input matrices for generalized Riccati equation. method : str, optional Set the method used for computing the result. Current methods are 'slycot' and 'scipy'. If set to None (default), try 'slycot' first and then 'scipy'. + stabilizing : bool, optional + If `method` is 'slycot', unstabilized eigenvalues will be returned + in the initial elements of `L`. Not supported for 'scipy'. Returns ------- X : 2D array (or matrix) - Solution to the Ricatti equation + Solution to the Riccati equation. L : 1D array - Closed loop eigenvalues + Closed loop eigenvalues. G : 2D array (or matrix) - Gain matrix + Gain matrix. """ # Decide what method to use @@ -558,14 +537,14 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, m = B.shape[1] # Check to make sure input matrices are the right shape and type - _check_shape(A_s, A, n, n, square=True) - _check_shape(B_s, B, n, m) - _check_shape(Q_s, Q, n, n, square=True, symmetric=True) - _check_shape(R_s, R, m, m, square=True, symmetric=True) + _check_shape(A, n, n, square=True, name=_As) + _check_shape(B, n, m, name=_Bs) + _check_shape(Q, n, n, square=True, symmetric=True, name=_Qs) + _check_shape(R, m, m, square=True, symmetric=True, name=_Rs) if E is not None: - _check_shape(E_s, E, n, n, square=True) + _check_shape(E, n, n, square=True, name=_Es) if S is not None: - _check_shape(S_s, S, n, m) + _check_shape(S, n, m, name=_Ss) # Figure out how to solve the problem if method == 'scipy': @@ -583,13 +562,13 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, else: L, _ = sp.linalg.eig(A - B @ G, E) - return _ssmatrix(X), L, _ssmatrix(G) + return X, L, G - # Make sure we can import required slycot routine + # Make sure we can import required Slycot routine try: from slycot import sg02ad except ImportError: - raise ControlSlycot("Can't find slycot module 'sg02ad'") + raise ControlSlycot("Can't find slycot module sg02ad") # Initialize optional matrices S = np.zeros((n, m)) if S is None else np.array(S, ndmin=2) @@ -612,7 +591,7 @@ def dare(A, B, Q, R, S=None, E=None, stabilizing=True, method=None, # Return the solution X, the closed-loop eigenvalues L and # the gain matrix G - return _ssmatrix(X), L, _ssmatrix(G) + return X, L, G # Utility function to decide on method to use @@ -626,15 +605,48 @@ def _slycot_or_scipy(method): # Utility function to check matrix dimensions -def _check_shape(name, M, n, m, square=False, symmetric=False): - if square and M.shape[0] != M.shape[1]: +def _check_shape(M, n, m, square=False, symmetric=False, name="??"): + """Check the shape and properties of a 2D array. + + This function can be used to check to make sure a 2D array_like has the + right shape, along with other properties. If not, an appropriate error + message is generated. + + Parameters + ---------- + M : array_like + Array to be checked. + n : int + Expected number of rows. + m : int + Expected number of columns. + square : bool, optional + If True, check to make sure the matrix is square. + symmetric : bool, optional + If True, check to make sure the matrix is symmetric. + name : str + Name of the matrix (for use in error messages). + + Returns + ------- + M : 2D array + Input array, converted to 2D if needed. + + """ + M = np.atleast_2d(M) + + if (square or symmetric) and M.shape[0] != M.shape[1]: raise ControlDimension("%s must be a square matrix" % name) if symmetric and not _is_symmetric(M): raise ControlArgument("%s must be a symmetric matrix" % name) if M.shape[0] != n or M.shape[1] != m: - raise ControlDimension("Incompatible dimensions of %s matrix" % name) + raise ControlDimension( + f"Incompatible dimensions of {name} matrix; " + f"expected ({n}, {m}) but found {M.shape}") + + return M # Utility function to check if a matrix is symmetric diff --git a/control/matlab/__init__.py b/control/matlab/__init__.py index b02d16d53..6414c9131 100644 --- a/control/matlab/__init__.py +++ b/control/matlab/__init__.py @@ -1,55 +1,20 @@ -# -*- coding: utf-8 -*- -""" -The :mod:`control.matlab` module contains a number of functions that emulate -some of the functionality of MATLAB. The intent of these functions is to -provide a simple interface to the python control systems library -(python-control) for people who are familiar with the MATLAB Control Systems -Toolbox (tm). -""" - -"""Copyright (c) 2009 by California Institute of Technology -All rights reserved. - -Copyright (c) 2011 by Eike Welk - - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. +# Original author: Richard M. Murray +# Creation date: 29 May 09 +# Pre-2014 revisions: Kevin K. Chen, Dec 2010 -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. +"""MATLAB compatibility subpackage. -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Author: Richard M. Murray -Date: 29 May 09 -Revised: Kevin K. Chen, Dec 10 - -$Id$ +This subpackage contains a number of functions that emulate some of +the functionality of MATLAB. The intent of these functions is to +provide a simple interface to the python control systems library +(python-control) for people who are familiar with the MATLAB Control +Systems Toolbox (tm). """ +# Silence unused imports (F401), * imports (F403), unknown symbols (F405) +# ruff: noqa: F401, F403, F405 + # Import MATLAB-like functions that are defined in other packages from scipy.signal import zpk2ss, ss2zpk, tf2zpk, zpk2tf from numpy import linspace, logspace @@ -84,10 +49,12 @@ from ..dtime import c2d from ..sisotool import sisotool from ..stochsys import lqe, dlqe +from ..nlsys import find_operating_point # Functions that are renamed in MATLAB pole, zero = poles, zeros freqresp = frequency_response +trim = find_operating_point # Import functions specific to Matlab compatibility package from .timeresp import * @@ -96,295 +63,3 @@ # Set up defaults corresponding to MATLAB conventions from ..config import * use_matlab_defaults() - -r""" -The following tables give an overview of the module ``control.matlab``. -They also show the implementation progress and the planned features of the -module. - -The symbols in the first column show the current state of a feature: - -* ``*`` : The feature is currently implemented. -* ``-`` : The feature is not planned for implementation. -* ``s`` : A similar feature from another library (Scipy) is imported into - the module, until the feature is implemented here. - - -Creating linear models ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`tf` create transfer function (TF) models -\* :func:`zpk` create zero/pole/gain (ZPK) models. -\* :func:`ss` create state-space (SS) models -\ dss create descriptor state-space models -\ delayss create state-space models with delayed terms -\* :func:`frd` create frequency response data (FRD) models -\ lti/exp create pure continuous-time delays (TF and - ZPK only) -\ filt specify digital filters -\- lti/set set/modify properties of LTI models -\- setdelaymodel specify internal delay model (state space - only) -\* :func:`rss` create a random continuous state space model -\* :func:`drss` create a random discrete state space model -== ========================== ============================================ - - -Data extraction ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`tfdata` extract numerators and denominators -\ lti/zpkdata extract zero/pole/gain data -\ lti/ssdata extract state-space matrices -\ lti/dssdata descriptor version of SSDATA -\ frd/frdata extract frequency response data -\ lti/get access values of LTI model properties -\ ss/getDelayModel access internal delay model (state space) -== ========================== ============================================ - - -Conversions ----------------------------------------------------------------------------- - -== ============================ ============================================ -\* :func:`tf` conversion to transfer function -\ zpk conversion to zero/pole/gain -\* :func:`ss` conversion to state space -\* :func:`frd` conversion to frequency data -\* :func:`c2d` continuous to discrete conversion -\ d2c discrete to continuous conversion -\ d2d resample discrete-time model -\ upsample upsample discrete-time LTI systems -\* :func:`ss2tf` state space to transfer function -\s :func:`~scipy.signal.ss2zpk` transfer function to zero-pole-gain -\* :func:`tf2ss` transfer function to state space -\s :func:`~scipy.signal.tf2zpk` transfer function to zero-pole-gain -\s :func:`~scipy.signal.zpk2ss` zero-pole-gain to state space -\s :func:`~scipy.signal.zpk2tf` zero-pole-gain to transfer function -== ============================ ============================================ - - -System interconnections ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`~control.append` group LTI models by appending inputs/outputs -\* :func:`~control.parallel` connect LTI models in parallel - (see also overloaded ``+``) -\* :func:`~control.series` connect LTI models in series - (see also overloaded ``*``) -\* :func:`~control.feedback` connect lti models with a feedback loop -\ lti/lft generalized feedback interconnection -\* :func:`~control.connect` arbitrary interconnection of lti models -\ sumblk summing junction (for use with connect) -\ strseq builds sequence of indexed strings - (for I/O naming) -== ========================== ============================================ - - -System gain and dynamics ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`dcgain` steady-state (D.C.) gain -\* :func:`bandwidth` system bandwidth -\ lti/norm h2 and Hinfinity norms of LTI models -\* :func:`pole` system poles -\* :func:`zero` system (transmission) zeros -\ lti/order model order (number of states) -\* :func:`~control.pzmap` pole-zero map (TF only) -\ lti/iopzmap input/output pole-zero map -\* :func:`damp` natural frequency, damping of system poles -\ esort sort continuous poles by real part -\ dsort sort discrete poles by magnitude -\ lti/stabsep stable/unstable decomposition -\ lti/modsep region-based modal decomposition -== ========================== ============================================ - - -Time-domain analysis ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`step` step response -\ stepinfo step response characteristics -\* :func:`impulse` impulse response -\* :func:`initial` free response with initial conditions -\* :func:`lsim` response to user-defined input signal -\ lsiminfo linear response characteristics -\ gensig generate input signal for LSIM -\ covar covariance of response to white noise -== ========================== ============================================ - - -Frequency-domain analysis ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`bode` Bode plot of the frequency response -\ lti/bodemag Bode magnitude diagram only -\ sigma singular value frequency plot -\* :func:`~control.nyquist` Nyquist plot -\* :func:`~control.nichols` Nichols plot -\* :func:`margin` gain and phase margins -\ lti/allmargin all crossover frequencies and margins -\* :func:`freqresp` frequency response -\* :func:`evalfr` frequency response at complex frequency s -== ========================== ============================================ - - -Model simplification ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`~control.minreal` minimal realization; pole/zero cancellation -\ ss/sminreal structurally minimal realization -\* :func:`~control.hsvd` hankel singular values (state contributions) -\* :func:`~control.balred` reduced-order approximations of LTI models -\* :func:`~control.modred` model order reduction -== ========================== ============================================ - - -Compensator design ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`rlocus` evans root locus -\* :func:`sisotool` SISO controller design -\* :func:`~control.place` pole placement -\ estim form estimator given estimator gain -\ reg form regulator given state-feedback and - estimator gains -== ========================== ============================================ - - -LQR/LQG design ----------------------------------------------------------------------------- - -== ========================== ============================================ -\ ss/lqg single-step LQG design -\* :func:`~control.lqr` linear quadratic (LQ) state-fbk regulator -\ dlqr discrete-time LQ state-feedback regulator -\ lqry LQ regulator with output weighting -\ lqrd discrete LQ regulator for continuous plant -\ ss/lqi Linear-Quadratic-Integral (LQI) controller -\ ss/kalman Kalman state estimator -\ ss/kalmd discrete Kalman estimator for cts plant -\ ss/lqgreg build LQG regulator from LQ gain and Kalman - estimator -\ ss/lqgtrack build LQG servo-controller -\ augstate augment output by appending states -== ========================== ============================================ - - -State-space (SS) models ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`rss` random stable cts-time state-space models -\* :func:`drss` random stable disc-time state-space models -\ ss2ss state coordinate transformation -\ canon canonical forms of state-space models -\* :func:`~control.ctrb` controllability matrix -\* :func:`~control.obsv` observability matrix -\* :func:`~control.gram` controllability and observability gramians -\ ss/prescale optimal scaling of state-space models. -\ balreal gramian-based input/output balancing -\ ss/xperm reorder states. -== ========================== ============================================ - - -Frequency response data (FRD) models ----------------------------------------------------------------------------- - -== ========================== ============================================ -\ frd/chgunits change frequency vector units -\ frd/fcat merge frequency responses -\ frd/fselect select frequency range or subgrid -\ frd/fnorm peak gain as a function of frequency -\ frd/abs entrywise magnitude of frequency response -\ frd/real real part of the frequency response -\ frd/imag imaginary part of the frequency response -\ frd/interp interpolate frequency response data -\* :func:`~control.mag2db` convert magnitude to decibels (dB) -\* :func:`~control.db2mag` convert decibels (dB) to magnitude -== ========================== ============================================ - - -Time delays ----------------------------------------------------------------------------- - -== ========================== ============================================ -\ lti/hasdelay true for models with time delays -\ lti/totaldelay total delay between each input/output pair -\ lti/delay2z replace delays by poles at z=0 or FRD phase - shift -\* :func:`~control.pade` pade approximation of time delays -== ========================== ============================================ - - -Model dimensions and characteristics ----------------------------------------------------------------------------- - -== ========================== ============================================ -\ class model type ('tf', 'zpk', 'ss', or 'frd') -\ isa test if model is of given type -\ tf/size model sizes -\ lti/ndims number of dimensions -\ lti/isempty true for empty models -\ lti/isct true for continuous-time models -\ lti/isdt true for discrete-time models -\ lti/isproper true for proper models -\ lti/issiso true for single-input/single-output models -\ lti/isstable true for models with stable dynamics -\ lti/reshape reshape array of linear models -== ========================== ============================================ - -Overloaded arithmetic operations ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* \+ and - add, subtract systems (parallel connection) -\* \* multiply systems (series connection) -\ / right divide -- sys1\*inv(sys2) -\- \\ left divide -- inv(sys1)\*sys2 -\ ^ powers of a given system -\ ' pertransposition -\ .' transposition of input/output map -\ .\* element-by-element multiplication -\ [..] concatenate models along inputs or outputs -\ lti/stack stack models/arrays along some dimension -\ lti/inv inverse of an LTI system -\ lti/conj complex conjugation of model coefficients -== ========================== ============================================ - -Matrix equation solvers and linear algebra ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`~control.lyap` solve continuous-time Lyapunov equations -\* :func:`~control.dlyap` solve discrete-time Lyapunov equations -\ lyapchol, dlyapchol square-root Lyapunov solvers -\* :func:`~control.care` solve continuous-time algebraic Riccati - equations -\* :func:`~control.dare` solve disc-time algebraic Riccati equations -\ gcare, gdare generalized Riccati solvers -\ bdschur block diagonalization of a square matrix -== ========================== ============================================ - - -Additional functions ----------------------------------------------------------------------------- - -== ========================== ============================================ -\* :func:`~control.gangof4` generate the Gang of 4 sensitivity plots -\* :func:`~numpy.linspace` generate a set of numbers that are linearly - spaced -\* :func:`~numpy.logspace` generate a set of numbers that are - logarithmically spaced -\* :func:`~control.unwrap` unwrap phase angle to give continuous curve -== ========================== ============================================ - -""" diff --git a/control/matlab/timeresp.py b/control/matlab/timeresp.py index fe8bfbd71..a0554ec9b 100644 --- a/control/matlab/timeresp.py +++ b/control/matlab/timeresp.py @@ -1,13 +1,16 @@ -""" -Time response routines in the Matlab compatibility package +# timeresp.py - time response routines in the MATLAB compatibility package + +"""Time response routines in the MATLAB compatibility package. + +Note that the return arguments are different than in the standard +control package.. -Note that the return arguments are different than in the standard control package. """ __all__ = ['step', 'stepinfo', 'impulse', 'initial', 'lsim'] def step(sys, T=None, input=0, output=None, return_x=False): - '''Step response of a linear system. + """Step response of a linear system. If the system has multiple inputs or outputs (MIMO), one input has to be selected for the simulation. Optionally, one output may be @@ -17,25 +20,26 @@ def step(sys, T=None, input=0, output=None, return_x=False): Parameters ---------- - sys: StateSpace, or TransferFunction - LTI system to simulate - T: array-like or number, optional + sys : `StateSpace` or `TransferFunction` + LTI system to simulate. + T : array_like or number, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given) - input: int + autocomputed if not given). + input : int Index of the input that will be used in this simulation. - output: int + output : int If given, index of the output that is returned by this simulation. + return_x : bool, optional + If True, return the state vector in addition to outputs. Returns ------- - yout: array - Response of the system - T: array - Time values of the output - xout: array (if selected) - Individual response of each x variable - + yout : array + Response of the system. + T : array + Time values of the output. + xout : array (if selected) + Individual response of each x variable. See Also -------- @@ -48,7 +52,7 @@ def step(sys, T=None, input=0, output=None, return_x=False): >>> G = rss(4) >>> yout, T = step(G) - ''' + """ from ..timeresp import step_response # Switch output argument order and transpose outputs @@ -59,13 +63,14 @@ def step(sys, T=None, input=0, output=None, return_x=False): def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, RiseTimeLimits=(0.1, 0.9)): - """Step response characteristics (Rise time, Settling Time, Peak and others) + """ + Step response characteristics (rise time, settling time, etc). Parameters ---------- - sysdata : StateSpace or TransferFunction or array_like - The system data. Either LTI system to similate (StateSpace, - TransferFunction), or a time series of step response data. + sysdata : `StateSpace` or `TransferFunction` or array_like + The system data. Either LTI system to simulate (`StateSpace`, + `TransferFunction`), or a time series of step response data. T : array_like or float, optional Time vector, or simulation time duration if a number (time vector is autocomputed if not given). @@ -76,39 +81,29 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, used for a given time series of response data. Scalar for SISO, (noutputs, ninputs) array_like for MIMO systems. SettlingTimeThreshold : float, optional - Defines the error to compute settling time (default = 0.02) - RiseTimeLimits : tuple (lower_threshold, upper_theshold) - Defines the lower and upper threshold for RiseTime computation + Defines the error to compute settling time (default = 0.02). + RiseTimeLimits : tuple (lower_threshold, upper_threshold) + Defines the lower and upper threshold for RiseTime computation. Returns ------- S : dict or list of list of dict - If `sysdata` corresponds to a SISO system, S is a dictionary + If `sysdata` corresponds to a SISO system, `S` is a dictionary containing: - RiseTime: - Time from 10% to 90% of the steady-state value. - SettlingTime: - Time to enter inside a default error of 2% - SettlingMin: - Minimum value after RiseTime - SettlingMax: - Maximum value after RiseTime - Overshoot: - Percentage of the Peak relative to steady value - Undershoot: - Percentage of undershoot - Peak: - Absolute peak value - PeakTime: - time of the Peak - SteadyStateValue: - Steady-state value + - 'RiseTime': Time from 10% to 90% of the steady-state value. + - 'SettlingTime': Time to enter inside a default error of 2%. + - 'SettlingMin': Minimum value after `RiseTime`. + - 'SettlingMax': Maximum value after `RiseTime`. + - 'Overshoot': Percentage of the peak relative to steady value. + - 'Undershoot': Percentage of undershoot. + - 'Peak': Absolute peak value. + - 'PeakTime': Time that the first peak value is obtained. + - 'SteadyStateValue': Steady-state value. If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. - To get the step response characteristics from the j-th input to the - i-th output, access ``S[i][j]`` - + To get the step response characteristics from the jth input to the + ith output, access ``S[i][j]``. See Also -------- @@ -132,7 +127,7 @@ def stepinfo(sysdata, T=None, yfinal=None, SettlingTimeThreshold=0.02, return S def impulse(sys, T=None, input=0, output=None, return_x=False): - '''Impulse response of a linear system. + """Impulse response of a linear system. If the system has multiple inputs or outputs (MIMO), one input has to be selected for the simulation. Optionally, one output may be @@ -142,24 +137,26 @@ def impulse(sys, T=None, input=0, output=None, return_x=False): Parameters ---------- - sys: StateSpace, TransferFunction - LTI system to simulate - T: array-like or number, optional + sys : `StateSpace` or `TransferFunction` + LTI system to simulate. + T : array_like or number, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given) - input: int + autocomputed if not given). + input : int Index of the input that will be used in this simulation. - output: int + output : int Index of the output that will be used in this simulation. + return_x : bool, optional + If True, return the state vector in addition to outputs. Returns ------- - yout: array - Response of the system - T: array - Time values of the output - xout: array (if selected) - Individual response of each x variable + yout : array + Response of the system. + T : array + Time values of the output. + xout : array (if selected) + Individual response of each x variable. See Also -------- @@ -172,7 +169,7 @@ def impulse(sys, T=None, input=0, output=None, return_x=False): >>> G = rss() >>> yout, T = impulse(G) - ''' + """ from ..timeresp import impulse_response # Switch output argument order and transpose outputs @@ -181,7 +178,7 @@ def impulse(sys, T=None, input=0, output=None, return_x=False): return (out[1], out[0], out[2]) if return_x else (out[1], out[0]) def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): - '''Initial condition response of a linear system. + """Initial condition response of a linear system. If the system has multiple outputs (?IMO), optionally, one output may be selected. If no selection is made for the output, all @@ -189,27 +186,29 @@ def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): Parameters ---------- - sys: StateSpace, or TransferFunction - LTI system to simulate - T: array-like or number, optional + sys : `StateSpace` or `TransferFunction` + LTI system to simulate. + T : array_like or number, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given) - X0: array-like object or number, optional - Initial condition (default = 0) - input: int + autocomputed if not given). + X0 : array_like object or number, optional + Initial condition (default = 0). + input : int This input is ignored, but present for compatibility with step and impulse. - output: int + output : int If given, index of the output that is returned by this simulation. + return_x : bool, optional + If True, return the state vector in addition to outputs. Returns ------- - yout: array - Response of the system - T: array - Time values of the output - xout: array (if selected) - Individual response of each x variable + yout : array + Response of the system. + T : array + Time values of the output. + xout : array (if selected) + Individual response of each x variable. See Also -------- @@ -222,7 +221,7 @@ def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): >>> G = rss(4) >>> yout, T = initial(G) - ''' + """ from ..timeresp import initial_response # Switch output argument order and transpose outputs @@ -232,33 +231,32 @@ def initial(sys, T=None, X0=0., input=None, output=None, return_x=False): def lsim(sys, U=0., T=None, X0=0.): - '''Simulate the output of a linear system. + """Simulate the output of a linear system. - As a convenience for parameters `U`, `X0`: - Numbers (scalars) are converted to constant arrays with the correct shape. - The correct shape is inferred from arguments `sys` and `T`. + As a convenience for parameters `U` and `X0`, numbers (scalars) are + converted to constant arrays with the correct shape. The correct + shape is inferred from arguments `sys` and `T`. Parameters ---------- - sys: LTI (StateSpace, or TransferFunction) - LTI system to simulate - U: array-like or number, optional - Input array giving input at each time `T` (default = 0). - - If `U` is ``None`` or ``0``, a special algorithm is used. This special - algorithm is faster than the general algorithm, which is used otherwise. - T: array-like, optional for discrete LTI `sys` + sys : `StateSpace` or `TransferFunction` + LTI system to simulate. + U : array_like or number, optional + Input array giving input at each time `T` (default = 0). If `U` is + None or 0, a special algorithm is used. This special algorithm is + faster than the general algorithm, which is used otherwise. + T : array_like, optional for discrete LTI `sys` Time steps at which the input is defined; values must be evenly spaced. - X0: array-like or number, optional + X0 : array_like or number, optional Initial condition (default = 0). Returns ------- - yout: array + yout : array Response of the system. - T: array + T : array Time values of the output. - xout: array + xout : array Time evolution of the state vector. See Also @@ -273,7 +271,7 @@ def lsim(sys, U=0., T=None, X0=0.): >>> T = np.linspace(0,10) >>> yout, T, xout = lsim(G, T=T) - ''' + """ from ..timeresp import forced_response # Switch output argument order and transpose outputs (and always return x) diff --git a/control/matlab/wrappers.py b/control/matlab/wrappers.py index 153342096..e244479ad 100644 --- a/control/matlab/wrappers.py +++ b/control/matlab/wrappers.py @@ -1,21 +1,24 @@ -""" -Wrappers for the MATLAB compatibility module +# wrappers.py - Wrappers for the MATLAB compatibility module. + +"""Wrappers for the MATLAB compatibility module. + """ -import numpy as np -from scipy.signal import zpk2tf import warnings from warnings import warn +import numpy as np +from scipy.signal import zpk2tf + +from ..exception import ControlArgument +from ..lti import LTI from ..statesp import ss from ..xferfcn import tf -from ..lti import LTI -from ..exception import ControlArgument __all__ = ['bode', 'nyquist', 'ngrid', 'rlocus', 'pzmap', 'dcgain', 'connect'] def bode(*args, **kwargs): - """bode(syslist[, omega, dB, Hz, deg, ...]) + """bode(sys[, omega, dB, Hz, deg, ...]) Bode plot of the frequency response. @@ -26,25 +29,24 @@ def bode(*args, **kwargs): sys : LTI, or list of LTI System for which the Bode response is plotted and give. Optionally a list of systems can be entered, or several systems can be - specified (i.e. several parameters). The sys arguments may also be + specified (i.e. several parameters). The `sys` arguments may also be interspersed with format strings. A frequency argument (array_like) - may also be added, some examples:: - - >>> bode(sys, w) # one system, freq vector # doctest: +SKIP - >>> bode(sys1, sys2, ..., sysN) # several systems # doctest: +SKIP - >>> bode(sys1, sys2, ..., sysN, w) # doctest: +SKIP - >>> bode(sys1, 'plotstyle1', ..., sysN, 'plotstyleN') # + plot formats # doctest: +SKIP - - omega: freq_range - Range of frequencies in rad/s + may also be added (see Examples). + omega : array + Range of frequencies in rad/s. dB : boolean - If True, plot result in dB + If True, plot result in dB. Hz : boolean - If True, plot frequency in Hz (omega must be provided in rad/sec) + If True, plot frequency in Hz (omega must be provided in rad/sec). deg : boolean - If True, return phase in degrees (else radians) + If True, return phase in degrees (else radians). plot : boolean - If True, plot magnitude and phase + If True, plot magnitude and phase. + + Returns + ------- + mag, phase, omega : array + Magnitude, phase, and frequencies represented in the Bode plot. Examples -------- @@ -52,15 +54,11 @@ def bode(*args, **kwargs): >>> sys = ss([[1, -2], [3, -4]], [[5], [7]], [[6, 8]], 9) >>> mag, phase, omega = bode(sys) + >>> bode(sys, w) # one system, freq vector # doctest: +SKIP + >>> bode(sys1, sys2, ..., sysN) # several systems # doctest: +SKIP + >>> bode(sys1, sys2, ..., sysN, w) # doctest: +SKIP + >>> bode(sys1, 'plotstyle1', ..., sysN, 'plotstyleN') # doctest: +SKIP - .. todo:: - - Document these use cases - - * >>> bode(sys, w) # doctest: +SKIP - * >>> bode(sys1, sys2, ..., sysN) # doctest: +SKIP - * >>> bode(sys1, sys2, ..., sysN, w) # doctest: +SKIP - * >>> bode(sys1, 'plotstyle1', ..., sysN, 'plotstyleN') # doctest: +SKIP """ from ..freqplot import bode_plot @@ -99,22 +97,28 @@ def nyquist(*args, plot=True, **kwargs): Parameters ---------- - sys1, ..., sysn : list of LTI + syslist : list of LTI List of linear input/output systems (single system is OK). omega : array_like Set of frequencies to be evaluated, in rad/sec. + omega_limits : array_like of two values + Set limits for plotted frequency range. If Hz=True the limits are + in Hz otherwise in rad/s. Specifying `omega` as a list of two + elements is equivalent to providing `omega_limits`. + plot : bool + If False, do not generate a plot. Returns ------- real : ndarray (or list of ndarray if len(syslist) > 1)) - real part of the frequency response array + Real part of the frequency response array. imag : ndarray (or list of ndarray if len(syslist) > 1)) - imaginary part of the frequency response array + Imaginary part of the frequency response array. omega : ndarray (or list of ndarray if len(syslist) > 1)) - frequencies in rad/s + Frequencies in rad/s. """ - from ..freqplot import nyquist_response, nyquist_plot + from ..freqplot import nyquist_plot, nyquist_response # If first argument is a list, assume python-control calling format if hasattr(args[0], '__iter__'): @@ -189,7 +193,7 @@ def _parse_freqplot_args(*args): if len(syslist) == 0: raise ControlArgument("no systems specified") elif len(syslist) == 1: - # If only one system given, retun just that system (not a list) + # If only one system given, return just that system (not a list) syslist = syslist[0] return syslist, omega, plotstyle, other @@ -212,11 +216,11 @@ def rlocus(*args, **kwargs): gains : array_like, optional Gains to use in computing plot of closed-loop poles. xlim : tuple or list, optional - Set limits of x axis, normally with tuple - (see :doc:`matplotlib:api/axes_api`). + Set limits of x axis (see `matplotlib.axes.Axes.set_xlim`). ylim : tuple or list, optional - Set limits of y axis, normally with tuple - (see :doc:`matplotlib:api/axes_api`). + Set limits of y axis (see `matplotlib.axes.Axes.set_ylim`). + plot : bool + If False, do not generate a plot. Returns ------- @@ -228,7 +232,7 @@ def rlocus(*args, **kwargs): Notes ----- - This function is a wrapper for :func:`~control.root_locus_plot`, + This function is a wrapper for `root_locus_plot`, with legacy return arguments. """ @@ -257,24 +261,24 @@ def pzmap(*args, **kwargs): Parameters ---------- - sys: LTI (StateSpace or TransferFunction) + sys : `StateSpace` or `TransferFunction` Linear system for which poles and zeros are computed. - plot: bool, optional - If ``True`` a graph is generated with Matplotlib, + plot : bool, optional + If True a graph is generated with matplotlib, otherwise the poles and zeros are only computed and returned. - grid: boolean (default = False) - If True plot omega-damping grid. + grid : boolean (default = False) + If True, plot omega-damping grid. Returns ------- - poles: array + poles : array The system's poles. - zeros: array + zeros : array The system's zeros. Notes ----- - This function is a wrapper for :func:`~control.pole_zero_plot`, + This function is a wrapper for `pole_zero_plot`, with legacy return arguments. """ @@ -296,43 +300,55 @@ def pzmap(*args, **kwargs): from ..nichols import nichols_grid + + def ngrid(): return nichols_grid() ngrid.__doc__ = nichols_grid.__doc__ def dcgain(*args): - '''Compute the gain of the system in steady state. + """dcgain(sys) \ + dcgain(num, den) \ + dcgain(Z, P, k) \ + dcgain(A, B, C, D) + + Compute the gain of the system in steady state. The function takes either 1, 2, 3, or 4 parameters: + * dcgain(sys) + * dcgain(num, den) + * dcgain(Z, P, k) + * dcgain(A, B, C, D) + Parameters ---------- - A, B, C, D: array-like + A, B, C, D : array_like A linear system in state space form. - Z, P, k: array-like, array-like, number + Z, P, k : array_like, array_like, number A linear system in zero, pole, gain form. - num, den: array-like + num, den : array_like A linear system in transfer function form. - sys: LTI (StateSpace or TransferFunction) + sys : `StateSpace` or `TransferFunction` A linear system object. Returns ------- - gain: ndarray + gain : ndarray The gain of each output versus each input: - :math:`y = gain \\cdot u` + :math:`y = gain \\cdot u`. Notes ----- This function is only useful for systems with invertible system - matrix ``A``. + matrix `A`. All systems are first converted to state space form. The function then computes: .. math:: gain = - C \\cdot A^{-1} \\cdot B + D - ''' + """ #Convert the parameters to state space form if len(args) == 4: A, B, C, D = args @@ -348,48 +364,51 @@ def dcgain(*args): sys, = args return sys.dcgain() else: - raise ValueError("Function ``dcgain`` needs either 1, 2, 3 or 4 " + raise ValueError("Function `dcgain` needs either 1, 2, 3 or 4 " "arguments.") from ..bdalg import connect as ct_connect + + def connect(*args): + """connect(sys, Q, inputv, outputv) - """Index-based interconnection of an LTI system. + Index-based interconnection of an LTI system. The system `sys` is a system typically constructed with `append`, with multiple inputs and outputs. The inputs and outputs are connected - according to the interconnection matrix `Q`, and then the final inputs and - outputs are trimmed according to the inputs and outputs listed in `inputv` - and `outputv`. + according to the interconnection matrix `Q`, and then the final inputs + and outputs are trimmed according to the inputs and outputs listed in + `inputv` and `outputv`. NOTE: Inputs and outputs are indexed starting at 1 and negative values correspond to a negative feedback interconnection. Parameters ---------- - sys : :class:`InputOutputSystem` + sys : `InputOutputSystem` System to be connected. Q : 2D array Interconnection matrix. First column gives the input to be connected. The second column gives the index of an output that is to be fed into that input. Each additional column gives the index of an additional - input that may be optionally added to that input. Negative - values mean the feedback is negative. A zero value is ignored. Inputs + input that may be optionally added to that input. Negative values + mean the feedback is negative. A zero value is ignored. Inputs and outputs are indexed starting at 1 to communicate sign information. inputv : 1D array - list of final external inputs, indexed starting at 1 + List of final external inputs, indexed starting at 1. outputv : 1D array - list of final external outputs, indexed starting at 1 + List of final external outputs, indexed starting at 1. Returns ------- - out : :class:`InputOutputSystem` + out : `InputOutputSystem` Connected and trimmed I/O system. See Also -------- - append, feedback, interconnect, negate, parallel, series + append, feedback, connect, negate, parallel, series Examples -------- diff --git a/control/modelsimp.py b/control/modelsimp.py index 06c3d350d..3352cc156 100644 --- a/control/modelsimp.py +++ b/control/modelsimp.py @@ -1,55 +1,29 @@ -#! TODO: add module docstring # modelsimp.py - tools for model simplification # -# Author: Steve Brunton, Kevin Chen, Lauren Padilla -# Date: 30 Nov 2010 -# -# This file contains routines for obtaining reduced order models -# -# Copyright (c) 2010 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ +# Initial authors: Steve Brunton, Kevin Chen, Lauren Padilla +# Creation date: 30 Nov 2010 + +"""Tools for model simplification. + +This module contains routines for obtaining reduced order models for state +space systems. + +""" + +import warnings # External packages and modules import numpy as np -import warnings -from .exception import ControlSlycot, ControlMIMONotImplemented, \ - ControlDimension -from .iosys import isdtime, isctime -from .statesp import StateSpace + +from .exception import ControlArgument, ControlDimension, ControlSlycot +from .iosys import isctime, isdtime from .statefbk import gram +from .statesp import StateSpace +from .timeresp import TimeResponseData -__all__ = ['hsvd', 'balred', 'modred', 'era', 'markov', 'minreal'] +__all__ = ['hankel_singular_values', 'balanced_reduction', 'model_reduction', + 'minimal_realization', 'eigensys_realization', 'markov', 'hsvd', + 'balred', 'modred', 'minreal', 'era'] # Hankel Singular Value Decomposition @@ -57,18 +31,18 @@ # The following returns the Hankel singular values, which are singular values # of the matrix formed by multiplying the controllability and observability # Gramians -def hsvd(sys): +def hankel_singular_values(sys): """Calculate the Hankel singular values. Parameters ---------- - sys : StateSpace - A state space system + sys : `StateSpace` + State space system. Returns ------- H : array - A list of Hankel singular values + List of Hankel singular values. See Also -------- @@ -79,7 +53,7 @@ def hsvd(sys): The Hankel singular values are the singular values of the Hankel operator. In practice, we compute the square root of the eigenvalues of the matrix formed by taking the product of the observability and controllability - gramians. There are other (more efficient) methods based on solving the + Gramians. There are other (more efficient) methods based on solving the Lyapunov equation in a particular way (more details soon). Examples @@ -90,7 +64,7 @@ def hsvd(sys): np.float64(0.25) """ - # TODO: implement for discrete time systems + # TODO: implement for discrete-time systems if (isdtime(sys, strict=True)): raise NotImplementedError("Function not implemented in discrete time") @@ -106,88 +80,145 @@ def hsvd(sys): return hsv[::-1] -def modred(sys, ELIM, method='matchdc'): - """ - Model reduction of `sys` by eliminating the states in `ELIM` using a given - method. +def model_reduction( + sys, elim_states=None, method='matchdc', elim_inputs=None, + elim_outputs=None, keep_states=None, keep_inputs=None, + keep_outputs=None, warn_unstable=True): + """Model reduction by input, output, or state elimination. + + This function produces a reduced-order model of a system by eliminating + specified inputs, outputs, and/or states from the original system. The + specific states, inputs, or outputs that are eliminated can be + specified by either listing the states, inputs, or outputs to be + eliminated or those to be kept. + + Two methods of state reduction are possible: 'truncate' removes the + states marked for elimination, while 'matchdc' replaces the eliminated + states with their equilibrium values (thereby keeping the input/output + gain unchanged at zero frequency ["DC"]). Parameters ---------- - sys: StateSpace - Original system to reduce - ELIM: array - Vector of states to eliminate - method: string - Method of removing states in `ELIM`: either ``'truncate'`` or - ``'matchdc'``. + sys : `StateSpace` + Original system to reduce. + elim_inputs, elim_outputs, elim_states : array of int or str, optional + Vector of inputs, outputs, or states to eliminate. Can be specified + either as an offset into the appropriate vector or as a signal name. + keep_inputs, keep_outputs, keep_states : array, optional + Vector of inputs, outputs, or states to keep. Can be specified + either as an offset into the appropriate vector or as a signal name. + method : string + Method of removing states: either 'truncate' or 'matchdc' (default). + warn_unstable : bool, option + If False, don't warn if system is unstable. Returns ------- - rsys: StateSpace - A reduced order model + rsys : `StateSpace` + Reduced order model. Raises ------ ValueError - Raised under the following conditions: - - * if `method` is not either ``'matchdc'`` or ``'truncate'`` + If `method` is not either 'matchdc' or 'truncate'. + NotImplementedError + If the 'matchdc' method is used for a discrete-time system. - * if eigenvalues of `sys.A` are not all in left half plane - (`sys` must be stable) + Warns + ----- + UserWarning + If eigenvalues of `sys.A` are not all stable. Examples -------- >>> G = ct.rss(4) - >>> Gr = ct.modred(G, [0, 2], method='matchdc') + >>> Gr = ct.model_reduction(G, [0, 2], method='matchdc') >>> Gr.nstates 2 - """ + See Also + -------- + balanced_reduction, minimal_realization - # Check for ss system object, need a utility for this? + Notes + ----- + The model_reduction function issues a warning if the system has + unstable eigenvalues, since in those situations the stability of the + reduced order model may be different than the stability of the full + model. No other checking is done, so users must to be careful not to + render a system unobservable or unreachable. - # TODO: Check for continous or discrete, only continuous supported for now - # if isCont(): - # dico = 'C' - # elif isDisc(): - # dico = 'D' - # else: - if (isctime(sys)): - dico = 'C' - else: - raise NotImplementedError("Function not implemented in discrete time") + States, inputs, and outputs can be specified using integer offsets or + using signal names. Slices can also be specified, but must use the + Python `slice` function. + + """ + if not isinstance(sys, StateSpace): + raise TypeError("system must be a StateSpace system") # Check system is stable - if np.any(np.linalg.eigvals(sys.A).real >= 0.0): - raise ValueError("Oops, the system is unstable!") - - ELIM = np.sort(ELIM) - # Create list of elements not to eliminate (NELIM) - NELIM = [i for i in range(len(sys.A)) if i not in ELIM] - # A1 is a matrix of all columns of sys.A not to eliminate - A1 = sys.A[:, NELIM[0]].reshape(-1, 1) - for i in NELIM[1:]: - A1 = np.hstack((A1, sys.A[:, i].reshape(-1, 1))) - A11 = A1[NELIM, :] - A21 = A1[ELIM, :] - # A2 is a matrix of all columns of sys.A to eliminate - A2 = sys.A[:, ELIM[0]].reshape(-1, 1) - for i in ELIM[1:]: - A2 = np.hstack((A2, sys.A[:, i].reshape(-1, 1))) - A12 = A2[NELIM, :] - A22 = A2[ELIM, :] - - C1 = sys.C[:, NELIM] - C2 = sys.C[:, ELIM] - B1 = sys.B[NELIM, :] - B2 = sys.B[ELIM, :] - - if method == 'matchdc': - # if matchdc, residualize + if warn_unstable: + if isctime(sys) and np.any(np.linalg.eigvals(sys.A).real >= 0.0) or \ + isdtime(sys) and np.any(np.abs(np.linalg.eigvals(sys.A)) >= 1): + warnings.warn("System is unstable; reduction may be meaningless") + + # Utility function to process keep/elim keywords + def _process_elim_or_keep(elim, keep, labels): + def _expand_key(key): + if key is None: + return [] + elif isinstance(key, str): + return labels.index(key) + elif isinstance(key, list): + return [_expand_key(k) for k in key] + elif isinstance(key, slice): + return range(len(labels))[key] + else: + return key + + elim = np.atleast_1d(_expand_key(elim)) + keep = np.atleast_1d(_expand_key(keep)) + + if len(elim) > 0 and len(keep) > 0: + raise ValueError( + "can't provide both 'keep' and 'elim' for same variables") + elif len(keep) > 0: + keep = np.sort(keep).tolist() + elim = [i for i in range(len(labels)) if i not in keep] + else: + elim = [] if elim is None else np.sort(elim).tolist() + keep = [i for i in range(len(labels)) if i not in elim] + return elim, keep + + # Determine which states to keep + elim_states, keep_states = _process_elim_or_keep( + elim_states, keep_states, sys.state_labels) + elim_inputs, keep_inputs = _process_elim_or_keep( + elim_inputs, keep_inputs, sys.input_labels) + elim_outputs, keep_outputs = _process_elim_or_keep( + elim_outputs, keep_outputs, sys.output_labels) + + # Create submatrix of states we are keeping + A11 = sys.A[:, keep_states][keep_states, :] # states we are keeping + A12 = sys.A[:, elim_states][keep_states, :] # needed for 'matchdc' + A21 = sys.A[:, keep_states][elim_states, :] + A22 = sys.A[:, elim_states][elim_states, :] + + B1 = sys.B[keep_states, :] + B2 = sys.B[elim_states, :] + + C1 = sys.C[:, keep_states] + C2 = sys.C[:, elim_states] + + # Figure out the new state space system + if method == 'matchdc' and A22.size > 0: + if sys.isdtime(strict=True): + raise NotImplementedError( + "'matchdc' not (yet) supported for discrete-time systems") + # if matchdc, residualize # Check if the matrix A22 is invertible - if np.linalg.matrix_rank(A22) != len(ELIM): + if np.linalg.matrix_rank(A22) != len(elim_states): raise ValueError("Matrix A22 is singular to working precision.") # Now precompute A22\A21 and A22\B2 (A22I = inv(A22)) @@ -203,40 +234,49 @@ def modred(sys, ELIM, method='matchdc'): Br = B1 - A12 @ A22I_B2 Cr = C1 - C2 @ A22I_A21 Dr = sys.D - C2 @ A22I_B2 - elif method == 'truncate': - # if truncate, simply discard state x2 + + elif method == 'truncate' or A22.size == 0: + # Get rid of unwanted states Ar = A11 Br = B1 Cr = C1 Dr = sys.D + else: raise ValueError("Oops, method is not supported!") + # Get rid of additional inputs and outputs + Br = Br[:, keep_inputs] + Cr = Cr[keep_outputs, :] + Dr = Dr[keep_outputs, :][:, keep_inputs] + rsys = StateSpace(Ar, Br, Cr, Dr) return rsys -def balred(sys, orders, method='truncate', alpha=None): - """Balanced reduced order model of sys of a given order. - States are eliminated based on Hankel singular value. - If sys has unstable modes, they are removed, the - balanced realization is done on the stable part, then - reinserted in accordance with the reference below. +def balanced_reduction(sys, orders, method='truncate', alpha=None): + """Balanced reduced order model of system of a given order. - Reference: Hsu,C.S., and Hou,D., 1991, - Reducing unstable linear control systems via real Schur transformation. - Electronics Letters, 27, 984-986. + States are eliminated based on Hankel singular value. If `sys` has + unstable modes, they are removed, the balanced realization is done on + the stable part, then reinserted in accordance with [1]_. + + References + ---------- + .. [1] C. S. Hsu and D. Hou, "Reducing unstable linear control + systems via real Schur transformation". Electronics Letters, + 27, 984-986, 1991. Parameters ---------- - sys: StateSpace - Original system to reduce - orders: integer or array of integer + sys : `StateSpace` + Original system to reduce. + orders : integer or array of integer Desired order of reduced order model (if a vector, returns a vector - of systems) - method: string - Method of removing states, either ``'truncate'`` or ``'matchdc'``. - alpha: float + of systems). + method : string + Method of removing states, either 'truncate' or 'matchdc'. + alpha : float Redefines the stability boundary for eigenvalues of the system matrix A. By default for continuous-time systems, alpha <= 0 defines the stability boundary for the real part of A's eigenvalues @@ -246,19 +286,18 @@ def balred(sys, orders, method='truncate', alpha=None): Returns ------- - rsys: StateSpace + rsys : `StateSpace` A reduced order model or a list of reduced order models if orders is a list. Raises ------ ValueError - If `method` is not ``'truncate'`` or ``'matchdc'`` + If `method` is not 'truncate' or 'matchdc'. ImportError - if slycot routine ab09ad, ab09md, or ab09nd is not found - + If slycot routine ab09ad, ab09md, or ab09nd is not found. ValueError - if there are more unstable modes than any value in orders + If there are more unstable modes than any value in orders. Examples -------- @@ -272,7 +311,7 @@ def balred(sys, orders, method='truncate', alpha=None): raise ValueError("supported methods are 'truncate' or 'matchdc'") elif method == 'truncate': try: - from slycot import ab09md, ab09ad + from slycot import ab09ad, ab09md except ImportError: raise ControlSlycot( "can't find slycot subroutine ab09md or ab09ad") @@ -284,7 +323,7 @@ def balred(sys, orders, method='truncate', alpha=None): # Check for ss system object, need a utility for this? - # TODO: Check for continous or discrete, only continuous supported for now + # TODO: Check for continuous or discrete, only continuous supported for now # if isCont(): # dico = 'C' # elif isDisc(): @@ -304,7 +343,7 @@ def balred(sys, orders, method='truncate', alpha=None): # check if orders is a list or a scalar try: - order = iter(orders) + iter(orders) except TypeError: # if orders is a scalar orders = [orders] @@ -340,27 +379,29 @@ def balred(sys, orders, method='truncate', alpha=None): return rsys -def minreal(sys, tol=None, verbose=True): - ''' +def minimal_realization(sys, tol=None, verbose=True): + """Eliminate uncontrollable or unobservable states. + Eliminates uncontrollable or unobservable states in state-space - models or cancelling pole-zero pairs in transfer functions. The - output sysr has minimal order and the same response - characteristics as the original model sys. + models or canceling pole-zero pairs in transfer functions. The + output `sysr` has minimal order and the same response + characteristics as the original model `sys`. Parameters ---------- - sys: StateSpace or TransferFunction - Original system - tol: real - Tolerance - verbose: bool - Print results if True + sys : `StateSpace` or `TransferFunction` + Original system. + tol : real + Tolerance. + verbose : bool + Print results if True. Returns ------- - rsys: StateSpace or TransferFunction - Cleaned model - ''' + rsys : `StateSpace` or `TransferFunction` + Cleaned model. + + """ sysr = sys.minreal(tol) if verbose: print("{nstates} states have been removed from the model".format( @@ -368,91 +409,196 @@ def minreal(sys, tol=None, verbose=True): return sysr -def era(YY, m, n, nin, nout, r): - """Calculate an ERA model of order `r` based on the impulse-response data - `YY`. +def _block_hankel(Y, m, n): + """Create a block Hankel matrix from impulse response.""" + q, p, _ = Y.shape + YY = Y.transpose(0, 2, 1) # transpose for reshape + + H = np.zeros((q*m, p*n)) + + for r in range(m): + # shift and add row to Hankel matrix + new_row = YY[:, r:r+n, :] + H[q*r:q*(r+1), :] = new_row.reshape((q, p*n)) + + return H + + +def eigensys_realization(arg, r, m=None, n=None, dt=True, transpose=False): + r"""eigensys_realization(YY, r) - .. note:: This function is not implemented yet. + Calculate ERA model based on impulse-response data. + + This function computes a discrete-time system + + .. math:: + + x[k+1] &= A x[k] + B u[k] \\\\ + y[k] &= C x[k] + D u[k] + + of order :math:`r` for a given impulse-response data (see [1]_). + + The function can be called with 2 arguments: + + * ``sysd, S = eigensys_realization(data, r)`` + * ``sysd, S = eigensys_realization(YY, r)`` + + where `data` is a `TimeResponseData` object, `YY` is a 1D or 3D + array, and r is an integer. Parameters ---------- - YY: array - `nout` x `nin` dimensional impulse-response data - m: integer - Number of rows in Hankel matrix - n: integer - Number of columns in Hankel matrix - nin: integer - Number of input variables - nout: integer - Number of output variables - r: integer - Order of model + YY : array_like + Impulse response from which the `StateSpace` model is estimated, 1D + or 3D array. + data : `TimeResponseData` + Impulse response from which the `StateSpace` model is estimated. + r : integer + Order of model. + m : integer, optional + Number of rows in Hankel matrix. Default is 2*r. + n : integer, optional + Number of columns in Hankel matrix. Default is 2*r. + dt : True or float, optional + True indicates discrete time with unspecified sampling time and a + positive float is discrete time with the specified sampling time. + It can be used to scale the `StateSpace` model in order to match the + unit-area impulse response of python-control. Default is True. + transpose : bool, optional + Assume that input data is transposed relative to the standard + :ref:`time-series-convention`. For `TimeResponseData` this parameter + is ignored. Default is False. Returns ------- - sys: StateSpace - A reduced order model sys=ss(Ar,Br,Cr,Dr) + sys : `StateSpace` + State space model of the specified order. + S : array + Singular values of Hankel matrix. Can be used to choose a good `r` + value. + + References + ---------- + .. [1] Samet Oymak and Necmiye Ozay, Non-asymptotic Identification of + LTI Systems from a Single Trajectory. https://arxiv.org/abs/1806.05722 Examples -------- - >>> rsys = era(YY, m, n, nin, nout, r) # doctest: +SKIP + >>> T = np.linspace(0, 10, 100) + >>> _, YY = ct.impulse_response(ct.tf([1], [1, 0.5], True), T) + >>> sysd, _ = ct.eigensys_realization(YY, r=1) + >>> T = np.linspace(0, 10, 100) + >>> response = ct.impulse_response(ct.tf([1], [1, 0.5], True), T) + >>> sysd, _ = ct.eigensys_realization(response, r=1) """ - raise NotImplementedError('This function is not implemented yet.') + if isinstance(arg, TimeResponseData): + YY = np.array(arg.outputs, ndmin=3) + if arg.transpose: + YY = np.transpose(YY) + else: + YY = np.array(arg, ndmin=3) + if transpose: + YY = np.transpose(YY) + + q, p, l = YY.shape + + if m is None: + m = 2*r + if n is None: + n = 2*r + + if m*q < r or n*p < r: + raise ValueError("Hankel parameters are to small") + + if (l-1) < m+n: + raise ValueError("not enough data for requested number of parameters") + + H = _block_hankel(YY[:, :, 1:], m, n+1) # Hankel matrix (q*m, p*(n+1)) + Hf = H[:, :-p] # first p*n columns of H + Hl = H[:, p:] # last p*n columns of H + + U,S,Vh = np.linalg.svd(Hf, True) + Ur =U[:, 0:r] + Vhr =Vh[0:r, :] + + # balanced realizations + Sigma_inv = np.diag(1./np.sqrt(S[0:r])) + Ar = Sigma_inv @ Ur.T @ Hl @ Vhr.T @ Sigma_inv + Br = Sigma_inv @ Ur.T @ Hf[:, 0:p]*dt # dt scaling for unit-area impulse + Cr = Hf[0:q, :] @ Vhr.T @ Sigma_inv + Dr = YY[:, :, 0] + + return StateSpace(Ar, Br, Cr, Dr, dt), S + +def markov(*args, m=None, transpose=False, dt=None, truncate=False): + """markov(Y, U, [, m]) -def markov(Y, U, m=None, transpose=False): - """Calculate the first `m` Markov parameters [D CB CAB ...] - from input `U`, output `Y`. + Calculate Markov parameters [D CB CAB ...] from data. - This function computes the Markov parameters for a discrete time system + This function computes the the first `m` Markov parameters [D CB CAB + ...] for a discrete-time system. .. math:: x[k+1] &= A x[k] + B u[k] \\\\ y[k] &= C x[k] + D u[k] - given data for u and y. The algorithm assumes that that C A^k B = 0 for - k > m-2 (see [1]_). Note that the problem is ill-posed if the length of - the input data is less than the desired number of Markov parameters (a - warning message is generated in this case). + given data for u and y. The algorithm assumes that that C A^k B = 0 + for k > m-2 (see [1]_). Note that the problem is ill-posed if the + length of the input data is less than the desired number of Markov + parameters (a warning message is generated in this case). + + The function can be called with either 1, 2 or 3 arguments: + + * ``H = markov(data)`` + * ``H = markov(data, m)`` + * ``H = markov(Y, U)`` + * ``H = markov(Y, U, m)`` + + where `data` is a `TimeResponseData` object, `YY` is a 1D or 3D + array, and r is an integer. Parameters ---------- Y : array_like - Output data. If the array is 1D, the system is assumed to be single - input. If the array is 2D and transpose=False, the columns of `Y` - are taken as time points, otherwise the rows of `Y` are taken as - time points. + Output data. If the array is 1D, the system is assumed to be + single input. If the array is 2D and `transpose` = False, the columns + of `Y` are taken as time points, otherwise the rows of `Y` are + taken as time points. U : array_like Input data, arranged in the same way as `Y`. + data : `TimeResponseData` + Response data from which the Markov parameters where estimated. + Input and output data must be 1D or 2D array. m : int, optional - Number of Markov parameters to output. Defaults to len(U). + Number of Markov parameters to output. Defaults to len(U). + dt : True of float, optional + True indicates discrete time with unspecified sampling time and a + positive float is discrete time with the specified sampling time. + It can be used to scale the Markov parameters in order to match + the unit-area impulse response of python-control. Default is True + for array_like and dt=data.time[1]-data.time[0] for + `TimeResponseData` as input. + truncate : bool, optional + Do not use first m equation for least squares. Default is False. transpose : bool, optional Assume that input data is transposed relative to the standard - :ref:`time-series-convention`. Default value is False. + :ref:`time-series-convention`. For `TimeResponseData` this parameter + is ignored. Default is False. Returns ------- H : ndarray - First m Markov parameters, [D CB CAB ...] + First m Markov parameters, [D CB CAB ...]. References ---------- .. [1] J.-N. Juang, M. Phan, L. G. Horta, and R. W. Longman, Identification of observer/Kalman filter Markov parameters - Theory and experiments. Journal of Guidance Control and Dynamics, 16(2), - 320-329, 2012. http://doi.org/10.2514/3.21006 - - Notes - ----- - Currently only works for SISO systems. - - This function does not currently comply with the Python Control Library - :ref:`time-series-convention` for representation of time series data. - Use `transpose=False` to make use of the standard convention (this - will be updated in a future release). + 320-329, 2012. https://doi.org/10.2514/3.21006 Examples -------- @@ -462,97 +608,125 @@ def markov(Y, U, m=None, transpose=False): >>> H = ct.markov(Y, U, 3, transpose=False) """ - # Convert input parameters to 2D arrays (if they aren't already) - Umat = np.array(U, ndmin=2) - Ymat = np.array(Y, ndmin=2) - - # If data is in transposed format, switch it around - if transpose: - Umat, Ymat = np.transpose(Umat), np.transpose(Ymat) - # Make sure the system is a SISO system - if Umat.shape[0] != 1 or Ymat.shape[0] != 1: - raise ControlMIMONotImplemented + # Convert input parameters to 2D arrays (if they aren't already) + # Get the system description + if len(args) < 1: + raise ControlArgument("not enough input arguments") + + if isinstance(args[0], TimeResponseData): + data = args[0] + Umat = np.array(data.inputs, ndmin=2) + Ymat = np.array(data.outputs, ndmin=2) + if dt is None: + dt = data.time[1] - data.time[0] + if not np.allclose(np.diff(data.time), dt): + raise ValueError("response time values must be equally " + "spaced.") + transpose = data.transpose + if data.transpose and not data.issiso: + Umat, Ymat = np.transpose(Umat), np.transpose(Ymat) + if len(args) == 2: + m = args[1] + elif len(args) > 2: + raise ControlArgument("too many positional arguments") + else: + if len(args) < 2: + raise ControlArgument("not enough input arguments") + Umat = np.array(args[1], ndmin=2) + Ymat = np.array(args[0], ndmin=2) + if dt is None: + dt = True + if transpose: + Umat, Ymat = np.transpose(Umat), np.transpose(Ymat) + if len(args) == 3: + m = args[2] + elif len(args) > 3: + raise ControlArgument("too many positional arguments") # Make sure the number of time points match if Umat.shape[1] != Ymat.shape[1]: raise ControlDimension( - "Input and output data are of differnent lengths") - n = Umat.shape[1] + "Input and output data are of different lengths") + l = Umat.shape[1] # If number of desired parameters was not given, set to size of input data if m is None: - m = Umat.shape[1] + m = l + + t = 0 + if truncate: + t = m + + q = Ymat.shape[0] # number of outputs + p = Umat.shape[0] # number of inputs # Make sure there is enough data to compute parameters - if m > n: + if m*p > (l-t): warnings.warn("Not enough data for requested number of parameters") + # the algorithm - Construct a matrix of control inputs to invert # - # Original algorithm (with mapping to standard order) - # - # RMM note, 24 Dec 2020: This algorithm sets the problem up correctly - # until the final column of the UU matrix is created, at which point it - # makes some modifications that I don't understand. This version of the - # algorithm does not seem to return the actual Markov parameters for a - # system. - # - # # Create the matrix of (shifted) inputs - # UU = np.transpose(Umat) - # for i in range(1, m-1): - # # Shift previous column down and add a zero at the top - # newCol = np.vstack((0, np.reshape(UU[0:n-1, i-1], (-1, 1)))) - # UU = np.hstack((UU, newCol)) - # - # # Shift previous column down and add a zero at the top - # Ulast = np.vstack((0, np.reshape(UU[0:n-1, m-2], (-1, 1)))) - # - # # Replace the elements of the last column new values (?) - # # Each row gets the sum of the rows above it (?) - # for i in range(n-1, 0, -1): - # Ulast[i] = np.sum(Ulast[0:i-1]) - # UU = np.hstack((UU, Ulast)) - # - # # Solve for the Markov parameters from Y = H @ UU - # # H = [[D], [CB], [CAB], ..., [C A^{m-3} B], [???]] - # H = np.linalg.lstsq(UU, np.transpose(Ymat))[0] - # - # # Markov parameters are in rows => transpose if needed - # return H if transpose else np.transpose(H) - - # - # New algorithm - Construct a matrix of control inputs to invert + # (q,l) = (q,p*m) @ (p*m,l) + # YY.T = H @ UU.T # # This algorithm sets up the following problem and solves it for # the Markov parameters # + # (l,q) = (l,p*m) @ (p*m,q) + # YY = UU @ H.T + # # [ y(0) ] [ u(0) 0 0 ] [ D ] # [ y(1) ] [ u(1) u(0) 0 ] [ C B ] # [ y(2) ] = [ u(2) u(1) u(0) ] [ C A B ] # [ : ] [ : : : : ] [ : ] - # [ y(n-1) ] [ u(n-1) u(n-2) u(n-3) ... u(n-m) ] [ C A^{m-2} B ] + # [ y(l-1) ] [ u(l-1) u(l-2) u(l-3) ... u(l-m) ] [ C A^{m-2} B ] + # + # truncated version t=m, do not use first m equation + # + # [ y(t) ] [ u(t) u(t-1) u(t-2) u(t-m) ] [ D ] + # [ y(t+1) ] [ u(t+1) u(t) u(t-1) u(t-m+1)] [ C B ] + # [ y(t+2) ] = [ u(t+2) u(t+1) u(t) u(t-m+2)] [ C B ] + # [ : ] [ : : : : ] [ : ] + # [ y(l-1) ] [ u(l-1) u(l-2) u(l-3) ... u(l-m) ] [ C A^{m-2} B ] # - # Note: if the number of Markov parameters (m) is less than the size of - # the input/output data (n), then this algorithm assumes C A^{j} B = 0 + # Note: This algorithm assumes C A^{j} B = 0 # for j > m-2. See equation (3) in # # J.-N. Juang, M. Phan, L. G. Horta, and R. W. Longman, Identification # of observer/Kalman filter Markov parameters - Theory and # experiments. Journal of Guidance Control and Dynamics, 16(2), - # 320-329, 2012. http://doi.org/10.2514/3.21006 + # 320-329, 2012. https://doi.org/10.2514/3.21006 # + # Set up the full problem # Create matrix of (shifted) inputs - UU = Umat - for i in range(1, m): - # Shift previous column down and add a zero at the top - new_row = np.hstack((0, UU[i-1, 0:-1])) - UU = np.vstack((UU, new_row)) - UU = np.transpose(UU) + UUT = np.zeros((p*m, l)) + for i in range(m): + # Shift previous column down and keep zeros at the top + UUT[i*p:(i+1)*p, i:] = Umat[:, :l-i] + + # Truncate first t=0 or t=m time steps, transpose the problem for lsq + YY = Ymat[:, t:].T + UU = UUT[:, t:].T + + # Solve for the Markov parameters from YY = UU @ H.T + HT, _, _, _ = np.linalg.lstsq(UU, YY, rcond=None) + H = HT.T/dt # scaling + + H = H.reshape(q, m, p) # output, time*input -> output, time, input + H = H.transpose(0, 2, 1) # output, input, time - # Invert and solve for Markov parameters - YY = np.transpose(Ymat) - H, _, _, _ = np.linalg.lstsq(UU, YY, rcond=None) + # for siso return a 1D array instead of a 3D array + if q == 1 and p == 1: + H = np.squeeze(H) # Return the first m Markov parameters - return H if transpose else np.transpose(H) + return H if not transpose else np.transpose(H) + +# Function aliases +hsvd = hankel_singular_values +balred = balanced_reduction +modred = model_reduction +minreal = minimal_realization +era = eigensys_realization diff --git a/control/nichols.py b/control/nichols.py index 5eafa594f..98775ddaf 100644 --- a/control/nichols.py +++ b/control/nichols.py @@ -1,27 +1,17 @@ # nichols.py - Nichols plot # -# Contributed by Allan McInnes -# - -"""nichols.py - -Functions for plotting Black-Nichols charts. +# Initial author: Allan McInnes -Routines in this module: - -nichols.nichols_plot aliased as nichols.nichols -nichols.nichols_grid -""" +"""Functions for plotting Black-Nichols charts.""" import matplotlib.pyplot as plt import matplotlib.transforms import numpy as np from . import config -from .ctrlplot import suptitle +from .ctrlplot import ControlPlot, _get_line_labels, _process_ax_keyword, \ + _process_legend_keywords, _process_line_labels, _update_plot_title from .ctrlutil import unwrap -from .freqplot import _default_frequency_range, _freqplot_defaults, \ - _get_line_labels, _process_ax_keyword from .lti import frequency_response from .statesp import StateSpace from .xferfcn import TransferFunction @@ -36,7 +26,7 @@ def nichols_plot( data, omega=None, *fmt, grid=None, title=None, ax=None, - legend_loc='upper left', **kwargs): + label=None, **kwargs): """Nichols plot for a system. Plots a Nichols plot for the system over a (optional) frequency range. @@ -44,32 +34,63 @@ def nichols_plot( Parameters ---------- data : list of `FrequencyResponseData` or `LTI` - List of LTI systems or :class:`FrequencyResponseData` objects. A + List of LTI systems or `FrequencyResponseData` objects. A single system or frequency response can also be passed. omega : array_like - Range of frequencies (list or bounds) in rad/sec - *fmt : :func:`matplotlib.pyplot.plot` format string, optional + Range of frequencies (list or bounds) in rad/sec. + *fmt : `matplotlib.pyplot.plot` format string, optional Passed to `matplotlib` as the format string for all lines in the plot. The `omega` parameter must be present (use omega=None if needed). grid : boolean, optional - True if the plot should include a Nichols-chart grid. Default is True. - legend_loc : str, optional - For plots with multiple lines, a legend will be included in the - given location. Default is 'upper left'. Use False to supress. - **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional + True if the plot should include a Nichols-chart grid. Default is + True and can be set using `config.defaults['nichols.grid']`. + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. Returns ------- - lines : array of Line2D - 1-D array of Line2D objects. The size of the array matches - the number of systems and the value of the array is a list of - Line2D objects for that system. + cplt : `ControlPlot` object + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : Array of `matplotlib.lines.Line2D` objects + Array containing information on each line in the plot. The shape + of the array matches the subplots shape and the value of the array + is a list of Line2D objects in that subplot. + cplt.axes : 2D ndarray of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. + + Other Parameters + ---------------- + ax : `matplotlib.axes.Axes`, optional + The matplotlib axes to draw the figure on. If not specified and + the current figure has a single axes, that axes is used. + Otherwise, a new figure is created. + label : str or array_like of str, optional + If present, replace automatically generated label(s) with given + label(s). If sysdata is a list, strings should be specified for each + system. + legend_loc : int or str, optional + Include a legend in the given location. Default is 'upper left', + with no legend for a single response. Use False to suppress legend. + rcParams : dict + Override the default parameters used for generating plots. + Default is set by `config.defaults['ctrlplot.rcParams']`. + show_legend : bool, optional + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on the + plot or `legend_loc` has been specified. + title : str, optional + Set the title of the plot. Defaults to plot type and system name(s). + """ # Get parameter values grid = config._get_param('nichols', 'grid', grid, True) - rcParams = config._get_param( - 'freqplot', 'rcParams', kwargs, _freqplot_defaults, pop=True) + label = _process_line_labels(label) + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) # If argument was a singleton, turn it into a list if not isinstance(data, (tuple, list)): @@ -85,6 +106,8 @@ def nichols_plot( raise NotImplementedError("MIMO Nichols plots not implemented") fig, ax_nichols = _process_ax_keyword(ax, rcParams=rcParams, squeeze=True) + legend_loc, _, show_legend = _process_legend_keywords( + kwargs, None, 'upper left') # Create a list of lines for the output out = np.empty(len(data), dtype=object) @@ -100,38 +123,43 @@ def nichols_plot( x = unwrap(np.degrees(phase), 360) y = 20*np.log10(mag) - # Decide on the system name + # Decide on the system name and label sysname = response.sysname if response.sysname is not None \ - else f"Unknown-{idx_sys}" + else f"Unknown-sys_{idx}" + label_ = sysname if label is None else label[idx] # Generate the plot - out[idx] = ax_nichols.plot(x, y, *fmt, label=sysname, **kwargs) + out[idx] = ax_nichols.plot(x, y, *fmt, label=label_, **kwargs) # Label the plot axes - plt.xlabel('Phase [deg]') - plt.ylabel('Magnitude [dB]') + ax_nichols.set_xlabel('Phase [deg]') + ax_nichols.set_ylabel('Magnitude [dB]') # Mark the -180 point - plt.plot([-180], [0], 'r+') + ax_nichols.plot([-180], [0], 'r+') # Add grid if grid: - nichols_grid() + nichols_grid(ax=ax_nichols) # List of systems that are included in this plot lines, labels = _get_line_labels(ax_nichols) # Add legend if there is more than one system plotted - if len(labels) > 1 and legend_loc is not False: + if show_legend == True or (show_legend != False and len(labels) > 1): with plt.rc_context(rcParams): - ax_nichols.legend(lines, labels, loc=legend_loc) + legend = ax_nichols.legend(lines, labels, loc=legend_loc) + else: + legend = None # Add the title - if title is None: - title = "Nichols plot for " + ", ".join(labels) - suptitle(title, fig=fig, rcParams=rcParams) + if ax is None: + if title is None: + title = "Nichols plot for " + ", ".join(labels) + _update_plot_title( + title, fig=fig, rcParams=rcParams, use_existing=False) - return out + return ControlPlot(out, ax_nichols, fig, legend=legend) def _inner_extents(ax): @@ -146,39 +174,40 @@ def _inner_extents(ax): def nichols_grid(cl_mags=None, cl_phases=None, line_style='dotted', ax=None, label_cl_phases=True): - """Nichols chart grid. + """Plot Nichols chart grid. - Plots a Nichols chart grid on the current axis, or creates a new chart + Plots a Nichols chart grid on the current axes, or creates a new chart if no plot already exists. Parameters ---------- - cl_mags : array-like (dB), optional + cl_mags : array_like (dB), optional Array of closed-loop magnitudes defining the iso-gain lines on a custom Nichols chart. - cl_phases : array-like (degrees), optional + cl_phases : array_like (degrees), optional Array of closed-loop phases defining the iso-phase lines on a custom Nichols chart. Must be in the range -360 < cl_phases < 0 line_style : string, optional :doc:`Matplotlib linestyle \ - ` - ax : matplotlib.axes.Axes, optional - Axes to add grid to. If ``None``, use ``plt.gca()``. - label_cl_phases: bool, optional - If True, closed-loop phase lines will be labelled. + `. + ax : `matplotlib.axes.Axes`, optional + Axes to add grid to. If None, use `matplotlib.pyplot.gca`. + label_cl_phases : bool, optional + If True, closed-loop phase lines will be labeled. Returns ------- - cl_mag_lines: list of `matplotlib.line.Line2D` - The constant closed-loop gain contours - cl_phase_lines: list of `matplotlib.line.Line2D` - The constant closed-loop phase contours - cl_mag_labels: list of `matplotlib.text.Text` - mcontour labels; each entry corresponds to the respective entry - in ``cl_mag_lines`` - cl_phase_labels: list of `matplotlib.text.Text` - ncontour labels; each entry corresponds to the respective entry - in ``cl_phase_lines`` + cl_mag_lines : list of `matplotlib.line.Line2D` + The constant closed-loop gain contours. + cl_phase_lines : list of `matplotlib.line.Line2D` + The constant closed-loop phase contours. + cl_mag_labels : list of `matplotlib.text.Text` + Magnitude contour labels; each entry corresponds to the respective + entry in `cl_mag_lines`. + cl_phase_labels : list of `matplotlib.text.Text` + Phase contour labels; each entry corresponds to the respective entry + in `cl_phase_lines`. + """ if ax is None: ax = plt.gca() @@ -311,15 +340,16 @@ def closed_loop_contours(Gcl_mags, Gcl_phases): Parameters ---------- - Gcl_mags : array-like + Gcl_mags : array_like Array of magnitudes of the contours - Gcl_phases : array-like + Gcl_phases : array_like Array of phases in radians of the contours Returns ------- contours : complex array Array of complex numbers corresponding to the contours. + """ # Compute the contours in Gcl-space. Since we're given closed-loop # magnitudes and phases, this is just a case of converting them into @@ -337,7 +367,7 @@ def m_circles(mags, phase_min=-359.75, phase_max=-0.25): Parameters ---------- - mags : array-like + mags : array_like Array of magnitudes in dB of the M-circles phase_min : degrees Minimum phase in degrees of the N-circles @@ -348,6 +378,7 @@ def m_circles(mags, phase_min=-359.75, phase_max=-0.25): ------- contours : complex array Array of complex numbers corresponding to the contours. + """ # Convert magnitudes and phase range into a grid suitable for # building contours @@ -363,7 +394,7 @@ def n_circles(phases, mag_min=-40.0, mag_max=12.0): Parameters ---------- - phases : array-like + phases : array_like Array of phases in degrees of the N-circles mag_min : dB Minimum magnitude in dB of the N-circles @@ -374,6 +405,7 @@ def n_circles(phases, mag_min=-40.0, mag_max=12.0): ------- contours : complex array Array of complex numbers corresponding to the contours. + """ # Convert phases and magnitude range into a grid suitable for # building contours diff --git a/control/nlsys.py b/control/nlsys.py index d6d3b1b76..30f06f819 100644 --- a/control/nlsys.py +++ b/control/nlsys.py @@ -1,109 +1,104 @@ # nlsys.py - input/output system module # RMM, 28 April 2019 # -# Additional features to add +# Additional features to add: # * Allow constant inputs for MIMO input_output_response (w/out ones) -# * Add support for constants/matrices as part of operators (1 + P) # * Add unit tests (and example?) for time-varying systems -# * Allow time vector for discrete time simulations to be multiples of dt -# * Check the way initial outputs for discrete time systems are handled -# +# * Allow time vector for discrete-time simulations to be multiples of dt +# * Check the way initial outputs for discrete-time systems are handled -"""The :mod:`~control.nlsys` module contains the -:class:`~control.NonlinearIOSystem` class that represents (possibly nonlinear) -input/output systems. The :class:`~control.NonlinearIOSystem` class is a -general class that defines any continuous or discrete time dynamical system. -Input/output systems can be simulated and also used to compute equilibrium -points and linearizations. +"""This module contains the `NonlinearIOSystem` class that +represents (possibly nonlinear) input/output systems. The +`NonlinearIOSystem` class is a general class that defines any +continuous- or discrete-time dynamical system. Input/output systems +can be simulated and also used to compute operating points and +linearizations. """ -import copy from warnings import warn import numpy as np import scipy as sp from . import config +from .config import _process_param, _process_kwargs from .iosys import InputOutputSystem, _parse_spec, _process_iosys_keywords, \ - _process_signal_list, common_timebase, isctime, isdtime -from .timeresp import _check_convert_array, _process_time_response, \ - TimeResponseData, TimeResponseList + common_timebase, iosys_repr, isctime, isdtime +from .timeresp import TimeResponseData, TimeResponseList, \ + _check_convert_array, _process_time_response, _timeresp_aliases __all__ = ['NonlinearIOSystem', 'InterconnectedSystem', 'nlsys', 'input_output_response', 'find_eqpt', 'linearize', - 'interconnect', 'connection_table'] + 'interconnect', 'connection_table', 'OperatingPoint', + 'find_operating_point'] class NonlinearIOSystem(InputOutputSystem): - """Nonlinear I/O system. + """Nonlinear input/output system model. - Creates an :class:`~control.InputOutputSystem` for a nonlinear system by - specifying a state update function and an output function. The new system - can be a continuous or discrete time system (Note: discrete-time systems - are not yet supported by most functions.) + Creates an `InputOutputSystem` for a nonlinear system + by specifying a state update function and an output function. The new + system can be a continuous or discrete-time system. Nonlinear I/O + systems are usually created with the `nlsys` factory + function. Parameters ---------- updfcn : callable Function returning the state update function - `updfcn(t, x, u, params) -> array` + ``updfcn(t, x, u, params) -> array`` - where `x` is a 1-D array with shape (nstates,), `u` is a 1-D array - with shape (ninputs,), `t` is a float representing the currrent - time, and `params` is a dict containing the values of parameters - used by the function. + where `t` is a float representing the current time, `x` is a 1-D + array with shape (nstates,), `u` is a 1-D array with shape + (ninputs,), and `params` is a dict containing the values of + parameters used by the function. outfcn : callable Function returning the output at the given state `outfcn(t, x, u, params) -> array` - where the arguments are the same as for `upfcn`. + where the arguments are the same as for `updfcn`. - inputs : int, list of str or None, optional - Description of the system inputs. This can be given as an integer - count or as a list of strings that name the individual signals. - If an integer count is specified, the names of the signal will be - of the form `s[i]` (where `s` is one of `u`, `y`, or `x`). If - this parameter is not given or given as `None`, the relevant - quantity will be determined when possible based on other - information provided to functions using the system. - - outputs : int, list of str or None, optional - Description of the system outputs. Same format as `inputs`. + inputs, outputs, states : int, list of str or None, optional + Description of the system inputs, outputs, and states. See + `control.nlsys` for more details. - states : int, list of str, or None, optional - Description of the system states. Same format as `inputs`. + params : dict, optional + Parameter values for the systems. Passed to the evaluation functions + for the system as default values, overriding internal defaults. dt : timebase, optional The timebase for the system, used to specify whether the system is operating in continuous or discrete time. It can have the following values: - * dt = 0: continuous time system (default) - * dt > 0: discrete time system with sampling period 'dt' - * dt = True: discrete time with unspecified sampling period - * dt = None: no timebase specified + * `dt` = 0: continuous-time system (default) + * `dt` > 0: discrete-time system with sampling period `dt` + * `dt` = True: discrete time with unspecified sampling period + * `dt` = None: no timebase specified + Attributes + ---------- + ninputs, noutputs, nstates : int + Number of input, output and state variables. + shape : tuple + 2-tuple of I/O system dimension, (noutputs, ninputs). + input_labels, output_labels, state_labels : list of str + Names for the input, output, and state variables. name : string, optional - System name (used for specifying signals). If unspecified, a - generic name is generated with a unique integer id. - - params : dict, optional - Parameter values for the systems. Passed to the evaluation - functions for the system as default values, overriding internal - defaults. + System name. See Also -------- - InputOutputSystem : Input/output system class. + nlsys, InputOutputSystem Notes ----- - The :class:`~control.InputOuputSystem` class (and its subclasses) makes - use of two special methods for implementing much of the work of the class: + The `InputOutputSystem` class (and its subclasses) makes use of two + special methods for implementing much of the work of the class: * _rhs(t, x, u): compute the right hand side of the differential or difference equation for the system. If not specified, the system @@ -157,17 +152,21 @@ def __init__(self, updfcn, outfcn=None, params=None, **kwargs): self._current_params = {} if params is None else params.copy() def __str__(self): - return f"{InputOutputSystem.__str__(self)}\n\n" + \ + out = f"{InputOutputSystem.__str__(self)}" + if len(self.params) > 0: + out += f"\nParameters: {[p for p in self.params.keys()]}" + out += "\n\n" + \ f"Update: {self.updfcn}\n" + \ f"Output: {self.outfcn}" + return out # Return the value of a static nonlinear system def __call__(sys, u, params=None, squeeze=None): - """Evaluate a (static) nonlinearity at a given input value + """Evaluate a (static) nonlinearity at a given input value. - If a nonlinear I/O system has no internal state, then evaluating the - system at an input `u` gives the output `y = F(u)`, determined by the - output function. + If a nonlinear I/O system has no internal state, then evaluating + the system at an input `u` gives the output ``y = F(u)``, + determined by the output function. Parameters ---------- @@ -175,14 +174,15 @@ def __call__(sys, u, params=None, squeeze=None): Parameter values for the system. Passed to the evaluation function for the system as default values, overriding internal defaults. squeeze : bool, optional - If True and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If False, return the - system output as a 2D array even if the system is SISO. Default - value set by config.defaults['control.squeeze_time_response']. + If True and if the system has a single output, return the + system output as a 1D array rather than a 2D array. If + False, return the system output as a 2D array even if the + system is SISO. Default value set by + `config.defaults['control.squeeze_time_response']`. """ # Make sure the call makes sense - if not sys._isstatic(): + if sys.nstates != 0: raise TypeError( "function evaluation is only supported for static " "input/output systems") @@ -193,14 +193,14 @@ def __call__(sys, u, params=None, squeeze=None): # Evaluate the function on the argument out = sys._out(0, np.array((0,)), np.asarray(u)) - _, out = _process_time_response( - None, out, issiso=sys.issiso(), squeeze=squeeze) + out = _process_time_response( + out, issiso=sys.issiso(), squeeze=squeeze) return out def __mul__(self, other): """Multiply two input/output systems (series interconnection)""" # Convert 'other' to an I/O system if needed - other = _convert_static_iosystem(other) + other = _convert_to_iosystem(other) if not isinstance(other, InputOutputSystem): return NotImplemented @@ -210,7 +210,7 @@ def __mul__(self, other): "can't multiply systems with incompatible inputs and outputs") # Make sure timebase are compatible - dt = common_timebase(other.dt, self.dt) + common_timebase(other.dt, self.dt) # Create a new system to handle the composition inplist = [(0, i) for i in range(other.ninputs)] @@ -232,7 +232,7 @@ def __mul__(self, other): def __rmul__(self, other): """Pre-multiply an input/output systems by a scalar/matrix""" # Convert other to an I/O system if needed - other = _convert_static_iosystem(other) + other = _convert_to_iosystem(other) if not isinstance(other, InputOutputSystem): return NotImplemented @@ -242,7 +242,7 @@ def __rmul__(self, other): "inputs and outputs") # Make sure timebase are compatible - dt = common_timebase(self.dt, other.dt) + common_timebase(self.dt, other.dt) # Create a new system to handle the composition inplist = [(0, i) for i in range(self.ninputs)] @@ -264,7 +264,7 @@ def __rmul__(self, other): def __add__(self, other): """Add two input/output systems (parallel interconnection)""" # Convert other to an I/O system if needed - other = _convert_static_iosystem(other) + other = _convert_to_iosystem(other) if not isinstance(other, InputOutputSystem): return NotImplemented @@ -285,7 +285,7 @@ def __add__(self, other): def __radd__(self, other): """Parallel addition of input/output system to a compatible object.""" # Convert other to an I/O system if needed - other = _convert_static_iosystem(other) + other = _convert_to_iosystem(other) if not isinstance(other, InputOutputSystem): return NotImplemented @@ -306,14 +306,14 @@ def __radd__(self, other): def __sub__(self, other): """Subtract two input/output systems (parallel interconnection)""" # Convert other to an I/O system if needed - other = _convert_static_iosystem(other) + other = _convert_to_iosystem(other) if not isinstance(other, InputOutputSystem): return NotImplemented # Make sure number of input and outputs match if self.ninputs != other.ninputs or self.noutputs != other.noutputs: raise ValueError( - "can't substract systems with incompatible numbers of " + "can't subtract systems with incompatible numbers of " "inputs or outputs") ninputs = self.ninputs noutputs = self.noutputs @@ -330,7 +330,7 @@ def __sub__(self, other): def __rsub__(self, other): """Parallel subtraction of I/O system to a compatible object.""" # Convert other to an I/O system if needed - other = _convert_static_iosystem(other) + other = _convert_to_iosystem(other) if not isinstance(other, InputOutputSystem): return NotImplemented return other - self @@ -340,7 +340,7 @@ def __neg__(self): if self.ninputs is None or self.noutputs is None: raise ValueError("Can't determine number of inputs or outputs") - # Create a new selftem to hold the negation + # Create a new system to hold the negation inplist = [(0, i) for i in range(self.ninputs)] outlist = [(0, i, -1) for i in range(self.noutputs)] newsys = InterconnectedSystem( @@ -356,7 +356,11 @@ def __truediv__(self, other): else: return NotImplemented - def _update_params(self, params, warning=False): + # Determine if a system is static (memoryless) + def _isstatic(self): + return self.nstates == 0 + + def _update_params(self, params): # Update the current parameter values self._current_params = self.params.copy() if params: @@ -367,25 +371,25 @@ def _rhs(self, t, x, u): Private function used to compute the right hand side of an input/output system model. Intended for fast evaluation; for a more - user-friendly interface you may want to use :meth:`dynamics`. + user-friendly interface you may want to use `dynamics`. """ return np.asarray( self.updfcn(t, x, u, self._current_params)).reshape(-1) def dynamics(self, t, x, u, params=None): - """Compute the dynamics of a differential or difference equation. + """Dynamics of a differential or difference equation. Given time `t`, input `u` and state `x`, returns the value of the - right hand side of the dynamical system. If the system is continuous, - returns the time derivative + right hand side of the dynamical system. If the system is a + continuous-time system, returns the time derivative:: - dx/dt = f(t, x, u[, params]) + dx/dt = updfcn(t, x, u[, params]) - where `f` is the system's (possibly nonlinear) dynamics function. - If the system is discrete-time, returns the next value of `x`: + where `updfcn` is the system's (possibly nonlinear) update function. + If the system is discrete time, returns the next value of `x`:: - x[t+dt] = f(t, x[t], u[t][, params]) + x[t+dt] = updfcn(t, x[t], u[t][, params]) where `t` is a scalar. @@ -395,17 +399,18 @@ def dynamics(self, t, x, u, params=None): Parameters ---------- t : float - the time at which to evaluate + Time at which to evaluate. x : array_like - current state + Current state. u : array_like - input + Current input. params : dict, optional - system parameter values + System parameter values. Returns ------- dx/dt or x[t+dt] : ndarray + """ self._update_params(params) return self._rhs( @@ -417,7 +422,7 @@ def _out(self, t, x, u): Private function used to compute the output of of an input/output system model given the state, input, parameters. Intended for fast evaluation; for a more user-friendly interface you may want to use - :meth:`output`. + `output`. """ # @@ -433,61 +438,58 @@ def _out(self, t, x, u): self.outfcn(t, x, u, self._current_params)).reshape(-1) def output(self, t, x, u, params=None): - """Compute the output of the system + """Compute the output of the system. Given time `t`, input `u` and state `x`, returns the output of the - system: + system:: - y = g(t, x, u[, params]) + y = outfcn(t, x, u[, params]) The inputs `x` and `u` must be of the correct length. Parameters ---------- t : float - the time at which to evaluate + The time at which to evaluate. x : array_like - current state + Current state. u : array_like - input + Current input. params : dict, optional - system parameter values + System parameter values. Returns ------- y : ndarray + """ self._update_params(params) return self._out( t, np.asarray(x).reshape(-1), np.asarray(u).reshape(-1)) def feedback(self, other=1, sign=-1, params=None): - """Feedback interconnection between two input/output systems + """Feedback interconnection between two I/O systems. Parameters ---------- - sys1: InputOutputSystem - The primary process. - sys2: InputOutputSystem - The feedback process (often a feedback controller). - sign: scalar, optional - The sign of feedback. `sign` = -1 indicates negative feedback, - and `sign` = 1 indicates positive feedback. `sign` is an optional - argument; it assumes a value of -1 if not specified. + other : `InputOutputSystem` + System in the feedback path. + + sign : float, optional + Gain to use in feedback path. Defaults to -1. + + params : dict, optional + Parameter values for the overall system. Passed to the + evaluation functions for the system as default values, + overriding defaults for the individual systems. Returns ------- - out: InputOutputSystem - - Raises - ------ - ValueError - if the inputs, outputs, or timebases of the systems are - incompatible. + `NonlinearIOSystem` """ # Convert sys2 to an I/O system if needed - other = _convert_static_iosystem(other) + other = _convert_to_iosystem(other) # Make sure systems can be interconnected if self.noutputs != other.ninputs or other.noutputs != self.ninputs: @@ -505,7 +507,7 @@ def feedback(self, other=1, sign=-1, params=None): (self, other), inplist=inplist, outlist=outlist, params=params, dt=dt) - # Set up the connecton map manually + # Set up the connection map manually newsys.set_connect_map(np.block( [[np.zeros((self.ninputs, self.noutputs)), sign * np.eye(self.ninputs, other.noutputs)], @@ -516,22 +518,29 @@ def feedback(self, other=1, sign=-1, params=None): # Return the newly created system return newsys - def linearize(self, x0, u0, t=0, params=None, eps=1e-6, + def linearize(self, x0, u0=None, t=0, params=None, eps=1e-6, copy_names=False, **kwargs): """Linearize an input/output system at a given state and input. - Return the linearization of an input/output system at a given state - and input value as a StateSpace system. See - :func:`~control.linearize` for complete documentation. + Return the linearization of an input/output system at a given + operating point (or state and input value) as a `StateSpace` system. + See `linearize` for complete documentation. """ - from .statesp import StateSpace - # - # If the linearization is not defined by the subclass, perform a - # numerical linearization use the `_rhs()` and `_out()` member - # functions. + # Default method: if the linearization is not defined by the + # subclass, perform a numerical linearization use the `_rhs()` and + # `_out()` member functions. # + from .statesp import StateSpace + + # Allow first argument to be an operating point + if isinstance(x0, OperatingPoint): + u0 = x0.inputs if u0 is None else u0 + x0 = x0.states + elif u0 is None: + u0 = 0 + # Process nominal states and inputs x0, nstates = _process_vector_argument(x0, "x0", self.nstates) u0, ninputs = _process_vector_argument(u0, "u0", self.ninputs) @@ -581,14 +590,60 @@ class InterconnectedSystem(NonlinearIOSystem): """Interconnection of a set of input/output systems. This class is used to implement a system that is an interconnection of - input/output systems. The sys consists of a collection of subsystems + input/output systems. The system consists of a collection of subsystems whose inputs and outputs are connected via a connection map. The overall system inputs and outputs are subsets of the subsystem inputs and outputs. - The function :func:`~control.interconnect` should be used to create an + The `interconnect` factory function should be used to create an interconnected I/O system since it performs additional argument processing and checking. + Parameters + ---------- + syslist : list of `NonlinearIOSystem` + List of state space systems to interconnect. + connections : list of connections + Description of the internal connections between the subsystem. See + `interconnect` for details. + inplist, outlist : list of input and output connections + Description of the inputs and outputs for the overall system. See + `interconnect` for details. + inputs, outputs, states : int, list of str or None, optional + Description of the system inputs, outputs, and states. See + `control.nlsys` for more details. + params : dict, optional + Parameter values for the systems. Passed to the evaluation functions + for the system as default values, overriding internal defaults. + connection_type : str + Type of connection: 'explicit' (or None) for explicitly listed + set of connections, 'implicit' for connections made via signal names. + + Attributes + ---------- + ninputs, noutputs, nstates : int + Number of input, output and state variables. + shape : tuple + 2-tuple of I/O system dimension, (noutputs, ninputs). + name : string, optional + System name. + connect_map : 2D array + Mapping of subsystem outputs to subsystem inputs. + input_map : 2D array + Mapping of system inputs to subsystem inputs. + output_map : 2D array + Mapping of (stacked) subsystem outputs and inputs to system outputs. + input_labels, output_labels, state_labels : list of str + Names for the input, output, and state variables. + input_offset, output_offset, state_offset : list of int + Offset to the subsystem inputs, outputs, and states in the overall + system input, output, and state arrays. + syslist_index : dict + Index of the subsystem with key given by the name of the subsystem. + + See Also + -------- + interconnect, NonlinearIOSystem, LinearICSystem + """ def __init__(self, syslist, connections=None, inplist=None, outlist=None, params=None, warn_duplicate=None, connection_type=None, @@ -706,6 +761,11 @@ def __init__(self, syslist, connections=None, inplist=None, outlist=None, if outputs is None and outlist is not None: outputs = len(outlist) + if params is None: + params = {} + for sys in self.syslist: + params = params | sys.params + # Create updfcn and outfcn def updfcn(t, x, u, params): self._update_params(params) @@ -769,13 +829,78 @@ def outfcn(t, x, u, params): index + "; combining with previous entries") self.output_map[index + j, ylist_index] += gain - def _update_params(self, params, warning=False): + def __str__(self): + import textwrap + out = InputOutputSystem.__str__(self) + + out += f"\n\nSubsystems ({len(self.syslist)}):\n" + for sys in self.syslist: + out += "\n".join(textwrap.wrap( + iosys_repr(sys, format='info'), width=78, + initial_indent=" * ", subsequent_indent=" ")) + "\n" + + # Build a list of input, output, and inpout signals + input_list, output_list, inpout_list = [], [], [] + for sys in self.syslist: + input_list += [sys.name + "." + lbl for lbl in sys.input_labels] + output_list += [sys.name + "." + lbl for lbl in sys.output_labels] + inpout_list = input_list + output_list + + # Define a utility function to generate the signal + def cxn_string(signal, gain, first): + if gain == 1: + return (" + " if not first else "") + f"{signal}" + elif gain == -1: + return (" - " if not first else "-") + f"{signal}" + elif gain > 0: + return (" + " if not first else "") + f"{gain} * {signal}" + elif gain < 0: + return (" - " if not first else "-") + \ + f"{abs(gain)} * {signal}" + + out += "\nConnections:\n" + for i in range(len(input_list)): + first = True + cxn = f"{input_list[i]} <- " + if np.any(self.connect_map[i]): + for j in range(len(output_list)): + if self.connect_map[i, j]: + cxn += cxn_string( + output_list[j], self.connect_map[i,j], first) + first = False + if np.any(self.input_map[i]): + for j in range(len(self.input_labels)): + if self.input_map[i, j]: + cxn += cxn_string( + self.input_labels[j], self.input_map[i, j], first) + first = False + out += "\n".join(textwrap.wrap( + cxn, width=78, initial_indent=" * ", + subsequent_indent=" ")) + "\n" + + out += "\nOutputs:" + for i in range(len(self.output_labels)): + first = True + cxn = f"{self.output_labels[i]} <- " + if np.any(self.output_map[i]): + for j in range(len(inpout_list)): + if self.output_map[i, j]: + cxn += cxn_string( + output_list[j], self.output_map[i, j], first) + first = False + out += "\n" + "\n".join(textwrap.wrap( + cxn, width=78, initial_indent=" * ", + subsequent_indent=" ")) + + return out + + def _update_params(self, params): for sys in self.syslist: local = sys.params.copy() # start with system parameters local.update(self.params) # update with global params if params: local.update(params) # update with locally passed parameters - sys._update_params(local, warning=warning) + sys._update_params(local) def _rhs(self, t, x, u): # Make sure state and input are vectors @@ -812,6 +937,7 @@ def _out(self, t, x, u): # Make the full set of subsystem outputs to system output return self.output_map @ ylist + # Find steady state (static) inputs and outputs def _compute_static_io(self, t, x, u): # Figure out the total number of inputs and outputs (ninputs, noutputs) = self.connect_map.shape @@ -968,11 +1094,10 @@ def set_output_map(self, output_map): self.noutputs = output_map.shape[0] def unused_signals(self): - """Find unused subsystem inputs and outputs + """Find unused subsystem inputs and outputs. Returns ------- - unused_inputs : dict A mapping from tuple of indices (isys, isig) to string '{sys}.{sig}', for all unused subsystem inputs. @@ -1007,18 +1132,18 @@ def unused_signals(self): {outputs[i][:2]: outputs[i][2] for i in unused_sysout}) def connection_table(self, show_names=False, column_width=32): - """Print table of connections inside an interconnected system model. + """Table of connections inside an interconnected system. - Intended primarily for :class:`InterconnectedSystems` that have been + Intended primarily for `InterconnectedSystem`'s that have been connected implicitly using signal names. Parameters ---------- show_names : bool, optional - Instead of printing out the system number, print out the name of - each system. Default is False because system name is not usually - specified when performing implicit interconnection using - :func:`interconnect`. + Instead of printing out the system number, print out the name + of each system. Default is False because system name is not + usually specified when performing implicit interconnection + using `interconnect`. column_width : int, optional Character width of printed columns. @@ -1033,6 +1158,7 @@ def connection_table(self, show_names=False, column_width=32): e | input | C u | C | P y | P | output + """ print('signal'.ljust(10) + '| source'.ljust(column_width) + \ @@ -1106,13 +1232,14 @@ def _find_outputs_by_basename(self, basename): for sig, isig in sys.output_index.items() if sig == (basename)} + # TODO: change to internal function? (not sure users need to see this) def check_unused_signals( - self, ignore_inputs=None, ignore_outputs=None, warning=True): - """Check for unused subsystem inputs and outputs + self, ignore_inputs=None, ignore_outputs=None, print_warning=True): + """Check for unused subsystem inputs and outputs. Check to see if there are any unused signals and return a list of - unused input and output signal descriptions. If `warning` is True - and any unused inputs or outputs are found, emit a warning. + unused input and output signal descriptions. If `warning` is + True and any unused inputs or outputs are found, emit a warning. Parameters ---------- @@ -1130,13 +1257,16 @@ def check_unused_signals( If the 'sig' form is used, all subsystem outputs with that name are considered ignored. + print_warning : bool, optional + If True, print a warning listing any unused signals. + Returns ------- - dropped_inputs: list of tuples + dropped_inputs : list of tuples A list of the dropped input signals, with each element of the list in the form of (isys, isig). - dropped_outputs: list of tuples + dropped_outputs : list of tuples A list of the dropped output signals, with each element of the list in the form of (osys, osig). @@ -1186,25 +1316,25 @@ def check_unused_signals( used_ignored_inputs = set(ignore_input_map) - set(unused_inputs) used_ignored_outputs = set(ignore_output_map) - set(unused_outputs) - if warning and dropped_inputs: + if print_warning and dropped_inputs: msg = ('Unused input(s) in InterconnectedSystem: ' + '; '.join(f'{inp}={unused_inputs[inp]}' for inp in dropped_inputs)) warn(msg) - if warning and dropped_outputs: + if print_warning and dropped_outputs: msg = ('Unused output(s) in InterconnectedSystem: ' + '; '.join(f'{out} : {unused_outputs[out]}' for out in dropped_outputs)) warn(msg) - if warning and used_ignored_inputs: + if print_warning and used_ignored_inputs: msg = ('Input(s) specified as ignored is (are) used: ' + '; '.join(f'{inp} : {ignore_input_map[inp]}' for inp in used_ignored_inputs)) warn(msg) - if warning and used_ignored_outputs: + if print_warning and used_ignored_outputs: msg = ('Output(s) specified as ignored is (are) used: ' + '; '.join(f'{out}={ignore_output_map[out]}' for out in used_ignored_outputs)) @@ -1213,39 +1343,42 @@ def check_unused_signals( return dropped_inputs, dropped_outputs -def nlsys( - updfcn, outfcn=None, inputs=None, outputs=None, states=None, **kwargs): +def nlsys(updfcn, outfcn=None, **kwargs): """Create a nonlinear input/output system. - Creates an :class:`~control.InputOutputSystem` for a nonlinear system by - specifying a state update function and an output function. The new system - can be a continuous or discrete time system. + Creates an `InputOutputSystem` for a nonlinear system by specifying a + state update function and an output function. The new system can be a + continuous or discrete-time system. Parameters ---------- - updfcn : callable + updfcn : callable (or `StateSpace`) Function returning the state update function - `updfcn(t, x, u, params) -> array` + ``updfcn(t, x, u, params) -> array`` where `x` is a 1-D array with shape (nstates,), `u` is a 1-D array - with shape (ninputs,), `t` is a float representing the currrent + with shape (ninputs,), `t` is a float representing the current time, and `params` is a dict containing the values of parameters used by the function. + If a `StateSpace` system is passed as the update function, + then a nonlinear I/O system is created that implements the linear + dynamics of the state space system. + outfcn : callable Function returning the output at the given state - `outfcn(t, x, u, params) -> array` + ``outfcn(t, x, u, params) -> array`` - where the arguments are the same as for `upfcn`. + where the arguments are the same as for `updfcn`. inputs : int, list of str or None, optional Description of the system inputs. This can be given as an integer count or as a list of strings that name the individual signals. If an integer count is specified, the names of the signal will be - of the form `s[i]` (where `s` is one of `u`, `y`, or `x`). If - this parameter is not given or given as `None`, the relevant + of the form 's[i]' (where 's' is one of 'u', 'y', or 'x'). If + this parameter is not given or given as None, the relevant quantity will be determined when possible based on other information provided to functions using the system. @@ -1260,25 +1393,30 @@ def nlsys( operating in continuous or discrete time. It can have the following values: - * dt = 0: continuous time system (default) - * dt > 0: discrete time system with sampling period 'dt' - * dt = True: discrete time with unspecified sampling period - * dt = None: no timebase specified + * `dt` = 0: continuous-time system (default) + * `dt` > 0: discrete-time system with sampling period `dt` + * `dt` = True: discrete time with unspecified sampling period + * `dt` = None: no timebase specified name : string, optional System name (used for specifying signals). If unspecified, a - generic name is generated with a unique integer id. + generic name 'sys[id]' is generated with a unique integer id. params : dict, optional - Parameter values for the systems. Passed to the evaluation - functions for the system as default values, overriding internal - defaults. + Parameter values for the system. Passed to the evaluation functions + for the system as default values, overriding internal defaults. Returns ------- - sys : :class:`NonlinearIOSystem` + sys : `NonlinearIOSystem` Nonlinear input/output system. + Other Parameters + ---------------- + input_prefix, output_prefix, state_prefix : string, optional + Set the prefix for input, output, and state signals. Defaults = + 'u', 'y', 'x'. + See Also -------- ss, tf @@ -1286,7 +1424,7 @@ def nlsys( Examples -------- >>> def kincar_update(t, x, u, params): - ... l = params.get('l', 1) # wheelbase + ... l = params['l'] # wheelbase ... return np.array([ ... np.cos(x[2]) * u[0], # x velocity ... np.sin(x[2]) * u[0], # y velocity @@ -1297,20 +1435,46 @@ def nlsys( ... return x[0:2] # x, y position >>> >>> kincar = ct.nlsys( - ... kincar_update, kincar_output, states=3, inputs=2, outputs=2) + ... kincar_update, kincar_output, states=3, inputs=2, outputs=2, + ... params={'l': 1}) >>> >>> timepts = np.linspace(0, 10) >>> response = ct.input_output_response( ... kincar, timepts, [10, 0.05 * np.sin(timepts)]) + """ - return NonlinearIOSystem( - updfcn, outfcn, inputs=inputs, outputs=outputs, states=states, **kwargs) + from .iosys import _extended_system_name + from .statesp import StateSpace + + if isinstance(updfcn, StateSpace): + sys_ss = updfcn + kwargs['inputs'] = kwargs.get('inputs', sys_ss.input_labels) + kwargs['outputs'] = kwargs.get('outputs', sys_ss.output_labels) + kwargs['states'] = kwargs.get('states', sys_ss.state_labels) + kwargs['name'] = kwargs.get('name', _extended_system_name( + sys_ss.name, prefix_suffix_name='converted')) + + sys_nl = NonlinearIOSystem( + lambda t, x, u, params: + sys_ss.A @ np.atleast_1d(x) + sys_ss.B @ np.atleast_1d(u), + lambda t, x, u, params: + sys_ss.C @ np.atleast_1d(x) + sys_ss.D @ np.atleast_1d(u), + **kwargs) + + if sys_nl.nstates != sys_ss.nstates or sys_nl.shape != sys_ss.shape: + raise ValueError( + "new input, output, or state specification " + "doesn't match system size") + + return sys_nl + else: + return NonlinearIOSystem(updfcn, outfcn, **kwargs) def input_output_response( - sys, T, U=0., X0=0, params=None, ignore_errors=False, - transpose=False, return_x=False, squeeze=None, - solve_ivp_kwargs=None, t_eval='T', **kwargs): + sys, timepts=None, inputs=0., initial_state=0., params=None, + ignore_errors=False, transpose=False, return_states=False, + squeeze=None, solve_ivp_kwargs=None, evaluation_times='T', **kwargs): """Compute the output response of a system to a given input. Simulate a dynamical system with a given input and return its output @@ -1318,108 +1482,119 @@ def input_output_response( Parameters ---------- - sys : NonlinearIOSystem or list of NonlinearIOSystem + sys : `NonlinearIOSystem` or list of `NonlinearIOSystem` I/O system(s) for which input/output response is simulated. - - T : array-like + timepts (or T) : array_like Time steps at which the input is defined; values must be evenly spaced. - - U : array-like, list, or number, optional - Input array giving input at each time `T` (default = 0). If a list - is specified, each element in the list will be treated as a portion - of the input and broadcast (if necessary) to match the time vector. - - X0 : array-like, list, or number, optional + inputs (or U) : array_like, list, or number, optional + Input array giving input at each time in `timepts` (default = + 0). If a list is specified, each element in the list will be + treated as a portion of the input and broadcast (if necessary) to + match the time vector. + initial_state (or X0) : array_like, list, or number, optional Initial condition (default = 0). If a list is given, each element in the list will be flattened and stacked into the initial condition. If a smaller number of elements are given that the number of states in the system, the initial condition will be padded with zeros. - - t_eval : array-list, optional + evaluation_times (or t_eval) : array-list, optional List of times at which the time response should be computed. - Defaults to ``T``. - - return_x : bool, optional - If True, return the state vector when assigning to a tuple (default = - False). See :func:`forced_response` for more details. - If True, return the values of the state at each time (default = False). - + Defaults to `timepts`. + return_states (or return_x) : bool, optional + If True, return the state vector when assigning to a tuple. See + `forced_response` for more details. If True, return the values of + the state at each time Default is False. + params : dict, optional + Parameter values for the system. Passed to the evaluation functions + for the system as default values, overriding internal defaults. squeeze : bool, optional If True and if the system has a single output, return the system output as a 1D array rather than a 2D array. If False, return the - system output as a 2D array even if the system is SISO. Default value - set by config.defaults['control.squeeze_time_response']. + system output as a 2D array even if the system is SISO. Default + value set by `config.defaults['control.squeeze_time_response']`. Returns ------- - results : TimeResponseData - Time response represented as a :class:`TimeResponseData` object - containing the following properties: - - * time (array): Time values of the output. + response : `TimeResponseData` + Time response data object representing the input/output response. + When accessed as a tuple, returns ``(time, outputs)`` or ``(time, + outputs, states`` if `return_x` is True. If the input/output system + signals are named, these names will be used as labels for the time + response. If `sys` is a list of systems, returns a `TimeResponseList` + object. Results can be plotted using the `~TimeResponseData.plot` + method. See `TimeResponseData` for more detailed information. + response.time : array + Time values of the output. + response.outputs : array + Response of the system. If the system is SISO and `squeeze` is not + True, the array is 1D (indexed by time). If the system is not SISO + or `squeeze` is False, the array is 2D (indexed by output and time). + response.states : array + Time evolution of the state vector, represented as a 2D array + indexed by state and time. + response.inputs : array + Input(s) to the system, indexed by input and time. + response.params : dict + Parameters values used for the simulation. - * outputs (array): Response of the system. If the system is SISO and - `squeeze` is not True, the array is 1D (indexed by time). If the - system is not SISO or `squeeze` is False, the array is 2D (indexed - by output and time). - - * states (array): Time evolution of the state vector, represented as - a 2D array indexed by state and time. - - * inputs (array): Input(s) to the system, indexed by input and time. - - * params (dict): Parameters values used for the simulation. - - The return value of the system can also be accessed by assigning the - function to a tuple of length 2 (time, output) or of length 3 (time, - output, state) if ``return_x`` is ``True``. If the input/output - system signals are named, these names will be used as labels for the - time response. - - Other parameters + Other Parameters ---------------- + ignore_errors : bool, optional + If False (default), errors during computation of the trajectory + will raise a `RuntimeError` exception. If True, do not raise + an exception and instead set `response.success` to False and + place an error message in `response.message`. solve_ivp_method : str, optional - Set the method used by :func:`scipy.integrate.solve_ivp`. Defaults + Set the method used by `scipy.integrate.solve_ivp`. Defaults to 'RK45'. solve_ivp_kwargs : dict, optional - Pass additional keywords to :func:`scipy.integrate.solve_ivp`. - ignore_errors : bool, optional - If ``False`` (default), errors during computation of the trajectory - will raise a ``RuntimeError`` exception. If ``True``, do not raise - an exception and instead set ``results.success`` to ``False`` and - place an error message in ``results.message``. + Pass additional keywords to `scipy.integrate.solve_ivp`. + transpose : bool, default=False + If True, transpose all input and output arrays (for backward + compatibility with MATLAB and `scipy.signal.lsim`). Raises ------ TypeError If the system is not an input/output system. ValueError - If time step does not match sampling time (for discrete time systems). + If time step does not match sampling time (for discrete-time systems). Notes ----- - 1. If a smaller number of initial conditions are given than the number of - states in the system, the initial conditions will be padded with - zeros. This is often useful for interconnected control systems where - the process dynamics are the first system and all other components - start with zero initial condition since this can be specified as - [xsys_0, 0]. A warning is issued if the initial conditions are padded - and and the final listed initial state is not zero. - - 2. If discontinuous inputs are given, the underlying SciPy numerical - integration algorithms can sometimes produce erroneous results due - to the default tolerances that are used. The `ivp_method` and - `ivp_keywords` parameters can be used to tune the ODE solver and - produce better results. In particular, using 'LSODA' as the - `ivp_method` or setting the `rtol` parameter to a smaller value - (e.g. using `ivp_kwargs={'rtol': 1e-4}`) can provide more accurate - results. + If a smaller number of initial conditions are given than the number of + states in the system, the initial conditions will be padded with zeros. + This is often useful for interconnected control systems where the + process dynamics are the first system and all other components start + with zero initial condition since this can be specified as [xsys_0, 0]. + A warning is issued if the initial conditions are padded and and the + final listed initial state is not zero. + + If discontinuous inputs are given, the underlying SciPy numerical + integration algorithms can sometimes produce erroneous results due to + the default tolerances that are used. The `ivp_method` and + `ivp_keywords` parameters can be used to tune the ODE solver and + produce better results. In particular, using 'LSODA' as the + `ivp_method` or setting the `rtol` parameter to a smaller value + (e.g. using ``ivp_kwargs={'rtol': 1e-4}``) can provide more accurate + results. """ # # Process keyword arguments # + _process_kwargs(kwargs, _timeresp_aliases) + T = _process_param('timepts', timepts, kwargs, _timeresp_aliases) + U = _process_param('inputs', inputs, kwargs, _timeresp_aliases, sigval=0.) + X0 = _process_param( + 'initial_state', initial_state, kwargs, _timeresp_aliases, sigval=0.) + return_x = _process_param( + 'return_states', return_states, kwargs, _timeresp_aliases, + sigval=False) + # TODO: replace default value of evaluation_times with None? + t_eval = _process_param( + 'evaluation_times', evaluation_times, kwargs, _timeresp_aliases, + sigval='T') # Figure out the method to be used solve_ivp_kwargs = solve_ivp_kwargs.copy() if solve_ivp_kwargs else {} @@ -1444,9 +1619,10 @@ def input_output_response( sysdata, responses = sys, [] for sys in sysdata: responses.append(input_output_response( - sys, T, U=U, X0=X0, params=params, transpose=transpose, - return_x=return_x, squeeze=squeeze, t_eval=t_eval, - solve_ivp_kwargs=solve_ivp_kwargs, **kwargs)) + sys, timepts=T, inputs=U, initial_state=X0, params=params, + transpose=transpose, return_states=return_x, squeeze=squeeze, + evaluation_times=t_eval, solve_ivp_kwargs=solve_ivp_kwargs, + **kwargs)) return TimeResponseList(responses) # Sanity checking on the input @@ -1481,7 +1657,7 @@ def input_output_response( if isinstance(U, (tuple, list)) and len(U) != ntimepts: U_elements = [] for i, u in enumerate(U): - u = np.array(u) # convert everyting to an array + u = np.array(u) # convert everything to an array # Process this input if u.ndim == 0 or (u.ndim == 1 and u.shape[0] != T.shape[0]): # Broadcast array to the length of the time input @@ -1517,7 +1693,7 @@ def input_output_response( legal_shapes = [(ninputs, ntimepts)] U = _check_convert_array( - U, legal_shapes, 'Parameter ``U``: ', squeeze=False) + U, legal_shapes, 'Parameter `U`: ', squeeze=False) # Always store the input as a 2D array U = U.reshape(-1, ntimepts) @@ -1555,8 +1731,8 @@ def ufun(t): dt = (t - T[idx-1]) / (T[idx] - T[idx-1]) return U[..., idx-1] * (1. - dt) + U[..., idx] * dt - # Check to make sure this is not a static function - if nstates == 0: # No states => map input to output + # Check to make sure see if this is a static function + if sys.nstates == 0: # Make sure the user gave a time vector for evaluation (or 'T') if t_eval is None: # User overrode t_eval with None, but didn't give us the times... @@ -1612,7 +1788,7 @@ def ivp_rhs(t, x): # Make sure the time vector is uniformly spaced dt = t_eval[1] - t_eval[0] if not np.allclose(t_eval[1:] - t_eval[:-1], dt): - raise ValueError("parameter ``t_eval``: time values must be " + raise ValueError("parameter `t_eval`: time values must be " "equally spaced") # Make sure the sample time matches the given time @@ -1623,11 +1799,11 @@ def ivp_rhs(t, x): # TODO: this test is brittle if dt = sys.dt # First make sure that time increment is bigger than sampling time # if dt < sys.dt: - # raise ValueError("Time steps ``T`` must match sampling time") + # raise ValueError("Time steps `T` must match sampling time") # Check to make sure sampling time matches time increments if not np.isclose(dt, sys.dt): - raise ValueError("Time steps ``T`` must be equal to " + raise ValueError("Time steps `T` must be equal to " "sampling time") # Compute the solution @@ -1665,89 +1841,230 @@ def ivp_rhs(t, x): success=soln.success, message=message) -def find_eqpt(sys, x0, u0=None, y0=None, t=0, params=None, - iu=None, iy=None, ix=None, idx=None, dx0=None, - return_y=False, return_result=False): - """Find the equilibrium point for an input/output system. +class OperatingPoint(): + """Operating point of nonlinear I/O system. + + The OperatingPoint class stores the operating point of a nonlinear + system, consisting of the state and input vectors for the system. The + main use for this class is as the return object for the + `find_operating_point` function and as an input to the + `linearize` function. + + Parameters + ---------- + states : array + State vector at the operating point. + inputs : array + Input vector at the operating point. + outputs : array, optional + Output vector at the operating point. + result : `scipy.optimize.OptimizeResult`, optional + Result from the `scipy.optimize.root` function, if available. + return_outputs, return_result : bool, optional + If set to True, then when accessed a tuple the output values + and/or result of the root finding function will be returned. + + Notes + ----- + In addition to accessing the elements of the operating point as + attributes, if accessed as a list then the object will return ``(x0, + u0[, y0, res])``, where `y0` and `res` are returned depending on the + `return_outputs` and `return_result` parameters. + + """ + def __init__( + self, states, inputs, outputs=None, result=None, + return_outputs=False, return_result=False): + self.states = states + self.inputs = inputs + + if outputs is None and return_outputs and not return_result: + raise ValueError("return_outputs specified but no outputs value") + self.outputs = outputs + self.return_outputs = return_outputs + + if result is None and return_result: + raise ValueError("return_result specified but no result value") + self.result = result + self.return_result = return_result + + # Implement iter to allow assigning to a tuple + def __iter__(self): + if self.return_outputs and self.return_result: + return iter((self.states, self.inputs, self.outputs, self.result)) + elif self.return_outputs: + return iter((self.states, self.inputs, self.outputs)) + elif self.return_result: + return iter((self.states, self.inputs, self.result)) + else: + return iter((self.states, self.inputs)) + + # Implement (thin) getitem to allow access via legacy indexing + def __getitem__(self, index): + return list(self.__iter__())[index] + + # Implement (thin) len to emulate legacy return value + def __len__(self): + return len(list(self.__iter__())) + + +def find_operating_point( + sys, initial_state=0., inputs=None, outputs=None, t=0, params=None, + input_indices=None, output_indices=None, state_indices=None, + deriv_indices=None, derivs=None, root_method=None, root_kwargs=None, + return_outputs=None, return_result=None, **kwargs): + """Find an operating point for an input/output system. + + An operating point for a nonlinear system is a state and input around + which a nonlinear system operates. This point is most commonly an + equilibrium point for the system, but in some cases a non-equilibrium + operating point can be used. + + This function attempts to find an operating point given a specification + for the desired inputs, outputs, states, or state updates of the system. + + In its simplest form, `find_operating_point` finds an equilibrium point + given either the desired input or desired output:: - Returns the value of an equilibrium point given the initial state and - either input value or desired output value for the equilibrium point. + xeq, ueq = find_operating_point(sys, x0, u0) + xeq, ueq = find_operating_point(sys, x0, u0, y0) + + The first form finds an equilibrium point for a given input u0 based on + an initial guess x0. The second form fixes the desired output values + and uses x0 and u0 as an initial guess to find the equilibrium point. + If no equilibrium point can be found, the function returns the + operating point that minimizes the state update (state derivative for + continuous-time systems, state difference for discrete-time systems). + + More complex operating points can be found by specifying which states, + inputs, or outputs should be used in computing the operating point, as + well as desired values of the states, inputs, outputs, or state + updates. Parameters ---------- - x0 : list of initial state values - Initial guess for the value of the state near the equilibrium point. - u0 : list of input values, optional - If `y0` is not specified, sets the equilibrium value of the input. If - `y0` is given, provides an initial guess for the value of the input. - Can be omitted if the system does not have any inputs. - y0 : list of output values, optional + sys : `NonlinearIOSystem` + I/O system for which the operating point is sought. + initial_state (or x0) : list of initial state values + Initial guess for the value of the state near the operating point. + inputs (or u0) : list of input values, optional + If `y0` is not specified, sets the value of the input. If `y0` is + given, provides an initial guess for the value of the input. Can + be omitted if the system does not have any inputs. + outputs (or y0) : list of output values, optional If specified, sets the desired values of the outputs at the - equilibrium point. + operating point. t : float, optional - Evaluation time, for time-varying systems + Evaluation time, for time-varying systems. params : dict, optional Parameter values for the system. Passed to the evaluation functions for the system as default values, overriding internal defaults. - iu : list of input indices, optional + input_indices (or iu) : list of input indices, optional If specified, only the inputs with the given indices will be fixed at - the specified values in solving for an equilibrium point. All other + the specified values in solving for an operating point. All other inputs will be varied. Input indices can be listed in any order. - iy : list of output indices, optional - If specified, only the outputs with the given indices will be fixed at - the specified values in solving for an equilibrium point. All other + output_indices (or iy) : list of output indices, optional + If specified, only the outputs with the given indices will be fixed + at the specified values in solving for an operating point. All other outputs will be varied. Output indices can be listed in any order. - ix : list of state indices, optional + state_indices (or ix) : list of state indices, optional If specified, states with the given indices will be fixed at the - specified values in solving for an equilibrium point. All other + specified values in solving for an operating point. All other states will be varied. State indices can be listed in any order. - dx0 : list of update values, optional + derivs (or dx0) : list of update values, optional If specified, the value of update map must match the listed value - instead of the default value of 0. - idx : list of state indices, optional + instead of the default value for an equilibrium point. + deriv_indices (or idx) : list of state indices, optional If specified, state updates with the given indices will have their update maps fixed at the values given in `dx0`. All other update - values will be ignored in solving for an equilibrium point. State + values will be ignored in solving for an operating point. State indices can be listed in any order. By default, all updates will be - fixed at `dx0` in searching for an equilibrium point. - return_y : bool, optional - If True, return the value of output at the equilibrium point. + fixed at `dx0` in searching for an operating point. + root_method : str, optional + Method to find the operating point. If specified, this parameter + is passed to the `scipy.optimize.root` function. + root_kwargs : dict, optional + Additional keyword arguments to pass `scipy.optimize.root`. + return_outputs : bool, optional + If True, return the value of outputs at the operating point. return_result : bool, optional If True, return the `result` option from the - :func:`scipy.optimize.root` function used to compute the equilibrium - point. + `scipy.optimize.root` function used to compute the + operating point. Returns ------- - xeq : array of states - Value of the states at the equilibrium point, or `None` if no - equilibrium point was found and `return_result` was False. - ueq : array of input values - Value of the inputs at the equilibrium point, or `None` if no - equilibrium point was found and `return_result` was False. - yeq : array of output values, optional - If `return_y` is True, returns the value of the outputs at the - equilibrium point, or `None` if no equilibrium point was found and - `return_result` was False. - result : :class:`scipy.optimize.OptimizeResult`, optional - If `return_result` is True, returns the `result` from the - :func:`scipy.optimize.root` function. + op_point : `OperatingPoint` + The solution represented as an `OperatingPoint` object. The main + attributes are `states` and `inputs`, which represent the state and + input arrays at the operating point. If accessed as a tuple, returns + `states`, `inputs`, and optionally `outputs` and `result` based on the + `return_outputs` and `return_result` parameters. See `OperatingPoint` + for a description of other attributes. + op_point.states : array + State vector at the operating point. + op_point.inputs : array + Input vector at the operating point. + op_point.outputs : array, optional + Output vector at the operating point. Notes ----- - For continuous time systems, equilibrium points are defined as points for - which the right hand side of the differential equation is zero: - :math:`f(t, x_e, u_e) = 0`. For discrete time systems, equilibrium points - are defined as points for which the right hand side of the difference - equation returns the current state: :math:`f(t, x_e, u_e) = x_e`. + For continuous-time systems, equilibrium points are defined as points + for which the right hand side of the differential equation is zero: + :math:`f(t, x_e, u_e) = 0`. For discrete-time systems, equilibrium + points are defined as points for which the right hand side of the + difference equation returns the current state: :math:`f(t, x_e, u_e) = + x_e`. + + Operating points are found using the `scipy.optimize.root` + function, which will attempt to find states and inputs that satisfy the + specified constraints. If no solution is found and `return_result` is + False, the returned state and input for the operating point will be + None. If `return_result` is True, then the return values from + `scipy.optimize.root` will be returned (but may not be valid). + If `root_method` is set to 'lm', then the least squares solution (in + the free variables) will be returned. """ from scipy.optimize import root + # Process keyword arguments + aliases = { + 'initial_state': (['x0', 'X0'], []), + 'inputs': (['u0'], []), + 'outputs': (['y0'], []), + 'derivs': (['dx0'], []), + 'input_indices': (['iu'], []), + 'output_indices': (['iy'], []), + 'state_indices': (['ix'], []), + 'deriv_indices': (['idx'], []), + 'return_outputs': ([], ['return_y']), + } + _process_kwargs(kwargs, aliases) + x0 = _process_param( + 'initial_state', initial_state, kwargs, aliases, sigval=0.) + u0 = _process_param('inputs', inputs, kwargs, aliases) + y0 = _process_param('outputs', outputs, kwargs, aliases) + dx0 = _process_param('derivs', derivs, kwargs, aliases) + iu = _process_param('input_indices', input_indices, kwargs, aliases) + iy = _process_param('output_indices', output_indices, kwargs, aliases) + ix = _process_param('state_indices', state_indices, kwargs, aliases) + idx = _process_param('deriv_indices', deriv_indices, kwargs, aliases) + return_outputs = _process_param( + 'return_outputs', return_outputs, kwargs, aliases) + if kwargs: + raise TypeError("unrecognized keyword(s): " + str(kwargs)) + + # Process arguments for the root function + root_kwargs = dict() if root_kwargs is None else root_kwargs + if root_method: + root_kwargs['method'] = root_method + # Figure out the number of states, inputs, and outputs - x0, nstates = _process_vector_argument(x0, "x0", sys.nstates) - u0, ninputs = _process_vector_argument(u0, "u0", sys.ninputs) - y0, noutputs = _process_vector_argument(y0, "y0", sys.noutputs) + x0, nstates = _process_vector_argument(x0, "initial_states", sys.nstates) + u0, ninputs = _process_vector_argument(u0, "inputs", sys.ninputs) + y0, noutputs = _process_vector_argument(y0, "outputs", sys.noutputs) # Make sure the input arguments match the sizes of the system if len(x0) != nstates or \ @@ -1769,7 +2086,7 @@ def state_rhs(z): return sys._rhs(t, z, u0) - z else: def state_rhs(z): return sys._rhs(t, z, u0) - result = root(state_rhs, x0) + result = root(state_rhs, x0, **root_kwargs) z = (result.x, u0, sys._out(t, result.x, u0)) else: @@ -1786,9 +2103,10 @@ def rootfun(z): return np.concatenate( (sys._rhs(t, x, u), sys._out(t, x, u) - y0), axis=0) - z0 = np.concatenate((x0, u0), axis=0) # Put variables together - result = root(rootfun, z0) # Find the eq point - x, u = np.split(result.x, [nstates]) # Split result back in two + # Find roots with (x, u) as free variables + z0 = np.concatenate((x0, u0), axis=0) + result = root(rootfun, z0, **root_kwargs) + x, u = np.split(result.x, [nstates]) z = (x, u, sys._out(t, x, u)) else: @@ -1828,10 +2146,10 @@ def rootfun(z): # Construct the index lists for mapping variables and constraints # - # The mechanism by which we implement the root finding function is to - # map the subset of variables we are searching over into the inputs - # and states, and then return a function that represents the equations - # we are trying to solve. + # The mechanism by which we implement the root finding function is + # to map the subset of variables we are searching over into the + # inputs and states, and then return a function that represents the + # equations we are trying to solve. # # To do this, we need to carry out the following operations: # @@ -1849,8 +2167,8 @@ def rootfun(z): # * output_vars: indices of outputs that must be constrained # # This index lists can all be precomputed based on the `iu`, `iy`, - # `ix`, and `idx` lists that were passed as arguments to `find_eqpt` - # and were processed above. + # `ix`, and `idx` lists that were passed as arguments to + # `find_operating_point` and were processed above. # Get the states and inputs that were not listed as fixed state_vars = (range(nstates) if not len(ix) @@ -1903,7 +2221,7 @@ def rootfun(z): z0 = np.concatenate((x[state_vars], u[input_vars]), axis=0) # Finally, call the root finding function - result = root(rootfun, z0) + result = root(rootfun, z0, **root_kwargs) # Extract out the results and insert into x and u x[state_vars] = result.x[:nstate_vars] @@ -1911,7 +2229,16 @@ def rootfun(z): z = (x, u, sys._out(t, x, u)) # Return the result based on what the user wants and what we found - if not return_y: + if return_result or result.success: + return OperatingPoint( + z[0], z[1], z[2], result, return_outputs, return_result) + else: + # Something went wrong, don't return anything + return OperatingPoint( + None, None, None, result, return_outputs, return_result) + + # TODO: remove code when ready + if not return_outputs: z = z[0:2] # Strip y from result if not desired if return_result: # Return whatever we got, along with the result dictionary @@ -1921,27 +2248,28 @@ def rootfun(z): return z else: # Something went wrong, don't return anything - return (None, None, None) if return_y else (None, None) + return (None, None, None) if return_outputs else (None, None) # Linearize an input/output system def linearize(sys, xeq, ueq=None, t=0, params=None, **kw): """Linearize an input/output system at a given state and input. - This function computes the linearization of an input/output system at a - given state and input value and returns a :class:`~control.StateSpace` - object. The evaluation point need not be an equilibrium point. + Compute the linearization of an I/O system at an operating point (state + and input) and returns a `StateSpace` object. The + operating point need not be an equilibrium point. Parameters ---------- - sys : InputOutputSystem - The system to be linearized - xeq : array - The state at which the linearization will be evaluated (does not need - to be an equilibrium state). - ueq : array + sys : `InputOutputSystem` + The system to be linearized. + xeq : array or `OperatingPoint` + The state or operating point at which the linearization will be + evaluated (does not need to be an equilibrium state). + ueq : array, optional The input at which the linearization will be evaluated (does not need - to correspond to an equlibrium state). + to correspond to an equilibrium state). Can be omitted if `xeq` is + an `OperatingPoint`. Defaults to 0. t : float, optional The time at which the linearization will be computed (for time-varying systems). @@ -1950,11 +2278,11 @@ def linearize(sys, xeq, ueq=None, t=0, params=None, **kw): for the system as default values, overriding internal defaults. name : string, optional Set the name of the linearized system. If not specified and - if `copy_names` is `False`, a generic name is generated - with a unique integer id. If `copy_names` is `True`, the new system + if `copy_names` is False, a generic name 'sys[id]' is generated + with a unique integer id. If `copy_names` is True, the new system name is determined by adding the prefix and suffix strings in - config.defaults['iosys.linearized_system_name_prefix'] and - config.defaults['iosys.linearized_system_name_suffix'], with the + `config.defaults['iosys.linearized_system_name_prefix']` and + `config.defaults['iosys.linearized_system_name_suffix']`, with the default being to add the suffix '$linearized'. copy_names : bool, Optional If True, Copy the names of the input signals, output signals, and @@ -1962,20 +2290,21 @@ def linearize(sys, xeq, ueq=None, t=0, params=None, **kw): Returns ------- - ss_sys : StateSpace - The linearization of the system, as a :class:`~control.StateSpace` + ss_sys : `StateSpace` + The linearization of the system, as a `StateSpace` object. Other Parameters ---------------- inputs : int, list of str or None, optional - Description of the system inputs. If not specified, the origional - system inputs are used. See :class:`InputOutputSystem` for more + Description of the system inputs. If not specified, the original + system inputs are used. See `InputOutputSystem` for more information. outputs : int, list of str or None, optional Description of the system outputs. Same format as `inputs`. states : int, list of str, or None, optional Description of the system states. Same format as `inputs`. + """ if not isinstance(sys, InputOutputSystem): raise TypeError("Can only linearize InputOutputSystem types") @@ -2011,29 +2340,29 @@ def interconnect( This function creates a new system that is an interconnection of a set of input/output systems. If all of the input systems are linear I/O systems - (type :class:`~control.StateSpace`) then the resulting system will be - a linear interconnected I/O system (type :class:`~control.LinearICSystem`) + (type `StateSpace`) then the resulting system will be + a linear interconnected I/O system (type `LinearICSystem`) with the appropriate inputs, outputs, and states. Otherwise, an - interconnected I/O system (type :class:`~control.InterconnectedSystem`) + interconnected I/O system (type `InterconnectedSystem`) will be created. Parameters ---------- - syslist : list of InputOutputSystems - The list of input/output systems to be connected + syslist : list of `NonlinearIOSystem` + The list of (state-based) input/output systems to be connected. connections : list of connections, optional - Description of the internal connections between the subsystems: + Description of the internal connections between the subsystems:: [connection1, connection2, ...] - Each connection is itself a list that describes an input to one of the - subsystems. The entries are of the form: + Each connection is itself a list that describes an input to one of + the subsystems. The entries are of the form:: [input-spec, output-spec1, output-spec2, ...] The input-spec can be in a number of different forms. The lowest - level representation is a tuple of the form `(subsys_i, inp_j)` + level representation is a tuple of the form ``(subsys_i, inp_j)`` where `subsys_i` is the index into `syslist` and `inp_j` is the index into the input vector for the subsystem. If the signal index is omitted, then all subsystem inputs are used. If systems and @@ -2041,38 +2370,38 @@ def interconnect( are also recognized. Finally, for multivariable systems the signal index can be given as a list, for example '(subsys_i, [inp_j1, ..., inp_jn])'; or as a slice, for example, 'sys.sig[i:j]'; or as a base - name `sys.sig` (which matches `sys.sig[i]`). + name 'sys.sig' (which matches 'sys.sig[i]'). Similarly, each output-spec should describe an output signal from one of the subsystems. The lowest level representation is a tuple - of the form `(subsys_i, out_j, gain)`. The input will be + of the form ``(subsys_i, out_j, gain)``. The input will be constructed by summing the listed outputs after multiplying by the gain term. If the gain term is omitted, it is assumed to be 1. If - the subsystem index `subsys_i` is omitted, then all outputs of the + the subsystem index 'subsys_i' is omitted, then all outputs of the subsystem are used. If systems and signals are given names, then the form 'sys.sig', ('sys', 'sig') or ('sys', 'sig', gain) are also recognized, and the special form '-sys.sig' can be used to specify - a signal with gain -1. Lists, slices, and base namess can also be + a signal with gain -1. Lists, slices, and base names can also be used, as long as the number of elements for each output spec - mataches the input spec. + matches the input spec. If omitted, the `interconnect` function will attempt to create the - interconnection map by connecting all signals with the same base names - (ignoring the system name). Specifically, for each input signal name - in the list of systems, if that signal name corresponds to the output - signal in any of the systems, it will be connected to that input (with - a summation across all signals if the output name occurs in more than - one system). - - The `connections` keyword can also be set to `False`, which will leave + interconnection map by connecting all signals with the same base + names (ignoring the system name). Specifically, for each input + signal name in the list of systems, if that signal name corresponds + to the output signal in any of the systems, it will be connected to + that input (with a summation across all signals if the output name + occurs in more than one system). + + The `connections` keyword can also be set to False, which will leave the connection map empty and it can be specified instead using the - low-level :func:`~control.InterconnectedSystem.set_connect_map` + low-level `InterconnectedSystem.set_connect_map` method. inplist : list of input connections, optional List of connections for how the inputs for the overall system are mapped to the subsystem inputs. The entries for a connection are - of the form: + of the form:: [input-spec1, input-spec2, ...] @@ -2086,7 +2415,7 @@ def interconnect( outlist : list of output connections, optional List of connections for how the outputs from the subsystems are mapped to overall system outputs. The entries for a connection are - of the form: + of the form:: [output-spec1, output-spec2, ...] @@ -2095,15 +2424,15 @@ def interconnect( term) to form the system output. If omitted, the output map can be specified using the - :func:`~control.InterconnectedSystem.set_output_map` method. + `InterconnectedSystem.set_output_map` method. inputs : int, list of str or None, optional Description of the system inputs. This can be given as an integer - count or as a list of strings that name the individual signals. If an - integer count is specified, the names of the signal will be of the - form `s[i]` (where `s` is one of `u`, `y`, or `x`). If this parameter - is not given or given as `None`, the relevant quantity will be - determined when possible based on other information provided to + count or as a list of strings that name the individual signals. If + an integer count is specified, the names of the signal will be of + the form 's[i]' (where 's' is one of 'u', 'y', or 'x'). If this + parameter is not given or given as None, the relevant quantity will + be determined when possible based on other information provided to functions using the system. outputs : int, list of str or None, optional @@ -2111,27 +2440,40 @@ def interconnect( states : int, list of str, or None, optional Description of the system states. Same format as `inputs`. The - default is `None`, in which case the states will be given names of the - form '.', for each subsys in syslist and each - state_name of each subsys. + default is None, in which case the states will be given names of + the form '', for each subsys in + syslist and each state_name of each subsys, where is the + value of `config.defaults['iosys.state_name_delim']`. params : dict, optional Parameter values for the systems. Passed to the evaluation functions - for the system as default values, overriding internal defaults. + for the system as default values, overriding internal defaults. If + not specified, defaults to parameters from subsystems. dt : timebase, optional The timebase for the system, used to specify whether the system is - operating in continuous or discrete time. It can have the following + operating in continuous or discrete-time. It can have the following values: - * dt = 0: continuous time system (default) - * dt > 0: discrete time system with sampling period 'dt' - * dt = True: discrete time with unspecified sampling period - * dt = None: no timebase specified + * `dt` = 0: continuous-time system (default) + * `dt` > 0`: discrete-time system with sampling period `dt` + * `dt` = True: discrete time with unspecified sampling period + * `dt` = None: no timebase specified name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. + + Returns + ------- + sys : `InterconnectedSystem` + `NonlinearIOSystem` consisting of the interconnected subsystems. + + Other Parameters + ---------------- + input_prefix, output_prefix, state_prefix : string, optional + Set the prefix for input, output, and state signals. Defaults = + 'u', 'y', 'x'. check_unused : bool, optional If True, check for unused sub-system signals. This check is @@ -2164,15 +2506,14 @@ def interconnect( warn_duplicate : None, True, or False, optional Control how warnings are generated if duplicate objects or names are - detected. In `None` (default), then warnings are generated for - systems that have non-generic names. If `False`, warnings are not - generated and if `True` then warnings are always generated. + detected. In None (default), then warnings are generated for + systems that have non-generic names. If False, warnings are not + generated and if True then warnings are always generated. debug : bool, default=False Print out information about how signals are being processed that may be useful in understanding why something is not working. - Examples -------- >>> P = ct.rss(2, 2, 2, strictly_proper=True, name='P') @@ -2201,7 +2542,7 @@ def interconnect( ... inplist=['C'], outlist=['P']) A feedback system can also be constructed using the - :func:`~control.summing_junction` function and the ability to + `summing_junction` function and the ability to automatically interconnect signals with the same names: >>> P = ct.tf(1, [1, 0], inputs='u', outputs='y') @@ -2214,38 +2555,37 @@ def interconnect( If a system is duplicated in the list of systems to be connected, a warning is generated and a copy of the system is created with the name of the new system determined by adding the prefix and suffix - strings in config.defaults['iosys.duplicate_system_name_prefix'] - and config.defaults['iosys.duplicate_system_name_suffix'], with the + strings in `config.defaults['iosys.duplicate_system_name_prefix']` + and `config.defaults['iosys.duplicate_system_name_suffix']`, with the default being to add the suffix '$copy' to the system name. In addition to explicit lists of system signals, it is possible to lists vectors of signals, using one of the following forms:: - (subsys, [i1, ..., iN], gain) signals with indices i1, ..., in - 'sysname.signal[i:j]' range of signal names, i through j-1 - 'sysname.signal[:]' all signals with given prefix + (subsys, [i1, ..., iN], gain) # signals with indices i1, ..., in + 'sysname.signal[i:j]' # range of signal names, i through j-1 + 'sysname.signal[:]' # all signals with given prefix While in many Python functions tuples can be used in place of lists, for the interconnect() function the only use of tuples should be in the specification of an input- or output-signal via the tuple notation - `(subsys_i, signal_j, gain)` (where `gain` is optional). If you get an + ``(subsys_i, signal_j, gain)`` (where `gain` is optional). If you get an unexpected error message about a specification being of the wrong type or not being found, check to make sure you are not using a tuple where you should be using a list. In addition to its use for general nonlinear I/O systems, the - :func:`~control.interconnect` function allows linear systems to be + `interconnect` function allows linear systems to be interconnected using named signals (compared with the - :func:`~control.connect` function, which uses signal indices) and to be - treated as both a :class:`~control.StateSpace` system as well as an - :class:`~control.InputOutputSystem`. + legacy `connect` function, which uses signal indices) and to be + treated as both a `StateSpace` system as well as an + `InputOutputSystem`. The `input` and `output` keywords can be used instead of `inputs` and `outputs`, for more natural naming of SISO systems. """ - from .statesp import LinearICSystem, StateSpace, _convert_to_statespace - from .xferfcn import TransferFunction + from .statesp import LinearICSystem, StateSpace dt = kwargs.pop('dt', None) # bypass normal 'dt' processing name, inputs, outputs, states, _ = _process_iosys_keywords(kwargs) @@ -2307,7 +2647,7 @@ def interconnect( # This includes signal lists such as ('sysname', ['sig1', 'sig2', ...]) # as well as slice-based specifications such as 'sysname.signal[i:j]'. # - dprint(f"Pre-processing connections:") + dprint("Pre-processing connections:") new_connections = [] for connection in connections: dprint(f" parsing {connection=}") @@ -2346,7 +2686,7 @@ def interconnect( # dprint(f"Pre-processing input connections: {inplist}") if not isinstance(inplist, list): - dprint(f" converting inplist to list") + dprint(" converting inplist to list") inplist = [inplist] new_inplist, new_inputs = [], [] if inplist_none else inputs @@ -2382,7 +2722,7 @@ def interconnect( new_connection.append((isys, isig, gain)) if len(new_connections) == 0: - # First time we have seen this signal => initalize + # First time we have seen this signal => initialize for cnx in new_connection: new_connections.append([cnx]) if inplist_none: @@ -2409,7 +2749,7 @@ def interconnect( else: if isinstance(connection, list): # Passed a list => create input map - dprint(f" detected input list") + dprint(" detected input list") signal_list = [] for spec in connection: isys, indices, gain = _parse_spec(syslist, spec, 'input') @@ -2435,7 +2775,7 @@ def interconnect( # dprint(f"Pre-processing output connections: {outlist}") if not isinstance(outlist, list): - dprint(f" converting outlist to list") + dprint(" converting outlist to list") outlist = [outlist] new_outlist, new_outputs = [], [] if outlist_none else outputs for iout, connection in enumerate(outlist): @@ -2509,17 +2849,17 @@ def _find_output_or_input_signal(spec): (syslist[isys].name, syslist[isys].input_labels[isig], gain)) return signal_list - + if isinstance(connection, list): # Passed a list => create input map - dprint(f" detected output list") + dprint(" detected output list") signal_list = [] for spec in connection: signal_list += _find_output_or_input_signal(spec) new_outlist.append(signal_list) else: new_outlist += _find_output_or_input_signal(connection) - + outlist, outputs = new_outlist, new_outputs dprint(f" {outlist=}\n {outputs=}") @@ -2543,7 +2883,7 @@ def _find_output_or_input_signal(spec): if add_unused: # Get all unused signals dropped_inputs, dropped_outputs = newsys.check_unused_signals( - ignore_inputs, ignore_outputs, warning=False) + ignore_inputs, ignore_outputs, print_warning=False) # Add on any unused signals that we aren't ignoring for isys, isig in dropped_inputs: @@ -2626,8 +2966,8 @@ def _process_vector_argument(arg, name, size): return val, nelem -# Utility function to create an I/O system from a static gain -def _convert_static_iosystem(sys): +# Utility function to create an I/O system (from number or array) +def _convert_to_iosystem(sys): # If we were given an I/O system, do nothing if isinstance(sys, InputOutputSystem): return sys @@ -2645,24 +2985,23 @@ def _convert_static_iosystem(sys): outputs=sys.shape[0], inputs=sys.shape[1], dt=None) def connection_table(sys, show_names=False, column_width=32): - """Print table of connections inside an interconnected system model. + """Print table of connections inside interconnected system. - Intended primarily for :class:`InterconnectedSystems` that have been + Intended primarily for `InterconnectedSystem`'s that have been connected implicitly using signal names. Parameters ---------- - sys : :class:`InterconnectedSystem` - Interconnected system object + sys : `InterconnectedSystem` + Interconnected system object. show_names : bool, optional Instead of printing out the system number, print out the name of each system. Default is False because system name is not usually specified when performing implicit interconnection using - :func:`interconnect`. + `interconnect`. column_width : int, optional Character width of printed columns. - Examples -------- >>> P = ct.ss(1,1,1,0, inputs='u', outputs='y', name='P') @@ -2674,8 +3013,13 @@ def connection_table(sys, show_names=False, column_width=32): e | input | C u | C | P y | P | output + """ assert isinstance(sys, InterconnectedSystem), "system must be"\ "an InterconnectedSystem." sys.connection_table(show_names=show_names, column_width=column_width) + + +# Short versions of function call +find_eqpt = find_operating_point diff --git a/control/optimal.py b/control/optimal.py index ce80eccfc..3242ac3fb 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -3,30 +3,41 @@ # RMM, 11 Feb 2021 # -"""The :mod:`control.optimal` module provides support for optimization-based -controllers for nonlinear systems with state and input constraints. +"""Optimization-based control module. -The docstring examples assume that the following import commands:: +This module provides support for optimization-based controllers for +nonlinear systems with state and input constraints. An optimal +control problem can be solved using the `solve_optimal_trajectory` +function or set up using the `OptimalControlProblem` class and then +solved using the `~OptimalControlProblem.compute_trajectory` method. +Utility functions are available to define common cost functions and +input/state constraints. Optimal estimation problems can be solved +using the `solve_optimal_estimate` function or by using the +`OptimalEstimationProblem` class and the +`~OptimalEstimationProblem.compute_estimate` method.. + +The docstring examples assume the following import commands:: >>> import numpy as np >>> import control as ct - >>> import control.optimal as obc + >>> import control.optimal as opt """ +import logging +import time +import warnings + import numpy as np import scipy as sp import scipy.optimize as opt + import control as ct -import warnings -import logging -import time from . import config -from .exception import ControlNotImplemented -from .iosys import _process_indices, _process_labels, \ - _process_control_disturbance_indices - +from .config import _process_param, _process_kwargs +from .iosys import _process_control_disturbance_indices, _process_labels +from .timeresp import _timeresp_aliases # Define module default parameter values _optimal_trajectory_methods = {'shooting', 'collocation'} @@ -38,20 +49,33 @@ 'optimal.solve_ivp_options': {}, } +# Parameter and keyword aliases +_optimal_aliases = { + # param: ([alias, ...], [legacy, ...]) + 'integral_cost': (['trajectory_cost', 'cost'], []), + 'initial_state': (['x0', 'X0'], []), + 'initial_input': (['u0', 'U0'], []), + 'final_state': (['xf'], []), + 'final_input': (['uf'], []), + 'initial_time': (['T0'], []), + 'trajectory_constraints': (['constraints'], []), + 'return_states': (['return_x'], []), +} + class OptimalControlProblem(): """Description of a finite horizon, optimal control problem. - The `OptimalControlProblem` class holds all of the information required to - specify an optimal control problem: the system dynamics, cost function, - and constraints. As much as possible, the information used to specify an - optimal control problem matches the notation and terminology of the SciPy - `optimize.minimize` module, with the hope that this makes it easier to - remember how to describe a problem. + The `OptimalControlProblem` class holds all of the information required + to specify an optimal control problem: the system dynamics, cost + function, and constraints. As much as possible, the information used + to specify an optimal control problem matches the notation and + terminology of `scipy.optimize` module, with the hope that + this makes it easier to remember how to describe a problem. Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the optimal input will be computed. timepts : 1D array_like List of times at which the optimal input should be computed. @@ -61,9 +85,9 @@ class OptimalControlProblem(): trajectory_constraints : list of constraints, optional List of constraints that should hold at each point in the time vector. Each element of the list should be an object of type - :class:`~scipy.optimize.LinearConstraint` with arguments `(A, lb, - ub)` or :class:`~scipy.optimize.NonlinearConstraint` with arguments - `(fun, lb, ub)`. The constraints will be applied at each time point + `scipy.optimize.LinearConstraint` with arguments ``(A, lb, + ub)`` or `scipy.optimize.NonlinearConstraint` with arguments + ``(fun, lb, ub)``. The constraints will be applied at each time point along the trajectory. terminal_cost : callable, optional Function that returns the terminal cost given the final state @@ -71,8 +95,8 @@ class OptimalControlProblem(): trajectory_method : string, optional Method to use for carrying out the optimization. Currently supported methods are 'shooting' and 'collocation' (continuous time only). The - default value is 'shooting' for discrete time systems and - 'collocation' for continuous time systems + default value is 'shooting' for discrete-time systems and + 'collocation' for continuous-time systems. initial_guess : (tuple of) 1D or 2D array_like Initial states and/or inputs to use as a guess for the optimal trajectory. For shooting methods, an array of inputs for each time @@ -82,34 +106,36 @@ class OptimalControlProblem(): shape (ninputs, ntimepts) or a 1D input of shape (ninputs,) that will be broadcast by extension of the time axis. log : bool, optional - If `True`, turn on logging messages (using Python logging module). - Use :py:func:`logging.basicConfig` to enable logging output + If True, turn on logging messages (using Python logging module). + Use `logging.basicConfig` to enable logging output (e.g., to a file). - Returns - ------- - ocp : OptimalControlProblem - Optimal control problem object, to be used in computing optimal - controllers. + Attributes + ---------- + constraint: list of SciPy constraint objects + List of `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` objects. + constraint_lb, constrain_ub, eqconst_value : list of float + List of constraint bounds. Other Parameters ---------------- - basis : BasisFamily, optional + basis : `BasisFamily`, optional Use the given set of basis functions for the inputs instead of setting the value of the input at each point in the timepts vector. terminal_constraints : list of constraints, optional List of constraints that should hold at the terminal point in time, in the same form as `trajectory_constraints`. solve_ivp_method : str, optional - Set the method used by :func:`scipy.integrate.solve_ivp`. + Set the method used by `scipy.integrate.solve_ivp`. solve_ivp_kwargs : str, optional - Pass additional keywords to :func:`scipy.integrate.solve_ivp`. + Pass additional keywords to `scipy.integrate.solve_ivp`. minimize_method : str, optional - Set the method used by :func:`scipy.optimize.minimize`. + Set the method used by `scipy.optimize.minimize`. minimize_options : str, optional - Set the options keyword used by :func:`scipy.optimize.minimize`. + Set the options keyword used by `scipy.optimize.minimize`. minimize_kwargs : str, optional - Pass additional keywords to :func:`scipy.optimize.minimize`. + Pass additional keywords to `scipy.optimize.minimize`. Notes ----- @@ -120,35 +146,36 @@ class OptimalControlProblem(): optimization over the inputs at each point in time, using the integral and terminal costs as well as the trajectory and terminal constraints. The `compute_trajectory` method sets up an optimization problem that - can be solved using :func:`scipy.optimize.minimize`. - - The `_cost_function` method takes the information computes the cost of the - trajectory generated by the proposed input. It does this by calling a - user-defined function for the integral_cost given the current states and - inputs at each point along the trajectory and then adding the value of a - user-defined terminal cost at the final point in the trajectory. - - The `_constraint_function` method evaluates the constraint functions along - the trajectory generated by the proposed input. As in the case of the - cost function, the constraints are evaluated at the state and input along - each point on the trajectory. This information is compared against the - constraint upper and lower bounds. The constraint function is processed - in the class initializer, so that it only needs to be computed once. + can be solved using `scipy.optimize.minimize`. + + The `_cost_function` method takes the information computes the cost of + the trajectory generated by the proposed input. It does this by calling + a user-defined function for the integral_cost given the current states + and inputs at each point along the trajectory and then adding the value + of a user-defined terminal cost at the final point in the trajectory. + + The `_constraint_function` method evaluates the constraint functions + along the trajectory generated by the proposed input. As in the case + of the cost function, the constraints are evaluated at the state and + input along each time point on the trajectory. This information is + compared against the constraint upper and lower bounds. The constraint + function is processed in the class initializer, so that it only needs + to be computed once. If `basis` is specified, then the optimization is done over coefficients of the basis elements. Otherwise, the optimization is performed over the - values of the input at the specified times (using linear interpolation for - continuous systems). + values of the input at the specified times (using linear interpolation + for continuous systems). - The default values for ``minimize_method``, ``minimize_options``, - ``minimize_kwargs``, ``solve_ivp_method``, and ``solve_ivp_options`` can - be set using config.defaults['optimal.']. + The default values for `minimize_method`, `minimize_options`, + `minimize_kwargs`, `solve_ivp_method`, and `solve_ivp_options` can + be set using `config.defaults['optimal.']`. """ def __init__( self, sys, timepts, integral_cost, trajectory_constraints=None, terminal_cost=None, terminal_constraints=None, initial_guess=None, - trajectory_method=None, basis=None, log=False, kwargs_check=True, + trajectory_method=None, basis=None, log=False, _kwargs_check=True, **kwargs): """Set up an optimal control problem.""" # Save the basic information for use later @@ -163,7 +190,7 @@ def __init__( if trajectory_method is None: trajectory_method = 'collocation' if sys.isctime() else 'shooting' elif trajectory_method not in _optimal_trajectory_methods: - raise NotImplementedError(f"Unkown method {method}") + raise NotImplementedError(f"Unknown method {trajectory_method}") self.shooting = trajectory_method in {'shooting'} self.collocation = trajectory_method in {'collocation'} @@ -189,10 +216,10 @@ def __init__( len(self.solve_ivp_kwargs) > 1: raise TypeError( "solve_ivp method, kwargs not allowed for" - " discrete time systems") + " discrete-time systems") # Make sure there were no extraneous keywords - if kwargs_check and kwargs: + if _kwargs_check and kwargs: raise TypeError("unrecognized keyword(s): ", str(kwargs)) self.trajectory_constraints = _process_constraints( @@ -278,7 +305,7 @@ def __init__( # Log information if log: - logging.info("New optimal control problem initailized") + logging.info("New optimal control problem initialized") # # Cost function @@ -287,15 +314,15 @@ def __init__( # time point and we use a trapezoidal approximation to compute the # integral cost, then add on the terminal cost. # - # For shooting methods, given the input U = [u[t_0], ... u[t_N]] we need to - # compute the cost of the trajectory generated by that input. This + # For shooting methods, given the input U = [u[t_0], ... u[t_N]] we need + # to compute the cost of the trajectory generated by that input. This # means we have to simulate the system to get the state trajectory X = # [x[t_0], ..., x[t_N]] and then compute the cost at each point: # # cost = sum_k integral_cost(x[t_k], u[t_k]) # + terminal_cost(x[t_N], u[t_N]) # - # The actual calculation is a bit more complex: for continuous time + # The actual calculation is a bit more complex: for continuous-time # systems, we use a trapezoidal approximation for the integral cost. # # The initial state used for generating the simulation is stored in the @@ -320,7 +347,8 @@ def _cost_function(self, coeffs): # Integrate the cost costs = np.array(costs) - # Approximate the integral using trapezoidal rule + + # Approximate the integral using trapezoidal rule cost = np.sum(0.5 * (costs[:-1] + costs[1:]) * dt) else: @@ -521,7 +549,7 @@ def _collocation_constraint(self, coeffs): fk = fkp1 else: raise NotImplementedError( - "collocation not yet implemented for discrete time systems") + "collocation not yet implemented for discrete-time systems") # Return the value of the constraint function return self.colloc_vals.reshape(-1) @@ -535,7 +563,7 @@ def _collocation_constraint(self, coeffs): # # The functions below are used to process the initial guess, which can # either consist of an input only (for shooting methods) or an input - # and/or state trajectory (for collocaiton methods). + # and/or state trajectory (for collocation methods). # # Note: The initial input guess is passed as the inputs at the given time # vector. If a basis is specified, this is converted to coefficient @@ -689,7 +717,7 @@ def _print_statistics(self, reset=True): # Compute the states and inputs from the coefficient vector # # These internal functions return the states and inputs at the - # collocation points given the ceofficient (optimizer state) vector. + # collocation points given the coefficient (optimizer state) vector. # They keep track of whether a shooting method is being used or not and # simulate the dynamics if needed. # @@ -724,7 +752,7 @@ def _compute_states_inputs(self, coeffs): return states, inputs - # Simulate the system dynamis to retrieve the state + # Simulate the system dynamics to retrieve the state def _simulate_states(self, x0, inputs): if self.log: logging.debug( @@ -732,6 +760,7 @@ def _simulate_states(self, x0, inputs): logging.debug("input =\n" + str(inputs)) # Simulate the system to get the state + # TODO: update to use response object; remove return_x _, _, states = ct.input_output_response( self.system, self.timepts, inputs, x0, return_x=True, solve_ivp_kwargs=self.solve_ivp_kwargs, t_eval=self.timepts) @@ -751,39 +780,50 @@ def _simulate_states(self, x0, inputs): def compute_trajectory( self, x, squeeze=None, transpose=None, return_states=True, initial_guess=None, print_summary=True, **kwargs): - """Compute the optimal input at state x + """Compute the optimal trajectory starting at state x. Parameters ---------- - x : array-like or number, optional + x : array_like or number, optional Initial state for the system. + initial_guess : (tuple of) 1D or 2D array_like + Initial states and/or inputs to use as a guess for the optimal + trajectory. For shooting methods, an array of inputs for each + time point should be specified. For collocation methods, the + initial guess is either the input vector or a tuple consisting + guesses for the state and the input. Guess should either be a + 2D vector of shape (ninputs, ntimepts) or a 1D input of shape + (ninputs,) that will be broadcast by extension of the time axis. return_states : bool, optional If True (default), return the values of the state at each time. squeeze : bool, optional - If True and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If False, return the - system output as a 2D array even if the system is SISO. Default - value set by config.defaults['control.squeeze_time_response']. + If True and if the system has a single output, return + the system output as a 1D array rather than a 2D array. If + False, return the system output as a 2D array even if + the system is SISO. Default value set by + `config.defaults['control.squeeze_time_response']`. transpose : bool, optional If True, assume that 2D input arrays are transposed from the standard format. Used to convert MATLAB-style inputs to our format. + print_summary : bool, optional + If True (default), print a short summary of the computation. Returns ------- - res : OptimalControlResult + res : `OptimalControlResult` Bundle object with the results of the optimal control problem. - res.success: bool + res.success : bool Boolean flag indicating whether the optimization was successful. res.time : array Time values of the input. res.inputs : array - Optimal inputs for the system. If the system is SISO and squeeze - is not True, the array is 1D (indexed by time). If the system is - not SISO or squeeze is False, the array is 2D (indexed by the - output number and time). + Optimal inputs for the system. If the system is SISO and + squeeze is not True, the array is 1D (indexed by time). + If the system is not SISO or squeeze is False, the array + is 2D (indexed by the output number and time). res.states : array - Time evolution of the state vector (if return_states=True). + Time evolution of the state vector. """ # Allow 'return_x` as a synonym for 'return_states' @@ -793,13 +833,13 @@ def compute_trajectory( # Store the initial state (for use in _constraint_function) self.x = x - # Allow the initial guess to be overriden + # Allow the initial guess to be overridden if initial_guess is None: initial_guess = self.initial_guess else: initial_guess = self._process_initial_guess(initial_guess) - # Call ScipPy optimizer + # Call SciPy optimizer res = sp.optimize.minimize( self._cost_function, initial_guess, constraints=self.constraints, **self.minimize_kwargs) @@ -811,29 +851,27 @@ def compute_trajectory( # Compute the current input to apply from the current state (MPC style) def compute_mpc(self, x, squeeze=None): - """Compute the optimal input at state x + """Compute the optimal input at state x. This function calls the :meth:`compute_trajectory` method and returns the input at the first time point. Parameters ---------- - x: array-like or number, optional + x : array_like or number, optional Initial state for the system. squeeze : bool, optional - If True and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If False, return the - system output as a 2D array even if the system is SISO. Default - value set by config.defaults['control.squeeze_time_response']. + If True and if the system has a single output, return + the system output as a 1D array rather than a 2D array. If + False, return the system output as a 2D array even if + the system is SISO. Default value set by + `config.defaults['control.squeeze_time_response']`. Returns ------- input : array - Optimal input for the system at the current time. If the system - is SISO and squeeze is not True, the array is 1D (indexed by - time). If the system is not SISO or squeeze is False, the array - is 2D (indexed by the output number and time). Set to `None` - if the optimization failed. + Optimal input for the system at the current time, as a 1D array + (even in the SISO case). Set to None if the optimization failed. """ res = self.compute_trajectory(x, squeeze=squeeze) @@ -841,16 +879,47 @@ def compute_mpc(self, x, squeeze=None): # Create an input/output system implementing an MPC controller def create_mpc_iosystem(self, **kwargs): - """Create an I/O system implementing an MPC controller""" + """Create an I/O system implementing an MPC controller. + + For a discrete-time system, creates an input/output system taking + the current state x and returning the control u to apply at the + current time step. + + Parameters + ---------- + name : str, optional + Name for the system controller. Defaults to a generic system + name of the form 'sys[i]'. + inputs : list of str, optional + Labels for the controller inputs. Defaults to the system state + labels. + outputs : list of str, optional + Labels for the controller outputs. Defaults to the system input + labels. + states : list of str, optional + Labels for the internal controller states, which consist either + of the input values over the horizon of the controller or the + coefficients of the basis functions. Defaults to strings of + the form 'x[i]'. + + Returns + ------- + `NonlinearIOSystem` + + Notes + ----- + Only works for discrete-time systems. + + """ # Check to make sure we are in discrete time if self.system.dt == 0: raise ct.ControlNotImplemented( - "MPC for continuous time systems not implemented") + "MPC for continuous-time systems not implemented") def _update(t, x, u, params={}): coeffs = x.reshape((self.system.ninputs, -1)) if self.basis: - # Keep the coeffecients unchanged + # Keep the coefficients unchanged # TODO: could compute input vector, shift, and re-project (?) self.initial_guess = coeffs else: @@ -885,8 +954,23 @@ def _output(t, x, u, params={}): class OptimalControlResult(sp.optimize.OptimizeResult): """Result from solving an optimal control problem. - This class is a subclass of :class:`scipy.optimize.OptimizeResult` with + This class is a subclass of `scipy.optimize.OptimizeResult` with additional attributes associated with solving optimal control problems. + It is used as the return type for optimal control problems. + + Parameters + ---------- + ocp : OptimalControlProblem + Optimal control problem that generated this solution. + res : scipy.minimize.OptimizeResult + Result of optimization. + print_summary : bool, optional + If True (default), print a short summary of the computation. + squeeze : bool, optional + If True and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If False, return the + system output as a 2D array even if the system is SISO. Default + value set by `config.defaults['control.squeeze_time_response']`. Attributes ---------- @@ -897,15 +981,14 @@ class OptimalControlResult(sp.optimize.OptimizeResult): associated with the optimal input. success : bool Whether or not the optimizer exited successful. - problem : OptimalControlProblem - Optimal control problem that generated this solution. cost : float Final cost of the return solution. - system_simulations, {cost, constraint, eqconst}_evaluations : int + system_simulations, cost_evaluations, constraint_evaluations, \ + eqconst_evaluations : int Number of system simulations and evaluations of the cost function, (inequality) constraint function, and equality constraint function - performed during the optimzation. - {cost, constraint, eqconst}_process_time : float + performed during the optimization. + cost_process_time, constraint_process_time, eqconst_process_time : float If logging was enabled, the amount of time spent evaluating the cost and constraint functions. @@ -950,17 +1033,18 @@ def __init__( # Compute the input for a nonlinear, (constrained) optimal control problem -def solve_ocp( - sys, timepts, X0, cost, trajectory_constraints=None, terminal_cost=None, - terminal_constraints=None, initial_guess=None, basis=None, squeeze=None, - transpose=None, return_states=True, print_summary=True, log=False, - **kwargs): +def solve_optimal_trajectory( + sys, timepts, initial_state=None, integral_cost=None, + trajectory_constraints=None, terminal_cost=None, + terminal_constraints=None, initial_guess=None, + basis=None, squeeze=None, transpose=None, return_states=True, + print_summary=True, log=False, **kwargs): r"""Compute the solution to an optimal control problem. The optimal trajectory (states and inputs) is computed so as to - approximately mimimize a cost function of the following form (for - continuous time systems): + approximately minimize a cost function of the following form (for + continuous-time systems): J(x(.), u(.)) = \int_0^T L(x(t), u(t)) dt + V(x(T)), @@ -975,134 +1059,135 @@ def solve_ocp( Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the optimal input will be computed. - timepts : 1D array_like List of times at which the optimal input should be computed. - - X0: array-like or number, optional + initial_state (or X0) : array_like or number, optional Initial condition (default = 0). - - cost : callable + integral_cost (or cost) : callable Function that returns the integral cost (L) given the current state - and input. Called as `cost(x, u)`. - - trajectory_constraints : list of tuples, optional - List of constraints that should hold at each point in the time vector. - Each element of the list should consist of a tuple with first element - given by :meth:`scipy.optimize.LinearConstraint` or - :meth:`scipy.optimize.NonlinearConstraint` and the remaining - elements of the tuple are the arguments that would be passed to those - functions. The following tuples are supported: - - * (LinearConstraint, A, lb, ub): The matrix A is multiplied by stacked - vector of the state and input at each point on the trajectory for - comparison against the upper and lower bounds. + and input. Called as ``integral_cost(x, u)``. + trajectory_constraints (or constraints) : list of tuples, optional + List of constraints that should hold at each point in the time + vector. Each element of the list should consist of a tuple with + first element given by `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` and the remaining elements of + the tuple are the arguments that would be passed to those functions. + The following tuples are supported: + + * (LinearConstraint, A, lb, ub): The matrix A is multiplied by + stacked vector of the state and input at each point on the + trajectory for comparison against the upper and lower bounds. * (NonlinearConstraint, fun, lb, ub): a user-specific constraint - function `fun(x, u)` is called at each point along the trajectory + function ``fun(x, u)`` is called at each point along the trajectory and compared against the upper and lower bounds. The constraints are applied at each time point along the trajectory. - terminal_cost : callable, optional Function that returns the terminal cost (V) given the final state and input. Called as terminal_cost(x, u). (For compatibility with the form of the cost function, u is passed even though it is often not part of the terminal cost.) - terminal_constraints : list of tuples, optional List of constraints that should hold at the end of the trajectory. Same format as `constraints`. - initial_guess : 1D or 2D array_like Initial inputs to use as a guess for the optimal input. The inputs should either be a 2D vector of shape (ninputs, len(timepts)) or a 1D input of shape (ninputs,) that will be broadcast by extension of the time axis. - - log : bool, optional - If `True`, turn on logging messages (using Python logging module). - - print_summary : bool, optional - If `True` (default), print a short summary of the computation. - - return_states : bool, optional - If True, return the values of the state at each time (default = True). - - squeeze : bool, optional - If True and if the system has a single output, return the system - output as a 1D array rather than a 2D array. If False, return the - system output as a 2D array even if the system is SISO. Default value - set by config.defaults['control.squeeze_time_response']. - - transpose : bool, optional - If True, assume that 2D input arrays are transposed from the standard - format. Used to convert MATLAB-style inputs to our format. + basis : `BasisFamily`, optional + Use the given set of basis functions for the inputs instead of + setting the value of the input at each point in the timepts vector. Returns ------- - res : OptimalControlResult + res : `OptimalControlResult` Bundle object with the results of the optimal control problem. - res.success : bool Boolean flag indicating whether the optimization was successful. - res.time : array Time values of the input. - res.inputs : array Optimal inputs for the system. If the system is SISO and squeeze is not True, the array is 1D (indexed by time). If the system is not SISO or squeeze is False, the array is 2D (indexed by the output number and time). - res.states : array - Time evolution of the state vector (if return_states=True). + Time evolution of the state vector. + + Other Parameters + ---------------- + log : bool, optional + If True, turn on logging messages (using Python logging module). + minimize_method : str, optional + Set the method used by `scipy.optimize.minimize`. + print_summary : bool, optional + If True (default), print a short summary of the computation. + return_states : bool, optional + If True (default), return the values of the state at each time. + squeeze : bool, optional + If True and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If False, return the + system output as a 2D array even if the system is SISO. Default + value set by `config.defaults['control.squeeze_time_response']`. + trajectory_method : string, optional + Method to use for carrying out the optimization. Currently supported + methods are 'shooting' and 'collocation' (continuous time only). The + default value is 'shooting' for discrete-time systems and + 'collocation' for continuous-time systems. + transpose : bool, optional + If True, assume that 2D input arrays are transposed from the standard + format. Used to convert MATLAB-style inputs to our format. Notes ----- - 1. For discrete time systems, the final value of the timepts vector - specifies the final time t_N, and the trajectory cost is computed - from time t_0 to t_{N-1}. Note that the input u_N does not affect - the state x_N and so it should always be returned as 0. Further, if - neither a terminal cost nor a terminal constraint is given, then the - input at time point t_{N-1} does not affect the cost function and - hence u_{N-1} will also be returned as zero. If you want the - trajectory cost to include state costs at time t_{N}, then you can - set `terminal_cost` to be the same function as `cost`. - - 2. Additional keyword parameters can be used to fine-tune the behavior - of the underlying optimization and integration functions. See - :func:`OptimalControlProblem` for more information. - - """ - # Process keyword arguments - if trajectory_constraints is None: - # Backwards compatibility - trajectory_constraints = kwargs.pop('constraints', []) + For discrete-time systems, the final value of the timepts vector + specifies the final time t_N, and the trajectory cost is computed from + time t_0 to t_{N-1}. Note that the input u_N does not affect the state + x_N and so it should always be returned as 0. Further, if neither a + terminal cost nor a terminal constraint is given, then the input at + time point t_{N-1} does not affect the cost function and hence u_{N-1} + will also be returned as zero. If you want the trajectory cost to + include state costs at time t_{N}, then you can set `terminal_cost` to + be the same function as `cost`. - # Allow 'return_x` as a synonym for 'return_states' - return_states = ct.config._get_param( - 'optimal', 'return_x', kwargs, return_states, pop=True) + Additional keyword parameters can be used to fine-tune the behavior of + the underlying optimization and integration functions. See + `OptimalControlProblem` for more information. - # Process (legacy) method keyword + """ + # Process parameter and keyword arguments + _process_kwargs(kwargs, _optimal_aliases) + X0 = _process_param( + 'initial_state', initial_state, kwargs, _optimal_aliases, sigval=None) + cost = _process_param( + 'integral_cost', integral_cost, kwargs, _optimal_aliases) + trajectory_constraints = _process_param( + 'trajectory_constraints', trajectory_constraints, kwargs, + _optimal_aliases) + return_states = _process_param( + 'return_states', return_states, kwargs, _optimal_aliases, sigval=True) + + # Process (legacy) method keyword (could be minimize or trajectory) if kwargs.get('method'): method = kwargs.pop('method') - if method not in optimal_methods: + if method not in _optimal_trajectory_methods: if kwargs.get('minimize_method'): raise ValueError("'minimize_method' specified more than once") warnings.warn( "'method' parameter is deprecated; assuming minimize_method", - DeprecationWarning) + FutureWarning) kwargs['minimize_method'] = method else: if kwargs.get('trajectory_method'): - raise ValueError("'trajectory_method' specified more than once") + raise ValueError( + "'trajectory_method' specified more than once") warnings.warn( "'method' parameter is deprecated; assuming trajectory_method", - DeprecationWarning) + FutureWarning) kwargs['trajectory_method'] = method # Set up the optimal control problem @@ -1119,9 +1204,9 @@ def solve_ocp( # Create a model predictive controller for an optimal control problem def create_mpc_iosystem( - sys, timepts, cost, constraints=None, terminal_cost=None, - terminal_constraints=None, log=False, **kwargs): - """Create a model predictive I/O control system + sys, timepts, integral_cost=None, trajectory_constraints=None, + terminal_cost=None, terminal_constraints=None, log=False, **kwargs): + """Create a model predictive I/O control system. This function creates an input/output system that implements a model predictive control for a system given the time points, cost function and @@ -1130,35 +1215,29 @@ def create_mpc_iosystem( Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the optimal input will be computed. - timepts : 1D array_like List of times at which the optimal input should be computed. - - cost : callable + integral_cost (or cost) : callable Function that returns the integral cost given the current state - and input. Called as cost(x, u). - - constraints : list of tuples, optional - List of constraints that should hold at each point in the time vector. - See :func:`~control.optimal.solve_ocp` for more details. - + and input. Called as ``integral_cost(x, u)``. + trajectory_constraints (or constraints) : list of tuples, optional + List of constraints that should hold at each point in the time + vector. See `solve_optimal_trajectory` for more details. terminal_cost : callable, optional Function that returns the terminal cost given the final state and input. Called as terminal_cost(x, u). - terminal_constraints : list of tuples, optional List of constraints that should hold at the end of the trajectory. Same format as `constraints`. - **kwargs - Additional parameters, passed to :func:`scipy.optimal.minimize` and - :class:`NonlinearIOSystem`. + Additional parameters, passed to `scipy.optimize.minimize` and + `~control.NonlinearIOSystem`. Returns ------- - ctrl : InputOutputSystem + ctrl : `InputOutputSystem` An I/O system taking the current state of the model system and returning the current input to be applied that minimizes the cost function while satisfying the constraints. @@ -1167,23 +1246,35 @@ def create_mpc_iosystem( ---------------- inputs, outputs, states : int or list of str, optional Set the names of the inputs, outputs, and states, as described in - :func:`~control.InputOutputSystem`. + `InputOutputSystem`. + log : bool, optional + If True, turn on logging messages (using Python logging module). + Use `logging.basicConfig` to enable logging output + (e.g., to a file). name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Notes ----- Additional keyword parameters can be used to fine-tune the behavior of - the underlying optimization and integrations functions. See - :func:`OptimalControlProblem` for more information. + the underlying optimization and integration functions. See + `OptimalControlProblem` for more information. """ from .iosys import InputOutputSystem + # Process parameter and keyword arguments + _process_kwargs(kwargs, _optimal_aliases) + cost = _process_param( + 'integral_cost', integral_cost, kwargs, _optimal_aliases) + constraints = _process_param( + 'trajectory_constraints', trajectory_constraints, kwargs, + _optimal_aliases) + # Grab the keyword arguments known by this function iosys_kwargs = {} - for kw in InputOutputSystem.kwargs_list: + for kw in InputOutputSystem._kwargs_list: if kw in kwargs: iosys_kwargs[kw] = kwargs.pop(kw) @@ -1210,22 +1301,22 @@ class OptimalEstimationProblem(): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the optimal input will be computed. - timepts: 1D array + timepts : 1D array Set up time points at which the inputs and outputs are given. integral_cost : callable Function that returns the integral cost given the estimated state, system inputs, and output error. Called as integral_cost(xhat, u, - v, w) where xhat is the estimated state, u is the appplied input to + v, w) where xhat is the estimated state, u is the applied input to the system, v is the estimated disturbance input, and w is the difference between the measured and the estimated output. trajectory_constraints : list of constraints, optional List of constraints that should hold at each point in the time vector. Each element of the list should be an object of type - :class:`~scipy.optimize.LinearConstraint` with arguments `(A, lb, - ub)` or :class:`~scipy.optimize.NonlinearConstraint` with arguments - `(fun, lb, ub)`. The constraints will be applied at each time point + `scipy.optimize.LinearConstraint` with arguments ``(A, lb, + ub)`` or `scipy.optimize.NonlinearConstraint` with arguments + ``(fun, lb, ub)``. The constraints will be applied at each time point along the trajectory. terminal_cost : callable, optional Function that returns the terminal cost given the initial estimated @@ -1243,15 +1334,17 @@ class OptimalEstimationProblem(): Specify the indices in the system input vector that correspond to the process disturbances. If value is an integer `m`, the last `m` system inputs are used. Otherwise, the value should be a slice or - a list of indices, as describedf for `control_indices`. If not - specified, defaults to the complement of the control indicies (see + a list of indices, as described for `control_indices`. If not + specified, defaults to the complement of the control indices (see also notes below). - Returns - ------- - oep : OptimalEstimationProblem - Optimal estimation problem object, to be used in computing optimal - estimates. + Attributes + ---------- + constraint: list of SciPy constraint objects + List of `scipy.optimize.LinearConstraint` or + `scipy.optimize.NonlinearConstraint` objects. + constraint_lb, constrain_ub, eqconst_value : list of float + List of constraint bounds. Other Parameters ---------------- @@ -1259,27 +1352,26 @@ class OptimalEstimationProblem(): List of constraints that should hold at the terminal point in time, in the same form as `trajectory_constraints`. solve_ivp_method : str, optional - Set the method used by :func:`scipy.integrate.solve_ivp`. + Set the method used by `scipy.integrate.solve_ivp`. solve_ivp_kwargs : str, optional - Pass additional keywords to :func:`scipy.integrate.solve_ivp`. + Pass additional keywords to `scipy.integrate.solve_ivp`. minimize_method : str, optional - Set the method used by :func:`scipy.optimize.minimize`. + Set the method used by `scipy.optimize.minimize`. minimize_options : str, optional - Set the options keyword used by :func:`scipy.optimize.minimize`. + Set the options keyword used by `scipy.optimize.minimize`. minimize_kwargs : str, optional - Pass additional keywords to :func:`scipy.optimize.minimize`. + Pass additional keywords to `scipy.optimize.minimize`. Notes ----- - To describe an optimal estimation problem we need an input/output system, - a set of time points, applied inputs and measured outputs, a cost - function, and (optionally) a set of constraints on the state and/or inputs - along the trajectory (and at the terminal time). This class sets up an - optimization over the state and disturbances at each point in time, using - the integral and terminal costs as well as the trajectory constraints. - The :func:`~control.optimal.OptimalEstimationProblem.compute_estimate` - method solves the underling optimization problem using - :func:`scipy.optimize.minimize`. + To describe an optimal estimation problem we need an input/output + system, a set of time points, applied inputs and measured outputs, a + cost function, and (optionally) a set of constraints on the state + and/or inputs along the trajectory (and at the terminal time). This + class sets up an optimization over the state and disturbances at + each point in time, using the integral and terminal costs as well as + the trajectory constraints. The `compute_estimate` method solves + the underling optimization problem using `scipy.optimize.minimize`. The control input and disturbance indices can be specified using the `control_indices` and `disturbance_indices` keywords. If only one is @@ -1302,9 +1394,9 @@ class OptimalEstimationProblem(): bounds. The constraint function is processed in the class initializer, so that it only needs to be computed once. - The default values for ``minimize_method``, ``minimize_options``, - ``minimize_kwargs``, ``solve_ivp_method``, and ``solve_ivp_options`` - can be set using config.defaults['optimal.']. + The default values for `minimize_method`, `minimize_options`, + `minimize_kwargs`, `solve_ivp_method`, and `solve_ivp_options` + can be set using `config.defaults['optimal.']`. """ def __init__( @@ -1639,17 +1731,17 @@ def _print_statistics(self, reset=True): # Optimal estimate computations # def compute_estimate( - self, Y, U, X0=None, initial_guess=None, - squeeze=None, print_summary=True): - """Compute the optimal input at state x + self, outputs=None, inputs=None, initial_state=None, + initial_guess=None, squeeze=None, print_summary=True, **kwargs): + """Compute the optimal input at state x. Parameters ---------- - Y : 2D array + outputs (or Y) : 2D array Measured outputs at each time point. - U : 2D array + inputs (or U) : 2D array Applied inputs at each time point. - X0 : 1D array + initial_state (or X0) : 1D array Expected initial value of the state. initial_guess : 2-tuple of 2D arrays A 2-tuple consisting of the estimated states and disturbance @@ -1659,13 +1751,13 @@ def compute_estimate( single measured output, return the system input and output as a 1D array rather than a 2D array. If False, return the system output as a 2D array even if the system is SISO. Default value - set by config.defaults['control.squeeze_time_response']. + set by `config.defaults['control.squeeze_time_response']`. print_summary : bool, optional - If `True` (default), print a short summary of the computation. + If True (default), print a short summary of the computation. Returns ------- - res : OptimalEstimationResult + res : `OptimalEstimationResult` Bundle object with the results of the optimal estimation problem. res.success : bool Boolean flag indicating whether the optimization was successful. @@ -1675,10 +1767,20 @@ def compute_estimate( Estimated disturbance inputs for the system trajectory. res.states : array Time evolution of the estimated state vector. - res.outputs: array + res.outputs : array Estimated measurement noise for the system trajectory. """ + # Argument and keyword processing + aliases = _timeresp_aliases | _optimal_aliases + _process_kwargs(kwargs, aliases) + Y = _process_param('outputs', outputs, kwargs, aliases) + U = _process_param('inputs', inputs, kwargs, aliases) + X0 = _process_param('initial_state', initial_state, kwargs, aliases) + + if kwargs: + raise TypeError("unrecognized keyword(s): ", str(kwargs)) + # Store the inputs and outputs (for use in _constraint_function) self.u = np.atleast_1d(U).reshape(-1, self.timepts.size) self.y = np.atleast_1d(Y).reshape(-1, self.timepts.size) @@ -1709,7 +1811,7 @@ def compute_estimate( # Process the initial guess initial_guess = self._process_initial_guess(initial_guess) - # Call ScipPy optimizer + # Call SciPy optimizer res = sp.optimize.minimize( self._cost_function, initial_guess, constraints=self.constraints, **self.minimize_kwargs) @@ -1718,7 +1820,6 @@ def compute_estimate( return OptimalEstimationResult( self, res, squeeze=squeeze, print_summary=print_summary) - # # Create an input/output system implementing an moving horizon estimator # @@ -1726,10 +1827,11 @@ def compute_estimate( # xhat, u, v, y for all previous time points. When the system update # function is called, # + def create_mhe_iosystem( self, estimate_labels=None, measurement_labels=None, control_labels=None, inputs=None, outputs=None, **kwargs): - """Create an I/O system implementing an MPC controller + """Create an I/O system implementing an MPC controller. This function creates an input/output system that implements a moving horizon estimator for a an optimal estimation problem. The @@ -1741,25 +1843,25 @@ def create_mhe_iosystem( estimate_labels : str or list of str, optional Set the name of the signals to use for the estimated state (estimator outputs). If a single string is specified, it - should be a format string using the variable ``i`` as an index. + should be a format string using the variable `i` as an index. Otherwise, a list of strings matching the size of the estimated state should be used. Default is "xhat[{i}]". These settings - can also be overriden using the `outputs` keyword. + can also be overridden using the `outputs` keyword. measurement_labels, control_labels : str or list of str, optional - Set the name of the measurement and control signal names + Set the names of the measurement and control signal names (estimator inputs). If a single string is specified, it should - be a format string using the variable ``i`` as an index. + be a format string using the variable `i` as an index. Otherwise, a list of strings matching the size of the system inputs and outputs should be used. Default is the signal names for the system outputs and control inputs. These settings can - also be overriden using the `inputs` keyword. + also be overridden using the `inputs` keyword. **kwargs, optional Additional keyword arguments to set system, input, and output - signal names; see :func:`~control.InputOutputSystem`. + signal names; see `InputOutputSystem`. Returns ------- - estim : InputOutputSystem + estim : `InputOutputSystem` An I/O system taking the measured output and applied input for the model system and returning the estimated state of the system, as determined by solving the optimal estimation problem. @@ -1770,13 +1872,13 @@ def create_mhe_iosystem( based on the signal names for the system model used in the optimal estimation problem. The system name and signal names can be overridden using the `name`, `input`, and `output` keywords, as - described in :func:`~control.InputOutputSystem`. + described in `InputOutputSystem`. """ # Check to make sure we are in discrete time if self.system.dt == 0: raise ct.ControlNotImplemented( - "MHE for continuous time systems not implemented") + "MHE for continuous-time systems not implemented") # Figure out the location of the disturbances self.ctrl_idx, self.dist_idx = \ @@ -1829,7 +1931,7 @@ def _mhe_update(t, xvec, uvec, params={}): # Compute the new states and disturbances est = self.compute_estimate( - Y, U, X0=xhat[:, 0], initial_guess=(xhat, V), + Y, U, initial_state=xhat[:, 0], initial_guess=(xhat, V), print_summary=False) # Restack the new state @@ -1853,12 +1955,26 @@ def _mhe_output(t, xvec, uvec, params={}): # Optimal estimation result class OptimalEstimationResult(sp.optimize.OptimizeResult): - """Result from solving an optimal estimationproblem. + """Result from solving an optimal estimation problem. - This class is a subclass of :class:`scipy.optimize.OptimizeResult` with + This class is a subclass of `scipy.optimize.OptimizeResult` with additional attributes associated with solving optimal estimation problems. + Parameters + ---------- + oep : OptimalEstimationProblem + Optimal estimation problem that generated this solution. + res : scipy.minimize.OptimizeResult + Result of optimization. + print_summary : bool, optional + If True (default), print a short summary of the computation. + squeeze : bool, optional + If True and if the system has a single output, return the system + output as a 1D array rather than a 2D array. If False, return the + system output as a 2D array even if the system is SISO. Default + value set by `config.defaults['control.squeeze_time_response']`. + Attributes ---------- states : ndarray @@ -1866,7 +1982,7 @@ class OptimalEstimationResult(sp.optimize.OptimizeResult): inputs : ndarray The disturbances associated with the estimated state trajectory. outputs : - The error between measured outputs and estiamted outputs. + The error between measured outputs and estimated outputs. success : bool Whether or not the optimizer exited successful. problem : OptimalControlProblem @@ -1876,8 +1992,8 @@ class OptimalEstimationResult(sp.optimize.OptimizeResult): system_simulations, {cost, constraint, eqconst}_evaluations : int Number of system simulations and evaluations of the cost function, (inequality) constraint function, and equality constraint function - performed during the optimzation. - {cost, constraint, eqconst}_process_time : float + performed during the optimization. + cost_process_time, constraint_process_time, eqconst_process_time : float If logging was enabled, the amount of time spent evaluating the cost and constraint functions. @@ -1922,51 +2038,58 @@ def __init__( self.outputs = response.outputs -# Compute the moving horizon estimate for a nonlinear system -def solve_oep( - sys, timepts, Y, U, trajectory_cost, X0=None, - trajectory_constraints=None, initial_guess=None, +# Compute the finite horizon estimate for a nonlinear system +def solve_optimal_estimate( + sys, timepts, outputs=None, inputs=None, integral_cost=None, + initial_state=None, trajectory_constraints=None, initial_guess=None, squeeze=None, print_summary=True, **kwargs): - """Compute the solution to a moving horizon estimation problem + """Compute the solution to a finite horizon estimation problem. + + This function computes the maximum likelihood estimate of a system + state given the input and output over a fixed horizon. The likelihood + is evaluated according to a cost function whose value is minimized + to compute the maximum likelihood estimate. Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the optimal input will be computed. timepts : 1D array_like List of times at which the optimal input should be computed. - Y, U: 2D array_like - Values of the outputs and inputs at each time point. - trajectory_cost : callable + outputs (or Y) : 2D array_like + Values of the outputs at each time point. + inputs (or U) : 2D array_like + Values of the inputs at each time point. + integral_cost (or cost) : callable Function that returns the cost given the current state - and input. Called as `cost(y, u, x0)`. - X0: 1D array_like, optional + and input. Called as ``cost(y, u, x0)``. + initial_state (or X0) : 1D array_like, optional Mean value of the initial condition (defaults to 0). trajectory_constraints : list of tuples, optional - List of constraints that should hold at each point in the time vector. - See :func:`solve_ocp` for more information. + List of constraints that should hold at each point in the time + vector. See `solve_optimal_trajectory` for more information. control_indices : int, slice, or list of int or string, optional Specify the indices in the system input vector that correspond to the control inputs. For more information on possible values, see - :func:`~control.optimal.OptimalEstimationProblem` + `OptimalEstimationProblem`. disturbance_indices : int, list of int, or slice, optional Specify the indices in the system input vector that correspond to the input disturbances. For more information on possible values, see - :func:`~control.optimal.OptimalEstimationProblem` + `OptimalEstimationProblem`. initial_guess : 2D array_like, optional Initial guess for the state estimate at each time point. print_summary : bool, optional - If `True` (default), print a short summary of the computation. + If True (default), print a short summary of the computation. squeeze : bool, optional If True and if the system has a single output, return the system output as a 1D array rather than a 2D array. If False, return the - system output as a 2D array even if the system is SISO. Default value - set by config.defaults['control.squeeze_time_response']. + system output as a 2D array even if the system is SISO. Default + value set by `config.defaults['control.squeeze_time_response']`. Returns ------- - res : TimeResponseData + res : `TimeResponseData` Bundle object with the estimated state and noise values. res.success : bool Boolean flag indicating whether the optimization was successful. @@ -1989,9 +2112,18 @@ def solve_oep( ----- Additional keyword parameters can be used to fine-tune the behavior of the underlying optimization and integration functions. See - :func:`~control.optimal.OptimalControlProblem` for more information. + `OptimalControlProblem` for more information. """ + aliases = _timeresp_aliases | _optimal_aliases + _process_kwargs(kwargs, aliases) + Y = _process_param('outputs', outputs, kwargs, aliases) + U = _process_param('inputs', inputs, kwargs, aliases) + X0 = _process_param( + 'initial_state', initial_state, kwargs, aliases) + trajectory_cost = _process_param( + 'integral_cost', integral_cost, kwargs, aliases) + # Set up the optimal control problem oep = OptimalEstimationProblem( sys, timepts, trajectory_cost, @@ -1999,7 +2131,7 @@ def solve_oep( # Solve for the optimal input from the current state return oep.compute_estimate( - Y, U, X0=X0, initial_guess=initial_guess, + Y, U, initial_state=X0, initial_guess=initial_guess, squeeze=squeeze, print_summary=print_summary) @@ -2008,11 +2140,11 @@ def solve_oep( # # Since a quadratic function is common as a cost function, we provide a # function that will take a Q and R matrix and return a callable that -# evaluates to associted quadratic cost. This is compatible with the way that +# evaluates to associated quadratic cost. This is compatible with the way that # the `_cost_function` evaluates the cost at each point in the trajectory. # def quadratic_cost(sys, Q, R, x0=0, u0=0): - """Create quadratic cost function + """Create quadratic cost function. Returns a quadratic cost function that can be used for an optimal control problem. The cost function is of the form @@ -2021,7 +2153,7 @@ def quadratic_cost(sys, Q, R, x0=0, u0=0): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the cost function is being defined. Q : 2D array_like Weighting matrix for state cost. Dimensions must match system state. @@ -2065,7 +2197,7 @@ def quadratic_cost(sys, Q, R, x0=0, u0=0): def gaussian_likelihood_cost(sys, Rv, Rw=None): - """Create cost function for Gaussian likelihoods + """Create cost function for Gaussian likelihoods. Returns a quadratic cost function that can be used for an optimal estimation problem. The cost function is of the form @@ -2074,7 +2206,7 @@ def gaussian_likelihood_cost(sys, Rv, Rw=None): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the cost function is being defined. Rv : 2D array_like Covariance matrix for input (or state) disturbances. @@ -2115,11 +2247,11 @@ def gaussian_likelihood_cost(sys, Rv, Rw=None): # Functions to create constraints: either polytopes (A x <= b) or ranges # (lb # <= x <= ub). # -# As in the cost function evaluation, the main "trick" in creating a constrain -# on the state or input is to properly evaluate the constraint on the stacked -# state and input vector at the current time point. The constraint itself -# will be called at each point along the trajectory (or the endpoint) via the -# constrain_function() method. +# As in the cost function evaluation, the main "trick" in creating a +# constraint on the state or input is to properly evaluate the constraint on +# the stacked state and input vector at the current time point. The +# constraint itself will be called at each point along the trajectory (or the +# endpoint) via the constrain_function() method. # # Note that these functions to not actually evaluate the constraint, they # simply return the information required to do so. We use the SciPy @@ -2127,19 +2259,19 @@ def gaussian_likelihood_cost(sys, Rv, Rw=None): # keep things consistent with the terminology in scipy.optimize. # def state_poly_constraint(sys, A, b): - """Create state constraint from polytope + """Create state constraint from polytope. Creates a linear constraint on the system state of the form A x <= b that can be used as an optimal control constraint (trajectory or terminal). Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. A : 2D array - Constraint matrix + Constraint matrix. b : 1D array - Upper bound for the constraint + Upper bound for the constraint. Returns ------- @@ -2162,16 +2294,16 @@ def state_poly_constraint(sys, A, b): def state_range_constraint(sys, lb, ub): - """Create state constraint from range + """Create state constraint from range. Creates a linear constraint on the system state that bounds the range of the individual states to be between `lb` and `ub`. The upper and lower - bounds can be set of `inf` and `-inf` to indicate there is no constraint + bounds can be set of 'inf' and '-inf' to indicate there is no constraint or to the same value to describe an equality constraint. Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. lb : 1D array Lower bound for each of the states. @@ -2199,19 +2331,19 @@ def state_range_constraint(sys, lb, ub): # Create a constraint polytope on the system input def input_poly_constraint(sys, A, b): - """Create input constraint from polytope + """Create input constraint from polytope. Creates a linear constraint on the system input of the form A u <= b that can be used as an optimal control constraint (trajectory or terminal). Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. A : 2D array - Constraint matrix + Constraint matrix. b : 1D array - Upper bound for the constraint + Upper bound for the constraint. Returns ------- @@ -2235,16 +2367,16 @@ def input_poly_constraint(sys, A, b): def input_range_constraint(sys, lb, ub): - """Create input constraint from polytope + """Create input constraint from polytope. Creates a linear constraint on the system input that bounds the range of the individual states to be between `lb` and `ub`. The upper and lower - bounds can be set of `inf` and `-inf` to indicate there is no constraint + bounds can be set of 'inf' and '-inf' to indicate there is no constraint or to the same value to describe an equality constraint. Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. lb : 1D array Lower bound for each of the inputs. @@ -2273,30 +2405,30 @@ def input_range_constraint(sys, lb, ub): # # Create a constraint polytope/range constraint on the system output # -# Unlike the state and input constraints, for the output constraint we need to -# do a function evaluation before applying the constraints. +# Unlike the state and input constraints, for the output constraint we need +# to do a function evaluation before applying the constraints. # -# TODO: for the special case of an LTI system, we can avoid the extra function -# call by multiplying the state by the C matrix for the system and then -# imposing a linear constraint: +# TODO: for the special case of an LTI system, we can avoid the extra +# function call by multiplying the state by the C matrix for the system and +# then imposing a linear constraint: # # np.hstack( # [A @ sys.C, np.zeros((A.shape[0], sys.ninputs))]) # def output_poly_constraint(sys, A, b): - """Create output constraint from polytope + """Create output constraint from polytope. Creates a linear constraint on the system output of the form A y <= b that can be used as an optimal control constraint (trajectory or terminal). Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. A : 2D array - Constraint matrix + Constraint matrix. b : 1D array - Upper bound for the constraint + Upper bound for the constraint. Returns ------- @@ -2323,16 +2455,16 @@ def _evaluate_output_poly_constraint(x, u): def output_range_constraint(sys, lb, ub): - """Create output constraint from range + """Create output constraint from range. Creates a linear constraint on the system output that bounds the range of the individual states to be between `lb` and `ub`. The upper and lower - bounds can be set of `inf` and `-inf` to indicate there is no constraint + bounds can be set of 'inf' and '-inf' to indicate there is no constraint or to the same value to describe an equality constraint. Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. lb : 1D array Lower bound for each of the outputs. @@ -2359,12 +2491,13 @@ def _evaluate_output_range_constraint(x, u): # Return a nonlinear constraint object based on the polynomial return (opt.NonlinearConstraint, _evaluate_output_range_constraint, lb, ub) + # # Create a constraint on the disturbance input # def disturbance_range_constraint(sys, lb, ub): - """Create constraint for bounded disturbances + """Create constraint for bounded disturbances. This function computes a constraint that puts a bound on the size of input disturbances. The output of this function can be passed as a @@ -2372,12 +2505,12 @@ def disturbance_range_constraint(sys, lb, ub): Parameters ---------- - sys : InputOutputSystem + sys : `InputOutputSystem` I/O system for which the constraint is being defined. lb : 1D array - Lower bound for each of the disturbancs. + Lower bound for each of the disturbance. ub : 1D array - Upper bound for each of the disturbances. + Upper bound for each of the disturbance. Returns ------- @@ -2403,6 +2536,7 @@ def disturbance_range_constraint(sys, lb, ub): # Utility functions # + # # Process trajectory constraints # @@ -2414,6 +2548,7 @@ def disturbance_range_constraint(sys, lb, ub): # internal representation (currently a tuple with the constraint type as the # first element. # + def _process_constraints(clist, name): if clist is None: clist = [] @@ -2429,7 +2564,7 @@ def _process_constraints(clist, name): if isinstance(constraint, tuple): # Original style of constraint ctype, fun, lb, ub = constraint - if not ctype in [opt.LinearConstraint, opt.NonlinearConstraint]: + if ctype not in [opt.LinearConstraint, opt.NonlinearConstraint]: raise TypeError(f"unknown {name} constraint type {ctype}") constraint_list.append(constraint) elif isinstance(constraint, opt.LinearConstraint): @@ -2442,3 +2577,8 @@ def _process_constraints(clist, name): constraint.lb, constraint.ub)) return constraint_list + + +# Convenience aliases +solve_ocp = solve_optimal_trajectory +solve_oep = solve_optimal_estimate diff --git a/control/passivity.py b/control/passivity.py index 0f4104186..605d8c726 100644 --- a/control/passivity.py +++ b/control/passivity.py @@ -1,11 +1,12 @@ -""" -Functions for passive control. +# passivity.py - functions for passive control +# +# Initial author: Mark Yeatman +# Creation date: July 17, 2022 -Author: Mark Yeatman -Date: July 17, 2022 -""" +"""Functions for passive control.""" import numpy as np + from control import statesp from control.exception import ControlArgument, ControlDimension @@ -20,39 +21,40 @@ def solve_passivity_LMI(sys, rho=None, nu=None): - """Compute passivity indices and/or solves feasiblity via a LMI. - - Constructs a linear matrix inequality (LMI) such that if a solution exists - and the last element of the solution is positive, the system `sys` is - passive. Inputs of None for `rho` or `nu` indicate that the function should - solve for that index (they are mutually exclusive, they can't both be - None, otherwise you're trying to solve a nonconvex bilinear matrix - inequality.) The last element of the output `solution` is either the output or input - passivity index, for `rho` = None and `nu` = None respectively. - - The sources for the algorithm are: + """Compute passivity indices and/or solves feasibility via a LMI. - McCourt, Michael J., and Panos J. Antsaklis - "Demonstrating passivity and dissipativity using computational - methods." - - Nicholas Kottenstette and Panos J. Antsaklis - "Relationships Between Positive Real, Passive Dissipative, & Positive - Systems", equation 36. + Constructs a linear matrix inequality (LMI) such that if a solution + exists and the last element of the solution is positive, the system + `sys` is passive. Inputs of None for `rho` or `nu` indicate that the + function should solve for that index (they are mutually exclusive, they + can't both be None, otherwise you're trying to solve a nonconvex + bilinear matrix inequality.) The last element of the output `solution` + is either the output or input passivity index, for `rho` = None and + `nu` = None, respectively. Parameters ---------- sys : LTI - System to be checked + System to be checked. rho : float or None - Output feedback passivity index + Output feedback passivity index. nu : float or None - Input feedforward passivity index + Input feedforward passivity index. Returns ------- solution : ndarray - The LMI solution + The LMI solution. + + References + ---------- + .. [1] McCourt, Michael J., and Panos J. Antsaklis, "Demonstrating + passivity and dissipativity using computational methods." + + .. [2] Nicholas Kottenstette and Panos J. Antsaklis, + "Relationships Between Positive Real, Passive Dissipative, & + Positive Systems", equation 36. + """ if cvx is None: raise ModuleNotFoundError("cvxopt required for passivity module") @@ -98,8 +100,9 @@ def make_P_basis_matrices(n, rho, nu): """Make list of matrix constraints for passivity LMI. Utility function to make basis matrices for a LMI from a - symmetric matrix P of size n by n representing a parametrized symbolic - matrix + symmetric matrix P of size n by n representing a parameterized + symbolic matrix. + """ matrix_list = [] for i in range(0, n): @@ -121,7 +124,7 @@ def P_pos_def_constraint(n): """Make a list of matrix constraints for P >= 0. Utility function to make basis matrices for a LMI that ensures - parametrized symbolic matrix of size n by n is positive definite + parameterized symbolic matrix of size n by n is positive definite """ matrix_list = [] for i in range(0, n): @@ -137,7 +140,7 @@ def P_pos_def_constraint(n): n = sys.nstates - # coefficents for passivity indices and feasibility matrix + # coefficients for passivity indices and feasibility matrix sys_matrix_list = make_P_basis_matrices(n, rho, nu) # get constants for numerical values of rho and nu @@ -175,12 +178,12 @@ def P_pos_def_constraint(n): return sol["x"] except ZeroDivisionError as e: - raise ValueError("The system is probably ill conditioned. " - "Consider perturbing the system matrices by a small amount." + raise ValueError( + "The system is probably ill conditioned. Consider perturbing " + "the system matrices by a small amount." ) from e - def get_output_fb_index(sys): """Return the output feedback passivity (OFP) index for the system. @@ -190,12 +193,13 @@ def get_output_fb_index(sys): Parameters ---------- sys : LTI - System to be checked + System to be checked. Returns ------- float - The OFP index + The OFP index. + """ sol = solve_passivity_LMI(sys, nu=0.0) if sol is None: @@ -205,11 +209,11 @@ def get_output_fb_index(sys): def get_input_ff_index(sys): - """Return the input feedforward passivity (IFP) index for the system. + """Input feedforward passivity (IFP) index for a system. - The IFP is the largest gain that can be placed in negative parallel - interconnection with a system such that the new interconnected system is - passive. + The input feedforward passivity (IFP) is the largest gain that can be + placed in negative parallel interconnection with a system such that the + new interconnected system is passive. Parameters ---------- @@ -219,7 +223,8 @@ def get_input_ff_index(sys): Returns ------- float - The IFP index + The IFP index. + """ sol = solve_passivity_LMI(sys, rho=0.0) if sol is None: @@ -255,17 +260,17 @@ def get_directional_index(sys): def ispassive(sys, ofp_index=0, ifp_index=0): r"""Indicate if a linear time invariant (LTI) system is passive. - Checks if system is passive with the given output feedback (OFP) and input - feedforward (IFP) passivity indices. + Checks if system is passive with the given output feedback (OFP) + and input feedforward (IFP) passivity indices. Parameters ---------- sys : LTI - System to be checked + System to be checked. ofp_index : float - Output feedback passivity index + Output feedback passivity index. ifp_index : float - Input feedforward passivity index + Input feedforward passivity index. Returns ------- @@ -278,18 +283,21 @@ def ispassive(sys, ofp_index=0, ifp_index=0): .. math:: V(x) >= 0 \land \dot{V}(x) <= y^T u - is equivalent to the default case of `ofp_index` = 0 and `ifp_index` = 0. - Note that computing the `ofp_index` and `ifp_index` for a system, then - using both values simultaneously as inputs to this function is not - guaranteed to have an output of True (the system might not be passive with - both indices at the same time). + is equivalent to the default case of `ofp_index` = 0 and `ifp_index` = + 0. Note that computing the `ofp_index` and `ifp_index` for a system, + then using both values simultaneously as inputs to this function is not + guaranteed to have an output of True (the system might not be passive + with both indices at the same time). For more details, see [1]_. References ---------- - .. [1] McCourt, Michael J., and Panos J. Antsaklis - "Demonstrating passivity and dissipativity using computational - methods." + + .. [1] McCourt, Michael J., and Panos J. Antsaklis "Demonstrating + passivity and dissipativity using computational methods." + Technical Report of the ISIS Group at the University of Notre + Dame. ISIS-2013-008, Aug. 2013. + """ return solve_passivity_LMI(sys, rho=ofp_index, nu=ifp_index) is not None diff --git a/control/phaseplot.py b/control/phaseplot.py index a885f2d5c..cf73d62a0 100644 --- a/control/phaseplot.py +++ b/control/phaseplot.py @@ -1,29 +1,22 @@ # phaseplot.py - generate 2D phase portraits # -# Author: Richard M. Murray -# Date: 23 Mar 2024 (legacy version information below) -# -# TODO -# * Allow multiple timepoints (and change timespec name to T?) -# * Update linestyles (color -> linestyle?) -# * Check for keyword compatibility with other plot routines -# * Set up configuration parameters (nyquist --> phaseplot) - -"""Module for generating 2D phase plane plots. - -The :mod:`control.phaseplot` module contains functions for generating 2D -phase plots. The base function for creating phase plane portraits is -:func:`~control.phase_plane_plot`, which generates a phase plane portrait -for a 2 state I/O system (with no inputs). In addition, several other -functions are available to create customized phase plane plots: - -* boxgrid: Generate a list of points along the edge of a box -* circlegrid: Generate list of points around a circle -* equilpoints: Plot equilibrium points in the phase plane -* meshgrid: Generate a list of points forming a mesh -* separatrices: Plot separatrices in the phase plane -* streamlines: Plot stream lines in the phase plane -* vectorfield: Plot a vector field in the phase plane +# Initial author: Richard M. Murray +# Creation date: 24 July 2011, converted from MATLAB version (2002); +# based on an original version by Kristi Morgansen + +"""Generate 2D phase portraits. + +This module contains functions for generating 2D phase plots. The base +function for creating phase plane portraits is `~control.phase_plane_plot`, +which generates a phase plane portrait for a 2 state I/O system (with no +inputs). Utility functions are available to customize the individual +elements of a phase plane portrait. + +The docstring examples assume the following import commands:: + + >>> import numpy as np + >>> import control as ct + >>> import control.phaseplot as pp """ @@ -36,9 +29,11 @@ from scipy.integrate import odeint from . import config -from .exception import ControlNotImplemented -from .freqplot import _add_arrows_to_line2D -from .nlsys import NonlinearIOSystem, find_eqpt, input_output_response +from .ctrlplot import ControlPlot, _add_arrows_to_line2D, _get_color, \ + _process_ax_keyword, _update_plot_title +from .exception import ControlArgument +from .nlsys import NonlinearIOSystem, find_operating_point, \ + input_output_response __all__ = ['phase_plane_plot', 'phase_plot', 'box_grid'] @@ -46,22 +41,27 @@ _phaseplot_defaults = { 'phaseplot.arrows': 2, # number of arrows around curve 'phaseplot.arrow_size': 8, # pixel size for arrows + 'phaseplot.arrow_style': None, # set arrow style 'phaseplot.separatrices_radius': 0.1 # initial radius for separatrices } + def phase_plane_plot( sys, pointdata=None, timedata=None, gridtype=None, gridspec=None, - plot_streamlines=True, plot_vectorfield=False, plot_equilpoints=True, - plot_separatrices=True, ax=None, suppress_warnings=False, **kwargs + plot_streamlines=None, plot_vectorfield=None, plot_streamplot=None, + plot_equilpoints=True, plot_separatrices=True, ax=None, + suppress_warnings=False, title=None, **kwargs ): """Plot phase plane diagram. This function plots phase plane data, including vector fields, stream lines, equilibrium points, and contour curves. + If none of plot_streamlines, plot_vectorfield, or plot_streamplot are + set, then plot_streamplot is used by default. Parameters ---------- - sys : NonlinearIOSystem or callable(t, x, ...) + sys : `NonlinearIOSystem` or callable(t, x, ...) I/O system or function used to generate phase plane data. If a function is given, the remaining arguments are drawn from the `params` keyword. @@ -88,51 +88,119 @@ def phase_plane_plot( Parameters to pass to system. For an I/O system, `params` should be a dict of parameters and values. For a callable, `params` should be dict with key 'args' and value given by a tuple (passed to callable). - color : str - Plot all elements in the given color (use `plot_={'color': c}` - to set the color in one element of the phase plot. - ax : Axes - Use the given axes for the plot instead of creating a new figure. + color : matplotlib color spec, optional + Plot all elements in the given color (use ``plot_`` = + {'color': c} to set the color in one element of the phase + plot (equilpoints, separatrices, streamlines, etc). + ax : `matplotlib.axes.Axes`, optional + The matplotlib axes to draw the figure on. If not specified and + the current figure has a single axes, that axes is used. + Otherwise, a new figure is created. Returns ------- - out : list of list of Artists - out[0] = list of Line2D objects (streamlines and separatrices) - out[1] = Quiver object (vector field arrows) - out[2] = list of Line2D objects (equilibrium points) - - Other parameters + cplt : `ControlPlot` object + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : array of list of `matplotlib.lines.Line2D` + Array of list of `matplotlib.artist.Artist` objects: + + - lines[0] = list of Line2D objects (streamlines, separatrices). + - lines[1] = Quiver object (vector field arrows). + - lines[2] = list of Line2D objects (equilibrium points). + - lines[3] = StreamplotSet object (lines with arrows). + + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + + Other Parameters ---------------- + arrows : int + Set the number of arrows to plot along the streamlines. The default + value can be set in `config.defaults['phaseplot.arrows']`. + arrow_size : float + Set the size of arrows to plot along the streamlines. The default + value can be set in `config.defaults['phaseplot.arrow_size']`. + arrow_style : matplotlib patch + Set the style of arrows to plot along the streamlines. The default + value can be set in `config.defaults['phaseplot.arrow_style']`. + dir : str, optional + Direction to draw streamlines: 'forward' to flow forward in time + from the reference points, 'reverse' to flow backward in time, or + 'both' to flow both forward and backward. The amount of time to + simulate in each direction is given by the `timedata` argument. plot_streamlines : bool or dict, optional - If `True` (default) then plot streamlines based on the pointdata - and gridtype. If set to a dict, pass on the key-value pairs in - the dict as keywords to :func:`~control.phaseplot.streamlines`. + If True then plot streamlines based on the pointdata and gridtype. + If set to a dict, pass on the key-value pairs in the dict as + keywords to `streamlines`. plot_vectorfield : bool or dict, optional - If `True` (default) then plot the vector field based on the pointdata - and gridtype. If set to a dict, pass on the key-value pairs in - the dict as keywords to :func:`~control.phaseplot.vectorfield`. + If True then plot the vector field based on the pointdata and + gridtype. If set to a dict, pass on the key-value pairs in the + dict as keywords to `phaseplot.vectorfield`. + plot_streamplot : bool or dict, optional + If True then use `matplotlib.axes.Axes.streamplot` function + to plot the streamlines. If set to a dict, pass on the key-value + pairs in the dict as keywords to `phaseplot.streamplot`. plot_equilpoints : bool or dict, optional - If `True` (default) then plot equilibrium points based in the phase + If True (default) then plot equilibrium points based in the phase plot boundary. If set to a dict, pass on the key-value pairs in the - dict as keywords to :func:`~control.phaseplot.equilpoints`. + dict as keywords to `phaseplot.equilpoints`. plot_separatrices : bool or dict, optional - If `True` (default) then plot separatrices starting from each + If True (default) then plot separatrices starting from each equilibrium point. If set to a dict, pass on the key-value pairs - in the dict as keywords to :func:`~control.phaseplot.separatrices`. + in the dict as keywords to `phaseplot.separatrices`. + rcParams : dict + Override the default parameters used for generating plots. + Default is set by `config.defaults['ctrlplot.rcParams']`. suppress_warnings : bool, optional - If set to `True`, suppress warning messages in generating trajectories. + If set to True, suppress warning messages in generating trajectories. + title : str, optional + Set the title of the plot. Defaults to plot type and system name(s). + + Notes + ----- + The default method for producing streamlines is determined based on which + keywords are specified, with `plot_streamplot` serving as the generic + default. If any of the `arrows`, `arrow_size`, `arrow_style`, or `dir` + keywords are used and neither `plot_streamlines` nor `plot_streamplot` is + set, then `plot_streamlines` will be set to True. If neither + `plot_streamlines` nor `plot_vectorfield` set set to True, then + `plot_streamplot` will be set to True. """ + # Check for legacy usage of plot_streamlines + streamline_keywords = [ + 'arrows', 'arrow_size', 'arrow_style', 'dir'] + if plot_streamlines is None: + if any([kw in kwargs for kw in streamline_keywords]): + warnings.warn( + "detected streamline keywords; use plot_streamlines to set", + FutureWarning) + plot_streamlines = True + if gridtype not in [None, 'meshgrid']: + warnings.warn( + "streamplots only support gridtype='meshgrid'; " + "falling back to streamlines") + plot_streamlines = True + + if plot_streamlines is None and plot_vectorfield is None \ + and plot_streamplot is None: + plot_streamplot = True + + if plot_streamplot and not plot_streamlines and not plot_vectorfield: + gridspec = gridspec or [25, 25] + # Process arguments params = kwargs.get('params', None) sys = _create_system(sys, params) pointdata = [-1, 1, -1, 1] if pointdata is None else pointdata + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) # Create axis if needed - if ax is None: - fig, ax = plt.gcf(), plt.gca() - else: - fig = None # don't modify figure + user_ax = ax + fig, ax = _process_ax_keyword(user_ax, squeeze=True, rcParams=rcParams) # Create copy of kwargs for later checking to find unused arguments initial_kwargs = dict(kwargs) @@ -146,7 +214,10 @@ def _create_kwargs(global_kwargs, local_kwargs, **other_kwargs): return new_kwargs # Create list for storing outputs - out = [[], None, None] + out = np.array([[], None, None, None], dtype=object) + + # the maximum zorder of stramlines, vectorfield or streamplot + flow_zorder = None # Plot out the main elements if plot_streamlines: @@ -154,9 +225,13 @@ def _create_kwargs(global_kwargs, local_kwargs, **other_kwargs): kwargs, plot_streamlines, gridspec=gridspec, gridtype=gridtype, ax=ax) out[0] += streamlines( - sys, pointdata, timedata, check_kwargs=False, + sys, pointdata, timedata, _check_kwargs=False, suppress_warnings=suppress_warnings, **kwargs_local) + new_zorder = max(elem.get_zorder() for elem in out[0]) + flow_zorder = max(flow_zorder, new_zorder) if flow_zorder \ + else new_zorder + # Get rid of keyword arguments handled by streamlines for kw in ['arrows', 'arrow_size', 'arrow_style', 'color', 'dir', 'params']: @@ -166,31 +241,62 @@ def _create_kwargs(global_kwargs, local_kwargs, **other_kwargs): if gridtype not in [None, 'boxgrid', 'meshgrid']: gridspec = None + if plot_vectorfield: + kwargs_local = _create_kwargs( + kwargs, plot_vectorfield, gridspec=gridspec, ax=ax) + out[1] = vectorfield( + sys, pointdata, _check_kwargs=False, **kwargs_local) + + new_zorder = out[1].get_zorder() + flow_zorder = max(flow_zorder, new_zorder) if flow_zorder \ + else new_zorder + + # Get rid of keyword arguments handled by vectorfield + for kw in ['color', 'params']: + initial_kwargs.pop(kw, None) + + if plot_streamplot: + if gridtype not in [None, 'meshgrid']: + raise ValueError( + "gridtype must be 'meshgrid' when using streamplot") + + kwargs_local = _create_kwargs( + kwargs, plot_streamplot, gridspec=gridspec, ax=ax) + out[3] = streamplot( + sys, pointdata, _check_kwargs=False, **kwargs_local) + + new_zorder = max(out[3].lines.get_zorder(), out[3].arrows.get_zorder()) + flow_zorder = max(flow_zorder, new_zorder) if flow_zorder \ + else new_zorder + + # Get rid of keyword arguments handled by streamplot + for kw in ['color', 'params']: + initial_kwargs.pop(kw, None) + + sep_zorder = flow_zorder + 1 if flow_zorder else None + if plot_separatrices: kwargs_local = _create_kwargs( kwargs, plot_separatrices, gridspec=gridspec, ax=ax) + kwargs_local['zorder'] = kwargs_local.get('zorder', sep_zorder) out[0] += separatrices( - sys, pointdata, check_kwargs=False, **kwargs_local) + sys, pointdata, _check_kwargs=False, **kwargs_local) + + sep_zorder = max(elem.get_zorder() for elem in out[0]) if out[0] \ + else None # Get rid of keyword arguments handled by separatrices for kw in ['arrows', 'arrow_size', 'arrow_style', 'params']: initial_kwargs.pop(kw, None) - if plot_vectorfield: - kwargs_local = _create_kwargs( - kwargs, plot_vectorfield, gridspec=gridspec, ax=ax) - out[1] = vectorfield( - sys, pointdata, check_kwargs=False, **kwargs_local) - - # Get rid of keyword arguments handled by vectorfield - for kw in ['color', 'params']: - initial_kwargs.pop(kw, None) + equil_zorder = sep_zorder + 1 if sep_zorder else None if plot_equilpoints: kwargs_local = _create_kwargs( kwargs, plot_equilpoints, gridspec=gridspec, ax=ax) + kwargs_local['zorder'] = kwargs_local.get('zorder', equil_zorder) out[2] = equilpoints( - sys, pointdata, check_kwargs=False, **kwargs_local) + sys, pointdata, _check_kwargs=False, **kwargs_local) # Get rid of keyword arguments handled by equilpoints for kw in ['params']: @@ -200,17 +306,20 @@ def _create_kwargs(global_kwargs, local_kwargs, **other_kwargs): if initial_kwargs: raise TypeError("unrecognized keywords: ", str(initial_kwargs)) - if fig is not None: - ax.set_title(f"Phase portrait for {sys.name}") + if user_ax is None: + if title is None: + title = f"Phase portrait for {sys.name}" + _update_plot_title(title, use_existing=False, rcParams=rcParams) ax.set_xlabel(sys.state_labels[0]) ax.set_ylabel(sys.state_labels[1]) + plt.tight_layout() - return out + return ControlPlot(out, ax, fig) def vectorfield( - sys, pointdata, gridspec=None, ax=None, suppress_warnings=False, - check_kwargs=True, **kwargs): + sys, pointdata, gridspec=None, zorder=None, ax=None, + suppress_warnings=False, _check_kwargs=True, **kwargs): """Plot a vector field in the phase plane. This function plots a vector field for a two-dimensional state @@ -218,7 +327,7 @@ def vectorfield( Parameters ---------- - sys : NonlinearIOSystem or callable(t, x, ...) + sys : `NonlinearIOSystem` or callable(t, x, ...) I/O system or function used to generate phase plane data. If a function is given, the remaining arguments are drawn from the `params` keyword. @@ -242,21 +351,30 @@ def vectorfield( Parameters to pass to system. For an I/O system, `params` should be a dict of parameters and values. For a callable, `params` should be dict with key 'args' and value given by a tuple (passed to callable). - color : str + color : matplotlib color spec, optional Plot the vector field in the given color. - ax : Axes + ax : `matplotlib.axes.Axes`, optional Use the given axes for the plot, otherwise use the current axes. Returns ------- out : Quiver - Other parameters + Other Parameters ---------------- + rcParams : dict + Override the default parameters used for generating plots. + Default is set by `config.defaults['ctrlplot.rcParams']`. suppress_warnings : bool, optional - If set to `True`, suppress warning messages in generating trajectories. + If set to True, suppress warning messages in generating trajectories. + zorder : float, optional + Set the zorder for the vectorfield. In not specified, it will be + automatically chosen by `matplotlib.axes.Axes.quiver`. """ + # Process keywords + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) + # Get system parameters params = kwargs.pop('params', None) @@ -274,10 +392,10 @@ def vectorfield( xlim, ylim, maxlim = _set_axis_limits(ax, pointdata) # Figure out the color to use - color = _get_color(kwargs, ax) + color = _get_color(kwargs, ax=ax) # Make sure all keyword arguments were processed - if check_kwargs and kwargs: + if _check_kwargs and kwargs: raise TypeError("unrecognized keywords: ", str(kwargs)) # Generate phase plane (quiver) data @@ -285,18 +403,132 @@ def vectorfield( sys._update_params(params) for i, x in enumerate(points): vfdata[i, :2] = x - vfdata[i, 2:] = sys._rhs(0, x, 0) + vfdata[i, 2:] = sys._rhs(0, x, np.zeros(sys.ninputs)) - out = ax.quiver( - vfdata[:, 0], vfdata[:, 1], vfdata[:, 2], vfdata[:, 3], - angles='xy', color=color) + with plt.rc_context(rcParams): + out = ax.quiver( + vfdata[:, 0], vfdata[:, 1], vfdata[:, 2], vfdata[:, 3], + angles='xy', color=color, zorder=zorder) + + return out + + +def streamplot( + sys, pointdata, gridspec=None, zorder=None, ax=None, vary_color=False, + vary_linewidth=False, cmap=None, norm=None, suppress_warnings=False, + _check_kwargs=True, **kwargs): + """Plot streamlines in the phase plane. + + This function plots the streamlines for a two-dimensional state + space system using the `matplotlib.axes.Axes.streamplot` function. + + Parameters + ---------- + sys : `NonlinearIOSystem` or callable(t, x, ...) + I/O system or function used to generate phase plane data. If a + function is given, the remaining arguments are drawn from the + `params` keyword. + pointdata : list or 2D array + List of the form [xmin, xmax, ymin, ymax] describing the + boundaries of the phase plot. + gridspec : list, optional + Specifies the size of the grid in the x and y axes on which to + generate points. + params : dict or list, optional + Parameters to pass to system. For an I/O system, `params` should be + a dict of parameters and values. For a callable, `params` should be + dict with key 'args' and value given by a tuple (passed to callable). + color : matplotlib color spec, optional + Plot the vector field in the given color. + ax : `matplotlib.axes.Axes`, optional + Use the given axes for the plot, otherwise use the current axes. + + Returns + ------- + out : StreamplotSet + Containter object with lines and arrows contained in the + streamplot. See `matplotlib.axes.Axes.streamplot` for details. + + Other Parameters + ---------------- + cmap : str or Colormap, optional + Colormap to use for varying the color of the streamlines. + norm : `matplotlib.colors.Normalize`, optional + Normalization map to use for scaling the colormap and linewidths. + rcParams : dict + Override the default parameters used for generating plots. + Default is set by `config.default['ctrlplot.rcParams']`. + suppress_warnings : bool, optional + If set to True, suppress warning messages in generating trajectories. + vary_color : bool, optional + If set to True, vary the color of the streamlines based on the + magnitude of the vector field. + vary_linewidth : bool, optional. + If set to True, vary the linewidth of the streamlines based on the + magnitude of the vector field. + zorder : float, optional + Set the zorder for the streamlines. In not specified, it will be + automatically chosen by `matplotlib.axes.Axes.streamplot`. + + """ + # Process keywords + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) + + # Get system parameters + params = kwargs.pop('params', None) + + # Create system from callable, if needed + sys = _create_system(sys, params) + + # Determine the points on which to generate the streamplot field + points, gridspec = _make_points(pointdata, gridspec, 'meshgrid') + grid_arr_shape = gridspec[::-1] + xs = points[:, 0].reshape(grid_arr_shape) + ys = points[:, 1].reshape(grid_arr_shape) + + # Create axis if needed + if ax is None: + ax = plt.gca() + + # Set the plotting limits + xlim, ylim, maxlim = _set_axis_limits(ax, pointdata) + + # Figure out the color to use + color = _get_color(kwargs, ax=ax) + + # Make sure all keyword arguments were processed + if _check_kwargs and kwargs: + raise TypeError("unrecognized keywords: ", str(kwargs)) + + # Generate phase plane (quiver) data + sys._update_params(params) + us_flat, vs_flat = np.transpose( + [sys._rhs(0, x, np.zeros(sys.ninputs)) for x in points]) + us, vs = us_flat.reshape(grid_arr_shape), vs_flat.reshape(grid_arr_shape) + + magnitudes = np.linalg.norm([us, vs], axis=0) + norm = norm or mpl.colors.Normalize() + normalized = norm(magnitudes) + cmap = plt.get_cmap(cmap) + + with plt.rc_context(rcParams): + default_lw = plt.rcParams['lines.linewidth'] + min_lw, max_lw = 0.25*default_lw, 2*default_lw + linewidths = normalized * (max_lw - min_lw) + min_lw \ + if vary_linewidth else None + color = magnitudes if vary_color else color + + out = ax.streamplot( + xs, ys, us, vs, color=color, linewidth=linewidths, cmap=cmap, + norm=norm, zorder=zorder) return out def streamlines( sys, pointdata, timedata=1, gridspec=None, gridtype=None, dir=None, - ax=None, check_kwargs=True, suppress_warnings=False, **kwargs): + zorder=None, ax=None, _check_kwargs=True, suppress_warnings=False, + **kwargs): """Plot stream lines in the phase plane. This function plots stream lines for a two-dimensional state space @@ -304,7 +536,7 @@ def streamlines( Parameters ---------- - sys : NonlinearIOSystem or callable(t, x, ...) + sys : `NonlinearIOSystem` or callable(t, x, ...) I/O system or function used to generate phase plane data. If a function is given, the remaining arguments are drawn from the `params` keyword. @@ -327,25 +559,48 @@ def streamlines( If gridtype is 'circlegrid', then `gridspec` is a 2-tuple specifying the radius and number of points around each point in the `pointdata` array. + dir : str, optional + Direction to draw streamlines: 'forward' to flow forward in time + from the reference points, 'reverse' to flow backward in time, or + 'both' to flow both forward and backward. The amount of time to + simulate in each direction is given by the `timedata` argument. params : dict or list, optional Parameters to pass to system. For an I/O system, `params` should be a dict of parameters and values. For a callable, `params` should be dict with key 'args' and value given by a tuple (passed to callable). color : str Plot the streamlines in the given color. - ax : Axes + ax : `matplotlib.axes.Axes`, optional Use the given axes for the plot, otherwise use the current axes. Returns ------- out : list of Line2D objects - Other parameters + Other Parameters ---------------- + arrows : int + Set the number of arrows to plot along the streamlines. The default + value can be set in `config.defaults['phaseplot.arrows']`. + arrow_size : float + Set the size of arrows to plot along the streamlines. The default + value can be set in `config.defaults['phaseplot.arrow_size']`. + arrow_style : matplotlib patch + Set the style of arrows to plot along the streamlines. The default + value can be set in `config.defaults['phaseplot.arrow_style']`. + rcParams : dict + Override the default parameters used for generating plots. + Default is set by `config.defaults['ctrlplot.rcParams']`. suppress_warnings : bool, optional - If set to `True`, suppress warning messages in generating trajectories. + If set to True, suppress warning messages in generating trajectories. + zorder : float, optional + Set the zorder for the streamlines. In not specified, it will be + automatically chosen by `matplotlib.axes.Axes.plot`. """ + # Process keywords + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) + # Get system parameters params = kwargs.pop('params', None) @@ -368,10 +623,10 @@ def streamlines( xlim, ylim, maxlim = _set_axis_limits(ax, pointdata) # Figure out the color to use - color = _get_color(kwargs, ax) + color = _get_color(kwargs, ax=ax) # Make sure all keyword arguments were processed - if check_kwargs and kwargs: + if _check_kwargs and kwargs: raise TypeError("unrecognized keywords: ", str(kwargs)) # Create reverse time system, if needed @@ -395,26 +650,25 @@ def streamlines( # Plot the trajectory (if there is one) if traj.shape[1] > 1: - out.append( - ax.plot(traj[0], traj[1], color=color)) - - # Add arrows to the lines at specified intervals - _add_arrows_to_line2D( - ax, out[-1][0], arrow_pos, arrowstyle=arrow_style, dir=1) + with plt.rc_context(rcParams): + out += ax.plot(traj[0], traj[1], color=color, zorder=zorder) + # Add arrows to the lines at specified intervals + _add_arrows_to_line2D( + ax, out[-1], arrow_pos, arrowstyle=arrow_style, dir=1) return out def equilpoints( - sys, pointdata, gridspec=None, color='k', ax=None, check_kwargs=True, - **kwargs): + sys, pointdata, gridspec=None, color='k', zorder=None, ax=None, + _check_kwargs=True, **kwargs): """Plot equilibrium points in the phase plane. This function plots the equilibrium points for a planar dynamical system. Parameters ---------- - sys : NonlinearIOSystem or callable(t, x, ...) + sys : `NonlinearIOSystem` or callable(t, x, ...) I/O system or function used to generate phase plane data. If a function is given, the remaining arguments are drawn from the `params` keyword. @@ -440,14 +694,26 @@ def equilpoints( dict with key 'args' and value given by a tuple (passed to callable). color : str Plot the equilibrium points in the given color. - ax : Axes + ax : `matplotlib.axes.Axes`, optional Use the given axes for the plot, otherwise use the current axes. Returns ------- out : list of Line2D objects + Other Parameters + ---------------- + rcParams : dict + Override the default parameters used for generating plots. + Default is set by `config.defaults['ctrlplot.rcParams']`. + zorder : float, optional + Set the zorder for the equilibrium points. In not specified, it will + be automatically chosen by `matplotlib.axes.Axes.plot`. + """ + # Process keywords + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) + # Get system parameters params = kwargs.pop('params', None) @@ -466,7 +732,7 @@ def equilpoints( points, _ = _make_points(pointdata, gridspec, 'meshgrid') # Make sure all keyword arguments were processed - if check_kwargs and kwargs: + if _check_kwargs and kwargs: raise TypeError("unrecognized keywords: ", str(kwargs)) # Search for equilibrium points @@ -475,15 +741,15 @@ def equilpoints( # Plot the equilibrium points out = [] for xeq in equilpts: - out.append( - ax.plot(xeq[0], xeq[1], marker='o', color=color)) - + with plt.rc_context(rcParams): + out += ax.plot( + xeq[0], xeq[1], marker='o', color=color, zorder=zorder) return out def separatrices( - sys, pointdata, timedata=None, gridspec=None, ax=None, - check_kwargs=True, suppress_warnings=False, **kwargs): + sys, pointdata, timedata=None, gridspec=None, zorder=None, ax=None, + _check_kwargs=True, suppress_warnings=False, **kwargs): """Plot separatrices in the phase plane. This function plots separatrices for a two-dimensional state space @@ -491,7 +757,7 @@ def separatrices( Parameters ---------- - sys : NonlinearIOSystem or callable(t, x, ...) + sys : `NonlinearIOSystem` or callable(t, x, ...) I/O system or function used to generate phase plane data. If a function is given, the remaining arguments are drawn from the `params` keyword. @@ -518,21 +784,41 @@ def separatrices( Parameters to pass to system. For an I/O system, `params` should be a dict of parameters and values. For a callable, `params` should be dict with key 'args' and value given by a tuple (passed to callable). - color : str - Plot the streamlines in the given color. - ax : Axes + color : matplotlib color spec, optional + Plot the separatrices in the given color. If a single color + specification is given, this is used for both stable and unstable + separatrices. If a tuple is given, the first element is used as + the color specification for stable separatrices and the second + element for unstable separatrices. + ax : `matplotlib.axes.Axes`, optional Use the given axes for the plot, otherwise use the current axes. Returns ------- out : list of Line2D objects - Other parameters + Other Parameters ---------------- + rcParams : dict + Override the default parameters used for generating plots. + Default is set by `config.defaults['ctrlplot.rcParams']`. suppress_warnings : bool, optional - If set to `True`, suppress warning messages in generating trajectories. + If set to True, suppress warning messages in generating trajectories. + zorder : float, optional + Set the zorder for the separatrices. In not specified, it will be + automatically chosen by `matplotlib.axes.Axes.plot`. + + Notes + ----- + The value of `config.defaults['separatrices_radius']` is used to set the + offset from the equilibrium point to the starting point of the separatix + traces, in the direction of the eigenvectors evaluated at that + equilibrium point. """ + # Process keywords + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) + # Get system parameters params = kwargs.pop('params', None) @@ -566,10 +852,10 @@ def separatrices( case (stable_color, unstable_color) | [stable_color, unstable_color]: pass case single_color: - stable_color = unstable_color = color + stable_color = unstable_color = single_color # Make sure all keyword arguments were processed - if check_kwargs and kwargs: + if _check_kwargs and kwargs: raise TypeError("unrecognized keywords: ", str(kwargs)) # Create a "reverse time" system to use for simulation @@ -581,10 +867,6 @@ def separatrices( # Plot separatrices by flowing backwards in time along eigenspaces out = [] for i, xeq in enumerate(equilpts): - # Plot the equilibrium points - out.append( - ax.plot(xeq[0], xeq[1], marker='o', color='k')) - # Figure out the linearization and eigenvectors evals, evecs = np.linalg.eig(sys.linearize(xeq, 0, params=params).A) @@ -623,14 +905,16 @@ def separatrices( # Plot the trajectory (if there is one) if traj.shape[1] > 1: - out.append(ax.plot( - traj[0], traj[1], color=color, linestyle=linestyle)) + with plt.rc_context(rcParams): + out += ax.plot( + traj[0], traj[1], color=color, + linestyle=linestyle, zorder=zorder) # Add arrows to the lines at specified intervals - _add_arrows_to_line2D( - ax, out[-1][0], arrow_pos, arrowstyle=arrow_style, - dir=1) - + with plt.rc_context(rcParams): + _add_arrows_to_line2D( + ax, out[-1], arrow_pos, arrowstyle=arrow_style, + dir=1) return out @@ -647,13 +931,13 @@ def boxgrid(xvals, yvals): Parameters ---------- - xvals, yvals: 1D array-like + xvals, yvals : 1D array_like Array of points defining the points on the lower and left edges of the box. Returns ------- - grid: 2D array + grid : 2D array Array with shape (p, 2) defining the points along the edges of the box, where p is the number of points around the edge. @@ -676,14 +960,14 @@ def meshgrid(xvals, yvals): Parameters ---------- - xvals, yvals: 1D array-like + xvals, yvals : 1D array_like Array of points defining the points on the lower and left edges of the box. Returns ------- - grid: 2D array - Array of points with shape (n * m, 2) defining the mesh + grid : 2D array + Array of points with shape (n * m, 2) defining the mesh. """ xvals, yvals = np.meshgrid(xvals, yvals) @@ -703,7 +987,7 @@ def circlegrid(centers, radius, num): Parameters ---------- - centers : 2D array-like + centers : 2D array_like Array of points with shape (p, 2) defining centers of the circles. radius : float Radius of the points to be generated around each center. @@ -712,7 +996,7 @@ def circlegrid(centers, radius, num): Returns ------- - grid: 2D array + grid : 2D array Array of points with shape (p * num, 2) defining the circles. """ @@ -724,6 +1008,7 @@ def circlegrid(centers, radius, num): theta in np.linspace(0, 2 * math.pi, num, endpoint=False)]) return grid + # # Internal utility functions # @@ -744,6 +1029,7 @@ def _create_system(sys, params): return NonlinearIOSystem( _update, _output, states=2, inputs=0, outputs=0, name="_callable") + # Set axis limits for the plot def _set_axis_limits(ax, pointdata): # Get the current axis limits @@ -786,7 +1072,7 @@ def _find_equilpts(sys, points, params=None): equilpts = [] for i, x0 in enumerate(points): # Look for an equilibrium point near this point - xeq, ueq = find_eqpt(sys, x0, 0, params=params) + xeq, ueq = find_operating_point(sys, x0, 0, params=params) if xeq is None: continue # didn't find anything @@ -887,39 +1173,24 @@ def _parse_arrow_keywords(kwargs): return arrow_pos, arrow_style -def _get_color(kwargs, ax=None): - if 'color' in kwargs: - return kwargs.pop('color') - - # If we were passed an axis, try to increment color from previous - color_cycle = plt.rcParams['axes.prop_cycle'].by_key()['color'] - if ax is not None: - color_offset = 0 - if len(ax.lines) > 0: - last_color = ax.lines[-1].get_color() - if last_color in color_cycle: - color_offset = color_cycle.index(last_color) + 1 - return color_cycle[color_offset % len(color_cycle)] - else: - return None - - +# TODO: move to ctrlplot? def _create_trajectory( sys, revsys, timepts, X0, params, dir, suppress_warnings=False, gridtype=None, gridspec=None, xlim=None, ylim=None): - # Comput ethe forward trajectory + # Compute the forward trajectory if dir == 'forward' or dir == 'both': fwdresp = input_output_response( - sys, timepts, X0=X0, params=params, ignore_errors=True) + sys, timepts, initial_state=X0, params=params, ignore_errors=True) if not fwdresp.success and not suppress_warnings: - warnings.warn(f"{X0=}, {fwdresp.message}") + warnings.warn(f"initial_state={X0}, {fwdresp.message}") # Compute the reverse trajectory if dir == 'reverse' or dir == 'both': revresp = input_output_response( - revsys, timepts, X0=X0, params=params, ignore_errors=True) + revsys, timepts, initial_state=X0, params=params, + ignore_errors=True) if not revresp.success and not suppress_warnings: - warnings.warn(f"{X0=}, {revresp.message}") + warnings.warn(f"initial_state={X0}, {revresp.message}") # Create the trace to plot if dir == 'forward': @@ -962,9 +1233,12 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, """(legacy) Phase plot for 2D dynamical systems. + .. deprecated:: 0.10.1 + This function is deprecated; use `phase_plane_plot` instead. + Produces a vector field or stream line plot for a planar system. This - function has been replaced by the :func:`~control.phase_plane_map` and - :func:`~control.phase_plane_plot` functions. + function has been replaced by the `phase_plane_map` and + `phase_plane_plot` functions. Call signatures: phase_plot(func, X, Y, ...) - display vector field on meshgrid @@ -978,8 +1252,8 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, ---------- func : callable(x, t, ...) Computes the time derivative of y (compatible with odeint). The - function should be the same for as used for :mod:`scipy.integrate`. - Namely, it should be a function of the form dxdt = F(t, x) that + function should be the same for as used for `scipy.integrate`. + Namely, it should be a function of the form dx/dt = F(t, x) that accepts a state x of dimension 2 and returns a derivative dx/dt of dimension 2. X, Y: 3-element sequences, optional, as [start, stop, npts] @@ -992,36 +1266,36 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, X0: ndarray of initial conditions, optional List of initial conditions from which streamlines are plotted. Each initial condition should be a pair of numbers. - T: array-like or number, optional + T: array_like or number, optional Length of time to run simulations that generate streamlines. If a single number, the same simulation time is used for all initial conditions. Otherwise, should be a list of length len(X0) that gives the simulation time for each initial condition. Default value = 50. lingrid : integer or 2-tuple of integers, optional - Argument is either N or (N, M). If X0 is given and X, Y are missing, - a grid of arrows is produced using the limits of the initial - conditions, with N grid points in each dimension or N grid points in x - and M grid points in y. + Argument is either N or (N, M). If X0 is given and X, Y are + missing, a grid of arrows is produced using the limits of the + initial conditions, with N grid points in each dimension or N grid + points in x and M grid points in y. lintime : integer or tuple (integer, float), optional - If a single integer N is given, draw N arrows using equally space time - points. If a tuple (N, lambda) is given, draw N arrows using + If a single integer N is given, draw N arrows using equally space + time points. If a tuple (N, lambda) is given, draw N arrows using exponential time constant lambda - timepts : array-like, optional + timepts : array_like, optional Draw arrows at the given list times [t1, t2, ...] tfirst : bool, optional - If True, call `func` with signature `func(t, x, ...)`. + If True, call `func` with signature ``func(t, x, ...)``. params: tuple, optional - List of parameters to pass to vector field: `func(x, t, *params)` + List of parameters to pass to vector field: ``func(x, t, *params)``. - See also + See Also -------- - box_grid : construct box-shaped grid of initial conditions + box_grid """ # Generate a deprecation warning warnings.warn( - "phase_plot is deprecated; use phase_plot_plot instead", + "phase_plot() is deprecated; use phase_plane_plot() instead", FutureWarning) # @@ -1038,9 +1312,9 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, # Get parameters to pass to function if parms: warnings.warn( - f"keyword 'parms' is deprecated; use 'params'", FutureWarning) + "keyword 'parms' is deprecated; use 'params'", FutureWarning) if params: - raise ControlArgument(f"duplicate keywords 'parms' and 'params'") + raise ControlArgument("duplicate keywords 'parms' and 'params'") else: params = parms @@ -1091,10 +1365,11 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, if scale is None: plt.quiver(x1, x2, dx[:,:,1], dx[:,:,2], angles='xy') elif (scale != 0): + plt.quiver(x1, x2, dx[:,:,0]*np.abs(scale), + dx[:,:,1]*np.abs(scale), angles='xy') #! TODO: optimize parameters for arrows #! TODO: figure out arguments to make arrows show up correctly - xy = plt.quiver(x1, x2, dx[:,:,0]*np.abs(scale), - dx[:,:,1]*np.abs(scale), angles='xy') + # xy = plt.quiver(...) # set(xy, 'LineWidth', PP_arrow_linewidth, 'Color', 'b') #! TODO: Tweak the shape of the plot @@ -1204,30 +1479,36 @@ def phase_plot(odefun, X=None, Y=None, scale=1, X0=None, T=None, #! TODO: figure out arguments to make arrows show up correctly plt.quiver(x1, x2, dx[:,:,0], dx[:,:,1], angles='xy') elif scale != 0 and Narrows > 0: + plt.quiver(x1, x2, dx[:,:,0]*abs(scale), dx[:,:,1]*abs(scale), + angles='xy') #! TODO: figure out arguments to make arrows show up correctly - xy = plt.quiver(x1, x2, dx[:,:,0]*abs(scale), dx[:,:,1]*abs(scale), - angles='xy') + # xy = plt.quiver(...) # set(xy, 'LineWidth', PP_arrow_linewidth) # set(xy, 'AutoScale', 'off') # set(xy, 'AutoScaleFactor', 0) if scale < 0: - bp = plt.plot(x1, x2, 'b.'); # add dots at base + plt.plot(x1, x2, 'b.'); # add dots at base + # bp = plt.plot(...) # set(bp, 'MarkerSize', PP_arrow_markersize) # Utility function for generating initial conditions around a box def box_grid(xlimp, ylimp): - """box_grid generate list of points on edge of box + """Generate list of points on edge of box. + + .. deprecated:: 0.10.0 + Use `phaseplot.boxgrid` instead. list = box_grid([xmin xmax xnum], [ymin ymax ynum]) generates a list of points that correspond to a uniform grid at the end of the box defined by the corners [xmin ymin] and [xmax ymax]. + """ # Generate a deprecation warning warnings.warn( - "box_grid is deprecated; use phaseplot.boxgrid instead", + "box_grid() is deprecated; use phaseplot.boxgrid() instead", FutureWarning) return boxgrid( @@ -1238,6 +1519,8 @@ def box_grid(xlimp, ylimp): # TODO: rename to something more useful (or remove??) def _find(condition): """Returns indices where ravel(a) is true. - Private implementation of deprecated matplotlib.mlab.find + + Private implementation of deprecated `matplotlib.mlab.find`. + """ return np.nonzero(np.ravel(condition))[0] diff --git a/control/pzmap.py b/control/pzmap.py index dd3f9e42b..42ba8e087 100644 --- a/control/pzmap.py +++ b/control/pzmap.py @@ -1,36 +1,41 @@ # pzmap.py - computations involving poles and zeros # -# Original author: Richard M. Murray -# Date: 7 Sep 2009 -# -# This file contains functions that compute poles, zeros and related -# quantities for a linear system, as well as the main functions for -# storing and plotting pole/zero and root locus diagrams. (The actual -# computation of root locus diagrams is in rlocus.py.) -# +# Initial author: Richard M. Murray +# Creation date: 7 Sep 2009 + +"""Computations involving poles and zeros. + +This module contains functions that compute poles, zeros and related +quantities for a linear system, as well as the main functions for +storing and plotting pole/zero and root locus diagrams. (The actual +computation of root locus diagrams is in rlocus.py.) + +""" import itertools import warnings -from math import pi import matplotlib.pyplot as plt import numpy as np -from numpy import cos, exp, imag, linspace, real, sin, sqrt +from numpy import imag, real from . import config -from .freqplot import _freqplot_defaults, _get_line_labels +from .config import _process_legacy_keyword +from .ctrlplot import ControlPlot, _get_color, _get_color_offset, \ + _get_line_labels, _process_ax_keyword, _process_legend_keywords, \ + _process_line_labels, _update_plot_title from .grid import nogrid, sgrid, zgrid from .iosys import isctime, isdtime -from .lti import LTI from .statesp import StateSpace from .xferfcn import TransferFunction -__all__ = ['pole_zero_map', 'pole_zero_plot', 'pzmap', 'PoleZeroData'] +__all__ = ['pole_zero_map', 'pole_zero_plot', 'pzmap', 'PoleZeroData', + 'PoleZeroList'] # Define default parameter values for this module _pzmap_defaults = { - 'pzmap.grid': None, # Plot omega-damping grid + 'pzmap.grid': False, # Plot omega-damping grid 'pzmap.marker_size': 6, # Size of the markers 'pzmap.marker_width': 1.5, # Width of the markers 'pzmap.expansion_factor': 1.8, # Amount to scale plots beyond features @@ -61,7 +66,7 @@ class PoleZeroData: system poles and zeros, as well as the gains and loci for root locus diagrams. - Attributes + Parameters ---------- poles : ndarray 1D array of system poles. @@ -69,39 +74,29 @@ class PoleZeroData: 1D array of system zeros. gains : ndarray, optional 1D array of gains for root locus plots. - loci : ndarray, optiona + loci : ndarray, optional 2D array of poles, with each row corresponding to a gain. sysname : str, optional System name. - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction`, optional System corresponding to the data. + dt : None, True or float, optional + System timebase (used for showing stability boundary). + sort_loci : bool, optional + Set to False to turn off sorting of loci into unique branches. """ def __init__( self, poles, zeros, gains=None, loci=None, dt=None, sysname=None, - sys=None): - """Create a pole/zero map object. - - Parameters - ---------- - poles : ndarray - 1D array of system poles. - zeros : ndarray - 1D array of system zeros. - gains : ndarray, optional - 1D array of gains for root locus plots. - loci : ndarray, optiona - 2D array of poles, with each row corresponding to a gain. - sysname : str, optional - System name. - sys : StateSpace or TransferFunction - System corresponding to the data. - - """ + sys=None, sort_loci=True): + from .rlocus import _RLSortRoots self.poles = poles self.zeros = zeros self.gains = gains - self.loci = loci + if loci is not None and sort_loci: + self.loci = _RLSortRoots(loci) + else: + self.loci = loci self.dt = dt self.sysname = sysname self.sys = sys @@ -113,27 +108,18 @@ def __iter__(self): def plot(self, *args, **kwargs): """Plot the pole/zero data. - See :func:`~control.pole_zero_plot` for description of arguments - and keywords. + See `pole_zero_plot` for description of arguments and keywords. """ - # If this is a root locus plot, use rlocus defaults for grid - if self.loci is not None: - from .rlocus import _rlocus_defaults - kwargs = kwargs.copy() - kwargs['grid'] = config._get_param( - 'rlocus', 'grid', kwargs.get('grid', None), _rlocus_defaults) - return pole_zero_plot(self, *args, **kwargs) class PoleZeroList(list): - """List of PoleZeroData objects.""" + """List of PoleZeroData objects with plotting capability.""" def plot(self, *args, **kwargs): """Plot pole/zero data. - See :func:`~control.pole_zero_plot` for description of arguments - and keywords. + See `pole_zero_plot` for description of arguments and keywords. """ return pole_zero_plot(self, *args, **kwargs) @@ -145,14 +131,14 @@ def pole_zero_map(sysdata): Parameters ---------- - sys : LTI system (StateSpace or TransferFunction) + sysdata : `StateSpace` or `TransferFunction` Linear system for which poles and zeros are computed. Returns ------- - pzmap_data : PoleZeroMap + pzmap_data : `PoleZeroMap` Pole/zero map containing the poles and zeros of the system. Use - `pzmap_data.plot()` or `pole_zero_plot(pzmap_data)` to plot the + ``pzmap_data.plot()`` or ``pole_zero_plot(pzmap_data)`` to plot the pole/zero map. """ @@ -172,13 +158,12 @@ def pole_zero_map(sysdata): # TODO: Implement more elegant cross-style axes. See: -# https://matplotlib.org/2.0.2/examples/axes_grid/demo_axisline_style.html -# https://matplotlib.org/2.0.2/examples/axes_grid/demo_curvelinear_grid.html +# https://matplotlib.org/2.0.2/examples/axes_grid/demo_axisline_style.html +# https://matplotlib.org/2.0.2/examples/axes_grid/demo_curvelinear_grid.html def pole_zero_plot( - data, plot=None, grid=None, title=None, marker_color=None, - marker_size=None, marker_width=None, legend_loc='upper right', - xlim=None, ylim=None, interactive=None, ax=None, scaling=None, - initial_gain=None, **kwargs): + data, plot=None, grid=None, title=None, color=None, marker_size=None, + marker_width=None, xlim=None, ylim=None, interactive=None, ax=None, + scaling=None, initial_gain=None, label=None, **kwargs): """Plot a pole/zero map for a linear system. If the system data include root loci, a root locus diagram for the @@ -189,60 +174,86 @@ def pole_zero_plot( Parameters ---------- - sysdata : List of PoleZeroData objects or LTI systems + data : List of `PoleZeroData` objects or `LTI` systems List of pole/zero response data objects generated by pzmap_response() - or rootlocus_response() that are to be plotted. If a list of systems + or root_locus_map() that are to be plotted. If a list of systems is given, the poles and zeros of those systems will be plotted. grid : bool or str, optional - If `True` plot omega-damping grid, if `False` show imaginary axis - for continuous time systems, unit circle for discrete time systems. - If `empty`, do not draw any additonal lines. Default value is set - by config.default['pzmap.grid'] or config.default['rlocus.grid']. + If True plot omega-damping grid, if False show imaginary + axis for continuous-time systems, unit circle for discrete-time + systems. If 'empty', do not draw any additional lines. Default + value is set by `config.defaults['pzmap.grid']` or + `config.defaults['rlocus.grid']`. plot : bool, optional - (legacy) If ``True`` a graph is generated with Matplotlib, + (legacy) If True a graph is generated with matplotlib, otherwise the poles and zeros are only computed and returned. If this argument is present, the legacy value of poles and zeros is returned. Returns ------- - lines : array of list of Line2D - Array of Line2D objects for each set of markers in the plot. The - shape of the array is given by (nsys, 2) where nsys is the number + cplt : `ControlPlot` object + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : array of list of `matplotlib.lines.Line2D` + The shape of the array is given by (nsys, 2) where nsys is the number of systems or responses passed to the function. The second index specifies the pzmap object type: - * lines[idx, 0]: poles - * lines[idx, 1]: zeros + - lines[idx, 0]: poles + - lines[idx, 1]: zeros - poles, zeros: list of arrays + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. + poles, zeros : list of arrays (legacy) If the `plot` keyword is given, the system poles and zeros are returned. Other Parameters ---------------- - scaling : str or list, optional - Set the type of axis scaling. Can be 'equal' (default), 'auto', or - a list of the form [xmin, xmax, ymin, ymax]. - title : str, optional - Set the title of the plot. Defaults plot type and system name(s). + ax : `matplotlib.axes.Axes`, optional + The matplotlib axes to draw the figure on. If not specified and + the current figure has a single axes, that axes is used. + Otherwise, a new figure is created. + color : matplotlib color spec, optional + Specify the color of the markers and lines. + initial_gain : float, optional + If given, the specified system gain will be marked on the plot. + interactive : bool, optional + Turn off interactive mode for root locus plots. + label : str or array_like of str, optional + If present, replace automatically generated label(s) with given + label(s). If data is a list, strings should be specified for each + system. + legend_loc : int or str, optional + Include a legend in the given location. Default is 'upper right', + with no legend for a single response. Use False to suppress legend. marker_color : str, optional Set the color of the markers used for poles and zeros. marker_size : int, optional Set the size of the markers used for poles and zeros. marker_width : int, optional Set the line width of the markers used for poles and zeros. - legend_loc : str, optional - For plots with multiple lines, a legend will be included in the - given location. Default is 'center right'. Use False to supress. + rcParams : dict + Override the default parameters used for generating plots. + Default is set by `config.defaults['ctrlplot.rcParams']`. + scaling : str or list, optional + Set the type of axis scaling. Can be 'equal' (default), 'auto', or + a list of the form [xmin, xmax, ymin, ymax]. + show_legend : bool, optional + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on the + plot or `legend_loc` has been specified. + title : str, optional + Set the title of the plot. Defaults to plot type and system name(s). xlim : list, optional Set the limits for the x axis. ylim : list, optional Set the limits for the y axis. - interactive : bool, optional - Turn off interactive mode for root locus plots. - initial_gain : float, optional - If given, the specified system gain will be marked on the plot. Notes ----- @@ -252,16 +263,31 @@ def pole_zero_plot( matplotlib.pyplot.gca().axis('auto') and then set the axis limits to the desired values. + Pole/zero plots that use the continuous-time omega-damping grid do not + work with the `ax` keyword argument, due to the way that axes grids + are implemented. The `grid` argument must be set to False or + 'empty' when using the `ax` keyword argument. + + The limits of the pole/zero plot are set based on the location features + in the plot, including the location of poles, zeros, and local maxima + of root locus curves. The locations of local maxima are expanded by a + buffer factor set by `config.defaults['phaseplot.buffer_factor']` that is + applied to the locations of the local maxima. The final axis limits + are set to by the largest features in the plot multiplied by an + expansion factor set by `config.defaults['phaseplot.expansion_factor']`. + The default value for the buffer factor is 1.05 (5% buffer around local + maxima) and the default value for the expansion factor is 1.8 (80% + increase in limits around the most distant features). + """ # Get parameter values - grid = config._get_param('pzmap', 'grid', grid, _pzmap_defaults) + label = _process_line_labels(label) marker_size = config._get_param('pzmap', 'marker_size', marker_size, 6) marker_width = config._get_param('pzmap', 'marker_width', marker_width, 1.5) - xlim_user, ylim_user = xlim, ylim - freqplot_rcParams = config._get_param( - 'freqplot', 'rcParams', kwargs, _freqplot_defaults, - pop=True, last=True) + user_color = _process_legacy_keyword(kwargs, 'marker_color', 'color', color) + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) user_ax = ax + xlim_user, ylim_user = xlim, ylim # If argument was a singleton, turn it into a tuple if not isinstance(data, (list, tuple)): @@ -288,8 +314,8 @@ def pole_zero_plot( # Legacy return value processing if plot is not None: warnings.warn( - "`pole_zero_plot` return values of poles, zeros is deprecated; " - "use pole_zero_map()", DeprecationWarning) + "pole_zero_plot() return value of poles, zeros is deprecated; " + "use pole_zero_map()", FutureWarning) # Extract out the values that we will eventually return poles = [response.poles for response in pzmap_responses] @@ -302,58 +328,49 @@ def pole_zero_plot( return poles, zeros # Initialize the figure - # TODO: turn into standard utility function (from plotutil.py?) - if user_ax is None: - fig = plt.gcf() - axs = fig.get_axes() - else: - fig = ax.figure - axs = [ax] - - if len(axs) > 1: - # Need to generate a new figure - fig, axs = plt.figure(), [] - - with plt.rc_context(freqplot_rcParams): - if grid and grid != 'empty': - plt.clf() - if all([isctime(dt=response.dt) for response in data]): - ax, fig = sgrid(scaling=scaling) - elif all([isdtime(dt=response.dt) for response in data]): - ax, fig = zgrid(scaling=scaling) - else: - raise ValueError( - "incompatible time bases; don't know how to grid") - # Store the limits for later use - xlim, ylim = ax.get_xlim(), ax.get_ylim() - elif len(axs) == 0: - if grid == 'empty': - # Leave off grid entirely + fig, ax = _process_ax_keyword( + user_ax, rcParams=rcParams, squeeze=True, create_axes=False) + legend_loc, _, show_legend = _process_legend_keywords( + kwargs, None, 'upper right') + + # Make sure there are no remaining keyword arguments + if kwargs: + raise TypeError("unrecognized keywords: ", str(kwargs)) + + if ax is None: + # Determine what type of grid to use + if rlocus_plot: + from .rlocus import _rlocus_defaults + grid = config._get_param('rlocus', 'grid', grid, _rlocus_defaults) + else: + grid = config._get_param('pzmap', 'grid', grid, _pzmap_defaults) + + # Create the axes with the appropriate grid + with plt.rc_context(rcParams): + if grid and grid != 'empty': + if all([isctime(dt=response.dt) for response in data]): + ax, fig = sgrid(scaling=scaling) + elif all([isdtime(dt=response.dt) for response in data]): + ax, fig = zgrid(scaling=scaling) + else: + raise ValueError( + "incompatible time bases; don't know how to grid") + # Store the limits for later use + xlim, ylim = ax.get_xlim(), ax.get_ylim() + elif grid == 'empty': ax = plt.axes() xlim = ylim = [np.inf, -np.inf] # use data to set limits else: - # draw stability boundary; use first response timebase ax, fig = nogrid(data[0].dt, scaling=scaling) xlim, ylim = ax.get_xlim(), ax.get_ylim() - else: - # Use the existing axes and any grid that is there - ax = axs[0] - - # Store the limits for later use - xlim, ylim = ax.get_xlim(), ax.get_ylim() - - # Issue a warning if the user tried to set the grid type - if grid: - warnings.warn("axis already exists; grid keyword ignored") + else: + # Store the limits for later use + xlim, ylim = ax.get_xlim(), ax.get_ylim() + if grid is not None: + warnings.warn("axis already exists; grid keyword ignored") - # Handle color cycle manually as all root locus segments - # of the same system are expected to be of the same color - color_cycle = plt.rcParams['axes.prop_cycle'].by_key()['color'] - color_offset = 0 - if len(ax.lines) > 0: - last_color = ax.lines[-1].get_color() - if last_color in color_cycle: - color_offset = color_cycle.index(last_color) + 1 + # Get color offset for the next line to be drawn + color_offset, color_cycle = _get_color_offset(ax) # Create a list of lines for the output out = np.empty( @@ -366,32 +383,33 @@ def pole_zero_plot( poles = response.poles zeros = response.zeros - # Get the color to use for this system - if marker_color is None: - color = color_cycle[(color_offset + idx) % len(color_cycle)] - else: - color = marker_color + # Get the color to use for this response + color = _get_color(user_color, offset=color_offset + idx) # Plot the locations of the poles and zeros if len(poles) > 0: - label = response.sysname if response.loci is None else None + if label is None: + label_ = response.sysname if response.loci is None else None + else: + label_ = label[idx] out[idx, 0] = ax.plot( real(poles), imag(poles), marker='x', linestyle='', markeredgecolor=color, markerfacecolor=color, markersize=marker_size, markeredgewidth=marker_width, - label=label) + color=color, label=label_) if len(zeros) > 0: out[idx, 1] = ax.plot( real(zeros), imag(zeros), marker='o', linestyle='', markeredgecolor=color, markerfacecolor='none', - markersize=marker_size, markeredgewidth=marker_width) + markersize=marker_size, markeredgewidth=marker_width, + color=color) # Plot the loci, if present if response.loci is not None: + label_ = response.sysname if label is None else label[idx] for locus in response.loci.transpose(): out[idx, 2] += ax.plot( - real(locus), imag(locus), color=color, - label=response.sysname) + real(locus), imag(locus), color=color, label=label_) # Compute the axis limits to use based on the response resp_xlim, resp_ylim = _compute_root_locus_limits(response) @@ -422,7 +440,7 @@ def pole_zero_plot( lines, labels = _get_line_labels(ax) # Add legend if there is more than one system plotted - if len(labels) > 1 and legend_loc is not False: + if show_legend or len(labels) > 1 and show_legend != False: if response.loci is None: # Use "x o" for the system label, via matplotlib tuple handler from matplotlib.legend_handler import HandlerTuple @@ -435,26 +453,30 @@ def pole_zero_plot( markeredgecolor=pole_line.get_markerfacecolor(), markerfacecolor='none', markersize=marker_size, markeredgewidth=marker_width) - handle = (pole_line, zero_line) - line_tuples.append(handle) + handle = (pole_line, zero_line) + line_tuples.append(handle) - with plt.rc_context(freqplot_rcParams): - ax.legend( + with plt.rc_context(rcParams): + legend = ax.legend( line_tuples, labels, loc=legend_loc, handler_map={tuple: HandlerTuple(ndivide=None)}) else: # Regular legend, with lines - with plt.rc_context(freqplot_rcParams): - ax.legend(lines, labels, loc=legend_loc) + with plt.rc_context(rcParams): + legend = ax.legend(lines, labels, loc=legend_loc) + else: + legend = None # Add the title if title is None: - title = "Pole/zero plot for " + ", ".join(labels) + title = ("Root locus plot for " if rlocus_plot + else "Pole/zero plot for ") + ", ".join(labels) if user_ax is None: - with plt.rc_context(freqplot_rcParams): - fig.suptitle(title) + _update_plot_title( + title, fig, rcParams=rcParams, frame='figure', + use_existing=False) - # Add dispather to handle choosing a point on the diagram + # Add dispatcher to handle choosing a point on the diagram if interactive: if len(pzmap_responses) > 1: raise NotImplementedError( @@ -474,7 +496,7 @@ def _click_dispatcher(event): _mark_root_locus_gain(ax, sys, K) # Display the parameters in the axes title - with plt.rc_context(freqplot_rcParams): + with plt.rc_context(rcParams): ax.set_title(_create_root_locus_label(sys, K, s)) ax.figure.canvas.draw() @@ -488,7 +510,7 @@ def _click_dispatcher(event): else: TypeError("system lists not supported with legacy return values") - return out + return ControlPlot(out, ax, fig, legend=legend) # Utility function to find gain corresponding to a click event @@ -538,7 +560,7 @@ def _mark_root_locus_gain(ax, sys, K): line.remove() del line - # Visualise clicked point, displaying all roots + # Visualize clicked point, displaying all roots # TODO: allow marker parameters to be set nump, denp = _systopoly1d(sys) root_array = _RLFindRoots(nump, denp, K.real) @@ -583,14 +605,16 @@ def _compute_root_locus_limits(response): # Find the local maxima of root locus curve xpeaks = np.where( np.diff(np.abs(locus.real)) < 0, locus.real[0:-1], 0) - xlim = [ - min(xlim[0], np.min(xpeaks) * rho), - max(xlim[1], np.max(xpeaks) * rho) - ] + if xpeaks.size > 0: + xlim = [ + min(xlim[0], np.min(xpeaks) * rho), + max(xlim[1], np.max(xpeaks) * rho) + ] ypeaks = np.where( np.diff(np.abs(locus.imag)) < 0, locus.imag[0:-1], 0) - ylim = max(ylim, np.max(ypeaks) * rho) + if ypeaks.size > 0: + ylim = max(ylim, np.max(ypeaks) * rho) if isctime(dt=response.dt): # Adjust the limits to include some space around features diff --git a/control/rlocus.py b/control/rlocus.py index dab21f4ac..c4ef8b40e 100644 --- a/control/rlocus.py +++ b/control/rlocus.py @@ -1,31 +1,26 @@ # rlocus.py - code for computing a root locus plot -# Code contributed by Ryan Krauss, 2010 # -# RMM, 17 June 2010: modified to be a standalone piece of code -# * Added BSD copyright info to file (per Ryan) -# * Added code to convert (num, den) to poly1d's if they aren't already. -# This allows Ryan's code to run on a standard signal.ltisys object -# or a control.TransferFunction object. -# * Added some comments to make sure I understand the code +# Initial author: Ryan Krauss +# Creation date: 2010 # -# RMM, 2 April 2011: modified to work with new LTI structure (see ChangeLog) -# * Not tested: should still work on signal.ltisys objects +# RMM, 17 June 2010: modified to be a standalone piece of code # -# Sawyer B. Fuller (minster@uw.edu) 21 May 2020: -# * added compatibility with discrete-time systems. +# RMM, 2 April 2011: modified to work with new LTI structure # +# Sawyer B. Fuller (minster@uw.edu) 21 May 2020: added compatibility +# with discrete-time systems. + +"""Code for computing a root locus plot.""" import warnings -from functools import partial -import matplotlib.pyplot as plt import numpy as np import scipy.signal # signal processing toolbox -from numpy import array, imag, poly1d, real, vstack, zeros_like +from numpy import poly1d, vstack, zeros_like from . import config +from .ctrlplot import ControlPlot from .exception import ControlMIMONotImplemented -from .iosys import isdtime from .lti import LTI from .xferfcn import _convert_to_transfer_function @@ -46,7 +41,7 @@ def root_locus_map(sysdata, gains=None): Parameters ---------- - sys : LTI system or list of LTI systems + sysdata : LTI system or list of LTI systems Linear input/output systems (SISO only, for now). gains : array_like, optional Gains to use in computing plot of closed-loop poles. If not given, @@ -54,16 +49,15 @@ def root_locus_map(sysdata, gains=None): Returns ------- - rldata : PoleZeroData or list of PoleZeroData - Root locus data object(s) corresponding to the . The loci of - the root locus diagram are available in the array - `rldata.loci`, indexed by the gain index and the locus index, - and the gains are in the array `rldata.gains`. + rldata : `PoleZeroData` or list of `PoleZeroData` + Root locus data object(s). The loci of the root locus diagram are + available in the array `rldata.loci`, indexed by the gain index and + the locus index, and the gains are in the array `rldata.gains`. Notes ----- For backward compatibility, the `rldata` return object can be - assigned to the tuple `roots, gains`. + assigned to the tuple ``(roots, gains)``. """ from .pzmap import PoleZeroData, PoleZeroList @@ -88,7 +82,7 @@ def root_locus_map(sysdata, gains=None): root_array = _RLSortRoots(root_array) responses.append(PoleZeroData( - sys.poles(), sys.zeros(), kvect, root_array, + sys.poles(), sys.zeros(), kvect, root_array, sort_loci=False, dt=sys.dt, sysname=sys.name, sys=sys)) if isinstance(sysdata, (list, tuple)): @@ -113,42 +107,68 @@ def root_locus_plot( Gains to use in computing plot of closed-loop poles. If not given, gains are chosen to include the main features of the root locus map. xlim : tuple or list, optional - Set limits of x axis, normally with tuple - (see :doc:`matplotlib:api/axes_api`). + Set limits of x axis (see `matplotlib.axes.Axes.set_xlim`). ylim : tuple or list, optional - Set limits of y axis, normally with tuple - (see :doc:`matplotlib:api/axes_api`). + Set limits of y axis (see `matplotlib.axes.Axes.set_ylim`). plot : bool, optional (legacy) If given, `root_locus_plot` returns the legacy return values of roots and gains. If False, just return the values with no plot. grid : bool or str, optional - If `True` plot omega-damping grid, if `False` show imaginary axis - for continuous time systems, unit circle for discrete time systems. - If `empty`, do not draw any additonal lines. Default value is set - by config.default['rlocus.grid']. - ax : :class:`matplotlib.axes.Axes` - Axes on which to create root locus plot + If True plot omega-damping grid, if False show imaginary axis + for continuous-time systems, unit circle for discrete-time systems. + If 'empty', do not draw any additional lines. Default value is set + by `config.defaults['rlocus.grid']`. initial_gain : float, optional Mark the point on the root locus diagram corresponding to the given gain. + color : matplotlib color spec, optional + Specify the color of the markers and lines. Returns ------- - lines : array of list of Line2D - Array of Line2D objects for each set of markers in the plot. The - shape of the array is given by (nsys, 3) where nsys is the number + cplt : `ControlPlot` object + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : array of list of `matplotlib.lines.Line2D` + The shape of the array is given by (nsys, 3) where nsys is the number of systems or responses passed to the function. The second index specifies the object type: - * lines[idx, 0]: poles - * lines[idx, 1]: zeros - * lines[idx, 2]: loci + - lines[idx, 0]: poles + - lines[idx, 1]: zeros + - lines[idx, 2]: loci + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. roots, gains : ndarray (legacy) If the `plot` keyword is given, returns the closed-loop root locations, arranged such that each row corresponds to a gain, and the array of gains (same as `gains` keyword argument if provided). + Other Parameters + ---------------- + ax : `matplotlib.axes.Axes`, optional + The matplotlib axes to draw the figure on. If not specified and + the current figure has a single axes, that axes is used. + Otherwise, a new figure is created. + label : str or array_like of str, optional + If present, replace automatically generated label(s) with the given + label(s). If sysdata is a list, strings should be specified for each + system. + legend_loc : int or str, optional + Include a legend in the given location. Default is 'center right', + with no legend for a single response. Use False to suppress legend. + show_legend : bool, optional + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on the + plot or `legend_loc` has been specified. + title : str, optional + Set the title of the plot. Defaults to plot type and system name(s). + Notes ----- The root_locus_plot function calls matplotlib.pyplot.axis('equal'), which @@ -157,15 +177,10 @@ def root_locus_plot( then set the axis limits to the desired values. """ - from .pzmap import pole_zero_plot - # Legacy parameters for oldkey in ['kvect', 'k']: gains = config._process_legacy_keyword(kwargs, oldkey, 'gains', gains) - # Set default parameters - grid = config._get_param('rlocus', 'grid', grid, _rlocus_defaults) - if isinstance(sysdata, list) and all( [isinstance(sys, LTI) for sys in sysdata]) or \ isinstance(sysdata, LTI): @@ -177,24 +192,24 @@ def root_locus_plot( # Process `plot` keyword # # See bode_plot for a description of how this keyword is handled to - # support legacy implementatoins of root_locus. + # support legacy implementations of root_locus. # if plot is not None: warnings.warn( - "`root_locus` return values of roots, gains is deprecated; " - "use root_locus_map()", DeprecationWarning) + "root_locus() return value of roots, gains is deprecated; " + "use root_locus_map()", FutureWarning) if plot is False: return responses.loci, responses.gains # Plot the root loci - out = responses.plot(grid=grid, **kwargs) + cplt = responses.plot(grid=grid, **kwargs) # Legacy processing: return locations of poles and zeros as a tuple if plot is True: return responses.loci, responses.gains - return out + return ControlPlot(cplt.lines, cplt.axes, cplt.figure) def _default_gains(num, den, xlim, ylim): @@ -202,8 +217,8 @@ def _default_gains(num, den, xlim, ylim): References ---------- - Ogata, K. (2002). Modern control engineering (4th ed.). Upper - Saddle River, NJ : New Delhi: Prentice Hall.. + .. [1] Ogata, K. (2002). Modern control engineering (4th + ed.). Upper Saddle River, NJ : New Delhi: Prentice Hall.. """ # Compute the break points on the real axis for the root locus plot @@ -379,7 +394,7 @@ def _k_max(num, den, real_break_points, k_break_points): def _systopoly1d(sys): - """Extract numerator and denominator polynomails for a system""" + """Extract numerator and denominator polynomials for a system""" # Allow inputs from the signal processing toolbox if (isinstance(sys, scipy.signal.lti)): nump = sys.num @@ -431,20 +446,18 @@ def _RLSortRoots(roots): one branch to another.""" sorted = zeros_like(roots) - for n, row in enumerate(roots): - if n == 0: - sorted[n, :] = row - else: - # sort the current row by finding the element with the - # smallest absolute distance to each root in the - # previous row - available = list(range(len(prevrow))) - for elem in row: - evect = elem - prevrow[available] - ind1 = abs(evect).argmin() - ind = available.pop(ind1) - sorted[n, ind] = elem - prevrow = sorted[n, :] + sorted[0] = roots[0] + for n, row in enumerate(roots[1:], start=1): + # sort the current row by finding the element with the + # smallest absolute distance to each root in the + # previous row + prevrow = sorted[n-1] + available = list(range(len(prevrow))) + for elem in row: + evect = elem - prevrow[available] + ind1 = abs(evect).argmin() + ind = available.pop(ind1) + sorted[n, ind] = elem return sorted diff --git a/control/robust.py b/control/robust.py index d5e5540fb..197222390 100644 --- a/control/robust.py +++ b/control/robust.py @@ -1,69 +1,40 @@ # robust.py - tools for robust control # -# Author: Steve Brunton, Kevin Chen, Lauren Padilla -# Date: 24 Dec 2010 -# -# This file contains routines for obtaining reduced order models -# -# Copyright (c) 2010 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ +# Initial authors: Steve Brunton, Kevin Chen, Lauren Padilla +# Creation date: 24 Dec 2010 + +"""Robust control synthesis algorithms.""" + +import warnings # External packages and modules import numpy as np -import warnings -from .exception import * + +from .exception import ControlSlycot from .statesp import StateSpace -from .statefbk import * def h2syn(P, nmeas, ncon): - """H_2 control synthesis for plant P. + """H2 control synthesis for plant P. Parameters ---------- - P: partitioned lti plant (State-space sys) - nmeas: number of measurements (input to controller) - ncon: number of control inputs (output from controller) + P : `StateSpace` + Partitioned LTI plant (state-space system). + nmeas : int + Number of measurements (input to controller). + ncon : int + Number of control inputs (output from controller). Returns ------- - K: controller to stabilize P (State-space sys) + K : `StateSpace` + Controller to stabilize `P`. Raises ------ ImportError - if slycot routine sb10hd is not loaded + If slycot routine sb10hd is not loaded. See Also -------- @@ -71,7 +42,7 @@ def h2syn(P, nmeas, ncon): Examples -------- - >>> # Unstable first order SISI system + >>> # Unstable first order SISO system >>> G = ct.tf([1], [1, -1], inputs=['u'], outputs=['y']) >>> all(G.poles() < 0) # Is G stable? False @@ -94,12 +65,6 @@ def h2syn(P, nmeas, ncon): # Check for ss system object, need a utility for this? # TODO: Check for continous or discrete, only continuous supported right now - # if isCont(): - # dico = 'C' - # elif isDisc(): - # dico = 'D' - # else: - dico = 'C' try: from slycot import sb10hd @@ -121,30 +86,37 @@ def h2syn(P, nmeas, ncon): def hinfsyn(P, nmeas, ncon): - """H_{inf} control synthesis for plant P. + # TODO: document significance of rcond + """H-infinity control synthesis for plant P. Parameters ---------- - P: partitioned lti plant - nmeas: number of measurements (input to controller) - ncon: number of control inputs (output from controller) + P : `StateSpace` + Partitioned LTI plant (state-space system). + nmeas : int + Number of measurements (input to controller). + ncon : int + Number of control inputs (output from controller). Returns ------- - K: controller to stabilize P (State-space sys) - CL: closed loop system (State-space sys) - gam: infinity norm of closed loop system - rcond: 4-vector, reciprocal condition estimates of: - 1: control transformation matrix - 2: measurement transformation matrix - 3: X-Riccati equation - 4: Y-Riccati equation - TODO: document significance of rcond + K : `StateSpace` + Controller to stabilize `P`. + CL : `StateSpace` + Closed loop system. + gam : float + Infinity norm of closed loop system. + rcond : list + 4-vector, reciprocal condition estimates of: + 1: control transformation matrix + 2: measurement transformation matrix + 3: X-Riccati equation + 4: Y-Riccati equation Raises ------ ImportError - if slycot routine sb10ad is not loaded + If slycot routine sb10ad is not loaded. See Also -------- @@ -152,7 +124,7 @@ def hinfsyn(P, nmeas, ncon): Examples -------- - >>> # Unstable first order SISI system + >>> # Unstable first order SISO system >>> G = ct.tf([1], [1,-1], inputs=['u'], outputs=['y']) >>> all(G.poles() < 0) False @@ -175,12 +147,6 @@ def hinfsyn(P, nmeas, ncon): # Check for ss system object, need a utility for this? # TODO: Check for continous or discrete, only continuous supported right now - # if isCont(): - # dico = 'C' - # elif isDisc(): - # dico = 'D' - # else: - dico = 'C' try: from slycot import sb10ad @@ -222,19 +188,20 @@ def _size_as_needed(w, wname, n): Returns ------- - w_: processed weighting function, a StateSpace object: - - if w is None, empty StateSpace object + w_: processed weighting function, a `StateSpace` object: + - if w is None, empty `StateSpace` object - if w is scalar, w_ will be w * eye(n) - - otherwise, w as StateSpace object + - otherwise, w as `StateSpace` object Raises ------ ValueError - - if w is not None or scalar, and doesn't have n inputs + If w is not None or scalar, and does not have n inputs. See Also -------- augw + """ from . import append, ss if w is not None: @@ -259,36 +226,37 @@ def augw(g, w1=None, w2=None, w3=None): one weighting must not be None. If a weighting w is scalar, it will be replaced by I*w, where I is - ny-by-ny for w1 and w3, and nu-by-nu for w2. + ny-by-ny for `w1` and `w3`, and nu-by-nu for `w2`. Parameters ---------- - g: LTI object, ny-by-nu - Plant - w1: None, scalar, or k1-by-ny LTI object - Weighting on S - w2: None, scalar, or k2-by-nu LTI object - Weighting on KS - w3: None, scalar, or k3-by-ny LTI object - Weighting on T + g : LTI object, ny-by-nu + Plant. + w1 : None, scalar, or k1-by-ny LTI object + Weighting on S. + w2 : None, scalar, or k2-by-nu LTI object + Weighting on KS. + w3 : None, scalar, or k3-by-ny LTI object + Weighting on T. Returns ------- - p: StateSpace - Plant augmented with weightings, suitable for submission to hinfsyn or - h2syn. + p : `StateSpace` + Plant augmented with weightings, suitable for submission to + `hinfsyn` or `h2syn`. Raises ------ ValueError - If all weightings are None + If all weightings are None. See Also -------- h2syn, hinfsyn, mixsyn + """ - from . import append, ss, connect + from . import append, connect, ss if w1 is None and w2 is None and w3 is None: raise ValueError("At least one weighting must not be None") @@ -337,12 +305,12 @@ def augw(g, w1=None, w2=None, w3=None): 1 + now1 + now2 + now3 + 2 * ny + niw2) # y -> w3 - q[niw1 + niw2:niw1 + niw2 + niw3, 1] = np.arange(1 + now1 + now2 + now3 + ny, - 1 + now1 + now2 + now3 + ny + niw3) + q[niw1 + niw2:niw1 + niw2 + niw3, 1] = np.arange( + 1 + now1 + now2 + now3 + ny, 1 + now1 + now2 + now3 + ny + niw3) # -y -> Iy; note the leading - - q[niw1 + niw2 + niw3:niw1 + niw2 + niw3 + ny, 1] = -np.arange(1 + now1 + now2 + now3 + ny, - 1 + now1 + now2 + now3 + 2 * ny) + q[niw1 + niw2 + niw3:niw1 + niw2 + niw3 + ny, 1] = -np.arange( + 1 + now1 + now2 + now3 + ny, 1 + now1 + now2 + now3 + 2 * ny) # Iu -> G q[niw1 + niw2 + niw3 + ny:niw1 + niw2 + niw3 + ny + nu, 1] = np.arange( @@ -375,20 +343,20 @@ def mixsyn(g, w1=None, w2=None, w3=None): Parameters ---------- - g: LTI - The plant for which controller must be synthesized - w1: None, or scalar or k1-by-ny LTI - Weighting on S = (1+G*K)**-1 - w2: None, or scalar or k2-by-nu LTI - Weighting on K*S - w3: None, or scalar or k3-by-ny LTI - Weighting on T = G*K*(1+G*K)**-1; + g : LTI + The plant for which controller must be synthesized. + w1 : None, or scalar or k1-by-ny LTI + Weighting on S = (1+G*K)**-1. + w2 : None, or scalar or k2-by-nu LTI + Weighting on K*S. + w3 : None, or scalar or k3-by-ny LTI + Weighting on T = G*K*(1+G*K)**-1. Returns ------- - k: StateSpace - Synthesized controller; - cl: StateSpace + k : `StateSpace` + Synthesized controller. + cl : `StateSpace` Closed system mapping evaluation inputs to evaluation outputs. Let p be the augmented plant, with:: @@ -396,21 +364,22 @@ def mixsyn(g, w1=None, w2=None, w3=None): [z] = [p11 p12] [w] [y] [p21 g] [u] - then cl is the system from w->z with `u = -k*y`. - - info: tuple - gamma: scalar - H-infinity norm of cl - rcond: array - Estimates of reciprocal condition numbers - computed during synthesis. See hinfsyn for details - - If a weighting w is scalar, it will be replaced by I*w, where I is - ny-by-ny for w1 and w3, and nu-by-nu for w2. + then cl is the system from w -> z with u = -k*y. + info : tuple + Two-tuple (`gamma`, `rcond`) containing additional information: + - `gamma` (scalar): H-infinity norm of cl. + - `rcond` (array): Estimates of reciprocal condition numbers + computed during synthesis. See hinfsyn for details. See Also -------- hinfsyn, augw + + Notes + ----- + If a weighting w is scalar, it will be replaced by I*w, where I is + ny-by-ny for `w1` and `w3`, and nu-by-nu for `w2`. + """ nmeas = g.noutputs ncon = g.ninputs diff --git a/control/sisotool.py b/control/sisotool.py index aca36e2d1..78be86b16 100644 --- a/control/sisotool.py +++ b/control/sisotool.py @@ -1,3 +1,7 @@ +# sisotool.py - interactive tool for SISO control design + +"""Interactive tool for SISO control design.""" + __all__ = ['sisotool', 'rootlocus_pid_designer'] import warnings @@ -27,7 +31,7 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, plotstr_rlocus='C0', rlocus_grid=False, omega=None, dB=None, Hz=None, deg=None, omega_limits=None, omega_num=None, margins_bode=True, tvect=None, kvect=None): - """Sisotool style collection of plots inspired by MATLAB's sisotool. + """Collection of plots inspired by MATLAB's sisotool. The left two plots contain the bode magnitude and phase diagrams. The top right plot is a clickable root locus plot, clicking on the @@ -37,32 +41,31 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, Parameters ---------- sys : LTI object - Linear input/output systems. If sys is SISO, use the same system for - the root locus and step response. If it is desired to see a different - step response than feedback(K*sys,1), such as a disturbance response, - sys can be provided as a two-input, two-output system (e.g. by using - :func:`bdgalg.connect' or :func:`iosys.interconnect`). For two-input, - two-output system, sisotool inserts the negative of the selected gain - K between the first output and first input and uses the second input - and output for computing the step response. To see the disturbance - response, configure your plant to have as its second input the - disturbance input. To view the step response with a feedforward - controller, give your plant two identical inputs, and sum your - feedback controller and your feedforward controller and multiply them - into your plant's second input. It is also possible to accomodate a + Linear input/output systems. If `sys` is SISO, use the same system + for the root locus and step response. If it is desired to see a + different step response than ``feedback(K*sys, 1)``, such as a + disturbance response, `sys` can be provided as a two-input, + two-output system. For two-input, two-output system, sisotool + inserts the negative of the selected gain `K` between the first + output and first input and uses the second input and output for + computing the step response. To see the disturbance response, + configure your plant to have as its second input the disturbance + input. To view the step response with a feedforward controller, + give your plant two identical inputs, and sum your feedback + controller and your feedforward controller and multiply them into + your plant's second input. It is also possible to accommodate a system with a gain in the feedback. initial_gain : float, optional Initial gain to use for plotting root locus. Defaults to 1 - (config.defaults['sisotool.initial_gain']). + (`config.defaults['sisotool.initial_gain']`). xlim_rlocus : tuple or list, optional - Control of x-axis range, normally with tuple - (see :doc:`matplotlib:api/axes_api`). + Control of x-axis range (see `matplotlib.axes.Axes.set_xlim`). ylim_rlocus : tuple or list, optional - control of y-axis range - plotstr_rlocus : :func:`matplotlib.pyplot.plot` format string, optional + Control of y-axis range (see `matplotlib.axes.Axes.set_ylim`). + plotstr_rlocus : `matplotlib.pyplot.plot` format string, optional Plotting style for the root locus plot(color, linestyle, etc). rlocus_grid : boolean (default = False) - If True plot s- or z-plane grid. + If True, plot s- or z-plane grid. omega : array_like List of frequencies in rad/sec to be used for bode plot. dB : boolean @@ -78,11 +81,11 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, auto-generated if omitted. omega_num : int Number of samples to plot. Defaults to - config.defaults['freqplot.number_of_samples']. + `config.defaults['freqplot.number_of_samples']`. margins_bode : boolean If True, plot gain and phase margin in the bode plot. tvect : list or ndarray, optional - List of timesteps to use for closed loop step response. + List of time steps to use for closed loop step response. Examples -------- @@ -136,7 +139,7 @@ def sisotool(sys, initial_gain=None, xlim_rlocus=None, ylim_rlocus=None, # ax=fig.axes[1]) ax_rlocus = fig.axes[1] root_locus_map(sys[0, 0]).plot( - xlim=xlim_rlocus, ylim=ylim_rlocus, grid=rlocus_grid, + xlim=xlim_rlocus, ylim=ylim_rlocus, initial_gain=initial_gain, ax=ax_rlocus) if rlocus_grid is False: # Need to generate grid manually, since root_locus_plot() won't @@ -189,7 +192,7 @@ def _SisotoolUpdate(sys, fig, K, bode_plot_params, tvect=None): sys_loop = sys if sys.issiso() else sys[0,0] - # Update the bodeplot + # Update the Bode plot bode_plot_params['data'] = frequency_response(sys_loop*K.real) bode_plot(**bode_plot_params, title=False) @@ -256,32 +259,32 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', """Manual PID controller design based on root locus using Sisotool. Uses `sisotool` to investigate the effect of adding or subtracting an - amount `deltaK` to the proportional, integral, or derivative (PID) gains of - a controller. One of the PID gains, `Kp`, `Ki`, or `Kd`, respectively, can - be modified at a time. `Sisotool` plots the step response, frequency + amount `deltaK` to the proportional, integral, or derivative (PID) gains + of a controller. One of the PID gains, `Kp`, `Ki`, or `Kd`, respectively, + can be modified at a time. `sisotool` plots the step response, frequency response, and root locus of the closed-loop system controlling the dynamical system specified by `plant`. Can be used with either non- interactive plots (e.g. in a Jupyter Notebook), or interactive plots. To use non-interactively, choose starting-point PID gains `Kp0`, `Ki0`, - and `Kd0` (you might want to start with all zeros to begin with), select - which gain you would like to vary (e.g. gain=`'P'`, `'I'`, or `'D'`), and - choose a value of `deltaK` (default 0.001) to specify by how much you - would like to change that gain. Repeatedly run `rootlocus_pid_designer` - with different values of `deltaK` until you are satisfied with the - performance for that gain. Then, to tune a different gain, e.g. `'I'`, - make sure to add your chosen `deltaK` to the previous gain you you were - tuning. - - Example: to examine the effect of varying `Kp` starting from an intial - value of 10, use the arguments `gain='P', Kp0=10` and try varying values + and `Kd0` (you might want to start with all zeros to begin with), + select which gain you would like to vary (e.g. `gain` = 'P', 'I', + or 'D'), and choose a value of `deltaK` (default 0.001) to specify + by how much you would like to change that gain. Repeatedly run + `rootlocus_pid_designer` with different values of `deltaK` until you + are satisfied with the performance for that gain. Then, to tune a + different gain, e.g. 'I', make sure to add your chosen `deltaK` to + the previous gain you you were tuning. + + Example: to examine the effect of varying `Kp` starting from an initial + value of 10, use the arguments ``gain='P', Kp0=10`` and try varying values of `deltaK`. Suppose a `deltaK` of 5 gives satisfactory performance. Then, to tune the derivative gain, add your selected `deltaK` to `Kp0` in the - next call using the arguments `gain='D', Kp0=15`, to see how adding + next call using the arguments ``gain='D', Kp0=15``, to see how adding different values of `deltaK` to your derivative gain affects performance. To use with interactive plots, you will need to enable interactive mode - if you are in a Jupyter Notebook, e.g. using `%matplotlib`. See + if you are in a Jupyter Notebook, e.g. using ``%matplotlib``. See `Interactive Plots `_ for more information. Click on a branch of the root locus plot to try different values of `deltaK`. Each click updates plots and prints the @@ -289,11 +292,11 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', glass on the plot to get more locations to click. Just make sure to deactivate magnification mode when you are done by clicking the magnifying glass. Otherwise you will not be able to be able to choose a gain on the - root locus plot. When you are done, `%matplotlib inline` returns to inline, - non-interactive ploting. + root locus plot. When you are done, ``%matplotlib inline`` returns to + inline, non-interactive plotting. - By default, all three PID terms are in the forward path C_f in the diagram - shown below, that is, + By default, all three PID terms are in the forward path C_f in the + diagram shown below, that is, C_f = Kp + Ki/s + Kd*s/(tau*s + 1). @@ -308,12 +311,12 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', | ----- C_b <-------| --------------------------------- - If `plant` is a discrete-time system, then the proportional, integral, and - derivative terms are given instead by Kp, Ki*dt/2*(z+1)/(z-1), and + If `plant` is a discrete-time system, then the proportional, integral, + and derivative terms are given instead by Kp, Ki*dt/2*(z+1)/(z-1), and Kd/dt*(z-1)/z, respectively. It is also possible to move the derivative term into the feedback path - `C_b` using `derivative_in_feedback_path=True`. This may be desired to + `C_b` using `derivative_in_feedback_path` = True. This may be desired to avoid that the plant is subject to an impulse function when the reference `r` is a step input. `C_b` is otherwise set to zero. @@ -322,42 +325,42 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', Parameters ---------- - plant : :class:`LTI` (:class:`TransferFunction` or :class:`StateSpace` system) + plant : `LTI` (`TransferFunction` or `StateSpace` system) The dynamical system to be controlled. - gain : string (optional) - Which gain to vary by `deltaK`. Must be one of `'P'`, `'I'`, or `'D'` - (proportional, integral, or derative). - sign : int (optional) + gain : string, optional + Which gain to vary by `deltaK`. Must be one of 'P', 'I', or 'D' + (proportional, integral, or derivative). + sign : int, optional The sign of deltaK gain perturbation. - input : string (optional) - The input used for the step response; must be `'r'` (reference) or - `'d'` (disturbance) (see figure above). - Kp0, Ki0, Kd0 : float (optional) + input_signal : string, optional + The input used for the step response; must be 'r' (reference) or + 'd' (disturbance) (see figure above). + Kp0, Ki0, Kd0 : float, optional Initial values for proportional, integral, and derivative gains, respectively. - deltaK : float (optional) - Perturbation value for gain specified by the `gain` keywoard. - tau : float (optional) + deltaK : float, optional + Perturbation value for gain specified by the `gain` keyword. + tau : float, optional The time constant associated with the pole in the continuous-time derivative term. This is required to make the derivative transfer function proper. - C_ff : float or :class:`LTI` system (optional) - Feedforward controller. If :class:`LTI`, must have timebase that is + C_ff : float or `LTI` system, optional + Feedforward controller. If `LTI`, must have timebase that is compatible with plant. - derivative_in_feedback_path : bool (optional) + derivative_in_feedback_path : bool, optional Whether to place the derivative term in feedback transfer function `C_b` instead of the forward transfer function `C_f`. - plot : bool (optional) + plot : bool, optional Whether to create Sisotool interactive plot. Returns ------- - closedloop : class:`StateSpace` system + closedloop : `StateSpace` system The closed-loop system using initial gains. Notes ----- - When running using iPython or Jupyter, use `%matplotlib` to configure + When running using iPython or Jupyter, use ``%matplotlib`` to configure the session for interactive support. """ @@ -382,7 +385,7 @@ def rootlocus_pid_designer(plant, gain='P', sign=+1, input_signal='r', prop = tf(1, 1, inputs='e', outputs='prop_e') integ = tf(1, [1, 0], inputs='e', outputs='int_e') deriv = tf([1, 0], [tau, 1], inputs='y', outputs='deriv') - else: # discrete-time + else: # discrete time prop = tf(1, 1, dt, inputs='e', outputs='prop_e') integ = tf([dt/2, dt/2], [1, -1], dt, inputs='e', outputs='int_e') deriv = tf([1, -1], [dt, 0], dt, inputs='y', outputs='deriv') diff --git a/control/statefbk.py b/control/statefbk.py index a385516ee..b6e9c9655 100644 --- a/control/statefbk.py +++ b/control/statefbk.py @@ -1,62 +1,28 @@ # statefbk.py - tools for state feedback control # -# Author: Richard M. Murray, Roberto Bucher -# Date: 31 May 2010 -# -# This file contains routines for designing state space controllers -# -# Copyright (c) 2010 by California Institute of Technology -# All rights reserved. -# -# Redistribution and use in source and binary forms, with or without -# modification, are permitted provided that the following conditions -# are met: -# -# 1. Redistributions of source code must retain the above copyright -# notice, this list of conditions and the following disclaimer. -# -# 2. Redistributions in binary form must reproduce the above copyright -# notice, this list of conditions and the following disclaimer in the -# documentation and/or other materials provided with the distribution. -# -# 3. Neither the name of the California Institute of Technology nor -# the names of its contributors may be used to endorse or promote -# products derived from this software without specific prior -# written permission. -# -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -# OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -# USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -# OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -# SUCH DAMAGE. -# -# $Id$ +# Initial authors: Richard M. Murray, Roberto Bucher +# Creation date: 31 May 2010 + +"""Routines for designing state space controllers.""" + +import warnings -# External packages and modules import numpy as np import scipy as sp -import warnings from . import statesp -from .mateqn import care, dare, _check_shape -from .statesp import StateSpace, _ssmatrix, _convert_to_statespace, ss +from .config import _process_legacy_keyword +from .exception import ControlArgument, ControlSlycot +from .iosys import _process_indices, _process_labels, isctime, isdtime from .lti import LTI -from .iosys import isdtime, isctime, _process_indices, _process_labels +from .mateqn import care, dare from .nlsys import NonlinearIOSystem, interconnect -from .exception import ControlSlycot, ControlArgument, ControlDimension, \ - ControlNotImplemented -from .config import _process_legacy_keyword +from .statesp import StateSpace, _ssmatrix, ss -# Make sure we have access to the right slycot routines +# Make sure we have access to the right Slycot routines try: from slycot import sb03md57 + # wrap without the deprecation warning def sb03md(n, C, A, U, dico, job='X',fact='N',trana='N',ldwork=None): ret = sb03md57(A, U, C, dico, job, fact, trana, ldwork) @@ -74,7 +40,7 @@ def sb03md(n, C, A, U, dico, job='X',fact='N',trana='N',ldwork=None): __all__ = ['ctrb', 'obsv', 'gram', 'place', 'place_varga', 'lqr', - 'dlqr', 'acker', 'create_statefbk_iosystem'] + 'dlqr', 'acker', 'place_acker', 'create_statefbk_iosystem'] # Pole placement @@ -86,28 +52,26 @@ def place(A, B, p): Parameters ---------- A : 2D array_like - Dynamics matrix + Dynamics matrix. B : 2D array_like - Input matrix + Input matrix. p : 1D array_like - Desired eigenvalue locations + Desired eigenvalue locations. Returns ------- - K : 2D array (or matrix) - Gain such that A - B K has eigenvalues given in p + K : 2D array + Gain such that A - B K has eigenvalues given in p. Notes ----- - Algorithm - This is a wrapper function for :func:`scipy.signal.place_poles`, which - implements the Tits and Yang algorithm [1]_. It will handle SISO, - MISO, and MIMO systems. If you want more control over the algorithm, - use :func:`scipy.signal.place_poles` directly. + This is a wrapper function for `scipy.signal.place_poles`, which + implements the Tits and Yang algorithm [1]_. It will handle SISO, MISO, + and MIMO systems. If you want more control over the algorithm, use + `scipy.signal.place_poles` directly. - Limitations - The algorithm will not place poles at the same location more - than rank(B) times. + Limitations: The algorithm will not place poles at the same location + more than rank(B) times. References ---------- @@ -123,44 +87,39 @@ def place(A, B, p): See Also -------- - place_varga, acker + place_acker, place_varga """ from scipy.signal import place_poles # Convert the system inputs to NumPy arrays - A_mat = np.array(A) - B_mat = np.array(B) - if (A_mat.shape[0] != A_mat.shape[1]): - raise ControlDimension("A must be a square matrix") - - if (A_mat.shape[0] != B_mat.shape[0]): - err_str = "The number of rows of A must equal the number of rows in B" - raise ControlDimension(err_str) + A_mat = _ssmatrix(A, square=True, name="A") + B_mat = _ssmatrix(B, axis=0, rows=A_mat.shape[0]) # Convert desired poles to numpy array placed_eigs = np.atleast_1d(np.squeeze(np.asarray(p))) result = place_poles(A_mat, B_mat, placed_eigs, method='YT') K = result.gain_matrix - return _ssmatrix(K) + return K def place_varga(A, B, p, dtime=False, alpha=None): - """Place closed loop eigenvalues. + """Place closed loop eigenvalues using Varga method. + K = place_varga(A, B, p, dtime=False, alpha=None) Parameters ---------- A : 2D array_like - Dynamics matrix + Dynamics matrix. B : 2D array_like - Input matrix + Input matrix. p : 1D array_like - Desired eigenvalue locations + Desired eigenvalue locations. dtime : bool, optional - False for continuous time pole placement or True for discrete time. - The default is dtime=False. + False (default) for continuous-time pole placement or True + for discrete time. alpha : float, optional If `dtime` is false then place_varga will leave the eigenvalues with real part less than alpha untouched. If `dtime` is true then @@ -172,43 +131,43 @@ def place_varga(A, B, p, dtime=False, alpha=None): Returns ------- - K : 2D array (or matrix) + K : 2D array Gain such that A - B K has eigenvalues given in p. See Also -------- - place, acker + place, place_acker Notes ----- - This function is a wrapper for the slycot function sb01bd, which - implements the pole placement algorithm of Varga [1]_. In contrast to the - algorithm used by place(), the Varga algorithm can place multiple poles at - the same location. The placement, however, may not be as robust. + This function is a wrapper for the Slycot function sb01bd, which + implements the pole placement algorithm of Varga [1]_. In contrast + to the algorithm used by `place`, the Varga algorithm can place + multiple poles at the same location. The placement, however, may + not be as robust. References ---------- - .. [1] Varga A. "A Schur method for pole assignment." IEEE Trans. Automatic - Control, Vol. AC-26, pp. 517-519, 1981. + .. [1] Varga A. "A Schur method for pole assignment." IEEE Trans. + Automatic Control, Vol. AC-26, pp. 517-519, 1981. Examples -------- >>> A = [[-1, -1], [0, 1]] >>> B = [[0], [1]] >>> K = ct.place_varga(A, B, [-2, -5]) + """ - # Make sure that SLICOT is installed + # Make sure that Slycot is installed try: from slycot import sb01bd except ImportError: - raise ControlSlycot("can't find slycot module 'sb01bd'") + raise ControlSlycot("can't find slycot module sb01bd") # Convert the system inputs to NumPy arrays - A_mat = np.array(A) - B_mat = np.array(B) - if (A_mat.shape[0] != A_mat.shape[1] or A_mat.shape[0] != B_mat.shape[0]): - raise ControlDimension("matrix dimensions are incorrect") + A_mat = _ssmatrix(A, square=True, name="A") + B_mat = _ssmatrix(B, axis=0, rows=A_mat.shape[0]) # Compute the system eigenvalues and convert poles to numpy array system_eigs = np.linalg.eig(A_mat)[0] @@ -225,60 +184,60 @@ def place_varga(A, B, p, dtime=False, alpha=None): # (if DICO='C') or with modulus less than alpha # (if DICO = 'D'). if dtime: - # For discrete time, slycot only cares about modulus, so just make + # For discrete time, Slycot only cares about modulus, so just make # alpha the smallest it can be. alpha = 0.0 else: # Choosing alpha=min_eig is insufficient and can lead to an # error or not having all the eigenvalues placed that we wanted. # Evidently, what python thinks are the eigs is not precisely - # the same as what slicot thinks are the eigs. So we need some + # the same as what Slycot thinks are the eigs. So we need some # numerical breathing room. The following is pretty heuristic, # but does the trick alpha = -2*abs(min(system_eigs.real)) elif dtime and alpha < 0.0: raise ValueError("Discrete time systems require alpha > 0") - # Call SLICOT routine to place the eigenvalues + # Call Slycot routine to place the eigenvalues A_z, w, nfp, nap, nup, F, Z = \ sb01bd(B_mat.shape[0], B_mat.shape[1], len(placed_eigs), alpha, A_mat, B_mat, placed_eigs, DICO) # Return the gain matrix, with MATLAB gain convention - return _ssmatrix(-F) + return -F # Contributed by Roberto Bucher -def acker(A, B, poles): +def place_acker(A, B, poles): """Pole placement using Ackermann method. Call: - K = acker(A, B, poles) + K = place_acker(A, B, poles) Parameters ---------- A, B : 2D array_like - State and input matrix of the system + State and input matrix of the system. poles : 1D array_like - Desired eigenvalue locations + Desired eigenvalue locations. Returns ------- - K : 2D array (or matrix) - Gains such that A - B K has given eigenvalues - + K : 2D array + Gains such that A - B K has given eigenvalues. + See Also -------- place, place_varga """ # Convert the inputs to matrices - a = _ssmatrix(A) - b = _ssmatrix(B) + A = _ssmatrix(A, square=True, name="A") + B = _ssmatrix(B, axis=0, rows=A.shape[0], name="B") # Make sure the system is controllable ct = ctrb(A, B) - if np.linalg.matrix_rank(ct) != a.shape[0]: + if np.linalg.matrix_rank(ct) != A.shape[0]: raise ValueError("System not reachable; pole placement invalid") # Compute the desired characteristic polynomial @@ -287,13 +246,13 @@ def acker(A, B, poles): # Place the poles using Ackermann's method # TODO: compute pmat using Horner's method (O(n) instead of O(n^2)) n = np.size(p) - pmat = p[n-1] * np.linalg.matrix_power(a, 0) + pmat = p[n-1] * np.linalg.matrix_power(A, 0) for i in np.arange(1, n): - pmat = pmat + p[n-i-1] * np.linalg.matrix_power(a, i) + pmat = pmat + p[n-i-1] * np.linalg.matrix_power(A, i) K = np.linalg.solve(ct, pmat) - K = K[-1][:] # Extract the last row - return _ssmatrix(K) + K = K[-1, :] # Extract the last row + return K def lqr(*args, **kwargs): @@ -319,13 +278,13 @@ def lqr(*args, **kwargs): Parameters ---------- A, B : 2D array_like - Dynamics and input matrices - sys : LTI StateSpace system - Linear system + Dynamics and input matrices. + sys : LTI `StateSpace` system + Linear system. Q, R : 2D array - State and input weight matrices + State and input weight matrices. N : 2D array, optional - Cross weight matrix + Cross weight matrix. integral_action : ndarray, optional If this keyword is specified, the controller includes integral action in addition to state feedback. The value of the @@ -336,17 +295,17 @@ def lqr(*args, **kwargs): additional rows and columns in the `Q` matrix. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first - and then 'scipy'. + 'slycot' and 'scipy'. If set to None (default), try 'slycot' + first and then 'scipy'. Returns ------- - K : 2D array (or matrix) - State feedback gains - S : 2D array (or matrix) - Solution to Riccati equation + K : 2D array + State feedback gains. + S : 2D array + Solution to Riccati equation. E : 1D array - Eigenvalues of the closed loop system + Eigenvalues of the closed loop system. See Also -------- @@ -356,7 +315,7 @@ def lqr(*args, **kwargs): ----- If the first argument is an LTI object, then this object will be used to define the dynamics and input matrices. Furthermore, if the LTI - object corresponds to a discrete time system, the ``dlqr()`` function + object corresponds to a discrete-time system, the `dlqr` function will be called. Examples @@ -369,7 +328,7 @@ def lqr(*args, **kwargs): # Process the arguments and figure out what inputs we received # - # If we were passed a discrete time system as the first arg, use dlqr() + # If we were passed a discrete-time system as the first arg, use dlqr() if isinstance(args[0], LTI) and isdtime(args[0], strict=True): # Call dlqr return dlqr(*args, **kwargs) @@ -438,7 +397,7 @@ def lqr(*args, **kwargs): raise TypeError("unrecognized keywords: ", str(kwargs)) # Compute the result (dimension and symmetry checking done in care()) - X, L, G = care(A, B, Q, R, N, None, method=method, S_s="N") + X, L, G = care(A, B, Q, R, N, None, method=method, _Ss="N") return G, X, L @@ -459,20 +418,20 @@ def dlqr(*args, **kwargs): * ``dlqr(A, B, Q, R)`` * ``dlqr(A, B, Q, R, N)`` - where `dsys` is a discrete-time :class:`StateSpace` system, and `A`, `B`, + where `dsys` is a discrete-time `StateSpace` system, and `A`, `B`, `Q`, `R`, and `N` are 2d arrays of appropriate dimension (`dsys.dt` must not be 0.) Parameters ---------- A, B : 2D array - Dynamics and input matrices - dsys : LTI :class:`StateSpace` - Discrete-time linear system + Dynamics and input matrices. + dsys : LTI `StateSpace` + Discrete-time linear system. Q, R : 2D array - State and input weight matrices + State and input weight matrices. N : 2D array, optional - Cross weight matrix + Cross weight matrix. integral_action : ndarray, optional If this keyword is specified, the controller includes integral action in addition to state feedback. The value of the @@ -483,17 +442,17 @@ def dlqr(*args, **kwargs): additional rows and columns in the `Q` matrix. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first - and then 'scipy'. + 'slycot' and 'scipy'. If set to None (default), try 'slycot' + first and then 'scipy'. Returns ------- - K : 2D array (or matrix) - State feedback gains - S : 2D array (or matrix) - Solution to Riccati equation + K : 2D array + State feedback gains. + S : 2D array + Solution to Riccati equation. E : 1D array - Eigenvalues of the closed loop system + Eigenvalues of the closed loop system. See Also -------- @@ -513,7 +472,7 @@ def dlqr(*args, **kwargs): if (len(args) < 3): raise ControlArgument("not enough input arguments") - # If we were passed a continus time system as the first arg, raise error + # If we were passed a continues time system as the first arg, raise error if isinstance(args[0], LTI) and isctime(args[0], strict=True): raise ControlArgument("dsys must be discrete time (dt != 0)") @@ -575,16 +534,18 @@ def dlqr(*args, **kwargs): raise TypeError("unrecognized keywords: ", str(kwargs)) # Compute the result (dimension and symmetry checking done in dare()) - S, E, K = dare(A, B, Q, R, N, method=method, S_s="N") - return _ssmatrix(K), _ssmatrix(S), E + S, E, K = dare(A, B, Q, R, N, method=method, _Ss="N") + return K, S, E -# Function to create an I/O sytems representing a state feedback controller +# Function to create an I/O systems representing a state feedback controller def create_statefbk_iosystem( - sys, gain, integral_action=None, estimator=None, controller_type=None, - xd_labels=None, ud_labels=None, gainsched_indices=None, + sys, gain, feedfwd_gain=None, integral_action=None, estimator=None, + controller_type=None, xd_labels=None, ud_labels=None, ref_labels=None, + feedfwd_pattern='trajgen', gainsched_indices=None, gainsched_method='linear', control_indices=None, state_indices=None, - name=None, inputs=None, outputs=None, states=None, **kwargs): + name=None, inputs=None, outputs=None, states=None, params=None, + **kwargs): r"""Create an I/O system using a (full) state feedback controller. This function creates an input/output system that implements a @@ -592,12 +553,12 @@ def create_statefbk_iosystem( .. math:: u = u_d - K_p (x - x_d) - K_i \int(C x - C x_d) - It can be called in the form:: + by calling ctrl, clsys = ct.create_statefbk_iosystem(sys, K) where `sys` is the process dynamics and `K` is the state (+ integral) - feedback gain (eg, from LQR). The function returns the controller + feedback gain (e.g., from LQR). The function returns the controller `ctrl` and the closed loop systems `clsys`, both as I/O systems. A gain scheduled controller can also be created, by passing a list of @@ -608,9 +569,21 @@ def create_statefbk_iosystem( where :math:`\mu` represents the scheduling variable. + Alternatively, a controller of the form + + .. math:: u = k_f r - K_p x - K_i \int(C x - r) + + can be created by calling + + ctrl, clsys = ct.create_statefbk_iosystem( + sys, K, kf, feedfwd_pattern='refgain') + + In either form, an estimator can also be used to compute the estimated + state from the input and output measurements. + Parameters ---------- - sys : NonlinearIOSystem + sys : `NonlinearIOSystem` The I/O system that represents the process dynamics. If no estimator is given, the output of this system should represent the full state. @@ -623,7 +596,7 @@ def create_statefbk_iosystem( represent the gains of the integral states of the controller. If a tuple is given, then it specifies a gain schedule. The tuple - should be of the form `(gains, points)` where gains is a list of + should be of the form ``(gains, points)`` where gains is a list of gains `K_j` and points is a list of values `mu_j` at which the gains are computed. The `gainsched_indices` parameter should be used to specify the scheduling variables. @@ -631,14 +604,15 @@ def create_statefbk_iosystem( If an I/O system is given, the error e = x - xd is passed to the system and the output is used as the feedback compensation term. - xd_labels, ud_labels : str or list of str, optional - Set the name of the signals to use for the desired state and - inputs. If a single string is specified, it should be a format - string using the variable `i` as an index. Otherwise, a list of - strings matching the size of `x_d` and `u_d`, respectively, should - be used. Default is "xd[{i}]" for xd_labels and "ud[{i}]" for - ud_labels. These settings can also be overridden using the - `inputs` keyword. + feedfwd_gain : array_like, optional + Specify the feedforward gain, `k_f`. Used only for the reference + gain design pattern. If not given and if `sys` is a `StateSpace` + (linear) system, will be computed as -1/(C (A-BK)^{-1}) B. + + feedfwd_pattern : str, optional + If set to 'refgain', the reference gain design pattern is used to + create the controller instead of the trajectory generation + ('trajgen') pattern. integral_action : ndarray, optional If this keyword is specified, the controller can include integral @@ -647,7 +621,7 @@ def create_statefbk_iosystem( multiplied by the current and desired state to generate the error for the internal integrator states of the control law. - estimator : NonlinearIOSystem, optional + estimator : `NonlinearIOSystem`, optional If an estimator is provided, use the states of the estimator as the system inputs for the controller. @@ -657,7 +631,7 @@ def create_statefbk_iosystem( the controller is the desired state `x_d`, the desired input `u_d`, and the system state `x` (or state estimate `xhat`, if an estimator is given). If value is an integer `q`, the first `q` - values of the `[x_d, u_d, x]` vector are used. Otherwise, the + values of the ``[x_d, u_d, x]`` vector are used. Otherwise, the value should be a slice or a list of indices. The list of indices can be specified as either integer offsets or as signal names. The default is to use the desired state `x_d`. @@ -665,36 +639,38 @@ def create_statefbk_iosystem( gainsched_method : str, optional The method to use for gain scheduling. Possible values are 'linear' (default), 'nearest', and 'cubic'. More information is available in - :func:`scipy.interpolate.griddata`. For points outside of the convex + `scipy.interpolate.griddata`. For points outside of the convex hull of the scheduling points, the gain at the nearest point is used. controller_type : 'linear' or 'nonlinear', optional Set the type of controller to create. The default for a linear gain is a linear controller implementing the LQR regulator. If the type - is 'nonlinear', a :class:NonlinearIOSystem is created instead, with + is 'nonlinear', a `NonlinearIOSystem` is created instead, with the gain `K` as a parameter (allowing modifications of the gain at runtime). If the gain parameter is a tuple, then a nonlinear, gain-scheduled controller is created. Returns ------- - ctrl : NonlinearIOSystem - Input/output system representing the controller. This system - takes as inputs the desired state `x_d`, the desired input - `u_d`, and either the system state `x` or the estimated state - `xhat`. It outputs the controller action `u` according to the - formula `u = u_d - K(x - x_d)`. If the keyword - `integral_action` is specified, then an additional set of - integrators is included in the control system (with the gain - matrix `K` having the integral gains appended after the state - gains). If a gain scheduled controller is specified, the gain - (proportional and integral) are evaluated using the scheduling - variables specified by `gainsched_indices`. - - clsys : NonlinearIOSystem + ctrl : `NonlinearIOSystem` + Input/output system representing the controller. For the 'trajgen' + design pattern (default), this system takes as inputs the desired + state `x_d`, the desired input `u_d`, and either the system state + `x` or the estimated state `xhat`. It outputs the controller + action `u` according to the formula u = u_d - K(x - x_d). For + the 'refgain' design pattern, the system takes as inputs the + reference input `r` and the system or estimated state. If the + keyword `integral_action` is specified, then an additional set of + integrators is included in the control system (with the gain matrix + `K` having the integral gains appended after the state gains). If + a gain scheduled controller is specified, the gain (proportional + and integral) are evaluated using the scheduling variables + specified by `gainsched_indices`. + + clsys : `NonlinearIOSystem` Input/output system representing the closed loop system. This - system takes as inputs the desired trajectory `(x_d, u_d)` and + system takes as inputs the desired trajectory (x_d, u_d) and outputs the system state `x` and the applied input `u` (vertically stacked). @@ -716,15 +692,28 @@ def create_statefbk_iosystem( specified as either integer offsets or as estimator/system output signal names. If not specified, defaults to the system states. - inputs, outputs : str, or list of str, optional + xd_labels, ud_labels, ref_labels : str or list of str, optional + Set the name of the signals to use for the desired state and inputs + or the reference inputs (for the 'refgain' design pattern). If a + single string is specified, it should be a format string using the + variable `i` as an index. Otherwise, a list of strings matching + the size of x_d and u_d, respectively, should be used. + Default is "xd[{i}]" for xd_labels and "ud[{i}]" for ud_labels. + These settings can also be overridden using the `inputs` keyword. + + inputs, outputs, states : str, or list of str, optional List of strings that name the individual signals of the transformed - system. If not given, the inputs and outputs are the same as the - original system. + system. If not given, the inputs, outputs, and states are the same + as the original system. name : string, optional - System name. If unspecified, a generic name is generated + System name. If unspecified, a generic name 'sys[id]' is generated with a unique integer id. + params : dict, optional + System parameter values. By default, these will be copied from + `sys` and `ctrl`, but can be overridden with this keyword. + Examples -------- >>> import control as ct @@ -747,12 +736,18 @@ def create_statefbk_iosystem( if not isinstance(sys, NonlinearIOSystem): raise ControlArgument("Input system must be I/O system") - # Process (legacy) keywords + # Process keywords + params = sys.params if params is None else params controller_type = _process_legacy_keyword( kwargs, 'type', 'controller_type', controller_type) if kwargs: raise TypeError("unrecognized keywords: ", str(kwargs)) + # Check for consistency of positional parameters + if feedfwd_gain is not None and feedfwd_pattern != 'refgain': + raise ControlArgument( + "feedfwd_gain specified but feedfwd_pattern != 'refgain'") + # Figure out what inputs to the system to use control_indices = _process_indices( control_indices, 'control', sys.input_labels, sys.ninputs) @@ -788,12 +783,12 @@ def create_statefbk_iosystem( if integral_action is not None: if not isinstance(integral_action, np.ndarray): raise ControlArgument("Integral action must pass an array") - elif integral_action.shape[1] != sys_nstates: + + C = np.atleast_2d(integral_action) + if C.shape[1] != sys_nstates: raise ControlArgument( "Integral gain size must match system state size") - else: - nintegrators = integral_action.shape[0] - C = integral_action + nintegrators = C.shape[0] else: # Create a C matrix with no outputs, just in case update gets called C = np.zeros((0, sys_nstates)) @@ -812,6 +807,10 @@ def create_statefbk_iosystem( # Check for gain scheduled controller if len(gain) != 2: raise ControlArgument("gain must be a 2-tuple for gain scheduling") + elif feedfwd_pattern != 'trajgen': + raise NotImplementedError( + "Gain scheduling is not implemented for pattern " + f"'{feedfwd_pattern}'") gains, points = gain[0:2] # Stack gains and points if past as a list @@ -819,7 +818,7 @@ def create_statefbk_iosystem( points = np.stack(points) gainsched = True - elif isinstance(gain, NonlinearIOSystem): + elif isinstance(gain, NonlinearIOSystem) and feedfwd_pattern != 'refgain': if controller_type not in ['iosystem', None]: raise ControlArgument( f"incompatible controller type '{controller_type}'") @@ -841,20 +840,29 @@ def create_statefbk_iosystem( raise ControlArgument(f"unknown controller_type '{controller_type}'") # Figure out the labels to use - xd_labels = _process_labels( - xd_labels, 'xd', ['xd[{i}]'.format(i=i) for i in range(sys_nstates)]) - ud_labels = _process_labels( - ud_labels, 'ud', ['ud[{i}]'.format(i=i) for i in range(sys_ninputs)]) - - # Create the signal and system names - if inputs is None: - inputs = xd_labels + ud_labels + estimator.output_labels + if feedfwd_pattern == 'trajgen': + xd_labels = _process_labels(xd_labels, 'xd', [ + 'xd[{i}]'.format(i=i) for i in range(sys_nstates)]) + ud_labels = _process_labels(ud_labels, 'ud', [ + 'ud[{i}]'.format(i=i) for i in range(sys_ninputs)]) + + # Create the signal and system names + if inputs is None: + inputs = xd_labels + ud_labels + estimator.output_labels + elif feedfwd_pattern == 'refgain': + ref_labels = _process_labels(ref_labels, 'r', [ + f'r[{i}]' for i in range(sys_ninputs)]) + if inputs is None: + inputs = ref_labels + estimator.output_labels + else: + raise NotImplementedError(f"unknown pattern '{feedfwd_pattern}'") + if outputs is None: outputs = [sys.input_labels[i] for i in control_indices] if states is None: states = nintegrators - # Process gainscheduling variables, if present + # Process gain scheduling variables, if present if gainsched: # Create a copy of the scheduling variable indices (default = xd) gainsched_indices = _process_indices( @@ -897,7 +905,7 @@ def _compute_gain(mu): return K # Define the controller system - if controller_type == 'nonlinear': + if controller_type == 'nonlinear' and feedfwd_pattern == 'trajgen': # Create an I/O system for the state feedback gains def _control_update(t, states, inputs, params): # Split input into desired state, nominal input, and current state @@ -926,12 +934,12 @@ def _control_output(t, states, inputs, params): return u - params = {} if gainsched else {'K': K} + ctrl_params = {} if gainsched else {'K': K} ctrl = NonlinearIOSystem( _control_update, _control_output, name=name, inputs=inputs, - outputs=outputs, states=states, params=params) + outputs=outputs, states=states, params=ctrl_params) - elif controller_type == 'iosystem': + elif controller_type == 'iosystem' and feedfwd_pattern == 'trajgen': # Use the passed system to compute feedback compensation def _control_update(t, states, inputs, params): # Split input into desired state, nominal input, and current state @@ -955,7 +963,7 @@ def _control_output(t, states, inputs, params): _control_update, _control_output, name=name, inputs=inputs, outputs=outputs, states=fbkctrl.state_labels, dt=fbkctrl.dt) - elif controller_type == 'linear' or controller_type is None: + elif controller_type in 'linear' and feedfwd_pattern == 'trajgen': # Create the matrices implementing the controller if isctime(sys): # Continuous time: integrator @@ -973,6 +981,37 @@ def _control_output(t, states, inputs, params): A_lqr, B_lqr, C_lqr, D_lqr, dt=sys.dt, name=name, inputs=inputs, outputs=outputs, states=states) + elif feedfwd_pattern == 'refgain': + if controller_type not in ['linear', 'iosystem']: + raise ControlArgument( + "refgain design pattern only supports linear controllers") + + if feedfwd_gain is None: + raise ControlArgument( + "'feedfwd_gain' required for reference gain pattern") + + # Check to make sure the reference gain is valid + Kf = np.atleast_2d(feedfwd_gain) + if Kf.ndim != 2 or Kf.shape[0] != sys.ninputs or \ + Kf.shape[1] != sys.ninputs: + raise ControlArgument("feedfwd_gain is not the right shape") + + # Create the matrices implementing the controller + # [r, x]->[u]: u = k_f r - K_p x - K_i \int(C x - r) + if isctime(sys): + # Continuous time: integrator + A_lqr = np.zeros((C.shape[0], C.shape[0])) + else: + # Discrete time: summer + A_lqr = np.eye(C.shape[0]) + B_lqr = np.hstack([-np.eye(C.shape[0], sys_ninputs), C]) + C_lqr = -K[:, sys_nstates:] # integral gain (opt) + D_lqr = np.hstack([Kf, -K]) + + ctrl = ss( + A_lqr, B_lqr, C_lqr, D_lqr, dt=sys.dt, name=name, + inputs=inputs, outputs=outputs, states=states) + else: raise ControlArgument(f"unknown controller_type '{controller_type}'") @@ -986,25 +1025,26 @@ def _control_output(t, states, inputs, params): [sys, ctrl] if estimator == sys else [sys, ctrl, estimator], name=sys.name + "_" + ctrl.name, add_unused=True, inplist=inplist, inputs=input_labels, - outlist=outlist, outputs=output_labels + outlist=outlist, outputs=output_labels, + params= ctrl.params | params ) return ctrl, closed def ctrb(A, B, t=None): - """Controllabilty matrix. + """Controllability matrix. Parameters ---------- A, B : array_like or string - Dynamics and input matrix of the system + Dynamics and input matrix of the system. t : None or integer - maximum time horizon of the controllability matrix, max = A.shape[0] + Maximum time horizon of the controllability matrix, max = A.shape[0]. Returns ------- - C : 2D array (or matrix) - Controllability matrix + C : 2D array + Controllability matrix. Examples -------- @@ -1016,21 +1056,22 @@ def ctrb(A, B, t=None): """ # Convert input parameters to matrices (if they aren't already) - amat = _ssmatrix(A) - bmat = _ssmatrix(B) - n = np.shape(amat)[0] - m = np.shape(bmat)[1] - + A = _ssmatrix(A, square=True, name="A") + n = A.shape[0] + + B = _ssmatrix(B, axis=0, rows=n, name="B") + m = B.shape[1] + if t is None or t > n: t = n # Construct the controllability matrix ctrb = np.zeros((n, t * m)) - ctrb[:, :m] = bmat + ctrb[:, :m] = B for k in range(1, t): - ctrb[:, k * m:(k + 1) * m] = np.dot(amat, ctrb[:, (k - 1) * m:k * m]) + ctrb[:, k * m:(k + 1) * m] = np.dot(A, ctrb[:, (k - 1) * m:k * m]) - return _ssmatrix(ctrb) + return ctrb def obsv(A, C, t=None): @@ -1039,14 +1080,14 @@ def obsv(A, C, t=None): Parameters ---------- A, C : array_like or string - Dynamics and output matrix of the system + Dynamics and output matrix of the system. t : None or integer - maximum time horizon of the controllability matrix, max = A.shape[0] - + Maximum time horizon of the controllability matrix, max = A.shape[0]. + Returns ------- - O : 2D array (or matrix) - Observability matrix + O : 2D array + Observability matrix. Examples -------- @@ -1056,24 +1097,24 @@ def obsv(A, C, t=None): np.int64(2) """ - # Convert input parameters to matrices (if they aren't already) - amat = _ssmatrix(A) - cmat = _ssmatrix(C) - n = np.shape(amat)[0] - p = np.shape(cmat)[0] - + A = _ssmatrix(A, square=True, name="A") + n = A.shape[0] + + C = _ssmatrix(C, cols=n, name="C") + p = C.shape[0] + if t is None or t > n: t = n # Construct the observability matrix obsv = np.zeros((t * p, n)) - obsv[:p, :] = cmat - + obsv[:p, :] = C + for k in range(1, t): - obsv[k * p:(k + 1) * p, :] = np.dot(obsv[(k - 1) * p:k * p, :], amat) + obsv[k * p:(k + 1) * p, :] = np.dot(obsv[(k - 1) * p:k * p, :], A) - return _ssmatrix(obsv) + return obsv def gram(sys, type): @@ -1081,28 +1122,28 @@ def gram(sys, type): Parameters ---------- - sys : StateSpace - System description + sys : `StateSpace` + System description. type : String Type of desired computation. `type` is either 'c' (controllability) or 'o' (observability). To compute the Cholesky factors of Gramians - use 'cf' (controllability) or 'of' (observability) + use 'cf' (controllability) or 'of' (observability). Returns ------- - gram : 2D array (or matrix) - Gramian of system + gram : 2D array + Gramian of system. Raises ------ ValueError - * if system is not instance of StateSpace class - * if `type` is not 'c', 'o', 'cf' or 'of' - * if system is unstable (sys.A has eigenvalues not in left half plane) + * If system is not instance of `StateSpace` class, or + * if `type` is not 'c', 'o', 'cf' or 'of', or + * if system is unstable (sys.A has eigenvalues not in left half plane). ControlSlycot - if slycot routine sb03md cannot be found - if slycot routine sb03od cannot be found + If slycot routine sb03md cannot be found or + if slycot routine sb03od cannot be found. Examples -------- @@ -1140,7 +1181,7 @@ def gram(sys, type): # Compute Gramian by the Slycot routine sb03md # make sure Slycot is installed if sb03md is None: - raise ControlSlycot("can't find slycot module 'sb03md'") + raise ControlSlycot("can't find slycot module sb03md") if type == 'c': tra = 'T' C = -sys.B @ sys.B.T @@ -1153,12 +1194,12 @@ def gram(sys, type): X, scale, sep, ferr, w = sb03md( n, C, A, U, dico, job='X', fact='N', trana=tra) gram = X - return _ssmatrix(gram) + return gram elif type == 'cf' or type == 'of': - # Compute cholesky factored gramian from slycot routine sb03od + # Compute Cholesky factored Gramian from Slycot routine sb03od if sb03od is None: - raise ControlSlycot("can't find slycot module 'sb03od'") + raise ControlSlycot("can't find slycot module sb03od") tra = 'N' n = sys.nstates Q = np.zeros((n, n)) @@ -1176,4 +1217,8 @@ def gram(sys, type): X, scale, w = sb03od( n, m, A, Q, C.transpose(), dico, fact='N', trans=tra) gram = X - return _ssmatrix(gram) + return gram + + +# Short versions of functions +acker = place_acker diff --git a/control/statesp.py b/control/statesp.py index 717fc9a73..65529b99d 100644 --- a/control/statesp.py +++ b/control/statesp.py @@ -1,74 +1,43 @@ -"""statesp.py - -State space representation and functions. +# statesp.py - state space class and related functions +# +# Initial author: Richard M. Murray +# Creation date: 24 May 2009 +# Pre-2014 revisions: Kevin K. Chen, Dec 2010 +# Use `git shortlog -n -s statesp.py` for full list of contributors -This file contains the StateSpace class, which is used to represent linear -systems in state space. This is the primary representation for the -python-control library. +"""State space class and related functions. -""" +This module contains the `StateSpace class`, which is used to +represent linear systems in state space. -"""Copyright (c) 2010 by California Institute of Technology -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Author: Richard M. Murray -Date: 24 May 09 -Revised: Kevin K. Chen, Dec 10 - -$Id$ """ import math -from copy import deepcopy -from warnings import warn +import sys from collections.abc import Iterable +from warnings import warn import numpy as np import scipy as sp import scipy.linalg -from numpy import (any, asarray, concatenate, cos, delete, empty, exp, eye, - isinf, ones, pad, sin, squeeze, zeros) +from numpy import array # noqa: F401 +from numpy import any, asarray, concatenate, cos, delete, empty, exp, eye, \ + isinf, pad, sin, squeeze, zeros from numpy.linalg import LinAlgError, eigvals, matrix_rank, solve from numpy.random import rand, randn from scipy.signal import StateSpace as signalStateSpace from scipy.signal import cont2discrete -from . import config -from .exception import ControlMIMONotImplemented, ControlSlycot, slycot_check +import control + +from . import bdalg, config +from .exception import ControlDimension, ControlMIMONotImplemented, \ + ControlSlycot, slycot_check from .frdata import FrequencyResponseData -from .iosys import (InputOutputSystem, _process_dt_keyword, - _process_iosys_keywords, _process_signal_list, - common_timebase, isdtime, issiso) +from .iosys import InputOutputSystem, NamedSignal, _process_iosys_keywords, \ + _process_signal_list, _process_subsys_index, common_timebase, issiso from .lti import LTI, _process_frequency_response +from .mateqn import _check_shape from .nlsys import InterconnectedSystem, NonlinearIOSystem try: @@ -76,8 +45,8 @@ except ImportError: ab13dd = None -__all__ = ['StateSpace', 'LinearICSystem', 'ss2io', 'tf2io', 'tf2ss', 'ssdata', - 'linfnorm', 'ss', 'rss', 'drss', 'summing_junction'] +__all__ = ['StateSpace', 'LinearICSystem', 'ss2io', 'tf2io', 'tf2ss', + 'ssdata', 'linfnorm', 'ss', 'rss', 'drss', 'summing_junction'] # Define module default parameter values _statesp_defaults = { @@ -91,7 +60,7 @@ class StateSpace(NonlinearIOSystem, LTI): r"""StateSpace(A, B, C, D[, dt]) - A class for representing state-space models. + State space representation for LTI input/output systems. The StateSpace class is used to represent state-space realizations of linear time-invariant (LTI) systems: @@ -101,75 +70,89 @@ class StateSpace(NonlinearIOSystem, LTI): dx/dt &= A x + B u \\ y &= C x + D u - where `u` is the input, `y` is the output, and `x` is the state. + where :math:`u` is the input, :math:`y` is the output, and + :math:`x` is the state. State space systems are usually created + with the `ss` factory function. Parameters ---------- - A, B, C, D: array_like + A, B, C, D : array_like System matrices of the appropriate dimensions. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous - time, True indicates discrete time with unspecified sampling - time, positive number is discrete time with specified - sampling time, None indicates unspecified timebase (either - continuous or discrete time). + System timebase. 0 (default) indicates continuous time, True + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, None + indicates unspecified timebase (either continuous or discrete time). Attributes ---------- ninputs, noutputs, nstates : int Number of input, output and state variables. - A, B, C, D : 2D arrays - System matrices defining the input/output dynamics. - dt : None, True or float - System timebase. 0 (default) indicates continuous time, True indicates - discrete time with unspecified sampling time, positive number is - discrete time with specified sampling time, None indicates unspecified - timebase (either continuous or discrete time). + shape : tuple + 2-tuple of I/O system dimension, (noutputs, ninputs). + input_labels, output_labels, state_labels : list of str + Names for the input, output, and state variables. + name : string, optional + System name. + + See Also + -------- + ss, InputOutputSystem, NonlinearIOSystem Notes ----- - The main data members in the ``StateSpace`` class are the A, B, C, and D + The main data members in the `StateSpace` class are the A, B, C, and D matrices. The class also keeps track of the number of states (i.e., the size of A). - A discrete time system is created by specifying a nonzero 'timebase', dt + A discrete-time system is created by specifying a nonzero 'timebase', dt when the system is constructed: - * dt = 0: continuous time system (default) - * dt > 0: discrete time system with sampling period 'dt' - * dt = True: discrete time with unspecified sampling period - * dt = None: no timebase specified + * `dt` = 0: continuous-time system (default) + * `dt` > 0: discrete-time system with sampling period `dt` + * `dt` = True: discrete time with unspecified sampling period + * `dt` = None: no timebase specified - Systems must have compatible timebases in order to be combined. A discrete - time system with unspecified sampling time (`dt = True`) can be combined - with a system having a specified sampling time; the result will be a - discrete time system with the sample time of the latter system. Similarly, - a system with timebase `None` can be combined with a system having any - timebase; the result will have the timebase of the latter system. - The default value of dt can be changed by changing the value of - ``control.config.defaults['control.default_dt']``. + Systems must have compatible timebases in order to be combined. A + discrete-time system with unspecified sampling time (`dt` = True) can + be combined with a system having a specified sampling time; the result + will be a discrete-time system with the sample time of the other + system. Similarly, a system with timebase None can be combined with a + system having any timebase; the result will have the timebase of the + other system. The default value of dt can be changed by changing the + value of `config.defaults['control.default_dt']`. A state space system is callable and returns the value of the transfer function evaluated at a point in the complex plane. See - :meth:`~control.StateSpace.__call__` for a more detailed description. - - StateSpace instances have support for IPython LaTeX output, - intended for pretty-printing in Jupyter notebooks. The LaTeX - output can be configured using - `control.config.defaults['statesp.latex_num_format']` and - `control.config.defaults['statesp.latex_repr_type']`. The LaTeX output is - tailored for MathJax, as used in Jupyter, and may look odd when - typeset by non-MathJax LaTeX systems. - - `control.config.defaults['statesp.latex_num_format']` is a format string - fragment, specifically the part of the format string after `'{:'` + `StateSpace.__call__` for a more detailed description. + + Subsystems corresponding to selected input/output pairs can be + created by indexing the state space system:: + + subsys = sys[output_spec, input_spec] + + The input and output specifications can be single integers, lists of + integers, or slices. In addition, the strings representing the names + of the signals can be used and will be replaced with the equivalent + signal offsets. The subsystem is created by truncating the inputs and + outputs, but leaving the full set of system states. + + StateSpace instances have support for IPython HTML/LaTeX output, intended + for pretty-printing in Jupyter notebooks. The HTML/LaTeX output can be + configured using `config.defaults['statesp.latex_num_format']` + and `config.defaults['statesp.latex_repr_type']`. The + HTML/LaTeX output is tailored for MathJax, as used in Jupyter, and + may look odd when typeset by non-MathJax LaTeX systems. + + `config.defaults['statesp.latex_num_format']` is a format string + fragment, specifically the part of the format string after '{:' used to convert floating-point numbers to strings. By default it - is `'.3g'`. + is '.3g'. - `control.config.defaults['statesp.latex_repr_type']` must either be - `'partitioned'` or `'separate'`. If `'partitioned'`, the A, B, C, D + `config.defaults['statesp.latex_repr_type']` must either be + 'partitioned' or 'separate'. If 'partitioned', the A, B, C, D matrices are shown as a single, partitioned matrix; if - `'separate'`, the matrices are shown separately. + 'separate', the matrices are shown separately. """ def __init__(self, *args, **kwargs): @@ -178,17 +161,13 @@ def __init__(self, *args, **kwargs): Construct a state space object. The default constructor is StateSpace(A, B, C, D), where A, B, C, D - are matrices or equivalent objects. To create a discrete time system, - use StateSpace(A, B, C, D, dt) where `dt` is the sampling time (or - True for unspecified sampling time). To call the copy constructor, - call StateSpace(sys), where sys is a StateSpace object. - - The `remove_useless_states` keyword can be used to scan the A, B, and - C matrices for rows or columns of zeros. If the zeros are such that a - particular state has no effect on the input-output dynamics, then that - state is removed from the A, B, and C matrices. If not specified, the - value is read from `config.defaults['statesp.remove_useless_states']` - (default = False). + are matrices or equivalent objects. To create a discrete-time + system, use StateSpace(A, B, C, D, dt) where `dt` is the sampling + time (or True for unspecified sampling time). To call the copy + constructor, call ``StateSpace(sys)``, where `sys` is a `StateSpace` + object. + + See `StateSpace` and `ss` for more information. """ # @@ -225,21 +204,24 @@ def __init__(self, *args, **kwargs): raise TypeError( "Expected 1, 4, or 5 arguments; received %i." % len(args)) - # Convert all matrices to standard form - A = _ssmatrix(A) - # if B is a 1D array, turn it into a column vector if it fits - if np.asarray(B).ndim == 1 and len(B) == A.shape[0]: - B = _ssmatrix(B, axis=0) - else: - B = _ssmatrix(B) - if np.asarray(C).ndim == 1 and len(C) == A.shape[0]: - C = _ssmatrix(C, axis=1) - else: - C = _ssmatrix(C, axis=0) # if this doesn't work, error below + # Convert all matrices to standard form (sizes checked later) + A = _ssmatrix(A, square=True, name="A") + B = _ssmatrix( + B, axis=0 if np.asarray(B).ndim == 1 and len(B) == A.shape[0] + else 1, name="B") + C = _ssmatrix( + C, axis=1 if np.asarray(C).ndim == 1 and len(C) == A.shape[0] + else 0, name="C") if np.isscalar(D) and D == 0 and B.shape[1] > 0 and C.shape[0] > 0: # If D is a scalar zero, broadcast it to the proper size D = np.zeros((C.shape[0], B.shape[1])) - D = _ssmatrix(D) + D = _ssmatrix(D, name="D") + + # If only direct term is present, adjust sizes of C and D if needed + if D.size > 0 and B.size == 0: + B = np.zeros((0, D.shape[1])) + if D.size > 0 and C.size == 0: + C = np.zeros((D.shape[0], 0)) # Matrices defining the linear system self.A = A @@ -247,6 +229,9 @@ def __init__(self, *args, **kwargs): self.C = C self.D = D + # Determine if the system is static (memoryless) + static = (A.size == 0) + # # Process keyword arguments # @@ -257,10 +242,10 @@ def __init__(self, *args, **kwargs): # Process iosys keywords defaults = args[0] if len(args) == 1 else \ - {'inputs': D.shape[1], 'outputs': D.shape[0], + {'inputs': B.shape[1], 'outputs': C.shape[0], 'states': A.shape[0]} name, inputs, outputs, states, dt = _process_iosys_keywords( - kwargs, defaults, static=(A.size == 0)) + kwargs, defaults, static=static) # Create updfcn and outfcn updfcn = lambda t, x, u, params: \ @@ -275,25 +260,16 @@ def __init__(self, *args, **kwargs): states=states, dt=dt, **kwargs) # Reset shapes if the system is static - if self._isstatic(): + if static: A.shape = (0, 0) B.shape = (0, self.ninputs) C.shape = (self.noutputs, 0) - # # Check to make sure everything is consistent - # - # Check that the matrix sizes are consistent - if A.shape[0] != A.shape[1] or self.nstates != A.shape[0]: - raise ValueError("A must be square.") - if self.nstates != B.shape[0]: - raise ValueError("A and B must have the same number of rows.") - if self.nstates != C.shape[1]: - raise ValueError("A and C must have the same number of columns.") - if self.ninputs != B.shape[1] or self.ninputs != D.shape[1]: - raise ValueError("B and D must have the same number of columns.") - if self.noutputs != C.shape[0] or self.noutputs != D.shape[0]: - raise ValueError("C and D must have the same number of rows.") + _check_shape(A, self.nstates, self.nstates, name="A") + _check_shape(B, self.nstates, self.ninputs, name="B") + _check_shape(C, self.noutputs, self.nstates, name="C") + _check_shape(D, self.noutputs, self.ninputs, name="D") # # Final processing @@ -355,20 +331,20 @@ def __init__(self, *args, **kwargs): def _get_states(self): warn("The StateSpace `states` attribute will be deprecated in a " "future release. Use `nstates` instead.", - DeprecationWarning, stacklevel=2) + FutureWarning, stacklevel=2) return self.nstates def _set_states(self, value): warn("The StateSpace `states` attribute will be deprecated in a " "future release. Use `nstates` instead.", - DeprecationWarning, stacklevel=2) + FutureWarning, stacklevel=2) self.nstates = value - #: Deprecated attribute; use :attr:`nstates` instead. + #: Deprecated attribute; use `nstates` instead. #: - #: The ``state`` attribute was used to store the number of states for : a + #: The `state` attribute was used to store the number of states for : a #: state space system. It is no longer used. If you need to access the - #: number of states, use :attr:`nstates`. + #: number of states, use `nstates`. states = property(_get_states, _set_states) def _remove_useless_states(self): @@ -403,23 +379,56 @@ def _remove_useless_states(self): def __str__(self): """Return string representation of the state space system.""" string = f"{InputOutputSystem.__str__(self)}\n\n" - string += "\n".join([ - "{} = {}\n".format(Mvar, + string += "\n\n".join([ + "{} = {}".format(Mvar, "\n ".join(str(M).splitlines())) for Mvar, M in zip(["A", "B", "C", "D"], [self.A, self.B, self.C, self.D])]) - if self.isdtime(strict=True): - string += f"\ndt = {self.dt}\n" return string - # represent to implement a re-loadable version - def __repr__(self): - """Print state-space system in loadable form.""" - # TODO: add input/output names (?) - return "StateSpace({A}, {B}, {C}, {D}{dt})".format( + def _repr_eval_(self): + # Loadable format + out = "StateSpace(\n{A},\n{B},\n{C},\n{D}".format( A=self.A.__repr__(), B=self.B.__repr__(), - C=self.C.__repr__(), D=self.D.__repr__(), - dt=(isdtime(self, strict=True) and ", {}".format(self.dt)) or '') + C=self.C.__repr__(), D=self.D.__repr__()) + + out += super()._dt_repr(separator=",\n", space="") + if len(labels := super()._label_repr()) > 0: + out += ",\n" + labels + + out += ")" + return out + + def _repr_html_(self): + """HTML representation of state-space model. + + Output is controlled by config options statesp.latex_repr_type, + statesp.latex_num_format, and statesp.latex_maxsize. + + The output is primarily intended for Jupyter notebooks, which + use MathJax to render the LaTeX, and the results may look odd + when processed by a 'conventional' LaTeX system. + + Returns + ------- + s : str + HTML/LaTeX representation of model, or None if either matrix + dimension is greater than statesp.latex_maxsize. + + """ + syssize = self.nstates + max(self.noutputs, self.ninputs) + if syssize > config.defaults['statesp.latex_maxsize']: + return None + elif config.defaults['statesp.latex_repr_type'] == 'partitioned': + return super()._repr_info_(html=True) + \ + "\n" + self._latex_partitioned() + elif config.defaults['statesp.latex_repr_type'] == 'separate': + return super()._repr_info_(html=True) + \ + "\n" + self._latex_separate() + else: + raise ValueError( + "Unknown statesp.latex_repr_type '{cfg}'".format( + cfg=config.defaults['statesp.latex_repr_type'])) def _latex_partitioned_stateless(self): """`Partitioned` matrix LaTeX representation for stateless systems @@ -428,23 +437,28 @@ def _latex_partitioned_stateless(self): Returns ------- - s : string with LaTeX representation of model + s : str + LaTeX representation of model. + """ + # Apply NumPy formatting + with np.printoptions(threshold=sys.maxsize): + D = eval(repr(self.D)) + lines = [ r'$$', - (r'\left(' + (r'\left[' + r'\begin{array}' + r'{' + 'rll' * self.ninputs + '}') ] - for Di in asarray(self.D): + for Di in asarray(D): lines.append('&'.join(_f2s(Dij) for Dij in Di) + '\\\\') lines.extend([ r'\end{array}' - r'\right)' - + self._latex_dt(), + r'\right]', r'$$']) return '\n'.join(lines) @@ -457,32 +471,38 @@ def _latex_partitioned(self): Returns ------- - s : string with LaTeX representation of model + s : str + LaTeX representation of model. + """ if self.nstates == 0: return self._latex_partitioned_stateless() + # Apply NumPy formatting + with np.printoptions(threshold=sys.maxsize): + A, B, C, D = ( + eval(repr(getattr(self, M))) for M in ['A', 'B', 'C', 'D']) + lines = [ r'$$', - (r'\left(' + (r'\left[' + r'\begin{array}' + r'{' + 'rll' * self.nstates + '|' + 'rll' * self.ninputs + '}') ] - for Ai, Bi in zip(asarray(self.A), asarray(self.B)): + for Ai, Bi in zip(asarray(A), asarray(B)): lines.append('&'.join([_f2s(Aij) for Aij in Ai] + [_f2s(Bij) for Bij in Bi]) + '\\\\') lines.append(r'\hline') - for Ci, Di in zip(asarray(self.C), asarray(self.D)): + for Ci, Di in zip(asarray(C), asarray(D)): lines.append('&'.join([_f2s(Cij) for Cij in Ci] + [_f2s(Dij) for Dij in Di]) + '\\\\') lines.extend([ r'\end{array}' - + r'\right)' - + self._latex_dt(), + + r'\right]', r'$$']) return '\n'.join(lines) @@ -494,7 +514,9 @@ def _latex_separate(self): Returns ------- - s : string with LaTeX representation of model + s : str + LaTeX representation of model. + """ lines = [ r'$$', @@ -503,7 +525,7 @@ def _latex_separate(self): def fmt_matrix(matrix, name): matlines = [name - + r' = \left(\begin{array}{' + + r' = \left[\begin{array}{' + 'rll' * matrix.shape[1] + '}'] for row in asarray(matrix): @@ -511,7 +533,7 @@ def fmt_matrix(matrix, name): + '\\\\') matlines.extend([ r'\end{array}' - r'\right)']) + r'\right]']) return matlines if self.nstates > 0: @@ -525,52 +547,11 @@ def fmt_matrix(matrix, name): lines.extend(fmt_matrix(self.D, 'D')) lines.extend([ - r'\end{array}' - + self._latex_dt(), + r'\end{array}', r'$$']) return '\n'.join(lines) - def _latex_dt(self): - if self.isdtime(strict=True): - if self.dt is True: - return r"~,~dt=~\mathrm{True}" - else: - fmt = config.defaults['statesp.latex_num_format'] - return f"~,~dt={self.dt:{fmt}}" - return "" - - def _repr_latex_(self): - """LaTeX representation of state-space model - - Output is controlled by config options statesp.latex_repr_type, - statesp.latex_num_format, and statesp.latex_maxsize. - - The output is primarily intended for Jupyter notebooks, which - use MathJax to render the LaTeX, and the results may look odd - when processed by a 'conventional' LaTeX system. - - - Returns - ------- - - s : string with LaTeX representation of model, or None if - either matrix dimension is greater than - statesp.latex_maxsize - - """ - syssize = self.nstates + max(self.noutputs, self.ninputs) - if syssize > config.defaults['statesp.latex_maxsize']: - return None - elif config.defaults['statesp.latex_repr_type'] == 'partitioned': - return self._latex_partitioned() - elif config.defaults['statesp.latex_repr_type'] == 'separate': - return self._latex_separate() - else: - raise ValueError( - "Unknown statesp.latex_repr_type '{cfg}'".format( - cfg=config.defaults['statesp.latex_repr_type'])) - # Negation of a system def __neg__(self): """Negate a state space system.""" @@ -595,6 +576,9 @@ def __add__(self, other): elif isinstance(other, np.ndarray): other = np.atleast_2d(other) + # Special case for SISO + if self.issiso(): + self = np.ones_like(other) * self if self.ninputs != other.shape[0]: raise ValueError("array has incompatible shape") A, B, C = self.A, self.B, self.C @@ -605,6 +589,12 @@ def __add__(self, other): return NotImplemented # let other.__rmul__ handle it else: + # Promote SISO object to compatible dimension + if self.issiso() and not other.issiso(): + self = np.ones((other.noutputs, other.ninputs)) * self + elif not self.issiso() and other.issiso(): + other = np.ones((self.noutputs, self.ninputs)) * other + # Check to make sure the dimensions are OK if ((self.ninputs != other.ninputs) or (self.noutputs != other.noutputs)): @@ -659,6 +649,10 @@ def __mul__(self, other): elif isinstance(other, np.ndarray): other = np.atleast_2d(other) + # Special case for SISO + if self.issiso(): + self = bdalg.append(*([self] * other.shape[0])) + # Dimension check after broadcasting if self.ninputs != other.shape[0]: raise ValueError("array has incompatible shape") A, C = self.A, self.C @@ -670,6 +664,12 @@ def __mul__(self, other): return NotImplemented # let other.__rmul__ handle it else: + # Promote SISO object to compatible dimension + if self.issiso() and not other.issiso(): + self = bdalg.append(*([self] * other.noutputs)) + elif not self.issiso() and other.issiso(): + other = bdalg.append(*([other] * self.ninputs)) + # Check to make sure the dimensions are OK if self.ninputs != other.noutputs: raise ValueError( @@ -709,56 +709,80 @@ def __rmul__(self, other): return StateSpace(self.A, B, self.C, D, self.dt) elif isinstance(other, np.ndarray): - C = np.atleast_2d(other) @ self.C - D = np.atleast_2d(other) @ self.D + other = np.atleast_2d(other) + # Special case for SISO transfer function + if self.issiso(): + self = bdalg.append(*([self] * other.shape[1])) + # Dimension check after broadcasting + if self.noutputs != other.shape[1]: + raise ValueError("array has incompatible shape") + C = other @ self.C + D = other @ self.D return StateSpace(self.A, self.B, C, D, self.dt) if not isinstance(other, StateSpace): return NotImplemented + # Promote SISO object to compatible dimension + if self.issiso() and not other.issiso(): + self = bdalg.append(*([self] * other.ninputs)) + elif not self.issiso() and other.issiso(): + other = bdalg.append(*([other] * self.noutputs)) + return other * self # TODO: general __truediv__ requires descriptor system support def __truediv__(self, other): """Division of state space systems by TFs, FRDs, scalars, and arrays""" - if not isinstance(other, (LTI, InputOutputSystem)): - return self * (1/other) - else: + # Let ``other.__rtruediv__`` handle it + try: + return self * (1 / other) + except ValueError: return NotImplemented - def __call__(self, x, squeeze=None, warn_infinite=True): - """Evaluate system's frequency response at complex frequencies. + def __rtruediv__(self, other): + """Division by state space system""" + return other * self**-1 - Returns the complex frequency response `sys(x)` where `x` is `s` for - continuous-time systems and `z` for discrete-time systems. + def __pow__(self, other): + """Power of a state space system""" + if not type(other) == int: + raise ValueError("Exponent must be an integer") + if self.ninputs != self.noutputs: + # System must have same number of inputs and outputs + return NotImplemented + if other < -1: + return (self**-1)**(-other) + elif other == -1: + try: + Di = scipy.linalg.inv(self.D) + except scipy.linalg.LinAlgError: + # D matrix must be nonsingular + return NotImplemented + Ai = self.A - self.B @ Di @ self.C + Bi = self.B @ Di + Ci = -Di @ self.C + return StateSpace(Ai, Bi, Ci, Di, self.dt) + elif other == 0: + return StateSpace([], [], [], np.eye(self.ninputs), self.dt) + elif other == 1: + return self + elif other > 1: + return self * (self**(other - 1)) - To evaluate at a frequency omega in radians per second, enter - ``x = omega * 1j``, for continuous-time systems, or - ``x = exp(1j * omega * dt)`` for discrete-time systems. Or use - :meth:`StateSpace.frequency_response`. + def __call__(self, x, squeeze=None, warn_infinite=True): + """Evaluate system transfer function at point in complex plane. - Parameters - ---------- - x : complex or complex 1D array_like - Complex frequencies - squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, - keep all indices (output, input and, if omega is array_like, - frequency) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_frequency_response']. - warn_infinite : bool, optional - If set to `False`, don't warn if frequency response is infinite. + Returns the value of the system's transfer function at a point `x` + in the complex plane, where `x` is `s` for continuous-time systems + and `z` for discrete-time systems. - Returns - ------- - fresp : complex ndarray - The frequency response of the system. If the system is SISO and - squeeze is not True, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is False, the first - two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If ``squeeze`` is True - then single-dimensional axes are removed. + See `LTI.__call__` for details. + + Examples + -------- + >>> G = ct.ss([[-1, -2], [3, -4]], [[5], [7]], [[6, 8]], [[9]]) + >>> fresp = G(1j) # evaluate at s = 1j """ # Use Slycot if available @@ -766,21 +790,23 @@ def __call__(self, x, squeeze=None, warn_infinite=True): return _process_frequency_response(self, x, out, squeeze=squeeze) def slycot_laub(self, x): - """Evaluate system's transfer function at complex frequency - using Laub's method from Slycot. + """Laub's method to evaluate response at complex frequency. - Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` - for a more user-friendly interface. + Evaluate transfer function at complex frequency using Laub's + method from Slycot. Expects inputs and outputs to be + formatted correctly. Use ``sys(x)`` for a more user-friendly + interface. Parameters ---------- x : complex array_like or complex - Complex frequency + Complex frequency. Returns ------- output : (number_outputs, number_inputs, len(x)) complex ndarray - Frequency response + Frequency response. + """ from slycot import tb05ad @@ -821,29 +847,30 @@ def slycot_laub(self, x): return out def horner(self, x, warn_infinite=True): - """Evaluate system's transfer function at complex frequency - using Laub's or Horner's method. - - Evaluates `sys(x)` where `x` is `s` for continuous-time systems and `z` - for discrete-time systems. + """Evaluate value of transfer function using Horner's method. - Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` - for a more user-friendly interface. + Evaluates ``sys(x)`` where `x` is a complex number `s` for + continuous-time systems and `z` for discrete-time systems. Expects + inputs and outputs to be formatted correctly. Use ``sys(x)`` for a + more user-friendly interface. Parameters ---------- - x : complex array_like or complex - Complex frequencies + x : complex + Complex frequency at which the transfer function is evaluated. + + warn_infinite : bool, optional + If True (default), generate a warning if `x` is a pole. Returns ------- - output : (self.noutputs, self.ninputs, len(x)) complex ndarray - Frequency response + complex Notes ----- - Attempts to use Laub's method from Slycot library, with a - fall-back to python code. + Attempts to use Laub's method from Slycot library, with a fall-back + to Python code. + """ # Make sure the argument is a 1D array of complex numbers x_arr = np.atleast_1d(x).astype(complex, copy=False) @@ -881,7 +908,7 @@ def horner(self, x, warn_infinite=True): xr = solve(x_idx * eye(self.nstates) - self.A, self.B) out[:, :, idx] = self.C @ xr + self.D except LinAlgError: - # Issue a warning messsage, for consistency with xferfcn + # Issue a warning message, for consistency with xferfcn if warn_infinite: warn("singular matrix in frequency response", RuntimeWarning) @@ -899,14 +926,14 @@ def freqresp(self, omega): """(deprecated) Evaluate transfer function at complex frequencies. .. deprecated::0.9.0 - Method has been given the more pythonic name - :meth:`StateSpace.frequency_response`. Or use - :func:`freqresp` in the MATLAB compatibility module. + Method has been given the more Pythonic name + `StateSpace.frequency_response`. Or use + `freqresp` in the MATLAB compatibility module. """ warn("StateSpace.freqresp(omega) will be removed in a " "future release of python-control; use " "sys.frequency_response(omega), or freqresp(sys, omega) in the " - "MATLAB compatibility module instead", DeprecationWarning) + "MATLAB compatibility module instead", FutureWarning) return self.frequency_response(omega) # Compute poles and zeros @@ -933,11 +960,11 @@ def zeros(self): if nu == 0: return np.array([]) else: - # Use SciPy generalized eigenvalue fucntion + # Use SciPy generalized eigenvalue function return sp.linalg.eigvals(out[8][0:nu, 0:nu], out[9][0:nu, 0:nu]).astype(complex) - except ImportError: # Slycot unavailable. Fall back to scipy. + except ImportError: # Slycot unavailable. Fall back to SciPy. if self.C.shape[0] != self.D.shape[1]: raise NotImplementedError( "StateSpace.zero only supports systems with the same " @@ -963,7 +990,17 @@ def zeros(self): # Feedback around a state space system def feedback(self, other=1, sign=-1): - """Feedback interconnection between two LTI systems.""" + """Feedback interconnection between two LTI objects. + + Parameters + ---------- + other : `InputOutputSystem` + System in the feedback path. + + sign : float, optional + Gain to use in feedback path. Defaults to -1. + + """ # Convert the system to state space, if possible try: other = _convert_to_statespace(other) @@ -1020,24 +1057,30 @@ def feedback(self, other=1, sign=-1): return StateSpace(A, B, C, D, dt) def lft(self, other, nu=-1, ny=-1): - """Return the Linear Fractional Transformation. + """Return the linear fractional transformation. A definition of the LFT operator can be found in Appendix A.7, - page 512 in the 2nd Edition, Multivariable Feedback Control by - Sigurd Skogestad. - - An alternative definition can be found here: + page 512 in [1]_. An alternative definition can be found here: https://www.mathworks.com/help/control/ref/lft.html Parameters ---------- - other : LTI - The lower LTI system + other : `StateSpace` + The lower LTI system. ny : int, optional Dimension of (plant) measurement output. nu : int, optional Dimension of (plant) control input. + Returns + ------- + `StateSpace` + + References + ---------- + .. [1] S. Skogestad, Multivariable Feedback Control. Second + edition, 2005. + """ other = _convert_to_statespace(other) # maximal values for nu, ny @@ -1075,7 +1118,7 @@ def lft(self, other, nu=-1, ny=-1): # well-posed check F = np.block([[np.eye(ny), -D22], [-Dbar11, np.eye(nu)]]) if matrix_rank(F) != ny + nu: - raise ValueError("lft not well-posed to working precision.") + raise ValueError("LFT not well-posed to working precision.") # solve for the resulting ss by solving for [y, u] using [x, # xbar] and [w1, w2]. @@ -1118,8 +1161,18 @@ def lft(self, other, nu=-1, ny=-1): return StateSpace(Ares, Bres, Cres, Dres, dt) def minreal(self, tol=0.0): - """Calculate a minimal realization, removes unobservable and - uncontrollable states""" + """Remove unobservable and uncontrollable states. + + Calculate a minimal realization for a state space system, + removing all unobservable and/or uncontrollable states. + + Parameters + ---------- + tol : float + Tolerance for determining whether states are unobservable + or uncontrollable. + + """ if self.nstates: try: from slycot import tb01pd @@ -1130,21 +1183,21 @@ def minreal(self, tol=0.0): A, B, C, nr = tb01pd(self.nstates, self.ninputs, self.noutputs, self.A, B, C, tol=tol) return StateSpace(A[:nr, :nr], B[:nr, :self.ninputs], - C[:self.noutputs, :nr], self.D) + C[:self.noutputs, :nr], self.D, self.dt) except ImportError: raise TypeError("minreal requires slycot tb01pd") else: return StateSpace(self) def returnScipySignalLTI(self, strict=True): - """Return a list of a list of :class:`scipy.signal.lti` objects. + """Return a list of a list of `scipy.signal.lti` objects. For instance, >>> out = ssobject.returnScipySignalLTI() # doctest: +SKIP >>> out[3][5] # doctest: +SKIP - is a :class:`scipy.signal.lti` object corresponding to the transfer + is a `scipy.signal.lti` object corresponding to the transfer function from the 6th input to the 4th output. Parameters @@ -1154,15 +1207,16 @@ def returnScipySignalLTI(self, strict=True): The timebase `ssobject.dt` cannot be None; it must be continuous (0) or discrete (True or > 0). False: - If `ssobject.dt` is None, continuous time - :class:`scipy.signal.lti` objects are returned. + If `ssobject.dt` is None, continuous-time + `scipy.signal.lti` objects are returned. Returns ------- - out : list of list of :class:`scipy.signal.StateSpace` - continuous time (inheriting from :class:`scipy.signal.lti`) - or discrete time (inheriting from :class:`scipy.signal.dlti`) - SISO objects + out : list of list of `scipy.signal.StateSpace` + Continuous time (inheriting from `scipy.signal.lti`) + or discrete time (inheriting from `scipy.signal.dlti`) + SISO objects. + """ if strict and self.dt is None: raise ValueError("with strict=True, dt cannot be None") @@ -1170,7 +1224,7 @@ def returnScipySignalLTI(self, strict=True): if self.dt: kwdt = {'dt': self.dt} else: - # scipy convention for continuous time lti systems: call without + # SciPy convention for continuous-time LTI systems: call without # dt keyword argument kwdt = {} @@ -1191,7 +1245,19 @@ def append(self, other): """Append a second model to the present model. The second model is converted to state-space if necessary, inputs and - outputs are appended and their order is preserved""" + outputs are appended and their order is preserved. + + Parameters + ---------- + other : `StateSpace` or `TransferFunction` + System to be appended. + + Returns + ------- + sys : `StateSpace` + System model with `other` appended to `self`. + + """ if not isinstance(other, StateSpace): other = _convert_to_statespace(other) @@ -1214,29 +1280,29 @@ def append(self, other): D[self.noutputs:, self.ninputs:] = other.D return StateSpace(A, B, C, D, self.dt) - def __getitem__(self, indices): + def __getitem__(self, key): """Array style access""" - if not isinstance(indices, Iterable) or len(indices) != 2: - raise IOError('must provide indices of length 2 for state space') - outdx, inpdx = indices - - # Convert int to slice to ensure that numpy doesn't drop the dimension - if isinstance(outdx, int): outdx = slice(outdx, outdx+1, 1) - if isinstance(inpdx, int): inpdx = slice(inpdx, inpdx+1, 1) + if not isinstance(key, Iterable) or len(key) != 2: + raise IOError("must provide indices of length 2 for state space") - if not isinstance(outdx, slice) or not isinstance(inpdx, slice): - raise TypeError(f"system indices must be integers or slices") + # Convert signal names to integer offsets + iomap = NamedSignal(self.D, self.output_labels, self.input_labels) + indices = iomap._parse_key(key, level=1) # ignore index checks + outdx, output_labels = _process_subsys_index( + indices[0], self.output_labels) + inpdx, input_labels = _process_subsys_index( + indices[1], self.input_labels) sysname = config.defaults['iosys.indexed_system_name_prefix'] + \ self.name + config.defaults['iosys.indexed_system_name_suffix'] return StateSpace( - self.A, self.B[:, inpdx], self.C[outdx, :], self.D[outdx, inpdx], - self.dt, name=sysname, - inputs=self.input_labels[inpdx], outputs=self.output_labels[outdx]) + self.A, self.B[:, inpdx], self.C[outdx, :], + self.D[outdx, :][:, inpdx], self.dt, + name=sysname, inputs=input_labels, outputs=output_labels) def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, name=None, copy_names=True, **kwargs): - """Convert a continuous time system to discrete time + """Convert a continuous-time system to discrete time. Creates a discrete-time system from a continuous-time system by sampling. Multiple methods of conversion are supported. @@ -1244,48 +1310,51 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Parameters ---------- Ts : float - Sampling period - method : {"gbt", "bilinear", "euler", "backward_diff", "zoh"} - Which method to use: - - * gbt: generalized bilinear transformation - * bilinear: Tustin's approximation ("gbt" with alpha=0.5) - * euler: Euler (or forward differencing) method ("gbt" with + Sampling period. + method : {'gbt', 'bilinear', 'euler', 'backward_diff', 'zoh'} + Method to use for sampling: + + * 'gbt': generalized bilinear transformation + * 'backward_diff': Backwards difference ('gbt' with alpha=1.0) + * 'bilinear' (or 'tustin'): Tustin's approximation ('gbt' with + alpha=0.5) + * 'euler': Euler (or forward difference) method ('gbt' with alpha=0) - * backward_diff: Backwards differencing ("gbt" with alpha=1.0) - * zoh: zero-order hold (default) + * 'zoh': zero-order hold (default) alpha : float within [0, 1] - The generalized bilinear transformation weighting parameter, which - should only be specified with method="gbt", and is ignored - otherwise + The generalized bilinear transformation weighting parameter, + which should only be specified with method='gbt', and is + ignored otherwise. prewarp_frequency : float within [0, infinity) - The frequency [rad/s] at which to match with the input continuous- - time system's magnitude and phase (the gain=1 crossover frequency, - for example). Should only be specified with method='bilinear' or - 'gbt' with alpha=0.5 and ignored otherwise. + The frequency [rad/s] at which to match with the input + continuous-time system's magnitude and phase (the gain = 1 + crossover frequency, for example). Should only be specified + with `method` = 'bilinear' or 'gbt' with `alpha` = 0.5 and + ignored otherwise. name : string, optional - Set the name of the sampled system. If not specified and - if `copy_names` is `False`, a generic name is generated - with a unique integer id. If `copy_names` is `True`, the new system - name is determined by adding the prefix and suffix strings in - config.defaults['iosys.sampled_system_name_prefix'] and - config.defaults['iosys.sampled_system_name_suffix'], with the - default being to add the suffix '$sampled'. + Set the name of the sampled system. If not specified and if + `copy_names` is False, a generic name 'sys[id]' is + generated with a unique integer id. If `copy_names` is + True, the new system name is determined by adding the + prefix and suffix strings in + `config.defaults['iosys.sampled_system_name_prefix']` and + `config.defaults['iosys.sampled_system_name_suffix']`, with + the default being to add the suffix '$sampled'. copy_names : bool, Optional If True, copy the names of the input signals, output signals, and states to the sampled system. Returns ------- - sysd : StateSpace - Discrete-time system, with sampling rate Ts + sysd : `StateSpace` + Discrete-time system, with sampling rate `Ts`. Other Parameters ---------------- inputs : int, list of str or None, optional - Description of the system inputs. If not specified, the origional - system inputs are used. See :class:`InputOutputSystem` for more - information. + Description of the system inputs. If not specified, the + original system inputs are used. See `InputOutputSystem` for + more information. outputs : int, list of str or None, optional Description of the system outputs. Same format as `inputs`. states : int, list of str, or None, optional @@ -1293,7 +1362,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Notes ----- - Uses :func:`scipy.signal.cont2discrete` + Uses `scipy.signal.cont2discrete`. Examples -------- @@ -1302,7 +1371,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, """ if not self.isctime(): - raise ValueError("System must be continuous time system") + raise ValueError("System must be continuous-time system") if prewarp_frequency is not None: if method in ('bilinear', 'tustin') or \ (method == 'gbt' and alpha == 0.5): @@ -1324,7 +1393,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, return StateSpace(sysd, **kwargs) def dcgain(self, warn_infinite=False): - """Return the zero-frequency gain + """Return the zero-frequency ("DC") gain. The zero-frequency gain of a continuous-time state-space system is given by: @@ -1339,26 +1408,28 @@ def dcgain(self, warn_infinite=False): ---------- warn_infinite : bool, optional By default, don't issue a warning message if the zero-frequency - gain is infinite. Setting `warn_infinite` to generate the warning - message. + gain is infinite. Setting `warn_infinite` to generate the + warning message. Returns ------- gain : (noutputs, ninputs) ndarray or scalar Array or scalar value for SISO systems, depending on - config.defaults['control.squeeze_frequency_response']. - The value of the array elements or the scalar is either the - zero-frequency (or DC) gain, or `inf`, if the frequency response - is singular. + `config.defaults['control.squeeze_frequency_response']`. The + value of the array elements or the scalar is either the + zero-frequency (or DC) gain, or `inf`, if the frequency + response is singular. For real valued systems, the empty imaginary part of the complex zero-frequency response is discarded and a real array or scalar is returned. + """ return self._dcgain(warn_infinite) + # TODO: decide if we need this function (already in NonlinearIOSystem def dynamics(self, t, x, u=None, params=None): - """Compute the dynamics of the system + """Compute the dynamics of the system. Given input `u` and state `x`, returns the dynamics of the state-space system. If the system is continuous, returns the time derivative dx/dt @@ -1366,25 +1437,25 @@ def dynamics(self, t, x, u=None, params=None): dx/dt = A x + B u where A and B are the state-space matrices of the system. If the - system is discrete-time, returns the next value of `x`: + system is discrete time, returns the next value of `x`: x[t+dt] = A x[t] + B u[t] The inputs `x` and `u` must be of the correct length for the system. - The first argument `t` is ignored because :class:`StateSpace` systems + The first argument `t` is ignored because `StateSpace` systems are time-invariant. It is included so that the dynamics can be passed - to numerical integrators, such as :func:`scipy.integrate.solve_ivp` - and for consistency with :class:`IOSystem` systems. + to numerical integrators, such as `scipy.integrate.solve_ivp` + and for consistency with `InputOutputSystem` models. Parameters ---------- t : float (ignored) - time + Time. x : array_like - current state + Current state. u : array_like (optional) - input, zero if omitted + Input, zero if omitted. Returns ------- @@ -1406,8 +1477,9 @@ def dynamics(self, t, x, u=None, params=None): return (self.A @ x).reshape((-1,)) \ + (self.B @ u).reshape((-1,)) # return as row vector + # TODO: decide if we need this function (already in NonlinearIOSystem def output(self, t, x, u=None, params=None): - """Compute the output of the system + """Compute the output of the system. Given input `u` and state `x`, returns the output `y` of the state-space system: @@ -1416,25 +1488,26 @@ def output(self, t, x, u=None, params=None): where A and B are the state-space matrices of the system. - The first argument `t` is ignored because :class:`StateSpace` systems + The first argument `t` is ignored because `StateSpace` systems are time-invariant. It is included so that the dynamics can be passed - to most numerical integrators, such as scipy's `integrate.solve_ivp` - and for consistency with :class:`IOSystem` systems. + to most numerical integrators, such as SciPy's `integrate.solve_ivp` + and for consistency with `InputOutputSystem` models. The inputs `x` and `u` must be of the correct length for the system. Parameters ---------- t : float (ignored) - time + Time. x : array_like - current state + Current state. u : array_like (optional) - input (zero if omitted) + Input (zero if omitted). Returns ------- y : ndarray + """ if params is not None: warn("params keyword ignored for StateSpace object") @@ -1452,17 +1525,20 @@ def output(self, t, x, u=None, params=None): return (self.C @ x).reshape((-1,)) \ + (self.D @ u).reshape((-1,)) # return as row vector + # convenience alias, import needs submodule to avoid circular imports + initial_response = control.timeresp.initial_response + class LinearICSystem(InterconnectedSystem, StateSpace): """Interconnection of a set of linear input/output systems. This class is used to implement a system that is an interconnection of linear input/output systems. It has all of the structure of an - :class:`~control.InterconnectedSystem`, but also maintains the required - elements of the :class:`StateSpace` class structure, allowing it to be - passed to functions that expect a :class:`StateSpace` system. + `InterconnectedSystem`, but also maintains the required + elements of the `StateSpace` class structure, allowing it to be + passed to functions that expect a `StateSpace` system. - This class is generated using :func:`~control.interconnect` and + This class is generated using `interconnect` and not called directly. """ @@ -1472,7 +1548,7 @@ def __init__(self, io_sys, ss_sys=None, connection_type=None): # Because this is a "hybrid" object, the initialization proceeds in # stages. We first create an empty InputOutputSystem of the # appropriate size, then copy over the elements of the - # InterconnectedIOSystem class. From there we compute the + # InterconnectedSystem class. From there we compute the # linearization of the system (if needed) and then populate the # StateSpace parameters. # @@ -1493,7 +1569,7 @@ def __init__(self, io_sys, ss_sys=None, connection_type=None): self.params = io_sys.params self.connection_type = connection_type - # If we didnt' get a state space system, linearize the full system + # If we didn't' get a state space system, linearize the full system if ss_sys is None: ss_sys = self.linearize(0, 0) @@ -1503,19 +1579,48 @@ def __init__(self, io_sys, ss_sys=None, connection_type=None): outputs=io_sys.output_labels, states=io_sys.state_labels, params=io_sys.params, remove_useless_states=False) - # Use StateSpace.__call__ to evaluate at a given complex value - def __call__(self, *args, **kwargs): - return StateSpace.__call__(self, *args, **kwargs) + # Use StateSpace.__call__ to evaluate at a given complex value + def __call__(self, *args, **kwargs): + return StateSpace.__call__(self, *args, **kwargs) + + def __str__(self): + string = InterconnectedSystem.__str__(self) + "\n\n" + string += "\n\n".join([ + "{} = {}".format(Mvar, + "\n ".join(str(M).splitlines())) + for Mvar, M in zip(["A", "B", "C", "D"], + [self.A, self.B, self.C, self.D])]) + return string + + # Use InputOutputSystem repr for 'eval' since we can't recreate structure + # (without this, StateSpace._repr_eval_ gets used...) + def _repr_eval_(self): + return InputOutputSystem._repr_eval_(self) + + def _repr_html_(self): + syssize = self.nstates + max(self.noutputs, self.ninputs) + if syssize > config.defaults['statesp.latex_maxsize']: + return None + elif config.defaults['statesp.latex_repr_type'] == 'partitioned': + return InterconnectedSystem._repr_info_(self, html=True) + \ + "\n" + StateSpace._latex_partitioned(self) + elif config.defaults['statesp.latex_repr_type'] == 'separate': + return InterconnectedSystem._repr_info_(self, html=True) + \ + "\n" + StateSpace._latex_separate(self) + else: + raise ValueError( + "Unknown statesp.latex_repr_type '{cfg}'".format( + cfg=config.defaults['statesp.latex_repr_type'])) # The following text needs to be replicated from StateSpace in order for - # this entry to show up properly in sphinx doccumentation (not sure why, + # this entry to show up properly in sphinx documentation (not sure why, # but it was the only way to get it to work). # - #: Deprecated attribute; use :attr:`nstates` instead. + #: Deprecated attribute; use `nstates` instead. #: - #: The ``state`` attribute was used to store the number of states for : a + #: The `state` attribute was used to store the number of states for : a #: state space system. It is no longer used. If you need to access the - #: number of states, use :attr:`nstates`. + #: number of states, use `nstates`. states = property(StateSpace._get_states, StateSpace._set_states) @@ -1525,13 +1630,15 @@ def ss(*args, **kwargs): Create a state space system. - The function accepts either 1, 2, 4 or 5 parameters: + The function accepts either 1, 4 or 5 positional parameters: ``ss(sys)`` + Convert a linear system into space system form. Always creates a - new system, even if sys is already a state space system. + new system, even if `sys` is already a state space system. ``ss(A, B, C, D)`` + Create a state space system from the matrices of its state and output equations: @@ -1541,6 +1648,7 @@ def ss(*args, **kwargs): y &= C x + D u ``ss(A, B, C, D, dt)`` + Create a discrete-time state space system from the matrices of its state and output equations: @@ -1549,39 +1657,53 @@ def ss(*args, **kwargs): x[k+1] &= A x[k] + B u[k] \\ y[k] &= C x[k] + D u[k] - The matrices can be given as *array like* data types or strings. - Everything that the constructor of :class:`numpy.matrix` accepts is - permissible here too. + The matrices can be given as 2D array_like data types. For SISO + systems, `B` and `C` can be given as 1D arrays and D can be given + as a scalar. + - ``ss(args, inputs=['u1', ..., 'up'], outputs=['y1', ..., 'yq'], states=['x1', ..., 'xn'])`` + ``ss(*args, inputs=['u1', ..., 'up'], outputs=['y1', ..., 'yq'], states=['x1', ..., 'xn'])`` Create a system with named input, output, and state signals. Parameters ---------- - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction` A linear system. A, B, C, D : array_like or string System, control, output, and feed forward matrices. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous - time, True indicates discrete time with unspecified sampling - time, positive number is discrete time with specified - sampling time, None indicates unspecified timebase (either - continuous or discrete time). - inputs, outputs, states : str, or list of str, optional - List of strings that name the individual signals. If this parameter - is not given or given as `None`, the signal names will be of the - form `s[i]` (where `s` is one of `u`, `y`, or `x`). See - :class:`InputOutputSystem` for more information. - name : string, optional - System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + System timebase. 0 (default) indicates continuous time, True + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, None + indicates unspecified timebase (either continuous or discrete time). + remove_useless_states : bool, optional + If True, remove states that have no effect on the input/output + dynamics. If not specified, the value is read from + `config.defaults['statesp.remove_useless_states']` (default = False). + method : str, optional + Set the method used for converting a transfer function to a state + space system. Current methods are 'slycot' and 'scipy'. If set to + None (default), try 'slycot' first and then 'scipy' (SISO only). Returns ------- - out: :class:`StateSpace` + out : `StateSpace` Linear input/output system. + Other Parameters + ---------------- + inputs, outputs, states : str, or list of str, optional + List of strings that name the individual signals. If this parameter + is not given or given as None, the signal names will be of the + form 's[i]' (where 's' is one of 'u', 'y', or 'x'). See + `InputOutputSystem` for more information. + input_prefix, output_prefix, state_prefix : string, optional + Set the prefix for input, output, and state signals. Defaults = + 'u', 'y', 'x'. + name : string, optional + System name (used for specifying signals). If unspecified, a generic + name 'sys[id]' is generated with a unique integer id. + Raises ------ ValueError @@ -1589,22 +1711,22 @@ def ss(*args, **kwargs): See Also -------- - tf, ss2tf, tf2ss + StateSpace, nlsys, tf, ss2tf, tf2ss, zpk Notes ----- If a transfer function is passed as the sole positional argument, the system will be converted to state space form in the same way as calling - :func:`~control.tf2ss`. The `method` keyword can be used to select the + `tf2ss`. The `method` keyword can be used to select the method for conversion. Examples -------- - Create a Linear I/O system object from matrices. + Create a linear I/O system object from matrices: >>> G = ct.ss([[-1, -2], [3, -4]], [[5], [7]], [[6, 8]], [[9]]) - Convert a TransferFunction to a StateSpace object. + Convert a transfer function to a state space system: >>> sys_tf = ct.tf([2.], [1., 3]) >>> sys2 = ct.ss(sys_tf) @@ -1614,8 +1736,8 @@ def ss(*args, **kwargs): if len(args) > 0 and (hasattr(args[0], '__call__') or args[0] is None) \ and not isinstance(args[0], (InputOutputSystem, LTI)): # Function as first (or second) argument => assume nonlinear IO system - warn("using ss to create nonlinear I/O systems is deprecated; " - "use nlsys()", DeprecationWarning) + warn("using ss() to create nonlinear I/O systems is deprecated; " + "use nlsys()", FutureWarning) return NonlinearIOSystem(*args, **kwargs) elif len(args) == 4 or len(args) == 5: @@ -1630,7 +1752,7 @@ def ss(*args, **kwargs): warn("state labels specified for " "non-unique state space realization") - # Allow method to be specified (eg, tf2ss) + # Allow method to be specified (e.g., tf2ss) method = kwargs.pop('method', None) # Create a state space system from an LTI system @@ -1660,10 +1782,10 @@ def ss2io(*args, **kwargs): This function will be removed in a future version of python-control. The `ss` function can be used directly to produce an I/O system. - Create an :class:`~control.StateSpace` system with the given signal - and system names. See :func:`~control.ss` for more details. + Create an `StateSpace` system with the given signal + and system names. See `ss` for more details. """ - warn("ss2io is deprecated; use ss()", DeprecationWarning) + warn("ss2io() is deprecated; use ss()", FutureWarning) return StateSpace(*args, **kwargs) @@ -1680,18 +1802,20 @@ def tf2io(*args, **kwargs): The function accepts either 1 or 2 parameters: ``tf2io(sys)`` + Convert a linear system into space space form. Always creates - a new system, even if sys is already a StateSpace object. + a new system, even if `sys` is already a `StateSpace` object. ``tf2io(num, den)`` + Create a linear I/O system from its numerator and denominator polynomial coefficients. - For details see: :func:`tf` + For details see: `tf`. Parameters ---------- - sys : LTI (StateSpace or TransferFunction) + sys : `StateSpace` or `TransferFunction` A linear system. num : array_like, or list of list of array_like Polynomial coefficients of the numerator. @@ -1700,7 +1824,7 @@ def tf2io(*args, **kwargs): Returns ------- - out : StateSpace + out : `StateSpace` New I/O system (in state space form). Other Parameters @@ -1710,22 +1834,21 @@ def tf2io(*args, **kwargs): system. If not given, the inputs and outputs are the same as the original system. name : string, optional - System name. If unspecified, a generic name is generated + System name. If unspecified, a generic name 'sys[id]' is generated with a unique integer id. Raises ------ ValueError - if `num` and `den` have invalid or unequal dimensions, or if an + If `num` and `den` have invalid or unequal dimensions, or if an invalid number of arguments is passed in. TypeError - if `num` or `den` are of incorrect type, or if sys is not a - TransferFunction object. + If `num` or `den` are of incorrect type, or if `sys` is not a + `TransferFunction` object. See Also -------- - ss2io - tf2ss + ss2io, tf2ss Examples -------- @@ -1739,7 +1862,7 @@ def tf2io(*args, **kwargs): (2, 2, 8) """ - warn("tf2io is deprecated; use tf2ss() or tf()", DeprecationWarning) + warn("tf2io() is deprecated; use tf2ss() or tf()", FutureWarning) return tf2ss(*args, **kwargs) @@ -1751,28 +1874,30 @@ def tf2ss(*args, **kwargs): The function accepts either 1 or 2 parameters: ``tf2ss(sys)`` + Convert a transfer function into space space form. Equivalent to `ss(sys)`. ``tf2ss(num, den)`` + Create a state space system from its numerator and denominator polynomial coefficients. - For details see: :func:`tf` + For details see: `tf`. Parameters ---------- - sys : LTI (StateSpace or TransferFunction) - A linear system + sys : `StateSpace` or `TransferFunction` + A linear system. num : array_like, or list of list of array_like - Polynomial coefficients of the numerator + Polynomial coefficients of the numerator. den : array_like, or list of list of array_like - Polynomial coefficients of the denominator + Polynomial coefficients of the denominator. Returns ------- - out : StateSpace - New linear system in state space form + out : `StateSpace` + New linear system in state space form. Other Parameters ---------------- @@ -1781,34 +1906,32 @@ def tf2ss(*args, **kwargs): system. If not given, the inputs and outputs are the same as the original system. name : string, optional - System name. If unspecified, a generic name is generated + System name. If unspecified, a generic name 'sys[id]' is generated with a unique integer id. method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first - and then 'scipy' (SISO only). + 'slycot' and 'scipy'. If set to None (default), try 'slycot' + first and then 'scipy' (SISO only). Raises ------ ValueError - if `num` and `den` have invalid or unequal dimensions, or if an - invalid number of arguments is passed in + If `num` and `den` have invalid or unequal dimensions, or if an + invalid number of arguments is passed in. TypeError - if `num` or `den` are of incorrect type, or if sys is not a - TransferFunction object + If `num` or `den` are of incorrect type, or if `sys` is not a + `TransferFunction` object. See Also -------- - ss - tf - ss2tf + ss, tf, ss2tf Notes ----- - The ``slycot`` routine used to convert a transfer function into state - space form appears to have a bug and in some (rare) instances may not - return a system with the same poles as the input transfer function. - For SISO systems, setting ``method=scipy`` can be used as an alternative. + The `slycot` routine used to convert a transfer function into state space + form appears to have a bug and in some (rare) instances may not return + a system with the same poles as the input transfer function. For SISO + systems, setting `method` = 'scipy' can be used as an alternative. Examples -------- @@ -1840,47 +1963,51 @@ def ssdata(sys): Parameters ---------- - sys : LTI (StateSpace, or TransferFunction) - LTI system whose data will be returned + sys : `StateSpace` or `TransferFunction` + LTI system whose data will be returned. Returns ------- - (A, B, C, D): list of matrices - State space data for the system + A, B, C, D : ndarray + State space data for the system. + """ ss = _convert_to_statespace(sys) return ss.A, ss.B, ss.C, ss.D +# TODO: combine with sysnorm? def linfnorm(sys, tol=1e-10): - """L-infinity norm of a linear system + """L-infinity norm of a linear system. Parameters ---------- - sys : LTI (StateSpace or TransferFunction) - system to evalute L-infinity norm of + sys : `StateSpace` or `TransferFunction` + System to evaluate L-infinity norm of. tol : real scalar - tolerance on norm estimate + Tolerance on norm estimate. Returns ------- gpeak : non-negative scalar - L-infinity norm + L-infinity norm. fpeak : non-negative scalar - Frequency, in rad/s, at which gpeak occurs + Frequency, in rad/s, at which gpeak occurs. + See Also + -------- + slycot.ab13dd + + Notes + ----- For stable systems, the L-infinity and H-infinity norms are equal; for unstable systems, the H-infinity norm is infinite, while the L-infinity norm is finite if the system has no poles on the imaginary axis. - See also - -------- - slycot.ab13dd : the Slycot routine linfnorm that does the calculation """ - if ab13dd is None: - raise ControlSlycot("Can't find slycot module 'ab13dd'") + raise ControlSlycot("Can't find slycot module ab13dd") a, b, c, d = ssdata(_convert_to_statespace(sys)) e = np.eye(a.shape[0]) @@ -1893,7 +2020,7 @@ def linfnorm(sys, tol=1e-10): # ab13dd doesn't accept empty A, B, C, D; # static gain case is easy enough to compute gpeak = scipy.linalg.svdvals(d)[0] - # max svd is constant with freq; arbitrarily choose 0 as peak + # max SVD is constant with freq; arbitrarily choose 0 as peak fpeak = 0 return gpeak, fpeak @@ -1915,51 +2042,47 @@ def rss(states=1, outputs=1, inputs=1, strictly_proper=False, **kwargs): Parameters ---------- - inputs : int, list of str, or None - Description of the system inputs. This can be given as an integer - count or as a list of strings that name the individual signals. If an - integer count is specified, the names of the signal will be of the - form `s[i]` (where `s` is one of `u`, `y`, or `x`). - outputs : int, list of str, or None - Description of the system outputs. Same format as `inputs`. - states : int, list of str, or None - Description of the system states. Same format as `inputs`. + states, outputs, inputs : int, list of str, or None + Description of the system states, outputs, and inputs. This can be + given as an integer count or as a list of strings that name the + individual signals. If an integer count is specified, the names of + the signal will be of the form 's[i]' (where 's' is one of 'x', + 'y', or 'u'). strictly_proper : bool, optional - If set to 'True', returns a proper system (no direct term). + If set to True, returns a proper system (no direct term). dt : None, True or float, optional - System timebase. 0 (default) indicates continuous - time, True indicates discrete time with unspecified sampling - time, positive number is discrete time with specified - sampling time, None indicates unspecified timebase (either - continuous or discrete time). + System timebase. 0 (default) indicates continuous time, True + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, None + indicates unspecified timebase (either continuous or discrete time). name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Returns ------- - sys : StateSpace + sys : `StateSpace` The randomly created linear system. Raises ------ ValueError - if any input is not a positive integer. + If any input is not a positive integer. Notes ----- If the number of states, inputs, or outputs is not specified, then the - missing numbers are assumed to be 1. If dt is not specified or is given - as 0 or None, the poles of the returned system will always have a - negative real part. If dt is True or a postive float, the poles of the - returned system will have magnitude less than 1. + missing numbers are assumed to be 1. If `dt` is not specified or is + given as 0 or None, the poles of the returned system will always have a + negative real part. If `dt` is True or a positive float, the poles of + the returned system will have magnitude less than 1. """ # Process keyword arguments kwargs.update({'states': states, 'outputs': outputs, 'inputs': inputs}) name, inputs, outputs, states, dt = _process_iosys_keywords(kwargs) - # Figure out the size of the sytem + # Figure out the size of the system nstates, _ = _process_signal_list(states) ninputs, _ = _process_signal_list(inputs) noutputs, _ = _process_signal_list(outputs) @@ -1979,9 +2102,9 @@ def drss(*args, **kwargs): Create a stable, discrete-time, random state space system. - Create a stable *discrete time* random state space object. This - function calls :func:`rss` using either the `dt` keyword provided by - the user or `dt=True` if not specified. + Create a stable *discrete-time* random state space object. This + function calls `rss` using either the `dt` keyword provided by + the user or `dt` = True if not specified. Examples -------- @@ -2002,7 +2125,7 @@ def drss(*args, **kwargs): elif dt is None: warn("drss called with unspecified timebase; " "system may be interpreted as continuous time") - kwargs['dt'] = True # force rss to generate discrete time sys + kwargs['dt'] = True # force rss to generate discrete-time sys else: dt = True kwargs['dt'] = True @@ -2021,37 +2144,37 @@ def summing_junction( inputs=None, output=None, dimension=None, prefix='u', **kwargs): """Create a summing junction as an input/output system. - This function creates a static input/output system that outputs the sum of - the inputs, potentially with a change in sign for each individual input. - The input/output system that is created by this function can be used as a - component in the :func:`~control.interconnect` function. + This function creates a static input/output system that outputs the sum + of the inputs, potentially with a change in sign for each individual + input. The input/output system that is created by this function can be + used as a component in the `interconnect` function. Parameters ---------- inputs : int, string or list of strings - Description of the inputs to the summing junction. This can be given - as an integer count, a string, or a list of strings. If an integer - count is specified, the names of the input signals will be of the form - `u[i]`. + Description of the inputs to the summing junction. This can be + given as an integer count, a string, or a list of strings. If an + integer count is specified, the names of the input signals will be + of the form 'u[i]'. output : string, optional Name of the system output. If not specified, the output will be 'y'. dimension : int, optional The dimension of the summing junction. If the dimension is set to a positive integer, a multi-input, multi-output summing junction will be created. The input and output signal names will be of the form - `[i]` where `signal` is the input/output signal name specified - by the `inputs` and `output` keywords. Default value is `None`. + '[i]' where 'signal' is the input/output signal name specified + by the `inputs` and `output` keywords. Default value is None. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. prefix : string, optional If `inputs` is an integer, create the names of the states using the given prefix (default = 'u'). The names of the input will be of the - form `prefix[i]`. + form 'prefix[i]'. Returns ------- - sys : static StateSpace + sys : `StateSpace` Linear input/output system object with no states and only a direct term that implements the summing junction. @@ -2146,33 +2269,46 @@ def _parse_list(signals, signame='input', prefix='u'): # Utility functions # -def _ssmatrix(data, axis=1): +def _ssmatrix(data, axis=1, square=None, rows=None, cols=None, name=None): """Convert argument to a (possibly empty) 2D state space matrix. - The axis keyword argument makes it convenient to specify that if the input - is a vector, it is a row (axis=1) or column (axis=0) vector. + This function can be used to process the matrices that define a + state-space system. The axis keyword argument makes it convenient + to specify that if the input is a vector, it is a row (axis=1) or + column (axis=0) vector. Parameters ---------- data : array, list, or string - Input data defining the contents of the 2D array + Input data defining the contents of the 2D array. axis : 0 or 1 - If input data is 1D, which axis to use for return object. The default - is 1, corresponding to a row matrix. + If input data is 1D, which axis to use for return object. The + default is 1, corresponding to a row matrix. + square : bool, optional + If set to True, check that the input matrix is square. + rows : int, optional + If set, check that the input matrix has the given number of rows. + cols : int, optional + If set, check that the input matrix has the given number of columns. + name : str, optional + Name of the state-space matrix being checked (for error messages). Returns ------- arr : 2D array, with shape (0, 0) if a is empty """ - # Convert the data into an array + # Process the name of the object, if available + name = "" if name is None else " " + name + + # Convert the data into an array (always making a copy) arr = np.array(data, dtype=float) ndim = arr.ndim shape = arr.shape # Change the shape of the array into a 2D array if (ndim > 2): - raise ValueError("state-space matrix must be 2-dimensional") + raise ValueError(f"state-space matrix{name} must be 2-dimensional") elif (ndim == 2 and shape == (1, 0)) or \ (ndim == 1 and shape == (0, )): @@ -2187,6 +2323,21 @@ def _ssmatrix(data, axis=1): # Passed a constant; turn into a matrix shape = (1, 1) + # Check to make sure any conditions are satisfied + if square and shape[0] != shape[1]: + raise ControlDimension( + f"state-space matrix{name} must be a square matrix") + + if rows is not None and shape[0] != rows: + raise ControlDimension( + f"state-space matrix{name} has the wrong number of rows; " + f"expected {rows} instead of {shape[0]}") + + if cols is not None and shape[1] != cols: + raise ControlDimension( + f"state-space matrix{name} has the wrong number of columns; " + f"expected {cols} instead of {shape[1]}") + # Create the actual object used to store the result return arr.reshape(shape) @@ -2200,7 +2351,7 @@ def _f2s(f): """ fmt = "{:" + config.defaults['statesp.latex_num_format'] + "}" sraw = fmt.format(f) - # significand-exponent + # significant-exponent se = sraw.lower().split('e') # whole-fraction wf = se[0].split('.') @@ -2221,9 +2372,9 @@ def _f2s(f): def _convert_to_statespace(sys, use_prefix_suffix=False, method=None): """Convert a system to state space form (if needed). - If sys is already a state space, then it is returned. If sys is a - transfer function object, then it is converted to a state space and - returned. + If `sys` is already a state space object, then it is returned. If + `sys` is a transfer function object, then it is converted to a state + space and returned. Note: no renaming of inputs and outputs is performed; this should be done by the calling function. @@ -2265,7 +2416,7 @@ def _convert_to_statespace(sys, use_prefix_suffix=False, method=None): ssout[3][:sys.noutputs, :states], ssout[4], sys.dt) elif method in [None, 'scipy']: - # Scipy tf->ss can't handle MIMO, but SISO is OK + # SciPy tf->ss can't handle MIMO, but SISO is OK maxn = max(max(len(n) for n in nrow) for nrow in sys.num) maxd = max(max(len(d) for d in drow) @@ -2274,7 +2425,7 @@ def _convert_to_statespace(sys, use_prefix_suffix=False, method=None): D = empty((sys.noutputs, sys.ninputs), dtype=float) for i, j in itertools.product(range(sys.noutputs), range(sys.ninputs)): - D[i, j] = sys.num[i][j][0] / sys.den[i][j][0] + D[i, j] = sys.num_array[i, j][0] / sys.den_array[i, j][0] newsys = StateSpace([], [], [], D, sys.dt) else: if not issiso(sys): @@ -2298,7 +2449,7 @@ def _convert_to_statespace(sys, use_prefix_suffix=False, method=None): # If this is a matrix, try to create a constant feedthrough try: - D = _ssmatrix(np.atleast_2d(sys)) + D = _ssmatrix(np.atleast_2d(sys), name="D") return StateSpace([], [], [], D, dt=None) except Exception: diff --git a/control/stochsys.py b/control/stochsys.py index fe11a4fb5..756d83e13 100644 --- a/control/stochsys.py +++ b/control/stochsys.py @@ -1,14 +1,11 @@ # stochsys.py - stochastic systems module # RMM, 16 Mar 2022 -# -# This module contains functions that are intended to be used for analysis -# and design of stochastic control systems, mainly involving Kalman -# filtering and its variants. -# -"""The :mod:`~control.stochsys` module contains functions for analyzing and -designing stochastic (control) systems, including white noise processes and -Kalman filtering. +"""Stochastic systems module. + +This module contains functions for analyzing and designing stochastic +(control) systems, including white noise processes and Kalman +filtering. """ @@ -16,19 +13,20 @@ __maintainer__ = "Richard Murray" __email__ = "murray@cds.caltech.edu" +import warnings +from math import sqrt + import numpy as np import scipy as sp -from math import sqrt -from .statesp import StateSpace +from .config import _process_legacy_keyword +from .exception import ControlArgument, ControlNotImplemented +from .iosys import _process_control_disturbance_indices, _process_labels, \ + isctime, isdtime from .lti import LTI -from .iosys import InputOutputSystem, isctime, isdtime, _process_indices, \ - _process_labels, _process_control_disturbance_indices +from .mateqn import _check_shape, care, dare from .nlsys import NonlinearIOSystem -from .mateqn import care, dare, _check_shape -from .statesp import StateSpace, _ssmatrix -from .exception import ControlArgument, ControlNotImplemented -from .config import _process_legacy_keyword +from .statesp import StateSpace __all__ = ['lqe', 'dlqe', 'create_estimator_iosystem', 'white_noise', 'correlation'] @@ -38,8 +36,9 @@ def lqe(*args, **kwargs): r"""lqe(A, G, C, QN, RN, [, NN]) - Linear quadratic estimator design (Kalman filter) for continuous-time - systems. Given the system + Continuous-time linear quadratic estimator (Kalman filter). + + Given the continuous-time system .. math:: @@ -72,12 +71,12 @@ def lqe(*args, **kwargs): Parameters ---------- A, G, C : 2D array_like - Dynamics, process noise (disturbance), and output matrices - sys : LTI (StateSpace or TransferFunction) + Dynamics, process noise (disturbance), and output matrices. + sys : `StateSpace` or `TransferFunction` Linear I/O system, with the process noise input taken as the system input. QN, RN : 2D array_like - Process and sensor noise covariance matrices + Process and sensor noise covariance matrices. NN : 2D array, optional Cross covariance matrix. Not currently implemented. method : str, optional @@ -88,22 +87,22 @@ def lqe(*args, **kwargs): Returns ------- L : 2D array - Kalman estimator gain + Kalman estimator gain. P : 2D array - Solution to Riccati equation + Solution to Riccati equation: .. math:: A P + P A^T - (P C^T + G N) R^{-1} (C P + N^T G^T) + G Q G^T = 0 E : 1D array - Eigenvalues of estimator poles eig(A - L C) + Eigenvalues of estimator poles eig(A - L C). Notes ----- If the first argument is an LTI object, then this object will be used to define the dynamics, noise and output matrices. Furthermore, if the - LTI object corresponds to a discrete time system, the ``dlqe()`` + LTI object corresponds to a discrete-time system, the `dlqe` function will be called. Examples @@ -127,7 +126,7 @@ def lqe(*args, **kwargs): # Process the arguments and figure out what inputs we received # - # If we were passed a discrete time system as the first arg, use dlqe() + # If we were passed a discrete-time system as the first arg, use dlqe() if isinstance(args[0], LTI) and isdtime(args[0], strict=True): # Call dlqe return dlqe(*args, **kwargs) @@ -165,28 +164,31 @@ def lqe(*args, **kwargs): # Get the cross-covariance matrix, if given if (len(args) > index + 2): - NN = np.array(args[index+2], ndmin=2, dtype=float) + # NN = np.array(args[index+2], ndmin=2, dtype=float) raise ControlNotImplemented("cross-covariance not implemented") else: + pass # For future use (not currently used below) - NN = np.zeros((QN.shape[0], RN.shape[1])) + # NN = np.zeros((QN.shape[0], RN.shape[1])) + # Check dimensions of G (needed before calling care()) - _check_shape("QN", QN, G.shape[1], G.shape[1]) + _check_shape(QN, G.shape[1], G.shape[1], name="QN") # Compute the result (dimension and symmetry checking done in care()) P, E, LT = care(A.T, C.T, G @ QN @ G.T, RN, method=method, - B_s="C", Q_s="QN", R_s="RN", S_s="NN") - return _ssmatrix(LT.T), _ssmatrix(P), E + _Bs="C", _Qs="QN", _Rs="RN", _Ss="NN") + return LT.T, P, E # contributed by Sawyer B. Fuller def dlqe(*args, **kwargs): r"""dlqe(A, G, C, QN, RN, [, N]) - Linear quadratic estimator design (Kalman filter) for discrete-time - systems. Given the system + Discrete-time linear quadratic estimator (Kalman filter). + + Given the system .. math:: @@ -202,36 +204,36 @@ def dlqe(*args, **kwargs): .. math:: x_e[n+1] = A x_e[n] + B u[n] + L(y[n] - C x_e[n] - D u[n]) - produces a state estimate x_e[n] that minimizes the expected squared error - using the sensor measurements y. The noise cross-correlation `NN` is - set to zero when omitted. + produces a state estimate x_e[n] that minimizes the expected squared + error using the sensor measurements y. The noise cross-correlation `NN` + is set to zero when omitted. Parameters ---------- - A, G : 2D array_like - Dynamics and noise input matrices + A, G, C : 2D array_like + Dynamics, process noise (disturbance), and output matrices. QN, RN : 2D array_like - Process and sensor noise covariance matrices + Process and sensor noise covariance matrices. NN : 2D array, optional - Cross covariance matrix (not yet supported) + Cross covariance matrix (not yet supported). method : str, optional Set the method used for computing the result. Current methods are - 'slycot' and 'scipy'. If set to None (default), try 'slycot' first - and then 'scipy'. + 'slycot' and 'scipy'. If set to None (default), try 'slycot' + first and then 'scipy'. Returns ------- L : 2D array - Kalman estimator gain + Kalman estimator gain. P : 2D array - Solution to Riccati equation + Solution to Riccati equation. .. math:: A P + P A^T - (P C^T + G N) R^{-1} (C P + N^T G^T) + G Q G^T = 0 E : 1D array - Eigenvalues of estimator poles eig(A - L C) + Eigenvalues of estimator poles eig(A - L C). Examples -------- @@ -257,9 +259,9 @@ def dlqe(*args, **kwargs): if (len(args) < 3): raise ControlArgument("not enough input arguments") - # If we were passed a continus time system as the first arg, raise error + # If we were passed a continuous time system as the first arg, raise error if isinstance(args[0], LTI) and isctime(args[0], strict=True): - raise ControlArgument("dlqr() called with a continuous time system") + raise ControlArgument("dlqr() called with a continuous-time system") # If we were passed a state space system, use that to get system matrices if isinstance(args[0], StateSpace): @@ -289,16 +291,16 @@ def dlqe(*args, **kwargs): # NN = np.zeros(QN.size(0),RN.size(1)) # NG = G @ NN if len(args) > index + 2: - NN = np.array(args[index+2], ndmin=2, dtype=float) - raise ControlNotImplemented("cross-covariance not yet implememented") + # NN = np.array(args[index+2], ndmin=2, dtype=float) + raise ControlNotImplemented("cross-covariance not yet implemented") # Check dimensions of G (needed before calling care()) - _check_shape("QN", QN, G.shape[1], G.shape[1]) + _check_shape(QN, G.shape[1], G.shape[1], name="QN") # Compute the result (dimension and symmetry checking done in dare()) P, E, LT = dare(A.T, C.T, G @ QN @ G.T, RN, method=method, - B_s="C", Q_s="QN", R_s="RN", S_s="NN") - return _ssmatrix(LT.T), _ssmatrix(P), E + _Bs="C", _Qs="QN", _Rs="RN", _Ss="NN") + return LT.T, P, E # Function to create an estimator @@ -314,20 +316,20 @@ def create_estimator_iosystem( r"""Create an I/O system implementing a linear quadratic estimator. This function creates an input/output system that implements a - continuous time state estimator of the form + continuous-time state estimator of the form .. math:: d \hat{x}/dt &= A \hat{x} + B u - L (C \hat{x} - y) \\ - dP/dt &= A P + P A^T + F Q_N F^T - P C^T R_N^{-1} C P \\ + dP/dt &= A P + P A^T + G Q_N G^T - P C^T R_N^{-1} C P \\ L &= P C^T R_N^{-1} - or a discrete time state estimator of the form + or a discrete-time state estimator of the form .. math:: \hat{x}[k+1] &= A \hat{x}[k] + B u[k] - L (C \hat{x}[k] - y[k]) \\ - P[k+1] &= A P A^T + F Q_N F^T - A P C^T R_e^{-1} C P A \\ + P[k+1] &= A P A^T + G Q_N G^T - A P C^T R_e^{-1} C P A \\ L &= A P C^T R_e^{-1} where :math:`R_e = R_N + C P C^T`. It can be called in the form:: @@ -342,7 +344,7 @@ def create_estimator_iosystem( Parameters ---------- - sys : StateSpace + sys : `StateSpace` The linear I/O system that represents the process dynamics. QN, RN : ndarray Disturbance and measurement noise covariance matrices. @@ -351,7 +353,7 @@ def create_estimator_iosystem( state covariance. G : ndarray, optional Disturbance matrix describing how the disturbances enters the - dynamics. Defaults to sys.B. + dynamics. Defaults to `sys.B`. C : ndarray, optional If the system has full state output, define the measured values to be used by the estimator. Otherwise, use the system output as the @@ -359,7 +361,7 @@ def create_estimator_iosystem( Returns ------- - estim : InputOutputSystem + estim : `InputOutputSystem` Input/output system representing the estimator. This system takes the system output y and input u and generates the estimated state xhat. @@ -397,21 +399,21 @@ def create_estimator_iosystem( measurement_labels, control_labels : str or list of str, optional Set the name of the measurement and control signal names (estimator inputs). If a single string is specified, it should be a format - string using the variable ``i`` as an index. Otherwise, a list of + string using the variable `i` as an index. Otherwise, a list of strings matching the size of the system inputs and outputs should be used. Default is the signal names for the system measurements and - known control inputs. These settings can also be overriden using the + known control inputs. These settings can also be overridden using the `inputs` keyword. inputs, outputs, states : int or list of str, optional Set the names of the inputs, outputs, and states, as described in - :func:`~control.InputOutputSystem`. Overrides signal labels. + `InputOutputSystem`. Overrides signal labels. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. + name 'sys[id]' is generated with a unique integer id. Notes ----- - This function can be used with the ``create_statefbk_iosystem()`` function + This function can be used with the `create_statefbk_iosystem` function to create a closed loop, output-feedback, state space controller:: K, _, _ = ct.lqr(sys, Q, R) @@ -422,11 +424,16 @@ def create_estimator_iosystem( resp = ct.input_output_response(est, T, [Y, U], [X0, P0]) - If desired, the ``correct`` parameter can be set to ``False`` to allow + If desired, the `correct` parameter can be set to False to allow prediction with no additional measurement information:: resp = ct.input_output_response( - est, T, 0, [X0, P0], param={'correct': False) + est, T, 0, [X0, P0], params={'correct': False) + + References + ---------- + .. [1] R. M. Murray, `Optimization-Based Control + `_, 2023. """ @@ -460,12 +467,13 @@ def create_estimator_iosystem( # Set the input and direct matrices B = sys.B[:, ctrl_idx] if not np.allclose(sys.D, 0): - raise NotImplemented("nonzero 'D' matrix not yet implemented") + raise NotImplementedError("nonzero 'D' matrix not yet implemented") # Set the output matrices if C is not None: - # Make sure that we have the full system output - if not np.array_equal(sys.C, np.eye(sys.nstates)): + # Make sure we have full system output (allowing for numerical errors) + if sys.C.shape[0] != sys.nstates or \ + not np.allclose(sys.C, np.eye(sys.nstates)): raise ValueError("System output must be full state") # Make sure that the output matches the size of RN @@ -478,11 +486,13 @@ def create_estimator_iosystem( # Generate the disturbance matrix (G) if G is None: G = sys.B if len(dist_idx) == 0 else sys.B[:, dist_idx] + G = _check_shape(G, sys.nstates, len(dist_idx), name='G') # Initialize the covariance matrix if P0 is None: - # Initalize P0 to the steady state value + # Initialize P0 to the steady state value _, P0, _ = lqe(A, G, C, QN, RN) + P0 = _check_shape(P0, sys.nstates, sys.nstates, symmetric=True, name='P0') # Figure out the labels to use estimate_labels = _process_labels( @@ -497,7 +507,7 @@ def create_estimator_iosystem( else: # Generate labels corresponding to measured values from C measurement_labels = _process_labels( - measurement_labels, 'measurement', + measurement_labels, 'measurement', [f'y[{i}]' for i in range(C.shape[0])]) control_labels = _process_labels( control_labels, 'control', @@ -505,6 +515,10 @@ def create_estimator_iosystem( inputs = measurement_labels + control_labels if inputs is None \ else inputs + # Process the disturbance covariances and check size + QN = _check_shape(QN, G.shape[1], G.shape[1], square=True, name='QN') + RN = _check_shape(RN, C.shape[0], C.shape[0], square=True, name='RN') + if isinstance(covariance_labels, str): # Generate the list of labels using the argument as a format string covariance_labels = [ @@ -590,8 +604,8 @@ def white_noise(T, Q, dt=0): """Generate a white noise signal with specified intensity. This function generates a (multi-variable) white noise signal of - specified intensity as either a sampled continous time signal or a - discrete time signal. A white noise signal along a 1D array + specified intensity as either a sampled continuous time signal or a + discrete-time signal. A white noise signal along a 1D array of linearly spaced set of times T can be computing using V = ct.white_noise(T, Q, dt) @@ -604,6 +618,21 @@ def white_noise(T, Q, dt=0): covariance Q at each point in time (without any scaling based on the sample time). + Parameters + ---------- + T : 1D array_like + Array of linearly spaced times. + Q : 2D array_like + Noise intensity matrix of dimension nxn. + dt : float, optional + If 0, generate continuous-time noise signal, otherwise discrete time. + + Returns + ------- + V : array + Noise signal indexed as ``V[i, j]`` where `i` is the signal index and + `j` is the time index. + """ # Convert input arguments to arrays T = np.atleast_1d(T) @@ -637,15 +666,16 @@ def white_noise(T, Q, dt=0): def correlation(T, X, Y=None, squeeze=True): """Compute the correlation of time signals. - For a time series X(t) (and optionally Y(t)), the correlation() function - computes the correlation matrix E(X'(t+tau) X(t)) or the cross-correlation - matrix E(X'(t+tau) Y(t)]: + For a time series X(t) (and optionally Y(t)), the correlation() + function computes the correlation matrix E(X'(t+tau) X(t)) or the + cross-correlation matrix E(X'(t+tau) Y(t)]: tau, Rtau = correlation(T, X[, Y]) - The signal X (and Y, if present) represent a continuous time signal - sampled at times T. The return value provides the correlation Rtau - between X(t+tau) and X(t) at a set of time offets tau. + The signal X (and Y, if present) represent a continuous or + discrete-time signal sampled at times T. The return value provides the + correlation Rtau between X(t+tau) and X(t) at a set of time offsets + tau. Parameters ---------- @@ -663,6 +693,10 @@ def correlation(T, X, Y=None, squeeze=True): Returns ------- + tau : array + Array of time offsets. + Rtau : array + Correlation for each offset tau. """ T = np.atleast_1d(T) diff --git a/control/sysnorm.py b/control/sysnorm.py index 6737dc5c0..fecdd7095 100644 --- a/control/sysnorm.py +++ b/control/sysnorm.py @@ -1,77 +1,76 @@ -# -*- coding: utf-8 -*- -"""sysnorm.py +# sysnorm.py - functions for computing system norms +# +# Initial author: Henrik Sandberg +# Creation date: 21 Dec 2023 -Functions for computing system norms. +"""Functions for computing system norms.""" -Routine in this module: - -norm - -Created on Thu Dec 21 08:06:12 2023 -Author: Henrik Sandberg -""" +import warnings import numpy as np -import scipy as sp import numpy.linalg as la -import warnings import control as ct -__all__ = ['norm'] +__all__ = ['system_norm', 'norm'] #------------------------------------------------------------------------------ def _h2norm_slycot(sys, print_warning=True): """H2 norm of a linear system. For internal use. Requires Slycot. - See also + See Also -------- - ``slycot.ab13bd`` : the Slycot routine that does the calculation - https://github.com/python-control/Slycot/issues/199 : Post on issue with ``ab13bf`` + slycot.ab13bd + """ - + # See: https://github.com/python-control/Slycot/issues/199 try: from slycot import ab13bd except ImportError: - ct.ControlSlycot("Can't find slycot module ``ab13bd``!") + ct.ControlSlycot("Can't find slycot module ab13bd") try: from slycot.exceptions import SlycotArithmeticError - except ImportError: - raise ct.ControlSlycot("Can't find slycot class ``SlycotArithmeticError``!") + except ImportError: + raise ct.ControlSlycot( + "Can't find slycot class SlycotArithmeticError") A, B, C, D = ct.ssdata(ct.ss(sys)) n = A.shape[0] m = B.shape[1] p = C.shape[0] - + dico = 'C' if sys.isctime() else 'D' # Continuous or discrete time - jobn = 'H' # H2 (and not L2 norm) + jobn = 'H' # H2 (and not L2 norm) if n == 0: # ab13bd does not accept empty A, B, C if dico == 'C': if any(D.flat != 0): if print_warning: - warnings.warn("System has a direct feedthrough term!", UserWarning) + warnings.warn( + "System has a direct feedthrough term!", UserWarning) return float("inf") else: return 0.0 elif dico == 'D': return np.sqrt(D@D.T) - + try: norm = ab13bd(dico, jobn, n, m, p, A, B, C, D) except SlycotArithmeticError as e: if e.info == 3: if print_warning: - warnings.warn("System has pole(s) on the stability boundary!", UserWarning) + warnings.warn( + "System has pole(s) on the stability boundary!", + UserWarning) return float("inf") elif e.info == 5: if print_warning: - warnings.warn("System has a direct feedthrough term!", UserWarning) + warnings.warn( + "System has a direct feedthrough term!", UserWarning) return float("inf") elif e.info == 6: if print_warning: @@ -83,34 +82,37 @@ def _h2norm_slycot(sys, print_warning=True): #------------------------------------------------------------------------------ -def norm(system, p=2, tol=1e-6, print_warning=True, method=None): - """Computes norm of system. - +def system_norm(system, p=2, tol=1e-6, print_warning=True, method=None): + """Computes the input/output norm of system. + Parameters ---------- - system : LTI (:class:`StateSpace` or :class:`TransferFunction`) - System in continuous or discrete time for which the norm should be computed. + system : LTI (`StateSpace` or `TransferFunction`) + System in continuous or discrete time for which the norm should + be computed. p : int or str - Type of norm to be computed. ``p=2`` gives the H2 norm, and ``p='inf'`` gives the L-infinity norm. + Type of norm to be computed. `p` = 2 gives the H2 norm, and + `p` = 'inf' gives the L-infinity norm. tol : float - Relative tolerance for accuracy of L-infinity norm computation. Ignored - unless ``p='inf'``. + Relative tolerance for accuracy of L-infinity norm + computation. Ignored unless `p` = 'inf'. print_warning : bool Print warning message in case norm value may be uncertain. method : str, optional Set the method used for computing the result. Current methods are - ``'slycot'`` and ``'scipy'``. If set to ``None`` (default), try ``'slycot'`` first - and then ``'scipy'``. - + 'slycot' and 'scipy'. If set to None (default), try 'slycot' first + and then 'scipy'. + Returns ------- norm_value : float Norm value of system. - + Notes ----- - Does not yet compute the L-infinity norm for discrete time systems with pole(s) in z=0 unless Slycot is used. - + Does not yet compute the L-infinity norm for discrete-time systems + with pole(s) at the origin unless Slycot is used. + Examples -------- >>> Gc = ct.tf([1], [1, 2, 1]) @@ -118,34 +120,37 @@ def norm(system, p=2, tol=1e-6, print_warning=True, method=None): 0.5 >>> round(ct.norm(Gc, 'inf', tol=1e-5, method='scipy'), 3) np.float64(1.0) + """ - if not isinstance(system, (ct.StateSpace, ct.TransferFunction)): - raise TypeError('Parameter ``system``: must be a ``StateSpace`` or ``TransferFunction``') - + raise TypeError( + "Parameter `system`: must be a `StateSpace` or `TransferFunction`") + G = ct.ss(system) A = G.A B = G.B C = G.C D = G.D - + # Decide what method to use method = ct.mateqn._slycot_or_scipy(method) - + # ------------------- # H2 norm computation # ------------------- - if p == 2: + if p == 2: # -------------------- # Continuous time case # -------------------- if G.isctime(): - + # Check for cases with infinite norm - poles_real_part = G.poles().real + poles_real_part = G.poles().real if any(np.isclose(poles_real_part, 0.0)): # Poles on imaginary axis if print_warning: - warnings.warn("Poles close to, or on, the imaginary axis. Norm value may be uncertain.", UserWarning) + warnings.warn( + "Poles close to, or on, the imaginary axis. " + "Norm value may be uncertain.", UserWarning) return float('inf') elif any(poles_real_part > 0.0): # System unstable if print_warning: @@ -153,107 +158,130 @@ def norm(system, p=2, tol=1e-6, print_warning=True, method=None): return float('inf') elif any(D.flat != 0): # System has direct feedthrough if print_warning: - warnings.warn("System has a direct feedthrough term!", UserWarning) - return float('inf') - - else: + warnings.warn( + "System has a direct feedthrough term!", UserWarning) + return float('inf') + + else: # Use slycot, if available, to compute (finite) norm if method == 'slycot': - return _h2norm_slycot(G, print_warning) - - # Else use scipy + return _h2norm_slycot(G, print_warning) + + # Else use scipy else: - P = ct.lyap(A, B@B.T, method=method) # Solve for controllability Gramian - - # System is stable to reach this point, and P should be positive semi-definite. - # Test next is a precaution in case the Lyapunov equation is ill conditioned. - if any(la.eigvals(P).real < 0.0): + # Solve for controllability Gramian + P = ct.lyap(A, B@B.T, method=method) + + # System is stable to reach this point, and P should be + # positive semi-definite. Test next is a precaution in + # case the Lyapunov equation is ill conditioned. + if any(la.eigvals(P).real < 0.0): if print_warning: - warnings.warn("There appears to be poles close to the imaginary axis. Norm value may be uncertain.", UserWarning) + warnings.warn( + "There appears to be poles close to the " + "imaginary axis. Norm value may be uncertain.", + UserWarning) return float('inf') else: - norm_value = np.sqrt(np.trace(C@P@C.T)) # Argument in sqrt should be non-negative + # Argument in sqrt should be non-negative + norm_value = np.sqrt(np.trace(C@P@C.T)) if np.isnan(norm_value): - raise ct.ControlArgument("Norm computation resulted in NaN.") + raise ct.ControlArgument( + "Norm computation resulted in NaN.") else: return norm_value - + # ------------------ # Discrete time case # ------------------ elif G.isdtime(): - + # Check for cases with infinite norm poles_abs = abs(G.poles()) if any(np.isclose(poles_abs, 1.0)): # Poles on imaginary axis if print_warning: - warnings.warn("Poles close to, or on, the complex unit circle. Norm value may be uncertain.", UserWarning) + warnings.warn( + "Poles close to, or on, the complex unit circle. " + "Norm value may be uncertain.", UserWarning) return float('inf') elif any(poles_abs > 1.0): # System unstable if print_warning: warnings.warn("System is unstable!", UserWarning) return float('inf') - else: # Use slycot, if available, to compute (finite) norm if method == 'slycot': - return _h2norm_slycot(G, print_warning) - - # Else use scipy + return _h2norm_slycot(G, print_warning) + + # Else use scipy else: P = ct.dlyap(A, B@B.T, method=method) - - # System is stable to reach this point, and P should be positive semi-definite. - # Test next is a precaution in case the Lyapunov equation is ill conditioned. + + # System is stable to reach this point, and P should be + # positive semi-definite. Test next is a precaution in + # case the Lyapunov equation is ill conditioned. if any(la.eigvals(P).real < 0.0): if print_warning: - warnings.warn("Warning: There appears to be poles close to the complex unit circle. Norm value may be uncertain.", UserWarning) + warnings.warn( + "There appears to be poles close to the complex " + "unit circle. Norm value may be uncertain.", + UserWarning) return float('inf') else: - norm_value = np.sqrt(np.trace(C@P@C.T + D@D.T)) # Argument in sqrt should be non-negative + # Argument in sqrt should be non-negative + norm_value = np.sqrt(np.trace(C@P@C.T + D@D.T)) if np.isnan(norm_value): - raise ct.ControlArgument("Norm computation resulted in NaN.") + raise ct.ControlArgument( + "Norm computation resulted in NaN.") else: - return norm_value - + return norm_value + # --------------------------- # L-infinity norm computation # --------------------------- - elif p == "inf": - + elif p == "inf": + # Check for cases with infinite norm poles = G.poles() if G.isdtime(): # Discrete time if any(np.isclose(abs(poles), 1.0)): # Poles on unit circle if print_warning: - warnings.warn("Poles close to, or on, the complex unit circle. Norm value may be uncertain.", UserWarning) + warnings.warn( + "Poles close to, or on, the complex unit circle. " + "Norm value may be uncertain.", UserWarning) return float('inf') else: # Continuous time if any(np.isclose(poles.real, 0.0)): # Poles on imaginary axis if print_warning: - warnings.warn("Poles close to, or on, the imaginary axis. Norm value may be uncertain.", UserWarning) + warnings.warn( + "Poles close to, or on, the imaginary axis. " + "Norm value may be uncertain.", UserWarning) return float('inf') - + # Use slycot, if available, to compute (finite) norm if method == 'slycot': return ct.linfnorm(G, tol)[0] - + # Else use scipy else: - - # ------------------ + + # ------------------ # Discrete time case # ------------------ - # Use inverse bilinear transformation of discrete time system to s-plane if no poles on |z|=1 or z=0. - # Allows us to use test for continuous time systems next. + # Use inverse bilinear transformation of discrete-time system + # to s-plane if no poles on |z|=1 or z=0. Allows us to use + # test for continuous-time systems next. if G.isdtime(): Ad = A Bd = B Cd = C Dd = D if any(np.isclose(la.eigvals(Ad), 0.0)): - raise ct.ControlArgument("L-infinity norm computation for discrete time system with pole(s) in z=0 currently not supported unless Slycot installed.") - + raise ct.ControlArgument( + "L-infinity norm computation for discrete-time " + "system with pole(s) in z=0 currently not supported " + "unless Slycot installed.") + # Inverse bilinear transformation In = np.eye(len(Ad)) Adinv = la.inv(Ad+In) @@ -261,7 +289,7 @@ def norm(system, p=2, tol=1e-6, print_warning=True, method=None): B = 2*Adinv@Bd C = 2*Cd@Adinv D = Dd - Cd@Adinv@Bd - + # -------------------- # Continuous time case # -------------------- @@ -269,15 +297,19 @@ def _Hamilton_matrix(gamma): """Constructs Hamiltonian matrix. For internal use.""" R = Ip*gamma**2 - D.T@D invR = la.inv(R) - return np.block([[A+B@invR@D.T@C, B@invR@B.T], [-C.T@(Ip+D@invR@D.T)@C, -(A+B@invR@D.T@C).T]]) + return np.block([ + [A+B@invR@D.T@C, B@invR@B.T], + [-C.T@(Ip+D@invR@D.T)@C, -(A+B@invR@D.T@C).T]]) gaml = la.norm(D,ord=2) # Lower bound gamu = max(1.0, 2.0*gaml) # Candidate upper bound - Ip = np.eye(len(D)) - - while any(np.isclose(la.eigvals(_Hamilton_matrix(gamu)).real, 0.0)): # Find actual upper bound + Ip = np.eye(len(D)) + + while any(np.isclose( + la.eigvals(_Hamilton_matrix(gamu)).real, 0.0)): + # Find actual upper bound gamu *= 2.0 - + while (gamu-gaml)/gamu > tol: gam = (gamu+gaml)/2.0 if any(np.isclose(la.eigvals(_Hamilton_matrix(gam)).real, 0.0)): @@ -285,10 +317,13 @@ def _Hamilton_matrix(gamma): else: gamu = gam return gam - + # ---------------------- # Other norm computation # ---------------------- else: - raise ct.ControlArgument(f"Norm computation for p={p} currently not supported.") + raise ct.ControlArgument( + f"Norm computation for p={p} currently not supported.") + +norm = system_norm diff --git a/control/tests/bdalg_test.py b/control/tests/bdalg_test.py index b9e26e8c0..cec10f904 100644 --- a/control/tests/bdalg_test.py +++ b/control/tests/bdalg_test.py @@ -1,23 +1,21 @@ -"""bdalg_test.py - test suite for block diagram algebra +"""bdalg_test.py - test suite for block diagram algebra. RMM, 30 Mar 2011 (based on TestBDAlg from v0.4a) """ +import control as ctrl import numpy as np -from numpy import sort import pytest - -import control as ctrl -from control.xferfcn import TransferFunction +from control.bdalg import _ensure_tf, append, connect, feedback +from control.lti import poles, zeros from control.statesp import StateSpace -from control.bdalg import feedback, append, connect -from control.lti import zeros, poles +from control.tests.conftest import assert_tf_close_coeff +from control.xferfcn import TransferFunction +from numpy import sort class TestFeedback: - """These are tests for the feedback function in bdalg.py. Currently, some - of the tests are not implemented, or are not working properly. TODO: these - need to be fixed.""" + """Tests for the feedback function in bdalg.py.""" @pytest.fixture def tsys(self): @@ -179,7 +177,7 @@ def testTFTF(self, tsys): [[[1., 4., 9., 8., 5.]]]) def testLists(self, tsys): - """Make sure that lists of various lengths work for operations""" + """Make sure that lists of various lengths work for operations.""" sys1 = ctrl.tf([1, 1], [1, 2]) sys2 = ctrl.tf([1, 3], [1, 4]) sys3 = ctrl.tf([1, 5], [1, 6]) @@ -236,7 +234,7 @@ def testLists(self, tsys): sort(zeros(sys1 + sys2 + sys3 + sys4 + sys5))) def testMimoSeries(self, tsys): - """regression: bdalg.series reverses order of arguments""" + """regression: bdalg.series reverses order of arguments.""" g1 = ctrl.ss([], [], [], [[1, 2], [0, 3]]) g2 = ctrl.ss([], [], [], [[1, 0], [2, 3]]) ref = g2 * g1 @@ -269,7 +267,7 @@ def test_feedback_args(self, tsys): def testConnect(self, tsys): sys = append(tsys.sys2, tsys.sys3) # two siso systems - with pytest.warns(DeprecationWarning, match="use `interconnect`"): + with pytest.warns(FutureWarning, match="use interconnect()"): # should not raise error connect(sys, [[1, 2], [2, -2]], [2], [1, 2]) connect(sys, [[1, 2], [2, 0]], [2], [1, 2]) @@ -349,16 +347,523 @@ def test_bdalg_udpate_names_errors(): sys2 = ctrl.rss(2, 1, 1) with pytest.raises(ValueError, match="number of inputs does not match"): - sys = ctrl.series(sys1, sys2, inputs=2) + ctrl.series(sys1, sys2, inputs=2) with pytest.raises(ValueError, match="number of outputs does not match"): - sys = ctrl.series(sys1, sys2, outputs=2) + ctrl.series(sys1, sys2, outputs=2) with pytest.raises(ValueError, match="number of states does not match"): - sys = ctrl.series(sys1, sys2, states=2) + ctrl.series(sys1, sys2, states=2) with pytest.raises(ValueError, match="number of states does not match"): - sys = ctrl.series(ctrl.tf(sys1), ctrl.tf(sys2), states=2) + ctrl.series(ctrl.tf(sys1), ctrl.tf(sys2), states=2) with pytest.raises(TypeError, match="unrecognized keywords"): - sys = ctrl.series(sys1, sys2, dt=1) + ctrl.series(sys1, sys2, dt=1) + + +class TestEnsureTf: + """Test `_ensure_tf`.""" + + @pytest.mark.parametrize( + "arraylike_or_tf, dt, tf", + [ + ( + ctrl.TransferFunction([1], [1, 2, 3]), + None, + ctrl.TransferFunction([1], [1, 2, 3]), + ), + ( + ctrl.TransferFunction([1], [1, 2, 3]), + 0, + ctrl.TransferFunction([1], [1, 2, 3]), + ), + ( + 2, + None, + ctrl.TransferFunction([2], [1]), + ), + ( + np.array([2]), + None, + ctrl.TransferFunction([2], [1]), + ), + ( + np.array([[2]]), + None, + ctrl.TransferFunction([2], [1]), + ), + ( + np.array( + [ + [2, 0, 3], + [1, 2, 3], + ] + ), + None, + ctrl.TransferFunction( + [ + [[2], [0], [3]], + [[1], [2], [3]], + ], + [ + [[1], [1], [1]], + [[1], [1], [1]], + ], + ), + ), + ( + np.array([2, 0, 3]), + None, + ctrl.TransferFunction( + [ + [[2], [0], [3]], + ], + [ + [[1], [1], [1]], + ], + ), + ), + ], + ) + def test_ensure(self, arraylike_or_tf, dt, tf): + """Test nominal cases.""" + ensured_tf = _ensure_tf(arraylike_or_tf, dt) + assert_tf_close_coeff(tf, ensured_tf) + + @pytest.mark.parametrize( + "arraylike_or_tf, dt, exception", + [ + ( + ctrl.TransferFunction([1], [1, 2, 3]), + 0.1, + ValueError, + ), + ( + ctrl.TransferFunction([1], [1, 2, 3], 0.1), + 0, + ValueError, + ), + ( + np.ones((1, 1, 1)), + None, + ValueError, + ), + ( + np.ones((1, 1, 1, 1)), + None, + ValueError, + ), + ], + ) + def test_error_ensure(self, arraylike_or_tf, dt, exception): + """Test error cases.""" + with pytest.raises(exception): + _ensure_tf(arraylike_or_tf, dt) + + +class TestTfCombineSplit: + """Test `combine_tf` and `split_tf`.""" + + @pytest.mark.parametrize( + "tf_array, tf", + [ + # Continuous-time + ( + [ + [ctrl.TransferFunction([1], [1, 1])], + [ctrl.TransferFunction([2], [1, 0])], + ], + ctrl.TransferFunction( + [ + [[1]], + [[2]], + ], + [ + [[1, 1]], + [[1, 0]], + ], + ), + ), + # Discrete-time + ( + [ + [ctrl.TransferFunction([1], [1, 1], dt=1)], + [ctrl.TransferFunction([2], [1, 0], dt=1)], + ], + ctrl.TransferFunction( + [ + [[1]], + [[2]], + ], + [ + [[1, 1]], + [[1, 0]], + ], + dt=1, + ), + ), + # Scalar + ( + [ + [2], + [ctrl.TransferFunction([2], [1, 0])], + ], + ctrl.TransferFunction( + [ + [[2]], + [[2]], + ], + [ + [[1]], + [[1, 0]], + ], + ), + ), + # Matrix + ( + [ + [np.eye(3)], + [ + ctrl.TransferFunction( + [ + [[2], [0], [3]], + [[1], [2], [3]], + ], + [ + [[1], [1], [1]], + [[1], [1], [1]], + ], + ) + ], + ], + ctrl.TransferFunction( + [ + [[1], [0], [0]], + [[0], [1], [0]], + [[0], [0], [1]], + [[2], [0], [3]], + [[1], [2], [3]], + ], + [ + [[1], [1], [1]], + [[1], [1], [1]], + [[1], [1], [1]], + [[1], [1], [1]], + [[1], [1], [1]], + ], + ), + ), + # Inhomogeneous + ( + [ + [np.eye(3)], + [ + ctrl.TransferFunction( + [ + [[2], [0]], + [[1], [2]], + ], + [ + [[1], [1]], + [[1], [1]], + ], + ), + ctrl.TransferFunction( + [ + [[3]], + [[3]], + ], + [ + [[1]], + [[1]], + ], + ), + ], + ], + ctrl.TransferFunction( + [ + [[1], [0], [0]], + [[0], [1], [0]], + [[0], [0], [1]], + [[2], [0], [3]], + [[1], [2], [3]], + ], + [ + [[1], [1], [1]], + [[1], [1], [1]], + [[1], [1], [1]], + [[1], [1], [1]], + [[1], [1], [1]], + ], + ), + ), + # Discrete-time + ( + [ + [2], + [ctrl.TransferFunction([2], [1, 0], dt=0.1)], + ], + ctrl.TransferFunction( + [ + [[2]], + [[2]], + ], + [ + [[1]], + [[1, 0]], + ], + dt=0.1, + ), + ), + ], + ) + def test_combine_tf(self, tf_array, tf): + """Test combining transfer functions.""" + tf_combined = ctrl.combine_tf(tf_array) + assert_tf_close_coeff(tf_combined, tf) + + @pytest.mark.parametrize( + "tf_array, tf", + [ + ( + np.array( + [ + [ctrl.TransferFunction([1], [1, 1])], + ], + dtype=object, + ), + ctrl.TransferFunction( + [ + [[1]], + ], + [ + [[1, 1]], + ], + ), + ), + ( + np.array( + [ + [ctrl.TransferFunction([1], [1, 1])], + [ctrl.TransferFunction([2], [1, 0])], + ], + dtype=object, + ), + ctrl.TransferFunction( + [ + [[1]], + [[2]], + ], + [ + [[1, 1]], + [[1, 0]], + ], + ), + ), + ( + np.array( + [ + [ctrl.TransferFunction([1], [1, 1], dt=1)], + [ctrl.TransferFunction([2], [1, 0], dt=1)], + ], + dtype=object, + ), + ctrl.TransferFunction( + [ + [[1]], + [[2]], + ], + [ + [[1, 1]], + [[1, 0]], + ], + dt=1, + ), + ), + ( + np.array( + [ + [ctrl.TransferFunction([2], [1], dt=0.1)], + [ctrl.TransferFunction([2], [1, 0], dt=0.1)], + ], + dtype=object, + ), + ctrl.TransferFunction( + [ + [[2]], + [[2]], + ], + [ + [[1]], + [[1, 0]], + ], + dt=0.1, + ), + ), + ], + ) + def test_split_tf(self, tf_array, tf): + """Test splitting transfer functions.""" + tf_split = ctrl.split_tf(tf) + # Test entry-by-entry + for i in range(tf_split.shape[0]): + for j in range(tf_split.shape[1]): + assert_tf_close_coeff( + tf_split[i, j], + tf_array[i, j], + ) + # Test combined + assert_tf_close_coeff( + ctrl.combine_tf(tf_split), + ctrl.combine_tf(tf_array), + ) + + @pytest.mark.parametrize( + "tf_array, exception", + [ + # Wrong timesteps + ( + [ + [ctrl.TransferFunction([1], [1, 1], 0.1)], + [ctrl.TransferFunction([2], [1, 0], 0.2)], + ], + ValueError, + ), + ( + [ + [ctrl.TransferFunction([1], [1, 1], 0.1)], + [ctrl.TransferFunction([2], [1, 0], 0)], + ], + ValueError, + ), + # Too few dimensions + ( + [ + ctrl.TransferFunction([1], [1, 1]), + ctrl.TransferFunction([2], [1, 0]), + ], + ValueError, + ), + # Too many dimensions + ( + [ + [[ctrl.TransferFunction([1], [1, 1], 0.1)]], + [[ctrl.TransferFunction([2], [1, 0], 0)]], + ], + ValueError, + ), + # Incompatible dimensions + ( + [ + [ + ctrl.TransferFunction( + [ + [ + [1], + ] + ], + [ + [ + [1, 1], + ] + ], + ), + ctrl.TransferFunction( + [ + [[2], [1]], + [[1], [3]], + ], + [ + [[1, 0], [1, 0]], + [[1, 0], [1, 0]], + ], + ), + ], + ], + ValueError, + ), + ( + [ + [ + ctrl.TransferFunction( + [ + [[2], [1]], + [[1], [3]], + ], + [ + [[1, 0], [1, 0]], + [[1, 0], [1, 0]], + ], + ), + ctrl.TransferFunction( + [ + [ + [1], + ] + ], + [ + [ + [1, 1], + ] + ], + ), + ], + ], + ValueError, + ), + ( + [ + [ + ctrl.TransferFunction( + [ + [[2], [1]], + [[1], [3]], + ], + [ + [[1, 0], [1, 0]], + [[1, 0], [1, 0]], + ], + ), + ctrl.TransferFunction( + [ + [[2], [1]], + [[1], [3]], + ], + [ + [[1, 0], [1, 0]], + [[1, 0], [1, 0]], + ], + ), + ], + [ + ctrl.TransferFunction( + [ + [[2], [1], [1]], + [[1], [3], [2]], + ], + [ + [[1, 0], [1, 0], [1, 0]], + [[1, 0], [1, 0], [1, 0]], + ], + ), + ctrl.TransferFunction( + [ + [[2], [1]], + [[1], [3]], + ], + [ + [[1, 0], [1, 0]], + [[1, 0], [1, 0]], + ], + ), + ], + ], + ValueError, + ), + ], + ) + def test_error_combine_tf(self, tf_array, exception): + """Test error cases.""" + with pytest.raises(exception): + ctrl.combine_tf(tf_array) diff --git a/control/tests/bspline_test.py b/control/tests/bspline_test.py index 0ac59094d..e15915182 100644 --- a/control/tests/bspline_test.py +++ b/control/tests/bspline_test.py @@ -11,11 +11,9 @@ import numpy as np import pytest -import scipy as sp import control as ct import control.flatsys as fs -import control.optimal as opt def test_bspline_basis(): Tf = 10 @@ -182,40 +180,40 @@ def test_kinematic_car_multivar(): def test_bspline_errors(): # Breakpoints must be a 1D array, in increasing order with pytest.raises(NotImplementedError, match="not yet supported"): - basis = fs.BSplineFamily([[0, 1, 3], [0, 2, 3]], [3, 3]) + fs.BSplineFamily([[0, 1, 3], [0, 2, 3]], [3, 3]) with pytest.raises(ValueError, match="breakpoints must be convertable to a 1D array"): - basis = fs.BSplineFamily([[[0, 1], [0, 1]], [[0, 1], [0, 1]]], [3, 3]) + fs.BSplineFamily([[[0, 1], [0, 1]], [[0, 1], [0, 1]]], [3, 3]) with pytest.raises(ValueError, match="must have at least 2 values"): - basis = fs.BSplineFamily([10], 2) + fs.BSplineFamily([10], 2) with pytest.raises(ValueError, match="must be strictly increasing"): - basis = fs.BSplineFamily([1, 3, 2], 2) + fs.BSplineFamily([1, 3, 2], 2) # Smoothness can't be more than dimension of splines - basis = fs.BSplineFamily([0, 1], 4, 3) # OK + fs.BSplineFamily([0, 1], 4, 3) # OK with pytest.raises(ValueError, match="degree must be greater"): - basis = fs.BSplineFamily([0, 1], 4, 4) # not OK + fs.BSplineFamily([0, 1], 4, 4) # not OK # nvars must be an integer with pytest.raises(TypeError, match="vars must be an integer"): - basis = fs.BSplineFamily([0, 1], 4, 3, vars=['x1', 'x2']) + fs.BSplineFamily([0, 1], 4, 3, vars=['x1', 'x2']) # degree, smoothness must match nvars with pytest.raises(ValueError, match="length of 'degree' does not match"): - basis = fs.BSplineFamily([0, 1], [4, 4, 4], 3, vars=2) + fs.BSplineFamily([0, 1], [4, 4, 4], 3, vars=2) # degree, smoothness must be list of ints - basis = fs.BSplineFamily([0, 1], [4, 4], 3, vars=2) # OK + fs.BSplineFamily([0, 1], [4, 4], 3, vars=2) # OK with pytest.raises(ValueError, match="could not parse 'degree'"): - basis = fs.BSplineFamily([0, 1], [4, '4'], 3, vars=2) + fs.BSplineFamily([0, 1], [4, '4'], 3, vars=2) # degree must be strictly positive with pytest.raises(ValueError, match="'degree'; must be at least 1"): - basis = fs.BSplineFamily([0, 1], 0, 1) + fs.BSplineFamily([0, 1], 0, 1) # smoothness must be non-negative with pytest.raises(ValueError, match="'smoothness'; must be at least 0"): - basis = fs.BSplineFamily([0, 1], 2, -1) + fs.BSplineFamily([0, 1], 2, -1) diff --git a/control/tests/config_test.py b/control/tests/config_test.py index 947dc95aa..be3fba5c9 100644 --- a/control/tests/config_test.py +++ b/control/tests/config_test.py @@ -108,6 +108,9 @@ def test_fbs_bode(self, mplcleanup): np.testing.assert_almost_equal(mag_x[0], 0.001, decimal=6) np.testing.assert_almost_equal(mag_y[0], 10, decimal=3) + # Make sure x-axis label is Gain + assert mag_axis.get_ylabel() == "Gain" + # Get the phase line phase_axis = plt.gcf().axes[1] phase_line = phase_axis.get_lines() @@ -153,6 +156,9 @@ def test_matlab_bode(self, mplcleanup): np.testing.assert_almost_equal(mag_x[0], 0.001, decimal=6) np.testing.assert_almost_equal(mag_y[0], 20*log10(10), decimal=3) + # Make sure x-axis label is Gain + assert mag_axis.get_ylabel() == "Magnitude [dB]" + # Get the phase line phase_axis = plt.gcf().axes[1] phase_line = phase_axis.get_lines() @@ -319,3 +325,42 @@ def test_system_indexing(self): indexed_system_name_suffix='POST') sys2 = sys[1:, 1:] assert sys2.name == 'PRE' + sys.name + 'POST' + + @pytest.mark.parametrize("kwargs", [ + {}, + {'name': 'mysys'}, + {'inputs': 1}, + {'inputs': 'u'}, + {'outputs': 1}, + {'outputs': 'y'}, + {'states': 1}, + {'states': 'x'}, + {'inputs': 1, 'outputs': 'y', 'states': 'x'}, + {'dt': 0.1} + ]) + def test_repr_format(self, kwargs): + sys = ct.ss([[1]], [[1]], [[1]], [[0]], **kwargs) + new = eval(repr(sys), None, {'StateSpace':ct.StateSpace, 'array':np.array}) + for attr in ['A', 'B', 'C', 'D']: + assert getattr(new, attr) == getattr(sys, attr) + for prop in ['input_labels', 'output_labels', 'state_labels']: + assert getattr(new, attr) == getattr(sys, attr) + if 'name' in kwargs: + assert new.name == sys.name + + +def test_config_context_manager(): + # Make sure we can temporarily set the value of a parameter + default_val = ct.config.defaults['statesp.latex_repr_type'] + with ct.config.defaults({'statesp.latex_repr_type': 'new value'}): + assert ct.config.defaults['statesp.latex_repr_type'] != default_val + assert ct.config.defaults['statesp.latex_repr_type'] == 'new value' + assert ct.config.defaults['statesp.latex_repr_type'] == default_val + + # OK to call the context manager and not do anything with it + ct.config.defaults({'statesp.latex_repr_type': 'new value'}) + assert ct.config.defaults['statesp.latex_repr_type'] == default_val + + with pytest.raises(ValueError, match="unknown parameter 'unknown'"): + with ct.config.defaults({'unknown': 'new value'}): + pass diff --git a/control/tests/conftest.py b/control/tests/conftest.py index 2330e3818..c10dcc225 100644 --- a/control/tests/conftest.py +++ b/control/tests/conftest.py @@ -1,7 +1,4 @@ -"""conftest.py - pytest local plugins and fixtures""" - -import os -from contextlib import contextmanager +"""conftest.py - pytest local plugins, fixtures, marks and functions.""" import matplotlib as mpl import numpy as np @@ -9,6 +6,7 @@ import control + # some common pytest marks. These can be used as test decorators or in # pytest.param(marks=) slycotonly = pytest.mark.skipif( @@ -61,28 +59,65 @@ def mplcleanup(): @pytest.fixture(scope="function") def legacy_plot_signature(): - """Turn off warnings for calls to plotting functions with old signatures""" + """Turn off warnings for calls to plotting functions with old signatures.""" import warnings warnings.filterwarnings( 'ignore', message='passing systems .* is deprecated', - category=DeprecationWarning) + category=FutureWarning) warnings.filterwarnings( - 'ignore', message='.* return values of .* is deprecated', - category=DeprecationWarning) + 'ignore', message='.* return value of .* is deprecated', + category=FutureWarning) yield warnings.resetwarnings() @pytest.fixture(scope="function") def ignore_future_warning(): - """Turn off warnings for functions that generate FutureWarning""" + """Turn off warnings for functions that generate FutureWarning.""" import warnings warnings.filterwarnings( 'ignore', message='.*deprecated', category=FutureWarning) yield warnings.resetwarnings() - -# Allow pytest.mark.slow to mark slow tests (skip with pytest -m "not slow") + def pytest_configure(config): + """Allow pytest.mark.slow to mark slow tests. + + skip with pytest -m "not slow" + """ config.addinivalue_line("markers", "slow: mark test as slow to run") + + +def assert_tf_close_coeff(actual, desired, rtol=1e-5, atol=1e-8): + """Check if two transfer functions have close coefficients. + + Parameters + ---------- + actual, desired : TransferFunction + Transfer functions to compare. + rtol : float + Relative tolerance for ``np.testing.assert_allclose``. + atol : float + Absolute tolerance for ``np.testing.assert_allclose``. + + Raises + ------ + AssertionError + """ + # Check number of outputs and inputs + assert actual.noutputs == desired.noutputs + assert actual.ninputs == desired.ninputs + # Check timestep + assert actual.dt == desired.dt + # Check coefficient arrays + for i in range(actual.noutputs): + for j in range(actual.ninputs): + np.testing.assert_allclose( + actual.num[i][j], + desired.num[i][j], + rtol=rtol, atol=atol) + np.testing.assert_allclose( + actual.den[i][j], + desired.den[i][j], + rtol=rtol, atol=atol) diff --git a/control/tests/ctrlplot_test.py b/control/tests/ctrlplot_test.py new file mode 100644 index 000000000..bf8a075ae --- /dev/null +++ b/control/tests/ctrlplot_test.py @@ -0,0 +1,815 @@ +# ctrlplot_test.py - test out control plotting utilities +# RMM, 27 Jun 2024 + +import inspect +import itertools +import warnings + +import matplotlib.pyplot as plt +import numpy as np +import pytest + +import control as ct + +# List of all plotting functions +resp_plot_fcns = [ + # response function plotting function + (ct.frequency_response, ct.bode_plot), + (ct.frequency_response, ct.nichols_plot), + (ct.singular_values_response, ct.singular_values_plot), + (ct.gangof4_response, ct.gangof4_plot), + (ct.describing_function_response, ct.describing_function_plot), + (None, ct.phase_plane_plot), + (ct.pole_zero_map, ct.pole_zero_plot), + (ct.nyquist_response, ct.nyquist_plot), + (ct.root_locus_map, ct.root_locus_plot), + (ct.initial_response, ct.time_response_plot), + (ct.step_response, ct.time_response_plot), + (ct.impulse_response, ct.time_response_plot), + (ct.forced_response, ct.time_response_plot), + (ct.input_output_response, ct.time_response_plot), +] + +nolabel_plot_fcns = [ct.describing_function_plot, ct.phase_plane_plot] +legacy_plot_fcns = [ct.gangof4_plot] +multiaxes_plot_fcns = [ct.bode_plot, ct.gangof4_plot, ct.time_response_plot] +deprecated_fcns = [ct.phase_plot] + + +# Utility function to make sure legends are OK +def assert_legend(cplt, expected_texts): + # Check to make sure the labels are OK in legend + legend = None + for ax in cplt.axes.flatten(): + legend = ax.get_legend() + if legend is not None: + break + if expected_texts is None: + assert legend is None + else: + assert legend is not None + legend_texts = [entry.get_text() for entry in legend.get_texts()] + assert legend_texts == expected_texts + + +def setup_plot_arguments(resp_fcn, plot_fcn, compute_time_response=True): + # Create some systems to use + sys1 = ct.rss(2, 1, 1, strictly_proper=True, name="sys[1]") + sys1c = ct.rss(2, 1, 1, strictly_proper=True, name="sys[1]_C") + sys2 = ct.rss(2, 1, 1, strictly_proper=True, name="sys[2]") + + # Set up arguments + kwargs = resp_kwargs = plot_kwargs = meth_kwargs = {} + argsc = None + match resp_fcn, plot_fcn: + case ct.describing_function_response, _: + sys1 = ct.tf([1], [1, 2, 2, 1], name="sys[1]") + sys2 = ct.tf([1.1], [1, 2, 2, 1], name="sys[2]") + F = ct.descfcn.saturation_nonlinearity(1) + amp = np.linspace(1, 4, 10) + args1 = (sys1, F, amp) + args2 = (sys2, F, amp) + resp_kwargs = plot_kwargs = {'refine': False} + + case ct.gangof4_response, _: + args1 = (sys1, sys1c) + args2 = (sys2, sys1c) + + case ct.frequency_response, ct.nichols_plot: + args1 = (sys1, None) # to allow *fmt in linestyle test + args2 = (sys2, ) + meth_kwargs = {'plot_type': 'nichols'} + + case ct.frequency_response, ct.bode_plot: + args1 = (sys1, None) # to allow *fmt in linestyle test + args2 = (sys2, ) + + case ct.singular_values_response, ct.singular_values_plot: + args1 = (sys1, None) # to allow *fmt in linestyle test + args2 = (sys2, ) + + case ct.root_locus_map, ct.root_locus_plot: + args1 = (sys1, ) + args2 = (sys2, ) + plot_kwargs = {'interactive': False} + + case (ct.forced_response | ct.input_output_response, _): + timepts = np.linspace(1, 10) + U = np.sin(timepts) + if compute_time_response: + args1 = (resp_fcn(sys1, timepts, U), ) + args2 = (resp_fcn(sys2, timepts, U), ) + argsc = (resp_fcn([sys1, sys2], timepts, U), ) + else: + args1 = (sys1, timepts, U) + args2 = (sys2, timepts, U) + argsc = None + + case (ct.impulse_response | ct.initial_response | ct.step_response, _): + if compute_time_response: + args1 = (resp_fcn(sys1), ) + args2 = (resp_fcn(sys2), ) + argsc = (resp_fcn([sys1, sys2]), ) + else: + args1 = (sys1, ) + args2 = (sys2, ) + argsc = ([sys1, sys2], ) + + case (None, ct.phase_plane_plot): + args1 = (sys1, ) + args2 = (sys2, ) + plot_kwargs = {'plot_streamlines': True} + + case _, _: + args1 = (sys1, ) + args2 = (sys2, ) + + return args1, args2, argsc, kwargs, meth_kwargs, plot_kwargs, resp_kwargs + + +# Make sure we didn't miss any plotting functions +def test_find_respplot_functions(): + # Get the list of plotting functions + plot_fcns = {respplot[1] for respplot in resp_plot_fcns} + + # Look through every object in the package + found = 0 + for name, obj in inspect.getmembers(ct): + # Skip anything that is outside of this module + if inspect.getmodule(obj) is not None and \ + not inspect.getmodule(obj).__name__.startswith('control'): + # Skip anything that isn't part of the control package + continue + + # Only look for non-deprecated functions ending in 'plot' + if not inspect.isfunction(obj) or name[-4:] != 'plot' or \ + obj in deprecated_fcns: + continue + + # Make sure that we have this on our list of functions + assert obj in plot_fcns + found += 1 + + assert found == len(plot_fcns) + + +@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns) +@pytest.mark.usefixtures('mplcleanup') +def test_plot_ax_processing(resp_fcn, plot_fcn): + # Set up arguments + args, _, _, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \ + setup_plot_arguments(resp_fcn, plot_fcn, compute_time_response=False) + get_line_color = lambda cplt: cplt.lines.reshape(-1)[0][0].get_color() + match resp_fcn, plot_fcn: + case None, ct.phase_plane_plot: + get_line_color = None + warnings.warn("ct.phase_plane_plot returns nonstandard lines") + + # Call the plot through the response function + if resp_fcn is not None: + resp = resp_fcn(*args, **kwargs, **resp_kwargs) + cplt1 = resp.plot(**kwargs, **meth_kwargs) + else: + # No response function available; just plot the data + cplt1 = plot_fcn(*args, **kwargs, **plot_kwargs) + assert isinstance(cplt1, ct.ControlPlot) + + # Call the plot directly, plotting on top of previous plot + if plot_fcn == ct.time_response_plot: + # Can't call the time_response_plot() with system => reuse data + cplt2 = plot_fcn(resp, **kwargs, **plot_kwargs) + else: + cplt2 = plot_fcn(*args, **kwargs, **plot_kwargs) + assert isinstance(cplt2, ct.ControlPlot) + + # Plot should have landed on top of previous plot, in different colors + assert cplt2.figure == cplt1.figure + assert np.all(cplt2.axes == cplt1.axes) + assert len(cplt2.lines[0]) == len(cplt1.lines[0]) + if get_line_color is not None: + assert get_line_color(cplt2) != get_line_color(cplt1) + + # Pass axes explicitly + if resp_fcn is not None: + cplt3 = resp.plot(**kwargs, **meth_kwargs, ax=cplt1.axes) + else: + cplt3 = plot_fcn(*args, **kwargs, **plot_kwargs, ax=cplt1.axes) + assert cplt3.figure == cplt1.figure + + # Plot should have landed on top of previous plot, in different colors + assert np.all(cplt3.axes == cplt1.axes) + assert len(cplt3.lines[0]) == len(cplt1.lines[0]) + if get_line_color is not None: + assert get_line_color(cplt3) != get_line_color(cplt1) + assert get_line_color(cplt3) != get_line_color(cplt2) + + # + # Plot on a user-contructed figure + # + + # Store modified properties from previous figure + cplt_titlesize = cplt3.figure._suptitle.get_fontsize() + cplt_labelsize = \ + cplt3.axes.reshape(-1)[0].get_yticklabels()[0].get_fontsize() + + # Set up some axes with a known title + fig, axs = plt.subplots(2, 3) + title = "User-constructed figure" + plt.suptitle(title) + titlesize = fig._suptitle.get_fontsize() + assert titlesize != cplt_titlesize + labelsize = axs[0, 0].get_yticklabels()[0].get_fontsize() + assert labelsize != cplt_labelsize + + # Figure out what to pass as the ax keyword + match resp_fcn, plot_fcn: + case _, ct.bode_plot: + ax = [axs[0, 1], axs[1, 1]] + + case ct.gangof4_response, _: + ax = [axs[0, 1], axs[0, 2], axs[1, 1], axs[1, 2]] + + case (ct.forced_response | ct.input_output_response, _): + ax = [axs[0, 1], axs[1, 1]] + + case _, _: + ax = [axs[0, 1]] + + # Call the plotting function, passing the axes + if resp_fcn is not None: + resp = resp_fcn(*args, **kwargs, **resp_kwargs) + resp.plot(**kwargs, **meth_kwargs, ax=ax) + else: + # No response function available; just plot the data + plot_fcn(*args, **kwargs, **plot_kwargs, ax=ax) + + # Make sure the plot ended up in the right place + assert len(axs[0, 0].get_lines()) == 0 # upper left + assert len(axs[0, 1].get_lines()) != 0 # top middle + assert len(axs[1, 0].get_lines()) == 0 # lower left + if resp_fcn != ct.gangof4_response: + assert len(axs[1, 2].get_lines()) == 0 # lower right (normally empty) + else: + assert len(axs[1, 2].get_lines()) != 0 # gangof4 uses this axes + + # Check to make sure original settings did not change + assert fig._suptitle.get_text() == title + assert fig._suptitle.get_fontsize() == titlesize + assert ax[0].get_yticklabels()[0].get_fontsize() == labelsize + + # Make sure that docstring documents ax keyword + if plot_fcn not in legacy_plot_fcns: + if plot_fcn in multiaxes_plot_fcns: + assert "ax : array of `matplotlib.axes.Axes`, optional" \ + in plot_fcn.__doc__ + else: + assert "ax : `matplotlib.axes.Axes`, optional" in plot_fcn.__doc__ + + +@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns) +@pytest.mark.usefixtures('mplcleanup') +def test_plot_label_processing(resp_fcn, plot_fcn): + # Set up arguments + args1, args2, argsc, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \ + setup_plot_arguments(resp_fcn, plot_fcn) + default_labels = ["sys[1]", "sys[2]"] + expected_labels = ["sys1_", "sys2_"] + match resp_fcn, plot_fcn: + case ct.gangof4_response, _: + default_labels = ["P=sys[1]", "P=sys[2]"] + + if plot_fcn in nolabel_plot_fcns: + pytest.skip(f"labels not implemented for {plot_fcn}") + + # Generate the first plot, with default labels + cplt1 = plot_fcn(*args1, **kwargs, **plot_kwargs) + assert isinstance(cplt1, ct.ControlPlot) + assert_legend(cplt1, None) + + # Generate second plot with default labels + cplt2 = plot_fcn(*args2, **kwargs, **plot_kwargs) + assert isinstance(cplt2, ct.ControlPlot) + assert_legend(cplt2, default_labels) + plt.close() + + # Generate both plots at the same time + if len(args1) == 1 and plot_fcn != ct.time_response_plot: + cplt = plot_fcn([*args1, *args2], **kwargs, **plot_kwargs) + assert isinstance(cplt, ct.ControlPlot) + assert_legend(cplt, default_labels) + elif len(args1) == 1 and plot_fcn == ct.time_response_plot: + # Use TimeResponseList.plot() to generate combined response + cplt = argsc[0].plot(**kwargs, **meth_kwargs) + assert isinstance(cplt, ct.ControlPlot) + assert_legend(cplt, default_labels) + plt.close() + + # Generate plots sequentially, with updated labels + cplt1 = plot_fcn( + *args1, **kwargs, **plot_kwargs, label=expected_labels[0]) + assert isinstance(cplt1, ct.ControlPlot) + assert_legend(cplt1, None) + + cplt2 = plot_fcn( + *args2, **kwargs, **plot_kwargs, label=expected_labels[1]) + assert isinstance(cplt2, ct.ControlPlot) + assert_legend(cplt2, expected_labels) + plt.close() + + # Generate both plots at the same time, with updated labels + if len(args1) == 1 and plot_fcn != ct.time_response_plot: + cplt = plot_fcn( + [*args1, *args2], **kwargs, **plot_kwargs, + label=expected_labels) + assert isinstance(cplt, ct.ControlPlot) + assert_legend(cplt, expected_labels) + elif len(args1) == 1 and plot_fcn == ct.time_response_plot: + # Use TimeResponseList.plot() to generate combined response + cplt = argsc[0].plot( + **kwargs, **meth_kwargs, label=expected_labels) + assert isinstance(cplt, ct.ControlPlot) + assert_legend(cplt, expected_labels) + plt.close() + + # Make sure that docstring documents label + if plot_fcn not in legacy_plot_fcns: + assert "label : str or array_like of str, optional" in plot_fcn.__doc__ + + +@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns) +@pytest.mark.usefixtures('mplcleanup') +def test_plot_linestyle_processing(resp_fcn, plot_fcn): + # Set up arguments + args1, args2, _, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \ + setup_plot_arguments(resp_fcn, plot_fcn) + + # Set line color + cplt1 = plot_fcn(*args1, **kwargs, **plot_kwargs, color='r') + assert cplt1.lines.reshape(-1)[0][0].get_color() == 'r' + + # Second plot, new line color + cplt2 = plot_fcn(*args2, **kwargs, **plot_kwargs, color='g') + assert cplt2.lines.reshape(-1)[0][0].get_color() == 'g' + + # Make sure that docstring documents line properties + if plot_fcn not in legacy_plot_fcns: + assert "line properties" in plot_fcn.__doc__ or \ + "color : matplotlib color spec, optional" in plot_fcn.__doc__ + + # Set other characteristics if documentation says we can + if "line properties" in plot_fcn.__doc__: + cplt = plot_fcn(*args1, **kwargs, **plot_kwargs, linewidth=5) + assert cplt.lines.reshape(-1)[0][0].get_linewidth() == 5 + + # If fmt string is allowed, use it to set line color and style + if "*fmt" in plot_fcn.__doc__: + cplt = plot_fcn(*args1, 'r--', **kwargs, **plot_kwargs) + assert cplt.lines.reshape(-1)[0][0].get_color() == 'r' + assert cplt.lines.reshape(-1)[0][0].get_linestyle() == '--' + + +@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns) +@pytest.mark.usefixtures('mplcleanup') +def test_siso_plot_legend_processing(resp_fcn, plot_fcn): + # Set up arguments + args1, args2, argsc, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \ + setup_plot_arguments(resp_fcn, plot_fcn) + default_labels = ["sys[1]", "sys[2]"] + match resp_fcn, plot_fcn: + case ct.gangof4_response, _: + # Multi-axes plot => test in next function + return + + if plot_fcn in nolabel_plot_fcns: + # Make sure that using legend keywords generates an error + with pytest.raises(TypeError, match="unexpected|unrecognized"): + cplt = plot_fcn(*args1, legend_loc=None) + with pytest.raises(TypeError, match="unexpected|unrecognized"): + cplt = plot_fcn(*args1, legend_map=None) + with pytest.raises(TypeError, match="unexpected|unrecognized"): + cplt = plot_fcn(*args1, show_legend=None) + return + + # Single system, with forced legend + cplt = plot_fcn(*args1, **kwargs, **plot_kwargs, show_legend=True) + assert_legend(cplt, default_labels[:1]) + plt.close() + + # Single system, with forced location + cplt = plot_fcn(*args1, **kwargs, **plot_kwargs, legend_loc=10) + assert cplt.axes[0, 0].get_legend()._loc == 10 + plt.close() + + # Generate two plots, but turn off legends + if len(args1) == 1 and plot_fcn != ct.time_response_plot: + cplt = plot_fcn( + [*args1, *args2], **kwargs, **plot_kwargs, show_legend=False) + assert_legend(cplt, None) + elif len(args1) == 1 and plot_fcn == ct.time_response_plot: + # Use TimeResponseList.plot() to generate combined response + cplt = argsc[0].plot(**kwargs, **meth_kwargs, show_legend=False) + assert_legend(cplt, None) + plt.close() + + # Make sure that docstring documents legend_loc, show_legend + assert "legend_loc : int or str, optional" in plot_fcn.__doc__ + assert "show_legend : bool, optional" in plot_fcn.__doc__ + + # Make sure that single axes plots generate an error with legend_map + if plot_fcn not in multiaxes_plot_fcns: + with pytest.raises(TypeError, match="unexpected"): + cplt = plot_fcn(*args1, legend_map=False) + else: + assert "legend_map : array of str" in plot_fcn.__doc__ + + +@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns) +@pytest.mark.usefixtures('mplcleanup') +def test_mimo_plot_legend_processing(resp_fcn, plot_fcn): + # Generate the response that we will use for plotting + match resp_fcn, plot_fcn: + case ct.frequency_response, ct.bode_plot: + resp = ct.frequency_response([ct.rss(4, 2, 2), ct.rss(3, 2, 2)]) + case ct.step_response, ct.time_response_plot: + resp = ct.step_response([ct.rss(4, 2, 2), ct.rss(3, 2, 2)]) + case ct.gangof4_response, ct.gangof4_plot: + resp = ct.gangof4_response(ct.rss(4, 1, 1), ct.rss(3, 1, 1)) + case _, ct.time_response_plot: + # Skip remaining time response plots to avoid duplicate tests + return + case _, _: + # Skip everything else that doesn't support multi-axes plots + assert plot_fcn not in multiaxes_plot_fcns + return + + # Generate a standard plot with legend in the center + cplt1 = resp.plot(legend_loc=10) + assert cplt1.axes.ndim == 2 + for legend_idx, ax in enumerate(cplt1.axes.flatten()): + if ax.get_legend() is not None: + break; + assert legend_idx != 0 # Make sure legend is not in first subplot + assert ax.get_legend()._loc == 10 + plt.close() + + # Regenerate the plot with no legend + cplt2 = resp.plot(show_legend=False) + for ax in cplt2.axes.flatten(): + if ax.get_legend() is not None: + break; + assert ax.get_legend() is None + plt.close() + + # Regenerate the plot with no legend in a different way + cplt2 = resp.plot(legend_loc=False) + for ax in cplt2.axes.flatten(): + if ax.get_legend() is not None: + break; + assert ax.get_legend() is None + plt.close() + + # Regenerate the plot with no legend in a different way + cplt2 = resp.plot(legend_map=False) + for ax in cplt2.axes.flatten(): + if ax.get_legend() is not None: + break; + assert ax.get_legend() is None + plt.close() + + # Put the legend in a different (first) subplot + legend_map = np.full(cplt2.shape, None, dtype=object) + legend_map[0, 0] = 5 + legend_map[-1, -1] = 6 + cplt3 = resp.plot(legend_map=legend_map) + assert cplt3.axes[0, 0].get_legend()._loc == 5 + assert cplt3.axes[-1, -1].get_legend()._loc == 6 + plt.close() + + +@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns) +@pytest.mark.usefixtures('mplcleanup') +def test_plot_title_processing(resp_fcn, plot_fcn): + # Set up arguments + args1, args2, argsc, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \ + setup_plot_arguments(resp_fcn, plot_fcn) + default_title = "sys[1], sys[2]" + match resp_fcn, plot_fcn: + case ct.gangof4_response, _: + default_title = "P=sys[1], C=sys[1]_C, P=sys[2], C=sys[1]_C" + + # Store the expected title prefix + match resp_fcn, plot_fcn: + case _, ct.bode_plot: + title_prefix = "Bode plot for " + case _, ct.nichols_plot: + title_prefix = "Nichols plot for " + case _, ct.singular_values_plot: + title_prefix = "Singular values for " + case _, ct.gangof4_plot: + title_prefix = "Gang of Four for " + case _, ct.describing_function_plot: + title_prefix = "Nyquist plot for " + case _, ct.phase_plane_plot: + title_prefix = "Phase portrait for " + case _, ct.pole_zero_plot: + title_prefix = "Pole/zero plot for " + case _, ct.nyquist_plot: + title_prefix = "Nyquist plot for " + case _, ct.root_locus_plot: + title_prefix = "Root locus plot for " + case ct.initial_response, _: + title_prefix = "Initial response for " + case ct.step_response, _: + title_prefix = "Step response for " + case ct.impulse_response, _: + title_prefix = "Impulse response for " + case ct.forced_response, _: + title_prefix = "Forced response for " + case ct.input_output_response, _: + title_prefix = "Input/output response for " + case _: + raise RuntimeError(f"didn't recognize {resp_fcn}, {plot_fcn}") + + # Generate the first plot, with default title + cplt1 = plot_fcn(*args1, **kwargs, **plot_kwargs) + assert cplt1.figure._suptitle._text.startswith(title_prefix) + + # Skip functions not intended for sequential calling + if plot_fcn not in nolabel_plot_fcns: + # Generate second plot with default title + cplt2 = plot_fcn(*args2, **kwargs, **plot_kwargs) + assert cplt1.figure._suptitle._text == title_prefix + default_title + plt.close() + + # Generate both plots at the same time + if len(args1) == 1 and plot_fcn != ct.time_response_plot: + cplt = plot_fcn([*args1, *args2], **kwargs, **plot_kwargs) + assert cplt.figure._suptitle._text == title_prefix + default_title + elif len(args1) == 1 and plot_fcn == ct.time_response_plot: + # Use TimeResponseList.plot() to generate combined response + cplt = argsc[0].plot(**kwargs, **meth_kwargs) + assert cplt.figure._suptitle._text == title_prefix + default_title + plt.close() + + # Generate plots sequentially, with updated titles + cplt1 = plot_fcn( + *args1, **kwargs, **plot_kwargs, title="My first title") + cplt2 = plot_fcn( + *args2, **kwargs, **plot_kwargs, title="My new title") + assert cplt2.figure._suptitle._text == "My new title" + plt.close() + + # Update using set_plot_title + cplt2.set_plot_title("Another title") + assert cplt2.figure._suptitle._text == "Another title" + plt.close() + + # Generate the plots with no title + cplt = plot_fcn( + *args1, **kwargs, **plot_kwargs, title=False) + assert cplt.figure._suptitle == None + plt.close() + + # Make sure that docstring documents title + if plot_fcn not in legacy_plot_fcns: + assert "title : str, optional" in plot_fcn.__doc__ + + +@pytest.mark.parametrize("plot_fcn", multiaxes_plot_fcns) +@pytest.mark.usefixtures('mplcleanup') +def test_tickmark_label_processing(plot_fcn): + # Generate the response that we will use for plotting + match plot_fcn: + case ct.bode_plot: + resp = ct.frequency_response(ct.rss(4, 2, 2)) + case ct.time_response_plot: + resp = ct.step_response(ct.rss(4, 2, 2)) + case ct.gangof4_plot: + resp = ct.gangof4_response(ct.rss(4, 1, 1), ct.rss(3, 1, 1)) + case _: + pytest.fail("unknown plot_fcn") + + # Turn off axis sharing => all axes have ticklabels + cplt = resp.plot(sharex=False, sharey=False) + for i, j in itertools.product( + range(cplt.axes.shape[0]), range(cplt.axes.shape[1])): + assert len(cplt.axes[i, j].get_xticklabels()) > 0 + assert len(cplt.axes[i, j].get_yticklabels()) > 0 + plt.clf() + + # Turn on axis sharing => only outer axes have ticklabels + cplt = resp.plot(sharex=True, sharey=True) + for i, j in itertools.product( + range(cplt.axes.shape[0]), range(cplt.axes.shape[1])): + if i < cplt.axes.shape[0] - 1: + assert len(cplt.axes[i, j].get_xticklabels()) == 0 + else: + assert len(cplt.axes[i, j].get_xticklabels()) > 0 + + if j > 0: + assert len(cplt.axes[i, j].get_yticklabels()) == 0 + else: + assert len(cplt.axes[i, j].get_yticklabels()) > 0 + + +@pytest.mark.parametrize("resp_fcn, plot_fcn", resp_plot_fcns) +@pytest.mark.usefixtures('mplcleanup', 'editsdefaults') +def test_rcParams(resp_fcn, plot_fcn): + # Set up arguments + args1, args2, argsc, kwargs, meth_kwargs, plot_kwargs, resp_kwargs = \ + setup_plot_arguments(resp_fcn, plot_fcn) + # Create new set of rcParams + my_rcParams = {} + for key in ct.ctrlplot.rcParams: + match plt.rcParams[key]: + case 8 | 9 | 10: + my_rcParams[key] = plt.rcParams[key] + 1 + case 'medium': + my_rcParams[key] = 11.5 + case 'large': + my_rcParams[key] = 9.5 + case _: + raise ValueError(f"unknown rcParam type for {key}") + checked_params = my_rcParams.copy() # make sure we check everything + + # Generate a figure with the new rcParams + if plot_fcn not in nolabel_plot_fcns: + cplt = plot_fcn( + *args1, **kwargs, **plot_kwargs, rcParams=my_rcParams, + show_legend=True) + else: + cplt = plot_fcn(*args1, **kwargs, **plot_kwargs, rcParams=my_rcParams) + + # Check lower left figure (should always have ticks, labels) + ax, fig = cplt.axes[-1, 0], cplt.figure + + # Check to make sure new settings were used + assert ax.xaxis.get_label().get_fontsize() == my_rcParams['axes.labelsize'] + assert ax.yaxis.get_label().get_fontsize() == my_rcParams['axes.labelsize'] + checked_params.pop('axes.labelsize') + + assert ax.title.get_fontsize() == my_rcParams['axes.titlesize'] + checked_params.pop('axes.titlesize') + + assert ax.get_xticklabels()[0].get_fontsize() == \ + my_rcParams['xtick.labelsize'] + checked_params.pop('xtick.labelsize') + + assert ax.get_yticklabels()[0].get_fontsize() == \ + my_rcParams['ytick.labelsize'] + checked_params.pop('ytick.labelsize') + + assert fig._suptitle.get_fontsize() == my_rcParams['figure.titlesize'] + checked_params.pop('figure.titlesize') + + if plot_fcn not in nolabel_plot_fcns: + for ax in cplt.axes.flatten(): + legend = ax.get_legend() + if legend is not None: + break + assert legend is not None + assert legend.get_texts()[0].get_fontsize() == \ + my_rcParams['legend.fontsize'] + checked_params.pop('legend.fontsize') + + # Make sure we checked everything + assert not checked_params + plt.close() + + # Change the default rcParams + ct.ctrlplot.rcParams.update(my_rcParams) + if plot_fcn not in nolabel_plot_fcns: + cplt = plot_fcn( + *args1, **kwargs, **plot_kwargs, show_legend=True) + else: + cplt = plot_fcn(*args1, **kwargs, **plot_kwargs) + + # Check everything + ax, fig = cplt.axes[-1, 0], cplt.figure + assert ax.xaxis.get_label().get_fontsize() == my_rcParams['axes.labelsize'] + assert ax.yaxis.get_label().get_fontsize() == my_rcParams['axes.labelsize'] + assert ax.title.get_fontsize() == my_rcParams['axes.titlesize'] + assert ax.get_xticklabels()[0].get_fontsize() == \ + my_rcParams['xtick.labelsize'] + assert ax.get_yticklabels()[0].get_fontsize() == \ + my_rcParams['ytick.labelsize'] + assert fig._suptitle.get_fontsize() == my_rcParams['figure.titlesize'] + if plot_fcn not in nolabel_plot_fcns: + for ax in cplt.axes.flatten(): + legend = ax.get_legend() + if legend is not None: + break + assert legend is not None + assert legend.get_texts()[0].get_fontsize() == \ + my_rcParams['legend.fontsize'] + plt.close() + + # Make sure that resetting parameters works correctly + ct.reset_defaults() + for key in ct.ctrlplot.rcParams: + assert ct.defaults['ctrlplot.rcParams'][key] != my_rcParams[key] + assert ct.ctrlplot.rcParams[key] != my_rcParams[key] + + +def test_deprecation_warnings(): + sys = ct.rss(2, 2, 2) + lines = ct.step_response(sys).plot(overlay_traces=True) + with pytest.warns(FutureWarning, match="deprecated"): + assert len(lines[0, 0]) == 2 + + cplt = ct.step_response(sys).plot() + with pytest.warns(FutureWarning, match="deprecated"): + axs = ct.get_plot_axes(cplt) + assert np.all(axs == cplt.axes) + + with pytest.warns(FutureWarning, match="deprecated"): + axs = ct.get_plot_axes(cplt.lines) + assert np.all(axs == cplt.axes) + + with pytest.warns(FutureWarning, match="deprecated"): + ct.suptitle("updated title") + assert cplt.figure._suptitle.get_text() == "updated title" + + +def test_ControlPlot_init(): + sys = ct.rss(2, 2, 2) + cplt = ct.step_response(sys).plot() + + # Create a ControlPlot from data, without the axes or figure + cplt_raw = ct.ControlPlot(cplt.lines) + assert np.all(cplt_raw.lines == cplt.lines) + assert np.all(cplt_raw.axes == cplt.axes) + assert cplt_raw.figure == cplt.figure + + +def test_pole_zero_subplots(savefig=False): + ax_array = ct.pole_zero_subplots(2, 1, grid=[True, False]) + sys1 = ct.tf([1, 2], [1, 2, 3], name='sys1') + sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') + ct.root_locus_plot([sys1, sys2], ax=ax_array[0, 0]) + cplt = ct.root_locus_plot([sys1, sys2], ax=ax_array[1, 0]) + with pytest.warns(UserWarning, match="Tight layout not applied"): + cplt.set_plot_title("Root locus plots (w/ specified axes)") + if savefig: + plt.savefig("ctrlplot-pole_zero_subplots.png") + + # Single type of of grid for all axes + ax_array = ct.pole_zero_subplots(2, 2, grid='empty') + assert ax_array[0, 0].xaxis.get_label().get_text() == '' + + # Discrete system grid + ax_array = ct.pole_zero_subplots(2, 2, grid=True, dt=1) + assert ax_array[0, 0].xaxis.get_label().get_text() == 'Real' + assert ax_array[0, 0].get_lines()[0].get_color() == 'grey' + + +if __name__ == "__main__": + # + # Interactive mode: generate plots for manual viewing + # + # Running this script in python (or better ipython) will show a + # collection of figures that should all look OK on the screeen. + # + + # In interactive mode, turn on ipython interactive graphics + plt.ion() + + # Start by clearing existing figures + plt.close('all') + + # + # Combination plot + # + + P = ct.tf([0.02], [1, 0.1, 0.01]) # servomechanism + C1 = ct.tf([1, 1], [1, 0]) # unstable + L1 = P * C1 + C2 = ct.tf([1, 0.05], [1, 0]) # stable + L2 = P * C2 + + plt.rcParams.update(ct.rcParams) + fig = plt.figure(figsize=[7, 4]) + ax_mag = fig.add_subplot(2, 2, 1) + ax_phase = fig.add_subplot(2, 2, 3) + ax_nyquist = fig.add_subplot(1, 2, 2) + + ct.bode_plot( + [L1, L2], ax=[ax_mag, ax_phase], + label=["$L_1$ (unstable)", "$L_2$ (unstable)"], + show_legend=False) + ax_mag.set_title("Bode plot for $L_1$, $L_2$") + ax_mag.tick_params(labelbottom=False) + fig.align_labels() + + ct.nyquist_plot(L1, ax=ax_nyquist, label="$L_1$ (unstable)") + ct.nyquist_plot( + L2, ax=ax_nyquist, label="$L_2$ (stable)", + max_curve_magnitude=22, legend_loc='upper right') + ax_nyquist.set_title("Nyquist plot for $L_1$, $L_2$") + + fig.suptitle("Loop analysis for servomechanism control design") + plt.tight_layout() + plt.savefig('ctrlplot-servomech.png') + + plt.figure() + test_pole_zero_subplots(savefig=True) diff --git a/control/tests/descfcn_test.py b/control/tests/descfcn_test.py index ceeff1123..e91738e82 100644 --- a/control/tests/descfcn_test.py +++ b/control/tests/descfcn_test.py @@ -7,14 +7,15 @@ """ -import pytest +import math +import matplotlib.pyplot as plt import numpy as np +import pytest + import control as ct -import math -import matplotlib.pyplot as plt -from control.descfcn import saturation_nonlinearity, \ - friction_backlash_nonlinearity, relay_hysteresis_nonlinearity +from control.descfcn import friction_backlash_nonlinearity, \ + relay_hysteresis_nonlinearity, saturation_nonlinearity # Static function via a class @@ -187,13 +188,13 @@ def test_describing_function_plot(): assert len(response.intersections) == 1 assert len(plt.gcf().get_axes()) == 0 # make sure there is no plot - out = response.plot() + cplt = response.plot() assert len(plt.gcf().get_axes()) == 1 # make sure there is a plot - assert len(out[0]) == 4 and len(out[1]) == 1 + assert len(cplt.lines[0]) == 4 and len(cplt.lines[1]) == 1 # Call plot directly - out = ct.describing_function_plot(H_larger, F_saturation, amp, omega) - assert len(out[0]) == 4 and len(out[1]) == 1 + cplt = ct.describing_function_plot(H_larger, F_saturation, amp, omega) + assert len(cplt.lines[0]) == 4 and len(cplt.lines[1]) == 1 def test_describing_function_exceptions(): @@ -204,12 +205,12 @@ def test_describing_function_exceptions(): assert saturation(3) == 2 # Turn off the bias check - bias = ct.describing_function(saturation, 0, zero_check=False) + ct.describing_function(saturation, 0, zero_check=False) # Function should evaluate to zero at zero amplitude f = lambda x: x + 0.5 with pytest.raises(ValueError, match="must evaluate to zero"): - bias = ct.describing_function(f, 0, zero_check=True) + ct.describing_function(f, 0, zero_check=True) # Evaluate at a negative amplitude with pytest.raises(ValueError, match="cannot evaluate"): @@ -231,3 +232,8 @@ def test_describing_function_exceptions(): with pytest.raises(AttributeError, match="no property|unexpected keyword"): response = ct.describing_function_response(H_simple, F_saturation, amp) response.plot(unknown=None) + + # Describing function plot for non-describing function object + resp = ct.frequency_response(H_simple) + with pytest.raises(TypeError, match="data must be DescribingFunction"): + ct.describing_function_plot(resp) diff --git a/control/tests/discrete_test.py b/control/tests/discrete_test.py index cccb53708..9b87bd61b 100644 --- a/control/tests/discrete_test.py +++ b/control/tests/discrete_test.py @@ -1,4 +1,4 @@ -"""discrete_test.py - test discrete time classes +"""discrete_test.py - test discrete-time classes RMM, 9 Sep 2012 """ @@ -22,7 +22,7 @@ def tsys(self): class Tsys: pass T = Tsys() - # Single input, single output continuous and discrete time systems + # Single input, single output continuous and discrete-time systems sys = rss(3, 1, 1) T.siso_ss1 = StateSpace(sys.A, sys.B, sys.C, sys.D, None) T.siso_ss1c = StateSpace(sys.A, sys.B, sys.C, sys.D, 0.0) @@ -30,7 +30,7 @@ class Tsys: T.siso_ss2d = StateSpace(sys.A, sys.B, sys.C, sys.D, 0.2) T.siso_ss3d = StateSpace(sys.A, sys.B, sys.C, sys.D, True) - # Two input, two output continuous time system + # Two input, two output continuous-time system A = [[-3., 4., 2.], [-1., -3., 0.], [2., 5., 3.]] B = [[1., 4.], [-3., -3.], [-2., 1.]] C = [[4., 2., -3.], [1., 4., 3.]] @@ -38,7 +38,7 @@ class Tsys: T.mimo_ss1 = StateSpace(A, B, C, D, None) T.mimo_ss1c = StateSpace(A, B, C, D, 0) - # Two input, two output discrete time system + # Two input, two output discrete-time system T.mimo_ss1d = StateSpace(A, B, C, D, 0.1) # Same system, but with a different sampling time @@ -231,14 +231,14 @@ def testisctime(self, tsys): def testAddition(self, tsys): # State space addition - sys = tsys.siso_ss1 + tsys.siso_ss1d - sys = tsys.siso_ss1 + tsys.siso_ss1c - sys = tsys.siso_ss1c + tsys.siso_ss1 - sys = tsys.siso_ss1d + tsys.siso_ss1 - sys = tsys.siso_ss1c + tsys.siso_ss1c - sys = tsys.siso_ss1d + tsys.siso_ss1d - sys = tsys.siso_ss3d + tsys.siso_ss3d - sys = tsys.siso_ss1d + tsys.siso_ss3d + _sys = tsys.siso_ss1 + tsys.siso_ss1d + _sys = tsys.siso_ss1 + tsys.siso_ss1c + _sys = tsys.siso_ss1c + tsys.siso_ss1 + _sys = tsys.siso_ss1d + tsys.siso_ss1 + _sys = tsys.siso_ss1c + tsys.siso_ss1c + _sys = tsys.siso_ss1d + tsys.siso_ss1d + _sys = tsys.siso_ss3d + tsys.siso_ss3d + _sys = tsys.siso_ss1d + tsys.siso_ss3d with pytest.raises(ValueError): StateSpace.__add__(tsys.mimo_ss1c, tsys.mimo_ss1d) @@ -246,14 +246,14 @@ def testAddition(self, tsys): StateSpace.__add__(tsys.mimo_ss1d, tsys.mimo_ss2d) # Transfer function addition - sys = tsys.siso_tf1 + tsys.siso_tf1d - sys = tsys.siso_tf1 + tsys.siso_tf1c - sys = tsys.siso_tf1c + tsys.siso_tf1 - sys = tsys.siso_tf1d + tsys.siso_tf1 - sys = tsys.siso_tf1c + tsys.siso_tf1c - sys = tsys.siso_tf1d + tsys.siso_tf1d - sys = tsys.siso_tf2d + tsys.siso_tf2d - sys = tsys.siso_tf1d + tsys.siso_tf3d + _sys = tsys.siso_tf1 + tsys.siso_tf1d + _sys = tsys.siso_tf1 + tsys.siso_tf1c + _sys = tsys.siso_tf1c + tsys.siso_tf1 + _sys = tsys.siso_tf1d + tsys.siso_tf1 + _sys = tsys.siso_tf1c + tsys.siso_tf1c + _sys = tsys.siso_tf1d + tsys.siso_tf1d + _sys = tsys.siso_tf2d + tsys.siso_tf2d + _sys = tsys.siso_tf1d + tsys.siso_tf3d with pytest.raises(ValueError): TransferFunction.__add__(tsys.siso_tf1c, tsys.siso_tf1d) @@ -261,22 +261,22 @@ def testAddition(self, tsys): TransferFunction.__add__(tsys.siso_tf1d, tsys.siso_tf2d) # State space + transfer function - sys = tsys.siso_ss1c + tsys.siso_tf1c - sys = tsys.siso_tf1c + tsys.siso_ss1c - sys = tsys.siso_ss1d + tsys.siso_tf1d - sys = tsys.siso_tf1d + tsys.siso_ss1d + _sys = tsys.siso_ss1c + tsys.siso_tf1c + _sys = tsys.siso_tf1c + tsys.siso_ss1c + _sys = tsys.siso_ss1d + tsys.siso_tf1d + _sys = tsys.siso_tf1d + tsys.siso_ss1d with pytest.raises(ValueError): TransferFunction.__add__(tsys.siso_tf1c, tsys.siso_ss1d) def testMultiplication(self, tsys): # State space multiplication - sys = tsys.siso_ss1 * tsys.siso_ss1d - sys = tsys.siso_ss1 * tsys.siso_ss1c - sys = tsys.siso_ss1c * tsys.siso_ss1 - sys = tsys.siso_ss1d * tsys.siso_ss1 - sys = tsys.siso_ss1c * tsys.siso_ss1c - sys = tsys.siso_ss1d * tsys.siso_ss1d - sys = tsys.siso_ss1d * tsys.siso_ss3d + _sys = tsys.siso_ss1 * tsys.siso_ss1d + _sys = tsys.siso_ss1 * tsys.siso_ss1c + _sys = tsys.siso_ss1c * tsys.siso_ss1 + _sys = tsys.siso_ss1d * tsys.siso_ss1 + _sys = tsys.siso_ss1c * tsys.siso_ss1c + _sys = tsys.siso_ss1d * tsys.siso_ss1d + _sys = tsys.siso_ss1d * tsys.siso_ss3d with pytest.raises(ValueError): StateSpace.__mul__(tsys.mimo_ss1c, tsys.mimo_ss1d) @@ -284,13 +284,13 @@ def testMultiplication(self, tsys): StateSpace.__mul__(tsys.mimo_ss1d, tsys.mimo_ss2d) # Transfer function multiplication - sys = tsys.siso_tf1 * tsys.siso_tf1d - sys = tsys.siso_tf1 * tsys.siso_tf1c - sys = tsys.siso_tf1c * tsys.siso_tf1 - sys = tsys.siso_tf1d * tsys.siso_tf1 - sys = tsys.siso_tf1c * tsys.siso_tf1c - sys = tsys.siso_tf1d * tsys.siso_tf1d - sys = tsys.siso_tf1d * tsys.siso_tf3d + _sys = tsys.siso_tf1 * tsys.siso_tf1d + _sys = tsys.siso_tf1 * tsys.siso_tf1c + _sys = tsys.siso_tf1c * tsys.siso_tf1 + _sys = tsys.siso_tf1d * tsys.siso_tf1 + _sys = tsys.siso_tf1c * tsys.siso_tf1c + _sys = tsys.siso_tf1d * tsys.siso_tf1d + _sys = tsys.siso_tf1d * tsys.siso_tf3d with pytest.raises(ValueError): TransferFunction.__mul__(tsys.siso_tf1c, tsys.siso_tf1d) @@ -298,10 +298,10 @@ def testMultiplication(self, tsys): TransferFunction.__mul__(tsys.siso_tf1d, tsys.siso_tf2d) # State space * transfer function - sys = tsys.siso_ss1c * tsys.siso_tf1c - sys = tsys.siso_tf1c * tsys.siso_ss1c - sys = tsys.siso_ss1d * tsys.siso_tf1d - sys = tsys.siso_tf1d * tsys.siso_ss1d + _sys = tsys.siso_ss1c * tsys.siso_tf1c + _sys = tsys.siso_tf1c * tsys.siso_ss1c + _sys = tsys.siso_ss1d * tsys.siso_tf1d + _sys = tsys.siso_tf1d * tsys.siso_ss1d with pytest.raises(ValueError): TransferFunction.__mul__(tsys.siso_tf1c, tsys.siso_ss1d) @@ -309,13 +309,13 @@ def testMultiplication(self, tsys): def testFeedback(self, tsys): # State space feedback - sys = feedback(tsys.siso_ss1, tsys.siso_ss1d) - sys = feedback(tsys.siso_ss1, tsys.siso_ss1c) - sys = feedback(tsys.siso_ss1c, tsys.siso_ss1) - sys = feedback(tsys.siso_ss1d, tsys.siso_ss1) - sys = feedback(tsys.siso_ss1c, tsys.siso_ss1c) - sys = feedback(tsys.siso_ss1d, tsys.siso_ss1d) - sys = feedback(tsys.siso_ss1d, tsys.siso_ss3d) + _sys = feedback(tsys.siso_ss1, tsys.siso_ss1d) + _sys = feedback(tsys.siso_ss1, tsys.siso_ss1c) + _sys = feedback(tsys.siso_ss1c, tsys.siso_ss1) + _sys = feedback(tsys.siso_ss1d, tsys.siso_ss1) + _sys = feedback(tsys.siso_ss1c, tsys.siso_ss1c) + _sys = feedback(tsys.siso_ss1d, tsys.siso_ss1d) + _sys = feedback(tsys.siso_ss1d, tsys.siso_ss3d) with pytest.raises(ValueError): feedback(tsys.mimo_ss1c, tsys.mimo_ss1d) @@ -323,13 +323,13 @@ def testFeedback(self, tsys): feedback(tsys.mimo_ss1d, tsys.mimo_ss2d) # Transfer function feedback - sys = feedback(tsys.siso_tf1, tsys.siso_tf1d) - sys = feedback(tsys.siso_tf1, tsys.siso_tf1c) - sys = feedback(tsys.siso_tf1c, tsys.siso_tf1) - sys = feedback(tsys.siso_tf1d, tsys.siso_tf1) - sys = feedback(tsys.siso_tf1c, tsys.siso_tf1c) - sys = feedback(tsys.siso_tf1d, tsys.siso_tf1d) - sys = feedback(tsys.siso_tf1d, tsys.siso_tf3d) + _sys = feedback(tsys.siso_tf1, tsys.siso_tf1d) + _sys = feedback(tsys.siso_tf1, tsys.siso_tf1c) + _sys = feedback(tsys.siso_tf1c, tsys.siso_tf1) + _sys = feedback(tsys.siso_tf1d, tsys.siso_tf1) + _sys = feedback(tsys.siso_tf1c, tsys.siso_tf1c) + _sys = feedback(tsys.siso_tf1d, tsys.siso_tf1d) + _sys = feedback(tsys.siso_tf1d, tsys.siso_tf3d) with pytest.raises(ValueError): feedback(tsys.siso_tf1c, tsys.siso_tf1d) @@ -337,10 +337,11 @@ def testFeedback(self, tsys): feedback(tsys.siso_tf1d, tsys.siso_tf2d) # State space, transfer function - sys = feedback(tsys.siso_ss1c, tsys.siso_tf1c) - sys = feedback(tsys.siso_tf1c, tsys.siso_ss1c) - sys = feedback(tsys.siso_ss1d, tsys.siso_tf1d) - sys = feedback(tsys.siso_tf1d, tsys.siso_ss1d) + _sys = feedback(tsys.siso_ss1c, tsys.siso_tf1c) + _sys = feedback(tsys.siso_tf1c, tsys.siso_ss1c) + _sys = feedback(tsys.siso_ss1d, tsys.siso_tf1d) + + _sys = feedback(tsys.siso_tf1d, tsys.siso_ss1d) with pytest.raises(ValueError): feedback(tsys.siso_tf1c, tsys.siso_ss1d) @@ -416,11 +417,11 @@ def test_sample_system_prewarp_warning(self, tsys, plantname, discretization_typ wwarp = 1 Ts = 0.1 with pytest.warns(UserWarning, match="prewarp_frequency ignored: incompatible conversion"): - plant_d_warped = plant.sample(Ts, discretization_type, prewarp_frequency=wwarp) + plant.sample(Ts, discretization_type, prewarp_frequency=wwarp) with pytest.warns(UserWarning, match="prewarp_frequency ignored: incompatible conversion"): - plant_d_warped = sample_system(plant, Ts, discretization_type, prewarp_frequency=wwarp) + sample_system(plant, Ts, discretization_type, prewarp_frequency=wwarp) with pytest.warns(UserWarning, match="prewarp_frequency ignored: incompatible conversion"): - plant_d_warped = c2d(plant, Ts, discretization_type, prewarp_frequency=wwarp) + c2d(plant, Ts, discretization_type, prewarp_frequency=wwarp) def test_sample_system_errors(self, tsys): # Check errors @@ -463,7 +464,7 @@ def test_sample_tf(self, tsys): @pytest.mark.usefixtures("legacy_plot_signature") def test_discrete_bode(self, tsys): - # Create a simple discrete time system and check the calculation + # Create a simple discrete-time system and check the calculation sys = TransferFunction([1], [1, 0.5], 1) omega = [1, 2, 3] mag_out, phase_out, omega_out = bode(sys, omega, plot=True) @@ -473,7 +474,7 @@ def test_discrete_bode(self, tsys): np.testing.assert_array_almost_equal(phase_out, np.angle(H_z)) def test_signal_names(self, tsys): - "test that signal names are preserved in conversion to discrete-time" + "test that signal names are preserved in conversion to discrete time" ssc = StateSpace(tsys.siso_ss1c, inputs='u', outputs='y', states=['a', 'b', 'c']) ssd = ssc.sample(0.1) diff --git a/control/tests/docstrings_test.py b/control/tests/docstrings_test.py new file mode 100644 index 000000000..496df42a3 --- /dev/null +++ b/control/tests/docstrings_test.py @@ -0,0 +1,914 @@ +# docstrings_test.py - test for undocumented arguments +# RMM, 28 Jul 2024 +# +# This unit test looks through all functions in the package and attempts to +# identify arguments that are not documented. It will check anything that +# is an explicitly listed argument, as well as attempt to find keyword +# arguments that are extracted using kwargs.pop(), config._get_param(), or +# config.use_legacy_defaults. +# +# This module can also be run in standalone mode: +# +# python docstrings_test.py [verbose] +# +# where 'verbose' is an integer indicating what level of verbosity is +# desired (0 = only warnings/errors, 10 = everything). + +import inspect +import re + +import sys +import warnings + +import numpydoc.docscrape as npd +import pytest + +import control +import control.flatsys +import control.matlab + +# List of functions that we can skip testing (special cases) +function_skiplist = [ + control.ControlPlot.reshape, # needed for legacy interface + control.phase_plot, # legacy function + control.drss, # documention in rss + control.LinearICSystem, # intermediate I/O class + control.LTI, # intermediate I/O class + control.NamedSignal, # internal I/O class + control.TimeResponseList, # internal response class + control.FrequencyResponseList, # internal response class + control.NyquistResponseList, # internal response class + control.PoleZeroList, # internal response class + control.FrequencyResponseData, # check separately (iosys) + control.InterconnectedSystem, # check separately (iosys) + control.flatsys.FlatSystem, # check separately (iosys) +] + +# List of keywords that we can skip testing (special cases) +keyword_skiplist = { + control.input_output_response: ['method', 't_eval'], # solve_ivp_kwargs + control.nyquist_plot: ['color'], # separate check + control.optimal.solve_optimal_trajectory: + ['method', 'return_x'], # deprecated + control.sisotool: ['kvect'], # deprecated + control.nyquist_response: ['return_contour'], # deprecated + control.create_estimator_iosystem: ['state_labels'], # deprecated + control.bode_plot: ['sharex', 'sharey', 'margin_info'], # deprecated + control.eigensys_realization: ['arg'], # quasi-positional + control.find_operating_point: ['method'], # internal use + control.zpk: ['args'], # 'dt' (manual) + control.StateSpace.dynamics: ['params'], # not allowed + control.StateSpace.output: ['params'], # not allowed + control.flatsys.point_to_point: [ + 'method', 'options', # minimize_kwargs + ], + control.flatsys.solve_flat_optimal: [ + 'method', 'options', # minimize_kwargs + ], + control.optimal.OptimalControlProblem: [ + 'method', 'options' # solve_ivp_kwargs, minimize_kwargs + ], + control.optimal.OptimalControlResult: [ + 'return_x', 'return_states', 'transpose'], # legacy + control.optimal.OptimalControlProblem.compute_trajectory: [ + 'return_x', # legacy + ], + control.optimal.OptimalEstimationProblem: [ + 'method', 'options' # solve_ivp_kwargs, minimize_kwargs + ], + control.optimal.OptimalEstimationResult: [ + 'return_x', 'return_states', 'transpose'], # legacy + control.optimal.OptimalEstimationProblem.create_mhe_iosystem: [ + 'inputs', 'outputs', 'states', # doc'd elsewhere + ], +} + +# Set global variables +verbose = 0 # Level of verbosity (use -rP when running pytest) +standalone = False # Controls how failures are treated +max_summary_len = 64 # Maximum length of a summary line + +module_list = [ + (control, ""), (control.flatsys, "flatsys."), + (control.optimal, "optimal."), (control.phaseplot, "phaseplot."), + (control.matlab, "matlab.")] + +@pytest.mark.parametrize("module, prefix", module_list) +def test_parameter_docs(module, prefix): + checked = set() # Keep track of functions we have checked + + # Look through every object in the package + _info(f"Checking module {module}", 0) + for name, obj in inspect.getmembers(module): + if getattr(obj, '__module__', None): + objname = ".".join([obj.__module__.removeprefix("control."), name]) + else: + objname = name + _info(f"Checking object {objname}", 4) + + # Parse the docstring using numpydoc + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = None if obj is None else npd.FunctionDoc(obj) + + # Skip anything that is outside of this module + if inspect.getmodule(obj) is not None and \ + not inspect.getmodule(obj).__name__.startswith('control'): + # Skip anything that isn't part of the control package + _info(f"member '{objname}' is outside `control` module", 5) + continue + + # Skip non-top-level functions without documentation + if prefix != "" and inspect.getmodule(obj) != module and doc is None: + _info(f"skipping {objname} [no docstring]", 1) + continue + + # If this is a class, recurse through methods + # TODO: check top level documenation here (__init__, attributes?) + if inspect.isclass(obj): + _info(f"Checking class {objname}", 1) + + # Check member functions within the class + test_parameter_docs(obj, prefix + name + '.') + + # Drop through and continue checks as a function + + # Skip anything that is inherited, hidden, or already checked + if not (inspect.isfunction(obj) or inspect.isclass(obj) and + not issubclass(obj, Exception)) or \ + inspect.isclass(module) and name not in module.__dict__ \ + or name.startswith('_') or obj in function_skiplist \ + or obj in checked: + _info(f"skipping {objname} [inherited, hidden, or checked]", 4) + continue + + # Don't fail on non-top-level functions without parameter lists + _info(f"Checking function {objname} against numpydoc", 2) + _check_numpydoc_style(obj, doc) + + # Add this to the list of functions we have checked + checked.add(obj) + + # Get the docstring (skip w/ warning if there isn't one) + _info(f"Checking function {objname} against python-control", 2) + if obj.__doc__ is None: + _warn(f"{objname} is missing docstring", 2) + continue + elif doc is None: + _fail(f"{objname} docstring not parseable", 2) + continue + else: + docstring = inspect.getdoc(obj) + + if inspect.isclass(obj): + # Just check __init__() + source = inspect.getsource(obj.__init__) + else: + source = inspect.getsource(obj) + + # Skip deprecated functions (and check for proper annotation) + doc_extended = "\n".join(doc["Extended Summary"]) + if ".. deprecated::" in doc_extended: + _info(" [deprecated]", 2) + continue + elif re.search(name + r"(\(\))? is deprecated", doc_extended) or \ + "function is deprecated" in doc_extended: + _info(" [deprecated, but not numpydoc compliant]", 2) + _warn(f"{objname} deprecated, but not numpydoc compliant", 0) + continue + elif re.search(name + r"(\(\))? is deprecated", source): + _warn(f"{objname} is deprecated, but not documented", 1) + continue + + # Get the signature for the function + sig = inspect.signature(obj) + + # If first argument is *args, try to use docstring instead + sig = _replace_var_positional_with_docstring(sig, doc) + + # Skip functions whose documentation is found elsewhere + if doc["Parameters"] == [] and re.search( + r"See[\s]+`[\w.]+`[\s]+(for|and)", doc_extended): + _info("skipping {objname}; references another function", 4) + continue + + # Go through each parameter and make sure it is in the docstring + for argname, par in sig.parameters.items(): + # Look for arguments that we can skip + if argname == 'self' or argname[0] == '_' or \ + obj in keyword_skiplist and argname in keyword_skiplist[obj]: + continue + + # Check for positional arguments (*arg) + if par.kind == inspect.Parameter.VAR_POSITIONAL: + if f"*{argname}" not in docstring: + _fail( + f"{objname} has undocumented, unbound positional " + f"argument '{argname}'; " + "use docstring signature instead") + continue + + # Check for keyword arguments (then look at code for parsing) + elif par.kind == inspect.Parameter.VAR_KEYWORD: + # See if we documented the keyward argument directly + # if f"**{argname} :" in docstring: + # continue + + # Look for direct kwargs argument access + kwargnames = set() + for _, kwargname in re.findall( + argname + r"(\[|\.pop\(|\.get\()'([\w]+)'", source): + _info(f"Found direct keyword argument {kwargname}", 2) + if not kwargname.startswith('_'): + kwargnames.add(kwargname) + + # Look for kwargs accessed via _get_param + for kwargname in re.findall( + r"_get_param\(\s*'\w*',\s*'([\w]+)',\s*" + argname, + source): + _info(f"Found config keyword argument {kwargname}", 2) + kwargnames.add(kwargname) + + # Look for kwargs accessed via _process_legacy_keyword + for kwargname in re.findall( + r"_process_legacy_keyword\([\s]*" + argname + + r",[\s]*'[\w]+',[\s]*'([\w]+)'", source): + _info(f"Found legacy keyword argument {kwargname}", 2) + kwargnames.add(kwargname) + + for kwargname in kwargnames: + if obj in keyword_skiplist and \ + kwargname in keyword_skiplist[obj]: + continue + _info(f"Checking keyword argument {kwargname}", 3) + _check_parameter_docs( + name, kwargname, inspect.getdoc(obj), + prefix=prefix) + + # Make sure this argument is documented properly in docstring + else: + _info(f"Checking argument {argname}", 3) + _check_parameter_docs( + objname, argname, docstring, prefix=prefix) + + # Look at the return values + for val in doc["Returns"]: + if val.name == '' and \ + (match := re.search(r"([\w]+):", val.type)) is not None: + retname = match.group(1) + _warn( + f"{obj} return value '{retname}' " + "docstring missing space") + + # Look at the exceptions + for exc in doc["Raises"]: + _check_numpydoc_param( + obj.__name__, exc, noname_ok=True, section="Raises") + + +@pytest.mark.parametrize("module, prefix", [ + (control, ""), (control.flatsys, "flatsys."), + (control.optimal, "optimal."), (control.phaseplot, "phaseplot.") +]) +def test_deprecated_functions(module, prefix): + checked = set() # Keep track of functions we have checked + + # Look through every object in the package + for name, obj in inspect.getmembers(module): + # Skip anything that is outside of this module + if inspect.getmodule(obj) is not None and ( + not inspect.getmodule(obj).__name__.startswith('control') + or prefix != "" and inspect.getmodule(obj) != module): + # Skip anything that isn't part of the control package + continue + + if inspect.isclass(obj): + # Check member functions within the class + test_deprecated_functions(obj, prefix + name + '.') + + # Parse the docstring using numpydoc + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = None if obj is None else npd.FunctionDoc(obj) + + if inspect.isfunction(obj): + # Skip anything that is inherited, hidden, or checked + if inspect.isclass(module) and name not in module.__dict__ \ + or name[0] == '_' or obj in checked: + continue + else: + checked.add(obj) + + # Get the docstring (skip w/ warning if there isn't one) + if obj.__doc__ is None: + _warn(f"{obj} is missing docstring") + continue + else: + docstring = inspect.getdoc(obj) + source = inspect.getsource(obj) + + # Look for functions marked as deprecated in doc string + doc_extended = "\n".join(doc["Extended Summary"]) + if ".. deprecated::" in doc_extended: + # Make sure a FutureWarning is issued + if not re.search("FutureWarning", source): + _fail(f"{obj} deprecated but does not issue " + "FutureWarning") + else: + if re.search(name + r"(\(\))? is deprecated", docstring) or \ + re.search(name + r"(\(\))? is deprecated", source): + _fail( + f"{obj} deprecated but with non-standard " + "docs/warnings") + +# +# Tests for I/O system classes +# +# The tests below try to make sure that we document I/O system classes +# and the factory functions that create them in a uniform way. +# + +ct = control +fs = control.flatsys + +# Dictionary of factory functions associated with primary classes +iosys_class_factory_function = { + fs.FlatSystem: fs.flatsys, + ct.FrequencyResponseData: ct.frd, + ct.InterconnectedSystem: ct.interconnect, + ct.LinearICSystem: ct.interconnect, + ct.NonlinearIOSystem: ct.nlsys, + ct.StateSpace: ct.ss, + ct.TransferFunction: ct.tf, +} + +# +# List of arguments described in class docstrings +# +# These are the minimal arguments needed to initialize the class. Optional +# arguments should be documented in the factory functions and do not need +# to be duplicated in the class documentation (=> don't list here). +# +iosys_class_args = { + fs.FlatSystem: ['forward', 'reverse'], + ct.FrequencyResponseData: ['frdata', 'omega', 'dt'], + ct.NonlinearIOSystem: [ + 'updfcn', 'outfcn', 'inputs', 'outputs', 'states', 'params', 'dt'], + ct.StateSpace: ['A', 'B', 'C', 'D', 'dt'], + ct.TransferFunction: ['num', 'den', 'dt'], + ct.InterconnectedSystem: [ + 'syslist', 'connections', 'inplist', 'outlist', 'params'] +} + +# +# List of attributes described in class docstrings +# +# This is the list of attributes for the class that are not already listed +# as parameters used to initialize the class. These should all be defined +# in the class docstring. +# +# Attributes that are part of all I/O system classes should be listed in +# `std_iosys_class_attributes`. Attributes that are not commonly needed are +# defined as part of a parent class can just be documented there, and +# should be listed in `iosys_parent_attributes` (these will be searched +# using the MRO). + +std_iosys_class_attributes = [ + 'ninputs', 'noutputs', 'input_labels', 'output_labels', 'name', 'shape'] + +# List of attributes defined for specific I/O systems +iosys_class_attributes = { + fs.FlatSystem: [], + ct.FrequencyResponseData: [], + ct.NonlinearIOSystem: ['nstates', 'state_labels'], + ct.StateSpace: ['nstates', 'state_labels'], + ct.TransferFunction: [], + ct.InterconnectedSystem: [ + 'connect_map', 'input_map', 'output_map', + 'input_offset', 'output_offset', 'state_offset', 'syslist_index', + 'nstates', 'state_labels' ] +} + +# List of attributes defined in a parent class (no need to warn) +iosys_parent_attributes = [ + 'input_index', 'output_index', 'state_index', # rarely used + 'states', 'nstates', 'state_labels', # not need in TF, FRD + 'params', 'outfcn', 'updfcn', # NL I/O, SS overlap + 'repr_format' # rarely used +] + +# +# List of arguments described (only) in factory function docstrings +# +# These lists consist of the arguments that should be documented in the +# factory functions and should not be duplicated in the class +# documentation, even though in some cases they are actually processed in +# the class __init__ function. +# +std_factory_args = [ + 'inputs', 'outputs', 'name', 'input_prefix', 'output_prefix'] + +factory_args = { + fs.flatsys: ['states', 'state_prefix'], + ct.frd: ['sys'], + ct.nlsys: ['state_prefix'], + ct.ss: ['sys', 'states', 'state_prefix'], + ct.tf: ['sys'], + ct.interconnect: ['dt'] +} + + +@pytest.mark.parametrize( + "cls, fcn, args", + [(cls, iosys_class_factory_function[cls], iosys_class_args[cls]) + for cls in iosys_class_args.keys()]) +def test_iosys_primary_classes(cls, fcn, args): + docstring = inspect.getdoc(cls) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = npd.FunctionDoc(cls) + _check_numpydoc_style(cls, doc) + + # Make sure the typical arguments are there + for argname in args + std_iosys_class_attributes + \ + iosys_class_attributes[cls]: + _check_parameter_docs(cls.__name__, argname, docstring) + + # Make sure we reference the factory function + if re.search( + f"`(~[\\w.]*)*{fcn.__name__}`" + r"[\s]+factory[\s]+function", "\n".join(doc["Extended Summary"]), + re.DOTALL) is None: + _fail( + f"{cls.__name__} summary does not reference factory function " + f"{fcn.__name__}") + + if doc["See Also"] == []: + _fail( + f'{cls.__name__} does not have "See Also" section; ' + f"must include and reference {fcn.__name__}") + else: + found_factory_function = False + for name, _ in doc["See Also"][0][0]: + if name == f"{fcn.__name__}": + found_factory_function = True + break; + if not found_factory_function: + _fail( + f'{cls.__name__} "See Also" section does not reference ' + f"factory function {fcn.__name__}") + + # Make sure we don't reference parameters from the factory function + for argname in factory_args[fcn]: + if re.search(f"[\\s]+{argname}(, .*)*[\\s]*:", docstring) is not None: + _fail( + f"{cls.__name__} references factory function parameter " + f"'{argname}'") + + +@pytest.mark.parametrize("cls", iosys_class_args.keys()) +def test_iosys_attribute_lists(cls, ignore_future_warning): + fcn = iosys_class_factory_function[cls] + + # Create a system that we can scan for attributes + sys = ct.rss(2, 1, 1) + ignore_args = [] + match fcn: + case ct.tf: + sys = ct.tf(sys) + ignore_args = ['state_labels'] + case ct.frd: + sys = ct.frd(sys, [0.1, 1, 10]) + ignore_args = ['state_labels'] + ignore_args += ['fresp', 'response'] # deprecated + case ct.interconnect: + sys = ct.nlsys(sys, name='sys') + sys = ct.interconnect([sys], inplist='sys.u', outlist='sys.y') + case ct.nlsys: + sys = ct.nlsys(sys) + case fs.flatsys: + sys = fs.flatsys(sys) + sys = fs.flatsys(sys.forward, sys.reverse) + + docstring = inspect.getdoc(cls) + for name, value in inspect.getmembers(sys): + if name.startswith('_') or name in ignore_args or \ + inspect.ismethod(value): + # Skip hidden and ignored attributes; methods checked elsewhere + continue + + # Try to find documentation in primary class + if _check_parameter_docs( + cls.__name__, name, docstring, fail_if_missing=False): + continue + + # Couldn't find in main documentation; look in parent classes + for parent in cls.__mro__: + if parent == object: + _fail( + f"{cls.__name__} attribute '{name}' not documented") + break + + if _check_parameter_docs( + parent.__name__, name, inspect.getdoc(parent), + fail_if_missing=False): + if name not in iosys_parent_attributes + factory_args[fcn]: + _warn( + f"{cls.__name__} attribute '{name}' only documented " + f"in parent class {parent.__name__}") + break + + +@pytest.mark.parametrize("cls", [ct.InputOutputSystem, ct.LTI]) +def test_iosys_container_classes(cls): + # Create a system that we can scan for attributes + sys = cls(states=2, outputs=1, inputs=1) + + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = npd.FunctionDoc(cls) + _check_numpydoc_style(cls, doc) + + for name, obj in inspect.getmembers(sys): + if name.startswith('_') or inspect.ismethod(obj): + # Skip hidden variables; class methods are checked elsewhere + continue + + # Look through all classes in hierarchy + _info(f"{name=}", 1) + for parent in cls.__mro__: + if parent == object: + _fail( + f"{cls.__name__} attribute '{name}' not documented") + break + + _info(f" {parent=}", 2) + if _check_parameter_docs( + parent.__name__, name, inspect.getdoc(parent), + fail_if_missing=False): + break + + +@pytest.mark.parametrize("cls", [ct.LTI, ct.LinearICSystem]) +def test_iosys_intermediate_classes(cls): + docstring = inspect.getdoc(cls) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = npd.FunctionDoc(cls) + _check_numpydoc_style(cls, doc) + + # Make sure there is not a parameters section + # TODO: replace with numpdoc check + if re.search(r"\nParameters\n----", docstring) is not None: + _fail(f"intermediate {cls} docstring contains Parameters section") + return + + +@pytest.mark.parametrize("fcn", factory_args.keys()) +def test_iosys_factory_functions(fcn): + docstring = inspect.getdoc(fcn) + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = npd.FunctionDoc(fcn) + _check_numpydoc_style(fcn, doc) + + cls = list(iosys_class_factory_function.keys())[ + list(iosys_class_factory_function.values()).index(fcn)] + + # Make sure we reference parameters in class and factory function docstring + for argname in iosys_class_args[cls] + std_factory_args + factory_args[fcn]: + _check_parameter_docs(fcn.__name__, argname, docstring) + + # Make sure we don't reference any class attributes + for argname in std_iosys_class_attributes + iosys_class_attributes[cls]: + if argname in std_factory_args: + continue + if re.search(f"[\\s]+{argname}(, .*)*[\\s]*:", docstring) is not None: + _fail( + f"{fcn.__name__} references class attribute '{argname}'") + + +# Utility function to check for an argument in a docstring +def _check_parameter_docs( + funcname, argname, docstring, prefix="", fail_if_missing=True): + funcname = prefix + funcname + + # Find the "Parameters" section of docstring, where we start searching + # TODO: rewrite to use numpydoc + if not (match := re.search(r"\nParameters\n----", docstring)): + if fail_if_missing: + _fail(f"{funcname} docstring missing Parameters section") + return False # for standalone mode + else: + return False + else: + start = match.start() + + # Find the "Returns" section of the docstring (to be skipped, if present) + match_returns = re.search(r"\nReturns\n----", docstring) + + # Find the "Other Parameters" section of the docstring, if present + match_other = re.search(r"\nOther Parameters\n----", docstring) + + # Remove the returns section from docstring, in case output arguments + # match input argument names (it happens...) + if match_other and match_returns: + docstring = docstring[start:match_returns.start()] + \ + docstring[match_other.start():] + elif match_returns: + docstring = docstring[start:match_returns.start()] + else: + docstring = docstring[start:] + + # Look for the parameter name in the docstring + argname_ = argname + r"( \(or .*\))*" + if match := re.search( + "\n" + r"((\w+|\.{3}), )*" + argname_ + r"(, (\w+|\.{3}))*:", + docstring): + # Found the string, but not in numpydoc form + _warn(f"{funcname}: {argname} docstring missing space") + + elif not (match := re.search( + "\n" + r"((\w+|\.{3}), )*" + argname_ + r"(, (\w+|\.{3}))* :", + docstring)): + if fail_if_missing: + _fail(f"{funcname} '{argname}' not documented") + return False # for standalone mode + else: + _info(f"{funcname} '{argname}' not documented (OK)", 6) + return False + + # Make sure there isn't another instance + second_match = re.search( + "\n" + r"((\w+|\.{3}), )*" + argname + r"(, (\w+|\.{3}))*[ ]*:", + docstring[match.end():]) + if second_match: + _fail(f"{funcname} '{argname}' documented twice") + return False # for standalone mode + + return True + + +# Utility function to check numpydoc style consistency +def _check_numpydoc_style(obj, doc): + name = ".".join([obj.__module__.removeprefix("control."), obj.__name__]) + + # Standard checks for all objects + summary = "\n".join(doc["Summary"]) + if len(doc["Summary"]) > 1: + _warn(f"{name} summary is more than one line") + if summary and summary[-1] != '.' and re.match(":$", summary) is None: + _warn(f"{name} summary doesn't end in period") + if summary[0:1].islower(): + _warn(f"{name} summary starts with lower case letter") + if len(summary) > max_summary_len: + _warn(f"{name} summary is longer than {max_summary_len} characters") + + # Look for Python objects that are not marked properly + python_objects = ['True', 'False', 'None'] + for pyobj in python_objects: + for section in ["Extended Summary", "Notes"]: + text = "\n".join(doc[section]) + if re.search(f"`{pyobj}`", text) is not None: + _warn(f"{pyobj} appears in {section} for {name} with backticks") + + control_classes = [ + 'InputOutputSystem', 'NonlinearIOSystem', 'StateSpace', + 'TransferFunction', 'FrequencyResponseData', 'LinearICSystem', + 'Flatsystem', 'InterconnectedSystem', 'TimeResponseData', + 'NyquistResponseData', 'PoleZeroData', 'RootLocusData', + 'ControlPlot', 'OperatingPoint', 'flatsys.Flatsystem'] + for pyobj in control_classes: + if obj.__name__ == pyobj: + continue + for section in ["Extended Summary", "Notes"]: + text = "\n".join(doc[section]) + if re.search(f"[^`]{pyobj}[^`.]", text) is not None: + _warn(f"{pyobj} in {section} for {name} w/o backticks") + + for section in [ + "Parameters", "Returns", "Additional Parameters", "Yields"]: + if section not in doc: + continue + for arg in doc[section]: + text = arg.type + "\n".join(arg.desc) + if re.search(f"(^|[^`]){pyobj}([^`.]|$)", text) is not None: + _warn(f"{pyobj} in {section} for {name} w/o backticks") + + if inspect.isclass(obj): + # Specialized checks for classes + if doc["Returns"] != []: + _fail(f'Class {name} should not have "Returns" section') + + elif inspect.isfunction(obj): + # Specialized checks for functions + if doc["Returns"] == [] and obj.__doc__ and 'return' in obj.__doc__: + _fail(f'Class {name} does not have a "Returns" section') + + else: + raise TypeError("unknown object type for {obj}") + + for param in doc["Parameters"] + doc["Other Parameters"]: + _check_numpydoc_param(name, param, section="Parameters") + for param in doc["Attributes"]: + _check_numpydoc_param(name, param, section="Attributes") + for param in doc["Returns"]: + _check_numpydoc_param( + name, param, empty_ok=True, noname_ok=True, section="Returns") + for param in doc["Yields"]: + _check_numpydoc_param( + name, param, empty_ok=True, noname_ok=True, section="Yields") + + +# Utility function for checking NumPyDoc parametres +def _check_numpydoc_param( + name, param, empty_ok=False, noname_ok=False, section="??"): + param_desc = "\n".join(param.desc) + param_name = f"{name} " + \ + (f" '{param.name}'" if param.name != '' else f" '{param.type}'") + + # Check for empty section + if param.name == "" and param.type == '': + _fail(f"Empty {section} section in {name}") + + # Make sure we have a name and description + if param.name == "" and not noname_ok: + _fail(f"{param_name} has improperly formatted parameter") + return + elif param_desc == "": + if not empty_ok: + _warn(f"{param_name} isn't documented") + return + + # Description should end in a period (colon also allowed) + if re.search(r"\.$|\.[\s]|:$", param_desc, re.MULTILINE) is None: + _warn(f"{param_name} description doesn't contain period") + if param_desc[0:1].islower(): + _warn(f"{param_name} description starts with lower case letter") + + # Look for Python objects that are not marked properly + python_objects = ['True', 'False', 'None'] + for pyobj in python_objects: + if re.search(f"`{pyobj}`", param_desc) is not None: + _warn(f"{pyobj} appears in {param_name} description with backticks") + + +# Utility function to replace positional signature with docstring signature +def _replace_var_positional_with_docstring(sig, doc): + # If no documentation is available, there is nothing we can do... + if doc is None: + return sig + + # Check to see if the first argument is positional + parameter_items = iter(sig.parameters.items()) + try: + argname, par = next(parameter_items) + if par.kind != inspect.Parameter.VAR_POSITIONAL or \ + (signature := doc["Signature"]) == '': + return sig + except StopIteration: + return sig + + # Try parsing the docstring signature + arg_list = [] + while (1): + if (match_fcn := re.match( + r"^([\s]*\|[\s]*)*[\w]+\(", signature)) is None: + break + arg_idx = match_fcn.span(0)[1] + while (1): + match_arg = re.match( + r"[\s]*([\w]+)(,|,\[|\[,|\)|\]\))(,[\s]*|[\s]*[.]{3},[\s]*)*", + signature[arg_idx:]) + if match_arg is None: + break + else: + arg_idx += match_arg.span(0)[1] + arg_list.append(match_arg.group(1)) + signature = signature[arg_idx:] + if arg_list == []: + return sig + + # Create the new parameter list + parameter_list = [ + inspect.Parameter(arg, inspect.Parameter.POSITIONAL_ONLY) + for arg in arg_list] + + # Add any remaining parameters that were in the original signature + for argname, par in parameter_items: + if argname not in arg_list: + parameter_list.append(par) + + # Return the new signature + return sig.replace(parameters=parameter_list) + + +# Utility function to warn with verbose output +def _info(str, level): + if verbose > level: + print(" " * level + str) + +def _warn(str, level=-1): + print("WARN: " + " " * level + str) + if not standalone: + warnings.warn(str, stacklevel=2) + +def _fail(str, level=-1): + if verbose > level: + print("FAIL: " + " " * level + str) + if not standalone: + pytest.fail(str) + +# +# Test function for the unit test +# +class simple_class: + def simple_function(arg1, arg2, opt1=None, **kwargs): + """Simple function for testing.""" + kwargs['test'] = None + +Failed = pytest.fail.Exception + +doc_header = simple_class.simple_function.__doc__ + "\n" +doc_parameters = "\nParameters\n----------\n" +doc_arg1 = "arg1 : int\n Argument 1.\n" +doc_arg2 = "arg2 : int\n Argument 2.\n" +doc_arg2_nospace = "arg2: int\n Argument 2.\n" +doc_arg3 = "arg3 : int\n Non-existent argument 1.\n" +doc_opt1 = "opt1 : int\n Keyword argument 1.\n" +doc_test = "test : int\n Internal keyword argument 1.\n" +doc_returns = "\nReturns\n-------\n" +doc_ret = "out : int\n" +doc_ret_nospace = "out: int\n" + +@pytest.mark.parametrize("docstring, exception, match", [ + (None, UserWarning, "missing docstring"), + (doc_header + doc_parameters + doc_arg1 + doc_arg2 + doc_opt1 + + doc_test + doc_returns + doc_ret, None, ""), + (doc_header + doc_parameters + doc_arg1 + doc_arg2 + doc_opt1 + doc_test, + None, ""), # no return section (OK) + (doc_header + doc_parameters + doc_arg1 + doc_arg2_nospace + doc_opt1 + + doc_test + doc_returns + doc_ret, UserWarning, "missing space"), + (doc_header + doc_parameters + doc_arg1 + doc_opt1 + + doc_test + doc_returns + doc_ret, Failed, "'arg2' not documented"), + (doc_header + doc_parameters + doc_arg1 + doc_arg2 + doc_arg2 + doc_opt1 + + doc_test + doc_returns + doc_ret, Failed, "'arg2' documented twice"), + (doc_header + doc_parameters + doc_arg1 + doc_arg2 + doc_opt1 + + doc_returns + doc_ret, Failed, "'test' not documented"), + (doc_header + doc_parameters + doc_arg1 + doc_arg2_nospace + doc_opt1 + + doc_test + doc_returns + doc_ret_nospace, UserWarning, "missing space"), + (doc_header + doc_returns + doc_ret_nospace, + Failed, "missing Parameters section"), + (doc_header + "\nSee `other_function` for details", None, ""), + (doc_header + "\n.. deprecated::", None, ""), + (doc_header + "\n\n simple_function() is deprecated", + UserWarning, "deprecated, but not numpydoc compliant"), +]) +def test_check_parameter_docs(docstring, exception, match): + simple_class.simple_function.__doc__ = docstring + if exception is None: + # Pass prefix to allow empty parameters to work + assert test_parameter_docs(simple_class, "test") is None + elif exception in [UserWarning]: + with pytest.warns(exception, match=match): + test_parameter_docs(simple_class, "") is None + elif exception in [Failed]: + with pytest.raises(exception, match=match): + test_parameter_docs(simple_class, "") is None + + +if __name__ == "__main__": + verbose = 0 if len(sys.argv) == 1 else int(sys.argv[1]) + standalone = True + + for module, prefix in module_list: + _info(f"--- test_parameter_docs(): {module.__name__} ----", 0) + test_parameter_docs(module, prefix) + + for module, prefix in module_list: + _info(f"--- test_deprecated_functions(): {module.__name__} ----", 0) + test_deprecated_functions + + for cls, fcn, args in [ + (cls, iosys_class_factory_function[cls], iosys_class_args[cls]) + for cls in iosys_class_args.keys()]: + _info(f"--- test_iosys_primary_classes(): {cls.__name__} ----", 0) + test_iosys_primary_classes(cls, fcn, args) + + for cls in iosys_class_args.keys(): + _info(f"--- test_iosys_attribute_lists(): {cls.__name__} ----", 0) + with warnings.catch_warnings(): + warnings.simplefilter('ignore', FutureWarning) + test_iosys_attribute_lists(cls, None) + + for cls in [ct.InputOutputSystem, ct.LTI]: + _info(f"--- test_iosys_container_classes(): {cls.__name__} ----", 0) + test_iosys_container_classes(cls) + + for cls in [ct.LTI, ct.LinearICSystem]: + _info(f"--- test_iosys_intermediate_classes(): {cls.__name__} ----", 0) + test_iosys_intermediate_classes(cls) + + for fcn in factory_args.keys(): + _info(f"--- test_iosys_factory_functions(): {fcn.__name__} ----", 0) + test_iosys_factory_functions(fcn) diff --git a/control/tests/flatsys_test.py b/control/tests/flatsys_test.py index a12bf1480..c53cf2e9c 100644 --- a/control/tests/flatsys_test.py +++ b/control/tests/flatsys_test.py @@ -198,9 +198,10 @@ def test_kinematic_car_ocp( with warnings.catch_warnings(): warnings.filterwarnings( 'ignore', message="unable to solve", category=UserWarning) - traj_ocp = fs.solve_flat_ocp( + traj_ocp = fs.solve_flat_optimal( vehicle_flat, timepts, x0, u0, - cost=traj_cost, constraints=input_constraints, + trajectory_cost=traj_cost, + trajectory_constraints=input_constraints, terminal_cost=terminal_cost, basis=basis, initial_guess=initial_guess, minimize_kwargs={'method': method}, @@ -383,7 +384,7 @@ def test_flat_solve_ocp(self, basis): terminal_cost = opt.quadratic_cost( flat_sys, 1e3, 1e3, x0=xf, u0=uf) - traj_cost = fs.solve_flat_ocp( + traj_cost = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, terminal_cost=terminal_cost, basis=basis) @@ -397,7 +398,7 @@ def test_flat_solve_ocp(self, basis): # Solve with trajectory and terminal cost functions trajectory_cost = opt.quadratic_cost(flat_sys, 0, 1, x0=xf, u0=uf) - traj_cost = fs.solve_flat_ocp( + traj_cost = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, terminal_cost=terminal_cost, trajectory_cost=trajectory_cost, basis=basis) @@ -420,7 +421,7 @@ def test_flat_solve_ocp(self, basis): assert np.any(x_cost[0, :] < lb[0]) or np.any(x_cost[0, :] > ub[0]) \ or np.any(x_cost[1, :] < lb[1]) or np.any(x_cost[1, :] > ub[1]) - traj_const = fs.solve_flat_ocp( + traj_const = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, terminal_cost=terminal_cost, trajectory_cost=trajectory_cost, trajectory_constraints=constraints, basis=basis, @@ -443,15 +444,38 @@ def test_flat_solve_ocp(self, basis): # Use alternative keywords as well nl_constraints = [ (sp.optimize.NonlinearConstraint, lambda x, u: x, lb, ub)] - traj_nlconst = fs.solve_flat_ocp( + traj_nlconst = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, - cost=trajectory_cost, terminal_cost=terminal_cost, - constraints=nl_constraints, basis=basis, + trajectory_cost=trajectory_cost, terminal_cost=terminal_cost, + trajectory_constraints=nl_constraints, basis=basis, ) x_nlconst, u_nlconst = traj_nlconst.eval(timepts) np.testing.assert_almost_equal(x_const, x_nlconst) np.testing.assert_almost_equal(u_const, u_nlconst) + def test_solve_flat_ocp_scalar_timepts(self): + # scalar timepts gives expected result + f = fs.LinearFlatSystem(ct.ss(ct.tf([1],[1,1]))) + + def terminal_cost(x, u): + return (x-5).dot(x-5)+u.dot(u) + + traj1 = fs.solve_flat_ocp(f, [0, 1], x0=[23], + terminal_cost=terminal_cost) + + traj2 = fs.solve_flat_ocp(f, 1, x0=[23], + terminal_cost=terminal_cost) + + teval = np.linspace(0, 1, 101) + + r1 = traj1.response(teval) + r2 = traj2.response(teval) + + np.testing.assert_array_equal(r1.x, r2.x) + np.testing.assert_array_equal(r1.y, r2.y) + np.testing.assert_array_equal(r1.u, r2.u) + + def test_bezier_basis(self): bezier = fs.BezierFamily(4) time = np.linspace(0, 1, 100) @@ -519,7 +543,6 @@ def test_point_to_point_errors(self): x0 = [1, 0]; u0 = [0] xf = [0, 0]; uf = [0] Tf = 10 - T = np.linspace(0, Tf, 500) # Cost function timepts = np.linspace(0, Tf, 10) @@ -595,6 +618,11 @@ def test_point_to_point_errors(self): flat_sys, timepts, x0, u0, xf, uf, constraints=[(None, 0, 0, 0)], basis=fs.PolyFamily(8)) + # too few timepoints + with pytest.raises(ct.ControlArgument, match="at least three time points"): + fs.point_to_point( + flat_sys, timepts[:2], x0, u0, xf, uf, basis=fs.PolyFamily(10), cost=cost_fcn) + # Unsolvable optimization constraint = [opt.input_range_constraint(flat_sys, -0.01, 0.01)] with pytest.warns(UserWarning, match="unable to solve"): @@ -629,7 +657,6 @@ def test_solve_flat_ocp_errors(self): x0 = [1, 0]; u0 = [0] xf = [0, 0]; uf = [0] Tf = 10 - T = np.linspace(0, Tf, 500) # Cost function timepts = np.linspace(0, Tf, 10) @@ -639,7 +666,7 @@ def test_solve_flat_ocp_errors(self): # Solving without basis specified should be OK (may generate warning) with warnings.catch_warnings(): warnings.simplefilter("ignore") - traj = fs.solve_flat_ocp(flat_sys, timepts, x0, u0, cost_fcn) + traj = fs.solve_flat_optimal(flat_sys, timepts, x0, u0, cost_fcn) x, u = traj.eval(timepts) np.testing.assert_array_almost_equal(x0, x[:, 0]) if not traj.success: @@ -652,40 +679,41 @@ def test_solve_flat_ocp_errors(self): # Solving without a cost function generates an error with pytest.raises(TypeError, match="cost required"): - traj = fs.solve_flat_ocp(flat_sys, timepts, x0, u0) + traj = fs.solve_flat_optimal(flat_sys, timepts, x0, u0) # Try to optimize with insufficient degrees of freedom with pytest.raises(ValueError, match="basis set is too small"): - traj = fs.solve_flat_ocp( - flat_sys, timepts, x0, u0, cost=cost_fcn, + traj = fs.solve_flat_optimal( + flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn, basis=fs.PolyFamily(2)) # Solve with the errors in the various input arguments with pytest.raises(ValueError, match="Initial state: Wrong shape"): - traj = fs.solve_flat_ocp( + traj = fs.solve_flat_optimal( flat_sys, timepts, np.zeros(3), u0, cost_fcn) with pytest.raises(ValueError, match="Initial input: Wrong shape"): - traj = fs.solve_flat_ocp( + traj = fs.solve_flat_optimal( flat_sys, timepts, x0, np.zeros(3), cost_fcn) # Constraint that isn't a constraint with pytest.raises(TypeError, match="must be a list"): - traj = fs.solve_flat_ocp( + traj = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, cost_fcn, - constraints=np.eye(2), basis=fs.PolyFamily(8)) + trajectory_constraints=np.eye(2), basis=fs.PolyFamily(8)) # Unknown constraint type with pytest.raises(TypeError, match="unknown constraint type"): - traj = fs.solve_flat_ocp( + traj = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, cost_fcn, - constraints=[(None, 0, 0, 0)], basis=fs.PolyFamily(8)) + trajectory_constraints=[(None, 0, 0, 0)], + basis=fs.PolyFamily(8)) # Method arguments, parameters - traj_method = fs.solve_flat_ocp( - flat_sys, timepts, x0, u0, cost=cost_fcn, + traj_method = fs.solve_flat_optimal( + flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn, basis=fs.PolyFamily(6), minimize_method='slsqp') - traj_kwarg = fs.solve_flat_ocp( - flat_sys, timepts, x0, u0, cost=cost_fcn, + traj_kwarg = fs.solve_flat_optimal( + flat_sys, timepts, x0, u0, trajectory_cost=cost_fcn, basis=fs.PolyFamily(6), minimize_kwargs={'method': 'slsqp'}) np.testing.assert_allclose( traj_method.eval(timepts)[0], traj_kwarg.eval(timepts)[0], @@ -693,7 +721,7 @@ def test_solve_flat_ocp_errors(self): # Unrecognized keywords with pytest.raises(TypeError, match="unrecognized keyword"): - traj_method = fs.solve_flat_ocp( + traj_method = fs.solve_flat_optimal( flat_sys, timepts, x0, u0, cost_fcn, solve_ivp_method=None) @pytest.mark.parametrize( diff --git a/control/tests/frd_test.py b/control/tests/frd_test.py index e50af3c92..1b370c629 100644 --- a/control/tests/frd_test.py +++ b/control/tests/frd_test.py @@ -3,8 +3,6 @@ RvP, 4 Oct 2012 """ -import sys as pysys - import numpy as np import matplotlib.pyplot as plt import pytest @@ -13,7 +11,7 @@ from control.statesp import StateSpace from control.xferfcn import TransferFunction from control.frdata import frd, _convert_to_frd, FrequencyResponseData -from control import bdalg, evalfr, freqplot +from control import bdalg, freqplot from control.tests.conftest import slycotonly from control.exception import pandas_check @@ -182,10 +180,55 @@ def testFeedback(self, frd_fcn): f1.feedback().frequency_response(chkpts)[0], h1.feedback().frequency_response(chkpts)[0]) - def testFeedback2(self): - h2 = StateSpace([[-1.0, 0], [0, -2.0]], [[0.4], [0.1]], - [[1.0, 0], [0, 1]], [[0.0], [0.0]]) - # h2.feedback([[0.3, 0.2], [0.1, 0.1]]) + def testAppendSiso(self): + # Create frequency responses + d1 = np.array([1 + 2j, 1 - 2j, 1 + 4j, 1 - 4j, 1 + 6j, 1 - 6j]) + d2 = d1 + 2 + d3 = d1 - 1j + w = np.arange(d1.shape[-1]) + frd1 = FrequencyResponseData(d1, w) + frd2 = FrequencyResponseData(d2, w) + frd3 = FrequencyResponseData(d3, w) + # Create appended frequency responses + d_app_1 = np.zeros((2, 2, d1.shape[-1]), dtype=complex) + d_app_1[0, 0, :] = d1 + d_app_1[1, 1, :] = d2 + d_app_2 = np.zeros((3, 3, d1.shape[-1]), dtype=complex) + d_app_2[0, 0, :] = d1 + d_app_2[1, 1, :] = d2 + d_app_2[2, 2, :] = d3 + # Test appending two FRDs + frd_app_1 = frd1.append(frd2) + np.testing.assert_allclose(d_app_1, frd_app_1.frdata) + # Test appending three FRDs + frd_app_2 = frd1.append(frd2).append(frd3) + np.testing.assert_allclose(d_app_2, frd_app_2.frdata) + + def testAppendMimo(self): + # Create frequency responses + rng = np.random.default_rng(1234) + n = 100 + w = np.arange(n) + d1 = rng.uniform(size=(2, 2, n)) + 1j * rng.uniform(size=(2, 2, n)) + d2 = rng.uniform(size=(3, 1, n)) + 1j * rng.uniform(size=(3, 1, n)) + d3 = rng.uniform(size=(1, 2, n)) + 1j * rng.uniform(size=(1, 2, n)) + frd1 = FrequencyResponseData(d1, w) + frd2 = FrequencyResponseData(d2, w) + frd3 = FrequencyResponseData(d3, w) + # Create appended frequency responses + d_app_1 = np.zeros((5, 3, d1.shape[-1]), dtype=complex) + d_app_1[:2, :2, :] = d1 + d_app_1[2:, 2:, :] = d2 + d_app_2 = np.zeros((6, 5, d1.shape[-1]), dtype=complex) + d_app_2[:2, :2, :] = d1 + d_app_2[2:5, 2:3, :] = d2 + d_app_2[5:, 3:, :] = d3 + # Test appending two FRDs + frd_app_1 = frd1.append(frd2) + np.testing.assert_allclose(d_app_1, frd_app_1.frdata) + # Test appending three FRDs + frd_app_2 = frd1.append(frd2).append(frd3) + np.testing.assert_allclose(d_app_2, frd_app_2.frdata) def testAuto(self): omega = np.logspace(-1, 2, 10) @@ -208,7 +251,6 @@ def testNyquist(self, frd_fcn): freqplot.nyquist(f1) # plt.savefig('/dev/null', format='svg') - @slycotonly @pytest.mark.parametrize( "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData]) def testMIMO(self, frd_fcn): @@ -226,7 +268,6 @@ def testMIMO(self, frd_fcn): sys.frequency_response(chkpts)[1], f1.frequency_response(chkpts)[1]) - @slycotonly @pytest.mark.parametrize( "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData]) def testMIMOfb(self, frd_fcn): @@ -245,7 +286,6 @@ def testMIMOfb(self, frd_fcn): f1.frequency_response(chkpts)[1], f2.frequency_response(chkpts)[1]) - @slycotonly @pytest.mark.parametrize( "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData]) def testMIMOfb2(self, frd_fcn): @@ -266,7 +306,6 @@ def testMIMOfb2(self, frd_fcn): f1.frequency_response(chkpts)[1], f2.frequency_response(chkpts)[1]) - @slycotonly @pytest.mark.parametrize( "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData]) def testMIMOMult(self, frd_fcn): @@ -285,7 +324,6 @@ def testMIMOMult(self, frd_fcn): (f1*f2).frequency_response(chkpts)[1], (sys*sys).frequency_response(chkpts)[1]) - @slycotonly @pytest.mark.parametrize( "frd_fcn", [ct.frd, ct.FRD, ct.FrequencyResponseData]) def testMIMOSmooth(self, frd_fcn): @@ -317,7 +355,6 @@ def testAgainstOctave(self): np.array([[1.0, 0], [0, 0], [0, 1]]), np.eye(3), np.zeros((3, 2))) omega = np.logspace(-1, 2, 10) - chkpts = omega[::3] f1 = frd(sys, omega) np.testing.assert_array_almost_equal( (f1.frequency_response([1.0])[0] * @@ -334,13 +371,13 @@ def test_frequency_mismatch(self, recwarn): sys1 = frd([1, 2, 3], [4, 5, 6]) sys2 = frd([2, 3, 4], [5, 6, 7]) with pytest.raises(NotImplementedError): - sys = sys1 + sys2 + sys1 + sys2 # One frequency range is a subset of another sys1 = frd([1, 2, 3], [4, 5, 6]) sys2 = frd([2, 3], [4, 5]) with pytest.raises(NotImplementedError): - sys = sys1 + sys2 + sys1 + sys2 def test_size_mismatch(self): sys1 = frd(ct.rss(2, 2, 2), np.logspace(-1, 1, 10)) @@ -348,16 +385,16 @@ def test_size_mismatch(self): # Different number of inputs sys2 = frd(ct.rss(3, 1, 2), np.logspace(-1, 1, 10)) with pytest.raises(ValueError): - sys = sys1 + sys2 + sys1 + sys2 # Different number of outputs sys2 = frd(ct.rss(3, 2, 1), np.logspace(-1, 1, 10)) with pytest.raises(ValueError): - sys = sys1 + sys2 + sys1 + sys2 # Inputs and outputs don't match with pytest.raises(ValueError): - sys = sys2 * sys1 + sys2 * sys1 # Feedback mismatch with pytest.raises(ValueError): @@ -372,47 +409,47 @@ def test_operator_conversion(self): sys_add = frd_tf + 2 chk_add = frd_tf + frd_2 np.testing.assert_array_almost_equal(sys_add.omega, chk_add.omega) - np.testing.assert_array_almost_equal(sys_add.fresp, chk_add.fresp) + np.testing.assert_array_almost_equal(sys_add.frdata, chk_add.frdata) sys_radd = 2 + frd_tf chk_radd = frd_2 + frd_tf np.testing.assert_array_almost_equal(sys_radd.omega, chk_radd.omega) - np.testing.assert_array_almost_equal(sys_radd.fresp, chk_radd.fresp) + np.testing.assert_array_almost_equal(sys_radd.frdata, chk_radd.frdata) sys_sub = frd_tf - 2 chk_sub = frd_tf - frd_2 np.testing.assert_array_almost_equal(sys_sub.omega, chk_sub.omega) - np.testing.assert_array_almost_equal(sys_sub.fresp, chk_sub.fresp) + np.testing.assert_array_almost_equal(sys_sub.frdata, chk_sub.frdata) sys_rsub = 2 - frd_tf chk_rsub = frd_2 - frd_tf np.testing.assert_array_almost_equal(sys_rsub.omega, chk_rsub.omega) - np.testing.assert_array_almost_equal(sys_rsub.fresp, chk_rsub.fresp) + np.testing.assert_array_almost_equal(sys_rsub.frdata, chk_rsub.frdata) sys_mul = frd_tf * 2 chk_mul = frd_tf * frd_2 np.testing.assert_array_almost_equal(sys_mul.omega, chk_mul.omega) - np.testing.assert_array_almost_equal(sys_mul.fresp, chk_mul.fresp) + np.testing.assert_array_almost_equal(sys_mul.frdata, chk_mul.frdata) sys_rmul = 2 * frd_tf chk_rmul = frd_2 * frd_tf np.testing.assert_array_almost_equal(sys_rmul.omega, chk_rmul.omega) - np.testing.assert_array_almost_equal(sys_rmul.fresp, chk_rmul.fresp) + np.testing.assert_array_almost_equal(sys_rmul.frdata, chk_rmul.frdata) sys_rdiv = 2 / frd_tf chk_rdiv = frd_2 / frd_tf np.testing.assert_array_almost_equal(sys_rdiv.omega, chk_rdiv.omega) - np.testing.assert_array_almost_equal(sys_rdiv.fresp, chk_rdiv.fresp) + np.testing.assert_array_almost_equal(sys_rdiv.frdata, chk_rdiv.frdata) sys_pow = frd_tf**2 chk_pow = frd(sys_tf**2, np.logspace(-1, 1, 10)) np.testing.assert_array_almost_equal(sys_pow.omega, chk_pow.omega) - np.testing.assert_array_almost_equal(sys_pow.fresp, chk_pow.fresp) + np.testing.assert_array_almost_equal(sys_pow.frdata, chk_pow.frdata) sys_pow = frd_tf**-2 chk_pow = frd(sys_tf**-2, np.logspace(-1, 1, 10)) np.testing.assert_array_almost_equal(sys_pow.omega, chk_pow.omega) - np.testing.assert_array_almost_equal(sys_pow.fresp, chk_pow.fresp) + np.testing.assert_array_almost_equal(sys_pow.frdata, chk_pow.frdata) # Assertion error if we try to raise to a non-integer power with pytest.raises(ValueError): @@ -422,17 +459,238 @@ def test_operator_conversion(self): sys_add = frd_2 + sys_tf chk_add = frd_2 + frd_tf np.testing.assert_array_almost_equal(sys_add.omega, chk_add.omega) - np.testing.assert_array_almost_equal(sys_add.fresp, chk_add.fresp) + np.testing.assert_array_almost_equal(sys_add.frdata, chk_add.frdata) + + # Test broadcasting with SISO system + sys_tf_mimo = TransferFunction([1], [1, 0]) * np.eye(2) + frd_tf_mimo = frd(sys_tf_mimo, np.logspace(-1, 1, 10)) + result = FrequencyResponseData.__rmul__(frd_tf, frd_tf_mimo) + expected = frd(sys_tf_mimo * sys_tf, np.logspace(-1, 1, 10)) + np.testing.assert_array_almost_equal(expected.omega, result.omega) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) # Input/output mismatch size mismatch in rmul sys1 = frd(ct.rss(2, 2, 2), np.logspace(-1, 1, 10)) + sys2 = frd(ct.rss(3, 3, 3), np.logspace(-1, 1, 10)) with pytest.raises(ValueError): - FrequencyResponseData.__rmul__(frd_2, sys1) + FrequencyResponseData.__rmul__(sys2, sys1) # Make sure conversion of something random generates exception with pytest.raises(TypeError): FrequencyResponseData.__add__(frd_tf, 'string') + def test_add_sub_mimo_siso(self): + omega = np.logspace(-1, 1, 10) + sys_mimo = frd(ct.rss(2, 2, 2), omega) + sys_siso = frd(ct.rss(2, 1, 1), omega) + + for op, expected_fresp in [ + (FrequencyResponseData.__add__, sys_mimo.frdata + sys_siso.frdata), + (FrequencyResponseData.__radd__, sys_mimo.frdata + sys_siso.frdata), + (FrequencyResponseData.__sub__, sys_mimo.frdata - sys_siso.frdata), + (FrequencyResponseData.__rsub__, -sys_mimo.frdata + sys_siso.frdata), + ]: + result = op(sys_mimo, sys_siso) + np.testing.assert_array_almost_equal(omega, result.omega) + np.testing.assert_array_almost_equal(expected_fresp, result.frdata) + + @pytest.mark.parametrize( + "left, right, expected", + [ + ( + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + TransferFunction([2], [1, 0]), + np.eye(3), + TransferFunction( + [ + [[2], [0], [0]], + [[0], [2], [0]], + [[0], [0], [2]], + ], + [ + [[1, 0], [1], [1]], + [[1], [1, 0], [1]], + [[1], [1], [1, 0]], + ], + ), + ), + ] + ) + def test_mul_mimo_siso(self, left, right, expected): + result = frd(left, np.logspace(-1, 1, 10)).__mul__(right) + expected_frd = frd(expected, np.logspace(-1, 1, 10)) + np.testing.assert_array_almost_equal(expected_frd.omega, result.omega) + np.testing.assert_array_almost_equal(expected_frd.frdata, result.frdata) + + @slycotonly + def test_truediv_mimo_siso(self): + omega = np.logspace(-1, 1, 10) + tf_mimo = TransferFunction([1], [1, 0]) * np.eye(2) + frd_mimo = frd(tf_mimo, omega) + tf_siso = TransferFunction([1], [1, 1]) + frd_siso = frd(tf_siso, omega) + expected = frd(tf_mimo.__truediv__(tf_siso), omega) + ss_siso = ct.tf2ss(tf_siso) + + # Test division of MIMO FRD by SISO FRD + result = frd_mimo.__truediv__(frd_siso) + np.testing.assert_array_almost_equal(expected.omega, result.omega) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) + + # Test division of MIMO FRD by SISO TF + result = frd_mimo.__truediv__(tf_siso) + np.testing.assert_array_almost_equal(expected.omega, result.omega) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) + + # Test division of MIMO FRD by SISO TF + result = frd_mimo.__truediv__(ss_siso) + np.testing.assert_array_almost_equal(expected.omega, result.omega) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) + + @slycotonly + def test_rtruediv_mimo_siso(self): + omega = np.logspace(-1, 1, 10) + tf_mimo = TransferFunction([1], [1, 0]) * np.eye(2) + frd_mimo = frd(tf_mimo, omega) + ss_mimo = ct.tf2ss(tf_mimo) + tf_siso = TransferFunction([1], [1, 1]) + frd_siso = frd(tf_siso, omega) + expected = frd(tf_siso.__rtruediv__(tf_mimo), omega) + + # Test division of MIMO FRD by SISO FRD + result = frd_siso.__rtruediv__(frd_mimo) + np.testing.assert_array_almost_equal(expected.omega, result.omega) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) + + # Test division of MIMO TF by SISO FRD + result = frd_siso.__rtruediv__(tf_mimo) + np.testing.assert_array_almost_equal(expected.omega, result.omega) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) + + # Test division of MIMO SS by SISO FRD + result = frd_siso.__rtruediv__(ss_mimo) + np.testing.assert_array_almost_equal(expected.omega, result.omega) + np.testing.assert_array_almost_equal(expected.frdata, result.frdata) + + + @pytest.mark.parametrize( + "left, right, expected", + [ + ( + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + np.eye(3), + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[2], [0], [0]], + [[0], [2], [0]], + [[0], [0], [2]], + ], + [ + [[1, 0], [1], [1]], + [[1], [1, 0], [1]], + [[1], [1], [1, 0]], + ], + ), + ), + ] + ) + def test_rmul_mimo_siso(self, left, right, expected): + result = frd(right, np.logspace(-1, 1, 10)).__rmul__(left) + expected_frd = frd(expected, np.logspace(-1, 1, 10)) + np.testing.assert_array_almost_equal(expected_frd.omega, result.omega) + np.testing.assert_array_almost_equal(expected_frd.frdata, result.frdata) + def test_eval(self): sys_tf = ct.tf([1], [1, 2, 1]) frd_tf = frd(sys_tf, np.logspace(-1, 1, 3)) @@ -454,9 +712,15 @@ def test_eval(self): def test_freqresp_deprecated(self): sys_tf = ct.tf([1], [1, 2, 1]) frd_tf = frd(sys_tf, np.logspace(-1, 1, 3)) - with pytest.warns(DeprecationWarning): + with pytest.warns(FutureWarning): frd_tf.freqresp(1.) + with pytest.warns(FutureWarning, match="use complex"): + np.testing.assert_equal(frd_tf.response, frd_tf.complex) + + with pytest.warns(FutureWarning, match="use frdata"): + np.testing.assert_equal(frd_tf.fresp, frd_tf.frdata) + def test_repr_str(self): # repr printing array = np.array @@ -464,25 +728,27 @@ def test_repr_str(self): [1.0, 0.9+0.1j, 0.1+2j, 0.05+3j], [0.1, 1.0, 10.0, 100.0], name='sys0') sys1 = ct.frd( - sys0.fresp, sys0.omega, smooth=True, name='sys1') - ref0 = "FrequencyResponseData(" \ - "array([[[1. +0.j , 0.9 +0.1j, 0.1 +2.j , 0.05+3.j ]]])," \ - " array([ 0.1, 1. , 10. , 100. ]))" - ref1 = ref0[:-1] + ", smooth=True)" + sys0.frdata, sys0.omega, smooth=True, name='sys1') + ref_common = "FrequencyResponseData(\n" \ + "array([[[1. +0.j , 0.9 +0.1j, 0.1 +2.j , 0.05+3.j ]]]),\n" \ + "array([ 0.1, 1. , 10. , 100. ])," + ref0 = ref_common + "\nname='sys0', outputs=1, inputs=1)" + ref1 = ref_common + " smooth=True," + \ + "\nname='sys1', outputs=1, inputs=1)" sysm = ct.frd( - np.matmul(array([[1], [2]]), sys0.fresp), sys0.omega, name='sysm') + np.matmul(array([[1], [2]]), sys0.frdata), sys0.omega, name='sysm') - assert repr(sys0) == ref0 - assert repr(sys1) == ref1 + assert ct.iosys_repr(sys0, format='eval') == ref0 + assert ct.iosys_repr(sys1, format='eval') == ref1 - sys0r = eval(repr(sys0)) - np.testing.assert_array_almost_equal(sys0r.fresp, sys0.fresp) + sys0r = eval(ct.iosys_repr(sys0, format='eval')) + np.testing.assert_array_almost_equal(sys0r.frdata, sys0.frdata) np.testing.assert_array_almost_equal(sys0r.omega, sys0.omega) - sys1r = eval(repr(sys1)) - np.testing.assert_array_almost_equal(sys1r.fresp, sys1.fresp) + sys1r = eval(ct.iosys_repr(sys1, format='eval')) + np.testing.assert_array_almost_equal(sys1r.frdata, sys1.frdata) np.testing.assert_array_almost_equal(sys1r.omega, sys1.omega) - assert(sys1.ifunc is not None) + assert(sys1._ifunc is not None) refs = """: {sysname} Inputs (1): ['u[0]'] @@ -503,28 +769,31 @@ def test_repr_str(self): Outputs (1): ['y[0]'] Input 1 to output 1: -Freq [rad/s] Response ------------- --------------------- - 0.100 1 +0j - 1.000 0.9 +0.1j - 10.000 0.1 +2j - 100.000 0.05 +3j + + Freq [rad/s] Response + ------------ --------------------- + 0.100 1 +0j + 1.000 0.9 +0.1j + 10.000 0.1 +2j + 100.000 0.05 +3j + Input 2 to output 1: -Freq [rad/s] Response ------------- --------------------- - 0.100 2 +0j - 1.000 1.8 +0.2j - 10.000 0.2 +4j - 100.000 0.1 +6j""" + + Freq [rad/s] Response + ------------ --------------------- + 0.100 2 +0j + 1.000 1.8 +0.2j + 10.000 0.2 +4j + 100.000 0.1 +6j""" assert str(sysm) == refm def test_unrecognized_keyword(self): h = TransferFunction([1], [1, 2, 2]) omega = np.logspace(-1, 2, 10) with pytest.raises(TypeError, match="unrecognized keyword"): - sys = FrequencyResponseData(h, omega, unknown=None) + FrequencyResponseData(h, omega, unknown=None) with pytest.raises(TypeError, match="unrecognized keyword"): - sys = ct.frd(h, omega, unknown=None) + ct.frd(h, omega, unknown=None) def test_named_signals(): @@ -564,7 +833,7 @@ def test_to_pandas(): # Check to make sure the data make senses np.testing.assert_equal(df['omega'], resp.omega) - np.testing.assert_equal(df['H_{y[0], u[0]}'], resp.fresp[0, 0]) + np.testing.assert_equal(df['H_{y[0], u[0]}'], resp.frdata[0, 0]) def test_frequency_response(): @@ -609,3 +878,49 @@ def test_frequency_response(): assert mag_nosq_sq.shape == mag_default.shape assert phase_nosq_sq.shape == phase_default.shape assert omega_nosq_sq.shape == omega_default.shape + + +def test_signal_labels(): + # Create a system response for a SISO system + sys = ct.rss(4, 1, 1) + fresp = ct.frequency_response(sys) + + # Make sure access via strings works + np.testing.assert_equal( + fresp.magnitude['y[0]'], fresp.magnitude) + np.testing.assert_equal( + fresp.phase['y[0]'], fresp.phase) + + # Make sure errors are generated if key is unknown + with pytest.raises(ValueError, match="unknown signal name 'bad'"): + fresp.magnitude['bad'] + + # Create a system response for a MIMO system + sys = ct.rss(4, 2, 2) + fresp = ct.frequency_response(sys) + + # Make sure access via strings works + np.testing.assert_equal( + fresp.magnitude['y[0]', 'u[1]'], + fresp.magnitude[0, 1]) + np.testing.assert_equal( + fresp.phase['y[0]', 'u[1]'], + fresp.phase[0, 1]) + np.testing.assert_equal( + fresp.complex['y[0]', 'u[1]'], + fresp.complex[0, 1]) + + # Make sure access via lists of strings works + np.testing.assert_equal( + fresp.complex[['y[1]', 'y[0]'], 'u[0]'], + fresp.complex[[1, 0], 0]) + + # Make sure errors are generated if key is unknown + with pytest.raises(ValueError, match="unknown signal name 'bad'"): + fresp.magnitude['bad'] + + with pytest.raises(ValueError, match="unknown signal name 'bad'"): + fresp.complex[['y[1]', 'bad']] + + with pytest.raises(ValueError, match=r"unknown signal name 'y\[0\]'"): + fresp.complex['y[1]', 'y[0]'] # second index = input name diff --git a/control/tests/freqplot_test.py b/control/tests/freqplot_test.py index f7105cb96..b3770486c 100644 --- a/control/tests/freqplot_test.py +++ b/control/tests/freqplot_test.py @@ -1,13 +1,14 @@ # freqplot_test.py - test out frequency response plots # RMM, 23 Jun 2023 -import pytest -import control as ct +import re import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np +import pytest + +import control as ct -from control.tests.conftest import slycotonly, editsdefaults pytestmark = pytest.mark.usefixtures("mplcleanup") # @@ -61,7 +62,7 @@ def test_response_plots( ovlout, ovlinp, clear=True): # Use figure frame for suptitle to speed things up - ct.set_defaults('freqplot', suptitle_frame='figure') + ct.set_defaults('freqplot', title_frame='figure') # Save up the keyword arguments kwargs = dict( @@ -82,21 +83,22 @@ def test_response_plots( # Plot the frequency response plt.figure() - out = response.plot(**kwargs) + cplt = response.plot(**kwargs) # Check the shape if ovlout and ovlinp: - assert out.shape == (pltmag + pltphs, 1) + assert cplt.lines.shape == (pltmag + pltphs, 1) elif ovlout: - assert out.shape == (pltmag + pltphs, sys.ninputs) + assert cplt.lines.shape == (pltmag + pltphs, sys.ninputs) elif ovlinp: - assert out.shape == (sys.noutputs * (pltmag + pltphs), 1) + assert cplt.lines.shape == (sys.noutputs * (pltmag + pltphs), 1) else: - assert out.shape == (sys.noutputs * (pltmag + pltphs), sys.ninputs) + assert cplt.lines.shape == \ + (sys.noutputs * (pltmag + pltphs), sys.ninputs) # Make sure all of the outputs are of the right type nlines_plotted = 0 - for ax_lines in np.nditer(out, flags=["refs_ok"]): + for ax_lines in np.nditer(cplt.lines, flags=["refs_ok"]): for line in ax_lines.item() or []: assert isinstance(line, mpl.lines.Line2D) nlines_plotted += 1 @@ -124,13 +126,12 @@ def test_response_plots( assert len(ax.get_lines()) > 1 # Update the title so we can see what is going on - fig = out[0, 0][0].axes.figure - ct.suptitle( - fig._suptitle._text + + cplt.set_plot_title( + cplt.figure._suptitle._text + f" [{sys.noutputs}x{sys.ninputs}, pm={pltmag}, pp={pltphs}," f" sm={shrmag}, sp={shrphs}, sf={shrfrq}]", # TODO: ", " # f"oo={ovlout}, oi={ovlinp}, ss={secsys}]", # TODO: add back - frame='figure', fontsize='small') + frame='figure') # Get rid of the figure to free up memory if clear: @@ -140,8 +141,8 @@ def test_response_plots( # Use the manaul response to verify that different settings are working def test_manual_response_limits(): # Default response: limits should be the same across rows - out = manual_response.plot() - axs = ct.get_plot_axes(out) + cplt = manual_response.plot() + axs = cplt.axes for i in range(manual_response.noutputs): for j in range(1, manual_response.ninputs): # Everything in the same row should have the same limits @@ -157,7 +158,7 @@ def test_manual_response_limits(): @pytest.mark.usefixtures("editsdefaults") def test_line_styles(plt_fcn): # Use figure frame for suptitle to speed things up - ct.set_defaults('freqplot', suptitle_frame='figure') + ct.set_defaults('freqplot', title_frame='figure') # Define a couple of systems for testing sys1 = ct.tf([1], [1, 2, 1], name='sys1') @@ -165,7 +166,7 @@ def test_line_styles(plt_fcn): sys3 = ct.tf([0.2, 0.1], [1, 0.1, 0.3, 0.1, 0.1], name='sys3') # Create a plot for the first system, with custom styles - lines_default = plt_fcn(sys1) + plt_fcn(sys1) # Now create a plot using *fmt customization lines_fmt = plt_fcn(sys2, None, 'r--') @@ -265,7 +266,7 @@ def test_gangof4_plots(savefigs=False): @pytest.mark.usefixtures("editsdefaults") def test_first_arg_listable(response_cmd, return_type): # Use figure frame for suptitle to speed things up - ct.set_defaults('freqplot', suptitle_frame='figure') + ct.set_defaults('freqplot', title_frame='figure') sys = ct.rss(2, 1, 1) @@ -301,11 +302,11 @@ def test_first_arg_listable(response_cmd, return_type): @pytest.mark.usefixtures("editsdefaults") def test_bode_share_options(): # Use figure frame for suptitle to speed things up - ct.set_defaults('freqplot', suptitle_frame='figure') + ct.set_defaults('freqplot', title_frame='figure') # Default sharing should share along rows and cols for mag and phase - lines = ct.bode_plot(manual_response) - axs = ct.get_plot_axes(lines) + cplt = ct.bode_plot(manual_response) + axs = cplt.axes for i in range(axs.shape[0]): for j in range(axs.shape[1]): # Share y limits along rows @@ -316,8 +317,8 @@ def test_bode_share_options(): # Sharing along y axis for mag but not phase plt.figure() - lines = ct.bode_plot(manual_response, share_phase='none') - axs = ct.get_plot_axes(lines) + cplt = ct.bode_plot(manual_response, share_phase='none') + axs = cplt.axes for i in range(int(axs.shape[0] / 2)): for j in range(axs.shape[1]): if i != 0: @@ -329,8 +330,8 @@ def test_bode_share_options(): # Turn off sharing for magnitude and phase plt.figure() - lines = ct.bode_plot(manual_response, sharey='none') - axs = ct.get_plot_axes(lines) + cplt = ct.bode_plot(manual_response, sharey='none') + axs = cplt.axes for i in range(int(axs.shape[0] / 2)): for j in range(axs.shape[1]): if i != 0: @@ -344,7 +345,7 @@ def test_bode_share_options(): # Turn off sharing in x axes plt.figure() - lines = ct.bode_plot(manual_response, sharex='none') + cplt = ct.bode_plot(manual_response, sharex='none') # TODO: figure out what to check @@ -354,17 +355,17 @@ def test_freqplot_plot_type(plot_type): response = ct.singular_values_response(ct.rss(2, 1, 1)) else: response = ct.frequency_response(ct.rss(2, 1, 1)) - lines = response.plot(plot_type=plot_type) + cplt = response.plot(plot_type=plot_type) if plot_type == 'bode': - assert lines.shape == (2, 1) + assert cplt.lines.shape == (2, 1) else: - assert lines.shape == (1, ) + assert cplt.lines.shape == (1, ) @pytest.mark.parametrize("plt_fcn", [ct.bode_plot, ct.singular_values_plot]) @pytest.mark.usefixtures("editsdefaults") def test_freqplot_omega_limits(plt_fcn): # Use figure frame for suptitle to speed things up - ct.set_defaults('freqplot', suptitle_frame='figure') + ct.set_defaults('freqplot', title_frame='figure') # Utility function to check visible limits def _get_visible_limits(ax): @@ -378,14 +379,14 @@ def _get_visible_limits(ax): ct.tf([1], [1, 2, 1]), np.logspace(-1, 1)) # Generate a plot without overridding the limits - lines = plt_fcn(response) - ax = ct.get_plot_axes(lines) + cplt = plt_fcn(response) + ax = cplt.axes np.testing.assert_allclose( _get_visible_limits(ax.reshape(-1)[0]), np.array([0.1, 10])) # Now reset the limits - lines = plt_fcn(response, omega_limits=(1, 100)) - ax = ct.get_plot_axes(lines) + cplt = plt_fcn(response, omega_limits=(1, 100)) + ax = cplt.axes np.testing.assert_allclose( _get_visible_limits(ax.reshape(-1)[0]), np.array([1, 100])) @@ -393,21 +394,40 @@ def _get_visible_limits(ax): def test_gangof4_trace_labels(): P1 = ct.rss(2, 1, 1, name='P1') P2 = ct.rss(3, 1, 1, name='P2') - C = ct.rss(1, 1, 1, name='C') + C1 = ct.rss(1, 1, 1, name='C1') + C2 = ct.rss(1, 1, 1, name='C2') # Make sure default labels are as expected - out = ct.gangof4_response(P1, C).plot() - out = ct.gangof4_response(P2, C).plot() - axs = ct.get_plot_axes(out) + cplt = ct.gangof4_response(P1, C1).plot() + cplt = ct.gangof4_response(P2, C2).plot() + axs = cplt.axes + legend = axs[0, 1].get_legend().get_texts() + assert legend[0].get_text() == 'P=P1, C=C1' + assert legend[1].get_text() == 'P=P2, C=C2' + plt.close() + + # Suffix truncation + cplt = ct.gangof4_response(P1, C1).plot() + cplt = ct.gangof4_response(P2, C1).plot() + axs = cplt.axes + legend = axs[0, 1].get_legend().get_texts() + assert legend[0].get_text() == 'P=P1' + assert legend[1].get_text() == 'P=P2' + plt.close() + + # Prefix turncation + cplt = ct.gangof4_response(P1, C1).plot() + cplt = ct.gangof4_response(P1, C2).plot() + axs = cplt.axes legend = axs[0, 1].get_legend().get_texts() - assert legend[0].get_text() == 'None' - assert legend[1].get_text() == 'None' + assert legend[0].get_text() == 'C=C1' + assert legend[1].get_text() == 'C=C2' plt.close() # Override labels - out = ct.gangof4_response(P1, C).plot(label='xxx, line1, yyy') - out = ct.gangof4_response(P2, C).plot(label='xxx, line2, yyy') - axs = ct.get_plot_axes(out) + cplt = ct.gangof4_response(P1, C1).plot(label='xxx, line1, yyy') + cplt = ct.gangof4_response(P2, C2).plot(label='xxx, line2, yyy') + axs = cplt.axes legend = axs[0, 1].get_legend().get_texts() assert legend[0].get_text() == 'xxx, line1, yyy' assert legend[1].get_text() == 'xxx, line2, yyy' @@ -417,16 +437,16 @@ def test_gangof4_trace_labels(): @pytest.mark.parametrize( "plt_fcn", [ct.bode_plot, ct.singular_values_plot, ct.nyquist_plot]) @pytest.mark.usefixtures("editsdefaults") -def test_freqplot_trace_labels(plt_fcn): +def test_freqplot_line_labels(plt_fcn): sys1 = ct.rss(2, 1, 1, name='sys1') sys2 = ct.rss(3, 1, 1, name='sys2') # Use figure frame for suptitle to speed things up - ct.set_defaults('freqplot', suptitle_frame='figure') + ct.set_defaults('freqplot', title_frame='figure') # Make sure default labels are as expected - out = plt_fcn([sys1, sys2]) - axs = ct.get_plot_axes(out) + cplt = plt_fcn([sys1, sys2]) + axs = cplt.axes if axs.ndim == 1: legend = axs[0].get_legend().get_texts() else: @@ -436,8 +456,8 @@ def test_freqplot_trace_labels(plt_fcn): plt.close() # Override labels all at once - out = plt_fcn([sys1, sys2], label=['line1', 'line2']) - axs = ct.get_plot_axes(out) + cplt = plt_fcn([sys1, sys2], label=['line1', 'line2']) + axs = cplt.axes if axs.ndim == 1: legend = axs[0].get_legend().get_texts() else: @@ -447,9 +467,9 @@ def test_freqplot_trace_labels(plt_fcn): plt.close() # Override labels one at a time - out = plt_fcn(sys1, label='line1') - out = plt_fcn(sys2, label='line2') - axs = ct.get_plot_axes(out) + cplt = plt_fcn(sys1, label='line1') + cplt = plt_fcn(sys2, label='line2') + axs = cplt.axes if axs.ndim == 1: legend = axs[0].get_legend().get_texts() else: @@ -458,32 +478,28 @@ def test_freqplot_trace_labels(plt_fcn): assert legend[1].get_text() == 'line2' plt.close() - if plt_fcn == ct.bode_plot: - # Multi-dimensional data - sys1 = ct.rss(2, 2, 2, name='sys1') - sys2 = ct.rss(3, 2, 2, name='sys2') - - # Check out some errors first - with pytest.raises(ValueError, match="number of labels must match"): - ct.bode_plot([sys1, sys2], label=['line1']) - - with pytest.xfail(reason="need better broadcast checking on labels"): - with pytest.raises( - ValueError, match="labels must be given for each"): - ct.bode_plot(sys1, overlay_inputs=True, label=['line1']) - - # Now do things that should work - out = ct.bode_plot( - [sys1, sys2], - label=[ - [['line1', 'line1'], ['line1', 'line1']], - [['line2', 'line2'], ['line2', 'line2']], - ]) - axs = ct.get_plot_axes(out) - legend = axs[0, -1].get_legend().get_texts() - assert legend[0].get_text() == 'line1' - assert legend[1].get_text() == 'line2' - plt.close() + +@pytest.mark.skip(reason="line label override not yet implemented") +@pytest.mark.parametrize("kwargs, labels", [ + ({}, ['sys1', 'sys2']), + ({'overlay_outputs': True}, [ + 'x sys1 out1 y', 'x sys1 out2 y', 'x sys2 out1 y', 'x sys2 out2 y']), +]) +def test_line_labels_bode(kwargs, labels): + # Multi-dimensional data + sys1 = ct.rss(2, 2, 2) + sys2 = ct.rss(3, 2, 2) + + # Check out some errors first + with pytest.raises(ValueError, match="number of labels must match"): + ct.bode_plot([sys1, sys2], label=['line1']) + + cplt = ct.bode_plot([sys1, sys2], label=labels, **kwargs) + axs = cplt.axes + legend_texts = axs[0, -1].get_legend().get_texts() + for i, legend in enumerate(legend_texts): + assert legend.get_text() == labels[i] + plt.close() @pytest.mark.parametrize( @@ -499,28 +515,28 @@ def test_freqplot_ax_keyword(plt_fcn, ninputs, noutputs): pytest.skip("MIMO not implemented for Nyquist/Nichols") # Use figure frame for suptitle to speed things up - ct.set_defaults('freqplot', suptitle_frame='figure') + ct.set_defaults('freqplot', title_frame='figure') # System to use sys = ct.rss(4, ninputs, noutputs) # Create an initial figure - out1 = plt_fcn(sys) + cplt1 = plt_fcn(sys) # Draw again on the same figure, using array - axs = ct.get_plot_axes(out1) - out2 = plt_fcn(sys, ax=axs) - np.testing.assert_equal(ct.get_plot_axes(out1), ct.get_plot_axes(out2)) + axs = cplt1.axes + cplt2 = plt_fcn(sys, ax=axs) + np.testing.assert_equal(cplt1.axes, cplt2.axes) # Pass things in as a list instead axs_list = axs.tolist() - out3 = plt_fcn(sys, ax=axs) - np.testing.assert_equal(ct.get_plot_axes(out1), ct.get_plot_axes(out3)) + cplt3 = plt_fcn(sys, ax=axs) + np.testing.assert_equal(cplt1.axes, cplt3.axes) # Flatten the list axs_list = axs.squeeze().tolist() - out3 = plt_fcn(sys, ax=axs_list) - np.testing.assert_equal(ct.get_plot_axes(out1), ct.get_plot_axes(out3)) + cplt4 = plt_fcn(sys, ax=axs_list) + np.testing.assert_equal(cplt1.axes, cplt4.axes) def test_mixed_systypes(): @@ -536,47 +552,56 @@ def test_mixed_systypes(): resp_tf = ct.frequency_response(sys_tf) resp_ss = ct.frequency_response(sys_ss) plt.figure() - ct.bode_plot([resp_tf, resp_ss, sys_frd1, sys_frd2], plot_phase=False) - ct.suptitle("bode_plot([resp_tf, resp_ss, sys_frd1, sys_frd2])") + cplt = ct.bode_plot( + [resp_tf, resp_ss, sys_frd1, sys_frd2], plot_phase=False) + cplt.set_plot_title("bode_plot([resp_tf, resp_ss, sys_frd1, sys_frd2])") # Same thing, but using frequency response plt.figure() resp = ct.frequency_response([sys_tf, sys_ss, sys_frd1, sys_frd2]) - resp.plot(plot_phase=False) - ct.suptitle("frequency_response([sys_tf, sys_ss, sys_frd1, sys_frd2])") + cplt = resp.plot(plot_phase=False) + cplt.set_plot_title( + "frequency_response([sys_tf, sys_ss, sys_frd1, sys_frd2])") # Same thing, but using bode_plot plt.figure() - resp = ct.bode_plot([sys_tf, sys_ss, sys_frd1, sys_frd2], plot_phase=False) - ct.suptitle("bode_plot([sys_tf, sys_ss, sys_frd1, sys_frd2])") + cplt = ct.bode_plot([sys_tf, sys_ss, sys_frd1, sys_frd2], plot_phase=False) + cplt.set_plot_title("bode_plot([sys_tf, sys_ss, sys_frd1, sys_frd2])") def test_suptitle(): - sys = ct.rss(2, 2, 2) + sys = ct.rss(2, 2, 2, strictly_proper=True) # Default location: center of axes - out = ct.bode_plot(sys) + cplt = ct.bode_plot(sys) assert plt.gcf()._suptitle._x != 0.5 # Try changing the the title - ct.suptitle("New title") + cplt.set_plot_title("New title") assert plt.gcf()._suptitle._text == "New title" # Change the location of the title - ct.suptitle("New title", frame='figure') + cplt.set_plot_title("New title", frame='figure') assert plt.gcf()._suptitle._x == 0.5 # Change the location of the title back - ct.suptitle("New title", frame='axes') + cplt.set_plot_title("New title", frame='axes') assert plt.gcf()._suptitle._x != 0.5 # Bad frame with pytest.raises(ValueError, match="unknown"): - ct.suptitle("New title", frame='nowhere') + cplt.set_plot_title("New title", frame='nowhere') # Bad keyword - with pytest.raises(AttributeError, match="unexpected keyword|no property"): - ct.suptitle("New title", unknown=None) + with pytest.raises( + TypeError, match="unexpected keyword|no property"): + cplt.set_plot_title("New title", unknown=None) + + # Make sure title is still there if we display margins underneath + sys = ct.rss(2, 1, 1, name='sys') + cplt = ct.bode_plot(sys, display_margins=True) + assert re.match(r"^Bode plot for sys$", cplt.figure._suptitle._text) + assert re.match(r"^sys: Gm = .*, Pm = .*$", cplt.axes[0, 0].get_title()) @pytest.mark.parametrize("plt_fcn", [ct.bode_plot, ct.singular_values_plot]) @@ -599,6 +624,95 @@ def test_freqplot_errors(plt_fcn): plt_fcn(response, omega_limits=[1e2, 1e-2]) +def test_freqresplist_unknown_kw(): + sys1 = ct.rss(2, 1, 1) + sys2 = ct.rss(2, 1, 1) + resp = ct.frequency_response([sys1, sys2]) + assert isinstance(resp, ct.FrequencyResponseList) + + with pytest.raises(AttributeError, match="unexpected keyword"): + resp.plot(unknown=True) + +@pytest.mark.parametrize("nsys, display_margins, gridkw, match", [ + (1, True, {}, None), + (1, False, {}, None), + (1, False, {}, None), + (1, True, {'grid': True}, None), + (1, 'overlay', {}, None), + (1, 'overlay', {'grid': True}, None), + (1, 'overlay', {'grid': False}, None), + (2, True, {}, None), + (2, 'overlay', {}, "not supported for multi-trace plots"), + (2, True, {'grid': 'overlay'}, None), + (3, True, {'grid': True}, None), +]) +def test_display_margins(nsys, display_margins, gridkw, match): + sys1 = ct.tf([10], [1, 1, 1, 1], name='sys1') + sys2 = ct.tf([20], [2, 2, 2, 1], name='sys2') + sys3 = ct.tf([30], [2, 3, 3, 1], name='sys3') + + sysdata = [sys1, sys2, sys3][0:nsys] + + plt.figure() + if match is None: + cplt = ct.bode_plot(sysdata, display_margins=display_margins, **gridkw) + else: + with pytest.raises(NotImplementedError, match=match): + ct.bode_plot(sysdata, display_margins=display_margins, **gridkw) + return + + cplt.set_plot_title( + cplt.figure._suptitle._text + f" [d_m={display_margins}, {gridkw=}") + + # Make sure the grid is there if it should be + if gridkw.get('grid') or not display_margins: + assert all( + [line.get_visible() for line in cplt.axes[0, 0].get_xgridlines()]) + else: + assert not any( + [line.get_visible() for line in cplt.axes[0, 0].get_xgridlines()]) + + # Make sure margins are displayed + if display_margins == True: + ax_title = cplt.axes[0, 0].get_title() + assert len(ax_title.split('\n')) == nsys + elif display_margins == 'overlay': + assert cplt.axes[0, 0].get_title() == '' + + +def test_singular_values_plot_colors(): + # Define some systems for testing + sys1 = ct.rss(4, 2, 2, strictly_proper=True) + sys2 = ct.rss(4, 2, 2, strictly_proper=True) + + # Get the default color cycle + color_cycle = plt.rcParams['axes.prop_cycle'].by_key()['color'] + + # Plot the systems individually and make sure line colors are OK + cplt = ct.singular_values_plot(sys1) + assert cplt.lines.size == 1 + assert len(cplt.lines[0]) == 2 + assert cplt.lines[0][0].get_color() == color_cycle[0] + assert cplt.lines[0][1].get_color() == color_cycle[0] + + cplt = ct.singular_values_plot(sys2) + assert cplt.lines.size == 1 + assert len(cplt.lines[0]) == 2 + assert cplt.lines[0][0].get_color() == color_cycle[1] + assert cplt.lines[0][1].get_color() == color_cycle[1] + plt.close('all') + + # Plot the systems as a list and make sure colors are OK + cplt = ct.singular_values_plot([sys1, sys2]) + assert cplt.lines.size == 2 + assert len(cplt.lines[0]) == 2 + assert len(cplt.lines[1]) == 2 + assert cplt.lines[0][0].get_color() == color_cycle[0] + assert cplt.lines[0][1].get_color() == color_cycle[0] + assert cplt.lines[1][0].get_color() == color_cycle[1] + assert cplt.lines[1][1].get_color() == color_cycle[1] + + if __name__ == "__main__": # # Interactive mode: generate plots for manual viewing @@ -638,7 +752,7 @@ def test_freqplot_errors(plt_fcn): for args in test_cases: test_response_plots(*args, ovlinp=False, ovlout=False, clear=False) - # Reset suptitle_frame to the default value + # Reset title_frame to the default value ct.reset_defaults() # Define and run a selected set of interesting tests @@ -652,3 +766,6 @@ def test_freqplot_errors(plt_fcn): # of them for use in the documentation). # test_mixed_systypes() + test_display_margins(2, True, {}) + test_display_margins(2, 'overlay', {}) + test_display_margins(2, True, {'grid': True}) diff --git a/control/tests/freqresp_test.py b/control/tests/freqresp_test.py index 555adf332..a268d38eb 100644 --- a/control/tests/freqresp_test.py +++ b/control/tests/freqresp_test.py @@ -60,7 +60,7 @@ def test_freqresp_siso(ss_siso): ctrl.frequency_response(ss_siso, omega) -@pytest.mark.filterwarnings("ignore:freqresp is deprecated") +@pytest.mark.filterwarnings(r"ignore:freqresp\(\) is deprecated") @slycotonly def test_freqresp_mimo_legacy(ss_mimo): """Test MIMO frequency response calls""" @@ -112,7 +112,7 @@ def test_nyquist_basic(ss_siso): # Check known warnings happened as expected assert len(record) == 2 assert re.search("encirclements was a non-integer", str(record[0].message)) - assert re.search("return values .* deprecated", str(record[1].message)) + assert re.search("return value .* deprecated", str(record[1].message)) response = nyquist_response(tf_siso, omega=np.logspace(-1, 1, 10)) assert len(response.contour) == 10 @@ -276,7 +276,7 @@ def dsystem_type(request, dsystem_dt): @pytest.mark.parametrize("dsystem_type", ['sssiso', 'ssmimo', 'tf'], indirect=True) def test_discrete(dsystem_type): - """Test discrete time frequency response""" + """Test discrete-time frequency response""" dsys = dsystem_type # Set frequency range to just below Nyquist freq (for Bode) omega_ok = np.linspace(10e-4, 0.99, 100) * np.pi / dsys.dt @@ -673,7 +673,7 @@ def test_singular_values_plot(tsystem): sys = tsystem.sys for omega_ref, sigma_ref in zip(tsystem.omegas, tsystem.sigmas): response = singular_values_response(sys, omega_ref) - sigma = np.real(response.fresp[:, 0, :]) + sigma = np.real(response.frdata[:, 0, :]) np.testing.assert_almost_equal(sigma, sigma_ref) diff --git a/control/tests/interconnect_test.py b/control/tests/interconnect_test.py index 604488ca5..aea3cbbc6 100644 --- a/control/tests/interconnect_test.py +++ b/control/tests/interconnect_test.py @@ -15,7 +15,6 @@ import pytest import numpy as np -import scipy as sp import math import control as ct @@ -46,15 +45,15 @@ def test_summing_junction(inputs, output, dimension, D): def test_summation_exceptions(): # Bad input description with pytest.raises(ValueError, match="could not parse input"): - sumblk = ct.summing_junction(np.pi, 'y') + ct.summing_junction(np.pi, 'y') # Bad output description with pytest.raises(ValueError, match="could not parse output"): - sumblk = ct.summing_junction('u', np.pi) + ct.summing_junction('u', np.pi) # Bad input dimension with pytest.raises(ValueError, match="unrecognized dimension"): - sumblk = ct.summing_junction('u', 'y', dimension=False) + ct.summing_junction('u', 'y', dimension=False) @pytest.mark.parametrize("dim", [1, 3]) @@ -346,7 +345,7 @@ def test_interconnect_exceptions(): # NonlinearIOSytem with pytest.raises(TypeError, match="unrecognized keyword"): - nlios = ct.NonlinearIOSystem( + ct.NonlinearIOSystem( None, lambda t, x, u, params: u*u, input_count=1, output_count=1) # Summing junction @@ -666,15 +665,29 @@ def test_interconnect_params(): # Create a nominally unstable system sys1 = ct.nlsys( lambda t, x, u, params: params['a'] * x[0] + u[0], - states=1, inputs='u', outputs='y', params={'a': 1}) + states=1, inputs='u', outputs='y', params={'a': 2, 'c':2}) # Simple system for serial interconnection sys2 = ct.nlsys( None, lambda t, x, u, params: u[0], - inputs='r', outputs='u') + inputs='r', outputs='u', params={'a': 4, 'b': 3}) - # Create a series interconnection + # Make sure default parameters get set as expected sys = ct.interconnect([sys1, sys2], inputs='r', outputs='y') + assert sys.params == {'a': 4, 'c': 2, 'b': 3} + assert sys.dynamics(0, [1], [0]).item() == 4 + + # Make sure we can override the parameters + sys = ct.interconnect( + [sys1, sys2], inputs='r', outputs='y', params={'b': 1}) + assert sys.params == {'b': 1} + assert sys.dynamics(0, [1], [0]).item() == 2 + assert sys.dynamics(0, [1], [0], params={'a': 5}).item() == 5 + + # Create final series interconnection, with proper parameter values + sys = ct.interconnect( + [sys1, sys2], inputs='r', outputs='y', params={'a': 1}) + assert sys.params == {'a': 1} # Make sure we can call the update function sys.updfcn(0, [0], [0], {}) diff --git a/control/tests/iosys_test.py b/control/tests/iosys_test.py index cf4e3dd43..5d741ae83 100644 --- a/control/tests/iosys_test.py +++ b/control/tests/iosys_test.py @@ -10,13 +10,14 @@ import re import warnings -import pytest +from math import sqrt import numpy as np -from math import sqrt +import pytest +import scipy import control as ct - +import control.flatsys as fs class TestIOSys: @@ -77,7 +78,7 @@ def test_tf2io(self, tsys): # Create a transfer function from the state space system linsys = tsys.siso_linsys tfsys = ct.ss2tf(linsys) - with pytest.warns(DeprecationWarning, match="use tf2ss"): + with pytest.warns(FutureWarning, match="use tf2ss"): iosys = ct.tf2io(tfsys) # Verify correctness via simulation @@ -90,13 +91,13 @@ def test_tf2io(self, tsys): # Make sure that non-proper transfer functions generate an error tfsys = ct.tf('s') with pytest.raises(ValueError): - with pytest.warns(DeprecationWarning, match="use tf2ss"): + with pytest.warns(FutureWarning, match="use tf2ss"): iosys=ct.tf2io(tfsys) def test_ss2io(self, tsys): # Create an input/output system from the linear system linsys = tsys.siso_linsys - with pytest.warns(DeprecationWarning, match="use ss"): + with pytest.warns(FutureWarning, match="use ss"): iosys = ct.ss2io(linsys) np.testing.assert_allclose(linsys.A, iosys.A) np.testing.assert_allclose(linsys.B, iosys.B) @@ -104,7 +105,7 @@ def test_ss2io(self, tsys): np.testing.assert_allclose(linsys.D, iosys.D) # Try adding names to things - with pytest.warns(DeprecationWarning, match="use ss"): + with pytest.warns(FutureWarning, match="use ss"): iosys_named = ct.ss2io(linsys, inputs='u', outputs='y', states=['x1', 'x2'], name='iosys_named') assert iosys_named.find_input('u') == 0 @@ -740,7 +741,7 @@ def test_nonsquare_bdalg(self, tsys): ct.series(*args) def test_discrete(self, tsys): - """Test discrete time functionality""" + """Test discrete-time functionality""" # Create some linear and nonlinear systems to play with linsys = ct.StateSpace( [[-1, 1], [0, -2]], [[0], [1]], [[1, 0]], [[0]], True) @@ -772,7 +773,7 @@ def test_discrete(self, tsys): np.testing.assert_allclose(ios_y, lin_y,atol=0.002,rtol=0.) def test_discrete_iosys(self, tsys): - """Create a discrete time system from scratch""" + """Create a discrete-time system from scratch""" linsys = ct.StateSpace( [[-1, 1], [0, -2]], [[0], [1]], [[1, 0]], [[0]], True) @@ -929,6 +930,8 @@ def test_params(self, tsys): ios_secord_update = ct.NonlinearIOSystem( secord_update, secord_output, inputs=1, outputs=1, states=2, params={'omega0':2, 'zeta':0}) + lin_secord_update = ct.linearize(ios_secord_update, [0, 0], [0]) + w_update, v_update = np.linalg.eig(lin_secord_update.A) # Make sure the default parameters haven't changed lin_secord_check = ct.linearize(ios_secord_default, [0, 0], [0]) @@ -958,7 +961,7 @@ def test_params(self, tsys): ios_series_default_local, [0, 0, 0, 0], [0]) w, v = np.linalg.eig(lin_series_default_local.A) np.testing.assert_array_almost_equal( - np.sort(w), np.sort(np.concatenate((w_default, [2j, -2j])))) + w, np.concatenate([w_update, w_update])) # Show that we can change the parameters at linearization lin_series_override = ct.linearize( @@ -1408,7 +1411,7 @@ def test_operand_incompatible(self, Pout, Pin, C, op): C = ct.rss(2, 2, 3) with pytest.raises(ValueError, match="incompatible"): - PC = op(P, C) + op(P, C) @pytest.mark.parametrize( "C, op", [ @@ -1578,7 +1581,7 @@ def test_linear_interconnection(): # Make sure call works properly response = io_connect.frequency_response(1) np.testing.assert_allclose( - response.fresp[:, :, 0], io_connect.C @ np.linalg.inv( + response.frdata[:, :, 0], io_connect.C @ np.linalg.inv( 1j * np.eye(io_connect.nstates) - io_connect.A) @ io_connect.B + \ io_connect.D) @@ -1705,9 +1708,9 @@ def test_interconnect_unused_input(): with pytest.warns( UserWarning, match=r"Unused input\(s\) in InterconnectedSystem"): - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y']) + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y']) with warnings.catch_warnings(): # no warning if output explicitly ignored, various argument forms @@ -1715,45 +1718,43 @@ def test_interconnect_unused_input(): # strip out matrix warnings warnings.filterwarnings("ignore", "the matrix subclass", category=PendingDeprecationWarning) - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y'], - ignore_inputs=['n']) + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y'], + ignore_inputs=['n']) - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y'], - ignore_inputs=['s.n']) + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y'], + ignore_inputs=['s.n']) # no warning if auto-connect disabled - h = ct.interconnect([g,s,k], - connections=False) + ct.interconnect([g,s,k], + connections=False) # warn if explicity ignored input in fact used with pytest.warns( UserWarning, - match=r"Input\(s\) specified as ignored is \(are\) used:") \ - as record: - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y'], - ignore_inputs=['u','n']) + match=r"Input\(s\) specified as ignored is \(are\) used:"): + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y'], + ignore_inputs=['u','n']) with pytest.warns( UserWarning, - match=r"Input\(s\) specified as ignored is \(are\) used:") \ - as record: - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y'], - ignore_inputs=['k.e','n']) + match=r"Input\(s\) specified as ignored is \(are\) used:"): + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y'], + ignore_inputs=['k.e','n']) # error if ignored signal doesn't exist with pytest.raises(ValueError): - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y'], - ignore_inputs=['v']) + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y'], + ignore_inputs=['v']) def test_interconnect_unused_output(): @@ -1775,10 +1776,10 @@ def test_interconnect_unused_output(): with pytest.warns( UserWarning, - match=r"Unused output\(s\) in InterconnectedSystem:") as record: - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y']) + match=r"Unused output\(s\) in InterconnectedSystem:"): + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y']) # no warning if output explicitly ignored @@ -1787,43 +1788,43 @@ def test_interconnect_unused_output(): # strip out matrix warnings warnings.filterwarnings("ignore", "the matrix subclass", category=PendingDeprecationWarning) - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y'], - ignore_outputs=['dy']) + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y'], + ignore_outputs=['dy']) - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y'], - ignore_outputs=['g.dy']) + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y'], + ignore_outputs=['g.dy']) # no warning if auto-connect disabled - h = ct.interconnect([g,s,k], - connections=False) + ct.interconnect([g,s,k], + connections=False) # warn if explicity ignored output in fact used with pytest.warns( UserWarning, match=r"Output\(s\) specified as ignored is \(are\) used:"): - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y'], - ignore_outputs=['dy','u']) + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y'], + ignore_outputs=['dy','u']) with pytest.warns( UserWarning, match=r"Output\(s\) specified as ignored is \(are\) used:"): - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y'], - ignore_outputs=['dy', ('k.u')]) + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y'], + ignore_outputs=['dy', ('k.u')]) # error if ignored signal doesn't exist with pytest.raises(ValueError): - h = ct.interconnect([g,s,k], - inputs=['r'], - outputs=['y'], - ignore_outputs=['v']) + ct.interconnect([g,s,k], + inputs=['r'], + outputs=['y'], + ignore_outputs=['v']) def test_interconnect_add_unused(): @@ -1896,11 +1897,11 @@ def test_input_output_broadcasting(): # Specify only some of the initial conditions with pytest.warns(UserWarning, match="X0 too short; padding"): - resp_short = ct.input_output_response(sys, T, [U[0], [0, 1]], [X0, 1]) + ct.input_output_response(sys, T, [U[0], [0, 1]], [X0, 1]) # Make sure that inconsistent settings don't work with pytest.raises(ValueError, match="inconsistent"): - resp_bad = ct.input_output_response( + ct.input_output_response( sys, T, (U[0, :], U[:2, :-1]), [X0, P0]) @pytest.mark.parametrize("nstates, ninputs, noutputs", [ @@ -1942,7 +1943,7 @@ def test_nonuniform_timepts(nstates, noutputs, ninputs): def test_ss_nonlinear(): """Test ss() for creating nonlinear systems""" - with pytest.warns(DeprecationWarning, match="use nlsys()"): + with pytest.warns(FutureWarning, match="use nlsys()"): secord = ct.ss(secord_update, secord_output, inputs='u', outputs='y', states = ['x1', 'x2'], name='secord') assert secord.name == 'secord' @@ -1963,12 +1964,12 @@ def test_ss_nonlinear(): np.testing.assert_almost_equal(ss_response.outputs, io_response.outputs) # Make sure that optional keywords are allowed - with pytest.warns(DeprecationWarning, match="use nlsys()"): + with pytest.warns(FutureWarning, match="use nlsys()"): secord = ct.ss(secord_update, secord_output, dt=True) assert ct.isdtime(secord) # Make sure that state space keywords are flagged - with pytest.warns(DeprecationWarning, match="use nlsys()"): + with pytest.warns(FutureWarning, match="use nlsys()"): with pytest.raises(TypeError, match="unrecognized keyword"): ct.ss(secord_update, remove_useless_states=True) @@ -2087,6 +2088,101 @@ def test_find_eqpt(x0, ix, u0, iu, y0, iy, dx0, idx, dt, x_expect, u_expect): np.testing.assert_allclose(np.array(ueq), u_expect, atol=1e-6) +# Test out new operating point version of find_eqpt +def test_find_operating_point(): + dt = 1 + sys = ct.NonlinearIOSystem( + eqpt_rhs, eqpt_out, dt=dt, states=3, inputs=2, outputs=2) + + # Conditions that lead to no exact solution (from previous unit test) + x0 = 0; ix = None + u0 = [-1, 0]; iu = None + y0 = None; iy = None + dx0 = None; idx = None + + # Default version: no equilibrium solution => returns None + op_point = ct.find_operating_point( + sys, x0, u0, y0, ix=ix, iu=iu, iy=iy, dx0=dx0, idx=idx) + assert op_point.states is None + assert op_point.inputs is None + assert op_point.result.success is False + + # Change the method to Levenberg-Marquardt (gives nearest point) + op_point = ct.find_operating_point( + sys, x0, u0, y0, ix=ix, iu=iu, iy=iy, dx0=dx0, idx=idx, + root_method='lm') + assert op_point.states is not None + assert op_point.inputs is not None + assert op_point.result.success is True + + # Make sure we get a solution if we ask for the result explicitly + op_point = ct.find_operating_point( + sys, x0, u0, y0, ix=ix, iu=iu, iy=iy, dx0=dx0, idx=idx, + return_result=True) + assert op_point.states is not None + assert op_point.inputs is not None + assert op_point.result.success is False + + # Check to make sure unknown keywords are caught + with pytest.raises(TypeError, match="unrecognized keyword"): + ct.find_operating_point(sys, x0, u0, unknown=None) + + +def test_operating_point(): + dt = 1 + sys = ct.NonlinearIOSystem( + eqpt_rhs, eqpt_out, dt=dt, states=3, inputs=2, outputs=2) + + # Find the operating point near the origin + op_point = ct.find_operating_point(sys, 0, 0) + + # Linearize the old fashioned way + linsys_orig = ct.linearize(sys, op_point.states, op_point.inputs) + + # Linearize around the operating point + linsys_oppt = ct.linearize(sys, op_point) + + np.testing.assert_allclose(linsys_orig.A, linsys_oppt.A) + np.testing.assert_allclose(linsys_orig.B, linsys_oppt.B) + np.testing.assert_allclose(linsys_orig.C, linsys_oppt.C) + np.testing.assert_allclose(linsys_orig.D, linsys_oppt.D) + + # Call find_operating_point with method and keyword arguments + op_point = ct.find_operating_point( + sys, 0, 0, root_method='lm', root_kwargs={'tol': 1e-6}) + + # Make sure we can get back the right arguments in a tuple + op_point = ct.find_operating_point(sys, 0, 0, return_outputs=True) + assert len(op_point) == 3 + assert isinstance(op_point[0], np.ndarray) + assert isinstance(op_point[1], np.ndarray) + assert isinstance(op_point[2], np.ndarray) + + with pytest.warns( + (FutureWarning, PendingDeprecationWarning), match="return_outputs"): + op_point = ct.find_operating_point(sys, 0, 0, return_y=True) + assert len(op_point) == 3 + assert isinstance(op_point[0], np.ndarray) + assert isinstance(op_point[1], np.ndarray) + assert isinstance(op_point[2], np.ndarray) + + # Make sure we can get back the right arguments in a tuple + op_point = ct.find_operating_point(sys, 0, 0, return_result=True) + assert len(op_point) == 3 + assert isinstance(op_point[0], np.ndarray) + assert isinstance(op_point[1], np.ndarray) + assert isinstance(op_point[2], scipy.optimize.OptimizeResult) + + # Make sure we can get back the right arguments in a tuple + op_point = ct.find_operating_point( + sys, 0, 0, return_result=True, return_outputs=True) + assert len(op_point) == 4 + assert isinstance(op_point[0], np.ndarray) + assert isinstance(op_point[1], np.ndarray) + assert isinstance(op_point[2], np.ndarray) + assert isinstance(op_point[3], scipy.optimize.OptimizeResult) + + def test_iosys_sample(): csys = ct.rss(2, 1, 1) dsys = csys.sample(0.1) @@ -2168,3 +2264,158 @@ def test_update_names(): with pytest.raises(TypeError, match=".* takes 1 positional argument"): sys.update_names(5) + + +def test_signal_indexing(): + # Response with two outputs, no traces + resp = ct.initial_response(ct.rss(4, 2, 1, strictly_proper=True)) + assert resp.outputs['y[0]'].shape == resp.outputs.shape[1:] + assert resp.outputs[0, 0].item() == 0 + + # Implicitly squeezed response + resp = ct.step_response(ct.rss(4, 1, 1, strictly_proper=True)) + for key in [ ['y[0]', 'y[0]'], ('y[0]', 'u[0]') ]: + with pytest.raises(IndexError, match=r"signal name\(s\) not valid"): + resp.outputs.__getitem__(key) + + # Explicitly squeezed response + resp = ct.step_response( + ct.rss(4, 2, 1, strictly_proper=True), squeeze=True) + assert resp.outputs['y[0]'].shape == resp.outputs.shape[1:] + with pytest.raises(IndexError, match=r"signal name\(s\) not valid"): + resp.outputs['y[0]', 'u[0]'] + + +@pytest.mark.parametrize("fcn, spec, expected, missing", [ + (ct.ss, {}, "states=4, outputs=3, inputs=2", r"dt|name"), + (ct.tf, {}, "outputs=3, inputs=2", r"dt|states|name"), + (ct.frd, {}, "outputs=3, inputs=2", r"dt|states|name"), + (ct.ss, {'dt': 0.1}, ".*\ndt=0.1,\nstates=4, outputs=3, inputs=2", r"name"), + (ct.tf, {'dt': 0.1}, ".*\ndt=0.1,\noutputs=3, inputs=2", r"states|name"), + (ct.frd, {'dt': 0.1}, ".*\ndt=0.1,\noutputs=3, inputs=2", r"states|name"), + (ct.ss, {'dt': True}, "\ndt=True,\nstates=4, outputs=3, inputs=2", r"name"), + (ct.ss, {'dt': None}, "\ndt=None,\nstates=4, outputs=3, inputs=2", r"name"), + (ct.ss, {'dt': 0}, "states=4, outputs=3, inputs=2", r"dt|name"), + (ct.ss, {'name': 'mysys'}, "\nname='mysys'", r"dt"), + (ct.tf, {'name': 'mysys'}, "\nname='mysys'", r"dt|states"), + (ct.frd, {'name': 'mysys'}, "\nname='mysys'", r"dt|states"), + (ct.ss, {'inputs': ['u1']}, + r"[\n]states=4, outputs=3, inputs=\['u1'\]", r"dt|name"), + (ct.tf, {'inputs': ['u1']}, + r"[\n]outputs=3, inputs=\['u1'\]", r"dt|name"), + (ct.frd, {'inputs': ['u1'], 'name': 'sampled'}, + r"[\n]name='sampled', outputs=3, inputs=\['u1'\]", r"dt"), + (ct.ss, {'outputs': ['y1']}, + r"[\n]states=4, outputs=\['y1'\], inputs=2", r"dt|name"), + (ct.ss, {'name': 'mysys', 'inputs': ['u1']}, + r"[\n]name='mysys', states=4, outputs=3, inputs=\['u1'\]", r"dt"), + (ct.ss, {'name': 'mysys', 'states': [ + 'long_state_1', 'long_state_2', 'long_state_3']}, + r"[\n]name='.*', states=\[.*\],\noutputs=3, inputs=2\)", r"dt"), +]) +@pytest.mark.parametrize("format", ['info', 'eval']) +def test_iosys_repr(fcn, spec, expected, missing, format): + spec['outputs'] = spec.get('outputs', 3) + spec['inputs'] = spec.get('inputs', 2) + if fcn is ct.ss: + spec['states'] = spec.get('states', 4) + + sys = ct.rss(**spec) + match fcn: + case ct.frd: + omega = np.logspace(-1, 1) + sys = fcn(sys, omega, name=spec.get('name')) + case ct.tf: + sys = fcn(sys, name=spec.get('name')) + assert sys.shape == (sys.noutputs, sys.ninputs) + + # Construct the 'info' format + info_expected = f"<{sys.__class__.__name__} {sys.name}: " \ + f"{sys.input_labels} -> {sys.output_labels}" + if sys.dt != 0: + info_expected += f", dt={sys.dt}>" + else: + info_expected += ">" + + # Make sure the default format is OK + out = repr(sys) + if ct.config.defaults['iosys.repr_format'] == 'info': + assert out == info_expected + else: + assert re.search(expected, out) != None + + # Now set the format to the given type and make sure things look right + sys.repr_format = format + out = repr(sys) + if format == 'eval': + assert re.search(expected, out) is not None + + if missing is not None: + assert re.search(missing, out) is None + + elif format == 'info': + assert out == info_expected + + # Make sure we can change back to the default format + sys.repr_format = None + + # Make sure the default format is OK + out = repr(sys) + if ct.config.defaults['iosys.repr_format'] == 'info': + assert out == info_expected + elif ct.config.defaults['iosys.repr_format'] == 'eval': + assert re.search(expected, out) != None + + +@pytest.mark.parametrize("fcn", [ct.ss, ct.tf, ct.frd, ct.nlsys, fs.flatsys]) +def test_relabeling(fcn): + sys = ct.rss(1, 1, 1, name="sys") + + # Rename the inputs, outputs, (states,) system + match fcn: + case ct.tf: + sys = fcn(sys, inputs='u', outputs='y', name='new') + case ct.frd: + sys = fcn(sys, [0.1, 1, 10], inputs='u', outputs='y', name='new') + case _: + sys = fcn(sys, inputs='u', outputs='y', states='x', name='new') + + assert sys.input_labels == ['u'] + assert sys.output_labels == ['y'] + if sys.nstates: + assert sys.state_labels == ['x'] + assert sys.name == 'new' + + +@pytest.mark.parametrize("fcn", [ct.ss, ct.tf, ct.frd, ct.nlsys, fs.flatsys]) +def test_signal_prefixing(fcn): + sys = ct.rss(2, 1, 1) + + # Recreate the system in different forms, with non-standard prefixes + match fcn: + case ct.ss: + sys = ct.ss( + sys.A, sys.B, sys.C, sys.D, state_prefix='xx', + input_prefix='uu', output_prefix='yy') + case ct.tf: + sys = ct.tf(sys) + sys = fcn(sys.num, sys.den, input_prefix='uu', output_prefix='yy') + case ct.frd: + freq = [0.1, 1, 10] + data = [sys(w * 1j) for w in freq] + sys = fcn(data, freq, input_prefix='uu', output_prefix='yy') + case ct.nlsys: + sys = ct.nlsys(sys) + sys = fcn( + sys.updfcn, sys.outfcn, inputs=1, outputs=1, states=2, + state_prefix='xx', input_prefix='uu', output_prefix='yy') + case fs.flatsys: + sys = fs.flatsys(sys) + sys = fcn( + sys.forward, sys.reverse, inputs=1, outputs=1, states=2, + state_prefix='xx', input_prefix='uu', output_prefix='yy') + + assert sys.input_labels == ['uu[0]'] + assert sys.output_labels == ['yy[0]'] + if sys.nstates: + assert sys.state_labels == ['xx[0]', 'xx[1]'] diff --git a/control/tests/kwargs_test.py b/control/tests/kwargs_test.py index 4d252ab19..566b35a28 100644 --- a/control/tests/kwargs_test.py +++ b/control/tests/kwargs_test.py @@ -11,24 +11,29 @@ # is a unit test that checks for unrecognized keywords. import inspect -import pytest import warnings -import matplotlib.pyplot as plt + +import pytest + +import numpy as np import control import control.flatsys - +import control.tests.descfcn_test as descfcn_test # List of all of the test modules where kwarg unit tests are defined import control.tests.flatsys_test as flatsys_test import control.tests.frd_test as frd_test import control.tests.freqplot_test as freqplot_test import control.tests.interconnect_test as interconnect_test +import control.tests.iosys_test as iosys_test import control.tests.optimal_test as optimal_test +import control.tests.statesp_test as statesp_test import control.tests.statefbk_test as statefbk_test import control.tests.stochsys_test as stochsys_test -import control.tests.trdata_test as trdata_test import control.tests.timeplot_test as timeplot_test -import control.tests.descfcn_test as descfcn_test +import control.tests.timeresp_test as timeresp_test +import control.tests.trdata_test as trdata_test + @pytest.mark.parametrize("module, prefix", [ (control, ""), (control.flatsys, "flatsys."), @@ -54,8 +59,9 @@ def test_kwarg_search(module, prefix): # Get the signature for the function sig = inspect.signature(obj) - # Skip anything that is inherited - if inspect.isclass(module) and obj.__name__ not in module.__dict__: + # Skip anything that is inherited or hidden + if inspect.isclass(module) and obj.__name__ not in module.__dict__ \ + or obj.__name__.startswith('_'): continue # See if there is a variable keyword argument @@ -93,6 +99,7 @@ def test_kwarg_search(module, prefix): @pytest.mark.parametrize( "function, nsssys, ntfsys, moreargs, kwargs", [(control.append, 2, 0, (), {}), + (control.combine_tf, 0, 0, ([[1, 0], [0, 1]], ), {}), (control.dlqe, 1, 0, ([[1]], [[1]]), {}), (control.dlqr, 1, 0, ([[1, 0], [0, 1]], [[1]]), {}), (control.drss, 0, 0, (2, 1, 1), {}), @@ -167,6 +174,7 @@ def test_unrecognized_kwargs(function, nsssys, ntfsys, moreargs, kwargs, (control.phase_plane_plot, 1, ([-1, 1, -1, 1], 1), {}), (control.phaseplot.streamlines, 1, ([-1, 1, -1, 1], 1), {}), (control.phaseplot.vectorfield, 1, ([-1, 1, -1, 1], ), {}), + (control.phaseplot.streamplot, 1, ([-1, 1, -1, 1], ), {}), (control.phaseplot.equilpoints, 1, ([-1, 1, -1, 1], ), {}), (control.phaseplot.separatrices, 1, ([-1, 1, -1, 1], ), {}), (control.singular_values_plot, 1, (), {})] @@ -241,6 +249,8 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo): 'append': test_unrecognized_kwargs, 'bode': test_response_plot_kwargs, 'bode_plot': test_response_plot_kwargs, + 'LTI.bode_plot': test_response_plot_kwargs, # tested via bode_plot + 'combine_tf': test_unrecognized_kwargs, 'create_estimator_iosystem': stochsys_test.test_estimator_errors, 'create_statefbk_iosystem': statefbk_test.TestStatefbk.test_statefbk_errors, 'describing_function_plot': test_matplotlib_kwargs, @@ -250,23 +260,34 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo): 'dlqr': test_unrecognized_kwargs, 'drss': test_unrecognized_kwargs, 'feedback': test_unrecognized_kwargs, + 'find_eqpt': iosys_test.test_find_operating_point, + 'find_operating_point': iosys_test.test_find_operating_point, 'flatsys.flatsys': test_unrecognized_kwargs, + 'forced_response': timeresp_test.test_timeresp_aliases, 'frd': frd_test.TestFRD.test_unrecognized_keyword, 'gangof4': test_matplotlib_kwargs, 'gangof4_plot': test_matplotlib_kwargs, + 'impulse_response': timeresp_test.test_timeresp_aliases, + 'initial_response': timeresp_test.test_timeresp_aliases, 'input_output_response': test_unrecognized_kwargs, 'interconnect': interconnect_test.test_interconnect_exceptions, 'time_response_plot': timeplot_test.test_errors, 'linearize': test_unrecognized_kwargs, 'lqe': test_unrecognized_kwargs, 'lqr': test_unrecognized_kwargs, + 'LTI.forced_response': statesp_test.test_convenience_aliases, + 'LTI.impulse_response': statesp_test.test_convenience_aliases, + 'LTI.initial_response': statesp_test.test_convenience_aliases, + 'LTI.step_response': statesp_test.test_convenience_aliases, 'negate': test_unrecognized_kwargs, 'nichols_plot': test_matplotlib_kwargs, + 'LTI.nichols_plot': test_matplotlib_kwargs, # tested via nichols_plot 'nichols': test_matplotlib_kwargs, 'nlsys': test_unrecognized_kwargs, 'nyquist': test_matplotlib_kwargs, 'nyquist_response': test_response_plot_kwargs, 'nyquist_plot': test_matplotlib_kwargs, + 'LTI.nyquist_plot': test_matplotlib_kwargs, # tested via nyquist_plot 'phase_plane_plot': test_matplotlib_kwargs, 'parallel': test_unrecognized_kwargs, 'pole_zero_plot': test_unrecognized_kwargs, @@ -279,11 +300,15 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo): 'set_defaults': test_unrecognized_kwargs, 'singular_values_plot': test_matplotlib_kwargs, 'ss': test_unrecognized_kwargs, + 'step_info': timeresp_test.test_timeresp_aliases, + 'step_response': timeresp_test.test_timeresp_aliases, + 'LTI.to_ss': test_unrecognized_kwargs, # tested via 'ss' 'ss2io': test_unrecognized_kwargs, 'ss2tf': test_unrecognized_kwargs, 'summing_junction': interconnect_test.test_interconnect_exceptions, 'suptitle': freqplot_test.test_suptitle, 'tf': test_unrecognized_kwargs, + 'LTI.to_tf': test_unrecognized_kwargs, # tested via 'ss' 'tf2io' : test_unrecognized_kwargs, 'tf2ss' : test_unrecognized_kwargs, 'sample_system' : test_unrecognized_kwargs, @@ -291,15 +316,21 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo): 'zpk': test_unrecognized_kwargs, 'flatsys.point_to_point': flatsys_test.TestFlatSys.test_point_to_point_errors, + 'flatsys.solve_flat_optimal': + flatsys_test.TestFlatSys.test_solve_flat_ocp_errors, 'flatsys.solve_flat_ocp': flatsys_test.TestFlatSys.test_solve_flat_ocp_errors, 'flatsys.FlatSystem.__init__': test_unrecognized_kwargs, 'optimal.create_mpc_iosystem': optimal_test.test_mpc_iosystem_rename, + 'optimal.solve_optimal_trajectory': optimal_test.test_ocp_argument_errors, 'optimal.solve_ocp': optimal_test.test_ocp_argument_errors, + 'optimal.solve_optimal_estimate': optimal_test.test_oep_argument_errors, 'optimal.solve_oep': optimal_test.test_oep_argument_errors, + 'ControlPlot.set_plot_title': freqplot_test.test_suptitle, 'FrequencyResponseData.__init__': frd_test.TestFRD.test_unrecognized_keyword, 'FrequencyResponseData.plot': test_response_plot_kwargs, + 'FrequencyResponseList.plot': freqplot_test.test_freqresplist_unknown_kw, 'DescribingFunctionResponse.plot': descfcn_test.test_describing_function_exceptions, 'InputOutputSystem.__init__': test_unrecognized_kwargs, @@ -308,15 +339,15 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo): 'flatsys.LinearFlatSystem.__init__': test_unrecognized_kwargs, 'NonlinearIOSystem.linearize': test_unrecognized_kwargs, 'NyquistResponseData.plot': test_response_plot_kwargs, + 'NyquistResponseList.plot': test_response_plot_kwargs, 'PoleZeroData.plot': test_response_plot_kwargs, + 'PoleZeroList.plot': test_response_plot_kwargs, 'InterconnectedSystem.__init__': interconnect_test.test_interconnect_exceptions, - 'StateSpace.__init__': - interconnect_test.test_interconnect_exceptions, - 'StateSpace.sample': test_unrecognized_kwargs, 'NonlinearIOSystem.__init__': interconnect_test.test_interconnect_exceptions, 'StateSpace.__init__': test_unrecognized_kwargs, + 'StateSpace.initial_response': timeresp_test.test_timeresp_aliases, 'StateSpace.sample': test_unrecognized_kwargs, 'TimeResponseData.__call__': trdata_test.test_response_copy, 'TimeResponseData.plot': timeplot_test.test_errors, @@ -331,10 +362,13 @@ def test_response_plot_kwargs(data_fcn, plot_fcn, mimo): optimal_test.test_ocp_argument_errors, 'optimal.OptimalEstimationProblem.__init__': optimal_test.test_oep_argument_errors, + 'optimal.OptimalEstimationProblem.compute_estimate': + stochsys_test.test_oep, 'optimal.OptimalEstimationProblem.create_mhe_iosystem': optimal_test.test_oep_argument_errors, 'phaseplot.streamlines': test_matplotlib_kwargs, 'phaseplot.vectorfield': test_matplotlib_kwargs, + 'phaseplot.streamplot': test_matplotlib_kwargs, 'phaseplot.equilpoints': test_matplotlib_kwargs, 'phaseplot.separatrices': test_matplotlib_kwargs, } diff --git a/control/tests/lti_test.py b/control/tests/lti_test.py index 734bdb40b..17dc7796e 100644 --- a/control/tests/lti_test.py +++ b/control/tests/lti_test.py @@ -1,15 +1,17 @@ """lti_test.py""" +import re + import numpy as np import pytest -from .conftest import editsdefaults import control as ct -from control import c2d, tf, ss, tf2ss, NonlinearIOSystem -from control.lti import LTI, evalfr, damp, dcgain, zeros, poles, bandwidth -from control import common_timebase, isctime, isdtime, issiso -from control.tests.conftest import slycotonly +from control import NonlinearIOSystem, c2d, common_timebase, isctime, \ + isdtime, issiso, ss, tf, tf2ss from control.exception import slycot_check +from control.lti import LTI, bandwidth, damp, dcgain, evalfr, poles, zeros +from control.tests.conftest import slycotonly + class TestLTI: @pytest.mark.parametrize("fun, args", [ @@ -22,10 +24,10 @@ def test_poles(self, fun, args): np.testing.assert_allclose(poles(sys), 42) with pytest.raises(AttributeError, match="no attribute 'pole'"): - pole_list = sys.pole() + sys.pole() with pytest.raises(AttributeError, match="no attribute 'pole'"): - pole_list = ct.pole(sys) + ct.pole(sys) @pytest.mark.parametrize("fun, args", [ [tf, (126, [-1, 42])], @@ -37,10 +39,10 @@ def test_zeros(self, fun, args): np.testing.assert_allclose(zeros(sys), 42) with pytest.raises(AttributeError, match="no attribute 'zero'"): - zero_list = sys.zero() + sys.zero() with pytest.raises(AttributeError, match="no attribute 'zero'"): - zero_list = ct.zero(sys) + ct.zero(sys) def test_issiso(self): assert issiso(1) @@ -71,7 +73,7 @@ def test_issiso_mimo(self): assert not issiso(sys, strict=True) def test_damp(self): - # Test the continuous time case. + # Test the continuous-time case. zeta = 0.1 wn = 42 p = -wn * zeta + 1j * wn * np.sqrt(1 - zeta**2) @@ -80,7 +82,7 @@ def test_damp(self): np.testing.assert_allclose(sys.damp(), expected) np.testing.assert_allclose(damp(sys), expected) - # Also test the discrete time case. + # Also test the discrete-time case. dt = 0.001 sys_dt = c2d(sys, dt, method='matched') p_zplane = np.exp(p*dt) @@ -291,7 +293,7 @@ def test_squeeze_exceptions(self, fcn): sys = fcn(ct.rss(2, 1, 1)) with pytest.raises(ValueError, match="unknown squeeze value"): - resp = sys.frequency_response([1], squeeze='siso') + sys.frequency_response([1], squeeze='siso') with pytest.raises(ValueError, match="unknown squeeze value"): sys([1j], squeeze='siso') with pytest.raises(ValueError, match="unknown squeeze value"): @@ -303,3 +305,104 @@ def test_squeeze_exceptions(self, fcn): sys([[0.1j, 1j], [1j, 10j]]) with pytest.raises(ValueError, match="must be 1D"): evalfr(sys, [[0.1j, 1j], [1j, 10j]]) + + +@pytest.mark.parametrize( + "outdx, inpdx, key", + [('y[0]', 'u[1]', (0, 1)), + (['y[0]'], ['u[1]'], (0, 1)), + (slice(0, 1, 1), slice(1, 2, 1), (0, 1)), + (['y[0]', 'y[1]'], ['u[1]', 'u[2]'], ([0, 1], [1, 2])), + ([0, 'y[1]'], ['u[1]', 2], ([0, 1], [1, 2])), + (slice(0, 2, 1), slice(1, 3, 1), ([0, 1], [1, 2])), + (['y[2]', 'y[1]'], ['u[2]', 'u[0]'], ([2, 1], [2, 0])), + ]) +@pytest.mark.parametrize("fcn", [ct.ss, ct.tf, ct.frd]) +def test_subsys_indexing(fcn, outdx, inpdx, key): + # Construct the base system and subsystem + sys = ct.rss(4, 3, 3) + subsys = sys[key] + + # Construct the system to be tested + match fcn: + case ct.frd: + omega = np.logspace(-1, 1) + sys = fcn(sys, omega) + subsys_chk = fcn(subsys, omega) + case _: + sys = fcn(sys) + subsys_chk = fcn(subsys) + + # Construct the subsystem + subsys_fcn = sys[outdx, inpdx] + + # Check to make sure everythng matches up + match fcn: + case ct.frd: + np.testing.assert_almost_equal( + subsys_fcn.complex, subsys_chk.complex) + case ct.ss: + np.testing.assert_almost_equal(subsys_fcn.A, subsys_chk.A) + np.testing.assert_almost_equal(subsys_fcn.B, subsys_chk.B) + np.testing.assert_almost_equal(subsys_fcn.C, subsys_chk.C) + np.testing.assert_almost_equal(subsys_fcn.D, subsys_chk.D) + case ct.tf: + omega = np.logspace(-1, 1) + np.testing.assert_almost_equal( + subsys_fcn.frequency_response(omega).complex, + subsys_chk.frequency_response(omega).complex) + + +@pytest.mark.parametrize("op", [ + '__mul__', '__rmul__', '__add__', '__radd__', '__sub__', '__rsub__']) +@pytest.mark.parametrize("fcn", [ct.ss, ct.tf, ct.frd]) +def test_scalar_algebra(op, fcn): + sys_ss = ct.rss(4, 2, 2) + match fcn: + case ct.ss: + sys = sys_ss + case ct.tf: + sys = ct.tf(sys_ss) + case ct.frd: + sys = ct.frd(sys_ss, [0.1, 1, 10]) + + scaled = getattr(sys, op)(2) + np.testing.assert_almost_equal(getattr(sys(1j), op)(2), scaled(1j)) + + +@pytest.mark.parametrize( + "fcn, args, kwargs, suppress, " + + "repr_expected, str_expected, latex_expected", [ + (ct.ss, (-1e-12, 1, 2, 3), {}, False, + r"StateSpace\([\s]*array\(\[\[-1.e-12\]\]\).*", + None, # standard Numpy formatting + r"10\^\{-12\}"), + (ct.ss, (-1e-12, 1, 3, 3), {}, True, + r"StateSpace\([\s]*array\(\[\[-0\.\]\]\).*", + None, # standard Numpy formatting + r"-0"), + (ct.tf, ([1, 1e-12, 1], [1, 2, 1]), {}, False, + r"\[1\.e\+00, 1\.e-12, 1.e\+00\]", + r"s\^2 \+ 1e-12 s \+ 1", + r"1 \\times 10\^\{-12\}"), + (ct.tf, ([1, 1e-12, 1], [1, 2, 1]), {}, True, + r"\[1\., 0., 1.\]", + r"s\^2 \+ 1", + r"\{s\^2 \+ 1\}"), +]) +@pytest.mark.usefixtures("editsdefaults") +def test_printoptions( + fcn, args, kwargs, suppress, + repr_expected, str_expected, latex_expected): + sys = fcn(*args, **kwargs) + + with np.printoptions(suppress=suppress): + # Test loadable representation + assert re.search(repr_expected, ct.iosys_repr(sys, 'eval')) is not None + + # Test string representation + if str_expected is not None: + assert re.search(str_expected, str(sys)) is not None + + # Test LaTeX/HTML representation + assert re.search(latex_expected, sys._repr_html_()) is not None diff --git a/control/tests/margin_test.py b/control/tests/margin_test.py index 07e21114f..43cd68ae3 100644 --- a/control/tests/margin_test.py +++ b/control/tests/margin_test.py @@ -12,12 +12,9 @@ from numpy import inf, nan from numpy.testing import assert_allclose -from control.frdata import FrequencyResponseData -from control.margins import (margin, phase_crossover_frequencies, - stability_margins) -from control.statesp import StateSpace -from control.xferfcn import TransferFunction -from control.exception import ControlMIMONotImplemented +from control import ControlMIMONotImplemented, FrequencyResponseData, \ + StateSpace, TransferFunction, margin, phase_crossover_frequencies, \ + stability_margins s = TransferFunction.s @@ -111,7 +108,6 @@ def test_margin_3input(tsys): out = margin((mag, phase*180/np.pi, omega_)) assert_allclose(out, np.array(refout)[[0, 1, 3, 4]], atol=1.5e-3) - @pytest.mark.parametrize( 'tfargs, omega_ref, gain_ref', [(([1], [1, 2, 3, 4]), [1.7325, 0.], [-0.5, 0.25]), @@ -119,7 +115,10 @@ def test_margin_3input(tsys): (([2], [1, 3, 3, 1]), [1.732, 0.], [-0.25, 2.]), ((np.array([3, 11, 3]) * 1e-4, [1., -2.7145, 2.4562, -0.7408], .1), [1.6235, 0.], [-0.28598, 1.88889]), + (([200.0], [1.0, 21.0, 20.0, 0.0]), + [4.47213595, 0], [-0.47619048, inf]), ]) +@pytest.mark.filterwarnings("error") def test_phase_crossover_frequencies(tfargs, omega_ref, gain_ref): """Test phase_crossover_frequencies() function""" sys = TransferFunction(*tfargs) diff --git a/control/tests/matlab2_test.py b/control/tests/matlab2_test.py index 5eedfc2ec..f8b0d2b40 100644 --- a/control/tests/matlab2_test.py +++ b/control/tests/matlab2_test.py @@ -16,7 +16,6 @@ from control.matlab import ss, step, impulse, initial, lsim, dcgain, ss2tf from control.timeresp import _check_convert_array -from control.tests.conftest import slycotonly class TestControlMatlab: @@ -49,7 +48,6 @@ def MIMO_mats(self): D = zeros((2, 2)) return A, B, C, D - @slycotonly def test_dcgain_mimo(self, MIMO_mats): """Test function dcgain with MIMO systems""" #Test MIMO systems @@ -88,7 +86,7 @@ def test_dcgain_2(self, SISO_mats): Z, P, k = scipy.signal.tf2zpk(num[0][-1], den) sys_ss = ss(A, B, C, D) - #Compute the gain with ``dcgain`` + #Compute the gain with `dcgain` gain_abcd = dcgain(A, B, C, D) gain_zpk = dcgain(Z, P, k) gain_numden = dcgain(np.squeeze(num), den) @@ -110,7 +108,7 @@ def test_dcgain_2(self, SISO_mats): decimal=6) def test_step(self, SISO_mats, MIMO_mats, mplcleanup): - """Test function ``step``.""" + """Test function `step`.""" figure(); plot_shape = (1, 3) #Test SISO system @@ -154,7 +152,8 @@ def test_impulse(self, SISO_mats, mplcleanup): t, y = impulse(sys, T) plot(t, y, label='t=0..2') - #Test system with direct feed-though, the function should print a warning. + # Test system with direct feedthough, the function should + # print a warning. D = [[0.5]] sys_ft = ss(A, B, C, D) with pytest.warns(UserWarning, match="has direct feedthrough"): @@ -231,7 +230,7 @@ def test_check_convert_shape(self): assert isinstance(arr, np.ndarray) assert not isinstance(arr, matrix) - #Convert array-like objects to arrays + #Convert array_like objects to arrays #Input is matrix, shape (1,3), must convert to array arr = _check_convert_array(matrix("1. 2 3"), [(3,), (1,3)], 'Test: ') assert isinstance(arr, np.ndarray) @@ -321,12 +320,12 @@ def test_lsim(self, SISO_mats, MIMO_mats): #T is None; - special handling: Value error self.assertRaises(ValueError, lsim(sys, U=0, T=None, x0=0)) #T="hello" : Wrong type - #TODO: better wording of error messages of ``lsim`` and - # ``_check_convert_array``, when wrong type is given. + #TODO: better wording of error messages of `lsim` and + # `_check_convert_array`, when wrong type is given. # Current error message is too cryptic. self.assertRaises(TypeError, lsim(sys, U=0, T="hello", x0=0)) #T=0; - T can not be zero dimensional, it determines the size of the - # input vector ``U`` + # input vector `U` self.assertRaises(ValueError, lsim(sys, U=0, T=0, x0=0)) #T is not monotonically increasing self.assertRaises(ValueError, lsim(sys, U=0, T=[0., 1., 2., 2., 3.], x0=0)) @@ -334,7 +333,7 @@ def test_lsim(self, SISO_mats, MIMO_mats): def assert_systems_behave_equal(self, sys1, sys2): ''' - Test if the behavior of two LTI systems is equal. Raises ``AssertionError`` + Test if the behavior of two LTI systems is equal. Raises `AssertionError` if the systems are not equal. Works only for SISO systems. @@ -344,7 +343,7 @@ def assert_systems_behave_equal(self, sys1, sys2): #gain of both systems must be the same assert_array_almost_equal(dcgain(sys1), dcgain(sys2)) - #Results of ``step`` simulation must be the same too + #Results of `step` simulation must be the same too y1, t1 = step(sys1) y2, t2 = step(sys2, t1) assert_array_almost_equal(y1, y2) diff --git a/control/tests/matlab_test.py b/control/tests/matlab_test.py index 2ba3d5df8..c6a45e2a2 100644 --- a/control/tests/matlab_test.py +++ b/control/tests/matlab_test.py @@ -83,6 +83,7 @@ class tsystems: @pytest.mark.usefixtures("fixedseed") +@pytest.mark.filterwarnings("ignore::FutureWarning") class TestMatlab: """Test matlab style functions""" @@ -110,7 +111,7 @@ def siso(self): @pytest.fixture def mimo(self): - """Create MIMO system, contains ``siso_ss1`` twice""" + """Create MIMO system, contains `siso_ss1` twice""" m = tsystems() A = np.array([[1., -2., 0., 0.], [3., -4., 0., 0.], @@ -129,33 +130,33 @@ def mimo(self): def testParallel(self, siso): """Call parallel()""" - sys1 = parallel(siso.ss1, siso.ss2) - sys1 = parallel(siso.ss1, siso.tf2) - sys1 = parallel(siso.tf1, siso.ss2) - sys1 = parallel(1, siso.ss2) - sys1 = parallel(1, siso.tf2) - sys1 = parallel(siso.ss1, 1) - sys1 = parallel(siso.tf1, 1) + _sys1 = parallel(siso.ss1, siso.ss2) + _sys1 = parallel(siso.ss1, siso.tf2) + _sys1 = parallel(siso.tf1, siso.ss2) + _sys1 = parallel(1, siso.ss2) + _sys1 = parallel(1, siso.tf2) + _sys1 = parallel(siso.ss1, 1) + _sys1 = parallel(siso.tf1, 1) def testSeries(self, siso): """Call series()""" - sys1 = series(siso.ss1, siso.ss2) - sys1 = series(siso.ss1, siso.tf2) - sys1 = series(siso.tf1, siso.ss2) - sys1 = series(1, siso.ss2) - sys1 = series(1, siso.tf2) - sys1 = series(siso.ss1, 1) - sys1 = series(siso.tf1, 1) + _sys1 = series(siso.ss1, siso.ss2) + _sys1 = series(siso.ss1, siso.tf2) + _sys1 = series(siso.tf1, siso.ss2) + _sys1 = series(1, siso.ss2) + _sys1 = series(1, siso.tf2) + _sys1 = series(siso.ss1, 1) + _sys1 = series(siso.tf1, 1) def testFeedback(self, siso): """Call feedback()""" - sys1 = feedback(siso.ss1, siso.ss2) - sys1 = feedback(siso.ss1, siso.tf2) - sys1 = feedback(siso.tf1, siso.ss2) - sys1 = feedback(1, siso.ss2) - sys1 = feedback(1, siso.tf2) - sys1 = feedback(siso.ss1, 1) - sys1 = feedback(siso.tf1, 1) + _sys1 = feedback(siso.ss1, siso.ss2) + _sys1 = feedback(siso.ss1, siso.tf2) + _sys1 = feedback(siso.tf1, siso.ss2) + _sys1 = feedback(1, siso.ss2) + _sys1 = feedback(1, siso.tf2) + _sys1 = feedback(siso.ss1, 1) + _sys1 = feedback(siso.tf1, 1) def testPoleZero(self, siso): """Call pole() and zero()""" @@ -173,6 +174,7 @@ def testPZmap(self, siso, subsys, mplcleanup): # pzmap(siso.ss1); not implemented # pzmap(siso.ss2); not implemented pzmap(getattr(siso, subsys)) + # TODO: check to make sure a plot got generated pzmap(getattr(siso, subsys), plot=False) def testStep(self, siso): @@ -312,7 +314,7 @@ def testLsim(self, siso): yout, _t, _xout = lsim(siso.tf3, u, t) np.testing.assert_array_almost_equal(yout, youttrue, decimal=4) - # test with initial value and special algorithm for ``U=0`` + # test with initial value and special algorithm for `U=0` u = 0 x0 = np.array([[.5], [1.]]) youttrue = np.array([11., 8.1494, 5.9361, 4.2258, 2.9118, 1.9092, @@ -376,7 +378,7 @@ def testDcgain(self, siso): num, den = sp.signal.ss2tf(A, B, C, D) sys_ss = siso.ss1 - # Compute the gain with ``dcgain`` + # Compute the gain with `dcgain` gain_abcd = dcgain(A, B, C, D) gain_zpk = dcgain(Z, P, k) gain_numden = dcgain(np.squeeze(num), den) @@ -404,6 +406,7 @@ def testDcgain_mimo(self, mimo): def testBode(self, siso, mplcleanup): """Call bode()""" + # TODO: make sure plots are generated bode(siso.ss1) bode(siso.tf1) bode(siso.tf2) @@ -579,10 +582,11 @@ def testOpers(self, siso): # siso.tf1 / siso.ss2 def testUnwrap(self): - """Call unwrap()""" + # control.matlab.unwrap phase = np.array(range(1, 100)) / 10. wrapped = phase % (2 * np.pi) unwrapped = unwrap(wrapped) + np.testing.assert_array_almost_equal(phase, unwrapped) def testSISOssdata(self, siso): """Call ssdata() @@ -689,7 +693,7 @@ def testFRD(self): omega = np.logspace(-1, 2, 10) frd1 = frd(h, omega) assert isinstance(frd1, FRD) - frd2 = frd(frd1.fresp[0, 0, :], omega) + frd2 = frd(frd1.frdata[0, 0, :], omega) assert isinstance(frd2, FRD) @slycotonly diff --git a/control/tests/modelsimp_test.py b/control/tests/modelsimp_test.py index 49c2afd58..e09446073 100644 --- a/control/tests/modelsimp_test.py +++ b/control/tests/modelsimp_test.py @@ -3,14 +3,18 @@ RMM, 30 Mar 2011 (based on TestModelSimp from v0.4a) """ +import warnings + import numpy as np import pytest - -from control import StateSpace, forced_response, tf, rss, c2d -from control.exception import ControlMIMONotImplemented +import control as ct +from control import StateSpace, TimeResponseData, c2d, forced_response, \ + impulse_response, rss, step_response, tf +from control.exception import ControlArgument, ControlDimension +from control.modelsimp import balred, eigensys_realization, hsvd, markov, \ + modred from control.tests.conftest import slycotonly -from control.modelsimp import balred, hsvd, markov, modred class TestModelsimp: @@ -33,36 +37,149 @@ def testHSVD(self): assert not isinstance(hsv, np.matrix) def testMarkovSignature(self): - U = np.array([[1., 1., 1., 1., 1.]]) + U = np.array([[1., 1., 1., 1., 1., 1., 1.]]) Y = U + response = TimeResponseData(time=np.arange(U.shape[-1]), + outputs=Y, + output_labels='y', + inputs=U, + input_labels='u', + ) + + # setup m = 3 - H = markov(Y, U, m, transpose=False) - Htrue = np.array([[1., 0., 0.]]) - np.testing.assert_array_almost_equal(H, Htrue) + Htrue = np.array([1., 0., 0.]) + Htrue_l = np.array([1., 0., 0., 0., 0., 0., 0.]) + + # test not enough input arguments + with pytest.raises(ControlArgument): + H = markov(Y) + with pytest.raises(ControlArgument): + H = markov() - # Make sure that transposed data also works - H = markov(np.transpose(Y), np.transpose(U), m, transpose=True) - np.testing.assert_array_almost_equal(H, np.transpose(Htrue)) + # too many positional arguments + with pytest.raises(ControlArgument): + H = markov(Y,U,m,1) + with pytest.raises(ControlArgument): + H = markov(response,m,1) - # Generate Markov parameters without any arguments + # too many positional arguments + with pytest.raises(ControlDimension): + U2 = np.hstack([U,U]) + H = markov(Y,U2,m) + + # not enough data + with pytest.warns(Warning): + H = markov(Y,U,8) + + # Basic Usage, m=l + H = markov(Y, U) + np.testing.assert_array_almost_equal(H, Htrue_l) + + H = markov(response) + np.testing.assert_array_almost_equal(H, Htrue_l) + + # Basic Usage, m H = markov(Y, U, m) np.testing.assert_array_almost_equal(H, Htrue) + H = markov(response, m) + np.testing.assert_array_almost_equal(H, Htrue) + + H = markov(Y, U, m=m) + np.testing.assert_array_almost_equal(H, Htrue) + + H = markov(response, m=m) + np.testing.assert_array_almost_equal(H, Htrue) + + response.transpose=False + H = markov(response, m=m) + np.testing.assert_array_almost_equal(H, Htrue) + + # Make sure that transposed data also works, siso + HT = markov(Y.T, U.T, m, transpose=True) + np.testing.assert_array_almost_equal(HT, np.transpose(Htrue)) + + response.transpose = True + HT = markov(response, m) + np.testing.assert_array_almost_equal(HT, np.transpose(Htrue)) + response.transpose=False + # Test example from docstring + # TODO: There is a problem here, last markov parameter does not fit + # the approximation error could be to big + Htrue = np.array([0, 1., -0.5]) T = np.linspace(0, 10, 100) U = np.ones((1, 100)) T, Y = forced_response(tf([1], [1, 0.5], True), T, U) - H = markov(Y, U, 3, transpose=False) + H = markov(Y, U, 4, dt=True) + np.testing.assert_array_almost_equal(H[:3], Htrue[:3]) + + response = forced_response(tf([1], [1, 0.5], True), T, U) + H = markov(response, 4, dt=True) + np.testing.assert_array_almost_equal(H[:3], Htrue[:3]) # Test example from issue #395 inp = np.array([1, 2]) outp = np.array([2, 4]) mrk = markov(outp, inp, 1, transpose=False) + np.testing.assert_almost_equal(mrk, 2.) + + # Test mimo example + # Mechanical Vibrations: Theory and Application, SI Edition, 1st ed. + # Figure 6.5 / Example 6.7 + m1, k1, c1 = 1., 4., 1. + m2, k2, c2 = 2., 2., 1. + k3, c3 = 6., 2. + + A = np.array([ + [0., 0., 1., 0.], + [0., 0., 0., 1.], + [-(k1+k2)/m1, (k2)/m1, -(c1+c2)/m1, c2/m1], + [(k2)/m2, -(k2+k3)/m2, c2/m2, -(c2+c3)/m2] + ]) + B = np.array([[0.,0.],[0.,0.],[1/m1,0.],[0.,1/m2]]) + C = np.array([[1.0, 0.0, 0.0, 0.0],[0.0, 1.0, 0.0, 0.0]]) + D = np.zeros((2,2)) + + sys = StateSpace(A, B, C, D) + dt = 0.25 + sysd = sys.sample(dt, method='zoh') + + T = np.arange(0,100,dt) + U = np.random.randn(sysd.B.shape[-1], len(T)) + response = forced_response(sysd, U=U) + Y = response.outputs + + m = 100 + _, Htrue = impulse_response(sysd, T=dt*(m-1)) + + + # test array_like + H = markov(Y, U, m, dt=dt) + np.testing.assert_array_almost_equal(H, Htrue) + + # test array_like, truncate + H = markov(Y, U, m, dt=dt, truncate=True) + np.testing.assert_array_almost_equal(H, Htrue) + + # test array_like, transpose + HT = markov(Y.T, U.T, m, dt=dt, transpose=True) + np.testing.assert_array_almost_equal(HT, np.transpose(Htrue)) + + # test response data + H = markov(response, m, dt=dt) + np.testing.assert_array_almost_equal(H, Htrue) + + # test response data + H = markov(response, m, dt=dt, truncate=True) + np.testing.assert_array_almost_equal(H, Htrue) + + # test response data, transpose + response.transpose = True + HT = markov(response, m, dt=dt) + np.testing.assert_array_almost_equal(HT, np.transpose(Htrue)) - # Make sure MIMO generates an error - U = np.ones((2, 100)) # 2 inputs (Y unchanged, with 1 output) - with pytest.raises(ControlMIMONotImplemented): - markov(Y, U, m) # Make sure markov() returns the right answer @pytest.mark.parametrize("k, m, n", @@ -84,14 +201,14 @@ def testMarkovResults(self, k, m, n): # 0 for k > m-2 (see modelsimp.py). # - # Generate stable continuous time system + # Generate stable continuous-time system Hc = rss(k, 1, 1) # Choose sampling time based on fastest time constant / 10 w, _ = np.linalg.eig(Hc.A) Ts = np.min(-np.real(w)) / 10. - # Convert to a discrete time system via sampling + # Convert to a discrete-time system via sampling Hd = c2d(Hc, Ts, 'zoh') # Compute the Markov parameters from state space @@ -99,17 +216,112 @@ def testMarkovResults(self, k, m, n): Hd.C @ np.linalg.matrix_power(Hd.A, i) @ Hd.B for i in range(m-1)]) + Mtrue = np.squeeze(Mtrue) + # Generate input/output data T = np.array(range(n)) * Ts U = np.cos(T) + np.sin(T/np.pi) - _, Y = forced_response(Hd, T, U, squeeze=True) - Mcomp = markov(Y, U, m) + + ir_true = impulse_response(Hd,T) + Mtrue_scaled = ir_true[1][:m] # Compare to results from markov() # experimentally determined probability to get non matching results # with rtot=1e-6 and atol=1e-8 due to numerical errors # for k=5, m=n=10: 0.015 % + T, Y = forced_response(Hd, T, U, squeeze=True) + Mcomp = markov(Y, U, m, dt=True) + Mcomp_scaled = markov(Y, U, m, dt=Ts) + + np.testing.assert_allclose(Mtrue, Mcomp, rtol=1e-6, atol=1e-8) + np.testing.assert_allclose(Mtrue_scaled, Mcomp_scaled, rtol=1e-6, atol=1e-8) + + response = forced_response(Hd, T, U, squeeze=True) + Mcomp = markov(response, m, dt=True) + Mcomp_scaled = markov(response, m, dt=Ts) + np.testing.assert_allclose(Mtrue, Mcomp, rtol=1e-6, atol=1e-8) + np.testing.assert_allclose( + Mtrue_scaled, Mcomp_scaled, rtol=1e-6, atol=1e-8) + + def testERASignature(self): + + # test siso + # Katayama, Subspace Methods for System Identification + # Example 6.1, Fibonacci sequence + H_true = np.array([0.,1.,1.,2.,3.,5.,8.,13.,21.,34.]) + + # A realization of fibonacci impulse response + A = np.array([[0., 1.],[1., 1.,]]) + B = np.array([[1.],[1.,]]) + C = np.array([[1., 0.,]]) + D = np.array([[0.,]]) + + T = np.arange(0,10,1) + sysd_true = StateSpace(A,B,C,D,True) + ir_true = impulse_response(sysd_true,T=T) + + # test TimeResponseData + sysd_est, _ = eigensys_realization(ir_true,r=2) + ir_est = impulse_response(sysd_est, T=T) + _, H_est = ir_est + + np.testing.assert_allclose(H_true, H_est, rtol=1e-6, atol=1e-8) + + # test ndarray + _, YY_true = ir_true + sysd_est, _ = eigensys_realization(YY_true,r=2) + ir_est = impulse_response(sysd_est, T=T) + _, H_est = ir_est + + np.testing.assert_allclose(H_true, H_est, rtol=1e-6, atol=1e-8) + + # test mimo + # Mechanical Vibrations: Theory and Application, SI Edition, 1st ed. + # Figure 6.5 / Example 6.7 + # m q_dd + c q_d + k q = f + m1, k1, c1 = 1., 4., 1. + m2, k2, c2 = 2., 2., 1. + k3, c3 = 6., 2. + + A = np.array([ + [0., 0., 1., 0.], + [0., 0., 0., 1.], + [-(k1+k2)/m1, (k2)/m1, -(c1+c2)/m1, c2/m1], + [(k2)/m2, -(k2+k3)/m2, c2/m2, -(c2+c3)/m2] + ]) + B = np.array([[0.,0.],[0.,0.],[1/m1,0.],[0.,1/m2]]) + C = np.array([[1.0, 0.0, 0.0, 0.0],[0.0, 1.0, 0.0, 0.0]]) + D = np.zeros((2,2)) + + sys = StateSpace(A, B, C, D) + + dt = 0.1 + T = np.arange(0,10,dt) + sysd_true = sys.sample(dt, method='zoh') + ir_true = impulse_response(sysd_true, T=T) + + # test TimeResponseData + sysd_est, _ = eigensys_realization(ir_true,r=4,dt=dt) + + step_true = step_response(sysd_true) + step_est = step_response(sysd_est) + + np.testing.assert_allclose(step_true.outputs, + step_est.outputs, + rtol=1e-6, atol=1e-8) + + # test ndarray + _, YY_true = ir_true + sysd_est, _ = eigensys_realization(YY_true,r=4,dt=dt) + + step_true = step_response(sysd_true, T=T) + step_est = step_response(sysd_est, T=T) + + np.testing.assert_allclose(step_true.outputs, + step_est.outputs, + rtol=1e-6, atol=1e-8) + def testModredMatchDC(self): #balanced realization computed in matlab for the transfer function: @@ -134,7 +346,7 @@ def testModredMatchDC(self): np.testing.assert_array_almost_equal(rsys.D, Drtrue, decimal=2) def testModredUnstable(self): - """Check if an error is thrown when an unstable system is given""" + """Check if warning is issued when an unstable system is given""" A = np.array( [[4.5418, 3.3999, 5.0342, 4.3808], [0.3890, 0.3599, 0.4195, 0.1760], @@ -144,7 +356,16 @@ def testModredUnstable(self): C = np.array([[1.0, 2.0, 3.0, 4.0], [1.0, 2.0, 3.0, 4.0]]) D = np.array([[0.0, 0.0], [0.0, 0.0]]) sys = StateSpace(A, B, C, D) - np.testing.assert_raises(ValueError, modred, sys, [2, 3]) + + # Make sure we get a warning message + with pytest.warns(UserWarning, match="System is unstable"): + newsys1 = modred(sys, [2, 3]) + + # Make sure we can turn the warning off + with warnings.catch_warnings(): + warnings.simplefilter('error') + newsys2 = ct.model_reduction(sys, [2, 3], warn_unstable=False) + np.testing.assert_equal(newsys1.A, newsys2.A) def testModredTruncate(self): #balanced realization computed in matlab for the transfer function: @@ -182,7 +403,7 @@ def testBalredTruncate(self): B = np.array([[2.], [0.], [0.], [0.]]) C = np.array([[0.5, 0.6875, 0.7031, 0.5]]) D = np.array([[0.]]) - + sys = StateSpace(A, B, C, D) orders = 2 rsys = balred(sys, orders, method='truncate') @@ -203,7 +424,7 @@ def testBalredTruncate(self): # Apply a similarity transformation Ar, Br, Cr = T @ Ar @ T, T @ Br, Cr @ T break - + # Make sure we got the correct answer np.testing.assert_array_almost_equal(Ar, Artrue, decimal=2) np.testing.assert_array_almost_equal(Br, Brtrue, decimal=4) @@ -223,12 +444,12 @@ def testBalredMatchDC(self): B = np.array([[2.], [0.], [0.], [0.]]) C = np.array([[0.5, 0.6875, 0.7031, 0.5]]) D = np.array([[0.]]) - + sys = StateSpace(A, B, C, D) orders = 2 rsys = balred(sys,orders,method='matchdc') Ar, Br, Cr, Dr = rsys.A, rsys.B, rsys.C, rsys.D - + # Result from MATLAB Artrue = np.array( [[-4.43094773, -4.55232904], @@ -236,7 +457,7 @@ def testBalredMatchDC(self): Brtrue = np.array([[1.36235673], [1.03114388]]) Crtrue = np.array([[1.36235673, 1.03114388]]) Drtrue = np.array([[-0.08383902]]) - + # Look for possible changes in state in slycot T1 = np.array([[1, 0], [0, -1]]) T2 = np.array([[-1, 0], [0, 1]]) @@ -246,9 +467,46 @@ def testBalredMatchDC(self): # Apply a similarity transformation Ar, Br, Cr = T @ Ar @ T, T @ Br, Cr @ T break - + # Make sure we got the correct answer np.testing.assert_array_almost_equal(Ar, Artrue, decimal=2) np.testing.assert_array_almost_equal(Br, Brtrue, decimal=4) np.testing.assert_array_almost_equal(Cr, Crtrue, decimal=4) np.testing.assert_array_almost_equal(Dr, Drtrue, decimal=4) + + +@pytest.mark.parametrize("kwargs, nstates, noutputs, ninputs", [ + ({'elim_states': [1, 3]}, 3, 3, 3), + ({'elim_inputs': [1, 2], 'keep_states': [1, 3]}, 2, 3, 1), + ({'elim_outputs': [1, 2], 'keep_inputs': [0, 1],}, 5, 1, 2), + ({'keep_states': [2, 0], 'keep_outputs': [0, 1]}, 2, 2, 3), + ({'keep_states': slice(0, 4, 2), 'keep_outputs': slice(None, 2)}, 2, 2, 3), + ({'keep_states': ['x[0]', 'x[3]'], 'keep_inputs': 'u[0]'}, 2, 3, 1), + ({'elim_inputs': [0, 1, 2]}, 5, 3, 0), # no inputs + ({'elim_outputs': [0, 1, 2]}, 5, 0, 3), # no outputs + ({'elim_states': [0, 1, 2, 3, 4]}, 0, 3, 3), # no states + ({'elim_states': [0, 1], 'keep_states': [1, 2]}, None, None, None), +]) +@pytest.mark.parametrize("method", ['truncate', 'matchdc']) +def test_model_reduction(method, kwargs, nstates, noutputs, ninputs): + sys = ct.rss(5, 3, 3) + + if nstates is None: + # Arguments should generate an error + with pytest.raises(ValueError, match="can't provide both"): + red = ct.model_reduction(sys, **kwargs, method=method) + return + else: + red = ct.model_reduction(sys, **kwargs, method=method) + + assert red.nstates == nstates + assert red.ninputs == ninputs + assert red.noutputs == noutputs + + if method == 'matchdc': + # Define a new system with truncated inputs and outputs + # (assumes we always keep the initial inputs and outputs) + chk = ct.ss( + sys.A, sys.B[:, :ninputs], sys.C[:noutputs, :], + sys.D[:noutputs, :][:, :ninputs]) + np.testing.assert_allclose(red(0), chk(0)) diff --git a/control/tests/namedio_test.py b/control/tests/namedio_test.py index f702e704b..ad74d27ba 100644 --- a/control/tests/namedio_test.py +++ b/control/tests/namedio_test.py @@ -8,7 +8,6 @@ created for that purpose. """ -import re from copy import copy import warnings @@ -34,8 +33,8 @@ def test_named_ss(): assert sys.input_labels == ['u[0]', 'u[1]'] assert sys.output_labels == ['y[0]', 'y[1]'] assert sys.state_labels == ['x[0]', 'x[1]'] - assert ct.InputOutputSystem.__repr__(sys) == \ - "['y[0]', 'y[1]']>" + assert ct.iosys_repr(sys, format='info') == \ + " ['y[0]', 'y[1]']>" # Pass the names as arguments sys = ct.ss( @@ -46,8 +45,8 @@ def test_named_ss(): assert sys.input_labels == ['u1', 'u2'] assert sys.output_labels == ['y1', 'y2'] assert sys.state_labels == ['x1', 'x2'] - assert ct.InputOutputSystem.__repr__(sys) == \ - "['y1', 'y2']>" + assert ct.iosys_repr(sys, format='info') == \ + " ['y1', 'y2']>" # Do the same with rss sys = ct.rss(['x1', 'x2', 'x3'], ['y1', 'y2'], 'u1', name='random') @@ -56,8 +55,8 @@ def test_named_ss(): assert sys.input_labels == ['u1'] assert sys.output_labels == ['y1', 'y2'] assert sys.state_labels == ['x1', 'x2', 'x3'] - assert ct.InputOutputSystem.__repr__(sys) == \ - "['y1', 'y2']>" + assert ct.iosys_repr(sys, format='info') == \ + " ['y1', 'y2']>" # List of classes that are expected @@ -285,7 +284,7 @@ def test_duplicate_sysname(): # strip out matrix warnings warnings.filterwarnings("ignore", "the matrix subclass", category=PendingDeprecationWarning) - res = sys * sys + sys * sys # Generate a warning if the system is named sys = ct.rss(4, 1, 1) @@ -293,7 +292,7 @@ def test_duplicate_sysname(): sys.updfcn, sys.outfcn, inputs=sys.ninputs, outputs=sys.noutputs, states=sys.nstates, name='sys') with pytest.warns(UserWarning, match="duplicate object found"): - res = sys * sys + sys * sys # Finding signals @@ -332,10 +331,10 @@ def test_find_signals(): # Invalid signal names def test_invalid_signal_names(): with pytest.raises(ValueError, match="invalid signal name"): - sys = ct.rss(4, inputs="input.signal", outputs=1) + ct.rss(4, inputs="input.signal", outputs=1) with pytest.raises(ValueError, match="invalid system name"): - sys = ct.rss(4, inputs=1, outputs=1, name="system.subsys") + ct.rss(4, inputs=1, outputs=1, name="system.subsys") # Negative system spect @@ -363,3 +362,20 @@ def test_negative_system_spec(): np.testing.assert_allclose(negfbk_negsig.B, negfbk_negsys.B) np.testing.assert_allclose(negfbk_negsig.C, negfbk_negsys.C) np.testing.assert_allclose(negfbk_negsig.D, negfbk_negsys.D) + + +# Named signal representations +def test_named_signal_repr(): + sys = ct.rss( + states=2, inputs=['u1', 'u2'], outputs=['y1', 'y2'], + state_prefix='xi') + resp = sys.step_response(np.linspace(0, 1, 3)) + + for signal in ['inputs', 'outputs', 'states']: + sig_orig = getattr(resp, signal) + sig_eval = eval(repr(sig_orig), + None, + {'array': np.array, + 'NamedSignal': ct.NamedSignal}) + assert sig_eval.signal_labels == sig_orig.signal_labels + assert sig_eval.trace_labels == sig_orig.trace_labels diff --git a/control/tests/nlsys_test.py b/control/tests/nlsys_test.py index 7f649e0cc..b14a619e0 100644 --- a/control/tests/nlsys_test.py +++ b/control/tests/nlsys_test.py @@ -7,15 +7,19 @@ """ -import pytest -import numpy as np import math +import re + +import numpy as np +import pytest + import control as ct + # Basic test of nlsys() def test_nlsys_basic(): def kincar_update(t, x, u, params): - l = params.get('l', 1) # wheelbase + l = params['l'] # wheelbase return np.array([ np.cos(x[2]) * u[0], # x velocity np.sin(x[2]) * u[0], # y velocity @@ -29,10 +33,11 @@ def kincar_output(t, x, u, params): kincar_update, kincar_output, states=['x', 'y', 'theta'], inputs=2, input_prefix='U', - outputs=2) + outputs=2, params={'l': 1}) assert kincar.input_labels == ['U[0]', 'U[1]'] assert kincar.output_labels == ['y[0]', 'y[1]'] assert kincar.state_labels == ['x', 'y', 'theta'] + assert kincar.params == {'l': 1} # Test nonlinear initial, step, and forced response @@ -93,7 +98,7 @@ def test_nlsys_impulse(): # Impulse_response (not implemented) with pytest.raises(ValueError, match="system must be LTI"): - resp_nl = ct.impulse_response(sys_nl, timepts) + ct.impulse_response(sys_nl, timepts) # Test nonlinear systems that are missing inputs or outputs @@ -154,3 +159,109 @@ def test_nlsys_empty_io(): resp = ct.forced_response(P, np.linspace(0, 1), 1) np.testing.assert_allclose(resp.states[:, -1], 1 - math.exp(-1)) + + +def test_ss2io(): + sys = ct.rss( + states=4, inputs=['u1', 'u2'], outputs=['y1', 'y2'], name='sys') + + # Standard conversion + nlsys = ct.nlsys(sys) + for attr in ['nstates', 'ninputs', 'noutputs']: + assert getattr(nlsys, attr) == getattr(sys, attr) + assert nlsys.name == 'sys$converted' + np.testing.assert_allclose( + nlsys.dynamics(0, [1, 2, 3, 4], [0, 0], {}), + sys.A @ np.array([1, 2, 3, 4])) + + # Put names back to defaults + nlsys = ct.nlsys( + sys, inputs=sys.ninputs, outputs=sys.noutputs, states=sys.nstates) + for attr, prefix in zip( + ['state_labels', 'input_labels', 'output_labels'], + ['x', 'u', 'y']): + for i in range(len(getattr(nlsys, attr))): + assert getattr(nlsys, attr)[i] == f"{prefix}[{i}]" + assert re.match(r"sys\$converted", nlsys.name) + + # Override the names with something new + nlsys = ct.nlsys( + sys, inputs=['U1', 'U2'], outputs=['Y1', 'Y2'], + states=['X1', 'X2', 'X3', 'X4'], name='nlsys') + for attr, prefix in zip( + ['state_labels', 'input_labels', 'output_labels'], + ['X', 'U', 'Y']): + for i in range(len(getattr(nlsys, attr))): + assert getattr(nlsys, attr)[i] == f"{prefix}{i+1}" + assert nlsys.name == 'nlsys' + + # Make sure dimension checking works + for attr in ['states', 'inputs', 'outputs']: + with pytest.raises(ValueError, match=r"new .* doesn't match"): + kwargs = {attr: getattr(sys, 'n' + attr) - 1} + nlsys = ct.nlsys(sys, **kwargs) + + +def test_ICsystem_str(): + sys1 = ct.rss(2, 2, 3, name='sys1', strictly_proper=True) + sys2 = ct.rss(2, 3, 2, name='sys2', strictly_proper=True) + + with pytest.warns(UserWarning, match="Unused") as record: + sys = ct.interconnect( + [sys1, sys2], inputs=['r1', 'r2'], outputs=['y1', 'y2'], + connections=[ + ['sys1.u[0]', '-sys2.y[0]', 'sys2.y[1]'], + ['sys1.u[1]', 'sys2.y[0]', '-sys2.y[1]'], + ['sys2.u[0]', 'sys2.y[0]', (0, 0, -1)], + ['sys2.u[1]', (1, 1, -2), (0, 1, -2)], + ], + inplist=['sys1.u[0]', 'sys1.u[1]'], + outlist=['sys2.y[0]', 'sys2.y[1]']) + assert len(record) == 2 + assert str(record[0].message).startswith("Unused input") + assert str(record[1].message).startswith("Unused output") + + ref = \ + r": sys\[[\d]+\]" + "\n" + \ + r"Inputs \(2\): \['r1', 'r2'\]" + "\n" + \ + r"Outputs \(2\): \['y1', 'y2'\]" + "\n" + \ + r"States \(4\): \['sys1_x\[0\].*'sys2_x\[1\]'\]" + "\n" + \ + "\n" + \ + r"Subsystems \(2\):" + "\n" + \ + r" \* \['y\[0\]', 'y\[1\]']>" + "\n" + \ + r" \* \[.*\]>" + "\n" + \ + "\n" + \ + r"Connections:" + "\n" + \ + r" \* sys1.u\[0\] <- -sys2.y\[0\] \+ sys2.y\[1\] \+ r1" + "\n" + \ + r" \* sys1.u\[1\] <- sys2.y\[0\] - sys2.y\[1\] \+ r2" + "\n" + \ + r" \* sys1.u\[2\] <-" + "\n" + \ + r" \* sys2.u\[0\] <- -sys1.y\[0\] \+ sys2.y\[0\]" + "\n" + \ + r" \* sys2.u\[1\] <- -2.0 \* sys1.y\[1\] - 2.0 \* sys2.y\[1\]" + \ + "\n\n" + \ + r"Outputs:" + "\n" + \ + r" \* y1 <- sys2.y\[0\]" + "\n" + \ + r" \* y2 <- sys2.y\[1\]" + \ + "\n\n" + \ + r"A = \[\[.*\]\]" + "\n\n" + \ + r"B = \[\[.*\]\]" + "\n\n" + \ + r"C = \[\[.*\]\]" + "\n\n" + \ + r"D = \[\[.*\]\]" + + assert re.match(ref, str(sys), re.DOTALL) + + +# Make sure nlsys str() works as expected +@pytest.mark.parametrize("params, expected", [ + ({}, r"States \(1\): \['x\[0\]'\]" + "\n\n"), + ({'a': 1}, r"States \(1\): \['x\[0\]'\]" + "\n" + + r"Parameters: \['a'\]" + "\n\n"), + ({'a': 1, 'b': 1}, r"States \(1\): \['x\[0\]'\]" + "\n" + + r"Parameters: \['a', 'b'\]" + "\n\n"), +]) +def test_nlsys_params_str(params, expected): + sys = ct.nlsys( + lambda t, x, u, params: -x, inputs=1, outputs=1, states=1, + params=params) + out = str(sys) + + assert re.search(expected, out) is not None diff --git a/control/tests/nyquist_test.py b/control/tests/nyquist_test.py index af9505354..42bb210c4 100644 --- a/control/tests/nyquist_test.py +++ b/control/tests/nyquist_test.py @@ -132,18 +132,17 @@ def test_nyquist_basic(): # Nyquist plot with poles on imaginary axis, omega specified # (can miss encirclements due to the imaginary poles at +/- 1j) sys = ct.tf([1], [1, 3, 2]) * ct.tf([1], [1, 0, 1]) - with pytest.warns(UserWarning, match="does not match") as records: + with warnings.catch_warnings(record=True) as records: count = ct.nyquist_response(sys, np.linspace(1e-3, 1e1, 1000)) - if len(records) == 0: - assert _Z(sys) == count + _P(sys) - - # Nyquist plot with poles on imaginary axis, omega specified, with contour - sys = ct.tf([1], [1, 3, 2]) * ct.tf([1], [1, 0, 1]) - with pytest.warns(UserWarning, match="does not match") as records: - count, contour = ct.nyquist_response( - sys, np.linspace(1e-3, 1e1, 1000), return_contour=True) - if len(records) == 0: - assert _Z(sys) == count + _P(sys) + if len(records) == 0: + # No warnings (it happens) => make sure count is correct + assert _Z(sys) == count + _P(sys) + elif len(records) == 1: + # Expected case: make sure warning is the right one + assert issubclass(records[0].category, UserWarning) + assert "encirclements does not match" in str(records[0].message) + else: + pytest.fail("multiple warnings in nyquist_response (?)") # Nyquist plot with poles on imaginary axis, return contour sys = ct.tf([1], [1, 3, 2]) * ct.tf([1], [1, 0, 1]) @@ -162,38 +161,40 @@ def test_nyquist_fbs_examples(): """Run through various examples from FBS2e to compare plots""" plt.figure() - ct.suptitle("Figure 10.4: L(s) = 1.4 e^{-s}/(s+1)^2") sys = ct.tf([1.4], [1, 2, 1]) * ct.tf(*ct.pade(1, 4)) response = ct.nyquist_response(sys) - response.plot() + cplt = response.plot() + cplt.set_plot_title("Figure 10.4: L(s) = 1.4 e^{-s}/(s+1)^2") assert _Z(sys) == response.count + _P(sys) plt.figure() - ct.suptitle("Figure 10.4: L(s) = 1/(s + a)^2 with a = 0.6") sys = 1/(s + 0.6)**3 response = ct.nyquist_response(sys) - response.plot() + cplt = response.plot() + cplt.set_plot_title("Figure 10.4: L(s) = 1/(s + a)^2 with a = 0.6") assert _Z(sys) == response.count + _P(sys) plt.figure() - ct.suptitle("Figure 10.6: L(s) = 1/(s (s+1)^2) - pole at the origin") sys = 1/(s * (s+1)**2) response = ct.nyquist_response(sys) - response.plot() + cplt = response.plot() + cplt.set_plot_title( + "Figure 10.6: L(s) = 1/(s (s+1)^2) - pole at the origin") assert _Z(sys) == response.count + _P(sys) plt.figure() - ct.suptitle("Figure 10.10: L(s) = 3 (s+6)^2 / (s (s+1)^2)") sys = 3 * (s+6)**2 / (s * (s+1)**2) response = ct.nyquist_response(sys) - response.plot() + cplt = response.plot() + cplt.set_plot_title("Figure 10.10: L(s) = 3 (s+6)^2 / (s (s+1)^2)") assert _Z(sys) == response.count + _P(sys) plt.figure() - ct.suptitle("Figure 10.10: L(s) = 3 (s+6)^2 / (s (s+1)^2) [zoom]") with pytest.warns(UserWarning, match="encirclements does not match"): response = ct.nyquist_response(sys, omega_limits=[1.5, 1e3]) - response.plot() + cplt = response.plot() + cplt.set_plot_title( + "Figure 10.10: L(s) = 3 (s+6)^2 / (s (s+1)^2) [zoom]") # Frequency limits for zoom give incorrect encirclement count # assert _Z(sys) == response.count + _P(sys) assert response.count == -1 @@ -208,9 +209,9 @@ def test_nyquist_fbs_examples(): def test_nyquist_arrows(arrows): sys = ct.tf([1.4], [1, 2, 1]) * ct.tf(*ct.pade(1, 4)) plt.figure(); - ct.suptitle("L(s) = 1.4 e^{-s}/(s+1)^2 / arrows = %s" % arrows) response = ct.nyquist_response(sys) - response.plot(arrows=arrows) + cplt = response.plot(arrows=arrows) + cplt.set_plot_title("L(s) = 1.4 e^{-s}/(s+1)^2 / arrows = %s" % arrows) assert _Z(sys) == response.count + _P(sys) @@ -237,14 +238,14 @@ def test_nyquist_encirclements(): plt.figure(); response = ct.nyquist_response(sys) - response.plot() - ct.suptitle("Stable system; encirclements = %d" % response.count) + cplt = response.plot() + cplt.set_plot_title("Stable system; encirclements = %d" % response.count) assert _Z(sys) == response.count + _P(sys) plt.figure(); response = ct.nyquist_response(sys * 3) - response.plot() - ct.suptitle("Unstable system; encirclements = %d" %response.count) + cplt = response.plot() + cplt.set_plot_title("Unstable system; encirclements = %d" %response.count) assert _Z(sys * 3) == response.count + _P(sys * 3) # System with pole at the origin @@ -252,8 +253,9 @@ def test_nyquist_encirclements(): plt.figure(); response = ct.nyquist_response(sys) - response.plot() - ct.suptitle("Pole at the origin; encirclements = %d" %response.count) + cplt = response.plot() + cplt.set_plot_title( + "Pole at the origin; encirclements = %d" %response.count) assert _Z(sys) == response.count + _P(sys) # Non-integer number of encirclements @@ -266,8 +268,9 @@ def test_nyquist_encirclements(): # strip out matrix warnings response = ct.nyquist_response( sys, omega_limits=[0.5, 1e3], encirclement_threshold=0.2) - response.plot() - ct.suptitle("Non-integer number of encirclements [%g]" %response.count) + cplt = response.plot() + cplt.set_plot_title( + "Non-integer number of encirclements [%g]" %response.count) @pytest.fixture @@ -281,8 +284,8 @@ def indentsys(): def test_nyquist_indent_default(indentsys): plt.figure(); response = ct.nyquist_response(indentsys) - response.plot() - ct.suptitle("Pole at origin; indent_radius=default") + cplt = response.plot() + cplt.set_plot_title("Pole at origin; indent_radius=default") assert _Z(indentsys) == response.count + _P(indentsys) @@ -292,7 +295,7 @@ def test_nyquist_indent_dont(indentsys): with pytest.warns() as record: count, contour = ct.nyquist_response( indentsys, omega=[0, 0.2, 0.3, 0.4], indent_radius=.1007, - plot=False, return_contour=True) + return_contour=True) np.testing.assert_allclose(contour[0], .1007+0.j) # second value of omega_vector is larger than indent_radius: not indented assert np.all(contour.real[2:] == 0.) @@ -308,8 +311,9 @@ def test_nyquist_indent_do(indentsys): response = ct.nyquist_response( indentsys, indent_radius=0.01, return_contour=True) count, contour = response - response.plot() - ct.suptitle("Pole at origin; indent_radius=0.01; encirclements = %d" % count) + cplt = response.plot() + cplt.set_plot_title( + "Pole at origin; indent_radius=0.01; encirclements = %d" % count) assert _Z(indentsys) == count + _P(indentsys) # indent radius is smaller than the start of the default omega vector # check that a quarter circle around the pole at origin has been added. @@ -318,7 +322,7 @@ def test_nyquist_indent_do(indentsys): # Make sure that the command also works if called directly as _plot() plt.figure() - with pytest.warns(DeprecationWarning, match=".* use nyquist_response()"): + with pytest.warns(FutureWarning, match=".* use nyquist_response()"): count, contour = ct.nyquist_plot( indentsys, indent_radius=0.01, return_contour=True) assert _Z(indentsys) == count + _P(indentsys) @@ -329,8 +333,8 @@ def test_nyquist_indent_do(indentsys): def test_nyquist_indent_left(indentsys): plt.figure(); response = ct.nyquist_response(indentsys, indent_direction='left') - response.plot() - ct.suptitle( + cplt = response.plot() + cplt.set_plot_title( "Pole at origin; indent_direction='left'; encirclements = %d" % response.count) assert _Z(indentsys) == response.count + _P(indentsys, indent='left') @@ -343,15 +347,15 @@ def test_nyquist_indent_im(): # Imaginary poles with standard indentation plt.figure(); response = ct.nyquist_response(sys) - response.plot() - ct.suptitle("Imaginary poles; encirclements = %d" % response.count) + cplt = response.plot() + cplt.set_plot_title("Imaginary poles; encirclements = %d" % response.count) assert _Z(sys) == response.count + _P(sys) # Imaginary poles with indentation to the left plt.figure(); response = ct.nyquist_response(sys, indent_direction='left') - response.plot(label_freq=300) - ct.suptitle( + cplt = response.plot(label_freq=300) + cplt.set_plot_title( "Imaginary poles; indent_direction='left'; encirclements = %d" % response.count) assert _Z(sys) == response.count + _P(sys, indent='left') @@ -361,8 +365,8 @@ def test_nyquist_indent_im(): with pytest.warns(UserWarning, match="encirclements does not match"): response = ct.nyquist_response( sys, np.linspace(0, 1e3, 1000), indent_direction='none') - response.plot() - ct.suptitle( + cplt = response.plot() + cplt.set_plot_title( "Imaginary poles; indent_direction='none'; encirclements = %d" % response.count) assert _Z(sys) == response.count + _P(sys) @@ -400,17 +404,17 @@ def test_linestyle_checks(): sys = ct.tf([100], [1, 1, 1]) # Set the line styles - lines = ct.nyquist_plot( + cplt = ct.nyquist_plot( sys, primary_style=[':', ':'], mirror_style=[':', ':']) - assert all([line.get_linestyle() == ':' for line in lines[0]]) + assert all([line.get_linestyle() == ':' for line in cplt.lines[0]]) # Set the line colors - lines = ct.nyquist_plot(sys, color='g') - assert all([line.get_color() == 'g' for line in lines[0]]) + cplt = ct.nyquist_plot(sys, color='g') + assert all([line.get_color() == 'g' for line in cplt.lines[0]]) # Turn off the mirror image - lines = ct.nyquist_plot(sys, mirror_style=False) - assert lines[0][2:] == [None, None] + cplt = ct.nyquist_plot(sys, mirror_style=False) + assert cplt.lines[0][2:] == [None, None] with pytest.raises(ValueError, match="invalid 'primary_style'"): ct.nyquist_plot(sys, primary_style=False) @@ -432,12 +436,14 @@ def test_nyquist_legacy(): sys = (0.02 * s**3 - 0.1 * s) / (s**4 + s**3 + s**2 + 0.25 * s + 0.04) with pytest.warns(UserWarning, match="indented contour may miss"): - response = ct.nyquist_plot(sys) + ct.nyquist_plot(sys) def test_discrete_nyquist(): - # Make sure we can handle discrete time systems with negative poles + # TODO: add tests to make sure plots make sense + + # Make sure we can handle discrete-time systems with negative poles sys = ct.tf(1, [1, -0.1], dt=1) * ct.tf(1, [1, 0.1], dt=1) - ct.nyquist_response(sys, plot=False) + ct.nyquist_response(sys) # system with a pole at the origin sys = ct.zpk([1,], [.3, 0], 1, dt=True) @@ -506,11 +512,20 @@ def test_nyquist_frd(): # Computing Nyquist response w/ different frequencies OK if given as a list nyqresp = ct.nyquist_response([sys1, sys2]) - out = nyqresp.plot() + nyqresp.plot() warnings.resetwarnings() +def test_no_indent_pole(): + s = ct.tf('s') + sys = ((1 + 5/s)/(1 + 0.5/s))**2 # Double-Lag-Compensator + + with pytest.raises(RuntimeError, match="evaluate at a pole"): + ct.nyquist_response( + sys, warn_encirclements=False, indent_direction='none') + + if __name__ == "__main__": # # Interactive mode: generate plots for manual viewing @@ -557,19 +572,19 @@ def test_nyquist_frd(): print("Unusual Nyquist plot") sys = ct.tf([1], [1, 3, 2]) * ct.tf([1], [1, 0, 1]) plt.figure() - ct.suptitle("Poles: %s" % - np.array2string(sys.poles(), precision=2, separator=',')) response = ct.nyquist_response(sys) - response.plot() + cplt = response.plot() + cplt.set_plot_title("Poles: %s" % + np.array2string(sys.poles(), precision=2, separator=',')) assert _Z(sys) == response.count + _P(sys) print("Discrete time systems") sys = ct.c2d(sys, 0.01) plt.figure() - ct.suptitle("Discrete-time; poles: %s" % - np.array2string(sys.poles(), precision=2, separator=',')) response = ct.nyquist_response(sys) - response.plot() + cplt = response.plot() + cplt.set_plot_title("Discrete-time; poles: %s" % + np.array2string(sys.poles(), precision=2, separator=',')) print("Frequency response data (FRD) systems") sys = ct.tf( @@ -578,5 +593,5 @@ def test_nyquist_frd(): sys1 = ct.frd(sys, np.logspace(-1, 1, 15), name='frd1') sys2 = ct.frd(sys, np.logspace(-2, 2, 20), name='frd2') plt.figure() - ct.nyquist_plot([sys, sys1, sys2]) - ct.suptitle("Mixed FRD, tf data") + cplt = ct.nyquist_plot([sys, sys1, sys2]) + cplt.set_plot_title("Mixed FRD, tf data") diff --git a/control/tests/optimal_test.py b/control/tests/optimal_test.py index f746db7d5..fa8fcb941 100644 --- a/control/tests/optimal_test.py +++ b/control/tests/optimal_test.py @@ -42,7 +42,7 @@ def test_continuous_lqr(method, npts): Tf = 10 timepts = np.linspace(0, Tf, npts) - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, timepts, x0, traj_cost, constraints, terminal_cost=term_cost, trajectory_method=method ) @@ -56,7 +56,7 @@ def test_continuous_lqr(method, npts): @pytest.mark.parametrize("method", ['shooting']) # TODO: add 'collocation' def test_finite_horizon_simple(method): - # Define a (discrete time) linear system with constraints + # Define a (discrete-time) linear system with constraints # Source: https://www.mpt3.org/UI/RegulationProblem # LTI prediction model (discrete time) @@ -77,11 +77,11 @@ def test_finite_horizon_simple(method): x0 = [4, 0] # Retrieve the full open-loop predictions - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, squeeze=True, trajectory_method=method, terminal_cost=cost) # include to match MPT3 formulation - t, u_openloop = res.time, res.inputs + _t, u_openloop = res.time, res.inputs np.testing.assert_almost_equal( u_openloop, [-1, -1, 0.1393, 0.3361, -5.204e-16], decimal=4) @@ -152,7 +152,7 @@ def test_discrete_lqr(): ] # Re-solve - res2 = opt.solve_ocp( + res2 = opt.solve_optimal_trajectory( sys, time, x0, integral_cost, trajectory_constraints, terminal_cost=terminal_cost, initial_guess=lqr_u) @@ -186,7 +186,6 @@ def test_mpc_iosystem_aircraft(): # compute the steady state values for a particular value of the input ud = np.array([0.8, -0.3]) xd = np.linalg.inv(np.eye(5) - A) @ B @ ud - yd = C @ xd # provide constraints on the system signals constraints = [opt.input_range_constraint(sys, [-5, -6], [5, 6])] @@ -216,7 +215,7 @@ def test_mpc_iosystem_aircraft(): def test_mpc_iosystem_rename(): - # Create a discrete time system (double integrator) + cost function + # Create a discrete-time system (double integrator) + cost function sys = ct.ss([[1, 1], [0, 1]], [[0], [1]], np.eye(2), 0, dt=True) cost = opt.quadratic_cost(sys, np.eye(2), np.eye(1)) timepts = np.arange(0, 5) @@ -264,7 +263,7 @@ def test_mpc_iosystem_continuous(): # Continuous time MPC controller not implemented with pytest.raises(NotImplementedError): - ctrl = opt.create_mpc_iosystem(sys, T, cost) + opt.create_mpc_iosystem(sys, T, cost) # Test various constraint combinations; need to use a somewhat convoluted @@ -315,7 +314,7 @@ def test_constraint_specification(constraint_list): # Compute optimal control and compare against MPT3 solution x0 = [4, 0] res = optctrl.compute_trajectory(x0, squeeze=True) - t, u_openloop = res.time, res.inputs + _t, u_openloop = res.time, res.inputs np.testing.assert_almost_equal( u_openloop, [-1, -1, 0.1393, 0.3361, -5.204e-16], decimal=3) @@ -352,7 +351,7 @@ def test_terminal_constraints(sys_args): # Find a path to the origin x0 = np.array([4, 3]) res = optctrl.compute_trajectory(x0, squeeze=True, return_x=True) - t, u1, x1 = res.time, res.inputs, res.states + _t, u1, x1 = res.time, res.inputs, res.states # Bug prior to SciPy 1.6 will result in incorrect results if NumpyVersion(sp.__version__) < '1.6.0': @@ -379,7 +378,7 @@ def test_terminal_constraints(sys_args): np.testing.assert_almost_equal(res.states, x1, decimal=4) # Re-run using a basis function and see if we get the same answer - res = opt.solve_ocp( + res = opt.solve_optimal_trajectory( sys, time, x0, cost, terminal_constraints=final_point, basis=flat.BezierFamily(8, Tf)) @@ -401,7 +400,7 @@ def test_terminal_constraints(sys_args): # Find a path to the origin res = optctrl.compute_trajectory( x0, squeeze=True, return_x=True, initial_guess=u1) - t, u2, x2 = res.time, res.inputs, res.states + _t, u2, x2 = res.time, res.inputs, res.states # Not all configurations are able to converge (?) if res.success: @@ -416,7 +415,7 @@ def test_terminal_constraints(sys_args): optctrl = opt.OptimalControlProblem( sys, time, cost, constraints, terminal_constraints=final_point) res = optctrl.compute_trajectory(x0, squeeze=True, return_x=True) - t, u3, x3 = res.time, res.inputs, res.states + _t, u3, x3 = res.time, res.inputs, res.states # Check the answers only if we converged if res.success: @@ -448,7 +447,7 @@ def test_optimal_logging(capsys): # Solve it, with logging turned on (with warning due to mixed constraints) with pytest.warns(sp.optimize.OptimizeWarning, match="Equality and inequality .* same element"): - res = opt.solve_ocp( + opt.solve_optimal_trajectory( sys, time, x0, cost, input_constraint, terminal_cost=cost, terminal_constraints=state_constraint, log=True) @@ -513,21 +512,21 @@ def test_ocp_argument_errors(): # Trajectory constraints not in the right form with pytest.raises(TypeError, match="constraints must be a list"): - res = opt.solve_ocp(sys, time, x0, cost, np.eye(2)) + opt.solve_optimal_trajectory(sys, time, x0, cost, np.eye(2)) # Terminal constraints not in the right form with pytest.raises(TypeError, match="constraints must be a list"): - res = opt.solve_ocp( + opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, terminal_constraints=np.eye(2)) # Initial guess in the wrong shape with pytest.raises(ValueError, match="initial guess is the wrong shape"): - res = opt.solve_ocp( + opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, initial_guess=np.zeros((4,1,1))) # Unrecognized arguments with pytest.raises(TypeError, match="unrecognized keyword"): - res = opt.solve_ocp( + opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, terminal_constraint=None) with pytest.raises(TypeError, match="unrecognized keyword"): @@ -541,21 +540,21 @@ def test_ocp_argument_errors(): # Unrecognized trajectory constraint type constraints = [(None, np.eye(3), [0, 0, 0], [0, 0, 0])] with pytest.raises(TypeError, match="unknown trajectory constraint type"): - res = opt.solve_ocp( + opt.solve_optimal_trajectory( sys, time, x0, cost, trajectory_constraints=constraints) # Unrecognized terminal constraint type with pytest.raises(TypeError, match="unknown terminal constraint type"): - res = opt.solve_ocp( + opt.solve_optimal_trajectory( sys, time, x0, cost, terminal_constraints=constraints) # Discrete time system checks: solve_ivp keywords not allowed sys = ct.rss(2, 1, 1, dt=True) with pytest.raises(TypeError, match="solve_ivp method, kwargs not allowed"): - res = opt.solve_ocp( + opt.solve_optimal_trajectory( sys, time, x0, cost, solve_ivp_method='LSODA') with pytest.raises(TypeError, match="solve_ivp method, kwargs not allowed"): - res = opt.solve_ocp( + opt.solve_optimal_trajectory( sys, time, x0, cost, solve_ivp_kwargs={'eps': 0.1}) @@ -583,7 +582,7 @@ def test_optimal_basis_simple(basis): x0 = [4, 0] # Basic optimal control problem - res1 = opt.solve_ocp( + res1 = opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, terminal_cost=cost, basis=basis, return_x=True) assert res1.success @@ -594,14 +593,14 @@ def test_optimal_basis_simple(basis): np.testing.assert_array_less(np.abs(res1.inputs[0]), 1 + 1e-6) # Pass an initial guess and rerun - res2 = opt.solve_ocp( + res2 = opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, initial_guess=0.99*res1.inputs, terminal_cost=cost, basis=basis, return_x=True) assert res2.success np.testing.assert_allclose(res2.inputs, res1.inputs, atol=0.01, rtol=0.01) # Run with logging turned on for code coverage - res3 = opt.solve_ocp( + res3 = opt.solve_optimal_trajectory( sys, time, x0, cost, constraints, terminal_cost=cost, basis=basis, return_x=True, log=True) assert res3.success @@ -629,7 +628,7 @@ def test_equality_constraints(): # Find a path to the origin x0 = np.array([4, 3]) res = optctrl.compute_trajectory(x0, squeeze=True, return_x=True) - t, u1, x1 = res.time, res.inputs, res.states + _t, u1, x1 = res.time, res.inputs, res.states # Bug prior to SciPy 1.6 will result in incorrect results if NumpyVersion(sp.__version__) < '1.6.0': @@ -649,7 +648,7 @@ def final_point_eval(x, u): # Find a path to the origin x0 = np.array([4, 3]) res = optctrl.compute_trajectory(x0, squeeze=True, return_x=True) - t, u2, x2 = res.time, res.inputs, res.states + _t, u2, x2 = res.time, res.inputs, res.states np.testing.assert_almost_equal(x2[:,-1], 0, decimal=4) np.testing.assert_almost_equal(u1, u2) np.testing.assert_almost_equal(x1, x2) @@ -732,8 +731,6 @@ def vehicle_output(t, x, u, params): initial_guess[0, :] = (xf[0] - x0[0]) / Tf # Steering = rate required to turn to proper slope in first segment - straight_seg_length = timepts[-2] - timepts[1] - curved_seg_length = (Tf - straight_seg_length)/2 approximate_angle = math.atan2(xf[1] - x0[1], xf[0] - x0[0]) initial_guess[1, 0] = approximate_angle / (timepts[1] - timepts[0]) initial_guess[1, -1] = -approximate_angle / (timepts[-1] - timepts[-2]) @@ -748,7 +745,7 @@ def vehicle_output(t, x, u, params): with warnings.catch_warnings(): warnings.filterwarnings( 'ignore', message="unable to solve", category=UserWarning) - result = opt.solve_ocp( + result = opt.solve_optimal_trajectory( vehicle, timepts, x0, traj_cost, constraints, terminal_cost=term_cost, initial_guess=initial_guess, trajectory_method=method, @@ -794,7 +791,7 @@ def test_oep_argument_errors(): # Unrecognized arguments with pytest.raises(TypeError, match="unrecognized keyword"): - res = opt.solve_oep(sys, timepts, Y, U, cost, unknown=True) + opt.solve_optimal_estimate(sys, timepts, Y, U, cost, unknown=True) with pytest.raises(TypeError, match="unrecognized keyword"): oep = opt.OptimalEstimationProblem(sys, timepts, cost, unknown=True) @@ -807,4 +804,4 @@ def test_oep_argument_errors(): # Incorrect number of signals with pytest.raises(ValueError, match="incorrect length"): oep = opt.OptimalEstimationProblem(sys, timepts, cost) - mhe = oep.create_mhe_iosystem(estimate_labels=['x1', 'x2', 'x3']) + oep.create_mhe_iosystem(estimate_labels=['x1', 'x2', 'x3']) diff --git a/control/tests/phaseplot_test.py b/control/tests/phaseplot_test.py index 18e06716f..fc4edcbea 100644 --- a/control/tests/phaseplot_test.py +++ b/control/tests/phaseplot_test.py @@ -10,11 +10,12 @@ """ import warnings +from math import pi +import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np import pytest -from math import pi import control as ct import control.phaseplot as pp @@ -116,18 +117,20 @@ def oscillator_ode(self, x, t, m=1., b=1, k=1, extra=None): [ct.phaseplot.separatrices, [5], {'params': {}, 'gridspec': [5, 5]}], [ct.phaseplot.separatrices, [5], {'color': ('r', 'g')}], ]) +@pytest.mark.usefixtures('mplcleanup') def test_helper_functions(func, args, kwargs): # Test with system sys = ct.nlsys( lambda t, x, u, params: [x[0] - 3*x[1], -3*x[0] + x[1]], states=2, inputs=0) - out = func(sys, [-1, 1, -1, 1], *args, **kwargs) + _out = func(sys, [-1, 1, -1, 1], *args, **kwargs) # Test with function rhsfcn = lambda t, x: sys.dynamics(t, x, 0, {}) - out = func(rhsfcn, [-1, 1, -1, 1], *args, **kwargs) + _out = func(rhsfcn, [-1, 1, -1, 1], *args, **kwargs) +@pytest.mark.usefixtures('mplcleanup') def test_system_types(): # Sample dynamical systems - inverted pendulum def invpend_ode(t, x, m=0, l=0, b=0, g=0): @@ -135,47 +138,133 @@ def invpend_ode(t, x, m=0, l=0, b=0, g=0): # Use callable form, with parameters (if not correct, will get /0 error) ct.phase_plane_plot( - invpend_ode, [-5, 5, 2, 2], params={'args': (1, 1, 0.2, 1)}) + invpend_ode, [-5, 5, -2, 2], params={'args': (1, 1, 0.2, 1)}, + plot_streamlines=True) # Linear I/O system ct.phase_plane_plot( - ct.ss([[0, 1], [-1, -1]], [[0], [1]], [[1, 0]], 0)) + ct.ss([[0, 1], [-1, -1]], [[0], [1]], [[1, 0]], 0), + plot_streamlines=True) +@pytest.mark.usefixtures('mplcleanup') def test_phaseplane_errors(): with pytest.raises(ValueError, match="invalid grid specification"): - ct.phase_plane_plot(ct.rss(2, 1, 1), gridspec='bad') - + ct.phase_plane_plot(ct.rss(2, 1, 1), gridspec='bad', + plot_streamlines=True) + with pytest.raises(ValueError, match="unknown grid type"): - ct.phase_plane_plot(ct.rss(2, 1, 1), gridtype='bad') - + ct.phase_plane_plot(ct.rss(2, 1, 1), gridtype='bad', + plot_streamlines=True) + with pytest.raises(ValueError, match="system must be planar"): - ct.phase_plane_plot(ct.rss(3, 1, 1)) + ct.phase_plane_plot(ct.rss(3, 1, 1), + plot_streamlines=True) with pytest.raises(ValueError, match="params must be dict with key"): def invpend_ode(t, x, m=0, l=0, b=0, g=0): return (x[1], -b/m*x[1] + (g*l/m) * np.sin(x[0])) ct.phase_plane_plot( - invpend_ode, [-5, 5, 2, 2], params={'stuff': (1, 1, 0.2, 1)}) + invpend_ode, [-5, 5, 2, 2], params={'stuff': (1, 1, 0.2, 1)}, + plot_streamlines=True) + + with pytest.raises(ValueError, match="gridtype must be 'meshgrid' when using streamplot"): + ct.phase_plane_plot(ct.rss(2, 1, 1), plot_streamlines=False, + plot_streamplot=True, gridtype='boxgrid') # Warning messages for invalid solutions: nonlinear spring mass system sys = ct.nlsys( lambda t, x, u, params: np.array( [x[1], -0.25 * (x[0] - 0.01 * x[0]**3) - 0.1 * x[1]]), states=2, inputs=0) - with pytest.warns(UserWarning, match=r"X0=array\(.*\), solve_ivp failed"): + with pytest.warns( + UserWarning, match=r"initial_state=\[.*\], solve_ivp failed"): ct.phase_plane_plot( sys, [-12, 12, -10, 10], 15, gridspec=[2, 9], - plot_separatrices=False) + plot_separatrices=False, plot_streamlines=True) # Turn warnings off with warnings.catch_warnings(): warnings.simplefilter("error") ct.phase_plane_plot( sys, [-12, 12, -10, 10], 15, gridspec=[2, 9], - plot_separatrices=False, suppress_warnings=True) - - + plot_streamlines=True, plot_separatrices=False, + suppress_warnings=True) + +@pytest.mark.usefixtures('mplcleanup') +def test_phase_plot_zorder(): + # some of these tests are a bit akward since the streamlines and separatrices + # are stored in the same list, so we separate them by color + key_color = "tab:blue" # must not be 'k', 'r', 'b' since they are used by separatrices + + def get_zorders(cplt): + max_zorder = lambda items: max([line.get_zorder() for line in items]) + assert isinstance(cplt.lines[0], list) + streamline_lines = [line for line in cplt.lines[0] if line.get_color() == key_color] + separatrice_lines = [line for line in cplt.lines[0] if line.get_color() != key_color] + streamlines = max_zorder(streamline_lines) if streamline_lines else None + separatrices = max_zorder(separatrice_lines) if separatrice_lines else None + assert cplt.lines[1] == None or isinstance(cplt.lines[1], mpl.quiver.Quiver) + quiver = cplt.lines[1].get_zorder() if cplt.lines[1] else None + assert cplt.lines[2] == None or isinstance(cplt.lines[2], list) + equilpoints = max_zorder(cplt.lines[2]) if cplt.lines[2] else None + assert cplt.lines[3] == None or isinstance(cplt.lines[3], mpl.streamplot.StreamplotSet) + streamplot = max(cplt.lines[3].lines.get_zorder(), cplt.lines[3].arrows.get_zorder()) if cplt.lines[3] else None + return streamlines, quiver, streamplot, separatrices, equilpoints + + def assert_orders(streamlines, quiver, streamplot, separatrices, equilpoints): + print(streamlines, quiver, streamplot, separatrices, equilpoints) + if streamlines is not None: + assert streamlines < separatrices < equilpoints + if quiver is not None: + assert quiver < separatrices < equilpoints + if streamplot is not None: + assert streamplot < separatrices < equilpoints + + def sys(t, x): + return np.array([4*x[1], -np.sin(4*x[0])]) + + # ensure correct zordering for all three flow types + res_streamlines = ct.phase_plane_plot(sys, plot_streamlines=dict(color=key_color)) + assert_orders(*get_zorders(res_streamlines)) + res_vectorfield = ct.phase_plane_plot(sys, plot_vectorfield=True) + assert_orders(*get_zorders(res_vectorfield)) + res_streamplot = ct.phase_plane_plot(sys, plot_streamplot=True) + assert_orders(*get_zorders(res_streamplot)) + + # ensure that zorder can still be overwritten + res_reversed = ct.phase_plane_plot(sys, plot_streamlines=dict(color=key_color, zorder=50), plot_vectorfield=dict(zorder=40), + plot_streamplot=dict(zorder=30), plot_separatrices=dict(zorder=20), plot_equilpoints=dict(zorder=10)) + streamlines, quiver, streamplot, separatrices, equilpoints = get_zorders(res_reversed) + assert streamlines > quiver > streamplot > separatrices > equilpoints + + +@pytest.mark.usefixtures('mplcleanup') +def test_stream_plot_magnitude(): + def sys(t, x): + return np.array([4*x[1], -np.sin(4*x[0])]) + + # plt context with linewidth + with plt.rc_context({'lines.linewidth': 4}): + res = ct.phase_plane_plot(sys, plot_streamplot=dict(vary_linewidth=True)) + linewidths = res.lines[3].lines.get_linewidths() + # linewidths are scaled to be between 0.25 and 2 times default linewidth + # but the extremes may not exist if there is no line at that point + assert min(linewidths) < 2 and max(linewidths) > 7 + + # make sure changing the colormap works + res = ct.phase_plane_plot(sys, plot_streamplot=dict(vary_color=True, cmap='viridis')) + assert res.lines[3].lines.get_cmap().name == 'viridis' + res = ct.phase_plane_plot(sys, plot_streamplot=dict(vary_color=True, cmap='turbo')) + assert res.lines[3].lines.get_cmap().name == 'turbo' + + # make sure changing the norm at least doesn't throw an error + ct.phase_plane_plot(sys, plot_streamplot=dict(vary_color=True, norm=mpl.colors.LogNorm())) + + + + +@pytest.mark.usefixtures('mplcleanup') def test_basic_phase_plots(savefigs=False): sys = ct.nlsys( lambda t, x, u, params: np.array([[0, 1], [-1, -1]]) @ x, @@ -184,7 +273,7 @@ def test_basic_phase_plots(savefigs=False): plt.figure() axis_limits = [-1, 1, -1, 1] T = 8 - ct.phase_plane_plot(sys, axis_limits, T) + ct.phase_plane_plot(sys, axis_limits, T, plot_streamlines=True) if savefigs: plt.savefig('phaseplot-dampedosc-default.png') @@ -197,7 +286,7 @@ def invpend_update(t, x, u, params): ct.phase_plane_plot( invpend, [-2*pi, 2*pi, -2, 2], 5, gridtype='meshgrid', gridspec=[5, 8], arrows=3, - plot_separatrices={'gridspec': [12, 9]}, + plot_separatrices={'gridspec': [12, 9]}, plot_streamlines=True, params={'m': 1, 'l': 1, 'b': 0.2, 'g': 1}) plt.xlabel(r"$\theta$ [rad]") plt.ylabel(r"$\dot\theta$ [rad/sec]") @@ -212,7 +301,8 @@ def oscillator_update(t, x, u, params): oscillator_update, states=2, inputs=0, name='nonlinear oscillator') plt.figure() - ct.phase_plane_plot(oscillator, [-1.5, 1.5, -1.5, 1.5], 0.9) + ct.phase_plane_plot(oscillator, [-1.5, 1.5, -1.5, 1.5], 0.9, + plot_streamlines=True) pp.streamlines( oscillator, np.array([[0, 0]]), 1.5, gridtype='circlegrid', gridspec=[0.5, 6], dir='both') @@ -222,6 +312,18 @@ def oscillator_update(t, x, u, params): if savefigs: plt.savefig('phaseplot-oscillator-helpers.png') + plt.figure() + ct.phase_plane_plot( + invpend, [-2*pi, 2*pi, -2, 2], + plot_streamplot=dict(vary_color=True, vary_density=True), + gridspec=[60, 20], params={'m': 1, 'l': 1, 'b': 0.2, 'g': 1} + ) + plt.xlabel(r"$\theta$ [rad]") + plt.ylabel(r"$\dot\theta$ [rad/sec]") + + if savefigs: + plt.savefig('phaseplot-invpend-streamplot.png') + if __name__ == "__main__": # diff --git a/control/tests/pzmap_test.py b/control/tests/pzmap_test.py index ce8adf6e7..64bbdee3e 100644 --- a/control/tests/pzmap_test.py +++ b/control/tests/pzmap_test.py @@ -16,7 +16,7 @@ from control import TransferFunction, config, pzmap -@pytest.mark.filterwarnings("ignore:.*return values.*:DeprecationWarning") +@pytest.mark.filterwarnings("ignore:.*return value.*:FutureWarning") @pytest.mark.parametrize("kwargs", [pytest.param(dict(), id="default"), pytest.param(dict(plot=False), id="plot=False"), @@ -53,7 +53,8 @@ def test_pzmap(kwargs, setdefaults, dt, editsdefaults, mplcleanup): if kwargs.get('plot', None) is None: pzkwargs['plot'] = True # use to get legacy return values - P, Z = pzmap(T, **pzkwargs) + with pytest.warns(FutureWarning, match="return value .* is deprecated"): + P, Z = pzmap(T, **pzkwargs) np.testing.assert_allclose(P, Pref, rtol=1e-3) np.testing.assert_allclose(Z, Zref, rtol=1e-3) @@ -96,7 +97,7 @@ def test_polezerodata(): # Legacy return format for plot in [True, False]: - with pytest.warns(DeprecationWarning, match=".* values .* deprecated"): + with pytest.warns(FutureWarning, match=".* value .* deprecated"): poles, zeros = ct.pole_zero_plot(pzdata, plot=False) np.testing.assert_equal(poles, sys.poles()) np.testing.assert_equal(zeros, sys.zeros()) @@ -110,16 +111,16 @@ def test_pzmap_raises(): sys1 = ct.rss(2, 1, 1) sys2 = sys1.sample(0.1) with pytest.raises(ValueError, match="incompatible time bases"): - pzdata = ct.pole_zero_plot([sys1, sys2], grid=True) + ct.pole_zero_plot([sys1, sys2], grid=True) with pytest.warns(UserWarning, match="axis already exists"): - fig, ax = plt.figure(), plt.axes() + _fig, ax = plt.figure(), plt.axes() ct.pole_zero_plot(sys1, ax=ax, grid='empty') def test_pzmap_limits(): sys = ct.tf([1, 2], [1, 2, 3]) - out = ct.pole_zero_plot(sys, xlim=[-1, 1], ylim=[-1, 1]) - ax = ct.get_plot_axes(out)[0, 0] + cplt = ct.pole_zero_plot(sys, xlim=[-1, 1], ylim=[-1, 1]) + ax = cplt.axes[0, 0] assert ax.get_xlim() == (-1, 1) assert ax.get_ylim() == (-1, 1) diff --git a/control/tests/response_test.py b/control/tests/response_test.py new file mode 100644 index 000000000..2b55ad103 --- /dev/null +++ b/control/tests/response_test.py @@ -0,0 +1,79 @@ +# response_test.py - test response/plot design pattern +# RMM, 13 Jan 2025 +# +# The standard pattern for control plots is to call a _response() or _map() +# function and then use the plot() method. However, it is also allowed to +# call the _plot() function directly, in which case the _response()/_map() +# function is called internally. +# +# If there are arguments that are allowed in _plot() that need to be +# processed by _response(), then we need to make sure that arguments are +# properly passed from _plot() to _response(). The unit tests in this file +# make sure that this functionality is implemented properly across all +# *relevant* _response/_map/plot pairs. +# +# Response/map function Plotting function Comments +# --------------------- ----------------- -------- +# describing_function_response describing_function_plot no passthru args +# forced_response time_response_plot no passthru args +# frequency_response bode_plot included below +# frequency_response nichols_plot included below +# gangof4_response gangof4_plot included below +# impulse_response time_response_plot no passthru args +# initial_response time_response_plot no passthru args +# input_output_response time_response_plot no passthru args +# nyquist_response nyquist_plot included below +# pole_zero_map pole_zero_plot no passthru args +# root_locus_map root_locus_plot included below +# singular_values_response singular_values_plot included below +# step_response time_response_plot no passthru args + +import matplotlib.pyplot as plt +import numpy as np +import pytest + +import control as ct + + +# List of parameters that should be processed by response function +@pytest.mark.parametrize("respfcn, plotfcn, respargs", [ + (ct.frequency_response, ct.bode_plot, + {'omega_limits': [1e-2, 1e2], 'omega_num': 50, 'Hz': True}), + (ct.frequency_response, ct.bode_plot, {'omega': np.logspace(2, 2)}), + (ct.frequency_response, ct.nichols_plot, {'omega': np.logspace(2, 2)}), + (ct.gangof4_response, ct.gangof4_plot, {'omega': np.logspace(2, 2)}), + (ct.gangof4_response, ct.gangof4_plot, + {'omega_limits': [1e-2, 1e2], 'omega_num': 50, 'Hz': True}), + (ct.nyquist_response, ct.nyquist_plot, + {'indent_direction': 'right', 'indent_radius': 0.1, 'indent_points': 100, + 'omega_num': 50, 'warn_nyquist': False}), + (ct.root_locus_map, ct.root_locus_plot, {'gains': np.linspace(1, 10, 5)}), + (ct.singular_values_response, ct.singular_values_plot, + {'omega_limits': [1e-2, 1e2], 'omega_num': 50, 'Hz': True}), + (ct.singular_values_response, ct.singular_values_plot, + {'omega': np.logspace(2, 2)}), +]) +@pytest.mark.usefixtures('mplcleanup') +def test_response_plot(respfcn, plotfcn, respargs): + if respfcn is ct.gangof4_response: + # Two arguments required + args = (ct.rss(4, 1, 1, strictly_proper=True), ct.rss(1, 1, 1)) + else: + # Single argument is enough + args = (ct.rss(4, 1, 1, strictly_proper=True), ) + + # Standard calling pattern - generate response, then plot + plt.figure() + resp = respfcn(*args, **respargs) + if plotfcn is ct.nichols_plot: + cplt_resp = resp.plot(plot_type='nichols') + else: + cplt_resp = resp.plot() + + # Alternative calling pattern - call plotting function directly + plt.figure() + cplt_plot = plotfcn(*args, **respargs) + + # Make sure the plots have the same elements + assert cplt_resp.lines.shape == cplt_plot.lines.shape + assert cplt_resp.axes.shape == cplt_plot.axes.shape diff --git a/control/tests/rlocus_test.py b/control/tests/rlocus_test.py index 15eb67d97..4d3a08206 100644 --- a/control/tests/rlocus_test.py +++ b/control/tests/rlocus_test.py @@ -45,7 +45,7 @@ def check_cl_poles(self, sys, pole_list, k_list): poles = np.sort(poles) np.testing.assert_array_almost_equal(poles, poles_expected) - @pytest.mark.filterwarnings("ignore:.*return values.*:DeprecationWarning") + @pytest.mark.filterwarnings("ignore:.*return value.*:FutureWarning") def testRootLocus(self, sys): """Basic root locus (no plot)""" klist = [-1, 0, 1] @@ -61,7 +61,7 @@ def testRootLocus(self, sys): np.testing.assert_allclose(klist, k_out) self.check_cl_poles(sys, roots, klist) - @pytest.mark.filterwarnings("ignore:.*return values.*:DeprecationWarning") + @pytest.mark.filterwarnings("ignore:.*return value.*:FutureWarning") def test_without_gains(self, sys): roots, kvect = root_locus(sys, plot=False) self.check_cl_poles(sys, roots, kvect) @@ -95,7 +95,7 @@ def test_root_locus_plot_grid(self, sys, grid, method): if grid == 'empty': assert n_gridlines == 0 assert not isinstance(ax, AA.Axes) - elif grid is False or method == 'pzmap' and grid is None: + elif grid is False: assert n_gridlines == 2 if sys.isctime() else 3 assert not isinstance(ax, AA.Axes) elif sys.isdtime(strict=True): @@ -109,7 +109,7 @@ def test_root_locus_plot_grid(self, sys, grid, method): # TODO: check validity of grid - @pytest.mark.filterwarnings("ignore:.*return values.*:DeprecationWarning") + @pytest.mark.filterwarnings("ignore:.*return value.*:FutureWarning") def test_root_locus_neg_false_gain_nonproper(self): """ Non proper TranferFunction with negative gain: Not implemented""" with pytest.raises(ValueError, match="with equal order"): @@ -134,7 +134,7 @@ def test_root_locus_zoom(self): ax_rlocus.set_xlim((-10.813628105112421, 14.760795435937652)) ax_rlocus.set_ylim((-35.61713798641108, 33.879716621220311)) plt.get_current_fig_manager().toolbar.mode = 'zoom rect' - _RLClickDispatcher(event, system, fig, ax_rlocus, '-') + _RLClickDispatcher(event, system, fig, ax_rlocus, '-') # noqa: F821 zoom_x = ax_rlocus.lines[-2].get_data()[0][0:5] zoom_y = ax_rlocus.lines[-2].get_data()[1][0:5] @@ -147,7 +147,7 @@ def test_root_locus_zoom(self): assert_array_almost_equal(zoom_x, zoom_x_valid) assert_array_almost_equal(zoom_y, zoom_y_valid) - @pytest.mark.filterwarnings("ignore:.*return values.*:DeprecationWarning") + @pytest.mark.filterwarnings("ignore:.*return value.*:FutureWarning") @pytest.mark.timeout(2) def test_rlocus_default_wn(self): """Check that default wn calculation works properly""" @@ -161,7 +161,6 @@ def test_rlocus_default_wn(self): # that will take a long time to do the calculation (minutes). # import scipy as sp - import signal # Define a system that exhibits this behavior sys = ct.tf(*sp.signal.zpk2tf( @@ -174,6 +173,7 @@ def test_rlocus_default_wn(self): "sys, grid, xlim, ylim, interactive", [ (ct.tf([1], [1, 2, 1]), None, None, None, False), ]) +@pytest.mark.usefixtures("mplcleanup") def test_root_locus_plots(sys, grid, xlim, ylim, interactive): ct.root_locus_map(sys).plot( grid=grid, xlim=xlim, ylim=ylim, interactive=interactive) @@ -182,13 +182,15 @@ def test_root_locus_plots(sys, grid, xlim, ylim, interactive): # Test deprecated keywords @pytest.mark.parametrize("keyword", ["kvect", "k"]) +@pytest.mark.usefixtures("mplcleanup") def test_root_locus_legacy(keyword): sys = ct.rss(2, 1, 1) - with pytest.warns(DeprecationWarning, match=f"'{keyword}' is deprecated"): + with pytest.warns(FutureWarning, match=f"'{keyword}' is deprecated"): ct.root_locus_plot(sys, **{keyword: [0, 1, 2]}) # Generate plots used in documentation +@pytest.mark.usefixtures("mplcleanup") def test_root_locus_documentation(savefigs=False): plt.figure() sys = ct.tf([1, 2], [1, 2, 3], name='SISO transfer function') @@ -204,9 +206,9 @@ def test_root_locus_documentation(savefigs=False): # TODO: generate event in order to generate real title plt.figure() - out = ct.root_locus_map(sys).plot(initial_gain=3.506) - ax = ct.get_plot_axes(out)[0, 0] - freqplot_rcParams = ct.config._get_param('freqplot', 'rcParams') + cplt = ct.root_locus_map(sys).plot(initial_gain=3.506) + ax = cplt.axes[0, 0] + freqplot_rcParams = ct.config._get_param('ctrlplot', 'rcParams') with plt.rc_context(freqplot_rcParams): ax.set_title( "Clicked at: -2.729+1.511j gain = 3.506 damping = 0.8748") @@ -227,6 +229,21 @@ def test_root_locus_documentation(savefigs=False): plt.savefig('rlocus-siso_multiple-nogrid.png') +# https://github.com/python-control/python-control/issues/1063 +def test_rlocus_singleton(): + # Generate a root locus map for a singleton + L = ct.tf([1, 1], [1, 2, 3]) + rldata = ct.root_locus_map(L, 1) + np.testing.assert_equal(rldata.gains, np.array([1])) + assert rldata.loci.shape == (1, 2) + + # Generate the root locus plot (no loci) + cplt = rldata.plot() + assert len(cplt.lines[0, 0]) == 1 # poles (one set of markers) + assert len(cplt.lines[0, 1]) == 1 # zeros + assert len(cplt.lines[0, 2]) == 2 # loci (two 0-length lines) + + if __name__ == "__main__": # # Interactive mode: generate plots for manual viewing @@ -278,6 +295,9 @@ def test_root_locus_documentation(savefigs=False): plt.figure() test_root_locus_plots( sys, grid=grid, xlim=xlim, ylim=ylim, interactive=interactive) + ct.suptitle( + f"sys={sys.name}, {grid=}, {xlim=}, {ylim=}, {interactive=}", + frame='figure') # Run tests that generate plots for the documentation test_root_locus_documentation(savefigs=True) diff --git a/control/tests/robust_test.py b/control/tests/robust_test.py index 146ae9e41..fc9c9570d 100644 --- a/control/tests/robust_test.py +++ b/control/tests/robust_test.py @@ -37,7 +37,7 @@ def testH2syn(self): """Test h2syn""" p = ss(-1, [[1, 1]], [[1], [1]], [[0, 1], [1, 0]]) k = h2syn(p, 1, 1) - # from Octave, which also uses SB10HD for H-2 synthesis: + # from Octave, which also uses SB10HD for H2 synthesis: # a= -1; b1= 1; b2= 1; c1= 1; c2= 1; d11= 0; d12= 1; d21= 1; d22= 0; # g = ss(a,[b1,b2],[c1;c2],[d11,d12;d21,d22]); # k = h2syn(g,1,1); @@ -48,6 +48,7 @@ def testH2syn(self): np.testing.assert_array_almost_equal(k.D, [[0]]) +@pytest.mark.filterwarnings("ignore:connect:FutureWarning") class TestAugw: # tolerance for system equality @@ -324,6 +325,7 @@ def testErrors(self): augw(g1by1, w3=g2by2) +@pytest.mark.filterwarnings("ignore:connect:FutureWarning") class TestMixsyn: """Test control.robust.mixsyn""" diff --git a/control/tests/sisotool_test.py b/control/tests/sisotool_test.py index 325b9c180..1fc744daa 100644 --- a/control/tests/sisotool_test.py +++ b/control/tests/sisotool_test.py @@ -153,6 +153,7 @@ def test_sisotool_initial_gain(self, tsys): with pytest.warns(FutureWarning): sisotool(tsys, kvect=1.2) + @pytest.mark.filterwarnings("ignore:connect:FutureWarning") def test_sisotool_mimo(self, sys222, sys221): # a 2x2 should not raise an error: sisotool(sys222) @@ -196,6 +197,7 @@ def test_pid_designer_1(self, plant, gain, sign, input_signal, Kp0, Ki0, Kd0, de {'input_signal':'r', 'Kp0':0.01, 'derivative_in_feedback_path':True}, {'input_signal':'d', 'Kp0':0.01, 'derivative_in_feedback_path':True}, {'input_signal':'r', 'Kd0':0.01, 'derivative_in_feedback_path':True}]) + @pytest.mark.filterwarnings("ignore:connect:FutureWarning") def test_pid_designer_2(self, plant, kwargs): rootlocus_pid_designer(plant, **kwargs) diff --git a/control/tests/statefbk_test.py b/control/tests/statefbk_test.py index 4a0472de7..3f4b4849a 100644 --- a/control/tests/statefbk_test.py +++ b/control/tests/statefbk_test.py @@ -7,15 +7,15 @@ import pytest import itertools import warnings -from math import pi, atan +from math import pi import control as ct -from control import lqe, dlqe, poles, rss, ss, tf +from control import poles, rss, ss, tf from control.exception import ControlDimension, ControlSlycot, \ ControlArgument, slycot_check from control.mateqn import care, dare from control.statefbk import (ctrb, obsv, place, place_varga, lqr, dlqr, - gram, acker) + gram, place_acker) from control.tests.conftest import slycotonly @@ -56,7 +56,27 @@ def testCtrbT(self): Wctrue = np.array([[5., 6.], [7., 8.]]) Wc = ctrb(A, B, t=t) np.testing.assert_array_almost_equal(Wc, Wctrue) - + + def testCtrbNdim1(self): + # gh-1097: treat 1-dim B as nx1 + A = np.array([[1., 2.], [3., 4.]]) + B = np.array([5., 7.]) + Wctrue = np.array([[5., 19.], [7., 43.]]) + Wc = ctrb(A, B) + np.testing.assert_array_almost_equal(Wc, Wctrue) + + def testCtrbRejectMismatch(self): + # gh-1097: check A, B for compatible shapes + with pytest.raises( + ControlDimension, match='.* A must be a square matrix'): + ctrb([[1,2]],[1]) + with pytest.raises( + ControlDimension, match='B has the wrong number of rows'): + ctrb([[1,2],[2,3]], 1) + with pytest.raises( + ControlDimension, match='B has the wrong number of rows'): + ctrb([[1,2],[2,3]], [[1,2]]) + def testObsvSISO(self): A = np.array([[1., 2.], [3., 4.]]) C = np.array([[5., 7.]]) @@ -70,7 +90,7 @@ def testObsvMIMO(self): Wotrue = np.array([[5., 6.], [7., 8.], [23., 34.], [31., 46.]]) Wo = obsv(A, C) np.testing.assert_array_almost_equal(Wo, Wotrue) - + def testObsvT(self): A = np.array([[1., 2.], [3., 4.]]) C = np.array([[5., 6.], [7., 8.]]) @@ -79,6 +99,26 @@ def testObsvT(self): Wo = obsv(A, C, t=t) np.testing.assert_array_almost_equal(Wo, Wotrue) + def testObsvNdim1(self): + # gh-1097: treat 1-dim C as 1xn + A = np.array([[1., 2.], [3., 4.]]) + C = np.array([5., 7.]) + Wotrue = np.array([[5., 7.], [26., 38.]]) + Wo = obsv(A, C) + np.testing.assert_array_almost_equal(Wo, Wotrue) + + def testObsvRejectMismatch(self): + # gh-1097: check A, C for compatible shapes + with pytest.raises( + ControlDimension, match='.* A must be a square matrix'): + obsv([[1,2]],[1]) + with pytest.raises( + ControlDimension, match='C has the wrong number of columns'): + obsv([[1,2],[2,3]], 1) + with pytest.raises( + ControlDimension, match='C has the wrong number of columns'): + obsv([[1,2],[2,3]], [[1],[2]]) + def testCtrbObsvDuality(self): A = np.array([[1.2, -2.3], [3.4, -4.5]]) B = np.array([[5.8, 6.9], [8., 9.1]]) @@ -128,15 +168,14 @@ def testGramRc(self): C = np.array([[4., 5.], [6., 7.]]) D = np.array([[13., 14.], [15., 16.]]) sys = ss(A, B, C, D) - Rctrue = np.array([[4.30116263, 5.6961343], - [0., 0.23249528]]) + Rctrue = np.array([[4.30116263, 5.6961343], [0., 0.23249528]]) Rc = gram(sys, 'cf') np.testing.assert_array_almost_equal(Rc, Rctrue) sysd = ct.c2d(sys, 0.2) Rctrue = np.array([[1.91488054, 2.53468814], [0. , 0.10290372]]) Rc = gram(sysd, 'cf') - np.testing.assert_array_almost_equal(Rc, Rctrue) + np.testing.assert_array_almost_equal(Rc, Rctrue) @slycotonly def testGramWo(self): @@ -149,7 +188,7 @@ def testGramWo(self): Wo = gram(sys, 'o') np.testing.assert_array_almost_equal(Wo, Wotrue) sysd = ct.c2d(sys, 0.2) - Wotrue = np.array([[ 1305.369179, -440.046414], + Wotrue = np.array([[ 1305.369179, -440.046414], [ -440.046414, 333.034844]]) Wo = gram(sysd, 'o') np.testing.assert_array_almost_equal(Wo, Wotrue) @@ -184,7 +223,7 @@ def testGramRo(self): Rotrue = np.array([[ 36.12989315, -12.17956588], [ 0. , 13.59018097]]) Ro = gram(sysd, 'of') - np.testing.assert_array_almost_equal(Ro, Rotrue) + np.testing.assert_array_almost_equal(Ro, Rotrue) def testGramsys(self): sys = tf([1.], [1., 1., 1.]) @@ -230,7 +269,7 @@ def testAcker(self, fixedseed): desired = poles(des) # Now place the poles using acker - K = acker(sys.A, sys.B, desired) + K = place_acker(sys.A, sys.B, desired) new = ss(sys.A - sys.B * K, sys.B, sys.C, sys.D) placed = poles(new) @@ -535,7 +574,7 @@ def test_dare(self, stabilizing): assert np.all(sgn * (np.abs(L) - 1) > 0) def test_lqr_discrete(self): - """Test overloading of lqr operator for discrete time systems""" + """Test overloading of lqr operator for discrete-time systems""" csys = ct.rss(2, 1, 1) dsys = ct.drss(2, 1, 1) Q = np.eye(2) @@ -548,7 +587,7 @@ def test_lqr_discrete(self): np.testing.assert_almost_equal(S_csys, S_expl) np.testing.assert_almost_equal(E_csys, E_expl) - # Calling lqr() with a discrete time system should call dlqr() + # Calling lqr() with a discrete-time system should call dlqr() K_lqr, S_lqr, E_lqr = ct.lqr(dsys, Q, R) K_dlqr, S_dlqr, E_dlqr = ct.dlqr(dsys, Q, R) np.testing.assert_almost_equal(K_lqr, K_dlqr) @@ -563,7 +602,7 @@ def test_lqr_discrete(self): np.testing.assert_almost_equal(S_asys, S_expl) np.testing.assert_almost_equal(E_asys, E_expl) - # Calling dlqr() with a continuous time system should raise an error + # Calling dlqr() with a continuous-time system should raise an error with pytest.raises(ControlArgument, match="dsys must be discrete"): K, S, E = ct.dlqr(csys, Q, R) @@ -744,7 +783,7 @@ def test_statefbk_iosys_unused(self): def test_lqr_integral_continuous(self): - # Generate a continuous time system for testing + # Generate a continuous-time system for testing sys = ct.rss(4, 4, 2, strictly_proper=True) sys.C = np.eye(4) # reset output to be full state C_int = np.eye(2, 4) # integrate outputs for first two states @@ -811,7 +850,7 @@ def test_lqr_integral_continuous(self): assert abs(ctrl_tf(1e-9)[1][1]) > 1e6 def test_lqr_integral_discrete(self): - # Generate a discrete time system for testing + # Generate a discrete-time system for testing sys = ct.drss(4, 4, 2, strictly_proper=True) sys.C = np.eye(4) # reset output to be full state C_int = np.eye(2, 4) # integrate outputs for first two states @@ -821,7 +860,7 @@ def test_lqr_integral_discrete(self): K, _, _ = ct.lqr( sys, np.eye(sys.nstates + nintegrators), np.eye(sys.ninputs), integral_action=C_int) - Kp, Ki = K[:, :sys.nstates], K[:, sys.nstates:] + Kp, _Ki = K[:, :sys.nstates], K[:, sys.nstates:] # Create an I/O system for the controller ctrl, clsys = ct.create_statefbk_iosystem( @@ -846,7 +885,7 @@ def test_lqr_integral_discrete(self): "rss_fun, lqr_fun", [(ct.rss, lqr), (ct.drss, dlqr)]) def test_lqr_errors(self, rss_fun, lqr_fun): - # Generate a discrete time system for testing + # Generate a discrete-time system for testing sys = rss_fun(4, 4, 2, strictly_proper=True) with pytest.raises(ControlArgument, match="must pass an array"): @@ -892,7 +931,7 @@ def test_statefbk_errors(self): with pytest.raises(ControlArgument, match="gain must be an array"): ctrl, clsys = ct.create_statefbk_iosystem(sys, "bad argument") - with pytest.warns(DeprecationWarning, match="'type' is deprecated"): + with pytest.warns(FutureWarning, match="'type' is deprecated"): ctrl, clsys = ct.create_statefbk_iosystem(sys, K, type='nonlinear') with pytest.raises(ControlArgument, match="duplicate keywords"): @@ -931,11 +970,11 @@ def unicycle_update(t, x, u, params): return ct.NonlinearIOSystem( unicycle_update, None, - inputs = ['v', 'phi'], - outputs = ['x', 'y', 'theta'], - states = ['x_', 'y_', 'theta_']) + inputs=['v', 'phi'], + outputs=['x', 'y', 'theta'], + states=['x_', 'y_', 'theta_'], + params={'a': 1}) # only used for testing params -from math import pi @pytest.mark.parametrize("method", ['nearest', 'linear', 'cubic']) def test_gainsched_unicycle(unicycle, method): @@ -1143,3 +1182,82 @@ def test_gainsched_errors(unicycle): ctrl, clsys = ct.create_statefbk_iosystem( unicycle, (gains, points), gainsched_indices=[3, 2], gainsched_method='unknown') + + +@pytest.mark.parametrize("ninputs, Kf", [ + (1, 1), + (1, None), + (2, np.diag([1, 1])), + (2, None), +]) +def test_refgain_pattern(ninputs, Kf): + sys = ct.rss(2, 2, ninputs, strictly_proper=True) + sys.C = np.eye(2) + + K, _, _ = ct.lqr(sys.A, sys.B, np.eye(sys.nstates), np.eye(sys.ninputs)) + if Kf is None: + # Make sure we get an error if we don't specify Kf + with pytest.raises(ControlArgument, match="'feedfwd_gain' required"): + ctrl, clsys = ct.create_statefbk_iosystem( + sys, K, Kf, feedfwd_pattern='refgain') + + # Now compute the gain to give unity zero frequency gain + C = np.eye(ninputs, sys.nstates) + Kf = -np.linalg.inv( + C @ np.linalg.inv(sys.A - sys.B @ K) @ sys.B) + ctrl, clsys = ct.create_statefbk_iosystem( + sys, K, Kf, feedfwd_pattern='refgain') + + np.testing.assert_almost_equal( + C @ clsys(0)[0:sys.nstates], np.eye(ninputs)) + + else: + ctrl, clsys = ct.create_statefbk_iosystem( + sys, K, Kf, feedfwd_pattern='refgain') + + manual = ct.feedback(sys, K) * Kf + np.testing.assert_almost_equal(clsys.A, manual.A) + np.testing.assert_almost_equal(clsys.B, manual.B) + np.testing.assert_almost_equal(clsys.C[:sys.nstates, :], manual.C) + np.testing.assert_almost_equal(clsys.D[:sys.nstates, :], manual.D) + + +def test_create_statefbk_errors(): + sys = ct.rss(2, 2, 1, strictly_proper=True) + sys.C = np.eye(2) + K = -np.ones((1, 4)) + Kf = 1 + + K, _, _ = ct.lqr(sys.A, sys.B, np.eye(sys.nstates), np.eye(sys.ninputs)) + with pytest.raises(NotImplementedError, match="unknown pattern"): + ct.create_statefbk_iosystem(sys, K, feedfwd_pattern='mypattern') + + with pytest.raises(ControlArgument, match="feedfwd_pattern != 'refgain'"): + ct.create_statefbk_iosystem(sys, K, Kf, feedfwd_pattern='trajgen') + + +def test_create_statefbk_params(unicycle): + Q = np.identity(unicycle.nstates) + R = np.identity(unicycle.ninputs) + gain, _, _ = ct.lqr(unicycle.linearize([0, 0, 0], [5, 0]), Q, R) + + # Create a linear controller + ctrl, clsys = ct.create_statefbk_iosystem(unicycle, gain) + assert [k for k in ctrl.params.keys()] == [] + assert [k for k in clsys.params.keys()] == ['a'] + assert clsys.params['a'] == 1 + + # Create a nonlinear controller + ctrl, clsys = ct.create_statefbk_iosystem( + unicycle, gain, controller_type='nonlinear') + assert [k for k in ctrl.params.keys()] == ['K'] + assert [k for k in clsys.params.keys()] == ['K', 'a'] + assert clsys.params['a'] == 1 + + # Override the default parameters + ctrl, clsys = ct.create_statefbk_iosystem( + unicycle, gain, controller_type='nonlinear', params={'a': 2, 'b': 1}) + assert [k for k in ctrl.params.keys()] == ['K'] + assert [k for k in clsys.params.keys()] == ['K', 'a', 'b'] + assert clsys.params['a'] == 2 + assert clsys.params['b'] == 1 diff --git a/control/tests/statesp_test.py b/control/tests/statesp_test.py index 6ddf9933e..3c1411f04 100644 --- a/control/tests/statesp_test.py +++ b/control/tests/statesp_test.py @@ -1,4 +1,4 @@ -"""statesp_test.py - test state space class +"""Tests for the StateSpace class. RMM, 30 Mar 2011 based on TestStateSp from v0.4a) RMM, 14 Jun 2019 statesp_array_test.py coverted from statesp_test.py to test @@ -7,23 +7,22 @@ convert to pytest """ +import operator + import numpy as np -from numpy.testing import assert_array_almost_equal import pytest -import operator from numpy.linalg import solve +from numpy.testing import assert_array_almost_equal from scipy.linalg import block_diag, eigvals import control as ct from control.config import defaults from control.dtime import sample_system -from control.lti import evalfr -from control.statesp import StateSpace, _convert_to_statespace, tf2ss, \ - _statesp_defaults, _rss_generate, linfnorm, ss, rss, drss +from control.lti import LTI, evalfr +from control.statesp import StateSpace, _convert_to_statespace, \ + _rss_generate, _statesp_defaults, drss, linfnorm, rss, ss, tf2ss from control.xferfcn import TransferFunction, ss2tf - -from .conftest import editsdefaults, slycotonly - +from .conftest import assert_tf_close_coeff, slycotonly class TestStateSpace: """Tests for the StateSpace class.""" @@ -121,28 +120,27 @@ def test_constructor(self, sys322ABCD, dt, argfun): np.testing.assert_almost_equal(sys.D, sys322ABCD[3]) assert sys.dt == dtref - @pytest.mark.parametrize("args, exc, errmsg", - [((True, ), TypeError, - "(can only take in|sys must be) a StateSpace"), - ((1, 2), TypeError, "1, 4, or 5 arguments"), - ((np.ones((3, 2)), np.ones((3, 2)), - np.ones((2, 2)), np.ones((2, 2))), - ValueError, "A must be square"), - ((np.ones((3, 3)), np.ones((2, 2)), - np.ones((2, 3)), np.ones((2, 2))), - ValueError, "A and B"), - ((np.ones((3, 3)), np.ones((3, 2)), - np.ones((2, 2)), np.ones((2, 2))), - ValueError, "A and C"), - ((np.ones((3, 3)), np.ones((3, 2)), - np.ones((2, 3)), np.ones((2, 3))), - ValueError, "B and D"), - ((np.ones((3, 3)), np.ones((3, 2)), - np.ones((2, 3)), np.ones((3, 2))), - ValueError, "C and D"), - ]) + @pytest.mark.parametrize( + "args, exc, errmsg", + [((True, ), TypeError, "(can only take in|sys must be) a StateSpace"), + ((1, 2), TypeError, "1, 4, or 5 arguments"), + ((np.ones((3, 2)), np.ones((3, 2)), + np.ones((2, 2)), np.ones((2, 2))), ValueError, + r"A must be a square matrix"), + ((np.ones((3, 3)), np.ones((2, 2)), + np.ones((2, 3)), np.ones((2, 2))), ValueError, + r"Incompatible dimensions of B matrix; expected \(3, 2\)"), + ((np.ones((3, 3)), np.ones((3, 2)), + np.ones((2, 2)), np.ones((2, 2))), ValueError, + r"Incompatible dimensions of C matrix; expected \(2, 3\)"), + ((np.ones((3, 3)), np.ones((3, 2)), + np.ones((2, 3)), np.ones((2, 3))), ValueError, + r"Incompatible dimensions of D matrix; expected \(2, 2\)"), + (([1j], 2, 3, 0), TypeError, "real number, not 'complex'"), + ]) def test_constructor_invalid(self, args, exc, errmsg): """Test invalid input to StateSpace() constructor""" + with pytest.raises(exc, match=errmsg): StateSpace(*args) with pytest.raises(exc, match=errmsg): @@ -247,7 +245,6 @@ def test_zero_siso(self, sys222): np.testing.assert_almost_equal(true_z, z) - @slycotonly def test_zero_mimo_sys322_square(self, sys322): """Evaluate the zeros of a square MIMO system.""" @@ -255,7 +252,6 @@ def test_zero_mimo_sys322_square(self, sys322): true_z = np.sort([44.41465, -0.490252, -5.924398]) np.testing.assert_array_almost_equal(z, true_z) - @slycotonly def test_zero_mimo_sys222_square(self, sys222): """Evaluate the zeros of a square MIMO system.""" @@ -319,6 +315,335 @@ def test_multiply_ss(self, sys222, sys322): np.testing.assert_array_almost_equal(sys.C, C) np.testing.assert_array_almost_equal(sys.D, D) + def test_add_sub_mimo_siso(self): + # Test SS with SS + ss_siso = StateSpace( + np.array([ + [1, 2], + [3, 4], + ]), + np.array([ + [1], + [4], + ]), + np.array([ + [1, 1], + ]), + np.array([ + [0], + ]), + ) + ss_siso_1 = StateSpace( + np.array([ + [1, 1], + [3, 1], + ]), + np.array([ + [3], + [-4], + ]), + np.array([ + [-1, 1], + ]), + np.array([ + [0.1], + ]), + ) + ss_siso_2 = StateSpace( + np.array([ + [1, 0], + [0, 1], + ]), + np.array([ + [0], + [2], + ]), + np.array([ + [0, 1], + ]), + np.array([ + [0], + ]), + ) + ss_mimo = ss_siso_1.append(ss_siso_2) + expected_add = ct.combine_tf([ + [ss2tf(ss_siso_1 + ss_siso), ss2tf(ss_siso)], + [ss2tf(ss_siso), ss2tf(ss_siso_2 + ss_siso)], + ]) + expected_sub = ct.combine_tf([ + [ss2tf(ss_siso_1 - ss_siso), -ss2tf(ss_siso)], + [-ss2tf(ss_siso), ss2tf(ss_siso_2 - ss_siso)], + ]) + for op, expected in [ + (StateSpace.__add__, expected_add), + (StateSpace.__radd__, expected_add), + (StateSpace.__sub__, expected_sub), + (StateSpace.__rsub__, -expected_sub), + ]: + result = op(ss_mimo, ss_siso) + assert_tf_close_coeff( + expected.minreal(), + ss2tf(result).minreal(), + ) + # Test SS with array + expected_add = ct.combine_tf([ + [ss2tf(1 + ss_siso), ss2tf(ss_siso)], + [ss2tf(ss_siso), ss2tf(1 + ss_siso)], + ]) + expected_sub = ct.combine_tf([ + [ss2tf(-1 + ss_siso), ss2tf(ss_siso)], + [ss2tf(ss_siso), ss2tf(-1 + ss_siso)], + ]) + for op, expected in [ + (StateSpace.__add__, expected_add), + (StateSpace.__radd__, expected_add), + (StateSpace.__sub__, expected_sub), + (StateSpace.__rsub__, -expected_sub), + ]: + result = op(ss_siso, np.eye(2)) + assert_tf_close_coeff( + expected.minreal(), + ss2tf(result).minreal(), + ) + + @slycotonly + @pytest.mark.parametrize( + "left, right, expected", + [ + ( + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + TransferFunction([2], [1, 0]), + np.eye(3), + TransferFunction( + [ + [[2], [0], [0]], + [[0], [2], [0]], + [[0], [0], [2]], + ], + [ + [[1, 0], [1], [1]], + [[1], [1, 0], [1]], + [[1], [1], [1, 0]], + ], + ), + ), + ] + ) + def test_mul_mimo_siso(self, left, right, expected): + result = tf2ss(left).__mul__(right) + assert_tf_close_coeff( + expected.minreal(), + ss2tf(result).minreal(), + ) + + @slycotonly + @pytest.mark.parametrize( + "left, right, expected", + [ + ( + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + np.eye(3), + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[2], [0], [0]], + [[0], [2], [0]], + [[0], [0], [2]], + ], + [ + [[1, 0], [1], [1]], + [[1], [1, 0], [1]], + [[1], [1], [1, 0]], + ], + ), + ), + ] + ) + def test_rmul_mimo_siso(self, left, right, expected): + result = tf2ss(right).__rmul__(left) + assert_tf_close_coeff( + expected.minreal(), + ss2tf(result).minreal(), + ) + + @slycotonly + @pytest.mark.parametrize("power", [0, 1, 3, -3]) + @pytest.mark.parametrize("sysname", ["sys222", "sys322"]) + def test_pow(self, request, sysname, power): + """Test state space powers.""" + sys = request.getfixturevalue(sysname) + result = sys**power + if power == 0: + expected = StateSpace([], [], [], np.eye(sys.ninputs), dt=0) + else: + sign = 1 if power > 0 else -1 + expected = sys**sign + for i in range(1,abs(power)): + expected *= sys**sign + np.testing.assert_allclose(expected.A, result.A) + np.testing.assert_allclose(expected.B, result.B) + np.testing.assert_allclose(expected.C, result.C) + np.testing.assert_allclose(expected.D, result.D) + + @slycotonly + @pytest.mark.parametrize("order", ["left", "right"]) + @pytest.mark.parametrize("sysname", ["sys121", "sys222", "sys322"]) + def test_pow_inv(self, request, sysname, order): + """Check for identity when multiplying by inverse. + + This holds approximately true for a few steps but is very + unstable due to numerical precision. Don't assume this in + real life. For testing purposes only! + """ + sys = request.getfixturevalue(sysname) + if order == "left": + combined = sys**-1 * sys + else: + combined = sys * sys**-1 + combined = combined.minreal() + np.testing.assert_allclose(combined.dcgain(), np.eye(sys.ninputs), + atol=1e-7) + T = np.linspace(0., 0.3, 100) + U = np.random.rand(sys.ninputs, len(T)) + R = combined.forced_response(T=T, U=U, squeeze=False) + # Check that the output is the same as the input + np.testing.assert_allclose(R.outputs, U) + + @slycotonly + def test_truediv(self, sys222, sys322): + """Test state space truediv""" + for sys in [sys222, sys322]: + # Divide by self + result = (sys.__truediv__(sys)).minreal() + expected = StateSpace([], [], [], np.eye(2), dt=0) + assert_tf_close_coeff( + ss2tf(expected).minreal(), + ss2tf(result).minreal(), + ) + # Divide by TF + result = sys.__truediv__(TransferFunction.s) + expected = ss2tf(sys) / TransferFunction.s + assert_tf_close_coeff( + expected.minreal(), + ss2tf(result).minreal(), + ) + + @slycotonly + def test_rtruediv(self, sys222, sys322): + """Test state space rtruediv""" + for sys in [sys222, sys322]: + result = (sys.__rtruediv__(sys)).minreal() + expected = StateSpace([], [], [], np.eye(2), dt=0) + assert_tf_close_coeff( + ss2tf(expected).minreal(), + ss2tf(result).minreal(), + ) + # Divide TF by SS + result = sys.__rtruediv__(TransferFunction.s) + expected = TransferFunction.s / sys + assert_tf_close_coeff( + expected.minreal(), + result.minreal(), + ) + # Divide array by SS + sys = tf2ss(TransferFunction([1, 2], [2, 1])) + result = sys.__rtruediv__(np.eye(2)) + expected = TransferFunction([2, 1], [1, 2]) * np.eye(2) + assert_tf_close_coeff( + expected.minreal(), + ss2tf(result).minreal(), + ) + @pytest.mark.parametrize("k", [2, -3.141, np.float32(2.718), np.array([[4.321], [5.678]])]) def test_truediv_ss_scalar(self, sys322, k): """Divide SS by scalar.""" @@ -364,8 +689,6 @@ def test_call(self, dt, omega, resp): with pytest.raises(AttributeError): sys.evalfr(omega) - - @slycotonly def test_freq_resp(self): """Evaluate the frequency response at multiple frequencies.""" @@ -392,7 +715,7 @@ def test_freq_resp(self): np.testing.assert_almost_equal(omega, true_omega) # Deprecated version of the call (should return warning) - with pytest.warns(DeprecationWarning, match="will be removed"): + with pytest.warns(FutureWarning, match="will be removed"): mag, phase, omega = sys.freqresp(true_omega) np.testing.assert_almost_equal(mag, true_mag) @@ -473,18 +796,22 @@ def test_array_access_ss_failure(self): with pytest.raises(IOError): sys1[0] - @pytest.mark.parametrize("outdx, inpdx", - [(0, 1), - (slice(0, 1, 1), 1), - (0, slice(1, 2, 1)), - (slice(0, 1, 1), slice(1, 2, 1)), - (slice(None, None, -1), 1), - (0, slice(None, None, -1)), - (slice(None, 2, None), 1), - (slice(None, None, 1), slice(None, None, 2)), - (0, slice(1, 2, 1)), - (slice(0, 1, 1), slice(1, 2, 1))]) - def test_array_access_ss(self, outdx, inpdx): + @pytest.mark.parametrize( + "outdx, inpdx", + [(0, 1), + (slice(0, 1, 1), 1), + (0, slice(1, 2, 1)), + (slice(0, 1, 1), slice(1, 2, 1)), + (slice(None, None, -1), 1), + (0, slice(None, None, -1)), + (slice(None, 2, None), 1), + (slice(None, None, 1), slice(None, None, 2)), + (0, slice(1, 2, 1)), + (slice(0, 1, 1), slice(1, 2, 1)), + # ([0, 1], [0]), # lists of indices + ]) + @pytest.mark.parametrize("named", [False, True]) + def test_array_access_ss(self, outdx, inpdx, named): sys1 = StateSpace( [[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]], @@ -492,20 +819,22 @@ def test_array_access_ss(self, outdx, inpdx): [[13., 14.], [15., 16.]], 1, inputs=['u0', 'u1'], outputs=['y0', 'y1']) - sys1_01 = sys1[outdx, inpdx] - + if named: + # Use names instead of numbers (and re-convert in statesp) + outnames = sys1.output_labels[outdx] + inpnames = sys1.input_labels[inpdx] + sys1_01 = sys1[outnames, inpnames] + else: + sys1_01 = sys1[outdx, inpdx] + # Convert int to slice to ensure that numpy doesn't drop the dimension if isinstance(outdx, int): outdx = slice(outdx, outdx+1, 1) if isinstance(inpdx, int): inpdx = slice(inpdx, inpdx+1, 1) - - np.testing.assert_array_almost_equal(sys1_01.A, - sys1.A) - np.testing.assert_array_almost_equal(sys1_01.B, - sys1.B[:, inpdx]) - np.testing.assert_array_almost_equal(sys1_01.C, - sys1.C[outdx, :]) - np.testing.assert_array_almost_equal(sys1_01.D, - sys1.D[outdx, inpdx]) + + np.testing.assert_array_almost_equal(sys1_01.A, sys1.A) + np.testing.assert_array_almost_equal(sys1_01.B, sys1.B[:, inpdx]) + np.testing.assert_array_almost_equal(sys1_01.C, sys1.C[outdx, :]) + np.testing.assert_array_almost_equal(sys1_01.D, sys1.D[outdx, inpdx]) assert sys1.dt == sys1_01.dt assert sys1_01.input_labels == sys1.input_labels[inpdx] @@ -728,19 +1057,24 @@ def test_lft(self): def test_repr(self, sys322): """Test string representation""" - ref322 = "\n".join(["StateSpace(array([[-3., 4., 2.],", - " [-1., -3., 0.],", - " [ 2., 5., 3.]]), array([[ 1., 4.],", - " [-3., -3.],", - " [-2., 1.]]), array([[ 4., 2., -3.],", - " [ 1., 4., 3.]]), array([[-2., 4.],", - " [ 0., 1.]]){dt})"]) - assert repr(sys322) == ref322.format(dt='') + ref322 = """StateSpace( +array([[-3., 4., 2.], + [-1., -3., 0.], + [ 2., 5., 3.]]), +array([[ 1., 4.], + [-3., -3.], + [-2., 1.]]), +array([[ 4., 2., -3.], + [ 1., 4., 3.]]), +array([[-2., 4.], + [ 0., 1.]]), +name='sys322'{dt}, states=3, outputs=2, inputs=2)""" + assert ct.iosys_repr(sys322, format='eval') == ref322.format(dt='') sysd = StateSpace(sys322.A, sys322.B, sys322.C, sys322.D, 0.4) - assert repr(sysd), ref322.format(dt=" == 0.4") + assert ct.iosys_repr(sysd, format='eval'), ref322.format(dt=",\ndt=0.4") array = np.array # noqa - sysd2 = eval(repr(sysd)) + sysd2 = eval(ct.iosys_repr(sysd, format='eval')) np.testing.assert_allclose(sysd.A, sysd2.A) np.testing.assert_allclose(sysd.B, sysd2.B) np.testing.assert_allclose(sysd.C, sysd2.C) @@ -749,31 +1083,31 @@ def test_repr(self, sys322): def test_str(self, sys322): """Test that printing the system works""" tsys = sys322 - tref = (": sys322\n" - "Inputs (2): ['u[0]', 'u[1]']\n" - "Outputs (2): ['y[0]', 'y[1]']\n" - "States (3): ['x[0]', 'x[1]', 'x[2]']\n" - "\n" - "A = [[-3. 4. 2.]\n" - " [-1. -3. 0.]\n" - " [ 2. 5. 3.]]\n" - "\n" - "B = [[ 1. 4.]\n" - " [-3. -3.]\n" - " [-2. 1.]]\n" - "\n" - "C = [[ 4. 2. -3.]\n" - " [ 1. 4. 3.]]\n" - "\n" - "D = [[-2. 4.]\n" - " [ 0. 1.]]\n") - assert str(tsys) == tref + tref = """: sys322 +Inputs (2): ['u[0]', 'u[1]'] +Outputs (2): ['y[0]', 'y[1]'] +States (3): ['x[0]', 'x[1]', 'x[2]']{dt} + +A = [[-3. 4. 2.] + [-1. -3. 0.] + [ 2. 5. 3.]] + +B = [[ 1. 4.] + [-3. -3.] + [-2. 1.]] + +C = [[ 4. 2. -3.] + [ 1. 4. 3.]] + +D = [[-2. 4.] + [ 0. 1.]]""" + assert str(tsys) == tref.format(dt='') tsysdtunspec = StateSpace( tsys.A, tsys.B, tsys.C, tsys.D, True, name=tsys.name) - assert str(tsysdtunspec) == tref + "\ndt = True\n" + assert str(tsysdtunspec) == tref.format(dt="\ndt = True") sysdt1 = StateSpace( tsys.A, tsys.B, tsys.C, tsys.D, 1., name=tsys.name) - assert str(sysdt1) == tref + "\ndt = {}\n".format(1.) + assert str(sysdt1) == tref.format(dt="\ndt = 1.0") def test_pole_static(self): """Regression: poles() of static gain is empty array.""" @@ -1042,7 +1376,7 @@ def test_statespace_defaults(self): "{} is {} but expected {}".format(k, defaults[k], v) -# test data for test_latex_repr below +# test data for test_html_repr below LTX_G1 = ([[np.pi, 1e100], [-1.23456789, 5e-23]], [[0], [1]], [[987654321, 0.001234]], @@ -1054,23 +1388,23 @@ def test_statespace_defaults(self): [[1.2345, -2e-200], [-1, 0]]) LTX_G1_REF = { - 'p3_p' : '$$\n\\left(\\begin{array}{rllrll|rll}\n3.&\\hspace{-1em}14&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{100}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n-1.&\\hspace{-1em}23&\\hspace{-1em}\\phantom{\\cdot}&5\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{-23}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\hline\n9.&\\hspace{-1em}88&\\hspace{-1em}\\cdot10^{8}&0.&\\hspace{-1em}00123&\\hspace{-1em}\\phantom{\\cdot}&5\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n$$', + 'p3_p': "<StateSpace sys: ['u[0]'] -> ['y[0]']{dt}>\n$$\n\\left[\\begin{{array}}{{rllrll|rll}}\n3.&\\hspace{{-1em}}14&\\hspace{{-1em}}\\phantom{{\\cdot}}&1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{100}}&0\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n-1.&\\hspace{{-1em}}23&\\hspace{{-1em}}\\phantom{{\\cdot}}&5\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{-23}}&1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\hline\n9.&\\hspace{{-1em}}88&\\hspace{{-1em}}\\cdot10^{{8}}&0.&\\hspace{{-1em}}00123&\\hspace{{-1em}}\\phantom{{\\cdot}}&5\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n$$", - 'p5_p' : '$$\n\\left(\\begin{array}{rllrll|rll}\n3.&\\hspace{-1em}1416&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{100}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n-1.&\\hspace{-1em}2346&\\hspace{-1em}\\phantom{\\cdot}&5\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{-23}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\hline\n9.&\\hspace{-1em}8765&\\hspace{-1em}\\cdot10^{8}&0.&\\hspace{-1em}001234&\\hspace{-1em}\\phantom{\\cdot}&5\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n$$', + 'p5_p': "<StateSpace sys: ['u[0]'] -> ['y[0]']{dt}>\n$$\n\\left[\\begin{{array}}{{rllrll|rll}}\n3.&\\hspace{{-1em}}1416&\\hspace{{-1em}}\\phantom{{\\cdot}}&1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{100}}&0\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n-1.&\\hspace{{-1em}}2346&\\hspace{{-1em}}\\phantom{{\\cdot}}&5\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{-23}}&1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\hline\n9.&\\hspace{{-1em}}8765&\\hspace{{-1em}}\\cdot10^{{8}}&0.&\\hspace{{-1em}}001234&\\hspace{{-1em}}\\phantom{{\\cdot}}&5\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n$$", - 'p3_s' : '$$\n\\begin{array}{ll}\nA = \\left(\\begin{array}{rllrll}\n3.&\\hspace{-1em}14&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{100}\\\\\n-1.&\\hspace{-1em}23&\\hspace{-1em}\\phantom{\\cdot}&5\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{-23}\\\\\n\\end{array}\\right)\n&\nB = \\left(\\begin{array}{rll}\n0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n\\\\\nC = \\left(\\begin{array}{rllrll}\n9.&\\hspace{-1em}88&\\hspace{-1em}\\cdot10^{8}&0.&\\hspace{-1em}00123&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n&\nD = \\left(\\begin{array}{rll}\n5\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n\\end{array}\n$$', + 'p3_s': "<StateSpace sys: ['u[0]'] -> ['y[0]']{dt}>\n$$\n\\begin{{array}}{{ll}}\nA = \\left[\\begin{{array}}{{rllrll}}\n3.&\\hspace{{-1em}}14&\\hspace{{-1em}}\\phantom{{\\cdot}}&1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{100}}\\\\\n-1.&\\hspace{{-1em}}23&\\hspace{{-1em}}\\phantom{{\\cdot}}&5\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{-23}}\\\\\n\\end{{array}}\\right]\n&\nB = \\left[\\begin{{array}}{{rll}}\n0\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n\\\\\nC = \\left[\\begin{{array}}{{rllrll}}\n9.&\\hspace{{-1em}}88&\\hspace{{-1em}}\\cdot10^{{8}}&0.&\\hspace{{-1em}}00123&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n&\nD = \\left[\\begin{{array}}{{rll}}\n5\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n\\end{{array}}\n$$", - 'p5_s' : '$$\n\\begin{array}{ll}\nA = \\left(\\begin{array}{rllrll}\n3.&\\hspace{-1em}1416&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{100}\\\\\n-1.&\\hspace{-1em}2346&\\hspace{-1em}\\phantom{\\cdot}&5\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{-23}\\\\\n\\end{array}\\right)\n&\nB = \\left(\\begin{array}{rll}\n0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n\\\\\nC = \\left(\\begin{array}{rllrll}\n9.&\\hspace{-1em}8765&\\hspace{-1em}\\cdot10^{8}&0.&\\hspace{-1em}001234&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n&\nD = \\left(\\begin{array}{rll}\n5\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n\\end{array}\n$$', + 'p5_s': "<StateSpace sys: ['u[0]'] -> ['y[0]']{dt}>\n$$\n\\begin{{array}}{{ll}}\nA = \\left[\\begin{{array}}{{rllrll}}\n3.&\\hspace{{-1em}}1416&\\hspace{{-1em}}\\phantom{{\\cdot}}&1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{100}}\\\\\n-1.&\\hspace{{-1em}}2346&\\hspace{{-1em}}\\phantom{{\\cdot}}&5\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{-23}}\\\\\n\\end{{array}}\\right]\n&\nB = \\left[\\begin{{array}}{{rll}}\n0\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n\\\\\nC = \\left[\\begin{{array}}{{rllrll}}\n9.&\\hspace{{-1em}}8765&\\hspace{{-1em}}\\cdot10^{{8}}&0.&\\hspace{{-1em}}001234&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n&\nD = \\left[\\begin{{array}}{{rll}}\n5\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n\\end{{array}}\n$$", } LTX_G2_REF = { - 'p3_p' : '$$\n\\left(\\begin{array}{rllrll}\n1.&\\hspace{-1em}23&\\hspace{-1em}\\phantom{\\cdot}&-2\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{-200}\\\\\n-1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n$$', + 'p3_p': "<StateSpace sys: ['u[0]', 'u[1]'] -> ['y[0]', 'y[1]']{dt}>\n$$\n\\left[\\begin{{array}}{{rllrll}}\n1.&\\hspace{{-1em}}23&\\hspace{{-1em}}\\phantom{{\\cdot}}&-2\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{-200}}\\\\\n-1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}&0\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n$$", - 'p5_p' : '$$\n\\left(\\begin{array}{rllrll}\n1.&\\hspace{-1em}2345&\\hspace{-1em}\\phantom{\\cdot}&-2\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{-200}\\\\\n-1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n$$', + 'p5_p': "<StateSpace sys: ['u[0]', 'u[1]'] -> ['y[0]', 'y[1]']{dt}>\n$$\n\\left[\\begin{{array}}{{rllrll}}\n1.&\\hspace{{-1em}}2345&\\hspace{{-1em}}\\phantom{{\\cdot}}&-2\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{-200}}\\\\\n-1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}&0\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n$$", - 'p3_s' : '$$\n\\begin{array}{ll}\nD = \\left(\\begin{array}{rllrll}\n1.&\\hspace{-1em}23&\\hspace{-1em}\\phantom{\\cdot}&-2\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{-200}\\\\\n-1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n\\end{array}\n$$', + 'p3_s': "<StateSpace sys: ['u[0]', 'u[1]'] -> ['y[0]', 'y[1]']{dt}>\n$$\n\\begin{{array}}{{ll}}\nD = \\left[\\begin{{array}}{{rllrll}}\n1.&\\hspace{{-1em}}23&\\hspace{{-1em}}\\phantom{{\\cdot}}&-2\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{-200}}\\\\\n-1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}&0\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n\\end{{array}}\n$$", - 'p5_s' : '$$\n\\begin{array}{ll}\nD = \\left(\\begin{array}{rllrll}\n1.&\\hspace{-1em}2345&\\hspace{-1em}\\phantom{\\cdot}&-2\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\cdot10^{-200}\\\\\n-1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n\\end{array}\\right)\n\\end{array}\n$$', + 'p5_s': "<StateSpace sys: ['u[0]', 'u[1]'] -> ['y[0]', 'y[1]']{dt}>\n$$\n\\begin{{array}}{{ll}}\nD = \\left[\\begin{{array}}{{rllrll}}\n1.&\\hspace{{-1em}}2345&\\hspace{{-1em}}\\phantom{{\\cdot}}&-2\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\cdot10^{{-200}}\\\\\n-1\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}&0\\phantom{{.}}&\\hspace{{-1em}}&\\hspace{{-1em}}\\phantom{{\\cdot}}\\\\\n\\end{{array}}\\right]\n\\end{{array}}\n$$", } refkey_n = {None: 'p3', '.3g': 'p3', '.5g': 'p5'} @@ -1081,19 +1415,19 @@ def test_statespace_defaults(self): (LTX_G2, LTX_G2_REF)]) @pytest.mark.parametrize("dt, dtref", [(0, ""), - (None, ""), - (True, r"~,~dt=~\mathrm{{True}}"), - (0.1, r"~,~dt={dt:{fmt}}")]) + (None, ", dt=None"), + (True, ", dt=True"), + (0.1, ", dt={dt:{fmt}}")]) @pytest.mark.parametrize("repr_type", [None, "partitioned", "separate"]) @pytest.mark.parametrize("num_format", [None, ".3g", ".5g"]) -def test_latex_repr(gmats, ref, dt, dtref, repr_type, num_format, editsdefaults): - """Test `._latex_repr_` with different config values +def test_html_repr(gmats, ref, dt, dtref, repr_type, num_format, editsdefaults): + """Test `._html_repr_` with different config values This is a 'gold image' test, so if you change behaviour, you'll need to regenerate the reference results. Try something like: control.reset_defaults() - print(f'p3_p : {g1._repr_latex_()!r}') + print(f'p3_p : {g1._repr_html_()!r}') """ from control import set_defaults if num_format is not None: @@ -1102,11 +1436,12 @@ def test_latex_repr(gmats, ref, dt, dtref, repr_type, num_format, editsdefaults) if repr_type is not None: set_defaults('statesp', latex_repr_type=repr_type) - g = StateSpace(*(gmats+(dt,))) + g = StateSpace(*(gmats + (dt,)), name='sys') refkey = "{}_{}".format(refkey_n[num_format], refkey_r[repr_type]) - dt_latex = dtref.format(dt=dt, fmt=defaults['statesp.latex_num_format']) - ref_latex = ref[refkey][:-3] + dt_latex + ref[refkey][-3:] - assert g._repr_latex_() == ref_latex + dt_html = dtref.format(dt=dt, fmt=defaults['statesp.latex_num_format']) + ref_html = ref[refkey].format(dt=dt_html) + assert g._repr_html_() == ref_html + assert g._repr_html_() == g._repr_markdown_() @pytest.mark.parametrize( @@ -1129,8 +1464,8 @@ def test_xferfcn_ndarray_precedence(op, tf, arr): assert isinstance(result, ct.StateSpace) -def test_latex_repr_testsize(editsdefaults): - # _repr_latex_ returns None when size > maxsize +def test_html_repr_testsize(editsdefaults): + # _repr_html_ returns None when size > maxsize from control import set_defaults maxsize = defaults['statesp.latex_maxsize'] @@ -1142,23 +1477,23 @@ def test_latex_repr_testsize(editsdefaults): assert ninputs > 0 g = rss(nstates, ninputs, noutputs) - assert isinstance(g._repr_latex_(), str) + assert isinstance(g._repr_html_(), str) set_defaults('statesp', latex_maxsize=maxsize - 1) - assert g._repr_latex_() is None + assert g._repr_html_() is None set_defaults('statesp', latex_maxsize=-1) - assert g._repr_latex_() is None + assert g._repr_html_() is None gstatic = ss([], [], [], 1) - assert gstatic._repr_latex_() is None + assert gstatic._repr_html_() is None class TestLinfnorm: # these are simple tests; we assume ab13dd is correct # python-control specific behaviour is: - # - checking for continuous- and discrete-time - # - scaling fpeak for discrete-time + # - checking for continuous and discrete time + # - scaling fpeak for discrete time # - handling static gains # the underdamped gpeak and fpeak are found from @@ -1180,6 +1515,7 @@ def dt_siso(self, request): return ct.c2d(systype(*sysargs), dt), refgpeak, reffpeak @slycotonly + @pytest.mark.usefixtures('ignore_future_warning') def test_linfnorm_ct_siso(self, ct_siso): sys, refgpeak, reffpeak = ct_siso gpeak, fpeak = linfnorm(sys) @@ -1187,6 +1523,7 @@ def test_linfnorm_ct_siso(self, ct_siso): np.testing.assert_allclose(fpeak, reffpeak) @slycotonly + @pytest.mark.usefixtures('ignore_future_warning') def test_linfnorm_dt_siso(self, dt_siso): sys, refgpeak, reffpeak = dt_siso gpeak, fpeak = linfnorm(sys) @@ -1195,6 +1532,7 @@ def test_linfnorm_dt_siso(self, dt_siso): np.testing.assert_allclose(fpeak, reffpeak) @slycotonly + @pytest.mark.usefixtures('ignore_future_warning') def test_linfnorm_ct_mimo(self, ct_siso): siso, refgpeak, reffpeak = ct_siso sys = ct.append(siso, siso) @@ -1274,3 +1612,46 @@ def test_tf2ss_mimo(): else: with pytest.raises(ct.ControlMIMONotImplemented): sys_ss = ct.ss(sys_tf) + +def test_convenience_aliases(): + sys = ct.StateSpace(1, 1, 1, 1) + + # Make sure the functions can be used as member function: i.e. they + # support an instance of StateSpace as the first argument and that + # they at least return the correct type + assert isinstance(sys.to_ss(), StateSpace) + assert isinstance(sys.to_tf(), TransferFunction) + assert isinstance(sys.bode_plot(), ct.ControlPlot) + assert isinstance(sys.nyquist_plot(), ct.ControlPlot) + assert isinstance(sys.nichols_plot(), ct.ControlPlot) + assert isinstance(sys.forced_response([0, 1], [1, 1]), + (ct.TimeResponseData, ct.TimeResponseList)) + assert isinstance(sys.impulse_response(), + (ct.TimeResponseData, ct.TimeResponseList)) + assert isinstance(sys.step_response(), + (ct.TimeResponseData, ct.TimeResponseList)) + assert isinstance(sys.initial_response(X0=1), + (ct.TimeResponseData, ct.TimeResponseList)) + + # Make sure that unrecognized keywords for response functions are caught + for method in [LTI.impulse_response, LTI.initial_response, + LTI.step_response]: + with pytest.raises(TypeError, match="unrecognized keyword"): + method(sys, unknown=True) + with pytest.raises(TypeError, match="unrecognized keyword"): + LTI.forced_response(sys, [0, 1], [1, 1], unknown=True) + + +# Test LinearICSystem __call__ +def test_linearic_call(): + import cmath + + sys1 = ct.rss(2, 1, 1, strictly_proper=True, name='sys1') + sys2 = ct.rss(2, 1, 1, strictly_proper=True, name='sys2') + + sys_ic = ct.interconnect( + [sys1, sys2], connections=['sys1.u', 'sys2.y'], + inplist='sys2.u', outlist='sys1.y') + + for s in [0, 1, 1j]: + assert cmath.isclose(sys_ic(s), (sys1 * sys2)(s)) diff --git a/control/tests/stochsys_test.py b/control/tests/stochsys_test.py index 8b846d4a0..6fc87461b 100644 --- a/control/tests/stochsys_test.py +++ b/control/tests/stochsys_test.py @@ -6,7 +6,7 @@ import control as ct import control.optimal as opt -from control import lqe, dlqe, rss, drss, tf, ss, ControlArgument, slycot_check +from control import lqe, dlqe, rss, tf, ControlArgument, slycot_check from math import log, pi # Utility function to check LQE answer @@ -88,7 +88,7 @@ def test_DLQE(method): check_DLQE(L, P, poles, G, QN, RN) def test_lqe_discrete(): - """Test overloading of lqe operator for discrete time systems""" + """Test overloading of lqe operator for discrete-time systems""" csys = ct.rss(2, 1, 1) dsys = ct.drss(2, 1, 1) Q = np.eye(1) @@ -101,7 +101,7 @@ def test_lqe_discrete(): np.testing.assert_almost_equal(S_csys, S_expl) np.testing.assert_almost_equal(E_csys, E_expl) - # Calling lqe() with a discrete time system should call dlqe() + # Calling lqe() with a discrete-time system should call dlqe() K_lqe, S_lqe, E_lqe = ct.lqe(dsys, Q, R) K_dlqe, S_dlqe, E_dlqe = ct.dlqe(dsys, Q, R) np.testing.assert_almost_equal(K_lqe, K_dlqe) @@ -116,7 +116,7 @@ def test_lqe_discrete(): np.testing.assert_almost_equal(S_asys, S_expl) np.testing.assert_almost_equal(E_asys, E_expl) - # Calling dlqe() with a continuous time system should raise an error + # Calling dlqe() with a continuous-time system should raise an error with pytest.raises(ControlArgument, match="called with a continuous"): K, S, E = ct.dlqe(csys, Q, R) @@ -225,26 +225,25 @@ def test_estimator_iosys_ctime(sys_args): def test_estimator_errors(): sys = ct.drss(4, 2, 2, strictly_proper=True) - P0 = np.eye(sys.nstates) QN = np.eye(sys.ninputs) RN = np.eye(sys.noutputs) with pytest.raises(TypeError, match="unrecognized keyword"): - estim = ct.create_estimator_iosystem(sys, QN, RN, unknown=True) + ct.create_estimator_iosystem(sys, QN, RN, unknown=True) with pytest.raises(ct.ControlArgument, match=".* system must be a linear"): sys_tf = ct.tf([1], [1, 1], dt=True) - estim = ct.create_estimator_iosystem(sys_tf, QN, RN) + ct.create_estimator_iosystem(sys_tf, QN, RN) with pytest.raises(ValueError, match="output must be full state"): C = np.eye(2, 4) - estim = ct.create_estimator_iosystem(sys, QN, RN, C=C) + ct.create_estimator_iosystem(sys, QN, RN, C=C) with pytest.raises(ValueError, match="output is the wrong size"): sys_fs = ct.drss(4, 4, 2, strictly_proper=True) sys_fs.C = np.eye(4) C = np.eye(1, 4) - estim = ct.create_estimator_iosystem(sys_fs, QN, RN, C=C) + ct.create_estimator_iosystem(sys_fs, QN, RN, C=C) def test_white_noise(): @@ -404,6 +403,10 @@ def test_oep(dt): np.testing.assert_allclose( est3.states[:, -1], res3.states[:, -1], atol=meas_mag, rtol=meas_mag) + # Make sure unknown keywords generate an error + with pytest.raises(TypeError, match="unrecognized keyword"): + est3 = oep1.compute_estimate(Y3, U, unknown=True) + @pytest.mark.slow def test_mhe(): @@ -426,7 +429,6 @@ def test_mhe(): V = np.array( [0 if i % 2 == 1 else 1 if i % 4 == 0 else -1 for i, t in enumerate(timepts)]).reshape(1, -1) * 0.1 - W = np.sin(timepts / dt) * 1e-3 # Create a moving horizon estimator traj_cost = opt.gaussian_likelihood_cost(sys, Rv, Rw) @@ -478,7 +480,6 @@ def test_indices(ctrl_indices, dist_indices): sysm = ct.ss(sys.A, sys.B[:, ctrl_idx], sys.C, sys.D[:, ctrl_idx]) # Set the simulation time based on the slowest system pole - from math import log T = 10 # Generate a system response with no disturbances diff --git a/control/tests/sysnorm_test.py b/control/tests/sysnorm_test.py index 68edad230..4b4c6c0e4 100644 --- a/control/tests/sysnorm_test.py +++ b/control/tests/sysnorm_test.py @@ -14,11 +14,11 @@ def test_norm_1st_order_stable_system(): """First-order stable continuous-time system""" s = ct.tf('s') - + G1 = 1/(s+1) assert np.allclose(ct.norm(G1, p='inf'), 1.0) # Comparison to norm computed in MATLAB assert np.allclose(ct.norm(G1, p=2), 0.707106781186547) # Comparison to norm computed in MATLAB - + Gd1 = ct.sample_system(G1, 0.1) assert np.allclose(ct.norm(Gd1, p='inf'), 1.0) # Comparison to norm computed in MATLAB assert np.allclose(ct.norm(Gd1, p=2), 0.223513699524858) # Comparison to norm computed in MATLAB @@ -27,12 +27,12 @@ def test_norm_1st_order_stable_system(): def test_norm_1st_order_unstable_system(): """First-order unstable continuous-time system""" s = ct.tf('s') - + G2 = 1/(1-s) assert np.allclose(ct.norm(G2, p='inf'), 1.0) # Comparison to norm computed in MATLAB with pytest.warns(UserWarning, match="System is unstable!"): assert ct.norm(G2, p=2) == float('inf') # Comparison to norm computed in MATLAB - + Gd2 = ct.sample_system(G2, 0.1) assert np.allclose(ct.norm(Gd2, p='inf'), 1.0) # Comparison to norm computed in MATLAB with pytest.warns(UserWarning, match="System is unstable!"): @@ -41,13 +41,13 @@ def test_norm_1st_order_unstable_system(): def test_norm_2nd_order_system_imag_poles(): """Second-order continuous-time system with poles on imaginary axis""" s = ct.tf('s') - + G3 = 1/(s**2+1) with pytest.warns(UserWarning, match="Poles close to, or on, the imaginary axis."): assert ct.norm(G3, p='inf') == float('inf') # Comparison to norm computed in MATLAB with pytest.warns(UserWarning, match="Poles close to, or on, the imaginary axis."): assert ct.norm(G3, p=2) == float('inf') # Comparison to norm computed in MATLAB - + Gd3 = ct.sample_system(G3, 0.1) with pytest.warns(UserWarning, match="Poles close to, or on, the complex unit circle."): assert ct.norm(Gd3, p='inf') == float('inf') # Comparison to norm computed in MATLAB @@ -68,7 +68,7 @@ def test_norm_3rd_order_mimo_system(): G4 = ct.ss(A,B,C,D) # Random system generated in MATLAB assert np.allclose(ct.norm(G4, p='inf'), 4.276759162964244) # Comparison to norm computed in MATLAB assert np.allclose(ct.norm(G4, p=2), 2.237461821810309) # Comparison to norm computed in MATLAB - + Gd4 = ct.sample_system(G4, 0.1) assert np.allclose(ct.norm(Gd4, p='inf'), 4.276759162964228) # Comparison to norm computed in MATLAB assert np.allclose(ct.norm(Gd4, p=2), 0.707434962289554) # Comparison to norm computed in MATLAB diff --git a/control/tests/timebase_test.py b/control/tests/timebase_test.py index 79b1492d7..c416d3fee 100644 --- a/control/tests/timebase_test.py +++ b/control/tests/timebase_test.py @@ -57,7 +57,7 @@ def test_composition(dt1, dt2, dt3, op, type): sys1 = Karray elif dt1 == 'float': sys1 = kfloat - + if isinstance(dt2, (int, float)) or dt2 is None: sys2 = ct.StateSpace(A, B, C, D, dt2) sys2 = type(sys2) @@ -97,3 +97,34 @@ def test_composition_override(dt): with pytest.raises(ValueError, match="incompatible timebases"): sys3 = ct.interconnect( [sys1, sys2], inputs='u1', outputs='y2', dt=dt) + + +# Make sure all system creation functions treat timebases uniformly +@pytest.mark.parametrize( + "fcn, args", [ + (ct.ss, [-1, 1, 1, 1]), + (ct.tf, [[1, 2], [3, 4, 5]]), + (ct.zpk, [[-1], [-2, -3], 1]), + (ct.frd, [[1, 1, 1], [1, 2, 3]]), + (ct.nlsys, [lambda t, x, u, params: -x, None]), + ]) +@pytest.mark.parametrize( + "kwargs, expected", [ + ({}, 0), + ({'dt': 0}, 0), + ({'dt': 0.1}, 0.1), + ({'dt': True}, True), + ({'dt': None}, None), + ]) +def test_default(fcn, args, kwargs, expected): + sys = fcn(*args, **kwargs) + assert sys.dt == expected + + # Some commands allow dt via extra argument + if fcn in [ct.ss, ct.tf, ct.zpk, ct.frd] and kwargs.get('dt'): + sys = fcn(*args, kwargs['dt']) + assert sys.dt == expected + + # Make sure an error is generated if dt is redundant + with pytest.warns(UserWarning, match="received multiple dt"): + sys = fcn(*args, kwargs['dt'], **kwargs) diff --git a/control/tests/timeplot_test.py b/control/tests/timeplot_test.py index 0fcc159be..888ff9080 100644 --- a/control/tests/timeplot_test.py +++ b/control/tests/timeplot_test.py @@ -1,13 +1,13 @@ # timeplot_test.py - test out time response plots # RMM, 23 Jun 2023 -import pytest -import control as ct import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np +import pytest -from control.tests.conftest import slycotonly, mplcleanup +import control as ct +from control.tests.conftest import slycotonly # Detailed test of (almost) all functionality # @@ -123,22 +123,22 @@ def test_response_plots( pltinp is False or response.ninputs == 0 or pltinp is None and response.plot_inputs is False): with pytest.raises(ValueError, match=".* no data to plot"): - out = response.plot(**kwargs) + cplt = response.plot(**kwargs) return None elif not pltout and pltinp == 'overlay': with pytest.raises(ValueError, match="can't overlay inputs"): - out = response.plot(**kwargs) + cplt = response.plot(**kwargs) return None elif pltinp in [True, 'overlay'] and response.ninputs == 0: with pytest.raises(ValueError, match=".* but no inputs"): - out = response.plot(**kwargs) + cplt = response.plot(**kwargs) return None - out = response.plot(**kwargs) + cplt = response.plot(**kwargs) # Make sure all of the outputs are of the right type nlines_plotted = 0 - for ax_lines in np.nditer(out, flags=["refs_ok"]): + for ax_lines in np.nditer(cplt.lines, flags=["refs_ok"]): for line in ax_lines.item(): assert isinstance(line, mpl.lines.Line2D) nlines_plotted += 1 @@ -179,7 +179,7 @@ def test_response_plots( assert len(ax.get_lines()) > 1 # Update the title so we can see what is going on - fig = out[0, 0][0].axes.figure + fig = cplt.figure fig.suptitle( fig._suptitle._text + f" [{sys.noutputs}x{sys.ninputs}, cs={cmbsig}, " @@ -193,46 +193,44 @@ def test_response_plots( @pytest.mark.usefixtures('mplcleanup') def test_axes_setup(): - get_plot_axes = ct.get_plot_axes - sys_2x3 = ct.rss(4, 2, 3) sys_2x3b = ct.rss(4, 2, 3) sys_3x2 = ct.rss(4, 3, 2) sys_3x1 = ct.rss(4, 3, 1) # Two plots of the same size leaves axes unchanged - out1 = ct.step_response(sys_2x3).plot() - out2 = ct.step_response(sys_2x3b).plot() - np.testing.assert_equal(get_plot_axes(out1), get_plot_axes(out2)) + cplt1 = ct.step_response(sys_2x3).plot() + cplt2 = ct.step_response(sys_2x3b).plot() + np.testing.assert_equal(cplt1.axes, cplt2.axes) plt.close() # Two plots of same net size leaves axes unchanged (unfortunately) - out1 = ct.step_response(sys_2x3).plot() - out2 = ct.step_response(sys_3x2).plot() + cplt1 = ct.step_response(sys_2x3).plot() + cplt2 = ct.step_response(sys_3x2).plot() np.testing.assert_equal( - get_plot_axes(out1).reshape(-1), get_plot_axes(out2).reshape(-1)) + cplt1.axes.reshape(-1), cplt2.axes.reshape(-1)) plt.close() # Plots of different shapes generate new plots - out1 = ct.step_response(sys_2x3).plot() - out2 = ct.step_response(sys_3x1).plot() - ax1_list = get_plot_axes(out1).reshape(-1).tolist() - ax2_list = get_plot_axes(out2).reshape(-1).tolist() + cplt1 = ct.step_response(sys_2x3).plot() + cplt2 = ct.step_response(sys_3x1).plot() + ax1_list = cplt1.axes.reshape(-1).tolist() + ax2_list = cplt2.axes.reshape(-1).tolist() for ax in ax1_list: assert ax not in ax2_list plt.close() # Passing a list of axes preserves those axes - out1 = ct.step_response(sys_2x3).plot() - out2 = ct.step_response(sys_3x1).plot() - out3 = ct.step_response(sys_2x3b).plot(ax=get_plot_axes(out1)) - np.testing.assert_equal(get_plot_axes(out1), get_plot_axes(out3)) + cplt1 = ct.step_response(sys_2x3).plot() + cplt2 = ct.step_response(sys_3x1).plot() + cplt3 = ct.step_response(sys_2x3b).plot(ax=cplt1.axes) + np.testing.assert_equal(cplt1.axes, cplt3.axes) plt.close() # Sending an axes array of the wrong size raises exception with pytest.raises(ValueError, match="not the right shape"): - out = ct.step_response(sys_2x3).plot() - ct.step_response(sys_3x1).plot(ax=get_plot_axes(out)) + cplt = ct.step_response(sys_2x3).plot() + ct.step_response(sys_3x1).plot(ax=cplt.axes) sys_2x3 = ct.rss(4, 2, 3) sys_2x3b = ct.rss(4, 2, 3) sys_3x2 = ct.rss(4, 3, 2) @@ -258,7 +256,7 @@ def test_combine_time_responses(): sys_mimo = ct.rss(4, 2, 2) timepts = np.linspace(0, 10, 100) - # Combine two response with ntrace = 0 + # Combine two responses with ntrace = 0 U = np.vstack([np.sin(timepts), np.cos(2*timepts)]) resp1 = ct.input_output_response(sys_mimo, timepts, U) @@ -293,6 +291,7 @@ def test_combine_time_responses(): combresp4 = ct.combine_time_responses( [resp1, resp2, resp3], trace_labels=labels) assert combresp4.trace_labels == labels + assert combresp4.trace_types == [None, None, 'step', 'step'] # Automatically generated trace label names and types resp5 = ct.step_response(sys_mimo, timepts) @@ -302,19 +301,25 @@ def test_combine_time_responses(): combresp5 = ct.combine_time_responses([resp1, resp5]) assert combresp5.trace_labels == [resp1.title] + \ ["test, trace 0", "test, trace 1"] - assert combresp4.trace_types == [None, None, 'step', 'step'] + assert combresp5.trace_types == [None, None, None] + + # ntraces = 0 with trace_types != None + # https://github.com/python-control/python-control/issues/1025 + resp6 = ct.forced_response(sys_mimo, timepts, U) + combresp6 = ct.combine_time_responses([resp1, resp6]) + assert combresp6.trace_types == [None, 'forced'] with pytest.raises(ValueError, match="must have the same number"): resp = ct.step_response(ct.rss(4, 2, 3), timepts) - combresp = ct.combine_time_responses([resp1, resp]) + ct.combine_time_responses([resp1, resp]) with pytest.raises(ValueError, match="trace labels does not match"): - combresp = ct.combine_time_responses( + ct.combine_time_responses( [resp1, resp2], trace_labels=["T1", "T2", "T3"]) with pytest.raises(ValueError, match="must have the same time"): resp = ct.step_response(ct.rss(4, 2, 3), timepts/2) - combresp6 = ct.combine_time_responses([resp1, resp]) + ct.combine_time_responses([resp1, resp]) @pytest.mark.parametrize("resp_fcn", [ @@ -344,26 +349,26 @@ def test_list_responses(resp_fcn): # Sequential plotting results in colors rotating plt.figure() - out1 = resp1.plot() - out2 = resp2.plot() - assert out1.shape == shape - assert out2.shape == shape + cplt1 = resp1.plot() + cplt2 = resp2.plot() + assert cplt1.shape == shape # legacy access (OK here) + assert cplt2.shape == shape # legacy access (OK here) for row in range(2): # just look at the outputs for col in range(shape[1]): - assert out1[row, col][0].get_color() == 'tab:blue' - assert out2[row, col][0].get_color() == 'tab:orange' + assert cplt1.lines[row, col][0].get_color() == 'tab:blue' + assert cplt2.lines[row, col][0].get_color() == 'tab:orange' plt.figure() resp_combined = resp_fcn([sys1, sys2], **kwargs) assert isinstance(resp_combined, ct.timeresp.TimeResponseList) assert resp_combined[0].time[-1] == max(resp1.time[-1], resp2.time[-1]) assert resp_combined[1].time[-1] == max(resp1.time[-1], resp2.time[-1]) - out = resp_combined.plot() - assert out.shape == shape + cplt = resp_combined.plot() + assert cplt.lines.shape == shape for row in range(2): # just look at the outputs for col in range(shape[1]): - assert out[row, col][0].get_color() == 'tab:blue' - assert out[row, col][1].get_color() == 'tab:orange' + assert cplt.lines[row, col][0].get_color() == 'tab:blue' + assert cplt.lines[row, col][1].get_color() == 'tab:orange' @slycotonly @@ -373,20 +378,20 @@ def test_linestyles(): sys_mimo = ct.tf2ss( [[[1], [0.1]], [[0.2], [1]]], [[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="MIMO") - out = ct.step_response(sys_mimo).plot('k--', plot_inputs=True) - for ax in np.nditer(out, flags=["refs_ok"]): + cplt = ct.step_response(sys_mimo).plot('k--', plot_inputs=True) + for ax in np.nditer(cplt.lines, flags=["refs_ok"]): for line in ax.item(): assert line.get_color() == 'k' assert line.get_linestyle() == '--' - out = ct.step_response(sys_mimo).plot( + cplt = ct.step_response(sys_mimo).plot( plot_inputs='overlay', overlay_signals=True, overlay_traces=True, output_props=[{'color': c} for c in ['blue', 'orange']], input_props=[{'color': c} for c in ['red', 'green']], trace_props=[{'linestyle': s} for s in ['-', '--']]) - assert out.shape == (1, 1) - lines = out[0, 0] + assert cplt.lines.shape == (1, 1) + lines = cplt.lines[0, 0] assert lines[0].get_color() == 'blue' and lines[0].get_linestyle() == '-' assert lines[1].get_color() == 'orange' and lines[1].get_linestyle() == '-' assert lines[2].get_color() == 'red' and lines[2].get_linestyle() == '-' @@ -397,41 +402,6 @@ def test_linestyles(): assert lines[7].get_color() == 'green' and lines[7].get_linestyle() == '--' -@pytest.mark.usefixtures('mplcleanup') -def test_rcParams(): - sys = ct.rss(2, 2, 2) - - # Create new set of rcParams - my_rcParams = {} - for key in [ - 'axes.labelsize', 'axes.titlesize', 'figure.titlesize', - 'legend.fontsize', 'xtick.labelsize', 'ytick.labelsize']: - match plt.rcParams[key]: - case 8 | 9 | 10: - my_rcParams[key] = plt.rcParams[key] + 1 - case 'medium': - my_rcParams[key] = 11.5 - case 'large': - my_rcParams[key] = 9.5 - case _: - raise ValueError(f"unknown rcParam type for {key}") - - # Generate a figure with the new rcParams - out = ct.step_response(sys).plot(rcParams=my_rcParams) - ax = out[0, 0][0].axes - fig = ax.figure - - # Check to make sure new settings were used - assert ax.xaxis.get_label().get_fontsize() == my_rcParams['axes.labelsize'] - assert ax.yaxis.get_label().get_fontsize() == my_rcParams['axes.labelsize'] - assert ax.title.get_fontsize() == my_rcParams['axes.titlesize'] - assert ax.get_xticklabels()[0].get_fontsize() == \ - my_rcParams['xtick.labelsize'] - assert ax.get_yticklabels()[0].get_fontsize() == \ - my_rcParams['ytick.labelsize'] - assert fig._suptitle.get_fontsize() == my_rcParams['figure.titlesize'] - - @pytest.mark.parametrize("resp_fcn", [ ct.step_response, ct.initial_response, ct.impulse_response, ct.forced_response, ct.input_output_response]) @@ -444,23 +414,20 @@ def test_timeplot_trace_labels(resp_fcn): # Figure out the expected shape of the system match resp_fcn: case ct.step_response | ct.impulse_response: - shape = (2, 2) kwargs = {} case ct.initial_response: - shape = (2, 1) kwargs = {} case ct.forced_response | ct.input_output_response: - shape = (4, 1) # outputs and inputs both plotted T = np.linspace(0, 10) U = [np.sin(T), np.cos(T)] kwargs = {'T': T, 'U': U} # Use figure frame for suptitle to speed things up - ct.set_defaults('freqplot', suptitle_frame='figure') + ct.set_defaults('freqplot', title_frame='figure') # Make sure default labels are as expected - out = resp_fcn([sys1, sys2], **kwargs).plot() - axs = ct.get_plot_axes(out) + cplt = resp_fcn([sys1, sys2], **kwargs).plot() + axs = cplt.axes if axs.ndim == 1: legend = axs[0].get_legend().get_texts() else: @@ -470,8 +437,8 @@ def test_timeplot_trace_labels(resp_fcn): plt.close() # Override labels all at once - out = resp_fcn([sys1, sys2], **kwargs).plot(label=['line1', 'line2']) - axs = ct.get_plot_axes(out) + cplt = resp_fcn([sys1, sys2], **kwargs).plot(label=['line1', 'line2']) + axs = cplt.axes if axs.ndim == 1: legend = axs[0].get_legend().get_texts() else: @@ -481,9 +448,9 @@ def test_timeplot_trace_labels(resp_fcn): plt.close() # Override labels one at a time - out = resp_fcn(sys1, **kwargs).plot(label='line1') - out = resp_fcn(sys2, **kwargs).plot(label='line2') - axs = ct.get_plot_axes(out) + cplt = resp_fcn(sys1, **kwargs).plot(label='line1') + cplt = resp_fcn(sys2, **kwargs).plot(label='line2') + axs = cplt.axes if axs.ndim == 1: legend = axs[0].get_legend().get_texts() else: @@ -513,10 +480,10 @@ def test_full_label_override(): labels_4d[i, j, k, 1] = "inp" + sys + trace + out # Test 4D labels - out = ct.step_response([sys1, sys2]).plot( + cplt = ct.step_response([sys1, sys2]).plot( overlay_signals=True, overlay_traces=True, plot_inputs=True, label=labels_4d) - axs = ct.get_plot_axes(out) + axs = cplt.axes assert axs.shape == (2, 1) legend_text = axs[0, 0].get_legend().get_texts() for i, label in enumerate(labels_2d[0]): @@ -526,10 +493,10 @@ def test_full_label_override(): assert legend_text[i].get_text() == label # Test 2D labels - out = ct.step_response([sys1, sys2]).plot( + cplt = ct.step_response([sys1, sys2]).plot( overlay_signals=True, overlay_traces=True, plot_inputs=True, label=labels_2d) - axs = ct.get_plot_axes(out) + axs = cplt.axes assert axs.shape == (2, 1) legend_text = axs[0, 0].get_legend().get_texts() for i, label in enumerate(labels_2d[0]): @@ -548,8 +515,8 @@ def test_relabel(): ct.step_response(sys1).plot() # Generate a new plot, which overwrites labels - out = ct.step_response(sys2).plot() - ax = ct.get_plot_axes(out) + cplt = ct.step_response(sys2).plot() + ax = cplt.axes assert ax[0, 0].get_ylabel() == 'y[0]' # Regenerate the first plot @@ -557,9 +524,9 @@ def test_relabel(): ct.step_response(sys1).plot() # Generate a new plt, without relabeling - out = ct.step_response(sys2).plot(relabel=False) - ax = ct.get_plot_axes(out) - assert ax[0, 0].get_ylabel() == 'y' + with pytest.warns(FutureWarning, match="deprecated"): + cplt = ct.step_response(sys2).plot(relabel=False) + assert cplt.axes[0, 0].get_ylabel() == 'y' def test_errors(): @@ -579,8 +546,8 @@ def test_errors(): for kw in ['input_props', 'output_props', 'trace_props']: propkw = {kw: {'color': 'green'}} with pytest.warns(UserWarning, match="ignored since fmt string"): - out = stepresp.plot('k-', **propkw) - assert out[0, 0][0].get_color() == 'k' + cplt = stepresp.plot('k-', **propkw) + assert cplt.lines[0, 0][0].get_color() == 'k' # Make sure TimeResponseLists also work stepresp = ct.step_response([sys, sys]) @@ -596,24 +563,24 @@ def test_legend_customization(): resp = ct.input_output_response(sys, timepts, U) # Generic input/output plot - out = resp.plot(overlay_signals=True) - axs = ct.get_plot_axes(out) + cplt = resp.plot(overlay_signals=True) + axs = cplt.axes assert axs[0, 0].get_legend()._loc == 7 # center right assert len(axs[0, 0].get_legend().get_texts()) == 2 assert axs[1, 0].get_legend() == None plt.close() # Hide legend - out = resp.plot(overlay_signals=True, show_legend=False) - axs = ct.get_plot_axes(out) + cplt = resp.plot(overlay_signals=True, show_legend=False) + axs = cplt.axes assert axs[0, 0].get_legend() == None assert axs[1, 0].get_legend() == None plt.close() # Put legend in both axes - out = resp.plot( + cplt = resp.plot( overlay_signals=True, legend_map=[['center left'], ['center right']]) - axs = ct.get_plot_axes(out) + axs = cplt.axes assert axs[0, 0].get_legend()._loc == 6 # center left assert len(axs[0, 0].get_legend().get_texts()) == 2 assert axs[1, 0].get_legend()._loc == 7 # center right @@ -713,7 +680,7 @@ def test_legend_customization(): plt.savefig('timeplot-mimo_ioresp-mt_tr.png') plt.figure() - out = ct.step_response(sys_mimo).plot( + cplt = ct.step_response(sys_mimo).plot( plot_inputs='overlay', overlay_signals=True, overlay_traces=True, output_props=[{'color': c} for c in ['blue', 'orange']], input_props=[{'color': c} for c in ['red', 'green']], @@ -725,22 +692,22 @@ def test_legend_customization(): resp_list = ct.step_response([sys1, sys2]) fig = plt.figure() - ct.combine_time_responses( + cplt = ct.combine_time_responses( [ct.step_response(sys1, resp_list[0].time), ct.step_response(sys2, resp_list[1].time)] ).plot(overlay_traces=True) - ct.suptitle("[Combine] " + fig._suptitle._text) + cplt.set_plot_title("[Combine] " + fig._suptitle._text) fig = plt.figure() ct.step_response(sys1).plot() - ct.step_response(sys2).plot() - ct.suptitle("[Sequential] " + fig._suptitle._text) + cplt = ct.step_response(sys2).plot() + cplt.set_plot_title("[Sequential] " + fig._suptitle._text) fig = plt.figure() ct.step_response(sys1).plot(color='b') - ct.step_response(sys2).plot(color='r') - ct.suptitle("[Seq w/color] " + fig._suptitle._text) + cplt = ct.step_response(sys2).plot(color='r') + cplt.set_plot_title("[Seq w/color] " + fig._suptitle._text) fig = plt.figure() - ct.step_response([sys1, sys2]).plot() - ct.suptitle("[List] " + fig._suptitle._text) + cplt = ct.step_response([sys1, sys2]).plot() + cplt.set_plot_title("[List] " + fig._suptitle._text) diff --git a/control/tests/timeresp_test.py b/control/tests/timeresp_test.py index e2d93be0e..8bbd27d73 100644 --- a/control/tests/timeresp_test.py +++ b/control/tests/timeresp_test.py @@ -5,7 +5,6 @@ import numpy as np import pytest -import scipy as sp import control as ct from control import StateSpace, TransferFunction, c2d, isctime, ss2tf, tf2ss @@ -68,7 +67,7 @@ def tsystem(self, request): siso_tf2 = copy(siso_ss1) siso_tf2.sys = ss2tf(siso_ss1.sys) - """MIMO system, contains ``siso_ss1`` twice""" + """MIMO system, contains `siso_ss1` twice""" mimo_ss1 = copy(siso_ss1) A = np.zeros((4, 4)) A[:2, :2] = siso_ss1.sys.A @@ -84,7 +83,7 @@ def tsystem(self, request): D[1:, 1:] = siso_ss1.sys.D mimo_ss1.sys = StateSpace(A, B, C, D) - """MIMO system, contains ``siso_ss2`` twice""" + """MIMO system, contains `siso_ss2` twice""" mimo_ss2 = copy(siso_ss2) A = np.zeros((4, 4)) A[:2, :2] = siso_ss2.sys.A @@ -336,15 +335,20 @@ def test_step_response_siso(self, tsystem, kwargs): @pytest.mark.parametrize("tsystem", ["mimo_ss1"], indirect=True) def test_step_response_mimo(self, tsystem): - """Test MIMO system, which contains ``siso_ss1`` twice.""" + """Test MIMO system, which contains `siso_ss1` twice.""" sys = tsystem.sys t = tsystem.t yref = tsystem.ystep _t, y_00 = step_response(sys, T=t, input=0, output=0) - _t, y_11 = step_response(sys, T=t, input=1, output=1) np.testing.assert_array_almost_equal(y_00, yref, decimal=4) + + _t, y_11 = step_response(sys, T=t, input=1, output=1) np.testing.assert_array_almost_equal(y_11, yref, decimal=4) + _t, y_01 = step_response( + sys, T=t, input_indices=[0], output_indices=[1]) + np.testing.assert_array_almost_equal(y_01, 0 * yref, decimal=4) + # Make sure we get the same result using MIMO step response response = step_response(sys, T=t) np.testing.assert_allclose(response.y[0, 0, :], y_00) @@ -354,11 +358,16 @@ def test_step_response_mimo(self, tsystem): np.testing.assert_allclose(response.u[0, 1, :], 0) np.testing.assert_allclose(response.u[1, 1, :], 1) + # Index lists not yet implemented + with pytest.raises(NotImplementedError, match="list of .* indices"): + step_response( + sys, timepts=t, input_indices=[0, 1], output_indices=[1]) + @pytest.mark.parametrize("tsystem", ["mimo_ss1"], indirect=True) def test_step_response_return(self, tsystem): """Verify continuous and discrete time use same return conventions.""" sysc = tsystem.sys - sysd = c2d(sysc, 1) # discrete time system + sysd = c2d(sysc, 1) # discrete-time system Tvec = np.linspace(0, 10, 11) # make sure to use integer times 0..10 Tc, youtc = step_response(sysc, Tvec, input=0) Td, youtd = step_response(sysd, Tvec, input=0) @@ -520,26 +529,36 @@ def test_impulse_response_mimo(self, tsystem): yref = tsystem.yimpulse _t, y_00 = impulse_response(sys, T=t, input=0, output=0) np.testing.assert_array_almost_equal(y_00, yref, decimal=4) + _t, y_11 = impulse_response(sys, T=t, input=1, output=1) np.testing.assert_array_almost_equal(y_11, yref, decimal=4) + _t, y_01 = impulse_response( + sys, T=t, input_indices=[0], output_indices=[1]) + np.testing.assert_array_almost_equal(y_01, 0 * yref, decimal=4) + yref_notrim = np.zeros((2, len(t))) yref_notrim[:1, :] = yref _t, yy = impulse_response(sys, T=t, input=0) np.testing.assert_array_almost_equal(yy[:,0,:], yref_notrim, decimal=4) + # Index lists not yet implemented + with pytest.raises(NotImplementedError, match="list of .* indices"): + impulse_response( + sys, timepts=t, input_indices=[0, 1], output_indices=[1]) + @pytest.mark.parametrize("tsystem", ["siso_tf1"], indirect=True) def test_discrete_time_impulse(self, tsystem): - # discrete time impulse sampled version should match cont time + # discrete-time impulse sampled version should match cont time dt = 0.1 t = np.arange(0, 3, dt) sys = tsystem.sys sysdt = sys.sample(dt, 'impulse') np.testing.assert_array_almost_equal(impulse_response(sys, t)[1], impulse_response(sysdt, t)[1]) - + def test_discrete_time_impulse_input(self): - # discrete time impulse input, Only one active input for each trace + # discrete-time impulse input, Only one active input for each trace A = [[.5, 0.25],[.0, .5]] B = [[1., 0,],[0., 1.]] C = [[1., 0.],[0., 1.]] @@ -734,10 +753,10 @@ def test_forced_response_invalid_d(self, tsystem): """Test invalid parameters dtime with sys.dt > 0.""" with pytest.raises(ValueError, match="can't both be zero"): forced_response(tsystem.sys) - with pytest.raises(ValueError, match="Parameter ``U``: Wrong shape"): + with pytest.raises(ValueError, match="Parameter `U`: Wrong shape"): forced_response(tsystem.sys, T=tsystem.t, U=np.random.randn(1, 12)) - with pytest.raises(ValueError, match="Parameter ``U``: Wrong shape"): + with pytest.raises(ValueError, match="Parameter `U`: Wrong shape"): forced_response(tsystem.sys, T=tsystem.t, U=np.random.randn(12)) with pytest.raises(ValueError, match="must match sampling time"): @@ -1178,7 +1197,6 @@ def test_squeeze_0_8_4(self, nstate, nout, ninp, squeeze, shape): # Generate system, time, and input vectors sys = ct.rss(nstate, nout, ninp, strictly_proper=True) tvec = np.linspace(0, 1, 8) - uvec =np.ones((sys.ninputs, 1)) @ np.reshape(np.sin(tvec), (1, 8)) _, yvec = ct.initial_response(sys, tvec, 1, squeeze=squeeze) assert yvec.shape == shape @@ -1247,13 +1265,14 @@ def test_to_pandas(): np.testing.assert_equal(df['x[1]'], resp.states[1]) # Change the time points - sys = ct.rss(2, 1, 1) + sys = ct.rss(2, 1, 2) T = np.linspace(0, timepts[-1]/2, timepts.size * 2) - resp = ct.input_output_response(sys, timepts, np.sin(timepts), t_eval=T) + resp = ct.input_output_response( + sys, timepts, [np.sin(timepts), 0], t_eval=T) df = resp.to_pandas() np.testing.assert_equal(df['time'], resp.time) - np.testing.assert_equal(df['u[0]'], resp.inputs) - np.testing.assert_equal(df['y[0]'], resp.outputs) + np.testing.assert_equal(df['u[0]'], resp.inputs[0]) + np.testing.assert_equal(df['y[0]'], resp.outputs[0]) np.testing.assert_equal(df['x[0]'], resp.states[0]) np.testing.assert_equal(df['x[1]'], resp.states[1]) @@ -1265,6 +1284,33 @@ def test_to_pandas(): np.testing.assert_equal(df['u[0]'], resp.inputs) np.testing.assert_equal(df['y[0]'], resp.inputs * 5) + # Multi-trace data + # https://github.com/python-control/python-control/issues/1087 + model = ct.rss( + states=['x0', 'x1'], outputs=['y0', 'y1'], + inputs=['u0', 'u1'], name='My Model') + T = np.linspace(0, 10, 100, endpoint=False) + X0 = np.zeros(model.nstates) + + res = ct.step_response(model, T=T, X0=X0, input=0) # extract single trace + df = res.to_pandas() + np.testing.assert_equal( + df[df['trace'] == 'From u0']['time'], res.time) + np.testing.assert_equal( + df[df['trace'] == 'From u0']['u0'], res.inputs['u0', 0]) + np.testing.assert_equal( + df[df['trace'] == 'From u0']['y1'], res.outputs['y1', 0]) + + res = ct.step_response(model, T=T, X0=X0) # all traces + df = res.to_pandas() + for i, label in enumerate(res.trace_labels): + np.testing.assert_equal( + df[df['trace'] == label]['time'], res.time) + np.testing.assert_equal( + df[df['trace'] == label]['u1'], res.inputs['u1', i]) + np.testing.assert_equal( + df[df['trace'] == label]['y0'], res.outputs['y0', i]) + @pytest.mark.skipif(pandas_check(), reason="pandas installed") def test_no_pandas(): @@ -1275,7 +1321,7 @@ def test_no_pandas(): # Convert to pandas with pytest.raises(ImportError, match="pandas"): - df = resp.to_pandas() + resp.to_pandas() # https://github.com/python-control/python-control/issues/1014 @@ -1318,3 +1364,103 @@ def test_step_info_nonstep(): assert step_info['Peak'] == 1 assert step_info['PeakTime'] == 0 assert isclose(step_info['SteadyStateValue'], 0.96) + + +def test_signal_labels(): + # Create a system response for a SISO system + sys = ct.rss(4, 1, 1) + response = ct.step_response(sys) + + # Make sure access via strings works + np.testing.assert_equal(response.states['x[2]'], response.states[2]) + + # Make sure access via lists of strings works + np.testing.assert_equal( + response.states[['x[1]', 'x[2]']], response.states[[1, 2]]) + + # Make sure errors are generated if key is unknown + with pytest.raises(ValueError, match="unknown signal name 'bad'"): + response.inputs['bad'] + + with pytest.raises(ValueError, match="unknown signal name 'bad'"): + response.states[['x[1]', 'bad']] + + # Create a system response for a MIMO system + sys = ct.rss(4, 2, 2) + response = ct.step_response(sys) + + # Make sure access via strings works + np.testing.assert_equal( + response.outputs['y[0]', 'u[1]'], + response.outputs[0, 1]) + np.testing.assert_equal( + response.states['x[2]', 'u[0]'], response.states[2, 0]) + + # Make sure access via lists of strings works + np.testing.assert_equal( + response.states[['x[1]', 'x[2]'], 'u[0]'], + response.states[[1, 2], 0]) + + np.testing.assert_equal( + response.outputs[['y[1]'], ['u[1]', 'u[0]']], + response.outputs[[1], [1, 0]]) + + # Make sure errors are generated if key is unknown + with pytest.raises(ValueError, match="unknown signal name 'bad'"): + response.inputs['bad'] + + with pytest.raises(ValueError, match="unknown signal name 'bad'"): + response.states[['x[1]', 'bad']] + + with pytest.raises(ValueError, match=r"unknown signal name 'x\[2\]'"): + response.states['x[1]', 'x[2]'] # second index = input name + + +def test_timeresp_aliases(): + sys = ct.rss(2, 1, 1) + timepts = np.linspace(0, 10, 10) + resp_long = ct.input_output_response(sys, timepts, 1, initial_state=[1, 1]) + + # Positional usage + resp_posn = ct.input_output_response(sys, timepts, 1, [1, 1]) + np.testing.assert_allclose(resp_long.states, resp_posn.states) + + # Aliases + resp_short = ct.input_output_response(sys, timepts, 1, X0=[1, 1]) + np.testing.assert_allclose(resp_long.states, resp_short.states) + + # Legacy + with pytest.warns(PendingDeprecationWarning, match="legacy"): + resp_legacy = ct.input_output_response(sys, timepts, 1, x0=[1, 1]) + np.testing.assert_allclose(resp_long.states, resp_legacy.states) + + # Check for multiple values: full keyword and alias + with pytest.raises(TypeError, match="multiple"): + ct.input_output_response( + sys, timepts, 1, initial_state=[1, 2], X0=[1, 1]) + + # Check for multiple values: positional and keyword + with pytest.raises(TypeError, match="multiple"): + ct.input_output_response( + sys, timepts, 1, [1, 2], initial_state=[1, 1]) + + # Check for multiple values: positional and alias + with pytest.raises(TypeError, match="multiple"): + ct.input_output_response( + sys, timepts, 1, [1, 2], X0=[1, 1]) + + # Make sure that LTI functions check for keywords + with pytest.raises(TypeError, match="unrecognized keyword"): + ct.forced_response(sys, timepts, 1, unknown=True) + + with pytest.raises(TypeError, match="unrecognized keyword"): + ct.impulse_response(sys, timepts, unknown=True) + + with pytest.raises(TypeError, match="unrecognized keyword"): + ct.initial_response(sys, timepts, [1, 2], unknown=True) + + with pytest.raises(TypeError, match="unrecognized keyword"): + ct.step_response(sys, timepts, unknown=True) + + with pytest.raises(TypeError, match="unrecognized keyword"): + ct.step_info(sys, timepts, unknown=True) diff --git a/control/tests/trdata_test.py b/control/tests/trdata_test.py index 7d0c20e7a..b84369d72 100644 --- a/control/tests/trdata_test.py +++ b/control/tests/trdata_test.py @@ -214,7 +214,7 @@ def test_response_copy(): # Unknown keyword with pytest.raises(TypeError, match="unrecognized keywords"): - response_bad_kw = response_mimo(input=0) + response_mimo(input=0) def test_trdata_labels(): diff --git a/control/tests/xferfcn_input_test.py b/control/tests/xferfcn_input_test.py index 46efbd257..c375d768a 100644 --- a/control/tests/xferfcn_input_test.py +++ b/control/tests/xferfcn_input_test.py @@ -64,15 +64,18 @@ def test_clean_part(num, fun, dtype): num_ = _clean_part(numa) ref_ = np.array(num, dtype=float, ndmin=3) - assert isinstance(num_, list) - assert np.all([isinstance(part, list) for part in num_]) + assert isinstance(num_, np.ndarray) + assert num_.ndim == 2 for i, numi in enumerate(num_): assert len(numi) == ref_.shape[1] for j, numj in enumerate(numi): np.testing.assert_allclose(numj, ref_[i, j, ...]) -@pytest.mark.parametrize("badinput", [[[0., 1.], [2., 3.]], "a"]) +@pytest.mark.parametrize("badinput", [ + # [[0., 1.], [2., 3.]], # OK: treated as static array + np.ones((2, 2, 2, 2)), + "a"]) def test_clean_part_bad_input(badinput): """Give the part cleaner invalid input type.""" with pytest.raises(TypeError): diff --git a/control/tests/xferfcn_test.py b/control/tests/xferfcn_test.py index cb5b38cba..d3db08ef6 100644 --- a/control/tests/xferfcn_test.py +++ b/control/tests/xferfcn_test.py @@ -14,7 +14,7 @@ isdtime, reset_defaults, rss, sample_system, set_defaults, ss, ss2tf, tf, tf2ss, zpk) from control.statesp import _convert_to_statespace -from control.tests.conftest import slycotonly +from control.tests.conftest import assert_tf_close_coeff, slycotonly from control.xferfcn import _convert_to_transfer_function @@ -30,13 +30,6 @@ class TestXferFcn: def test_constructor_bad_input_type(self): """Give the constructor invalid input types.""" - # MIMO requires lists of lists of vectors (not lists of vectors) - with pytest.raises(TypeError): - TransferFunction([[0., 1.], [2., 3.]], [[5., 2.], [3., 0.]]) - # good input - TransferFunction([[[0., 1.], [2., 3.]]], - [[[5., 2.], [3., 0.]]]) - # Single argument of the wrong type with pytest.raises(TypeError): TransferFunction([1]) @@ -56,6 +49,10 @@ def test_constructor_bad_input_type(self): [[4, 5], [6, 7]]], [[[6, 7], [4, 5]], [[2, 3]]]) + + with pytest.raises(TypeError, match="unsupported data type"): + ct.tf([1j], [1, 2, 3]) + # good input TransferFunction([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], @@ -113,9 +110,28 @@ def test_constructor_double_dt(self): def test_add_inconsistent_dimension(self): """Add two transfer function matrices of different sizes.""" - sys1 = TransferFunction([[[1., 2.]]], [[[4., 5.]]]) - sys2 = TransferFunction([[[4., 3.]], [[1., 2.]]], - [[[1., 6.]], [[2., 4.]]]) + sys1 = TransferFunction( + [ + [[1., 2.]], + [[2., -2.]], + [[2., 1.]], + ], + [ + [[4., 5.]], + [[5., 2.]], + [[3., 2.]], + ], + ) + sys2 = TransferFunction( + [ + [[4., 3.]], + [[1., 2.]], + ], + [ + [[1., 6.]], + [[2., 4.]], + ] + ) with pytest.raises(ValueError): sys1.__add__(sys2) with pytest.raises(ValueError): @@ -174,7 +190,6 @@ def test_reverse_sign_siso(self): np.testing.assert_allclose(sys2.num, [[[-1., -3., -5.]]]) np.testing.assert_allclose(sys2.den, [[[1., 6., 2., -1.]]]) - @slycotonly def test_reverse_sign_mimo(self): """Negate a MIMO system.""" num1 = [[[1., 2.], [0., 3.], [2., -1.]], @@ -190,8 +205,10 @@ def test_reverse_sign_mimo(self): for i in range(sys3.noutputs): for j in range(sys3.ninputs): - np.testing.assert_allclose(sys2.num[i][j], sys3.num[i][j]) - np.testing.assert_allclose(sys2.den[i][j], sys3.den[i][j]) + np.testing.assert_allclose( + sys2.num_array[i, j], sys3.num_array[i, j]) + np.testing.assert_allclose( + sys2.den_array[i, j], sys3.den_array[i, j]) # Tests for TransferFunction.__add__ @@ -214,7 +231,6 @@ def test_add_siso(self): np.testing.assert_allclose(sys3.num, [[[20., 4., -8]]]) np.testing.assert_allclose(sys3.den, [[[1., 6., 1., -7., -2., 1.]]]) - @slycotonly def test_add_mimo(self): """Add two MIMO systems.""" num1 = [[[1., 2.], [0., 3.], [2., -1.]], @@ -236,8 +252,8 @@ def test_add_mimo(self): for i in range(sys3.noutputs): for j in range(sys3.ninputs): - np.testing.assert_allclose(sys3.num[i][j], num3[i][j]) - np.testing.assert_allclose(sys3.den[i][j], den3[i][j]) + np.testing.assert_allclose(sys3.num_array[i, j], num3[i][j]) + np.testing.assert_allclose(sys3.den_array[i, j], den3[i][j]) # Tests for TransferFunction.__sub__ @@ -262,7 +278,6 @@ def test_subtract_siso(self): np.testing.assert_allclose(sys4.num, [[[-2., -6., 12., 10., 2.]]]) np.testing.assert_allclose(sys4.den, [[[1., 6., 1., -7., -2., 1.]]]) - @slycotonly def test_subtract_mimo(self): """Subtract two MIMO systems.""" num1 = [[[1., 2.], [0., 3.], [2., -1.]], @@ -284,8 +299,8 @@ def test_subtract_mimo(self): for i in range(sys3.noutputs): for j in range(sys3.ninputs): - np.testing.assert_allclose(sys3.num[i][j], num3[i][j]) - np.testing.assert_allclose(sys3.den[i][j], den3[i][j]) + np.testing.assert_allclose(sys3.num_array[i, j], num3[i][j]) + np.testing.assert_allclose(sys3.den_array[i, j], den3[i][j]) # Tests for TransferFunction.__mul__ @@ -313,7 +328,6 @@ def test_multiply_siso(self): np.testing.assert_allclose(sys3.num, sys4.num) np.testing.assert_allclose(sys3.den, sys4.den) - @slycotonly def test_multiply_mimo(self): """Multiply two MIMO systems.""" num1 = [[[1., 2.], [0., 3.], [2., -1.]], @@ -340,8 +354,8 @@ def test_multiply_mimo(self): for i in range(sys3.noutputs): for j in range(sys3.ninputs): - np.testing.assert_allclose(sys3.num[i][j], num3[i][j]) - np.testing.assert_allclose(sys3.den[i][j], den3[i][j]) + np.testing.assert_allclose(sys3.num_array[i, j], num3[i][j]) + np.testing.assert_allclose(sys3.den_array[i, j], den3[i][j]) # Tests for TransferFunction.__div__ @@ -390,19 +404,253 @@ def test_pow(self): with pytest.raises(ValueError): TransferFunction.__pow__(sys1, 0.5) - def test_slice(self): + def test_add_sub_mimo_siso(self): + for op in [ + TransferFunction.__add__, + TransferFunction.__radd__, + TransferFunction.__sub__, + TransferFunction.__rsub__, + ]: + tf_mimo = TransferFunction( + [ + [[1], [1]], + [[1], [1]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ) + tf_siso = TransferFunction([1], [5, 0]) + tf_arr = ct.split_tf(tf_mimo) + expected = ct.combine_tf([ + [op(tf_arr[0, 0], tf_siso), op(tf_arr[0, 1], tf_siso)], + [op(tf_arr[1, 0], tf_siso), op(tf_arr[1, 1], tf_siso)], + ]) + result = op(tf_mimo, tf_siso) + assert_tf_close_coeff(expected.minreal(), result.minreal()) + + @pytest.mark.parametrize( + "left, right, expected", + [ + ( + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + TransferFunction([2], [1, 0]), + np.eye(3), + TransferFunction( + [ + [[2], [0], [0]], + [[0], [2], [0]], + [[0], [0], [2]], + ], + [ + [[1, 0], [1], [1]], + [[1], [1, 0], [1]], + [[1], [1], [1, 0]], + ], + ), + ), + ] + ) + def test_mul_mimo_siso(self, left, right, expected): + """Test multiplication of a MIMO and a SISO system.""" + result = left.__mul__(right) + assert_tf_close_coeff(expected.minreal(), result.minreal()) + + @pytest.mark.parametrize( + "left, right, expected", + [ + ( + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + TransferFunction( + [ + [[2], [1]], + [[-1], [4]], + ], + [ + [[10, 1], [20, 1]], + [[20, 1], [30, 1]], + ], + ), + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[4], [2]], + [[-2], [8]], + ], + [ + [[10, 1, 0], [20, 1, 0]], + [[20, 1, 0], [30, 1, 0]], + ], + ), + ), + ( + np.eye(3), + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[2], [0], [0]], + [[0], [2], [0]], + [[0], [0], [2]], + ], + [ + [[1, 0], [1], [1]], + [[1], [1, 0], [1]], + [[1], [1], [1, 0]], + ], + ), + ), + ] + ) + def test_rmul_mimo_siso(self, left, right, expected): + """Test right multiplication of a MIMO and a SISO system.""" + result = right.__rmul__(left) + assert_tf_close_coeff(expected.minreal(), result.minreal()) + + @pytest.mark.parametrize( + "left, right, expected", + [ + ( + TransferFunction( + [ + [[1], [0], [0]], + [[0], [2], [0]], + [[0], [0], [3]], + ], + [ + [[1], [1], [1]], + [[1], [1], [1]], + [[1], [1], [1]], + ], + ), + TransferFunction([-2], [1, 0]), + TransferFunction( + [ + [[1, 0], [0], [0]], + [[0], [2, 0], [0]], + [[0], [0], [3, 0]], + ], + [ + [[-2], [1], [1]], + [[1], [-2], [1]], + [[1], [1], [-2]], + ], + ), + ), + ] + ) + def test_truediv_mimo_siso(self, left, right, expected): + """Test true division of a MIMO and a SISO system.""" + result = left.__truediv__(right) + assert_tf_close_coeff(expected.minreal(), result.minreal()) + + @pytest.mark.parametrize( + "left, right, expected", + [ + ( + np.eye(3), + TransferFunction([2], [1, 0]), + TransferFunction( + [ + [[1, 0], [0], [0]], + [[0], [1, 0], [0]], + [[0], [0], [1, 0]], + ], + [ + [[2], [1], [1]], + [[1], [2], [1]], + [[1], [1], [2]], + ], + ), + ), + ] + ) + def test_rtruediv_mimo_siso(self, left, right, expected): + """Test right true division of a MIMO and a SISO system.""" + result = right.__rtruediv__(left) + assert_tf_close_coeff(expected.minreal(), result.minreal()) + + @pytest.mark.parametrize("named", [False, True]) + def test_slice(self, named): sys = TransferFunction( [ [ [1], [2], [3]], [ [3], [4], [5]] ], [ [[1, 2], [1, 3], [1, 4]], [[1, 4], [1, 5], [1, 6]] ], inputs=['u0', 'u1', 'u2'], outputs=['y0', 'y1'], name='sys') - sys1 = sys[1:, 1:] + sys1 = sys[1:, 1:] if not named else sys['y1', ['u1', 'u2']] assert (sys1.ninputs, sys1.noutputs) == (2, 1) assert sys1.input_labels == ['u1', 'u2'] assert sys1.output_labels == ['y1'] assert sys1.name == 'sys$indexed' - sys2 = sys[:2, :2] + sys2 = sys[:2, :2] if not named else sys[['y0', 'y1'], ['u0', 'u1']] assert (sys2.ninputs, sys2.noutputs) == (2, 2) assert sys2.input_labels == ['u0', 'u1'] assert sys2.output_labels == ['y0', 'y1'] @@ -411,7 +659,7 @@ def test_slice(self): sys = TransferFunction( [ [ [1], [2], [3]], [ [3], [4], [5]] ], [ [[1, 2], [1, 3], [1, 4]], [[1, 4], [1, 5], [1, 6]] ], 0.5) - sys1 = sys[1:, 1:] + sys1 = sys[1:, 1:] if not named else sys[['y[1]'], ['u[1]', 'u[2]']] assert (sys1.ninputs, sys1.noutputs) == (2, 1) assert sys1.dt == 0.5 assert sys1.input_labels == ['u[1]', 'u[2]'] @@ -466,7 +714,6 @@ def test_call_dtime(self): sys = TransferFunction([1., 3., 5], [1., 6., 2., -1], 0.1) np.testing.assert_array_almost_equal(sys(1j), -0.5 - 0.5j) - @slycotonly def test_call_mimo(self): """Evaluate the frequency response of a MIMO system at one frequency.""" @@ -487,7 +734,7 @@ def test_call_mimo(self): def test_freqresp_deprecated(self): sys = TransferFunction([1., 3., 5], [1., 6., 2., -1.]) # Deprecated version of the call (should generate warning) - with pytest.warns(DeprecationWarning): + with pytest.warns(FutureWarning): sys.freqresp(1.) def test_frequency_response_siso(self): @@ -507,7 +754,6 @@ def test_frequency_response_siso(self): np.testing.assert_array_almost_equal(phase, truephase) np.testing.assert_array_almost_equal(omega, trueomega) - @slycotonly def test_freqresp_mimo(self): """Evaluate the MIMO magnitude and phase at multiple frequencies.""" num = [[[1., 2.], [0., 3.], [2., -1.]], @@ -604,7 +850,6 @@ def test_common_den_nonproper(self): _, den2, _ = tf2._common_den(allow_nonproper=True) np.testing.assert_array_almost_equal(den2, common_den_ref) - @slycotonly def test_pole_mimo(self): """Test for correct MIMO poles.""" sys = TransferFunction( @@ -642,7 +887,52 @@ def test_feedback_siso(self): np.testing.assert_allclose(sys4.num, [[[-1., 7., -16., 16., 0.]]]) np.testing.assert_allclose(sys4.den, [[[1., 0., 2., -8., 8., 0.]]]) - @slycotonly + def test_append(self): + """Test ``TransferFunction.append()``.""" + tf1 = TransferFunction( + [ + [[1], [1]] + ], + [ + [[10, 1], [20, 1]] + ], + ) + tf2 = TransferFunction( + [ + [[2], [2]] + ], + [ + [[10, 1], [1, 1]] + ], + ) + tf3 = TransferFunction([100], [100, 1]) + tf_exp_1 = TransferFunction( + [ + [[1], [1], [0], [0]], + [[0], [0], [2], [2]], + ], + [ + [[10, 1], [20, 1], [1], [1]], + [[1], [1], [10, 1], [1, 1]], + ], + ) + tf_exp_2 = TransferFunction( + [ + [[1], [1], [0], [0], [0]], + [[0], [0], [2], [2], [0]], + [[0], [0], [0], [0], [100]], + ], + [ + [[10, 1], [20, 1], [1], [1], [1]], + [[1], [1], [10, 1], [1, 1], [1]], + [[1], [1], [1], [1], [100, 1]], + ], + ) + tf_appended_1 = tf1.append(tf2) + assert_tf_close_coeff(tf_exp_1, tf_appended_1) + tf_appended_2 = tf1.append(tf2).append(tf3) + assert_tf_close_coeff(tf_exp_2, tf_appended_2) + def test_convert_to_transfer_function(self): """Test for correct state space to transfer function conversion.""" A = [[1., -2.], [-3., 4.]] @@ -661,10 +951,10 @@ def test_convert_to_transfer_function(self): for i in range(sys.noutputs): for j in range(sys.ninputs): - np.testing.assert_array_almost_equal(tfsys.num[i][j], - num[i][j]) - np.testing.assert_array_almost_equal(tfsys.den[i][j], - den[i][j]) + np.testing.assert_array_almost_equal( + tfsys.num_array[i, j], num[i][j]) + np.testing.assert_array_almost_equal( + tfsys.den_array[i, j], den[i][j]) def test_minreal(self): """Try the minreal function, and also test easy entry by creation @@ -729,7 +1019,6 @@ def test_state_space_conversion_mimo(self): np.testing.assert_array_almost_equal(H.num[1][0], H2.num[1][0]) np.testing.assert_array_almost_equal(H.den[1][0], H2.den[1][0]) - @slycotonly def test_indexing(self): """Test TF scalar indexing and slice""" tm = ss2tf(rss(5, 3, 3)) @@ -881,18 +1170,18 @@ def test_printing(self): """Print SISO""" sys = ss2tf(rss(4, 1, 1)) assert isinstance(str(sys), str) - assert isinstance(sys._repr_latex_(), str) + assert isinstance(sys._repr_html_(), str) # SISO, discrete time sys = sample_system(sys, 1) assert isinstance(str(sys), str) - assert isinstance(sys._repr_latex_(), str) + assert isinstance(sys._repr_html_(), str) @pytest.mark.parametrize( "args, output", - [(([0], [1]), "\n0\n-\n1\n"), - (([1.0001], [-1.1111]), "\n 1\n------\n-1.111\n"), - (([0, 1], [0, 1.]), "\n1\n-\n1\n"), + [(([0], [1]), " 0\n -\n 1"), + (([1.0001], [-1.1111]), " 1\n ------\n -1.111"), + (([0, 1], [0, 1.]), " 1\n -\n 1"), ]) def test_printing_polynomial_const(self, args, output): """Test _tf_polynomial_to_string for constant systems""" @@ -901,82 +1190,76 @@ def test_printing_polynomial_const(self, args, output): @pytest.mark.parametrize( "args, outputfmt", [(([1, 0], [2, 1]), - "\n {var}\n-------\n2 {var} + 1\n{dtstring}"), + " {var}\n -------\n 2 {var} + 1"), (([2, 0, -1], [1, 0, 0, 1.2]), - "\n2 {var}^2 - 1\n---------\n{var}^3 + 1.2\n{dtstring}")]) + " 2 {var}^2 - 1\n ---------\n {var}^3 + 1.2")]) @pytest.mark.parametrize("var, dt, dtstring", [("s", None, ''), ("z", True, ''), - ("z", 1, '\ndt = 1\n')]) + ("z", 1, 'dt = 1')]) def test_printing_polynomial(self, args, outputfmt, var, dt, dtstring): """Test _tf_polynomial_to_string for all other code branches""" - assert str(TransferFunction(*(args + (dt,)))).partition('\n\n')[2] == \ - outputfmt.format(var=var, dtstring=dtstring) + polystr = str(TransferFunction(*(args + (dt,)))).partition('\n\n') + if dtstring != '': + # Make sure the last line of the header has proper dt + assert polystr[0].split('\n')[3] == dtstring + else: + # Make sure there are only three header lines (sys, in, out) + assert len(polystr[0].split('\n')) == 4 + assert polystr[2] == outputfmt.format(var=var) - @slycotonly def test_printing_mimo(self): - """Print MIMO, continuous time""" + """Print MIMO, continuous-time""" sys = ss2tf(rss(4, 2, 3)) assert isinstance(str(sys), str) - assert isinstance(sys._repr_latex_(), str) + assert isinstance(sys._repr_html_(), str) @pytest.mark.parametrize( "zeros, poles, gain, output", [([0], [-1], 1, - '\n' - ' s\n' - '-----\n' - 's + 1\n'), + ' s\n' + ' -----\n' + ' s + 1'), ([-1], [-1], 1, - '\n' - 's + 1\n' - '-----\n' - 's + 1\n'), + ' s + 1\n' + ' -----\n' + ' s + 1'), ([-1], [1], 1, - '\n' - 's + 1\n' - '-----\n' - 's - 1\n'), + ' s + 1\n' + ' -----\n' + ' s - 1'), ([1], [-1], 1, - '\n' - 's - 1\n' - '-----\n' - 's + 1\n'), + ' s - 1\n' + ' -----\n' + ' s + 1'), ([-1], [-1], 2, - '\n' - '2 (s + 1)\n' - '---------\n' - ' s + 1\n'), + ' 2 (s + 1)\n' + ' ---------\n' + ' s + 1'), ([-1], [-1], 0, - '\n' - '0\n' - '-\n' - '1\n'), + ' 0\n' + ' -\n' + ' 1'), ([-1], [1j, -1j], 1, - '\n' - ' s + 1\n' - '-----------------\n' - '(s - 1j) (s + 1j)\n'), + ' s + 1\n' + ' -----------------\n' + ' (s - 1j) (s + 1j)'), ([4j, -4j], [2j, -2j], 2, - '\n' - '2 (s - 4j) (s + 4j)\n' - '-------------------\n' - ' (s - 2j) (s + 2j)\n'), + ' 2 (s - 4j) (s + 4j)\n' + ' -------------------\n' + ' (s - 2j) (s + 2j)'), ([1j, -1j], [-1, -4], 2, - '\n' - '2 (s - 1j) (s + 1j)\n' - '-------------------\n' - ' (s + 1) (s + 4)\n'), + ' 2 (s - 1j) (s + 1j)\n' + ' -------------------\n' + ' (s + 1) (s + 4)'), ([1], [-1 + 1j, -1 - 1j], 1, - '\n' - ' s - 1\n' - '-------------------------\n' - '(s + (1-1j)) (s + (1+1j))\n'), + ' s - 1\n' + ' -------------------------\n' + ' (s + (1-1j)) (s + (1+1j))'), ([1], [1 + 1j, 1 - 1j], 1, - '\n' - ' s - 1\n' - '-------------------------\n' - '(s - (1+1j)) (s - (1-1j))\n'), + ' s - 1\n' + ' -------------------------\n' + ' (s - (1+1j)) (s - (1-1j))'), ]) def test_printing_zpk(self, zeros, poles, gain, output): """Test _tf_polynomial_to_string for constant systems""" @@ -987,20 +1270,17 @@ def test_printing_zpk(self, zeros, poles, gain, output): @pytest.mark.parametrize( "zeros, poles, gain, format, output", [([1], [1 + 1j, 1 - 1j], 1, ".2f", - '\n' - ' 1.00\n' - '-------------------------------------\n' - '(s + (1.00-1.41j)) (s + (1.00+1.41j))\n'), + ' 1.00\n' + ' -------------------------------------\n' + ' (s + (1.00-1.41j)) (s + (1.00+1.41j))'), ([1], [1 + 1j, 1 - 1j], 1, ".3f", - '\n' - ' 1.000\n' - '-----------------------------------------\n' - '(s + (1.000-1.414j)) (s + (1.000+1.414j))\n'), + ' 1.000\n' + ' -----------------------------------------\n' + ' (s + (1.000-1.414j)) (s + (1.000+1.414j))'), ([1], [1 + 1j, 1 - 1j], 1, ".6g", - '\n' - ' 1\n' - '-------------------------------------\n' - '(s + (1-1.41421j)) (s + (1+1.41421j))\n') + ' 1\n' + ' -------------------------------------\n' + ' (s + (1-1.41421j)) (s + (1+1.41421j))') ]) def test_printing_zpk_format(self, zeros, poles, gain, format, output): """Test _tf_polynomial_to_string for constant systems""" @@ -1016,33 +1296,36 @@ def test_printing_zpk_format(self, zeros, poles, gain, format, output): "num, den, output", [([[[11], [21]], [[12], [22]]], [[[1, -3, 2], [1, 1, -6]], [[1, 0, 1], [1, -1, -20]]], - ('\n' - 'Input 1 to output 1:\n' - ' 11\n' - '---------------\n' - '(s - 2) (s - 1)\n' - '\n' - 'Input 1 to output 2:\n' - ' 12\n' - '-----------------\n' - '(s - 1j) (s + 1j)\n' - '\n' - 'Input 2 to output 1:\n' - ' 21\n' - '---------------\n' - '(s - 2) (s + 3)\n' - '\n' - 'Input 2 to output 2:\n' - ' 22\n' - '---------------\n' - '(s - 5) (s + 4)\n'))]) + ("""Input 1 to output 1: + + 11 + --------------- + (s - 2) (s - 1) + +Input 1 to output 2: + + 12 + ----------------- + (s - 1j) (s + 1j) + +Input 2 to output 1: + + 21 + --------------- + (s - 2) (s + 3) + +Input 2 to output 2: + + 22 + --------------- + (s - 5) (s + 4)"""))], + ) def test_printing_zpk_mimo(self, num, den, output): """Test _tf_polynomial_to_string for constant systems""" G = tf(num, den, display_format='zpk') res = str(G) assert res.partition('\n\n')[2] == output - @slycotonly def test_size_mismatch(self): """Test size mismacht""" sys1 = ss2tf(rss(2, 2, 2)) @@ -1065,63 +1348,79 @@ def test_size_mismatch(self): with pytest.raises(NotImplementedError): TransferFunction.feedback(sys2, sys1) - def test_latex_repr(self): + def test_html_repr(self): """Test latex printout for TransferFunction""" Hc = TransferFunction([1e-5, 2e5, 3e-4], - [1.2e34, 2.3e-4, 2.3e-45]) + [1.2e34, 2.3e-4, 2.3e-45], name='sys') Hd = TransferFunction([1e-5, 2e5, 3e-4], [1.2e34, 2.3e-4, 2.3e-45], - .1) + .1, name='sys') # TODO: make the multiplication sign configurable expmul = r'\times' - for var, H, suffix in zip(['s', 'z'], + for var, H, dtstr in zip(['s', 'z'], [Hc, Hd], - ['', r'\quad dt = 0.1']): - ref = (r'$$\frac{' + ['', ', dt=0.1']): + ref = (r"<TransferFunction sys: ['u[0]'] -> ['y[0]']" + + dtstr + r">" + "\n" + r'$$\dfrac{' r'1 ' + expmul + ' 10^{-5} ' + var + '^2 ' r'+ 2 ' + expmul + ' 10^{5} ' + var + ' + 0.0003' r'}{' r'1.2 ' + expmul + ' 10^{34} ' + var + '^2 ' r'+ 0.00023 ' + var + ' ' r'+ 2.3 ' + expmul + ' 10^{-45}' - r'}' + suffix + '$$') - assert H._repr_latex_() == ref + r'}' + '$$') + assert H._repr_html_() == ref @pytest.mark.parametrize( "Hargs, ref", [(([-1., 4.], [1., 3., 5.]), - "TransferFunction(array([-1., 4.]), array([1., 3., 5.]))"), + "TransferFunction(\n" + "array([-1., 4.]),\n" + "array([1., 3., 5.]),\n" + "outputs=1, inputs=1)"), (([2., 3., 0.], [1., -3., 4., 0], 2.0), - "TransferFunction(array([2., 3., 0.])," - " array([ 1., -3., 4., 0.]), 2.0)"), - + "TransferFunction(\n" + "array([2., 3., 0.]),\n" + "array([ 1., -3., 4., 0.]),\n" + "dt=2.0,\n" + "outputs=1, inputs=1)"), (([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], [[[6, 7], [4, 5]], [[2, 3], [0, 1]]]), - "TransferFunction([[array([1]), array([2, 3])]," - " [array([4, 5]), array([6, 7])]]," - " [[array([6, 7]), array([4, 5])]," - " [array([2, 3]), array([1])]])"), + "TransferFunction(\n" + "[[array([1]), array([2, 3])],\n" + " [array([4, 5]), array([6, 7])]],\n" + "[[array([6, 7]), array([4, 5])],\n" + " [array([2, 3]), array([1])]],\n" + "outputs=2, inputs=2)"), (([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], [[[6, 7], [4, 5]], [[2, 3], [0, 1]]], 0.5), - "TransferFunction([[array([1]), array([2, 3])]," - " [array([4, 5]), array([6, 7])]]," - " [[array([6, 7]), array([4, 5])]," - " [array([2, 3]), array([1])]], 0.5)") + "TransferFunction(\n" + "[[array([1]), array([2, 3])],\n" + " [array([4, 5]), array([6, 7])]],\n" + "[[array([6, 7]), array([4, 5])],\n" + " [array([2, 3]), array([1])]],\n" + "dt=0.5,\n" + "outputs=2, inputs=2)"), ]) - def test_repr(self, Hargs, ref): + def test_loadable_repr(self, Hargs, ref): """Test __repr__ printout.""" H = TransferFunction(*Hargs) - assert repr(H) == ref + rep = ct.iosys_repr(H, format='eval') + assert rep == ref # and reading back array = np.array # noqa - H2 = eval(H.__repr__()) + H2 = eval(rep) for p in range(len(H.num)): for m in range(len(H.num[0])): - np.testing.assert_array_almost_equal(H.num[p][m], H2.num[p][m]) - np.testing.assert_array_almost_equal(H.den[p][m], H2.den[p][m]) + np.testing.assert_array_almost_equal( + H.num_array[p, m], H2.num_array[p, m]) + np.testing.assert_array_almost_equal( + H.den_array[p, m], H2.den_array[p, m]) assert H.dt == H2.dt def test_sample_named_signals(self): @@ -1179,8 +1478,10 @@ def test_returnScipySignalLTI(self, mimotf): sslti = mimotf.returnScipySignalLTI(strict=False) for i in range(2): for j in range(3): - np.testing.assert_allclose(sslti[i][j].num, mimotf.num[i][j]) - np.testing.assert_allclose(sslti[i][j].den, mimotf.den[i][j]) + np.testing.assert_allclose( + sslti[i][j].num, mimotf.num_array[i, j]) + np.testing.assert_allclose( + sslti[i][j].den, mimotf.den_array[i, j]) if mimotf.dt == 0: assert sslti[i][j].dt is None else: @@ -1285,3 +1586,35 @@ def test_copy_names(create, args, kwargs, convert): cpy = convert(sys, inputs='myin', outputs='myout') assert cpy.input_labels == ['myin'] assert cpy.output_labels == ['myout'] + +s = ct.TransferFunction.s +@pytest.mark.parametrize("args, num, den", [ + (('s', ), [[[1, 0]]], [[[1]]]), # ctime + (('z', ), [[[1, 0]]], [[[1]]]), # dtime + ((1, 1), [[[1]]], [[[1]]]), # scalars as scalars + (([[1]], [[1]]), [[[1]]], [[[1]]]), # scalars as lists + (([[[1, 2]]], [[[3, 4]]]), [[[1, 2]]], [[[3, 4]]]), # SISO as lists + (([[np.array([1, 2])]], [[np.array([3, 4])]]), # SISO as arrays + [[[1, 2]]], [[[3, 4]]]), + (([[ [1], [2] ], [[1, 1], [1, 0] ]], # MIMO + [[ [1, 0], [1, 0] ], [[1, 2], [1] ]]), + [[ [1], [2] ], [[1, 1], [1, 0] ]], + [[ [1, 0], [1, 0] ], [[1, 2], [1] ]]), + (([[[1, 2], [3, 4]]], [[[5, 6]]]), # common denominator + [[[1, 2], [3, 4]]], [[[5, 6], [5, 6]]]), + (([ [1/s, 2/s], [(s+1)/(s+2), s]], ), # 2x2 from SISO + [[ [1], [2] ], [[1, 1], [1, 0] ]], # num + [[ [1, 0], [1, 0] ], [[1, 2], [1] ]]), # den + (([[1, 2], [3, 4]], [[[1, 0], [1, 0]]]), ValueError, + r"numerator has 2 output\(s\), but the denominator has 1 output"), +]) +def test_tf_args(args, num, den): + if isinstance(num, type): + exception, match = num, den + with pytest.raises(exception, match=match): + sys = ct.tf(*args) + else: + sys = ct.tf(*args) + chk = ct.tf(num, den) + np.testing.assert_equal(sys.num, chk.num) + np.testing.assert_equal(sys.den, chk.den) diff --git a/control/timeplot.py b/control/timeplot.py index 2eb7aec9b..545618f75 100644 --- a/control/timeplot.py +++ b/control/timeplot.py @@ -1,38 +1,28 @@ # timeplot.py - time plotting functions # RMM, 20 Jun 2023 -# -# This file contains routines for plotting out time responses. These -# functions can be called either as standalone functions or access from the -# TimeDataResponse class. -# -# Note: It might eventually make sense to put the functions here -# directly into timeresp.py. +"""Time plotting functions. + +This module contains routines for plotting out time responses. These +functions can be called either as standalone functions or access from +the TimeResponseData class. + +""" + +import itertools from warnings import warn -import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np from . import config -from .ctrlplot import _make_legend_labels, _update_suptitle +from .ctrlplot import ControlPlot, _make_legend_labels, \ + _process_legend_keywords, _update_plot_title __all__ = ['time_response_plot', 'combine_time_responses'] -# Default font dictionary -_timeplot_rcParams = mpl.rcParams.copy() -_timeplot_rcParams.update({ - 'axes.labelsize': 'small', - 'axes.titlesize': 'small', - 'figure.titlesize': 'medium', - 'legend.fontsize': 'x-small', - 'xtick.labelsize': 'small', - 'ytick.labelsize': 'small', -}) - # Default values for module parameter variables _timeplot_defaults = { - 'timeplot.rcParams': _timeplot_rcParams, 'timeplot.trace_props': [ {'linestyle': s} for s in ['-', '--', ':', '-.']], 'timeplot.output_props': [ @@ -42,6 +32,8 @@ {'color': c} for c in [ 'tab:red', 'tab:purple', 'tab:brown', 'tab:olive', 'tab:cyan']], 'timeplot.time_label': "Time [s]", + 'timeplot.sharex': 'col', + 'timeplot.sharey': False, } @@ -49,9 +41,8 @@ def time_response_plot( data, *fmt, ax=None, plot_inputs=None, plot_outputs=True, transpose=False, overlay_traces=False, overlay_signals=False, - legend_map=None, legend_loc=None, add_initial_zero=True, label=None, - trace_labels=None, title=None, relabel=True, show_legend=None, - **kwargs): + add_initial_zero=True, label=None, trace_labels=None, title=None, + relabel=True, **kwargs): """Plot the time response of an input/output system. This function creates a standard set of plots for the input/output @@ -61,22 +52,13 @@ def time_response_plot( Parameters ---------- - data : TimeResponseData + data : `TimeResponseData` Data to be plotted. - ax : array of Axes - The matplotlib Axes to draw the figure on. If not specified, the - Axes for the current figure are used or, if there is no current - figure with the correct number and shape of Axes, a new figure is - created. The default shape of the array should be (noutputs + - ninputs, ntraces), but if `overlay_traces` is set to `True` then - only one row is needed and if `overlay_signals` is set to `True` - then only one or two columns are needed (depending on plot_inputs - and plot_outputs). plot_inputs : bool or str, optional Sets how and where to plot the inputs: * False: don't plot the inputs * None: use value from time response data (default) - * 'overlay`: plot inputs overlaid with outputs + * 'overlay': plot inputs overlaid with outputs * True: plot the inputs on their own axes plot_outputs : bool, optional If False, suppress plotting of the outputs. @@ -86,6 +68,14 @@ def time_response_plot( overlay_signals : bool, optional If set to True, combine all input and output signals onto a single plot (for each). + sharex, sharey : str or bool, optional + Determine whether and how x- and y-axis limits are shared between + subplots. Can be set set to 'row' to share across all subplots in + a row, 'col' to set across all subplots in a column, 'all' to share + across all subplots, or False to allow independent limits. + Default values are False for `sharex' and 'col' for `sharey`, and + can be set using `config.defaults['timeplot.sharex']` and + `config.defaults['timeplot.sharey']`. transpose : bool, optional If transpose is False (default), signals are plotted from top to bottom, starting with outputs (if plotted) and then inputs. @@ -93,87 +83,109 @@ def time_response_plot( signals are plotted from left to right, starting with the inputs (if plotted) and then the outputs. Multi-trace responses are stacked vertically. - *fmt : :func:`matplotlib.pyplot.plot` format string, optional + *fmt : `matplotlib.pyplot.plot` format string, optional Passed to `matplotlib` as the format string for all lines in the plot. - **kwargs : :func:`matplotlib.pyplot.plot` keyword properties, optional + **kwargs : `matplotlib.pyplot.plot` keyword properties, optional Additional keywords passed to `matplotlib` to specify line properties. Returns ------- - out : array of list of Line2D - Array of Line2D objects for each line in the plot. The shape of - the array matches the subplots shape and the value of the array is a - list of Line2D objects in that subplot. + cplt : `ControlPlot` object + Object containing the data that were plotted. See `ControlPlot` + for more detailed information. + cplt.lines : 2D array of `matplotlib.lines.Line2D` + Array containing information on each line in the plot. The shape + of the array matches the subplots shape and the value of the array + is a list of Line2D objects in that subplot. + cplt.axes : 2D array of `matplotlib.axes.Axes` + Axes for each subplot. + cplt.figure : `matplotlib.figure.Figure` + Figure containing the plot. + cplt.legend : 2D array of `matplotlib.legend.Legend` + Legend object(s) contained in the plot. Other Parameters ---------------- add_initial_zero : bool Add an initial point of zero at the first time point for all inputs with type 'step'. Default is True. - input_props : array of dicts + ax : array of `matplotlib.axes.Axes`, optional + The matplotlib axes to draw the figure on. If not specified, the + axes for the current figure are used or, if there is no current + figure with the correct number and shape of axes, a new figure is + created. The shape of the array must match the shape of the + plotted data. + input_props : array of dict List of line properties to use when plotting combined inputs. The - default values are set by config.defaults['timeplot.input_props']. - label : str or array_like of str + default values are set by `config.defaults['timeplot.input_props']`. + label : str or array_like of str, optional If present, replace automatically generated label(s) with the given label(s). If more than one line is being generated, an array of labels should be provided with label[trace, :, 0] representing the output labels and label[trace, :, 1] representing the input labels. - legend_map : array of str, option - Location of the legend for multi-trace plots. Specifies an array + legend_map : array of str, optional + Location of the legend for multi-axes plots. Specifies an array of legend location strings matching the shape of the subplots, with each entry being either None (for no legend) or a legend location - string (see :func:`~matplotlib.pyplot.legend`). - legend_loc : str - Location of the legend within the axes for which it appears. This - value is used if legend_map is None. - output_props : array of dicts + string (see `~matplotlib.pyplot.legend`). + legend_loc : int or str, optional + Include a legend in the given location. Default is 'center right', + with no legend for a single response. Use False to suppress legend. + output_props : array of dict, optional List of line properties to use when plotting combined outputs. The - default values are set by config.defaults['timeplot.output_props']. + default values are set by `config.defaults['timeplot.output_props']`. + rcParams : dict + Override the default parameters used for generating plots. + Default is set by `config.defaults['ctrlplot.rcParams']`. relabel : bool, optional - By default, existing figures and axes are relabeled when new data - are added. If set to `False`, just plot new data on existing axes. + (deprecated) By default, existing figures and axes are relabeled + when new data are added. If set to False, just plot new data on + existing axes. show_legend : bool, optional - Force legend to be shown if ``True`` or hidden if ``False``. If - ``None``, then show legend when there is more than one line on an - axis or ``legend_loc`` or ``legend_map`` have been specified. + Force legend to be shown if True or hidden if False. If + None, then show legend when there is more than one line on an + axis or `legend_loc` or `legend_map` has been specified. time_label : str, optional Label to use for the time axis. - trace_props : array of dicts - List of line properties to use when plotting combined outputs. The - default values are set by config.defaults['timeplot.trace_props']. + title : str, optional + Set the title of the plot. Defaults to plot type and system name(s). + trace_labels : list of str, optional + Replace the default trace labels with the given labels. + trace_props : array of dict + List of line properties to use when plotting multiple traces. The + default values are set by `config.defaults['timeplot.trace_props']`. Notes ----- - 1. A new figure will be generated if there is no current figure or - the current figure has an incompatible number of axes. To - force the creation of a new figures, use `plt.figure()`. To reuse - a portion of an existing figure, use the `ax` keyword. - - 2. The line properties (color, linestyle, etc) can be set for the - entire plot using the `fmt` and/or `kwargs` parameter, which - are passed on to `matplotlib`. When combining signals or - traces, the `input_props`, `output_props`, and `trace_props` - parameters can be used to pass a list of dictionaries - containing the line properties to use. These input/output - properties are combined with the trace properties and finally - the kwarg properties to determine the final line properties. - - 3. The default plot properties, such as font sizes, can be set using - config.defaults[''timeplot.rcParams']. + A new figure will be generated if there is no current figure or the + current figure has an incompatible number of axes. To force the + creation of a new figures, use `plt.figure`. To reuse a portion of an + existing figure, use the `ax` keyword. + + The line properties (color, linestyle, etc) can be set for the entire + plot using the `fmt` and/or `kwargs` parameter, which are passed on to + `matplotlib`. When combining signals or traces, the `input_props`, + `output_props`, and `trace_props` parameters can be used to pass a list + of dictionaries containing the line properties to use. These + input/output properties are combined with the trace properties and + finally the kwarg properties to determine the final line properties. + + The default plot properties, such as font sizes, can be set using + `config.defaults[''timeplot.rcParams']`. """ - from .freqplot import _process_ax_keyword, _process_line_labels - from .iosys import InputOutputSystem - from .timeresp import TimeResponseData + from .ctrlplot import _process_ax_keyword, _process_line_labels # # Process keywords and set defaults # # Set up defaults + ax_user = ax + sharex = config._get_param('timeplot', 'sharex', kwargs, pop=True) + sharey = config._get_param('timeplot', 'sharey', kwargs, pop=True) time_label = config._get_param( 'timeplot', 'time_label', kwargs, _timeplot_defaults, pop=True) - rcParams = config._get_param( - 'timeplot', 'rcParams', kwargs, _timeplot_defaults, pop=True) + rcParams = config._get_param('ctrlplot', 'rcParams', kwargs, pop=True) if kwargs.get('input_props', None) and len(fmt) > 0: warn("input_props ignored since fmt string was present") @@ -193,9 +205,6 @@ def time_response_plot( 'timeplot', 'trace_props', kwargs, _timeplot_defaults, pop=True) tprop_len = len(trace_props) - # Set the title for the data - title = data.title if title == None else title - # Determine whether or not to plot the input data (and how) if plot_inputs is None: plot_inputs = data.plot_inputs @@ -287,7 +296,10 @@ def time_response_plot( nrows, ncols = ncols, nrows # See if we can use the current figure axes - fig, ax_array = _process_ax_keyword(ax, (nrows, ncols), rcParams=rcParams) + fig, ax_array = _process_ax_keyword( + ax, (nrows, ncols), rcParams=rcParams, sharex=sharex, sharey=sharey) + legend_loc, legend_map, show_legend = _process_legend_keywords( + kwargs, (nrows, ncols), 'center right') # # Map inputs/outputs and traces to axes @@ -381,7 +393,7 @@ def _make_line_label(signal_index, signal_labels, trace_index): # # To allow repeated calls to time_response_plot() to cycle through # colors, we store an offset in the figure object that we can - # retrieve at a later date, if needed. + # retrieve in a later call, if needed. # output_offset = fig._output_offset = getattr(fig, '_output_offset', 0) input_offset = fig._input_offset = getattr(fig, '_input_offset', 0) @@ -453,7 +465,8 @@ def _make_line_label(signal_index, signal_labels, trace_index): # Stop here if the user wants to control everything if not relabel: - return out + warn("relabel keyword is deprecated", FutureWarning) + return ControlPlot(out, ax_array, fig) # # Label the axes (including trace labels) @@ -549,7 +562,7 @@ def _make_line_label(signal_index, signal_labels, trace_index): # Create legends # # Legends can be placed manually by passing a legend_map array that - # matches the shape of the suplots, with each item being a string + # matches the shape of the subplots, with each item being a string # indicating the location of the legend for that axes (or None for no # legend). # @@ -560,18 +573,14 @@ def _make_line_label(signal_index, signal_labels, trace_index): # # Because plots can be built up by multiple calls to plot(), the legend # strings are created from the line labels manually. Thus an initial - # call to plot() may not generate any legends (eg, if no signals are + # call to plot() may not generate any legends (e.g., if no signals are # combined nor overlaid), but subsequent calls to plot() will need a # legend for each different line (system). # # Figure out where to put legends - if legend_map is None: + if show_legend != False and legend_map is None: legend_map = np.full(ax_array.shape, None, dtype=object) - if legend_loc == None: - legend_loc = 'center right' - else: - show_legend = True if show_legend is None else show_legend if transpose: if (overlay_signals or plot_inputs == 'overlay') and overlay_traces: @@ -596,6 +605,7 @@ def _make_line_label(signal_index, signal_labels, trace_index): else: # Put legend in the upper right legend_map[0, -1] = legend_loc + else: # regular layout if (overlay_signals or plot_inputs == 'overlay') and overlay_traces: # Put a legend in each plot for inputs and outputs @@ -619,29 +629,25 @@ def _make_line_label(signal_index, signal_labels, trace_index): else: # Put legend in the upper right legend_map[0, -1] = legend_loc - else: - # Make sure the legend map is the right size - legend_map = np.atleast_2d(legend_map) - if legend_map.shape != ax_array.shape: - raise ValueError("legend_map shape just match axes shape") - - # Turn legend on unless overridden by user - show_legend = True if show_legend is None else show_legend - # Create axis legends - for i in range(nrows): - for j in range(ncols): + if show_legend != False: + # Create axis legends + legend_array = np.full(ax_array.shape, None, dtype=object) + for i, j in itertools.product(range(nrows), range(ncols)): + if legend_map[i, j] is None: + continue ax = ax_array[i, j] labels = [line.get_label() for line in ax.get_lines()] if line_labels is None: labels = _make_legend_labels(labels, plot_inputs == 'overlay') # Update the labels to remove common strings - if show_legend != False and \ - (len(labels) > 1 or show_legend) and \ - legend_map[i, j] != None: + if show_legend == True or len(labels) > 1: with plt.rc_context(rcParams): - ax.legend(labels, loc=legend_map[i, j]) + legend_array[i, j] = ax.legend( + labels, loc=legend_map[i, j]) + else: + legend_array = None # # Update the plot title (= figure suptitle) @@ -653,28 +659,35 @@ def _make_line_label(signal_index, signal_labels, trace_index): # list of systems (e.g., "Step response for sys[1], sys[2]"). # - _update_suptitle(fig, title, rcParams=rcParams) + if ax_user is None and title is None: + title = data.title if title == None else title + _update_plot_title(title, fig, rcParams=rcParams) + elif ax_user is None: + _update_plot_title(title, fig, rcParams=rcParams, use_existing=False) - return out + return ControlPlot(out, ax_array, fig, legend=legend_map) def combine_time_responses(response_list, trace_labels=None, title=None): - """Combine multiple individual time responses into a multi-trace response. + """Combine individual time responses into multi-trace response. - This function combines multiple instances of :class:`TimeResponseData` - into a multi-trace :class:`TimeResponseData` object. + This function combines multiple instances of `TimeResponseData` + into a multi-trace `TimeResponseData` object. Parameters ---------- - response_list : list of :class:`TimeResponseData` objects - Reponses to be combined. + response_list : list of `TimeResponseData` objects + Responses to be combined. trace_labels : list of str, optional List of labels for each trace. If not specified, trace names are taken from the input data or set to None. + title : str, optional + Set the title to use when plotting. Defaults to plot type and + system name(s). Returns ------- - data : :class:`TimeResponseData` + data : `TimeResponseData` Multi-trace input/output data. """ @@ -736,9 +749,13 @@ def combine_time_responses(response_list, trace_labels=None, title=None): # Add on trace label and trace type if generate_trace_labels: - trace_labels.append(response.title) + trace_labels.append( + response.title if response.title is not None else + response.sysname if response.sysname is not None else + "unknown") trace_types.append( - None if response.trace_types is None else response.types[0]) + None if response.trace_types is None + else response.trace_types[0]) else: # Save the data diff --git a/control/timeresp.py b/control/timeresp.py index f844b1df4..bd549589a 100644 --- a/control/timeresp.py +++ b/control/timeresp.py @@ -1,14 +1,29 @@ -""" -timeresp.py - time-domain simulation routines. +# timeresp.py - time-domain simulation routines. +# +# Initial author: Eike Welk +# Creation date: 12 May 2011 +# +# Modified: Sawyer B. Fuller (minster@uw.edu) to add discrete-time +# capability and better automatic time vector creation +# Date: June 2020 +# +# Modified by Ilhan Polat to improve automatic time vector creation +# Date: August 17, 2020 +# +# Modified by Richard Murray to add TimeResponseData class +# Date: August 2021 +# +# Use `git shortlog -n -s statesp.py` for full list of contributors -The :mod:`~control.timeresp` module contains a collection of -functions that are used to compute time-domain simulations of LTI -systems. +"""Time domain simulation routines. + +This module contains a collection of functions that are used to +compute time-domain simulations of LTI systems. Arguments to time-domain simulations include a time vector, an input vector (when needed), and an initial condition vector. The most general function for simulating LTI systems the -:func:`forced_response` function, which has the form:: +`forced_response` function, which has the form:: t, y = forced_response(sys, T, U, X0) @@ -19,55 +34,6 @@ See :ref:`time-series-convention` for more information on how time series data are represented. -Copyright (c) 2011 by California Institute of Technology -All rights reserved. - -Copyright (c) 2011 by Eike Welk -Copyright (c) 2010 by SciPy Developers - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Initial Author: Eike Welk -Date: 12 May 2011 - -Modified: Sawyer B. Fuller (minster@uw.edu) to add discrete-time -capability and better automatic time vector creation -Date: June 2020 - -Modified by Ilhan Polat to improve automatic time vector creation -Date: August 17, 2020 - -Modified by Richard Murray to add TimeResponseData class -Date: August 2021 - -$Id$ """ import warnings @@ -79,22 +45,37 @@ from scipy.linalg import eig, eigvals, matrix_balance, norm from . import config -from .ctrlplot import _update_suptitle +from . config import _process_kwargs, _process_param from .exception import pandas_check -from .iosys import isctime, isdtime +from .iosys import NamedSignal, isctime, isdtime from .timeplot import time_response_plot __all__ = ['forced_response', 'step_response', 'step_info', 'initial_response', 'impulse_response', 'TimeResponseData', 'TimeResponseList'] +# Dictionary of aliases for time response commands +_timeresp_aliases = { + # param: ([alias, ...], [legacy, ...]) + 'timepts': (['T'], []), + 'inputs': (['U'], ['u']), + 'outputs': (['Y'], ['y']), + 'initial_state': (['X0'], ['x0']), + 'final_output': (['yfinal'], []), + 'return_states': (['return_x'], []), + 'evaluation_times': (['t_eval'], []), + 'timepts_num': (['T_num'], []), + 'input_indices': (['input'], []), + 'output_indices': (['output'], []), +} + class TimeResponseData: - """A class for returning time responses. + """Input/output system time response data. This class maintains and manipulates the data corresponding to the temporal response of an input/output system. It is used as the return - type for time domain simulations (step response, input/output response, + type for time domain simulations (`step_response`, `input_output_response`, etc). A time response consists of a time vector, an output vector, and @@ -107,131 +88,194 @@ class TimeResponseData: step responses for linear systems. For multi-trace responses, the same time vector must be used for all traces. - Time responses are accessed through either the raw data, stored as - :attr:`t`, :attr:`y`, :attr:`x`, :attr:`u`, or using a set of properties - :attr:`time`, :attr:`outputs`, :attr:`states`, :attr:`inputs`. When - accessing time responses via their properties, squeeze processing is - applied so that (by default) single-input, single-output systems will have - the output and input indices supressed. This behavior is set using the - ``squeeze`` keyword. + Time responses are accessed through either the raw data, stored as `t`, + `y`, `x`, `u`, or using a set of properties `time`, `outputs`, + `states`, `inputs`. When accessing time responses via their + properties, squeeze processing is applied so that (by default) + single-input, single-output systems will have the output and input + indices suppressed. This behavior is set using the `squeeze` parameter. + + Parameters + ---------- + time : 1D array + Time values of the output. Ignored if None. + outputs : ndarray + Output response of the system. This can either be a 1D array + indexed by time (for SISO systems or MISO systems with a specified + input), a 2D array indexed by output and time (for MIMO systems + with no input indexing, such as initial_response or forced + response) or trace and time (for SISO systems with multiple + traces), or a 3D array indexed by output, trace, and time (for + multi-trace input/output responses). + states : array, optional + Individual response of each state variable. This should be a 2D + array indexed by the state index and time (for single trace + systems) or a 3D array indexed by state, trace, and time. + inputs : array, optional + Inputs used to generate the output. This can either be a 1D array + indexed by time (for SISO systems or MISO/MIMO systems with a + specified input), a 2D array indexed either by input and time (for + a multi-input system) or trace and time (for a single-input, + multi-trace response), or a 3D array indexed by input, trace, and + time. + title : str, optional + Title of the data set (used as figure title in plotting). + squeeze : bool, optional + By default, if a system is single-input, single-output (SISO) then + the inputs and outputs are returned as a 1D array (indexed by time) + and if a system is multi-input or multi-output, then the inputs are + returned as a 2D array (indexed by input and time) and the outputs + are returned as either a 2D array (indexed by output and time) or a + 3D array (indexed by output, trace, and time). If `squeeze` = True, + access to the output response will remove single-dimensional + entries from the shape of the inputs and outputs even if the system + is not SISO. If squeeze=False, keep the input as a 2D or 3D array + (indexed by the input (if multi-input), trace (if single input) and + time) and the output as a 3D array (indexed by the output, trace, + and time) even if the system is SISO. The default value can be set + using `config.defaults['control.squeeze_time_response']`. Attributes ---------- t : 1D array Time values of the input/output response(s). This attribute is - normally accessed via the :attr:`time` property. - + normally accessed via the `time` property. y : 2D or 3D array Output response data, indexed either by output index and time (for single trace responses) or output, trace, and time (for multi-trace - responses). These data are normally accessed via the :attr:`outputs` + responses). These data are normally accessed via the `outputs` property, which performs squeeze processing. - x : 2D or 3D array, or None - State space data, indexed either by output number and time (for single - trace responses) or output, trace, and time (for multi-trace - responses). If no state data are present, value is ``None``. These - data are normally accessed via the :attr:`states` property, which + State space data, indexed either by output number and time (for + single trace responses) or output, trace, and time (for multi-trace + responses). If no state data are present, value is None. These + data are normally accessed via the `states` property, which performs squeeze processing. - u : 2D or 3D array, or None Input signal data, indexed either by input index and time (for single trace responses) or input, trace, and time (for multi-trace - responses). If no input data are present, value is ``None``. These - data are normally accessed via the :attr:`inputs` property, which + responses). If no input data are present, value is None. These + data are normally accessed via the `inputs` property, which performs squeeze processing. - - squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) - then the outputs (and inputs) are returned as a 1D array - (indexed by time) and if a system is multi-input or - multi-output, then the outputs are returned as a 2D array - (indexed by output and time) or a 3D array (indexed by output, - trace, and time). If ``squeeze=True``, access to the output - response will remove single-dimensional entries from the shape - of the inputs and outputs even if the system is not SISO. If - ``squeeze=False``, the output is returned as a 2D or 3D array - (indexed by the output [if multi-input], trace [if multi-trace] - and time) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_time_response']. - - transpose : bool, optional - If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). Default - value is False. - issiso : bool, optional - Set to ``True`` if the system generating the data is single-input, - single-output. If passed as ``None`` (default), the input data - will be used to set the value. - + Set to True if the system generating the data is single-input, + single-output. If passed as None (default), the input and output + data will be used to set the value. ninputs, noutputs, nstates : int Number of inputs, outputs, and states of the underlying system. - - input_labels, output_labels, state_labels : array of str - Names for the input, output, and state variables. - - success : bool, optional - If ``False``, result may not be valid (see - :func:`~control.input_output_response`). Defaults to ``True``. - - message : str, optional - Informational message if ``success`` is ``False``. - - sysname : str, optional - Name of the system that created the data. - params : dict, optional If system is a nonlinear I/O system, set parameter values. - - plot_inputs : bool, optional - Whether or not to plot the inputs by default (can be overridden in - the plot() method) - ntraces : int, optional Number of independent traces represented in the input/output - response. If ntraces is 0 (default) then the data represents a - single trace with the trace index surpressed in the data. - + response. If `ntraces` is 0 (default) then the data represents a + single trace with the trace index suppressed in the data. trace_labels : array of string, optional - Labels to use for traces (set to sysname it ntraces is 0) - + Labels to use for traces (set to sysname it `ntraces` is 0). trace_types : array of string, optional Type of trace. Currently only 'step' is supported, which controls the way in which the signal is plotted. + Other Parameters + ---------------- + input_labels, output_labels, state_labels : array of str, optional + Optional labels for the inputs, outputs, and states, given as a + list of strings matching the appropriate signal dimension. + sysname : str, optional + Name of the system that created the data. + transpose : bool, optional + If True, transpose all input and output arrays (for backward + compatibility with MATLAB and `scipy.signal.lsim`). Default value + is False. + return_x : bool, optional + If True, return the state vector when enumerating result by + assigning to a tuple (default = False). + plot_inputs : bool, optional + Whether or not to plot the inputs by default (can be overridden + in the `~TimeResponseData.plot` method). + multi_trace : bool, optional + If True, then 2D input array represents multiple traces. For + a MIMO system, the `input` attribute should then be set to + indicate which trace is being specified. Default is False. + success : bool, optional + If False, result may not be valid (see `input_output_response`). + message : str, optional + Informational message if `success` is False. + + See Also + -------- + input_output_response, forced_response, impulse_response, \ + initial_response, step_response, FrequencyResponseData + Notes ----- - 1. For backward compatibility with earlier versions of python-control, - this class has an ``__iter__`` method that allows it to be assigned - to a tuple with a variable number of elements. This allows the - following patterns to work: + The responses for individual elements of the time response can be + accessed using integers, slices, or lists of signal offsets or the + names of the appropriate signals:: + + sys = ct.rss(4, 2, 1) + resp = ct.initial_response(sys, initial_state=[1, 1, 1, 1]) + plt.plot(resp.time, resp.outputs['y[0]']) + + In the case of multi-trace data, the responses should be indexed using + the output signal name (or offset) and the input signal name (or + offset):: + + sys = ct.rss(4, 2, 2, strictly_proper=True) + resp = ct.step_response(sys) + plt.plot(resp.time, resp.outputs[['y[0]', 'y[1]'], 'u[0]'].T) - t, y = step_response(sys) - t, y, x = step_response(sys, return_x=True) + For backward compatibility with earlier versions of python-control, + this class has an `__iter__` method that allows it to be assigned to + a tuple with a variable number of elements. This allows the following + patterns to work:: - When using this (legacy) interface, the state vector is not affected by - the `squeeze` parameter. + t, y = step_response(sys) + t, y, x = step_response(sys, return_x=True) - 2. For backward compatibility with earlier version of python-control, - this class has ``__getitem__`` and ``__len__`` methods that allow the - return value to be indexed: + Similarly, the class has `__getitem__` and `__len__` methods that + allow the return value to be indexed: - response[0]: returns the time vector - response[1]: returns the output vector - response[2]: returns the state vector + * response[0]: returns the time vector + * response[1]: returns the output vector + * response[2]: returns the state vector - When using this (legacy) interface, the state vector is not affected by - the `squeeze` parameter. + When using this (legacy) interface, the state vector is not affected + by the `squeeze` parameter. - 3. The default settings for ``return_x``, ``squeeze`` and ``transpose`` - can be changed by calling the class instance and passing new values: + The default settings for `return_x`, `squeeze` and `transpose` + can be changed by calling the class instance and passing new values:: - response(tranpose=True).input + response(transpose=True).input - See :meth:`TimeResponseData.__call__` for more information. + See `TimeResponseData.__call__` for more information. """ + # + # Class attributes + # + # These attributes are defined as class attributes so that they are + # documented properly. They are "overwritten" in __init__. + # + + #: Squeeze processing parameter. + #: + #: By default, if a system is single-input, single-output (SISO) + #: then the inputs and outputs are returned as a 1D array (indexed + #: by time) and if a system is multi-input or multi-output, then + #: the inputs are returned as a 2D array (indexed by input and + #: time) and the outputs are returned as either a 2D array (indexed + #: by output and time) or a 3D array (indexed by output, trace, and + #: time). If squeeze=True, access to the output response will + #: remove single-dimensional entries from the shape of the inputs + #: and outputs even if the system is not SISO. If squeeze=False, + #: keep the input as a 2D or 3D array (indexed by the input (if + #: multi-input), trace (if single input) and time) and the output + #: as a 3D array (indexed by the output, trace, and time) even if + #: the system is SISO. The default value can be set using + #: config.defaults['control.squeeze_time_response']. + #: + #: :meta hide-value: + squeeze = None def __init__( self, time, outputs, states=None, inputs=None, issiso=None, @@ -243,85 +287,12 @@ def __init__( ): """Create an input/output time response object. - Parameters - ---------- - time : 1D array - Time values of the output. Ignored if None. - - outputs : ndarray - Output response of the system. This can either be a 1D array - indexed by time (for SISO systems or MISO systems with a specified - input), a 2D array indexed by output and time (for MIMO systems - with no input indexing, such as initial_response or forced - response) or trace and time (for SISO systems with multiple - traces), or a 3D array indexed by output, trace, and time (for - multi-trace input/output responses). - - states : array, optional - Individual response of each state variable. This should be a 2D - array indexed by the state index and time (for single trace - systems) or a 3D array indexed by state, trace, and time. - - inputs : array, optional - Inputs used to generate the output. This can either be a 1D - array indexed by time (for SISO systems or MISO/MIMO systems - with a specified input), a 2D array indexed either by input and - time (for a multi-input system) or trace and time (for a - single-input, multi-trace response), or a 3D array indexed by - input, trace, and time. - - title : str, optonal - Title of the data set (used as figure title in plotting). - - squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) - then the inputs and outputs are returned as a 1D array (indexed - by time) and if a system is multi-input or multi-output, then - the inputs are returned as a 2D array (indexed by input and - time) and the outputs are returned as either a 2D array (indexed - by output and time) or a 3D array (indexed by output, trace, and - time). If squeeze=True, access to the output response will - remove single-dimensional entries from the shape of the inputs - and outputs even if the system is not SISO. If squeeze=False, - keep the input as a 2D or 3D array (indexed by the input (if - multi-input), trace (if single input) and time) and the output - as a 3D array (indexed by the output, trace, and time) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_time_response']. - - Other parameters - ---------------- - input_labels, output_labels, state_labels: array of str, optional - Optional labels for the inputs, outputs, and states, given as a - list of strings matching the appropriate signal dimension. - - sysname : str, optional - Name of the system that created the data. - - transpose : bool, optional - If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). - Default value is False. - - return_x : bool, optional - If True, return the state vector when enumerating result by - assigning to a tuple (default = False). - - plot_inputs : bool, optional - Whether or not to plot the inputs by default (can be overridden - in the plot() method) - - multi_trace : bool, optional - If ``True``, then 2D input array represents multiple traces. For - a MIMO system, the ``input`` attribute should then be set to - indicate which trace is being specified. Default is ``False``. - - success : bool, optional - If ``False``, result may not be valid (see - :func:`~control.input_output_response`). + This function is used by the various time response functions, such + as `input_output_response` and `step_response` to store the + response of a simulation. It can be passed to `plot_time_response` + to plot the data, or the `~TimeResponseData.plot` method can be used. - message : str, optional - Informational message if ``success`` is ``False``. + See `TimeResponseData` for more information on parameters. """ # @@ -388,7 +359,7 @@ def __init__( # Make sure the shape is OK if multi_trace and \ (self.x.ndim != 3 or self.x.shape[1] != self.ntraces) or \ - not multi_trace and self.x.ndim != 2 : + not multi_trace and self.x.ndim != 2: raise ValueError("State vector is the wrong shape") # Make sure time dimension of state is the right length @@ -414,7 +385,7 @@ def __init__( self.u = np.array(inputs) self.plot_inputs = plot_inputs - # Make sure the shape is OK and figure out the nuumber of inputs + # Make sure the shape is OK and figure out the number of inputs if multi_trace and self.u.ndim == 3 and \ self.u.shape[1] == self.ntraces: self.ninputs = self.u.shape[0] @@ -484,23 +455,23 @@ def __call__(self, **kwargs): """Change value of processing keywords. Calling the time response object will create a copy of the object and - change the values of the keywords used to control the ``outputs``, - ``states``, and ``inputs`` properties. + change the values of the keywords used to control the `outputs`, + `states`, and `inputs` properties. Parameters ---------- squeeze : bool, optional - If squeeze=True, access to the output response will remove - single-dimensional entries from the shape of the inputs, outputs, - and states even if the system is not SISO. If squeeze=False, keep - the input as a 2D or 3D array (indexed by the input (if - multi-input), trace (if single input) and time) and the output and - states as a 3D array (indexed by the output/state, trace, and - time) even if the system is SISO. + If `squeeze` = True, access to the output response will remove + single-dimensional entries from the shape of the inputs, + outputs, and states even if the system is not SISO. If + `squeeze` = False, keep the input as a 2D or 3D array (indexed + by the input (if multi-input), trace (if single input) and + time) and the output and states as a 3D array (indexed by the + output/state, trace, and time) even if the system is SISO. transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. return_x : bool, optional @@ -559,16 +530,21 @@ def outputs(self): Output response of the system, indexed by either the output and time (if only a single input is given) or the output, trace, and time - (for multiple traces). See :attr:`TimeResponseData.squeeze` for a + (for multiple traces). See `TimeResponseData.squeeze` for a description of how this can be modified using the `squeeze` keyword. + Input and output signal names can be used to index the data in + place of integer offsets, with the input signal names being used to + access multi-input data. + :type: 1D, 2D, or 3D array """ - t, y = _process_time_response( - self.t, self.y, issiso=self.issiso, + # TODO: move to __init__ to avoid recomputing each time? + y = _process_time_response( + self.y, issiso=self.issiso, transpose=self.transpose, squeeze=self.squeeze) - return y + return NamedSignal(y, self.output_labels, self.input_labels) # Getter for states (implements squeeze processing) @property @@ -577,64 +553,65 @@ def states(self): Time evolution of the state vector, indexed indexed by either the state and time (if only a single trace is given) or the state, trace, - and time (for multiple traces). See :attr:`TimeResponseData.squeeze` + and time (for multiple traces). See `TimeResponseData.squeeze` for a description of how this can be modified using the `squeeze` keyword. + Input and output signal names can be used to index the data in + place of integer offsets, with the input signal names being used to + access multi-input data. + :type: 2D or 3D array """ - if self.x is None: - return None - - elif self.squeeze is True: - x = self.x.squeeze() + # TODO: move to __init__ to avoid recomputing each time? + x = _process_time_response( + self.x, transpose=self.transpose, + squeeze=self.squeeze, issiso=False) - elif self.ninputs == 1 and self.noutputs == 1 and \ - self.ntraces == 1 and self.x.ndim == 3 and \ + # Special processing for SISO case: always retain state index + if self.issiso and self.ntraces == 1 and x.ndim == 3 and \ self.squeeze is not False: # Single-input, single-output system with single trace - x = self.x[:, 0, :] - - else: - # Return the full set of data - x = self.x + x = x[:, 0, :] - # Transpose processing - if self.transpose: - x = np.transpose(x, np.roll(range(x.ndim), 1)) - - return x + return NamedSignal(x, self.state_labels, self.input_labels) # Getter for inputs (implements squeeze processing) @property def inputs(self): """Time response input vector. - Input(s) to the system, indexed by input (optiona), trace (optional), + Input(s) to the system, indexed by input (optional), trace (optional), and time. If a 1D vector is passed, the input corresponds to a scalar-valued input. If a 2D vector is passed, then it can either represent multiple single-input traces or a single multi-input trace. - The optional ``multi_trace`` keyword should be used to disambiguate + The optional `multi_trace` keyword should be used to disambiguate the two. If a 3D vector is passed, then it represents a multi-trace, multi-input signal, indexed by input, trace, and time. - See :attr:`TimeResponseData.squeeze` for a description of how the + Input and output signal names can be used to index the data in + place of integer offsets, with the input signal names being used to + access multi-input data. + + See `TimeResponseData.squeeze` for a description of how the dimensions of the input vector can be modified using the `squeeze` keyword. :type: 1D or 2D array """ + # TODO: move to __init__ to avoid recomputing each time? if self.u is None: return None - t, u = _process_time_response( - self.t, self.u, issiso=self.issiso, + u = _process_time_response( + self.u, issiso=self.issiso, transpose=self.transpose, squeeze=self.squeeze) - return u + return NamedSignal(u, self.input_labels, self.input_labels) # Getter for legacy state (implements non-standard squeeze processing) + # TODO: remove when no longer needed @property def _legacy_states(self): """Time response state vector (legacy version). @@ -698,8 +675,10 @@ def __len__(self): def to_pandas(self): """Convert response data to pandas data frame. - Creates a pandas data frame using the input, output, and state - labels for the time response. + Creates a pandas data frame using the input, output, and state labels + for the time response. The column labels are given by the input and + output (and state, when present) labels, with time labeled by 'time' + and traces (for multi-trace responses) labeled by 'trace'. """ if not pandas_check(): @@ -707,16 +686,23 @@ def to_pandas(self): import pandas # Create a dict for setting up the data frame - data = {'time': self.time} + data = {'time': np.tile( + self.time, self.ntraces if self.ntraces > 0 else 1)} + if self.ntraces > 0: + data['trace'] = np.hstack([ + np.full(self.time.size, label) for label in self.trace_labels]) if self.ninputs > 0: data.update( - {name: self.u[i] for i, name in enumerate(self.input_labels)}) + {name: self.u[i].reshape(-1) + for i, name in enumerate(self.input_labels)}) if self.noutputs > 0: data.update( - {name: self.y[i] for i, name in enumerate(self.output_labels)}) + {name: self.y[i].reshape(-1) + for i, name in enumerate(self.output_labels)}) if self.nstates > 0: data.update( - {name: self.x[i] for i, name in enumerate(self.state_labels)}) + {name: self.x[i].reshape(-1) + for i, name in enumerate(self.state_labels)}) return pandas.DataFrame(data) @@ -724,12 +710,13 @@ def to_pandas(self): def plot(self, *args, **kwargs): """Plot the time response data objects. - This method calls :func:`time_response_plot`, passing all arguments - and keywords. + This method calls `time_response_plot`, passing all arguments + and keywords. See `time_response_plot` for details. """ return time_response_plot(self, *args, **kwargs) + # # Time response data list class # @@ -739,25 +726,34 @@ def plot(self, *args, **kwargs): # class TimeResponseList(list): - """This class consist of a list of :class:`TimeResponseData` objects. + """List of TimeResponseData objects with plotting capability. + + This class consists of a list of `TimeResponseData` objects. It is a subclass of the Python `list` class, with a `plot` method that - plots the individual :class:`TimeResponseData` objects. + plots the individual `TimeResponseData` objects. """ def plot(self, *args, **kwargs): - out_full = None + """Plot a list of time responses. + + See `time_response_plot` for details. + + """ + from .ctrlplot import ControlPlot + + lines = None label = kwargs.pop('label', [None] * len(self)) for i, response in enumerate(self): - out = TimeResponseData.plot( + cplt = TimeResponseData.plot( response, *args, label=label[i], **kwargs) - if out_full is None: - out_full = out + if lines is None: + lines = cplt.lines else: # Append the lines in the new plot to previous lines - for row in range(out.shape[0]): - for col in range(out.shape[1]): - out_full[row, col] += out[row, col] - return out_full + for row in range(cplt.lines.shape[0]): + for col in range(cplt.lines.shape[1]): + lines[row, col] += cplt.lines[row, col] + return ControlPlot(lines, cplt.axes, cplt.figure) # Process signal labels @@ -811,16 +807,16 @@ def _process_labels(labels, signal, length): return labels -# Helper function for checking array-like parameters +# Helper function for checking array_like parameters def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, transpose=False): """Helper function for checking array_like parameters. - * Check type and shape of ``in_obj``. - * Convert ``in_obj`` to an array if necessary. - * Change shape of ``in_obj`` according to parameter ``squeeze``. - * If ``in_obj`` is a scalar (number) it is converted to an array with + * Check type and shape of `in_obj`. + * Convert `in_obj` to an array if necessary. + * Change shape of `in_obj` according to parameter `squeeze`. + * If `in_obj` is a scalar (number) it is converted to an array with a legal shape, that is filled with the scalar value. The function raises an exception when it detects an error. @@ -835,8 +831,8 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, The special value "any" means that there can be any number of elements in a certain dimension. - * ``(2, 3)`` describes an array with 2 rows and 3 columns - * ``(2, "any")`` describes an array with 2 rows and any number of + * (2, 3) describes an array with 2 rows and 3 columns + * (2, 'any') describes an array with 2 rows and any number of columns err_msg_start : str @@ -848,18 +844,18 @@ def _check_convert_array(in_obj, legal_shapes, err_msg_start, squeeze=False, If True, all dimensions with only one element are removed from the array. If False the array's shape is unmodified. - For example: - ``array([[1,2,3]])`` is converted to ``array([1, 2, 3])`` + For example: ``array([[1, 2, 3]])`` is converted to ``array([1, 2, + 3])``. transpose : bool, optional - If True, assume that 2D input arrays are transposed from the standard - format. Used to convert MATLAB-style inputs to our format. + If True, assume that 2D input arrays are transposed from the + standard format. Used to convert MATLAB-style inputs to our + format. Returns ------- - out_array : array - The checked and converted contents of ``in_obj``. + The checked and converted contents of `in_obj`. """ # convert nearly everything to an array. @@ -921,13 +917,15 @@ def shape_matches(s_legal, s_actual): # Forced response of a linear system -def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, - interpolate=False, return_x=None, squeeze=None): +def forced_response( + sysdata, timepts=None, inputs=0., initial_state=0., transpose=False, + params=None, interpolate=False, return_states=None, squeeze=None, + **kwargs): """Compute the output of a linear system given the input. - As a convenience for parameters `U`, `X0`: - Numbers (scalars) are converted to constant arrays with the correct shape. - The correct shape is inferred from arguments `sys` and `T`. + As a convenience for parameters `U`, `X0`: Numbers (scalars) are + converted to constant arrays with the correct shape. The correct shape + is inferred from arguments `sys` and `T`. For information on the **shape** of parameters `U`, `T`, `X0` and return values `T`, `yout`, `xout`, see :ref:`time-series-convention`. @@ -936,108 +934,100 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, ---------- sysdata : I/O system or list of I/O systems I/O system(s) for which forced response is computed. - - T : array_like, optional for discrete LTI `sys` - Time steps at which the input is defined; values must be evenly spaced. - - If None, `U` must be given and `len(U)` time steps of sys.dt are - simulated. If sys.dt is None or True (undetermined time step), a time - step of 1.0 is assumed. - - U : array_like or float, optional - Input array giving input at each time `T`. - If `U` is None or 0, `T` must be given, even for discrete - time systems. In this case, for continuous time systems, a direct - calculation of the matrix exponential is used, which is faster than the - general interpolating algorithm used otherwise. - - X0 : array_like or float, default=0. + timepts (or T) : array_like, optional for discrete LTI `sys` + Time steps at which the input is defined; values must be evenly + spaced. If None, `inputs` must be given and ``len(inputs)`` time + steps of `sys.dt` are simulated. If `sys.dt` is None or True + (undetermined time step), a time step of 1.0 is assumed. + inputs (or U) : array_like or float, optional + Input array giving input at each time in `timepts`. If `inputs` is + None or 0, `timepts` must be given, even for discrete-time + systems. In this case, for continuous-time systems, a direct + calculation of the matrix exponential is used, which is faster than + the general interpolating algorithm used otherwise. + initial_state (or X0) : array_like or float, default=0. Initial condition. - params : dict, optional If system is a nonlinear I/O system, set parameter values. - transpose : bool, default=False If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). - + compatibility with MATLAB and `scipy.signal.lsim`). interpolate : bool, default=False - If True and system is a discrete time system, the input will + If True and system is a discrete-time system, the input will be interpolated between the given time steps and the output will be given at system sampling rate. Otherwise, only return the output at the times given in `T`. No effect on continuous time simulations. - - return_x : bool, default=None - Used if the time response data is assigned to a tuple: - - * If False, return only the time and output vectors. - - * If True, also return the the state vector. - - * If None, determine the returned variables by - config.defaults['forced_response.return_x'], which was True - before version 0.9 and is False since then. - + return_states (or return_x) : bool, default=None + Used if the time response data is assigned to a tuple. If False, + return only the time and output vectors. If True, also return the + the state vector. If None, determine the returned variables by + `config.defaults['forced_response.return_x']`, which was True + before version 0.9 and is False since then. squeeze : bool, optional By default, if a system is single-input, single-output (SISO) then - the output response is returned as a 1D array (indexed by time). If - `squeeze` is True, remove single-dimensional entries from the shape of - the output even if the system is not SISO. If `squeeze` is False, keep - the output as a 2D array (indexed by the output number and time) - even if the system is SISO. The default behavior can be overridden by - config.defaults['control.squeeze_time_response']. + the output response is returned as a 1D array (indexed by time). + If `squeeze` is True, remove single-dimensional entries from + the shape of the output even if the system is not SISO. If + `squeeze` is False, keep the output as a 2D array (indexed by + the output number and time) even if the system is SISO. The default + behavior can be overridden by + `config.defaults['control.squeeze_time_response']`. Returns ------- - results : :class:`TimeResponseData` or :class:`TimeResponseList` - Time response represented as a :class:`TimeResponseData` object or - list of :class:`TimeResponseData` objects containing the following - properties: - - * time (array): Time values of the output. - - * outputs (array): Response of the system. If the system is SISO and - `squeeze` is not True, the array is 1D (indexed by time). If the - system is not SISO or `squeeze` is False, the array is 2D (indexed - by output and time). - - * states (array): Time evolution of the state vector, represented as - a 2D array indexed by state and time. - - * inputs (array): Input(s) to the system, indexed by input and time. - - The `plot()` method can be used to create a plot of the time - response(s) (see :func:`time_response_plot` for more information). + resp : `TimeResponseData` or `TimeResponseList` + Input/output response data object. When accessed as a tuple, + returns ``(time, outputs)`` (default) or ``(time, outputs, states)`` + if `return_x` is True. The `~TimeResponseData.plot` method can + be used to create a plot of the time response(s) (see + `time_response_plot` for more information). If `sysdata` is a list + of systems, a `TimeResponseList` object is returned, which acts as + a list of `TimeResponseData` objects with a `~TimeResponseList.plot` + method that will plot responses as multiple traces. See + `time_response_plot` for additional information. + resp.time : array + Time values of the output. + resp.outputs : array + Response of the system. If the system is SISO and `squeeze` is not + True, the array is 1D (indexed by time). If the system is not SISO or + `squeeze` is False, the array is 2D (indexed by output and time). + resp.states : array + Time evolution of the state vector, represented as a 2D array + indexed by state and time. + resp.inputs : array + Input(s) to the system, indexed by input and time. See Also -------- - step_response, initial_response, impulse_response, input_output_response + impulse_response, initial_response, input_output_response, \ + step_response, time_response_plot Notes ----- - 1. For discrete time systems, the input/output response is computed - using the :func:`scipy.signal.dlsim` function. + For discrete-time systems, the input/output response is computed + using the `scipy.signal.dlsim` function. - 2. For continuous time systems, the output is computed using the matrix - exponential `exp(A t)` and assuming linear interpolation of the - inputs between time points. + For continuous-time systems, the output is computed using the + matrix exponential exp(A t) and assuming linear interpolation + of the inputs between time points. - 3. If a nonlinear I/O system is passed to `forced_response`, the - `input_output_response` function is called instead. The main - difference between `input_output_response` and `forced_response` is - that `forced_response` is specialized (and optimized) for linear - systems. + If a nonlinear I/O system is passed to `forced_response`, the + `input_output_response` function is called instead. The main + difference between `input_output_response` and `forced_response` + is that `forced_response` is specialized (and optimized) for + linear systems. - 4. (legacy) The return value of the system can also be accessed by - assigning the function to a tuple of length 2 (time, output) or of - length 3 (time, output, state) if ``return_x`` is ``True``. + (legacy) The return value of the system can also be accessed by + assigning the function to a tuple of length 2 (time, output) or of + length 3 (time, output, state) if `return_x` is True. Examples -------- >>> G = ct.rss(4) - >>> T = np.linspace(0, 10) - >>> T, yout = ct.forced_response(G, T=T) + >>> timepts = np.linspace(0, 10) + >>> inputs = np.sin(timepts) + >>> tout, yout = ct.forced_response(G, timepts, inputs) See :ref:`time-series-convention` and :ref:`package-configuration-parameters`. @@ -1047,13 +1037,26 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, from .statesp import StateSpace, _convert_to_statespace from .xferfcn import TransferFunction + # Process keyword arguments + _process_kwargs(kwargs, _timeresp_aliases) + T = _process_param('timepts', timepts, kwargs, _timeresp_aliases) + U = _process_param('inputs', inputs, kwargs, _timeresp_aliases, sigval=0.) + X0 = _process_param( + 'initial_state', initial_state, kwargs, _timeresp_aliases, sigval=0.) + return_x = _process_param( + 'return_states', return_states, kwargs, _timeresp_aliases, sigval=None) + + if kwargs: + raise TypeError("unrecognized keyword(s): ", str(kwargs)) + # If passed a list, recursively call individual responses with given T if isinstance(sysdata, (list, tuple)): responses = [] for sys in sysdata: responses.append(forced_response( - sys, T, U=U, X0=X0, transpose=transpose, params=params, - interpolate=interpolate, return_x=return_x, squeeze=squeeze)) + sys, T, inputs=U, initial_state=X0, transpose=transpose, + params=params, interpolate=interpolate, + return_states=return_x, squeeze=squeeze)) return TimeResponseList(responses) else: sys = sysdata @@ -1067,8 +1070,8 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, sys, T, U, X0, params=params, transpose=transpose, return_x=return_x, squeeze=squeeze) else: - raise TypeError('Parameter ``sys``: must be a ``StateSpace`` or' - ' ``TransferFunction``)') + raise TypeError('Parameter `sys`: must be a `StateSpace` or' + ' `TransferFunction`)') # If return_x was not specified, figure out the default if return_x is None: @@ -1099,14 +1102,14 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, if U is not None: U = np.asarray(U) if T is not None: - # T must be array-like + # T must be array_like T = np.asarray(T) - # Set and/or check time vector in discrete time case + # Set and/or check time vector in discrete-time case if isdtime(sys): if T is None: if U is None or (U.ndim == 0 and U == 0.): - raise ValueError('Parameters ``T`` and ``U`` can\'t both be ' + raise ValueError('Parameters `T` and `U` can\'t both be ' 'zero for discrete-time simulation') # Set T to equally spaced samples with same length as U if U.ndim == 1: @@ -1120,12 +1123,12 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, U = np.full((n_inputs, T.shape[0]), U) else: if T is None: - raise ValueError('Parameter ``T`` is mandatory for continuous ' + raise ValueError('Parameter `T` is mandatory for continuous ' 'time systems.') # Test if T has shape (n,) or (1, n); T = _check_convert_array(T, [('any',), (1, 'any')], - 'Parameter ``T``: ', squeeze=True, + 'Parameter `T`: ', squeeze=True, transpose=transpose) n_steps = T.shape[0] # number of simulation steps @@ -1133,25 +1136,25 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, # equally spaced also implies strictly monotonic increase, dt = (T[-1] - T[0]) / (n_steps - 1) if not np.allclose(np.diff(T), dt): - raise ValueError("Parameter ``T``: time values must be equally " + raise ValueError("Parameter `T`: time values must be equally " "spaced.") # create X0 if not given, test if X0 has correct shape X0 = _check_convert_array(X0, [(n_states,), (n_states, 1)], - 'Parameter ``X0``: ', squeeze=True) + 'Parameter `X0`: ', squeeze=True) # Test if U has correct shape and type legal_shapes = [(n_steps,), (1, n_steps)] if n_inputs == 1 else \ [(n_inputs, n_steps)] U = _check_convert_array(U, legal_shapes, - 'Parameter ``U``: ', squeeze=False, + 'Parameter `U`: ', squeeze=False, transpose=transpose) xout = np.zeros((n_states, n_steps)) xout[:, 0] = X0 yout = np.zeros((n_outputs, n_steps)) - # Separate out the discrete and continuous time cases + # Separate out the discrete and continuous-time cases if isctime(sys, strict=True): # Solve the differential equation, copied from scipy.signal.ltisys. @@ -1207,13 +1210,13 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, # First make sure that time increment is bigger than sampling time # (with allowance for small precision errors) if dt < sys.dt and not np.isclose(dt, sys.dt): - raise ValueError("Time steps ``T`` must match sampling time") + raise ValueError("Time steps `T` must match sampling time") # Now check to make sure it is a multiple (with check against # sys.dt because floating point mod can have small errors if not (np.isclose(dt % sys.dt, 0) or np.isclose(dt % sys.dt, sys.dt)): - raise ValueError("Time steps ``T`` must be multiples of " + raise ValueError("Time steps `T` must be multiples of " "sampling time") sys_dt = sys.dt @@ -1223,7 +1226,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, # https://github.com/scipyscipy/blob/v1.6.1/scipy/signal/ltisys.py#L3462 scipy_out_samples = int(np.floor(spT[-1] / sys_dt)) + 1 if scipy_out_samples < n_steps: - # parantheses: order of evaluation is important + # parentheses: order of evaluation is important spT[-1] = spT[-1] * (n_steps / (spT[-1] / sys_dt + 1)) else: @@ -1232,7 +1235,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, # Discrete time simulation using signal processing toolbox dsys = (A, B, C, D, sys_dt) - # Use signal processing toolbox for the discrete time simulation + # Use signal processing toolbox for the discrete-time simulation # Transpose the input to match toolbox convention tout, yout, xout = sp.signal.dlsim(dsys, np.transpose(U), spT, X0) tout = tout + T[0] @@ -1261,7 +1264,7 @@ def forced_response(sysdata, T=None, U=0., X0=0., transpose=False, params=None, # Process time responses in a uniform way def _process_time_response( - tout, yout, issiso=False, transpose=None, squeeze=None): + signal, issiso=False, transpose=None, squeeze=None): """Process time response signals. This function processes the outputs (or inputs) of time response @@ -1269,43 +1272,36 @@ def _process_time_response( Parameters ---------- - T : 1D array - Time values of the output. Ignored if None. - - yout : ndarray - Response of the system. This can either be a 1D array indexed by time - (for SISO systems), a 2D array indexed by output and time (for MIMO - systems with no input indexing, such as initial_response or forced - response) or a 3D array indexed by output, input, and time. + signal : ndarray + Data to be processed. This can either be a 1D array indexed by + time (for SISO systems), a 2D array indexed by output and time (for + MIMO systems with no input indexing, such as initial_response or + forced response) or a 3D array indexed by output, input, and time. issiso : bool, optional - If ``True``, process data as single-input, single-output data. - Default is ``False``. + If True, process data as single-input, single-output data. + Default is False. transpose : bool, optional - If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). Default - value is False. + If True, transpose data (for backward compatibility with MATLAB and + `scipy.signal.lsim`). Default value is False. squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) then the - output response is returned as a 1D array (indexed by time). If - squeeze=True, remove single-dimensional entries from the shape of the - output even if the system is not SISO. If squeeze=False, keep the - output as a 3D array (indexed by the output, input, and time) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_time_response']. + By default, if a system is single-input, single-output (SISO) then + the signals are returned as a 1D array (indexed by time). If + `squeeze` = True, remove single-dimensional entries from the shape + of the signal even if the system is not SISO. If `squeeze` = False, + keep the signal as a 3D array (indexed by the output, input, and + time) even if the system is SISO. The default value can be set + using `config.defaults['control.squeeze_time_response']`. Returns ------- - T : 1D array - Time values of the output. - - yout : ndarray - Response of the system. If the system is SISO and squeeze is not - True, the array is 1D (indexed by time). If the system is not SISO or - squeeze is False, the array is either 2D (indexed by output and time) - or 3D (indexed by input, output, and time). + output : ndarray + Processed signal. If the system is SISO and squeeze is not True, + the array is 1D (indexed by time). If the system is not SISO or + squeeze is False, the array is either 2D (indexed by output and + time) or 3D (indexed by input, output, and time). """ # If squeeze was not specified, figure out the default (might remain None) @@ -1313,42 +1309,40 @@ def _process_time_response( squeeze = config.defaults['control.squeeze_time_response'] # Figure out whether and how to squeeze output data - if squeeze is True: # squeeze all dimensions - yout = np.squeeze(yout) - elif squeeze is False: # squeeze no dimensions + if squeeze is True: # squeeze all dimensions + signal = np.squeeze(signal) + elif squeeze is False: # squeeze no dimensions pass - elif squeeze is None: # squeeze signals if SISO + elif squeeze is None: # squeeze signals if SISO if issiso: - if yout.ndim == 3: - yout = yout[0][0] # remove input and output + if signal.ndim == 3: + signal = signal[0][0] # remove input and output else: - yout = yout[0] # remove input + signal = signal[0] # remove input else: raise ValueError("Unknown squeeze value") # See if we need to transpose the data back into MATLAB form if transpose: - # Transpose time vector in case we are using np.matrix - tout = np.transpose(tout) - # For signals, put the last index (time) into the first slot - yout = np.transpose(yout, np.roll(range(yout.ndim), 1)) + signal = np.transpose(signal, np.roll(range(signal.ndim), 1)) - # Return time, output, and (optionally) state - return tout, yout + # Return output + return signal def step_response( - sysdata, T=None, X0=0, input=None, output=None, T_num=None, - transpose=False, return_x=False, squeeze=None, params=None): + sysdata, timepts=None, initial_state=0., input_indices=None, + output_indices=None, timepts_num=None, transpose=False, + return_states=False, squeeze=None, params=None, **kwargs): # pylint: disable=W0622 """Compute the step response for a linear system. If the system has multiple inputs and/or multiple outputs, the step - response is computed for each input/output pair, with all other inputs set - to zero. Optionally, a single input and/or single output can be selected, - in which case all other inputs are set to 0 and all other outputs are - ignored. + response is computed for each input/output pair, with all other inputs + set to zero. Optionally, a single input and/or single output can be + selected, in which case all other inputs are set to 0 and all other + outputs are ignored. For information on the **shape** of parameters `T`, `X0` and return values `T`, `yout`, see :ref:`time-series-convention`. @@ -1357,62 +1351,55 @@ def step_response( ---------- sysdata : I/O system or list of I/O systems I/O system(s) for which step response is computed. - - T : array_like or float, optional - Time vector, or simulation time duration if a number. If T is not + timepts (or T) : array_like or float, optional + Time vector, or simulation time duration if a number. If `T` is not provided, an attempt is made to create it automatically from the - dynamics of sys. If sys is continuous-time, the time increment dt - is chosen small enough to show the fastest mode, and the simulation - time period tfinal long enough to show the slowest mode, excluding - poles at the origin and pole-zero cancellations. If this results in - too many time steps (>5000), dt is reduced. If sys is discrete-time, - only tfinal is computed, and final is reduced if it requires too - many simulation steps. - - X0 : array_like or float, optional + dynamics of the system. If the system continuous time, the time + increment dt is chosen small enough to show the fastest mode, and + the simulation time period tfinal long enough to show the slowest + mode, excluding poles at the origin and pole-zero cancellations. If + this results in too many time steps (>5000), dt is reduced. If the + system is discrete time, only tfinal is computed, and final is + reduced if it requires too many simulation steps. + initial_state (or X0) : array_like or float, optional Initial condition (default = 0). This can be used for a nonlinear system where the origin is not an equilibrium point. - - input : int, optional + input_indices (or input) : int or list of int, optional Only compute the step response for the listed input. If not specified, the step responses for each independent input are computed (as separate traces). - - output : int, optional + output_indices (or output) : int, optional Only report the step response for the listed output. If not specified, all outputs are reported. - params : dict, optional If system is a nonlinear I/O system, set parameter values. - - T_num : int, optional - Number of time steps to use in simulation if T is not provided as an - array (autocomputed if not given); ignored if sys is discrete-time. - + timepts_num (or T_num) : int, optional + Number of time steps to use in simulation if `T` is not provided as + an array (auto-computed if not given); ignored if the system is + discrete time. transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). Default + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. - - return_x : bool, optional - If True, return the state vector when assigning to a tuple (default = - False). See :func:`forced_response` for more details. - + return_states (or return_x) : bool, optional + If True, return the state vector when assigning to a tuple + (default = False). See `forced_response` for more details. squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) then the - output response is returned as a 1D array (indexed by time). If - squeeze=True, remove single-dimensional entries from the shape of the - output even if the system is not SISO. If squeeze=False, keep the - output as a 3D array (indexed by the output, input, and time) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_time_response']. + By default, if a system is single-input, single-output (SISO) then + the output response is returned as a 1D array (indexed by time). + If `squeeze` = True, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + `squeeze` = False, keep the output as a 3D array (indexed by the + output, input, and time) even if the system is SISO. The default + value can be set using + `config.defaults['control.squeeze_time_response']`. Returns ------- results : `TimeResponseData` or `TimeResponseList` - Time response represented as a :class:`TimeResponseData` object or - list of :class:`TimeResponseData` objects. See - :func:`forced_response` for additional information. + Time response represented as a `TimeResponseData` object or + list of `TimeResponseData` objects. See + `forced_response` for additional information. See Also -------- @@ -1433,6 +1420,24 @@ def step_response( from .statesp import _convert_to_statespace from .xferfcn import TransferFunction + # Process keyword arguments + _process_kwargs(kwargs, _timeresp_aliases) + T = _process_param('timepts', timepts, kwargs, _timeresp_aliases) + X0 = _process_param( + 'initial_state', initial_state, kwargs, _timeresp_aliases, sigval=0.) + input = _process_param( + 'input_indices', input_indices, kwargs, _timeresp_aliases) + output = _process_param( + 'output_indices', output_indices, kwargs, _timeresp_aliases) + return_x = _process_param( + 'return_states', return_states, kwargs, _timeresp_aliases, + sigval=False) + T_num = _process_param( + 'timepts_num', timepts_num, kwargs, _timeresp_aliases) + + if kwargs: + raise TypeError("unrecognized keyword(s): ", str(kwargs)) + # Create the time and input vectors if T is None or np.asarray(T).size == 1: T = _default_time_vector(sysdata, N=T_num, tfinal=T, is_step=True) @@ -1445,8 +1450,9 @@ def step_response( responses = [] for sys in sysdata: responses.append(step_response( - sys, T, X0=X0, input=input, output=output, T_num=T_num, - transpose=transpose, return_x=return_x, squeeze=squeeze, + sys, T, initial_state=X0, input_indices=input, + output_indices=output, timepts_num=T_num, + transpose=transpose, return_states=return_x, squeeze=squeeze, params=params)) return TimeResponseList(responses) else: @@ -1463,6 +1469,21 @@ def step_response( if isinstance(sys, LTI) and sys.nstates is None: sys = _convert_to_statespace(sys) + # Only single input and output are allowed for now + if isinstance(input, (list, tuple)): + if len(input_indices) > 1: + raise NotImplementedError("list of input indices not allowed") + input = input[0] + elif isinstance(input, str): + raise NotImplementedError("named inputs not allowed") + + if isinstance(output, (list, tuple)): + if len(output_indices) > 1: + raise NotImplementedError("list of output indices not allowed") + output = output[0] + elif isinstance(output, str): + raise NotImplementedError("named outputs not allowed") + # Set up arrays to handle the output ninputs = sys.ninputs if input is None else 1 noutputs = sys.noutputs if output is None else 1 @@ -1510,25 +1531,26 @@ def step_response( trace_types=trace_types, plot_inputs=False) -def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, - SettlingTimeThreshold=0.02, RiseTimeLimits=(0.1, 0.9)): - """ - Step response characteristics (Rise time, Settling Time, Peak and others). +def step_info( + sysdata, timepts=None, timepts_num=None, final_output=None, + params=None, SettlingTimeThreshold=0.02, RiseTimeLimits=(0.1, 0.9), + **kwargs): + """Step response characteristics (rise time, settling time, etc). Parameters ---------- - sysdata : StateSpace or TransferFunction or array_like - The system data. Either LTI system to simulate (StateSpace, - TransferFunction), or a time series of step response data. - T : array_like or float, optional + sysdata : `StateSpace` or `TransferFunction` or array_like + The system data. Either LTI system to simulate (`StateSpace`, + `TransferFunction`), or a time series of step response data. + timepts (or T) : array_like or float, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given, see :func:`step_response` for more detail). + auto-computed if not given, see `step_response` for more detail). Required, if sysdata is a time series of response data. - T_num : int, optional - Number of time steps to use in simulation if T is not provided as an - array; autocomputed if not given; ignored if sysdata is a + timepts_num (or T_num) : int, optional + Number of time steps to use in simulation if `T` is not provided as + an array; auto-computed if not given; ignored if sysdata is a discrete-time system or a time series or response data. - yfinal : scalar or array_like, optional + final_output (or yfinal) : scalar or array_like, optional Steady-state response. If not given, sysdata.dcgain() is used for systems to simulate and the last value of the the response data is used for a given time series of response data. Scalar for SISO, @@ -1536,43 +1558,33 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, params : dict, optional If system is a nonlinear I/O system, set parameter values. SettlingTimeThreshold : float, optional - Defines the error to compute settling time (default = 0.02) - RiseTimeLimits : tuple (lower_threshold, upper_theshold) - Defines the lower and upper threshold for RiseTime computation + Defines the error to compute settling time (default = 0.02). + RiseTimeLimits : tuple (lower_threshold, upper_threshold) + Defines the lower and upper threshold for RiseTime computation. Returns ------- S : dict or list of list of dict - If `sysdata` corresponds to a SISO system, S is a dictionary + If `sysdata` corresponds to a SISO system, `S` is a dictionary containing: - RiseTime: - Time from 10% to 90% of the steady-state value. - SettlingTime: - Time to enter inside a default error of 2% - SettlingMin: - Minimum value after RiseTime - SettlingMax: - Maximum value after RiseTime - Overshoot: - Percentage of the Peak relative to steady value - Undershoot: - Percentage of undershoot - Peak: - Absolute peak value - PeakTime: - time of the Peak - SteadyStateValue: - Steady-state value + - 'RiseTime': Time from 10% to 90% of the steady-state value. + - 'SettlingTime': Time to enter inside a default error of 2%. + - 'SettlingMin': Minimum value after `RiseTime`. + - 'SettlingMax': Maximum value after `RiseTime`. + - 'Overshoot': Percentage of the peak relative to steady value. + - 'Undershoot': Percentage of undershoot. + - 'Peak': Absolute peak value. + - 'PeakTime': Time that the first peak value is obtained. + - 'SteadyStateValue': Steady-state value. If `sysdata` corresponds to a MIMO system, `S` is a 2D list of dicts. - To get the step response characteristics from the j-th input to the - i-th output, access ``S[i][j]`` - + To get the step response characteristics from the jth input to the + ith output, access ``S[i][j]``. See Also -------- - step, lsim, initial, impulse + step_response, forced_response, initial_response, impulse_response Examples -------- @@ -1614,13 +1626,26 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, Peak: 1.209 PeakTime: 4.242 SteadyStateValue: -1.0 + """ from .nlsys import NonlinearIOSystem from .statesp import StateSpace from .xferfcn import TransferFunction + # Process keyword arguments + _process_kwargs(kwargs, _timeresp_aliases) + T = _process_param('timepts', timepts, kwargs, _timeresp_aliases) + T_num = _process_param( + 'timepts_num', timepts_num, kwargs, _timeresp_aliases) + yfinal = _process_param( + 'final_output', final_output, kwargs, _timeresp_aliases) + + if kwargs: + raise TypeError("unrecognized keyword(s): ", str(kwargs)) + if isinstance(sysdata, (StateSpace, TransferFunction, NonlinearIOSystem)): - T, Yout = step_response(sysdata, T, squeeze=False, params=params) + T, Yout = step_response( + sysdata, T, timepts_num=T_num, squeeze=False, params=params) if yfinal: InfValues = np.atleast_2d(yfinal) else: @@ -1720,15 +1745,15 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, steady_state_value = InfValue retij = { - 'RiseTime': rise_time, - 'SettlingTime': settling_time, - 'SettlingMin': settling_min, - 'SettlingMax': settling_max, - 'Overshoot': overshoot, - 'Undershoot': undershoot, - 'Peak': peak_value, - 'PeakTime': peak_time, - 'SteadyStateValue': steady_state_value + 'RiseTime': float(rise_time), + 'SettlingTime': float(settling_time), + 'SettlingMin': float(settling_min), + 'SettlingMax': float(settling_max), + 'Overshoot': float(overshoot), + 'Undershoot': float(undershoot), + 'Peak': float(peak_value), + 'PeakTime': float(peak_time), + 'SteadyStateValue': float(steady_state_value) } retrow.append(retij) @@ -1738,8 +1763,9 @@ def step_info(sysdata, T=None, T_num=None, yfinal=None, params=None, def initial_response( - sysdata, T=None, X0=0, output=None, T_num=None, params=None, - transpose=False, return_x=False, squeeze=None): + sysdata, timepts=None, initial_state=0, output_indices=None, + timepts_num=None, params=None, transpose=False, return_states=False, + squeeze=None, **kwargs): # pylint: disable=W0622 """Compute the initial condition response for a linear system. @@ -1754,53 +1780,44 @@ def initial_response( ---------- sysdata : I/O system or list of I/O systems I/O system(s) for which initial response is computed. - - sys : StateSpace or TransferFunction - LTI system to simulate - - T : array_like or float, optional + timepts (or T) : array_like or float, optional Time vector, or simulation time duration if a number (time vector is - autocomputed if not given; see :func:`step_response` for more detail) - - X0 : array_like or float, optional + auto-computed if not given; see `step_response` for more detail). + initial_state (or X0) : array_like or float, optional Initial condition (default = 0). Numbers are converted to constant arrays with the correct shape. - - output : int - Index of the output that will be used in this simulation. Set to None - to not trim outputs. - - T_num : int, optional - Number of time steps to use in simulation if T is not provided as an - array (autocomputed if not given); ignored if sys is discrete-time. - + output_indices (or output) : int + Index of the output that will be used in this simulation. Set + to None to not trim outputs. + timepts_num (or T_num) : int, optional + Number of time steps to use in simulation if `timepts` is not + provided as an array (auto-computed if not given); ignored if the + system is discrete time. params : dict, optional If system is a nonlinear I/O system, set parameter values. - transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). Default + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. - - return_x : bool, optional - If True, return the state vector when assigning to a tuple (default = - False). See :func:`forced_response` for more details. - + return_states (or return_x) : bool, optional + If True, return the state vector when assigning to a tuple + (default = False). See `forced_response` for more details. squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) then the - output response is returned as a 1D array (indexed by time). If - squeeze=True, remove single-dimensional entries from the shape of the - output even if the system is not SISO. If squeeze=False, keep the - output as a 2D array (indexed by the output number and time) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_time_response']. + By default, if a system is single-input, single-output (SISO) then + the output response is returned as a 1D array (indexed by time). + If `squeeze` = True, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + `squeeze` = False, keep the output as a 2D array (indexed by the + output number and time) even if the system is SISO. The default + value can be set using + `config.defaults['control.squeeze_time_response']`. Returns ------- results : `TimeResponseData` or `TimeResponseList` - Time response represented as a :class:`TimeResponseData` object or - list of :class:`TimeResponseData` objects. See - :func:`forced_response` for additional information. + Time response represented as a `TimeResponseData` object or + list of `TimeResponseData` objects. See + `forced_response` for additional information. See Also -------- @@ -1817,7 +1834,21 @@ def initial_response( >>> T, yout = ct.initial_response(G) """ - from .lti import LTI + # Process keyword arguments + _process_kwargs(kwargs, _timeresp_aliases) + T = _process_param('timepts', timepts, kwargs, _timeresp_aliases) + X0 = _process_param( + 'initial_state', initial_state, kwargs, _timeresp_aliases, sigval=0.) + output = _process_param( + 'output_indices', output_indices, kwargs, _timeresp_aliases) + return_x = _process_param( + 'return_states', return_states, kwargs, _timeresp_aliases, + sigval=False) + T_num = _process_param( + 'timepts_num', timepts_num, kwargs, _timeresp_aliases) + + if kwargs: + raise TypeError("unrecognized keyword(s): ", str(kwargs)) # Create the time and input vectors if T is None or np.asarray(T).size == 1: @@ -1831,8 +1862,9 @@ def initial_response( responses = [] for sys in sysdata: responses.append(initial_response( - sys, T, X0=X0, output=output, T_num=T_num, transpose=transpose, - return_x=return_x, squeeze=squeeze, params=params)) + sys, T, initial_state=X0, output_indices=output, + timepts_num=T_num, transpose=transpose, + return_states=return_x, squeeze=squeeze, params=params)) return TimeResponseList(responses) else: sys = sysdata @@ -1858,16 +1890,17 @@ def initial_response( def impulse_response( - sysdata, T=None, input=None, output=None, T_num=None, - transpose=False, return_x=False, squeeze=None): + sysdata, timepts=None, input_indices=None, output_indices=None, + timepts_num=None, transpose=False, return_states=False, squeeze=None, + **kwargs): # pylint: disable=W0622 """Compute the impulse response for a linear system. If the system has multiple inputs and/or multiple outputs, the impulse - response is computed for each input/output pair, with all other inputs set - to zero. Optionally, a single input and/or single output can be selected, - in which case all other inputs are set to 0 and all other outputs are - ignored. + response is computed for each input/output pair, with all other inputs + set to zero. Optionally, a single input and/or single output can be + selected, in which case all other inputs are set to 0 and all other + outputs are ignored. For information on the **shape** of parameters `T`, `X0` and return values `T`, `yout`, see :ref:`time-series-convention`. @@ -1875,49 +1908,44 @@ def impulse_response( Parameters ---------- sysdata : I/O system or list of I/O systems - I/O system(s) for which impluse response is computed. - - T : array_like or float, optional + I/O system(s) for which impulse response is computed. + timepts (or T) : array_like or float, optional Time vector, or simulation time duration if a scalar (time vector is - autocomputed if not given; see :func:`step_response` for more detail) - - input : int, optional + auto-computed if not given; see `step_response` for more detail). + input_indices (or input) : int, optional Only compute the impulse response for the listed input. If not specified, the impulse responses for each independent input are computed. - - output : int, optional + output_indices (or output) : int, optional Only report the step response for the listed output. If not specified, all outputs are reported. - - T_num : int, optional - Number of time steps to use in simulation if T is not provided as an - array (autocomputed if not given); ignored if sys is discrete-time. - + timepts_num (or T_num) : int, optional + Number of time steps to use in simulation if `T` is not provided as + an array (auto-computed if not given); ignored if the system is + discrete time. transpose : bool, optional If True, transpose all input and output arrays (for backward - compatibility with MATLAB and :func:`scipy.signal.lsim`). Default + compatibility with MATLAB and `scipy.signal.lsim`). Default value is False. - - return_x : bool, optional - If True, return the state vector when assigning to a tuple (default = - False). See :func:`forced_response` for more details. - + return_states (or return_x) : bool, optional + If True, return the state vector when assigning to a tuple + (default = False). See `forced_response` for more details. squeeze : bool, optional - By default, if a system is single-input, single-output (SISO) then the - output response is returned as a 1D array (indexed by time). If - squeeze=True, remove single-dimensional entries from the shape of the - output even if the system is not SISO. If squeeze=False, keep the - output as a 2D array (indexed by the output number and time) even if - the system is SISO. The default value can be set using - config.defaults['control.squeeze_time_response']. + By default, if a system is single-input, single-output (SISO) then + the output response is returned as a 1D array (indexed by time). + If `squeeze` = True, remove single-dimensional entries from the + shape of the output even if the system is not SISO. If + `squeeze` = False, keep the output as a 2D array (indexed by the + output number and time) even if the system is SISO. The default + value can be set using + `config.defaults['control.squeeze_time_response']`. Returns ------- results : `TimeResponseData` or `TimeResponseList` - Time response represented as a :class:`TimeResponseData` object or - list of :class:`TimeResponseData` objects. See - :func:`forced_response` for additional information. + Time response represented as a `TimeResponseData` object or + list of `TimeResponseData` objects. See + `forced_response` for additional information. See Also -------- @@ -1926,8 +1954,8 @@ def impulse_response( Notes ----- This function uses the `forced_response` function to compute the time - response. For continuous time systems, the initial condition is altered - to account for the initial impulse. For discrete-time aystems, the + response. For continuous-time systems, the initial condition is altered + to account for the initial impulse. For discrete-time systems, the impulse is sized so that it has unit area. The impulse response for nonlinear systems is not implemented. @@ -1940,6 +1968,22 @@ def impulse_response( from .lti import LTI from .statesp import _convert_to_statespace + # Process keyword arguments + _process_kwargs(kwargs, _timeresp_aliases) + T = _process_param('timepts', timepts, kwargs, _timeresp_aliases) + input = _process_param( + 'input_indices', input_indices, kwargs, _timeresp_aliases) + output = _process_param( + 'output_indices', output_indices, kwargs, _timeresp_aliases) + return_x = _process_param( + 'return_states', return_states, kwargs, _timeresp_aliases, + sigval=False) + T_num = _process_param( + 'timepts_num', timepts_num, kwargs, _timeresp_aliases) + + if kwargs: + raise TypeError("unrecognized keyword(s): ", str(kwargs)) + # Create the time and input vectors if T is None or np.asarray(T).size == 1: T = _default_time_vector(sysdata, N=T_num, tfinal=T, is_step=False) @@ -1968,11 +2012,25 @@ def impulse_response( # Check to make sure there is not a direct term if np.any(sys.D != 0) and isctime(sys): - warnings.warn("System has direct feedthrough: ``D != 0``. The " - "infinite impulse at ``t=0`` does not appear in the " + warnings.warn("System has direct feedthrough: `D != 0`. The " + "infinite impulse at `t=0` does not appear in the " "output.\n" "Results may be meaningless!") + # Only single input and output are allowed for now + if isinstance(input, (list, tuple)): + if len(input_indices) > 1: + raise NotImplementedError("list of input indices not allowed") + input = input[0] + elif isinstance(input, str): + raise NotImplementedError("named inputs not allowed") + + if isinstance(output, (list, tuple)): + if len(output_indices) > 1: + raise NotImplementedError("list of output indices not allowed") + output = output[0] + elif isinstance(output, str): + raise NotImplementedError("named outputs not allowed") # Set up arrays to handle the output ninputs = sys.ninputs if input is None else 1 @@ -1997,7 +2055,7 @@ def impulse_response( # # We can't put the impulse into U because there is no numerical # representation for it (infinitesimally short, infinitely high). - # See also: http://www.mathworks.com/support/tech-notes/1900/1901.html + # See also: https://www.mathworks.com/support/tech-notes/1900/1901.html # if isctime(sys): X0 = sys.B[:, i] @@ -2007,7 +2065,7 @@ def impulse_response( U = np.zeros((sys.ninputs, T.size)) U[i, 0] = 1./sys.dt # unit area impulse - # Simulate the impulse response fo this input + # Simulate the impulse response for this input response = forced_response(sys, T, U, X0) # Store the output (and states) @@ -2037,15 +2095,15 @@ def impulse_response( # utility function to find time period and time increment using pole locations def _ideal_tfinal_and_dt(sys, is_step=True): - """helper function to compute ideal simulation duration tfinal and dt, the - time increment. Usually called by _default_time_vector, whose job it is to - choose a realistic time vector. Considers both poles and zeros. + """Helper function to compute ideal simulation duration tfinal and dt, + the time increment. Usually called by _default_time_vector, whose job + it is to choose a realistic time vector. Considers both poles and zeros. For discrete-time models, dt is inherent and only tfinal is computed. Parameters ---------- - sys : StateSpace or TransferFunction + sys : `StateSpace` or `TransferFunction` The system whose time response is to be computed is_step : bool Scales the dc value by the magnitude of the nonzero mode since @@ -2069,14 +2127,14 @@ def _ideal_tfinal_and_dt(sys, is_step=True): and the simulation would be unnecessarily long and the plot is virtually an L shape since the decay is so fast. - Instead, a modal decomposition in time domain hence a truncated ZIR and ZSR - can be used such that only the modes that have significant effect on the - time response are taken. But the sensitivity of the eigenvalues complicate - the matter since dlambda = with = 1. Hence we can only work - with simple poles with this formulation. See Golub, Van Loan Section 7.2.2 - for simple eigenvalue sensitivity about the nonunity of . The size of - the response is dependent on the size of the eigenshapes rather than the - eigenvalues themselves. + Instead, a modal decomposition in time domain hence a truncated ZIR and + ZSR can be used such that only the modes that have significant effect + on the time response are taken. But the sensitivity of the eigenvalues + complicate the matter since dlambda = with = 1. Hence + we can only work with simple poles with this formulation. See Golub, + Van Loan Section 7.2.2 for simple eigenvalue sensitivity about the + nonunity of . The size of the response is dependent on the size of + the eigenshapes rather than the eigenvalues themselves. By Ilhan Polat, with modifications by Sawyer Fuller to integrate into python-control 2020.08.17 @@ -2113,7 +2171,7 @@ def _ideal_tfinal_and_dt(sys, is_step=True): # zero - negligible effect on tfinal m_z = np.abs(p) < sqrt_eps p = p[~m_z] - # Negative reals- treated as oscillary mode + # Negative reals- treated as oscillatory mode m_nr = (p.real < 0) & (np.abs(p.imag) < sqrt_eps) p_nr, p = p[m_nr], p[~m_nr] if p_nr.size > 0: @@ -2205,7 +2263,7 @@ def _ideal_tfinal_and_dt(sys, is_step=True): def _default_time_vector(sysdata, N=None, tfinal=None, is_step=True): """Returns a time vector that has a reasonable number of points. - if system is discrete-time, N is ignored """ + if system is discrete time, N is ignored """ from .lti import LTI if isinstance(sysdata, (list, tuple)): diff --git a/control/xferfcn.py b/control/xferfcn.py index ba9af3913..02ba72df4 100644 --- a/control/xferfcn.py +++ b/control/xferfcn.py @@ -1,72 +1,39 @@ -"""xferfcn.py +# xferfcn.py - transfer function class and related functions +# +# Initial author: Richard M. Murray +# Creation date: 24 May 2009 +# Pre-2014 revisions: Kevin K. Chen, Dec 2010 +# Use `git shortlog -n -s xferfcn.py` for full list of contributors -Transfer function representation and functions. +"""Transfer function class and related functions. -This file contains the TransferFunction class and also functions -that operate on transfer functions. This is the primary representation -for the python-control library. -""" - -"""Copyright (c) 2010 by California Institute of Technology -All rights reserved. - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions -are met: - -1. Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - -2. Redistributions in binary form must reproduce the above copyright - notice, this list of conditions and the following disclaimer in the - documentation and/or other materials provided with the distribution. - -3. Neither the name of the California Institute of Technology nor - the names of its contributors may be used to endorse or promote - products derived from this software without specific prior - written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS -"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT -LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS -FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CALTECH -OR THE CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, -SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT -LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF -USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND -ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT -OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF -SUCH DAMAGE. - -Author: Richard M. Murray -Date: 24 May 09 -Revised: Kevin K. Chen, Dec 10 - -$Id$ +This module contains the `TransferFunction` class and also functions +that operate on transfer functions. """ +import sys from collections.abc import Iterable +from copy import deepcopy +from itertools import chain, product +from re import sub +from warnings import warn -# External function declarations import numpy as np -from numpy import angle, array, empty, finfo, ndarray, ones, \ - polyadd, polymul, polyval, roots, sqrt, zeros, squeeze, exp, pi, \ - where, delete, real, poly, nonzero import scipy as sp -from scipy.signal import tf2zpk, zpk2tf, cont2discrete +# float64 needed in eval() call +from numpy import float64 # noqa: F401 +from numpy import array, delete, empty, exp, finfo, ndarray, nonzero, ones, \ + poly, polyadd, polymul, polyval, real, roots, sqrt, where, zeros from scipy.signal import TransferFunction as signalTransferFunction -from copy import deepcopy -from warnings import warn -from itertools import chain -from re import sub -from .lti import LTI, _process_frequency_response -from .iosys import InputOutputSystem, common_timebase, isdtime, \ - _process_iosys_keywords +from scipy.signal import cont2discrete, tf2zpk, zpk2tf + +from . import bdalg, config from .exception import ControlMIMONotImplemented from .frdata import FrequencyResponseData -from . import config +from .iosys import InputOutputSystem, NamedSignal, _process_iosys_keywords, \ + _process_subsys_index, common_timebase +from .lti import LTI, _process_frequency_response __all__ = ['TransferFunction', 'tf', 'zpk', 'ss2tf', 'tfdata'] @@ -81,77 +48,113 @@ class TransferFunction(LTI): """TransferFunction(num, den[, dt]) - A class for representing transfer functions. + Transfer function representation for LTI input/output systems. The TransferFunction class is used to represent systems in transfer - function form. + function form. Transfer functions are usually created with the + `tf` factory function. Parameters ---------- - num : array_like, or list of list of array_like - Polynomial coefficients of the numerator - den : array_like, or list of list of array_like - Polynomial coefficients of the denominator + num : 2D list of coefficient arrays + Polynomial coefficients of the numerator. + den : 2D list of coefficient arrays + Polynomial coefficients of the denominator. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous - time, True indicates discrete time with unspecified sampling - time, positive number is discrete time with specified - sampling time, None indicates unspecified timebase (either - continuous or discrete time). - display_format: None, 'poly' or 'zpk' - Set the display format used in printing the TransferFunction object. - Default behavior is polynomial display and can be changed by - changing config.defaults['xferfcn.display_format']. + System timebase. 0 (default) indicates continuous time, True + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, None indicates + unspecified timebase (either continuous or discrete time). Attributes ---------- - ninputs, noutputs, nstates : int - Number of input, output and state variables. - num, den : 2D list of array - Polynomial coefficients of the numerator and denominator. - dt : None, True or float - System timebase. 0 (default) indicates continuous time, True indicates - discrete time with unspecified sampling time, positive number is - discrete time with specified sampling time, None indicates unspecified - timebase (either continuous or discrete time). + ninputs, noutputs : int + Number of input and output signals. + shape : tuple + 2-tuple of I/O system dimension, (noutputs, ninputs). + input_labels, output_labels : list of str + Names for the input and output signals. + name : string, optional + System name. + num_array, den_array : 2D array of lists of float + Numerator and denominator polynomial coefficients as 2D array + of 1D array objects (of varying length). + num_list, den_list : 2D list of 1D array + Numerator and denominator polynomial coefficients as 2D lists + of 1D array objects (of varying length). + display_format : None, 'poly' or 'zpk' + Display format used in printing the TransferFunction object. + Default behavior is polynomial display and can be changed by + changing `config.defaults['xferfcn.display_format']`. + s : `TransferFunction` + Represents the continuous-time differential operator. + z : `TransferFunction` + Represents the discrete-time delay operator. + + See Also + -------- + tf, InputOutputSystem, FrequencyResponseData Notes ----- - The attribues 'num' and 'den' are 2-D lists of arrays containing MIMO - numerator and denominator coefficients. For example, + The numerator and denominator polynomials are stored as 2D arrays + with each element containing a 1D array of coefficients. These data + structures can be retrieved using `num_array` and `den_array`. For + example, + + >>> sys.num_array[2, 5] # doctest: +SKIP + + gives the numerator of the transfer function from the 6th input to the + 3rd output. (Note: a single 3D array structure cannot be used because + the numerators and denominators can have different numbers of + coefficients in each entry.) - >>> num[2][5] = numpy.array([1., 4., 8.]) # doctest: +SKIP + The attributes `num_list` and `den_list` are properties that return + 2D nested lists containing MIMO numerator and denominator coefficients. + For example, - means that the numerator of the transfer function from the 6th input to - the 3rd output is set to s^2 + 4s + 8. + >>> sys.num_list[2][5] # doctest: +SKIP - A discrete time transfer function is created by specifying a nonzero + For legacy purposes, this list-based representation can also be + obtained using `num` and `den`. + + A discrete-time transfer function is created by specifying a nonzero 'timebase' dt when the system is constructed: - * dt = 0: continuous time system (default) - * dt > 0: discrete time system with sampling period 'dt' - * dt = True: discrete time with unspecified sampling period - * dt = None: no timebase specified + * `dt` = 0: continuous-time system (default) + * `dt` > 0: discrete-time system with sampling period `dt` + * `dt` = True: discrete time with unspecified sampling period + * `dt` = None: no timebase specified - Systems must have compatible timebases in order to be combined. A discrete - time system with unspecified sampling time (`dt = True`) can be combined - with a system having a specified sampling time; the result will be a - discrete time system with the sample time of the latter system. Similarly, - a system with timebase `None` can be combined with a system having any - timebase; the result will have the timebase of the latter system. - The default value of dt can be changed by changing the value of - ``control.config.defaults['control.default_dt']``. + Systems must have compatible timebases in order to be combined. A + discrete-time system with unspecified sampling time (`dt` = True) can + be combined with a system having a specified sampling time; the result + will be a discrete-time system with the sample time of the other + system. Similarly, a system with timebase None can be combined with a + system having any timebase; the result will have the timebase of the + other system. The default value of dt can be changed by changing the + value of `config.defaults['control.default_dt']`. A transfer function is callable and returns the value of the transfer function evaluated at a point in the complex plane. See - :meth:`~control.TransferFunction.__call__` for a more detailed description. + `TransferFunction.__call__` for a more detailed description. + + Subsystems corresponding to selected input/output pairs can be + created by indexing the transfer function:: - The TransferFunction class defines two constants ``s`` and ``z`` that + subsys = sys[output_spec, input_spec] + + The input and output specifications can be single integers, lists of + integers, or slices. In addition, the strings representing the names + of the signals can be used and will be replaced with the equivalent + signal offsets. + + The TransferFunction class defines two constants `s` and `z` that represent the differentiation and delay operators in continuous and - discrete time. These can be used to create variables that allow algebraic - creation of transfer functions. For example, + discrete time. These can be used to create variables that allow + algebraic creation of transfer functions. For example, - >>> s = ct.TransferFunction.s + >>> s = ct.TransferFunction.s # or ct.tf('s') >>> G = (s + 1)/(s**2 + 2*s + 1) """ @@ -160,13 +163,15 @@ def __init__(self, *args, **kwargs): Construct a transfer function. - The default constructor is TransferFunction(num, den), where num and - den are lists of lists of arrays containing polynomial coefficients. - To create a discrete time transfer funtion, use TransferFunction(num, - den, dt) where 'dt' is the sampling time (or True for unspecified - sampling time). To call the copy constructor, call - TransferFunction(sys), where sys is a TransferFunction object - (continuous or discrete). + The default constructor is TransferFunction(num, den), where num + and den are 2D arrays of arrays containing polynomial coefficients. + To create a discrete-time transfer function, use + ``TransferFunction(num, den, dt)`` where `dt` is the sampling time + (or True for unspecified sampling time). To call the copy + constructor, call ``TransferFunction(sys)``, where `sys` is a + TransferFunction object (continuous or discrete). + + See `TransferFunction` and `tf` for more information. """ # @@ -199,8 +204,8 @@ def __init__(self, *args, **kwargs): raise TypeError("Needs 1, 2 or 3 arguments; received %i." % len(args)) - num = _clean_part(num) - den = _clean_part(den) + num = _clean_part(num, "numerator") + den = _clean_part(den, "denominator") # # Process keyword arguments @@ -209,23 +214,30 @@ def __init__(self, *args, **kwargs): # get initialized when defaults are not fully initialized yet. # Use 'poly' in these cases. - self.display_format = kwargs.pop( - 'display_format', - config.defaults.get('xferfcn.display_format', 'poly')) - - if self.display_format not in ('poly', 'zpk'): + self.display_format = kwargs.pop('display_format', None) + if self.display_format not in (None, 'poly', 'zpk'): raise ValueError("display_format must be 'poly' or 'zpk'," " got '%s'" % self.display_format) - # Determine if the transfer function is static (needed for dt) + # + # Determine if the transfer function is static (memoryless) + # + # True if and only if all of the numerator and denominator + # polynomials of the (MIMO) transfer function are zeroth order. + # static = True - for col in num + den: - for poly in col: - if len(poly) > 1: + for arr in [num, den]: + # Iterate using refs_OK since num and den are ndarrays of ndarrays + for poly_ in np.nditer(arr, flags=['refs_ok']): + if poly_.item().size > 1: static = False + break + if not static: + break + self._static = static # retain for later usage defaults = args[0] if len(args) == 1 else \ - {'inputs': len(num[0]), 'outputs': len(num)} + {'inputs': num.shape[1], 'outputs': num.shape[0]} name, inputs, outputs, states, dt = _process_iosys_keywords( kwargs, defaults, static=static) @@ -241,27 +253,17 @@ def __init__(self, *args, **kwargs): # Check to make sure everything is consistent # # Make sure numerator and denominator matrices have consistent sizes - if self.ninputs != len(den[0]): + if self.ninputs != den.shape[1]: raise ValueError( "The numerator has %i input(s), but the denominator has " - "%i input(s)." % (self.ninputs, len(den[0]))) - if self.noutputs != len(den): + "%i input(s)." % (self.ninputs, den.shape[1])) + if self.noutputs != den.shape[0]: raise ValueError( "The numerator has %i output(s), but the denominator has " - "%i output(s)." % (self.noutputs, len(den))) + "%i output(s)." % (self.noutputs, den.shape[0])) # Additional checks/updates on structure of the transfer function for i in range(self.noutputs): - # Make sure that each row has the same number of columns - if len(num[i]) != self.ninputs: - raise ValueError( - "Row 0 of the numerator matrix has %i elements, but row " - "%i has %i." % (self.ninputs, i, len(num[i]))) - if len(den[i]) != self.ninputs: - raise ValueError( - "Row 0 of the denominator matrix has %i elements, but row " - "%i has %i." % (self.ninputs, i, len(den[i]))) - # Check for zeros in numerator or denominator # TODO: Right now these checks are only done during construction. # It might be worthwhile to think of a way to perform checks if the @@ -269,8 +271,8 @@ def __init__(self, *args, **kwargs): for j in range(self.ninputs): # Check that we don't have any zero denominators. zeroden = True - for k in den[i][j]: - if k: + for k in den[i, j]: + if np.any(k): zeroden = False break if zeroden: @@ -280,16 +282,16 @@ def __init__(self, *args, **kwargs): # If we have zero numerators, set the denominator to 1. zeronum = True - for k in num[i][j]: - if k: + for k in num[i, j]: + if np.any(k): zeronum = False break if zeronum: den[i][j] = ones(1) # Store the numerator and denominator - self.num = num - self.den = den + self.num_array = num + self.den_array = den # # Final processing @@ -314,92 +316,62 @@ def __init__(self, *args, **kwargs): #: :meta hide-value: noutputs = 1 - #: Transfer function numerator polynomial (array) - #: - #: The numerator of the transfer function is stored as an 2D list of - #: arrays containing MIMO numerator coefficients, indexed by outputs and - #: inputs. For example, ``num[2][5]`` is the array of coefficients for - #: the numerator of the transfer function from the sixth input to the - #: third output. + #: Numerator polynomial coefficients as a 2D array of 1D coefficients. #: #: :meta hide-value: - num = [[0]] + num_array = None - #: Transfer function denominator polynomial (array) - #: - #: The numerator of the transfer function is store as an 2D list of - #: arrays containing MIMO numerator coefficients, indexed by outputs and - #: inputs. For example, ``den[2][5]`` is the array of coefficients for - #: the denominator of the transfer function from the sixth input to the - #: third output. + #: Denominator polynomial coefficients as a 2D array of 1D coefficients. #: #: :meta hide-value: - den = [[0]] + den_array = None - def __call__(self, x, squeeze=None, warn_infinite=True): - """Evaluate system's transfer function at complex frequencies. + # Numerator and denominator as lists of lists of lists + @property + def num_list(self): + """Numerator polynomial (as 2D nested list of 1D arrays).""" + return self.num_array.tolist() - Returns the complex frequency response `sys(x)` where `x` is `s` for - continuous-time systems and `z` for discrete-time systems. + @property + def den_list(self): + """Denominator polynomial (as 2D nested lists of 1D arrays).""" + return self.den_array.tolist() - In general the system may be multiple input, multiple output - (MIMO), where `m = self.ninputs` number of inputs and `p = - self.noutputs` number of outputs. + # Legacy versions (TODO: add DeprecationWarning in a later release?) + num, den = num_list, den_list - To evaluate at a frequency omega in radians per second, enter - ``x = omega * 1j``, for continuous-time systems, or - ``x = exp(1j * omega * dt)`` for discrete-time systems. Or use - :meth:`TransferFunction.frequency_response`. + def __call__(self, x, squeeze=None, warn_infinite=True): + """Evaluate system transfer function at point in complex plane. - Parameters - ---------- - x : complex or complex 1D array_like - Complex frequencies - squeeze : bool, optional - If squeeze=True, remove single-dimensional entries from the shape - of the output even if the system is not SISO. If squeeze=False, - keep all indices (output, input and, if omega is array_like, - frequency) even if the system is SISO. The default value can be - set using config.defaults['control.squeeze_frequency_response']. - If True and the system is single-input single-output (SISO), - return a 1D array rather than a 3D array. Default value (True) - set by config.defaults['control.squeeze_frequency_response']. - warn_infinite : bool, optional - If set to `False`, turn off divide by zero warning. + Returns the value of the system's transfer function at a point `x` + in the complex plane, where `x` is `s` for continuous-time systems + and `z` for discrete-time systems. - Returns - ------- - fresp : complex ndarray - The frequency response of the system. If the system is SISO and - squeeze is not True, the shape of the array matches the shape of - omega. If the system is not SISO or squeeze is False, the first - two dimensions of the array are indices for the output and input - and the remaining dimensions match omega. If ``squeeze`` is True - then single-dimensional axes are removed. + See `LTI.__call__` for details. """ out = self.horner(x, warn_infinite=warn_infinite) return _process_frequency_response(self, x, out, squeeze=squeeze) def horner(self, x, warn_infinite=True): - """Evaluate system's transfer function at complex frequency - using Horner's method. - - Evaluates `sys(x)` where `x` is `s` for continuous-time systems and `z` - for discrete-time systems. + """Evaluate value of transfer function using Horner's method. - Expects inputs and outputs to be formatted correctly. Use ``sys(x)`` - for a more user-friendly interface. + Evaluates ``sys(x)`` where `x` is a complex number `s` for + continuous-time systems and `z` for discrete-time systems. Expects + inputs and outputs to be formatted correctly. Use ``sys(x)`` for a + more user-friendly interface. Parameters ---------- - x : complex array_like or complex scalar - Complex frequencies + x : complex + Complex frequency at which the transfer function is evaluated. + + warn_infinite : bool, optional + If True (default), generate a warning if `x` is a pole. Returns ------- - output : (self.noutputs, self.ninputs, len(x)) complex ndarray - Frequency response + complex """ # Make sure the argument is a 1D array of complex numbers @@ -416,8 +388,8 @@ def horner(self, x, warn_infinite=True): with np.errstate(all='warn' if warn_infinite else 'ignore'): for i in range(self.noutputs): for j in range(self.ninputs): - out[i][j] = (polyval(self.num[i][j], x_arr) / - polyval(self.den[i][j], x_arr)) + out[i][j] = (polyval(self.num_array[i, j], x_arr) / + polyval(self.den_array[i, j], x_arr)) return out def _truncatecoeff(self): @@ -430,14 +402,14 @@ def _truncatecoeff(self): """ # Beware: this is a shallow copy. This should be okay. - data = [self.num, self.den] + data = [self.num_array, self.den_array] for p in range(len(data)): for i in range(self.noutputs): for j in range(self.ninputs): # Find the first nontrivial coefficient. nonzero = None - for k in range(data[p][i][j].size): - if data[p][i][j][k]: + for k in range(data[p][i, j].size): + if data[p][i, j][k]: nonzero = k break @@ -447,7 +419,7 @@ def _truncatecoeff(self): else: # Truncate the trivial coefficients. data[p][i][j] = data[p][i][j][nonzero:] - [self.num, self.den] = data + [self.num_array, self.den_array] = data def __str__(self, var=None): """String representation of the transfer function. @@ -455,28 +427,34 @@ def __str__(self, var=None): Based on the display_format property, the output will be formatted as either polynomials or in zpk form. """ + display_format = config.defaults['xferfcn.display_format'] if \ + self.display_format is None else self.display_format mimo = not self.issiso() if var is None: var = 's' if self.isctime() else 'z' - outstr = f"{InputOutputSystem.__str__(self)}\n" + outstr = f"{InputOutputSystem.__str__(self)}" for ni in range(self.ninputs): for no in range(self.noutputs): + outstr += "\n" if mimo: - outstr += "\nInput %i to output %i:" % (ni + 1, no + 1) + outstr += "\nInput %i to output %i:\n" % (ni + 1, no + 1) # Convert the numerator and denominator polynomials to strings. - if self.display_format == 'poly': - numstr = _tf_polynomial_to_string(self.num[no][ni], var=var) - denstr = _tf_polynomial_to_string(self.den[no][ni], var=var) - elif self.display_format == 'zpk': - num = self.num[no][ni] + if display_format == 'poly': + numstr = _tf_polynomial_to_string( + self.num_array[no, ni], var=var) + denstr = _tf_polynomial_to_string( + self.den_array[no, ni], var=var) + elif display_format == 'zpk': + num = self.num_array[no, ni] if num.size == 1 and num.item() == 0: # Catch a special case that SciPy doesn't handle - z, p, k = tf2zpk([1.], self.den[no][ni]) + z, p, k = tf2zpk([1.], self.den_array[no, ni]) k = 0 else: - z, p, k = tf2zpk(self.num[no][ni], self.den[no][ni]) + z, p, k = tf2zpk( + self.num[no][ni], self.den_array[no, ni]) numstr = _tf_factorized_polynomial_to_string( z, gain=k, var=var) denstr = _tf_factorized_polynomial_to_string(p, var=var) @@ -491,37 +469,48 @@ def __str__(self, var=None): if len(denstr) < dashcount: denstr = ' ' * ((dashcount - len(denstr)) // 2) + denstr - outstr += "\n" + numstr + "\n" + dashes + "\n" + denstr + "\n" - - # If this is a strict discrete time system, print the sampling time - if type(self.dt) != bool and self.isdtime(strict=True): - outstr += "\ndt = " + str(self.dt) + "\n" + outstr += "\n " + numstr + "\n " + dashes + "\n " + denstr return outstr - # represent to implement a re-loadable version - def __repr__(self): - """Print transfer function in loadable form""" + def _repr_eval_(self): + # Loadable format if self.issiso(): - return "TransferFunction({num}, {den}{dt})".format( - num=self.num[0][0].__repr__(), den=self.den[0][0].__repr__(), - dt=', {}'.format(self.dt) if isdtime(self, strict=True) - else '') + out = "TransferFunction(\n{num},\n{den}".format( + num=self.num_array[0, 0].__repr__(), + den=self.den_array[0, 0].__repr__()) else: - return "TransferFunction({num}, {den}{dt})".format( - num=self.num.__repr__(), den=self.den.__repr__(), - dt=', {}'.format(self.dt) if isdtime(self, strict=True) - else '') - - def _repr_latex_(self, var=None): - """LaTeX representation of transfer function, for Jupyter notebook""" + out = "TransferFunction(\n[" + for entry in [self.num_array, self.den_array]: + for i in range(self.noutputs): + out += "[" if i == 0 else "\n [" + linelen = 0 + for j in range(self.ninputs): + out += ", " if j != 0 else "" + numstr = np.array_repr(entry[i, j]) + if linelen + len(numstr) > 72: + out += "\n " + linelen = 0 + out += numstr + linelen += len(numstr) + out += "]," if i < self.noutputs - 1 else "]" + out += "],\n[" if entry is self.num_array else "]" + + out += super()._dt_repr(separator=",\n", space="") + if len(labels := self._label_repr()) > 0: + out += ",\n" + labels + + out += ")" + return out + def _repr_html_(self, var=None): + """HTML/LaTeX representation of xferfcn, for Jupyter notebook.""" + display_format = config.defaults['xferfcn.display_format'] if \ + self.display_format is None else self.display_format mimo = not self.issiso() - if var is None: var = 's' if self.isctime() else 'z' - - out = ['$$'] + out = [super()._repr_info_(html=True), '\n$$'] if mimo: out.append(r"\begin{bmatrix}") @@ -529,11 +518,14 @@ def _repr_latex_(self, var=None): for no in range(self.noutputs): for ni in range(self.ninputs): # Convert the numerator and denominator polynomials to strings. - if self.display_format == 'poly': - numstr = _tf_polynomial_to_string(self.num[no][ni], var=var) - denstr = _tf_polynomial_to_string(self.den[no][ni], var=var) - elif self.display_format == 'zpk': - z, p, k = tf2zpk(self.num[no][ni], self.den[no][ni]) + if display_format == 'poly': + numstr = _tf_polynomial_to_string( + self.num_array[no, ni], var=var) + denstr = _tf_polynomial_to_string( + self.den_array[no, ni], var=var) + elif display_format == 'zpk': + z, p, k = tf2zpk( + self.num_array[no, ni], self.den_array[no, ni]) numstr = _tf_factorized_polynomial_to_string( z, gain=k, var=var) denstr = _tf_factorized_polynomial_to_string(p, var=var) @@ -541,7 +533,7 @@ def _repr_latex_(self, var=None): numstr = _tf_string_to_latex(numstr, var=var) denstr = _tf_string_to_latex(denstr, var=var) - out += [r"\frac{", numstr, "}{", denstr, "}"] + out += [r"\dfrac{", numstr, "}{", denstr, "}"] if mimo and ni < self.ninputs - 1: out.append("&") @@ -552,20 +544,16 @@ def _repr_latex_(self, var=None): if mimo: out.append(r" \end{bmatrix}") - # See if this is a discrete time system with specific sampling time - if not (self.dt is None) and type(self.dt) != bool and self.dt > 0: - out += [r"\quad dt = ", str(self.dt)] - out.append("$$") return ''.join(out) def __neg__(self): """Negate a transfer function.""" - num = deepcopy(self.num) + num = deepcopy(self.num_array) for i in range(self.noutputs): for j in range(self.ninputs): - num[i][j] *= -1 + num[i, j] *= -1 return TransferFunction(num, self.den, self.dt) def __add__(self, other): @@ -582,6 +570,12 @@ def __add__(self, other): if not isinstance(other, TransferFunction): return NotImplemented + # Promote SISO object to compatible dimension + if self.issiso() and not other.issiso(): + self = np.ones((other.noutputs, other.ninputs)) * self + elif not self.issiso() and other.issiso(): + other = np.ones((self.noutputs, self.ninputs)) * other + # Check that the input-output sizes are consistent. if self.ninputs != other.ninputs: raise ValueError( @@ -595,14 +589,14 @@ def __add__(self, other): dt = common_timebase(self.dt, other.dt) # Preallocate the numerator and denominator of the sum. - num = [[[] for j in range(self.ninputs)] for i in range(self.noutputs)] - den = [[[] for j in range(self.ninputs)] for i in range(self.noutputs)] + num = _create_poly_array((self.noutputs, self.ninputs)) + den = _create_poly_array((self.noutputs, self.ninputs)) for i in range(self.noutputs): for j in range(self.ninputs): - num[i][j], den[i][j] = _add_siso( - self.num[i][j], self.den[i][j], - other.num[i][j], other.den[i][j]) + num[i, j], den[i, j] = _add_siso( + self.num_array[i, j], self.den_array[i, j], + other.num_array[i, j], other.den_array[i, j]) return TransferFunction(num, den, dt) @@ -623,28 +617,34 @@ def __mul__(self, other): from .statesp import StateSpace # Convert the second argument to a transfer function. - if isinstance(other, StateSpace): + if isinstance(other, (StateSpace, np.ndarray)): other = _convert_to_transfer_function(other) - elif isinstance(other, (int, float, complex, np.number, np.ndarray)): - other = _convert_to_transfer_function(other, inputs=self.ninputs, - outputs=self.noutputs) + elif isinstance(other, (int, float, complex, np.number)): + # Multiply by a scaled identity matrix (transfer function) + other = _convert_to_transfer_function(np.eye(self.ninputs) * other) if not isinstance(other, TransferFunction): return NotImplemented + # Promote SISO object to compatible dimension + if self.issiso() and not other.issiso(): + self = bdalg.append(*([self] * other.noutputs)) + elif not self.issiso() and other.issiso(): + other = bdalg.append(*([other] * self.ninputs)) + # Check that the input-output sizes are consistent. if self.ninputs != other.noutputs: raise ValueError( "C = A * B: A has %i column(s) (input(s)), but B has %i " "row(s)\n(output(s))." % (self.ninputs, other.noutputs)) - inputs = other.ninputs - outputs = self.noutputs + ninputs = other.ninputs + noutputs = self.noutputs dt = common_timebase(self.dt, other.dt) # Preallocate the numerator and denominator of the sum. - num = [[[0] for j in range(inputs)] for i in range(outputs)] - den = [[[1] for j in range(inputs)] for i in range(outputs)] + num = _create_poly_array((noutputs, ninputs), [0]) + den = _create_poly_array((noutputs, ninputs), [1]) # Temporary storage for the summands needed to find the (i, j)th # element of the product. @@ -652,17 +652,16 @@ def __mul__(self, other): den_summand = [[] for k in range(self.ninputs)] # Multiply & add. - for row in range(outputs): - for col in range(inputs): + for row in range(noutputs): + for col in range(ninputs): for k in range(self.ninputs): num_summand[k] = polymul( - self.num[row][k], other.num[k][col]) + self.num_array[row, k], other.num_array[k, col]) den_summand[k] = polymul( - self.den[row][k], other.den[k][col]) - num[row][col], den[row][col] = _add_siso( - num[row][col], den[row][col], + self.den_array[row, k], other.den_array[k, col]) + num[row, col], den[row, col] = _add_siso( + num[row, col], den[row, col], num_summand[k], den_summand[k]) - return TransferFunction(num, den, dt) def __rmul__(self, other): @@ -670,25 +669,31 @@ def __rmul__(self, other): # Convert the second argument to a transfer function. if isinstance(other, (int, float, complex, np.number)): - other = _convert_to_transfer_function(other, inputs=self.ninputs, - outputs=self.ninputs) + # Multiply by a scaled identity matrix (transfer function) + other = _convert_to_transfer_function(np.eye(self.noutputs) * other) else: other = _convert_to_transfer_function(other) + # Promote SISO object to compatible dimension + if self.issiso() and not other.issiso(): + self = bdalg.append(*([self] * other.ninputs)) + elif not self.issiso() and other.issiso(): + other = bdalg.append(*([other] * self.noutputs)) + # Check that the input-output sizes are consistent. if other.ninputs != self.noutputs: raise ValueError( "C = A * B: A has %i column(s) (input(s)), but B has %i " "row(s)\n(output(s))." % (other.ninputs, self.noutputs)) - inputs = self.ninputs - outputs = other.noutputs + ninputs = self.ninputs + noutputs = other.noutputs dt = common_timebase(self.dt, other.dt) # Preallocate the numerator and denominator of the sum. - num = [[[0] for j in range(inputs)] for i in range(outputs)] - den = [[[1] for j in range(inputs)] for i in range(outputs)] + num = _create_poly_array((noutputs, ninputs), [0]) + den = _create_poly_array((noutputs, ninputs), [1]) # Temporary storage for the summands needed to find the # (i, j)th element @@ -696,13 +701,15 @@ def __rmul__(self, other): num_summand = [[] for k in range(other.ninputs)] den_summand = [[] for k in range(other.ninputs)] - for i in range(outputs): # Iterate through rows of product. - for j in range(inputs): # Iterate through columns of product. + for i in range(noutputs): # Iterate through rows of product. + for j in range(ninputs): # Iterate through columns of product. for k in range(other.ninputs): # Multiply & add. - num_summand[k] = polymul(other.num[i][k], self.num[k][j]) - den_summand[k] = polymul(other.den[i][k], self.den[k][j]) + num_summand[k] = polymul( + other.num_array[i, k], self.num_array[k, j]) + den_summand[k] = polymul( + other.den_array[i, k], self.den_array[k, j]) num[i][j], den[i][j] = _add_siso( - num[i][j], den[i][j], + num[i, j], den[i, j], num_summand[k], den_summand[k]) return TransferFunction(num, den, dt) @@ -712,22 +719,25 @@ def __truediv__(self, other): """Divide two LTI objects.""" if isinstance(other, (int, float, complex, np.number)): - other = _convert_to_transfer_function( - other, inputs=self.ninputs, - outputs=self.ninputs) + # Multiply by a scaled identity matrix (transfer function) + other = _convert_to_transfer_function(np.eye(self.ninputs) * other) else: other = _convert_to_transfer_function(other) + # Special case for SISO ``other`` + if not self.issiso() and other.issiso(): + other = bdalg.append(*([other**-1] * self.noutputs)) + return self * other + if (self.ninputs > 1 or self.noutputs > 1 or other.ninputs > 1 or other.noutputs > 1): - raise NotImplementedError( - "TransferFunction.__truediv__ is currently \ - implemented only for SISO systems.") - + # TransferFunction.__truediv__ is currently implemented only for + # SISO systems. + return NotImplemented dt = common_timebase(self.dt, other.dt) - num = polymul(self.num[0][0], other.den[0][0]) - den = polymul(self.den[0][0], other.num[0][0]) + num = polymul(self.num_array[0, 0], other.den_array[0, 0]) + den = polymul(self.den_array[0, 0], other.num_array[0, 0]) return TransferFunction(num, den, dt) @@ -741,11 +751,16 @@ def __rtruediv__(self, other): else: other = _convert_to_transfer_function(other) + # Special case for SISO ``self`` + if self.issiso() and not other.issiso(): + self = bdalg.append(*([self**-1] * other.ninputs)) + return other * self + if (self.ninputs > 1 or self.noutputs > 1 or other.ninputs > 1 or other.noutputs > 1): - raise NotImplementedError( - "TransferFunction.__rtruediv__ is currently implemented only " - "for SISO systems.") + # TransferFunction.__rtruediv__ is currently implemented only for + # SISO systems + return NotImplemented return other / self @@ -761,47 +776,28 @@ def __pow__(self, other): def __getitem__(self, key): if not isinstance(key, Iterable) or len(key) != 2: - raise IOError('must provide indices of length 2 for transfer functions') - - key1, key2 = key - if not isinstance(key1, (int, slice)) or not isinstance(key2, (int, slice)): - raise TypeError(f"system indices must be integers or slices") - - # pre-process - if isinstance(key1, int): - key1 = slice(key1, key1 + 1, 1) - if isinstance(key2, int): - key2 = slice(key2, key2 + 1, 1) - # dim1 - start1, stop1, step1 = key1.start, key1.stop, key1.step - if step1 is None: - step1 = 1 - if start1 is None: - start1 = 0 - if stop1 is None: - stop1 = len(self.num) - # dim1 - start2, stop2, step2 = key2.start, key2.stop, key2.step - if step2 is None: - step2 = 1 - if start2 is None: - start2 = 0 - if stop2 is None: - stop2 = len(self.num[0]) - - num, den = [], [] - for i in range(start1, stop1, step1): - num_i = [] - den_i = [] - for j in range(start2, stop2, step2): - num_i.append(self.num[i][j]) - den_i.append(self.den[i][j]) - num.append(num_i) - den.append(den_i) - - # Save the label names - outputs = [self.output_labels[i] for i in range(start1, stop1, step1)] - inputs = [self.input_labels[j] for j in range(start2, stop2, step2)] + raise IOError( + "must provide indices of length 2 for transfer functions") + + # Convert signal names to integer offsets (via NamedSignal object) + iomap = NamedSignal( + np.empty((self.noutputs, self.ninputs)), + self.output_labels, self.input_labels) + indices = iomap._parse_key(key, level=1) # ignore index checks + outdx, outputs = _process_subsys_index( + indices[0], self.output_labels, slice_to_list=True) + inpdx, inputs = _process_subsys_index( + indices[1], self.input_labels, slice_to_list=True) + + # Construct the transfer function for the subsystem + num = _create_poly_array((len(outputs), len(inputs))) + den = _create_poly_array(num.shape) + for row, i in enumerate(outdx): + for col, j in enumerate(inpdx): + num[row, col] = self.num_array[i, j] + den[row, col] = self.den_array[i, j] + col += 1 + row += 1 # Create the system name sysname = config.defaults['iosys.indexed_system_name_prefix'] + \ @@ -811,17 +807,17 @@ def __getitem__(self, key): num, den, self.dt, inputs=inputs, outputs=outputs, name=sysname) def freqresp(self, omega): - """(deprecated) Evaluate transfer function at complex frequencies. + """Evaluate transfer function at complex frequencies. .. deprecated::0.9.0 - Method has been given the more pythonic name - :meth:`TransferFunction.frequency_response`. Or use - :func:`freqresp` in the MATLAB compatibility module. + Method has been given the more Pythonic name + `TransferFunction.frequency_response`. Or use + `freqresp` in the MATLAB compatibility module. """ warn("TransferFunction.freqresp(omega) will be removed in a " "future release of python-control; use " "sys.frequency_response(omega), or freqresp(sys, omega) in the " - "MATLAB compatibility module instead", DeprecationWarning) + "MATLAB compatibility module instead", FutureWarning) return self.frequency_response(omega) def poles(self): @@ -840,10 +836,20 @@ def zeros(self): "for SISO systems.") else: # for now, just give zeros of a SISO tf - return roots(self.num[0][0]).astype(complex) + return roots(self.num_array[0, 0]).astype(complex) def feedback(self, other=1, sign=-1): - """Feedback interconnection between two LTI objects.""" + """Feedback interconnection between two LTI objects. + + Parameters + ---------- + other : `InputOutputSystem` + System in the feedback path. + + sign : float, optional + Gain to use in feedback path. Defaults to -1. + + """ other = _convert_to_transfer_function(other) if (self.ninputs > 1 or self.noutputs > 1 or @@ -854,10 +860,10 @@ def feedback(self, other=1, sign=-1): "MIMO systems.") dt = common_timebase(self.dt, other.dt) - num1 = self.num[0][0] - den1 = self.den[0][0] - num2 = other.num[0][0] - den2 = other.den[0][0] + num1 = self.num_array[0, 0] + den1 = self.den_array[0, 0] + num2 = other.num_array[0, 0] + den2 = other.den_array[0, 0] num = polymul(num1, den2) den = polyadd(polymul(den2, den1), -sign * polymul(num2, num1)) @@ -869,8 +875,41 @@ def feedback(self, other=1, sign=-1): # But this does not work correctly because the state size will be too # large. + def append(self, other): + """Append a second model to the present model. + + The second model is converted to a transfer function if necessary, + inputs and outputs are appended and their order is preserved. + + Parameters + ---------- + other : `StateSpace` or `TransferFunction` + System to be appended. + + Returns + ------- + sys : `TransferFunction` + System model with `other` appended to `self`. + + """ + other = _convert_to_transfer_function(other) + + new_tf = bdalg.combine_tf([ + [self, np.zeros((self.noutputs, other.ninputs))], + [np.zeros((other.noutputs, self.ninputs)), other], + ]) + + return new_tf + def minreal(self, tol=None): - """Remove cancelling pole/zero pairs from a transfer function""" + """Remove canceling pole/zero pairs from a transfer function. + + Parameters + ---------- + tol : float + Tolerance for determining whether poles and zeros overlap. + + """ # based on octave minreal # default accuracy @@ -878,17 +917,17 @@ def minreal(self, tol=None): sqrt_eps = sqrt(float_info.epsilon) # pre-allocate arrays - num = [[[] for j in range(self.ninputs)] for i in range(self.noutputs)] - den = [[[] for j in range(self.ninputs)] for i in range(self.noutputs)] + num = _create_poly_array((self.noutputs, self.ninputs)) + den = _create_poly_array((self.noutputs, self.ninputs)) for i in range(self.noutputs): for j in range(self.ninputs): # split up in zeros, poles and gain newzeros = [] - zeros = roots(self.num[i][j]) - poles = roots(self.den[i][j]) - gain = self.num[i][j][0] / self.den[i][j][0] + zeros = roots(self.num_array[i, j]) + poles = roots(self.den_array[i, j]) + gain = self.num_array[i, j][0] / self.den_array[i, j][0] # check all zeros for z in zeros: @@ -903,21 +942,21 @@ def minreal(self, tol=None): newzeros.append(z) # poly([]) returns a scalar, but we always want a 1d array - num[i][j] = np.atleast_1d(gain * real(poly(newzeros))) - den[i][j] = np.atleast_1d(real(poly(poles))) + num[i, j] = np.atleast_1d(gain * real(poly(newzeros))) + den[i, j] = np.atleast_1d(real(poly(poles))) # end result return TransferFunction(num, den, self.dt) def returnScipySignalLTI(self, strict=True): - """Return a list of a list of :class:`scipy.signal.lti` objects. + """Return a 2D array of `scipy.signal.lti` objects. For instance, >>> out = tfobject.returnScipySignalLTI() # doctest: +SKIP - >>> out[3][5] # doctest: +SKIP + >>> out[3, 5] # doctest: +SKIP - is a :class:`scipy.signal.lti` object corresponding to the + is a `scipy.signal.lti` object corresponding to the transfer function from the 6th input to the 4th output. Parameters @@ -927,15 +966,15 @@ def returnScipySignalLTI(self, strict=True): The timebase `tfobject.dt` cannot be None; it must be continuous (0) or discrete (True or > 0). False: - if `tfobject.dt` is None, continuous time - :class:`scipy.signal.lti` objects are returned + if `tfobject.dt` is None, continuous-time + `scipy.signal.lti` objects are returned Returns ------- - out : list of list of :class:`scipy.signal.TransferFunction` - continuous time (inheriting from :class:`scipy.signal.lti`) - or discrete time (inheriting from :class:`scipy.signal.dlti`) - SISO objects + out : list of list of `scipy.signal.TransferFunction` + Continuous time (inheriting from `scipy.signal.lti`) + or discrete time (inheriting from `scipy.signal.dlti`) + SISO objects. """ if strict and self.dt is None: raise ValueError("with strict=True, dt cannot be None") @@ -943,7 +982,7 @@ def returnScipySignalLTI(self, strict=True): if self.dt: kwdt = {'dt': self.dt} else: - # scipy convention for continuous time lti systems: call without + # scipy convention for continuous-time LTI systems: call without # dt keyword argument kwdt = {} @@ -959,8 +998,7 @@ def returnScipySignalLTI(self, strict=True): return out def _common_den(self, imag_tol=None, allow_nonproper=False): - """ - Compute MIMO common denominators; return them and adjusted numerators. + """Compute MIMO common denominators; return them and adjusted numerators. This function computes the denominators per input containing all the poles of sys.den, and reports it as the array den. The @@ -980,27 +1018,23 @@ def _common_den(self, imag_tol=None, allow_nonproper=False): Returns ------- num: array - n by n by kd where n = max(sys.noutputs,sys.ninputs) - kd = max(denorder)+1 - Multi-dimensional array of numerator coefficients. num[i,j] - gives the numerator coefficient array for the ith output and jth - input; padded for use in td04ad ('C' option); matches the - denorder order; highest coefficient starts on the left. - If allow_nonproper=True and the order of a numerator exceeds the - order of the common denominator, num will be returned as None - + Multi-dimensional array of numerator coefficients with shape + (n, n, kd) array, where n = max(sys.noutputs, sys.ninputs), kd + = max(denorder) + 1. `num[i,j]` gives the numerator coefficient + array for the ith output and jth input; padded for use in + td04ad ('C' option); matches the denorder order; highest + coefficient starts on the left. If `allow_nonproper` = True + and the order of a numerator exceeds the order of the common + denominator, `num` will be returned as None. den: array - sys.ninputs by kd Multi-dimensional array of coefficients for common denominator - polynomial, one row per input. The array is prepared for use in - slycot td04ad, the first element is the highest-order polynomial - coefficient of s, matching the order in denorder. If denorder < - number of columns in den, the den is padded with zeros. - + polynomial with shape (sys.ninputs, kd) (one row per + input). The array is prepared for use in slycot td04ad, the + first element is the highest-order polynomial coefficient of + `s`, matching the order in denorder. If denorder < number of + columns in den, the den is padded with zeros. denorder: array of int, orders of den, one per input - - Examples -------- >>> num, den, denorder = sys._common_den() # doctest: +SKIP @@ -1084,7 +1118,7 @@ def _common_den(self, imag_tol=None, allow_nonproper=False): # create the denominator matching this input # coefficients should be padded on right, ending at maxindex maxindex = len(poles[j]) - den[j, :maxindex+1] = poly(poles[j]) + den[j, :maxindex+1] = poly(poles[j]).real denorder[j] = maxindex # now create the numerator, also padded on the right @@ -1100,7 +1134,7 @@ def _common_den(self, imag_tol=None, allow_nonproper=False): numpoly = poleset[i][j][2] * np.atleast_1d(poly(nwzeros)) # td04ad expects a proper transfer function. If the - # numerater has a higher order than the denominator, the + # numerator has a higher order than the denominator, the # padding will fail if len(numpoly) > maxindex + 1: if allow_nonproper: @@ -1114,7 +1148,8 @@ def _common_den(self, imag_tol=None, allow_nonproper=False): # numerator polynomial should be padded on left and right # ending at maxindex to line up with what td04ad expects. - num[i, j, maxindex+1-len(numpoly):maxindex+1] = numpoly + num[i, j, maxindex+1-len(numpoly):maxindex+1] = \ + numpoly.real # print(num[i, j]) if havenonproper: @@ -1124,7 +1159,7 @@ def _common_den(self, imag_tol=None, allow_nonproper=False): def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, name=None, copy_names=True, **kwargs): - """Convert a continuous-time system to discrete time + """Convert a continuous-time system to discrete time. Creates a discrete-time system from a continuous-time system by sampling. Multiple methods of conversion are supported. @@ -1132,56 +1167,58 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, Parameters ---------- Ts : float - Sampling period - method : {"gbt", "bilinear", "euler", "backward_diff", - "zoh", "matched"} + Sampling period. + method : {'gbt', 'bilinear', 'euler', 'backward_diff', 'zoh', 'matched'} Method to use for sampling: - * gbt: generalized bilinear transformation - * bilinear or tustin: Tustin's approximation ("gbt" with alpha=0.5) - * euler: Euler (or forward difference) method ("gbt" with alpha=0) - * backward_diff: Backwards difference ("gbt" with alpha=1.0) - * zoh: zero-order hold (default) + * 'gbt': generalized bilinear transformation + * 'backward_diff': Backwards difference ('gbt' with alpha=1.0) + * 'bilinear' (or 'tustin'): Tustin's approximation ('gbt' with + alpha=0.5) + * 'euler': Euler (or forward difference) method ('gbt' with + alpha=0) + * 'matched': pole-zero match method + * 'zoh': zero-order hold (default) alpha : float within [0, 1] - The generalized bilinear transformation weighting parameter, which - should only be specified with method="gbt", and is ignored - otherwise. See :func:`scipy.signal.cont2discrete`. + The generalized bilinear transformation weighting parameter, + which should only be specified with `method` = 'gbt', and is + ignored otherwise. See `scipy.signal.cont2discrete`. prewarp_frequency : float within [0, infinity) - The frequency [rad/s] at which to match with the input continuous- - time system's magnitude and phase (the gain=1 crossover frequency, - for example). Should only be specified with method='bilinear' or - 'gbt' with alpha=0.5 and ignored otherwise. + The frequency [rad/s] at which to match with the input + continuous- time system's magnitude and phase (the gain=1 + crossover frequency, for example). Should only be specified + with `method` = 'bilinear' or 'gbt' with `alpha` = 0.5 and + ignored otherwise. name : string, optional - Set the name of the sampled system. If not specified and - if `copy_names` is `False`, a generic name is generated - with a unique integer id. If `copy_names` is `True`, the new system + Set the name of the sampled system. If not specified and if + `copy_names` is False, a generic name 'sys[id]' is generated with + a unique integer id. If `copy_names` is True, the new system name is determined by adding the prefix and suffix strings in - config.defaults['iosys.sampled_system_name_prefix'] and - config.defaults['iosys.sampled_system_name_suffix'], with the + `config.defaults['iosys.sampled_system_name_prefix']` and + `config.defaults['iosys.sampled_system_name_suffix']`, with the default being to add the suffix '$sampled'. + copy_names : bool, Optional If True, copy the names of the input signals, output signals, and states to the sampled system. Returns ------- - sysd : TransferFunction system - Discrete-time system, with sample period Ts + sysd : `TransferFunction` system + Discrete-time system, with sample period Ts. Other Parameters ---------------- inputs : int, list of str or None, optional - Description of the system inputs. If not specified, the origional - system inputs are used. See :class:`InputOutputSystem` for more - information. + Description of the system inputs. If not specified, the + original system inputs are used. See `InputOutputSystem` for + more information. outputs : int, list of str or None, optional Description of the system outputs. Same format as `inputs`. Notes ----- - 1. Available only for SISO systems - - 2. Uses :func:`scipy.signal.cont2discrete` + Available only for SISO systems. Uses `scipy.signal.cont2discrete`. Examples -------- @@ -1190,7 +1227,7 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, """ if not self.isctime(): - raise ValueError("System must be continuous time system") + raise ValueError("System must be continuous-time system") if not self.issiso(): raise ControlMIMONotImplemented("Not implemented for MIMO systems") if method == "matched": @@ -1219,26 +1256,26 @@ def sample(self, Ts, method='zoh', alpha=None, prewarp_frequency=None, return TransferFunction(sysd, name=name, **kwargs) def dcgain(self, warn_infinite=False): - """Return the zero-frequency (or DC) gain. + """Return the zero-frequency ("DC") gain. - For a continous-time transfer function G(s), the DC gain is G(0) + For a continuous-time transfer function G(s), the DC gain is G(0) For a discrete-time transfer function G(z), the DC gain is G(1) Parameters ---------- warn_infinite : bool, optional By default, don't issue a warning message if the zero-frequency - gain is infinite. Setting `warn_infinite` to generate the warning - message. + gain is infinite. Setting `warn_infinite` to generate the + warning message. Returns ------- gain : (noutputs, ninputs) ndarray or scalar Array or scalar value for SISO systems, depending on - config.defaults['control.squeeze_frequency_response']. - The value of the array elements or the scalar is either the - zero-frequency (or DC) gain, or `inf`, if the frequency response - is singular. + `config.defaults['control.squeeze_frequency_response']`. The + value of the array elements or the scalar is either the + zero-frequency (or DC) gain, or `inf`, if the frequency + response is singular. For real valued systems, the empty imaginary part of the complex zero-frequency response is discarded and a real array or @@ -1253,44 +1290,37 @@ def dcgain(self, warn_infinite=False): """ return self._dcgain(warn_infinite) + # Determine if a system is static (memoryless) def _isstatic(self): - """returns True if and only if all of the numerator and denominator - polynomials of the (possibly MIMO) transfer function are zeroth order, - that is, if the system has no dynamics. """ - for list_of_polys in self.num, self.den: - for row in list_of_polys: - for poly in row: - if len(poly) > 1: - return False - return True + return self._static # Check done at initialization # Attributes for differentiation and delay # # These attributes are created here with sphinx docstrings so that the - # autodoc generated documentation has a description. The actual values of - # the class attributes are set at the bottom of the file to avoid problems - # with recursive calls. + # autodoc generated documentation has a description. The actual values + # of the class attributes are set at the bottom of the file to avoid + # problems with recursive calls. - #: Differentation operator (continuous time) + #: Differentiation operator (continuous time). #: - #: The ``s`` constant can be used to create continuous time transfer + #: The `s` constant can be used to create continuous-time transfer #: functions using algebraic expressions. #: - #: Example - #: ------- + #: Examples + #: -------- #: >>> s = TransferFunction.s # doctest: +SKIP #: >>> G = (s + 1)/(s**2 + 2*s + 1) # doctest: +SKIP #: #: :meta hide-value: s = None - #: Delay operator (discrete time) + #: Delay operator (discrete time). #: - #: The ``z`` constant can be used to create discrete time transfer + #: The `z` constant can be used to create discrete-time transfer #: functions using algebraic expressions. #: - #: Example - #: ------- + #: Examples + #: -------- #: >>> z = TransferFunction.z # doctest: +SKIP #: >>> G = 2 * z / (4 * z**3 + 3*z - 1) # doctest: +SKIP #: @@ -1328,10 +1358,13 @@ def _c2d_matched(sysC, Ts, **kwargs): # Utility function to convert a transfer function polynomial to a string # Borrowed from poly1d library def _tf_polynomial_to_string(coeffs, var='s'): - """Convert a transfer function polynomial to a string""" - + """Convert a transfer function polynomial to a string.""" thestr = "0" + # Apply NumPy formatting + with np.printoptions(threshold=sys.maxsize): + coeffs = eval(repr(coeffs)) + # Compute the number of coefficients N = len(coeffs) - 1 @@ -1375,7 +1408,10 @@ def _tf_polynomial_to_string(coeffs, var='s'): def _tf_factorized_polynomial_to_string(roots, gain=1, var='s'): - """Convert a factorized polynomial to a string""" + """Convert a factorized polynomial to a string.""" + # Apply NumPy formatting + with np.printoptions(threshold=sys.maxsize): + roots = eval(repr(roots)) if roots.size == 0: return _float2str(gain) @@ -1418,9 +1454,11 @@ def _tf_factorized_polynomial_to_string(roots, gain=1, var='s'): def _tf_string_to_latex(thestr, var='s'): - """ make sure to superscript all digits in a polynomial string - and convert float coefficients in scientific notation - to prettier LaTeX representation """ + """Superscript all digits in a polynomial string and convert + float coefficients in scientific notation to prettier LaTeX + representation. + + """ # TODO: make the multiplication sign configurable expmul = r' \\times' thestr = sub(var + r'\^(\d{2,})', var + r'^{\1}', thestr) @@ -1446,32 +1484,35 @@ def _convert_to_transfer_function( sys, inputs=1, outputs=1, use_prefix_suffix=False): """Convert a system to transfer function form (if needed). - If sys is already a transfer function, then it is returned. If sys is a - state space object, then it is converted to a transfer function and - returned. If sys is a scalar, then the number of inputs and outputs can be - specified manually, as in: + If `sys` is already a transfer function, then it is returned. If `sys` + is a state space object, then it is converted to a transfer function + and returned. If `sys` is a scalar, then the number of inputs and + outputs can be specified manually, as in:: + >>> from control.xferfcn import _convert_to_transfer_function >>> sys = _convert_to_transfer_function(3.) # Assumes inputs = outputs = 1 >>> sys = _convert_to_transfer_function(1., inputs=3, outputs=2) - In the latter example, sys's matrix transfer function is [[1., 1., 1.] - [1., 1., 1.]]. + In the latter example, the matrix transfer function for `sys` is:: - If sys is an array-like type, then it is converted to a constant-gain + [[1., 1., 1.] + [1., 1., 1.]]. + + If `sys` is an array_like type, then it is converted to a constant-gain transfer function. Note: no renaming of inputs and outputs is performed; this should be done by the calling function. - >>> sys = _convert_to_transfer_function([[1., 0.], [2., 3.]]) + Arrays can also be passed as an argument. For example:: + + sys = _convert_to_transfer_function([[1., 0.], [2., 3.]]) - In this example, the numerator matrix will be - [[[1.0], [0.0]], [[2.0], [3.0]]] - and the denominator matrix [[[1.0], [1.0]], [[1.0], [1.0]]] + will give a system with numerator matrix ``[[[1.0], [0.0]], [[2.0], + [3.0]]]`` and denominator matrix ``[[[1.0], [1.0]], [[1.0], [1.0]]]``. """ from .statesp import StateSpace - kwargs = {} if isinstance(sys, TransferFunction): return sys @@ -1486,20 +1527,20 @@ def _convert_to_transfer_function( den = [[[1.] for j in range(sys.ninputs)] for i in range(sys.noutputs)] else: + # Preallocate numerator and denominator arrays + num = [[[] for j in range(sys.ninputs)] + for i in range(sys.noutputs)] + den = [[[] for j in range(sys.ninputs)] + for i in range(sys.noutputs)] + try: # Use Slycot to make the transformation - # Make sure to convert system matrices to numpy arrays + # Make sure to convert system matrices to NumPy arrays from slycot import tb04ad tfout = tb04ad( sys.nstates, sys.ninputs, sys.noutputs, array(sys.A), array(sys.B), array(sys.C), array(sys.D), tol1=0.0) - # Preallocate outputs. - num = [[[] for j in range(sys.ninputs)] - for i in range(sys.noutputs)] - den = [[[] for j in range(sys.ninputs)] - for i in range(sys.noutputs)] - for i in range(sys.noutputs): for j in range(sys.ninputs): num[i][j] = list(tfout[6][i, j, :]) @@ -1508,16 +1549,13 @@ def _convert_to_transfer_function( den[i][j] = list(tfout[5][i, :]) except ImportError: - # If slycot is not available, use signal.lti (SISO only) - if sys.ninputs != 1 or sys.noutputs != 1: - raise ControlMIMONotImplemented("Not implemented for " + - "MIMO systems without slycot.") - - # Do the conversion using sp.signal.ss2tf - # Note that this returns a 2D array for the numerator - num, den = sp.signal.ss2tf(sys.A, sys.B, sys.C, sys.D) - num = squeeze(num) # Convert to 1D array - den = squeeze(den) # Probably not needed + # If slycot not available, do conversion using sp.signal.ss2tf + for j in range(sys.ninputs): + num_j, den_j = sp.signal.ss2tf( + sys.A, sys.B, sys.C, sys.D, input=j) + for i in range(sys.noutputs): + num[i][j] = num_j[i] + den[i][j] = den_j newsys = TransferFunction(num, den, sys.dt) if use_prefix_suffix: @@ -1533,7 +1571,7 @@ def _convert_to_transfer_function( elif isinstance(sys, FrequencyResponseData): raise TypeError("Can't convert given FRD to TransferFunction system.") - # If this is array-like, try to create a constant feedthrough + # If this is array_like, try to create a constant feedthrough try: D = array(sys, ndmin=2) outputs, inputs = D.shape @@ -1553,46 +1591,63 @@ def tf(*args, **kwargs): The function accepts either 1, 2, or 3 parameters: ``tf(sys)`` + Convert a linear system into transfer function form. Always creates - a new system, even if sys is already a TransferFunction object. + a new system, even if `sys` is already a `TransferFunction` object. ``tf(num, den)`` + Create a transfer function system from its numerator and denominator polynomial coefficients. If `num` and `den` are 1D array_like objects, the function creates a SISO system. - To create a MIMO system, `num` and `den` need to be 2D nested lists - of array_like objects. (A 3 dimensional data structure in total.) - (For details see note below.) + To create a MIMO system, `num` and `den` need to be 2D arrays of + of array_like objects (a 3 dimensional data structure in total; + for details see note below). If the denominator for all transfer + function is the same, `den` can be specified as a 1D array. ``tf(num, den, dt)`` - Create a discrete time transfer function system; dt can either be a - positive number indicating the sampling time or 'True' if no + + Create a discrete-time transfer function system; dt can either be a + positive number indicating the sampling time or True if no specific timebase is given. + ``tf([[G11, ..., G1m], ..., [Gp1, ..., Gpm]][, dt])`` + + Create a p x m MIMO system from SISO transfer functions Gij. See + `combine_tf` for more details. + ``tf('s')`` or ``tf('z')`` + Create a transfer function representing the differential operator ('s') or delay operator ('z'). Parameters ---------- - sys: LTI (StateSpace or TransferFunction) - A linear system - num: array_like, or list of list of array_like - Polynomial coefficients of the numerator - den: array_like, or list of list of array_like - Polynomial coefficients of the denominator - display_format: None, 'poly' or 'zpk' - Set the display format used in printing the TransferFunction object. + sys : `LTI` (`StateSpace` or `TransferFunction`) + A linear system that will be converted to a transfer function. + arr : 2D list of `TransferFunction` + 2D list of SISO transfer functions to create MIMO transfer function. + num : array_like, or list of list of array_like + Polynomial coefficients of the numerator. + den : array_like, or list of list of array_like + Polynomial coefficients of the denominator. + dt : None, True or float, optional + System timebase. 0 (default) indicates continuous time, True + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, None indicates + unspecified timebase (either continuous or discrete time). + display_format : None, 'poly' or 'zpk' + Set the display format used in printing the `TransferFunction` object. Default behavior is polynomial display and can be changed by - changing config.defaults['xferfcn.display_format'].. + changing `config.defaults['xferfcn.display_format']`. Returns ------- - out: :class:`TransferFunction` - The new linear system + sys : `TransferFunction` + The new linear system. Other Parameters ---------------- @@ -1600,31 +1655,31 @@ def tf(*args, **kwargs): List of strings that name the individual signals of the transformed system. If not given, the inputs and outputs are the same as the original system. + input_prefix, output_prefix : string, optional + Set the prefix for input and output signals. Defaults = 'u', 'y'. name : string, optional - System name. If unspecified, a generic name is generated + System name. If unspecified, a generic name 'sys[id]' is generated with a unique integer id. Raises ------ ValueError - if `num` and `den` have invalid or unequal dimensions + If `num` and `den` have invalid or unequal dimensions. TypeError - if `num` or `den` are of incorrect type + If `num` or `den` are of incorrect type. See Also -------- - TransferFunction - ss - ss2tf - tf2ss + TransferFunction, ss, ss2tf, tf2ss Notes ----- + MIMO transfer functions are created by passing a 2D array of coefficients: ``num[i][j]`` contains the polynomial coefficients of the numerator - for the transfer function from the (j+1)st input to the (i+1)st output. - ``den[i][j]`` works the same way. + for the transfer function from the (j+1)st input to the (i+1)st output, + and ``den[i][j]`` works the same way. - The list ``[2, 3, 4]`` denotes the polynomial :math:`2s^2 + 3s + 4`. + The list ``[2, 3, 4]`` denotes the polynomial :math:`2 s^2 + 3 s + 4`. The special forms ``tf('s')`` and ``tf('z')`` can be used to create transfer functions for differentiation and unit delays. @@ -1642,16 +1697,12 @@ def tf(*args, **kwargs): >>> s = ct.tf('s') >>> G = (s + 1)/(s**2 + 2*s + 1) - >>> # Convert a StateSpace to a TransferFunction object. + >>> # Convert a state space system to a transfer function: >>> sys_ss = ct.ss([[1, -2], [3, -4]], [[5], [7]], [[6, 8]], 9) >>> sys_tf = ct.tf(sys_ss) """ - - if len(args) == 2 or len(args) == 3: - return TransferFunction(*args, **kwargs) - - elif len(args) == 1 and isinstance(args[0], str): + if len(args) == 1 and isinstance(args[0], str): # Make sure there were no extraneous keywords if kwargs: raise TypeError("unrecognized keywords: ", str(kwargs)) @@ -1662,19 +1713,53 @@ def tf(*args, **kwargs): elif args[0] == 'z': return TransferFunction.z + elif len(args) == 1 and isinstance(args[0], list): + # Allow passing an array of SISO transfer functions + from .bdalg import combine_tf + return combine_tf(*args) + elif len(args) == 1: from .statesp import StateSpace - sys = args[0] - if isinstance(sys, StateSpace): + if isinstance(sys := args[0], StateSpace): return ss2tf(sys, **kwargs) elif isinstance(sys, TransferFunction): # Use copy constructor return TransferFunction(sys, **kwargs) + elif isinstance(data := args[0], np.ndarray) and data.ndim == 2 or \ + isinstance(data, list) and isinstance(data[0], list): + raise NotImplementedError( + "arrays of transfer functions not (yet) supported") else: raise TypeError("tf(sys): sys must be a StateSpace or " "TransferFunction object. It is %s." % type(sys)) - else: - raise ValueError("Needs 1 or 2 arguments; received %i." % len(args)) + + elif len(args) == 3: + if 'dt' in kwargs: + warn("received multiple dt arguments, " + f"using positional arg {args[2]}") + kwargs['dt'] = args[2] + args = args[:2] + + elif len(args) != 2: + raise ValueError("Needs 1, 2, or 3 arguments; received %i." % len(args)) + + # + # Process the numerator and denominator arguments + # + # If we got through to here, we have two arguments (num, den) and + # the keywords (including dt). The only thing left to do is look + # for some special cases, like having a common denominator. + # + num, den = args + + num = _clean_part(num, "numerator") + den = _clean_part(den, "denominator") + + if den.size == 1 and num.size > 1: + # Broadcast denominator to shape of numerator + den = np.broadcast_to(den, num.shape).copy() + + return TransferFunction(num, den, **kwargs) def zpk(zeros, poles, gain, *args, **kwargs): @@ -1696,29 +1781,28 @@ def zpk(zeros, poles, gain, *args, **kwargs): poles : array_like Array containing the location of poles. gain : float - System gain + System gain. dt : None, True or float, optional - System timebase. 0 (default) indicates continuous - time, True indicates discrete time with unspecified sampling - time, positive number is discrete time with specified - sampling time, None indicates unspecified timebase (either - continuous or discrete time). + System timebase. 0 (default) indicates continuous time, True + indicates discrete time with unspecified sampling time, positive + number is discrete time with specified sampling time, None + indicates unspecified timebase (either continuous or discrete time). inputs, outputs, states : str, or list of str, optional List of strings that name the individual signals. If this parameter - is not given or given as `None`, the signal names will be of the - form `s[i]` (where `s` is one of `u`, `y`, or `x`). See - :class:`InputOutputSystem` for more information. + is not given or given as None, the signal names will be of the + form 's[i]' (where 's' is one of 'u', 'y', or 'x'). See + `InputOutputSystem` for more information. name : string, optional System name (used for specifying signals). If unspecified, a generic - name is generated with a unique integer id. - display_format: None, 'poly' or 'zpk' - Set the display format used in printing the TransferFunction object. + name 'sys[id]' is generated with a unique integer id. + display_format : None, 'poly' or 'zpk', optional + Set the display format used in printing the `TransferFunction` object. Default behavior is polynomial display and can be changed by - changing config.defaults['xferfcn.display_format']. + changing `config.defaults['xferfcn.display_format']`. Returns ------- - out: :class:`TransferFunction` + out : `TransferFunction` Transfer function with given zeros, poles, and gain. Examples @@ -1744,32 +1828,36 @@ def ss2tf(*args, **kwargs): The function accepts either 1 or 4 parameters: ``ss2tf(sys)`` + Convert a linear system from state space into transfer function form. Always creates a new system. ``ss2tf(A, B, C, D)`` + Create a transfer function system from the matrices of its state and output equations. - For details see: :func:`tf` + For details see: `tf`. Parameters ---------- - sys: StateSpace - A linear system - A: array_like or string - System matrix - B: array_like or string - Control matrix - C: array_like or string - Output matrix - D: array_like or string - Feedthrough matrix + sys : `StateSpace` + A linear system. + A : array_like or string + System matrix. + B : array_like or string + Control matrix. + C : array_like or string + Output matrix. + D : array_like or string + Feedthrough matrix. + **kwargs : keyword arguments + Additional arguments passed to `tf` (e.g., signal names). Returns ------- - out: TransferFunction - New linear system in transfer function form + out : `TransferFunction` + New linear system in transfer function form. Other Parameters ---------------- @@ -1778,22 +1866,20 @@ def ss2tf(*args, **kwargs): system. If not given, the inputs and outputs are the same as the original system. name : string, optional - System name. If unspecified, a generic name is generated + System name. If unspecified, a generic name 'sys[id]' is generated with a unique integer id. Raises ------ ValueError - if matrix sizes are not self-consistent, or if an invalid number of - arguments is passed in + If matrix sizes are not self-consistent, or if an invalid number of + arguments is passed in. TypeError - if `sys` is not a StateSpace object + If `sys` is not a `StateSpace` object. See Also -------- - tf - ss - tf2ss + tf, ss, tf2ss Examples -------- @@ -1839,56 +1925,69 @@ def tfdata(sys): Parameters ---------- - sys: LTI (StateSpace, or TransferFunction) - LTI system whose data will be returned + sys : `StateSpace` or `TransferFunction` + LTI system whose data will be returned. Returns ------- - (num, den): numerator and denominator arrays - Transfer function coefficients (SISO only) + num, den : numerator and denominator arrays + Transfer function coefficients (SISO only). + """ tf = _convert_to_transfer_function(sys) return tf.num, tf.den -def _clean_part(data): +def _clean_part(data, name=""): """ Return a valid, cleaned up numerator or denominator - for the TransferFunction class. + for the `TransferFunction` class. Parameters ---------- - data: numerator or denominator of a transfer function. + data : numerator or denominator of a transfer function. Returns ------- data: list of lists of ndarrays, with int converted to float + """ valid_types = (int, float, complex, np.number) + unsupported_types = (complex, np.complexfloating) valid_collection = (list, tuple, ndarray) - if (isinstance(data, valid_types) or + if isinstance(data, np.ndarray) and data.ndim == 2 and \ + data.dtype == object and isinstance(data[0, 0], np.ndarray): + # Data is already in the right format + return data + elif isinstance(data, ndarray) and data.ndim == 3 and \ + isinstance(data[0, 0, 0], valid_types): + out = np.empty(data.shape[0:2], dtype=np.ndarray) + for i, j in product(range(out.shape[0]), range(out.shape[1])): + out[i, j] = data[i, j, :] + elif (isinstance(data, valid_types) or (isinstance(data, ndarray) and data.ndim == 0)): # Data is a scalar (including 0d ndarray) - data = [[array([data])]] - elif (isinstance(data, ndarray) and data.ndim == 3 and - isinstance(data[0, 0, 0], valid_types)): - data = [[array(data[i, j]) - for j in range(data.shape[1])] - for i in range(data.shape[0])] + out = np.empty((1,1), dtype=np.ndarray) + out[0, 0] = array([data]) elif (isinstance(data, valid_collection) and all([isinstance(d, valid_types) for d in data])): - data = [[array(data)]] - elif (isinstance(data, (list, tuple)) and - isinstance(data[0], (list, tuple)) and - (isinstance(data[0][0], valid_collection) and - all([isinstance(d, valid_types) for d in data[0][0]]))): - data = list(data) - for j in range(len(data)): - data[j] = list(data[j]) - for k in range(len(data[j])): - data[j][k] = array(data[j][k]) + out = np.empty((1,1), dtype=np.ndarray) + out[0, 0] = array(data) + elif isinstance(data, (list, tuple)) and \ + isinstance(data[0], (list, tuple)) and \ + (isinstance(data[0][0], valid_collection) and + all([isinstance(d, valid_types) for d in data[0][0]]) or \ + isinstance(data[0][0], valid_types)): + out = np.empty((len(data), len(data[0])), dtype=np.ndarray) + for i in range(out.shape[0]): + if len(data[i]) != out.shape[1]: + raise ValueError( + "Row 0 of the %s matrix has %i elements, but row " + "%i has %i." % (name, out.shape[1], i, len(data[i]))) + for j in range(out.shape[1]): + out[i, j] = np.atleast_1d(data[i][j]) else: # If the user passed in anything else, then it's unclear what # the meaning is. @@ -1897,20 +1996,39 @@ def _clean_part(data): "(for\nSISO), or lists of lists of vectors (for SISO or MIMO).") # Check for coefficients that are ints and convert to floats - for i in range(len(data)): - for j in range(len(data[i])): - for k in range(len(data[i][j])): - if isinstance(data[i][j][k], (int, np.int32, np.int64)): - data[i][j][k] = float(data[i][j][k]) + for i in range(out.shape[0]): + for j in range(out.shape[1]): + for k in range(len(out[i, j])): + if isinstance(out[i, j][k], (int, np.integer)): + out[i, j][k] = float(out[i, j][k]) + elif isinstance(out[i, j][k], unsupported_types): + raise TypeError( + f"unsupported data type: {type(out[i, j][k])}") + return out + + +# +# Define constants to represent differentiation, unit delay. +# +# Set the docstring explicitly to avoid having Sphinx document this as +# a method instead of a property/attribute. - return data - - -# Define constants to represent differentiation, unit delay TransferFunction.s = TransferFunction([1, 0], [1], 0, name='s') +TransferFunction.s.__doc__ = "Differentiation operator (continuous time)." + TransferFunction.z = TransferFunction([1, 0], [1], True, name='z') +TransferFunction.z.__doc__ = "Delay operator (discrete time)." def _float2str(value): _num_format = config.defaults.get('xferfcn.floating_point_format', ':.4g') return f"{value:{_num_format}}" + + +def _create_poly_array(shape, default=None): + out = np.empty(shape, dtype=np.ndarray) + if default is not None: + default = np.array(default) + for i, j in product(range(shape[0]), range(shape[1])): + out[i, j] = default + return out diff --git a/doc/.gitignore b/doc/.gitignore index 38303de2b..caaf55013 100644 --- a/doc/.gitignore +++ b/doc/.gitignore @@ -1,2 +1,2 @@ *.fig.bak -_static/ +.docfigs diff --git a/doc/Makefile b/doc/Makefile index dfd34f4f1..493fd7da5 100644 --- a/doc/Makefile +++ b/doc/Makefile @@ -1,5 +1,18 @@ -# Minimal makefile for Sphinx documentation -# +# Makefile for python-control Sphinx documentation +# RMM, 15 Jan 2025 + +FIGS = figures/classes.pdf +RST_FIGS = figures/flatsys-steering-compare.png \ + figures/iosys-predprey-open.png \ + figures/timeplot-servomech-combined.png \ + figures/steering-optimal.png figures/ctrlplot-servomech.png \ + figures/phaseplot-dampedosc-default.png \ + figures/timeplot-mimo_step-default.png \ + figures/freqplot-siso_bode-default.png \ + figures/pzmap-siso_ctime-default.png \ + figures/rlocus-siso_ctime-default.png \ + figures/stochastic-whitenoise-response.png \ + figures/xferfcn-delay-compare.png figures/descfcn-pade-backlash.png # You can set these variables from the command line. SPHINXOPTS = @@ -12,28 +25,46 @@ BUILDDIR = _build help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) -.PHONY: help Makefile +.PHONY: help Makefile html latexpdf doctest clean distclean -# Rules to create figures -FIGS = classes.pdf timeplot-mimo_step-default.png \ - freqplot-siso_bode-default.png rlocus-siso_ctime-default.png \ - phaseplot-dampedosc-default.png -classes.pdf: classes.fig - fig2dev -Lpdf $< $@ +# List of the first RST figure of each type in each file that is generated +figures/flatsys-steering-compare.png: flatsys.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" +figures/iosys-predprey-open.png: iosys.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" +figures/timeplot-servomech-combined.png: nlsys.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" +figures/steering-optimal.png: optimal.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" +figures/phaseplot-dampedosc-default.png: phaseplot.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" +figures/timeplot-mimo_step-default.png \ + figures/freqplot-siso_bode-default.png \ + figures/pzmap-siso_ctime-default.png \ + figures/rlocus-siso_ctime-default.png \ + figures/ctrlplot-servomech.png: response.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" -timeplot-mimo_step-default.png: ../control/tests/timeplot_test.py - PYTHONPATH=.. python $< +figures/stochastic-whitenoise-response.png: stochastic.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" -freqplot-siso_bode-default.png: ../control/tests/freqplot_test.py - PYTHONPATH=.. python $< +figures/xferfcn-delay-compare.png: xferfcn.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" -rlocus-siso_ctime-default.png: ../control/tests/rlocus_test.py - PYTHONPATH=.. python $< +figures/descfcn-pade-backlash.png: descfcn.rst + @$(SPHINXBUILD) -M doctest "$(SOURCEDIR)" "$(BUILDDIR)" -phaseplot-dampedosc-default.png: ../control/tests/phaseplot_test.py - PYTHONPATH=.. python $< +# Other figure rules +figure/classes.pdf: figure/classes.fig + make -C figures classes.pdf # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). -html pdf clean doctest: Makefile $(FIGS) +html latexpdf: Makefile $(FIGS) $(RST_FIGS) + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +doctest clean: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +distclean: clean + /bin/rm -rf generated diff --git a/doc/_static/css/custom.css b/doc/_static/css/custom.css new file mode 100644 index 000000000..41bbb3f9e --- /dev/null +++ b/doc/_static/css/custom.css @@ -0,0 +1,20 @@ +/* Center equations with equation numbers on the right */ +.math { + text-align: center; +} +.eqno { + float: right; +} + +/* Make code blocks show up in in dark grey, rather than RTD default (red) */ +code.literal { + color: #404040 !important; +} +/* Make py:obj objects non-bold by default */ +.py-obj .pre { + font-weight: normal; +} +/* Turn bold back on for py:obj objects that actually link to something */ +a .py-obj .pre { + font-weight: bold; +} diff --git a/doc/_templates/custom-class-template.rst b/doc/_templates/custom-class-template.rst index 53a76e905..1f01e7e8f 100644 --- a/doc/_templates/custom-class-template.rst +++ b/doc/_templates/custom-class-template.rst @@ -8,14 +8,33 @@ :inherited-members: :special-members: + {% block attributes %} + {% if attributes %} + .. rubric:: {{ _('Attributes') }} + + .. autosummary:: + :nosignatures: + + {% for item in attributes %} + {%- if not item.startswith('_') %} + ~{{ name }}.{{ item }} + {%- endif -%} + {%- endfor %} + {% endif %} + {% endblock %} + {% block methods %} {% if methods %} .. rubric:: {{ _('Methods') }} .. autosummary:: :nosignatures: - {% for item in methods %} - {%- if not item.startswith('_') %} + + {% for item in members %} + {%- if not item.startswith('_') and item not in attributes %} + ~{{ name }}.{{ item }} + {%- endif -%} + {%- if item == '__call__' %} ~{{ name }}.{{ item }} {%- endif -%} {%- endfor %} diff --git a/doc/_templates/list-class-template.rst b/doc/_templates/list-class-template.rst new file mode 100644 index 000000000..3c85596b3 --- /dev/null +++ b/doc/_templates/list-class-template.rst @@ -0,0 +1,8 @@ +{{ fullname | escape | underline}} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} + :members: plot + :no-inherited-members: + :show-inheritance: diff --git a/doc/classes.rst b/doc/classes.rst index 3bf8492ee..0ab508a3a 100644 --- a/doc/classes.rst +++ b/doc/classes.rst @@ -1,52 +1,99 @@ -.. _class-ref: .. currentmodule:: control +.. _class-ref: + ********************** -Control system classes +Control System Classes ********************** +Input/Output System Classes +=========================== + The classes listed below are used to represent models of input/output systems (both linear time-invariant and nonlinear). They are usually created from factory functions such as :func:`tf` and :func:`ss`, so the user should normally not need to instantiate these directly. - + +The following figure illustrates the relationship between the classes. + +.. image:: figures/classes.pdf + :width: 800 + :align: center + .. autosummary:: :toctree: generated/ :template: custom-class-template.rst + :nosignatures: InputOutputSystem + NonlinearIOSystem LTI StateSpace TransferFunction FrequencyResponseData - NonlinearIOSystem InterconnectedSystem LinearICSystem -The following figure illustrates the relationship between the classes and -some of the functions that can be used to convert objects from one class to -another: -.. image:: classes.pdf - :width: 800 +Response and Plotting Classes +============================= + +These classes are used as the outputs of `_response`, `_map`, and +`_plot` functions: -Additional classes -================== .. autosummary:: + :toctree: generated/ + :template: custom-class-template.rst + :nosignatures: + + ControlPlot + FrequencyResponseData + NyquistResponseData + PoleZeroData + TimeResponseData + +In addition, the following classes are used to store lists of +responses, which can then be plotted using the ``.plot()`` method: + +.. autosummary:: + :toctree: generated/ + :template: list-class-template.rst + :nosignatures: + + FrequencyResponseList + NyquistResponseList + PoleZeroList + TimeResponseList + +More information on the functions used to create these classes can be +found in the :ref:`response-chapter` chapter. + + +Nonlinear System Classes +======================== + +These classes are used for various nonlinear input/output system +operations: + +.. autosummary:: + :toctree: generated/ :template: custom-class-template.rst :nosignatures: DescribingFunctionNonlinearity DescribingFunctionResponse flatsys.BasisFamily + flatsys.BezierFamily + flatsys.BSplineFamily flatsys.FlatSystem flatsys.LinearFlatSystem flatsys.PolyFamily flatsys.SystemTrajectory + OperatingPoint optimal.OptimalControlProblem optimal.OptimalControlResult optimal.OptimalEstimationProblem optimal.OptimalEstimationResult -The use of these classes is described in more detail in the -:ref:`flatsys-module` module and the :ref:`optimal-module` module +More informaton on the functions used to create these classes can be +found in the :ref:`nonlinear-systems` chapter. diff --git a/doc/conf.py b/doc/conf.py index 7a45ba3f9..f07ee3fa2 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -23,14 +23,13 @@ try: import sphinx_rtd_theme html_theme = 'sphinx_rtd_theme' - html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] except ImportError: html_theme = 'default' # -- Project information ----------------------------------------------------- project = u'Python Control Systems Library' -copyright = u'2023, python-control.org' +copyright = u'2025, python-control.org' author = u'Python Control Developers' # Version information - read from the source code @@ -49,16 +48,15 @@ # If your documentation needs a minimal Sphinx version, state it here. # -needs_sphinx = '3.1' +needs_sphinx = '3.4' # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ - 'sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.napoleon', - 'sphinx.ext.intersphinx', 'sphinx.ext.imgmath', - 'sphinx.ext.autosummary', 'nbsphinx', 'numpydoc', - 'sphinx.ext.linkcode', 'sphinx.ext.doctest' + 'sphinx.ext.autodoc', 'sphinx.ext.todo', 'sphinx.ext.intersphinx', + 'sphinx.ext.imgmath', 'sphinx.ext.autosummary', 'nbsphinx', 'numpydoc', + 'sphinx.ext.linkcode', 'sphinx.ext.doctest', 'sphinx_copybutton' ] # scan documents for autosummary directives and generate stub pages for each. @@ -94,8 +92,9 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path . -exclude_patterns = [u'_build', 'Thumbs.db', '.DS_Store', - '*.ipynb_checkpoints'] +exclude_patterns = [ + u'_build', 'Thumbs.db', '.DS_Store', '*.ipynb_checkpoints', + 'releases/template.rst'] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' @@ -103,11 +102,15 @@ # This config value contains the locations and names of other projects that # should be linked to in this documentation. intersphinx_mapping = \ - {'scipy': ('https://docs.scipy.org/doc/scipy/reference', None), + {'scipy': ('https://docs.scipy.org/doc/scipy', None), 'numpy': ('https://numpy.org/doc/stable', None), - 'matplotlib': ('https://matplotlib.org/', None), + 'matplotlib': ('https://matplotlib.org/stable/', None), + 'python': ('https://docs.python.org/3/', None), } +# Don't generate external links to (local) keywords +intersphinx_disabled_reftypes = ["py:keyword"] + # If this is True, todo and todolist produce output, else they produce nothing. # The default is False. todo_include_todos = True @@ -120,6 +123,16 @@ # html_theme = 'sphinx_rtd_theme' +# Set the default role to render items in backticks as code +default_role = 'py:obj' + +# Align inline math with text +imgmath_use_preview = True + +# Skip prompts when using copy button +copybutton_prompt_text = r">>> |\.\.\. " +copybutton_prompt_is_regexp = True + # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. @@ -131,8 +144,7 @@ # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] -def setup(app): - app.add_css_file('css/custom.css') +html_css_files = ['css/custom.css'] # Custom sidebar templates, must be a dictionary that maps document names # to template names. @@ -212,7 +224,7 @@ def linkcode_resolve(domain, info): else: # specific version return base_url + "%s/control/%s%s" % (version, fn, linespec) -# Don't automaticall show all members of class in Methods & Attributes section +# Don't automatically show all members of class in Methods & Attributes section numpydoc_show_class_members = False # Don't create a Sphinx TOC for the lists of class methods and attributes @@ -248,8 +260,9 @@ def linkcode_resolve(domain, info): # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, 'PythonControlLibrary.tex', u'Python Control Library Documentation', - u'RMM', 'manual'), + (master_doc, 'python-control.tex', + u'Python Control Systems Library User Guide', + u'Python Control Developers', 'manual'), ] @@ -280,8 +293,21 @@ def linkcode_resolve(domain, info): doctest_global_setup = """ import numpy as np import control as ct -import control.optimal as obc +import control.optimal as opt import control.flatsys as fs import control.phaseplot as pp ct.reset_defaults() """ + +# -- Customization for python-control ---------------------------------------- +# +# This code does custom processing of docstrings for the python-control +# package. + +def process_docstring(app, what, name, obj, options, lines): + # Loop through each line in docstring and replace `sys` with :code:`sys` + for i in range(len(lines)): + lines[i] = lines[i].replace("`sys`", ":code:`sys`") + +def setup(app): + app.connect('autodoc-process-docstring', process_docstring) diff --git a/doc/config.rst b/doc/config.rst new file mode 100644 index 000000000..9313fdded --- /dev/null +++ b/doc/config.rst @@ -0,0 +1,664 @@ +.. currentmodule:: control + +.. _package-configuration-parameters: + +Package Configuration Parameters +================================ + +The python-control package can be customized to allow for different +default values for selected parameters. This includes the ability to +change the way system names are created, to set the style for various +types of plots, and to determine default solvers and parameters to use +in solving optimization problems. + +To set the default value of a configuration parameter, set the appropriate +element of the `config.defaults` dictionary:: + + ct.config.defaults['module.parameter'] = value + +The :func:`set_defaults` function can also be used to set multiple +configuration parameters at the same time:: + + ct.set_defaults('module', param1=val1, param2=val2, ...] + +Several functions available to set collections of parameters based on +standard configurations: + +.. autosummary:: + + reset_defaults + use_fbs_defaults + use_matlab_defaults + use_legacy_defaults + +.. Set the current module to be control.config.defaults so that each of + the parameters gets a link of the form control.config.defaults.. + This can be linked from the documentation using a the construct + `config.defaults['param'] `. + +.. currentmodule:: control.config.defaults + +Finally, the `config.defaults` object can be used as a context manager +for temporarily setting default parameters: + +.. testsetup:: + + import control as ct + +.. doctest:: + + >>> with ct.config.defaults({'iosys.repr_format': 'info'}): + ... sys = ct.rss(4, 2, 2, name='sys') + ... print(repr(sys)) + ['y[0]', 'y[1]']> + +System creation parameters +-------------------------- + +.. py:data:: control.default_dt + :type: int + :value: 0 + + Default value of `dt` when constructing new I/O systems. If `dt` + is not specified explicitly this value will be used. Set to None + to leave the timebase unspecified, 0 for continuous-time systems, + True for discrete-time systems. + +.. py:data:: iosys.converted_system_name_prefix + :type: str + :value: '' + + Prefix to add to system name when converting a system from one + representation to another. + +.. py:data:: iosys.converted_system_name_suffix + :type: str + :value: '$converted' + + Suffix to add to system name when converting a system from one + representation to another. + + +.. _config.defaults['iosys.duplicate_system_name_prefix']: + +.. py:data:: iosys.duplicate_system_name_prefix + :type: str + :value: '' + + Prefix to add to system name when making a copy of a system. + +.. py:data:: iosys.duplicate_system_name_suffix + :type: str + :value: '$copy' + + Suffix to add to system name when making a copy of a system. + +.. py:data:: iosys.indexed_system_name_prefix + :type: str + :value: '' + + Prefix to add to system name when extracting a subset of the inputs + and outputs. + +.. py:data:: iosys.indexed_system_name_suffix + :type: str + :value: '$indexed' + + Suffix to add to system name when extracting a subset of the inputs + and outputs. + +.. py:data:: iosys.linearized_system_name_prefix + :type: str + :value: '' + + Prefix to add to system name when linearizing a system using + :func:`linearize`. + +.. py:data:: iosys.linearized_system_name_suffix + :type: str + :value: '$linearized' + + Suffix to add to system name when linearizing a system using + :func:`linearize`. + +.. py:data:: iosys.sampled_system_name_prefix + :type: str + :value: '' + + Prefix to add to system name when sampling a system at a set of + frequency points in :func:`frd` or converting a continuous-time + system to discrete time in :func:`sample_system`. + +.. py:data:: iosys.sampled_system_name_suffix + :type: str + :value: '$sampled' + + Suffix to add to system name when sampling a system at a set of + frequency points in :func:`frd` or converting a continuous-time + system to discrete time in :func:`sample_system`. + +.. py:data:: iosys.state_name_delim + :type: str + :value: '_' + + Used by :func:`interconnect` to set the names of the states of the + interconnected system. If the state names are not explicitly given, + the states will be given names of the form + '', for each subsys in syslist and + each state_name of each subsys, where is the value of + config.defaults['iosys.state_name_delim']. + +.. py:data:: statesp.remove_useless_states + :type: bool + :value: False + + When creating a :class:`StateSpace` system, remove states that have no + effect on the input-output dynamics of the system. + + +System display parameters +------------------------- + +.. py:data:: iosys.repr_format + :type: str + :value: 'eval' + + Set the default format used by :func:`iosys_repr` to create the + representation of an :class:`InputOutputSystem`: + + * 'info' : [outputs]> + * 'eval' : system specific, loadable representation + * 'latex' : latex representation of the object + +.. py:data:: iosys.repr_show_count + :type: bool + :value: True + + If True, show the input, output, and state count when using + `iosys_repr` and the 'eval' format. Otherwise, the input, + output, and state values are repressed from the output unless + non-generic signal names are present. + +.. py:data:: xferfcn.display_format + :type: str + :value: 'poly' + + Set the display format used in printing the + :class:`TransferFunction` object: + + * 'poly': Single polynomial for numerator and denominator. + * 'zpk': Product of factors, showing poles and zeros. + +.. py:data:: xferfcn.floating_point_format + :type: str + :value: '.4g' + + Format to use for displaying coefficients in + :class:`TransferFunction` objects when generating string + representations. + +.. py:data:: statesp.latex_num_format + :type: str + :value: '.3g' + + Format to use for displaying coefficients in :class:`StateSpace` systems + when generating LaTeX representations. + +.. py:data:: statesp.latex_repr_type + :type: str + :value: 'partitioned' + + Used when generating LaTeX representations of :class:`StateSpace` + systems. If 'partitioned', the A, B, C, D matrices are shown as + a single, partitioned matrix; if 'separate', the matrices are + shown separately. + +.. py:data:: statesp.latex_maxsize + :type: int + :value: 10 + + Maximum number of states plus inputs or outputs for which the LaTeX + representation of the system dynamics will be generated. + + +Response parameters +------------------- + +.. py:data:: control.squeeze_frequency_response + :type: bool + :value: None + + Set the default value of the `squeeze` parameter for + :func:`frequency_response` and :class:`FrequencyResponseData` + objects. If None then if a system is single-input, single-output + (SISO) the outputs (and inputs) are returned as a 1D array (indexed by + frequency), and if a system is multi-input or multi-output, then the + outputs are returned as a 2D array (indexed by output and frequency) or + a 3D array (indexed by output, input (or trace), and frequency). If + `squeeze=True`, access to the output response will remove + single-dimensional entries from the shape of the inputs and outputs even + if the system is not SISO. If `squeeze=False`, the output is returned as + a 3D array (indexed by the output, input, and frequency) even if the + system is SISO. + +.. py:data:: control.squeeze_time_response + :type: bool + :value: None + + Set the default value of the `squeeze` parameter for + :func:`input_output_response` and other time response objects. By + default, if a system is single-input, single-output (SISO) then the + outputs (and inputs) are returned as a 1D array (indexed by time) and if + a system is multi-input or multi-output, then the outputs are returned + as a 2D array (indexed by output and time) or a 3D array (indexed by + output, input, and time). If `squeeze=True`, access to the output + response will remove single-dimensional entries from the shape of the + inputs and outputs even if the system is not SISO. If `squeeze=False`, + the output is returned as a 3D array (indexed by the output, input, and + time) even if the system is SISO. + +.. py:data:: forced_response.return_x + :type: bool + :value: False + + Determine whether :func:`forced_response` returns the values of the + states when the :class:`TimeResponseData` object is evaluated. The + default value was True before version 0.9 and is False since then. + + +Plotting parameters +------------------- + +.. py:data:: ctrlplot.rcParams + :type: dict + :value: {'axes.labelsize': 'small', 'axes.titlesize': 'small', 'figure.titlesize': 'medium', 'legend.fontsize': 'x-small', 'xtick.labelsize': 'small', 'ytick.labelsize': 'small'} + + Overrides the default matplotlib parameters used for generating + plots. This dictionary can also be accessed as `ct.rcParams`. + +.. py:data:: freqplot.dB + :type: bool + :value: False + + If True, the magnitude in :func:`bode_plot` is plotted in dB + (otherwise powers of 10). + +.. py:data:: freqplot.deg + :type: bool + :value: True + + If True, the phase in :func:`bode_plot` is plotted in degrees + (otherwise radians). + +.. py:data:: freqplot.feature_periphery_decades + :type: int + :value: 1 + + Number of decades in frequency to include on both sides of features + (poles, zeros) when generating frequency plots. + +.. py:data:: freqplot.freq_label + :type: bool + :value: 'Frequency [{units}]' + + Label for the frequency axis in frequency plots, with `units` set to + either 'rad/sec' or 'Hz' when the label is created. + +.. py:data:: freqplot.grid + :type: bool + :value: True + + Include grids for magnitude and phase in frequency plots. + +.. py:data:: freqplot.Hz + :type: bool + :value: False + + If True, use Hertz for frequency response plots (otherwise rad/sec). + +.. py:data:: freqplot.magnitude_label + :type: str + :value: 'Magnitude' + + Label to use on the magnitude portion of a frequency plot. Set to + 'Gain' by `use_fbs_defaults()`. + +.. py:data:: freqplot.number_of_samples + :type: int + :value: 1000 + + Number of frequency points to use in in frequency plots. + +.. py:data:: freqplot.share_magnitude + :type: str + :value: 'row' + + Determine whether and how axis limits are shared between the magnitude + variables in :func:`bode_plot`. Can be set set to 'row' to share across + all subplots in a row, 'col' to set across all subplots in a column, or + False to allow independent limits. + +.. py:data:: freqplot.share_phase + :type: str + :value: 'row' + + Determine whether and how axis limits are shared between the phase + variables in :func:`bode_plot`. Can be set set to 'row' to share across + all subplots in a row, 'col' to set across all subplots in a column, or + False to allow independent limits. + +.. py:data:: freqplot.share_frequency + :type: str + :value: 'col' + + Determine whether and how axis limits are shared between the frequency + axes in :func:`bode_plot`. Can be set set to 'row' to share across all + subplots in a row, 'col' to set across all subplots in a column, or + False to allow independent limits. + +.. py:data:: freqplot.title_frame + :type: str + :value: 'axes' + + Set the frame of reference used to center the plot title. If set to + 'axes', the horizontal position of the title will be centered relative + to the axes. If set to 'figure', it will be centered with respect to + the figure (faster execution). + +.. py:data:: freqplot.wrap_phase + :type: bool + :value: False + + If `wrap_phase` is False, then the phase will be unwrapped so that it + is continuously increasing or decreasing. If `wrap_phase` is True the + phase will be restricted to the range [-180, 180) (or [:math:`-\pi`, + :math:`\pi`) radians). If ``wrap_phase`` is specified as a float, the + phase will be offset by 360 degrees if it falls below the specified + value. + +.. py:data:: nichols.grid + :type: bool + :value: True + + Set to True if :func:`nichols_plot` should include a Nichols-chart + grid. + +.. py:data:: nyquist.arrows + :type: int + :value: 2 + + Specify the default number of arrows for :func:`nyquist_plot`. + +.. py:data:: nyquist.arrow_size + :type: float + :value: 8 + + Arrowhead width and length (in display coordinates) for + :func:`nyquist_plot`. + +.. py:data:: nyquist.circle_style + :type: dict + :value: {'color': 'black', 'linestyle': 'dashed', 'linewidth': 1} + + Style for unit circle in :func:`nyquist_plot`. + +.. py:data:: nyquist.encirclement_threshold + :type: float + :value: 0.05 + + Define the threshold in :func:`nyquist_response` for generating a + warning if the number of net encirclements is a non-integer value. + +.. py:data:: nyquist.indent_direction + :type: str + :value: 'right' + + Set the direction of indentation in :func:`nyquist_response` for poles + on the imaginary axis. Valid values are 'right', 'left', or 'none'. + +.. py:data:: nyquist.indent_points + :type: int + :value: 50 + + Set the number of points to insert in the Nyquist contour around poles + that are at or near the imaginary axis in :func:`nyquist_response`. + +.. py:data:: nyquist.indent_radius + :type: float + :value: 0.0001 + + Amount to indent the Nyquist contour around poles on or near the + imaginary axis in :func:`nyquist_response`. Portions of the Nyquist plot + corresponding to indented portions of the contour are plotted using a + different line style. + +.. py:data:: nyquist.max_curve_magnitude + :type: float + :value: 20 + + Restrict the maximum magnitude of the Nyquist plot in + :func:`nyquist_plot`. Portions of the Nyquist plot whose magnitude is + restricted are plotted using a different line style. + +.. py:data:: nyquist.max_curve_offset + :type: float + :value: 0.02 + + When plotting scaled portion of the Nyquist plot in + :func:`nyquist_plot`, increase/decrease the magnitude by this fraction + of the `max_curve_magnitude` to allow any overlaps between the primary and + mirror curves to be avoided. + +.. py:data:: nyquist.mirror_style + :type: list of str + :value: ['--', ':'] + + Linestyles for mirror image of the Nyquist curve in + :func:`nyquist_plot`. The first element is used for unscaled + portions of the Nyquist curve, the second element is used for + portions that are scaled (using `max_curve_magnitude`). If False + then omit the mirror image curve completely. + +.. py:data:: nyquist.primary_style + :type: list of str + :value: ['-', '-.'] + + Linestyles for primary image of the Nyquist curve in + :func:`nyquist_plot`. The first element is used for unscaled portions + of the Nyquist curve, the second element is used for portions that are + scaled (using max_curve_magnitude). + +.. py:data:: nyquist.start_marker + :type: str + :value: 'o' + + Matplotlib marker to use to mark the starting point of the Nyquist plot + in :func:`nyquist_plot`. + +.. py:data:: nyquist.start_marker_size + :type: float + :value: 4 + + Start marker size (in display coordinates) in :func:`nyquist_plot`. + +.. py:data:: phaseplot.arrows + :type: int + :value: 2 + + Set the default number of arrows in :func:`phase_plane_plot` and + :func:`phaseplot.streamlines`. + +.. py:data:: phaseplot.arrow_size + :type: float + :value: 8 + + Set the default size of arrows in :func:`phase_plane_plot` and + :func:`phaseplot.streamlines`. + +.. py:data:: phaseplot.arrow_style + :type: matplotlib patch + :value: None + + Set the default style for arrows in :func:`phase_plane_plot` and + :func:`phaseplot.streamlines`. If set to None, defaults to + + .. code:: + + mpl.patches.ArrowStyle( + 'simple', head_width=int(2 * arrow_size / 3), + head_length=arrow_size) + +.. py:data:: phaseplot.separatrices_radius + :type: float + :value: 0.1 + + In :func:`phaseplot.separatrices`, set the offset from the equilibrium + point to the starting point of the separatix traces, in the direction of + the eigenvectors evaluated at that equilibrium point. + +.. py:data:: pzmap.buffer_factor + :type: float + :value: 1.05 + + The limits of the pole/zero plot generated by :func:`pole_zero_plot` + are set based on the location features in the plot, including the + location of poles, zeros, and local maxima of root locus curves. The + locations of local maxima are expanded by the buffer factor set by + `buffer_factor`. + +.. py:data:: pzmap.expansion_factor + :type: float + :value: 1.8 + + The final axis limits of the pole/zero plot generated by + :func:`pole_zero_plot` are set to by the largest features in the plot + multiplied by an expansion factor set by `expansion_factor`. + +.. py:data:: pzmap.grid + :type: bool + :value: False + + If True plot omega-damping grid in :func:`pole_zero_plot`. If False + or None show imaginary axis for continuous-time systems, unit circle for + discrete-time systems. If 'empty', do not draw any additional lines. + + Note: this setting only applies to pole/zero plots. For root locus + plots, the 'rlocus.grid' parameter value is used as the default. + +.. py:data:: pzmap.marker_size + :type: float + :value: 6 + + Set the size of the markers used for poles and zeros in + :func:`pole_zero_plot`. + +.. py:data:: pzmap.marker_width + :type: float + :value: 1.5 + + Set the line width of the markers used for poles and zeros in + :func:`pole_zero_plot`. + +.. py:data:: rlocus.grid + :type: bool + :value: True + + If True, plot omega-damping grid in :func:`root_locus_plot`. If False + or None show imaginary axis for continuous-time systems, unit circle for + discrete-time systems. If 'empty', do not draw any additional lines. + +.. py:data:: sisotool.initial_gain + :type: float + :value: 1 + + Initial gain to use for plotting root locus in :func:`sisotool`. + +.. py:data:: timeplot.input_props + :type: list of dict + :value: [{'color': 'tab:red'}, {'color': 'tab:purple'}, {'color': 'tab:brown'}, {'color': 'tab:olive'}, {'color': 'tab:cyan'}] + + List of line properties to use when plotting combined inputs in + :func:`time_response_plot`. The line properties for each input will be + cycled through this list. + +.. py:data:: timeplot.output_props + :type: list of dict + :value: [{'color': 'tab:blue'}, {'color': 'tab:orange'}, {'color': 'tab:green'}, {'color': 'tab:pink'}, {'color': 'tab:gray'}] + + List of line properties to use when plotting combined outputs in + :func:`time_response_plot`. The line properties for each input will be + cycled through this list. + +.. py:data:: timeplot.trace_props + :type: list of dict + :value: [{'linestyle': '-'}, {'linestyle': '--'}, {'linestyle': ':'}, {'linestyle': '-.'}] + + List of line properties to use when plotting multiple traces in + :func:`time_response_plot`. The line properties for each input will be + cycled through this list. + +.. py:data:: timeplot.sharex + :type: str + :value: 'col' + + Determine whether and how x-axis limits are shared between subplots in + :func:`time_response_plot`. Can be set set to 'row' to share across all + subplots in a row, 'col' to set across all subplots in a column, 'all' + to share across all subplots, or False to allow independent limits. + +.. py:data:: timeplot.sharey + :type: bool + :value: False + + Determine whether and how y-axis limits are shared between subplots in + :func:`time_response_plot`. Can be set set to 'row' to share across all + subplots in a row, 'col' to set across all subplots in a column, 'all' + to share across all subplots, or False to allow independent limits. + +.. py:data:: timeplot.time_label + :type: str + :value: 'Time [s]' + + Label to use for the time axis in :func:`time_response_plot`. + + +Optimization parameters +----------------------- + +.. py:data:: optimal.minimize_method + :type: str + :value: None + + Set the method used by :func:`scipy.optimize.minimize` when called in + :func:`solve_optimal_trajectory` and :func:`solve_optimal_estimate`. + +.. py:data:: optimal.minimize_options + :type: dict + :value: {} + + Set the value of the options keyword used by + :func:`scipy.optimize.minimize` when called in + :func:`solve_optimal_trajectory` and :func:`solve_optimal_estimate`. + +.. py:data:: optimal.minimize_kwargs + :type: dict + :value: {} + + Set the keyword arguments passed to :func:`scipy.optimize.minimize` + when called in :func:`solve_optimal_trajectory` and + :func:`solve_optimal_estimate`. + +.. py:data:: optimal.solve_ivp_method + :type: str + :value: None + + Set the method used by :func:`scipy.integrate.solve_ivp` when called in + :func:`solve_optimal_trajectory` and :func:`solve_optimal_estimate`. + +.. py:data:: optimal.solve_ivp_options + :type: dict + :value: {} + + Set the value of the options keyword used by + :func:`scipy.integrate.solve_ivp` when called in + :func:`solve_optimal_trajectory` and :func:`solve_optimal_estimate`. diff --git a/doc/control.rst b/doc/control.rst deleted file mode 100644 index efd643d8a..000000000 --- a/doc/control.rst +++ /dev/null @@ -1,201 +0,0 @@ -.. _function-ref: - -****************** -Function reference -****************** - -.. Include header information from the main control module -.. automodule:: control - :no-members: - :no-inherited-members: - :no-special-members: - -System creation -=============== -.. autosummary:: - :toctree: generated/ - - ss - tf - frd - zpk - rss - drss - nlsys - - -System interconnections -======================= -.. autosummary:: - :toctree: generated/ - - append - connect - feedback - interconnect - negate - parallel - series - connection_table - - -Frequency domain plotting -========================= - -.. autosummary:: - :toctree: generated/ - - bode_plot - describing_function_plot - nyquist_plot - gangof4_plot - nichols_plot - nichols_grid - suptitle - -Note: For plotting commands that create multiple axes on the same plot, the -individual axes can be retrieved using the axes label (retrieved using the -`get_label` method for the matplotliib axes object). The following labels -are currently defined: - -* Bode plots: `control-bode-magnitude`, `control-bode-phase` -* Gang of 4 plots: `control-gangof4-s`, `control-gangof4-cs`, - `control-gangof4-ps`, `control-gangof4-t` - -Time domain simulation -====================== - -.. autosummary:: - :toctree: generated/ - - forced_response - impulse_response - initial_response - input_output_response - phase_plot - step_response - TimeResponseData - -Control system analysis -======================= -.. autosummary:: - :toctree: generated/ - - dcgain - describing_function - frequency_response - get_input_ff_index - get_output_fb_index - ispassive - margin - stability_margins - step_info - phase_crossover_frequencies - poles - zeros - pzmap - root_locus - sisotool - StateSpace.__call__ - TransferFunction.__call__ - -Matrix computations -=================== -.. autosummary:: - :toctree: generated/ - - care - ctrb - dare - dlyap - lyap - obsv - gram - -Control system synthesis -======================== -.. autosummary:: - :toctree: generated/ - - acker - create_statefbk_iosystem - dlqr - h2syn - hinfsyn - lqr - mixsyn - place - place_varga - rootlocus_pid_designer - -Model simplification tools -========================== -.. autosummary:: - :toctree: generated/ - - minreal - balred - hsvd - modred - era - markov - -Nonlinear system support -======================== -.. autosummary:: - :toctree: generated/ - - describing_function - find_eqpt - linearize - input_output_response - summing_junction - flatsys.point_to_point - -Stochastic system support -========================= -.. autosummary:: - :toctree: generated/ - - correlation - create_estimator_iosystem - dlqe - lqe - white_noise - -.. _utility-and-conversions: - -Utility functions and conversions -================================= -.. autosummary:: - :toctree: generated/ - - augw - bdschur - canonical_form - damp - db2mag - isctime - isdtime - issiso - issys - mag2db - modal_form - norm - observable_form - pade - reachable_form - reset_defaults - sample_system - set_defaults - similarity_transform - ss2tf - ssdata - tf2ss - tfdata - timebase - unwrap - use_fbs_defaults - use_matlab_defaults - - diff --git a/doc/conventions.rst b/doc/conventions.rst deleted file mode 100644 index 21f3ab82b..000000000 --- a/doc/conventions.rst +++ /dev/null @@ -1,333 +0,0 @@ -.. _conventions-ref: - -.. currentmodule:: control - -******************* -Library conventions -******************* - -The python-control library uses a set of standard conventions for the -way that different types of standard information used by the library. -Throughout this manual, we assume the `control` package has been -imported as `ct`. - -LTI system representation -========================= - -Linear time invariant (LTI) systems are represented in python-control in -state space, transfer function, or frequency response data (FRD) form. Most -functions in the toolbox will operate on any of these data types, and -functions for converting between compatible types are provided. - -State space systems -------------------- -The :class:`StateSpace` class is used to represent state-space realizations -of linear time-invariant (LTI) systems: - -.. math:: - - \frac{dx}{dt} &= A x + B u \\ - y &= C x + D u - -where u is the input, y is the output, and x is the state. - -To create a state space system, use the :func:`ss` function:: - - sys = ct.ss(A, B, C, D) - -State space systems can be manipulated using standard arithmetic operations -as well as the :func:`feedback`, :func:`parallel`, and :func:`series` -function. A full list of functions can be found in :ref:`function-ref`. - -Transfer functions ------------------- -The :class:`TransferFunction` class is used to represent input/output -transfer functions - -.. math:: - - G(s) = \frac{\text{num}(s)}{\text{den}(s)} - = \frac{a_0 s^m + a_1 s^{m-1} + \cdots + a_m} - {b_0 s^n + b_1 s^{n-1} + \cdots + b_n}, - -where n is generally greater than or equal to m (for a proper transfer -function). - -To create a transfer function, use the :func:`tf` function:: - - sys = ct.tf(num, den) - -Transfer functions can be manipulated using standard arithmetic operations -as well as the :func:`feedback`, :func:`parallel`, and :func:`series` -function. A full list of functions can be found in :ref:`function-ref`. - -Frequency response data (FRD) systems -------------------------------------- -The :class:`FrequencyResponseData` (FRD) class is used to represent systems in -frequency response data form. - -The main data members are `omega` and `fresp`, where `omega` is a 1D array -with the frequency points of the response, and `fresp` is a 3D array, with -the first dimension corresponding to the output index of the system, the -second dimension corresponding to the input index, and the 3rd dimension -corresponding to the frequency points in omega. - -FRD systems can be created with the :func:`~control.frd` factory function. -Frequency response data systems have a somewhat more limited set of -functions that are available, although all of the standard algebraic -manipulations can be performed. - -The FRD class is also used as the return type for the -:func:`frequency_response` function (and the equivalent method for the -:class:`StateSpace` and :class:`TransferFunction` classes). This -object can be assigned to a tuple using:: - - mag, phase, omega = response - -where `mag` is the magnitude (absolute value, not dB or log10) of the -system frequency response, `phase` is the wrapped phase in radians of -the system frequency response, and `omega` is the (sorted) frequencies -at which the response was evaluated. If the system is SISO and the -`squeeze` argument to :func:`frequency_response` is not True, -`magnitude` and `phase` are 1D, indexed by frequency. If the system -is not SISO or `squeeze` is False, the array is 3D, indexed by the -output, input, and frequency. If `squeeze` is True then -single-dimensional axes are removed. The processing of the `squeeze` -keyword can be changed by calling the response function with a new -argument:: - - mag, phase, omega = response(squeeze=False) - - -Discrete time systems ---------------------- -A discrete time system is created by specifying a nonzero 'timebase', dt. -The timebase argument can be given when a system is constructed: - -* `dt = 0`: continuous time system (default) -* `dt > 0`: discrete time system with sampling period 'dt' -* `dt = True`: discrete time with unspecified sampling period -* `dt = None`: no timebase specified - -Only the :class:`StateSpace`, :class:`TransferFunction`, and -:class:`InputOutputSystem` classes allow explicit representation of -discrete time systems. - -Systems must have compatible timebases in order to be combined. A discrete -time system with unspecified sampling time (`dt = True`) can be combined with -a system having a specified sampling time; the result will be a discrete time -system with the sample time of the latter system. Similarly, a system with -timebase `None` can be combined with a system having a specified timebase; the -result will have the timebase of the latter system. For continuous time -systems, the :func:`sample_system` function or the :meth:`StateSpace.sample` -and :meth:`TransferFunction.sample` methods can be used to create a discrete -time system from a continuous time system. See -:ref:`utility-and-conversions`. The default value of `dt` can be changed by -changing the value of `control.config.defaults['control.default_dt']`. - -Conversion between representations ----------------------------------- -LTI systems can be converted between representations either by calling the -constructor for the desired data type using the original system as the sole -argument or using the explicit conversion functions :func:`ss2tf` and -:func:`tf2ss`. - -Simulating LTI systems -====================== - -A number of functions are available for computing the output (and -state) response of an LTI systems: - -.. autosummary:: - :toctree: generated/ - - initial_response - step_response - impulse_response - forced_response - -Each of these functions returns a :class:`TimeResponseData` object -that contains the data for the time response (described in more detail -in the next section). - -The :func:`forced_response` system is the most general and allows by -the zero initial state response to be simulated as well as the -response from a non-zero initial condition. - -For linear time invariant (LTI) systems, the :func:`impulse_response`, -:func:`initial_response`, and :func:`step_response` functions will -automatically compute the time vector based on the poles and zeros of -the system. If a list of systems is passed, a common time vector will be -computed and a list of responses will be returned in the form of a -:class:`TimeResponseList` object. The :func:`forced_response` function can -also take a list of systems, to which a single common input is applied. -The :class:`TimeResponseList` object has a `plot()` method that will plot -each of the responses in turn, using a sequence of different colors with -appropriate titles and legends. - -In addition the :func:`input_output_response` function, which handles -simulation of nonlinear systems and interconnected systems, can be -used. For an LTI system, results are generally more accurate using -the LTI simulation functions above. The :func:`input_output_response` -function is described in more detail in the :ref:`iosys-module` section. - -.. currentmodule:: control -.. _time-series-convention: - -Time series data ----------------- -A variety of functions in the library return time series data: sequences of -values that change over time. A common set of conventions is used for -returning such data: columns represent different points in time, rows are -different components (e.g., inputs, outputs or states). For return -arguments, an array of times is given as the first returned argument, -followed by one or more arrays of variable values. This convention is used -throughout the library, for example in the functions -:func:`forced_response`, :func:`step_response`, :func:`impulse_response`, -and :func:`initial_response`. - -.. note:: - The convention used by python-control is different from the convention - used in the `scipy.signal - `_ library. In - Scipy's convention the meaning of rows and columns is interchanged. - Thus, all 2D values must be transposed when they are used with functions - from `scipy.signal`_. - -The time vector is a 1D array with shape (n, ):: - - T = [t1, t2, t3, ..., tn ] - -Input, state, and output all follow the same convention. Columns are different -points in time, rows are different components:: - - U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)] - [u2(t1), u2(t2), u2(t3), ..., u2(tn)] - ... - ... - [ui(t1), ui(t2), ui(t3), ..., ui(tn)]] - -(and similarly for `X`, `Y`). So, `U[:, 2]` is the system's input at the -third point in time; and `U[1]` or `U[1, :]` is the sequence of values for -the system's second input. - -When there is only one row, a 1D object is accepted or returned, which adds -convenience for SISO systems: - -The initial conditions are either 1D, or 2D with shape (j, 1):: - - X0 = [[x1] - [x2] - ... - ... - [xj]] - -Functions that return time responses (e.g., :func:`forced_response`, -:func:`impulse_response`, :func:`input_output_response`, -:func:`initial_response`, and :func:`step_response`) return a -:class:`TimeResponseData` object that contains the data for the time -response. These data can be accessed via the -:attr:`~TimeResponseData.time`, :attr:`~TimeResponseData.outputs`, -:attr:`~TimeResponseData.states` and :attr:`~TimeResponseData.inputs` -properties:: - - sys = ct.rss(4, 1, 1) - response = ct.step_response(sys) - plot(response.time, response.outputs) - -The dimensions of the response properties depend on the function being -called and whether the system is SISO or MIMO. In addition, some time -response function can return multiple "traces" (input/output pairs), -such as the :func:`step_response` function applied to a MIMO system, -which will compute the step response for each input/output pair. See -:class:`TimeResponseData` for more details. - -The time response functions can also be assigned to a tuple, which extracts -the time and output (and optionally the state, if the `return_x` keyword is -used). This allows simple commands for plotting:: - - t, y = ct.step_response(sys) - plot(t, y) - -The output of a MIMO LTI system can be plotted like this:: - - t, y = ct.forced_response(sys, t, u) - plot(t, y[0], label='y_0') - plot(t, y[1], label='y_1') - -The convention also works well with the state space form of linear -systems. If `D` is the feedthrough matrix (2D array) of a linear system, -and `U` is its input (array), then the feedthrough part of the system's -response, can be computed like this:: - - ft = D @ U - -Finally, the `to_pandas()` function can be used to create a pandas dataframe:: - - df = response.to_pandas() - -The column labels for the data frame are `time` and the labels for the input, -output, and state signals (`u[i]`, `y[i]`, and `x[i]` by default, but these -can be changed using the `inputs`, `outputs`, and `states` keywords when -constructing the system, as described in :func:`ss`, :func:`tf`, and other -system creation function. Note that when exporting to pandas, "rows" in the -data frame correspond to time and "cols" (DataSeries) correspond to signals. - -.. currentmodule:: control -.. _package-configuration-parameters: - -Package configuration parameters -================================ - -The python-control library can be customized to allow for different default -values for selected parameters. This includes the ability to set the style -for various types of plots and establishing the underlying representation for -state space matrices. - -To set the default value of a configuration variable, set the appropriate -element of the `control.config.defaults` dictionary:: - - ct.config.defaults['module.parameter'] = value - -The `~control.config.set_defaults` function can also be used to set multiple -configuration parameters at the same time:: - - ct.config.set_defaults('module', param1=val1, param2=val2, ...] - -Finally, there are also functions available set collections of variables based -on standard configurations. - -Selected variables that can be configured, along with their default values: - - * freqplot.dB (False): Bode plot magnitude plotted in dB (otherwise powers - of 10) - - * freqplot.deg (True): Bode plot phase plotted in degrees (otherwise radians) - - * freqplot.Hz (False): Bode plot frequency plotted in Hertz (otherwise - rad/sec) - - * freqplot.grid (True): Include grids for magnitude and phase plots - - * freqplot.number_of_samples (1000): Number of frequency points in Bode plots - - * freqplot.feature_periphery_decade (1.0): How many decades to include in - the frequency range on both sides of features (poles, zeros). - - * statesp.default_dt and xferfcn.default_dt (None): set the default value - of dt when constructing new LTI systems - - * statesp.remove_useless_states (True): remove states that have no effect - on the input-output dynamics of the system - -Additional parameter variables are documented in individual functions - -Functions that can be used to set standard configurations: - -.. autosummary:: - :toctree: generated/ - - reset_defaults - use_fbs_defaults - use_matlab_defaults - use_legacy_defaults diff --git a/doc/cruise-control.py b/doc/cruise-control.py deleted file mode 120000 index cfa1c8195..000000000 --- a/doc/cruise-control.py +++ /dev/null @@ -1 +0,0 @@ -../examples/cruise-control.py \ No newline at end of file diff --git a/doc/cruise.ipynb b/doc/cruise.ipynb deleted file mode 120000 index f712e2d5f..000000000 --- a/doc/cruise.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/cruise.ipynb \ No newline at end of file diff --git a/doc/descfcn.rst b/doc/descfcn.rst index 1e4a2f3fd..edff8603b 100644 --- a/doc/descfcn.rst +++ b/doc/descfcn.rst @@ -1,18 +1,26 @@ +.. currentmodule:: control + .. _descfcn-module: -******************** -Describing functions -******************** +Describing Functions +==================== For nonlinear systems consisting of a feedback connection between a -linear system and a static nonlinearity, it is possible to obtain a +linear system and a nonlinearity, it is possible to obtain a generalization of Nyquist's stability criterion based on the idea of describing functions. The basic concept involves approximating the -response of a static nonlinearity to an input :math:`u = A e^{j \omega -t}` as an output :math:`y = N(A) (A e^{j \omega t})`, where :math:`N(A) -\in \mathbb{C}` represents the (amplitude-dependent) gain and phase +response of a nonlinearity to an input :math:`u = A e^{j \omega t}` as +an output :math:`y = N(A) (A e^{j \omega t})`, where :math:`N(A) \in +\mathbb{C}` represents the (amplitude-dependent) gain and phase associated with the nonlinearity. +In the most common case, the nonlinearity will be a static, +time-invariant nonlinear function :math:`y = h(u)`. However, +describing functions can be defined for nonlinear input/output systems +that have some internal memory, such as hysteresis or backlash. For +simplicity, we take the nonlinearity to be static (memoryless) in the +description below, unless otherwise specified. + Stability analysis of a linear system :math:`H(s)` with a feedback nonlinearity :math:`F(x)` is done by looking for amplitudes :math:`A` and frequencies :math:`\omega` such that @@ -25,39 +33,45 @@ If such an intersection exists, it indicates that there may be a limit cycle of amplitude :math:`A` with frequency :math:`\omega`. Describing function analysis is a simple method, but it is approximate -because it assumes that higher harmonics can be neglected. +because it assumes that higher harmonics can be neglected. More +information on describing functions can be found in `Feedback Systems +`_, Section 10.5 +(Generalized Notions of Gain and Phase). + Module usage -============ +------------ -The function :func:`~control.describing_function` can be used to +The function :func:`describing_function` can be used to compute the describing function of a nonlinear function:: N = ct.describing_function(F, A) +where `F` is a scalar nonlinear function. + Stability analysis using describing functions is done by looking for -amplitudes :math:`a` and frequencies :math`\omega` such that +amplitudes :math:`A` and frequencies :math:`\omega` such that .. math:: H(j\omega) = \frac{-1}{N(A)} These points can be determined by generating a Nyquist plot in which -the transfer function :math:`H(j\omega)` intersections the negative +the transfer function :math:`H(j\omega)` intersects the negative reciprocal of the describing function :math:`N(A)`. The -:func:`~control.describing_function_response` function computes the +:func:`describing_function_response` function computes the amplitude and frequency of any points of intersection:: - response = ct.describing_function_response(H, F, amp_range[, omega_range]) - response.intersections # frequency, amplitude pairs + dfresp = ct.describing_function_response(H, F, amp_range[, omega_range]) + dfresp.intersections # frequency, amplitude pairs A Nyquist plot showing the describing function and the intersections -with the Nyquist curve can be generated using `response.plot()`, which -calls the :func:`~control.describing_function_plot` function. +with the Nyquist curve can be generated using ``dfresp.plot()``, which +calls the :func:`describing_function_plot` function. Pre-defined nonlinearities -========================== +-------------------------- To facilitate the use of common describing functions, the following nonlinearity constructors are predefined: @@ -76,17 +90,52 @@ nonlinearity:: F = ct.saturation_nonlinearity(1) -These functions use the -:class:`~control.DescribingFunctionNonlinearity`, which allows an -analytical description of the describing function. +These functions use the :class:`DescribingFunctionNonlinearity` class, +which allows an analytical description of the describing function. + + +Example +------- + +The following example demonstrates a more complicated interaction +between a (non-static) nonlinearity and a higher order transfer +function, resulting in multiple intersection points: + +.. testcode:: descfcn + + # Linear dynamics + H_simple = ct.tf([1], [1, 2, 2, 1]) + H_multiple = ct.tf(H_simple * ct.tf(*ct.pade(5, 4)) * 4, name='sys') + omega = np.logspace(-3, 3, 500) + + # Nonlinearity + F_backlash = ct.friction_backlash_nonlinearity(1) + amp = np.linspace(0.6, 5, 50) + + # Describing function plot + cplt = ct.describing_function_plot( + H_multiple, F_backlash, amp, omega, mirror_style=False) + +.. testcode:: descfcn + :hide: + + import matplotlib.pyplot as plt + plt.savefig('figures/descfcn-pade-backlash.png') + +.. image:: figures/descfcn-pade-backlash.png + Module classes and functions -============================ +---------------------------- .. autosummary:: - :toctree: generated/ :template: custom-class-template.rst - ~control.DescribingFunctionNonlinearity - ~control.friction_backlash_nonlinearity - ~control.relay_hysteresis_nonlinearity - ~control.saturation_nonlinearity + describing_function + describing_function_response + describing_function_plot + DescribingFunctionNonlinearity + friction_backlash_nonlinearity + relay_hysteresis_nonlinearity + saturation_nonlinearity + ~DescribingFunctionNonlinearity.__call__ + diff --git a/doc/describing_functions.ipynb b/doc/describing_functions.ipynb deleted file mode 120000 index 14bcb69a4..000000000 --- a/doc/describing_functions.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/describing_functions.ipynb \ No newline at end of file diff --git a/doc/develop.rst b/doc/develop.rst new file mode 100644 index 000000000..c9b6738a8 --- /dev/null +++ b/doc/develop.rst @@ -0,0 +1,815 @@ +.. currentmodule:: control + +*************** +Developer Notes +*************** + +This chapter contains notes for developers who wish to contribute to +the Python Control Systems Library (python-control). It is mainly a +listing of the practices that have evolved over the course of +development since the package was created in 2009. + + +Package Structure +================= + +The python-control package is maintained on GitHub, with documentation +hosted by ReadTheDocs and a mailing list on SourceForge: + + * Project home page: https://python-control.org + * Source code repository: https://github.com/python-control/python-control + * Documentation: https://python-control.readthedocs.io/ + * Issue tracker: https://github.com/python-control/python-control/issues + * Mailing list: https://sourceforge.net/p/python-control/mailman/ + +GitHub repository file and directory layout: + - **python-control/** - main repository + + * LICENSE, Manifest, pyproject.toml, README.rst - package information + + * **control/** - primary package source code + + + __init__.py, _version.py, config.py - package definition + and configuration + + + iosys.py, nlsys.py, lti.py, statesp.py, xferfcn.py, + frdata.py - I/O system classes + + + bdalg.py, delay.py, canonical.py, margins.py, + sysnorm.py, modelsimp.py, passivity.py, robust.py, + statefbk.py, stochsys.py - analysis and synthesis routines + + + ctrlplot.py, descfcn.py, freqplot.py, grid.py, + nichols.py, pzmap.py, rlocus.py, sisotool.py, + timeplot.py, timeresp.py - response and plotting routines + + + ctrlutil.py, dtime.py, exception.py, mateqn.py - utility functions + + + phaseplot.py - phase plot module + + + optimal.py - optimal control module + + + **flatsys/** - flat systems subpackage + + - __init__.py, basis.py, bezier.py, bspline.py, flatsys.py, + linflat.py, poly.py, systraj.py - subpackage files + + + **matlab/** - MATLAB compatibility subpackage + + - __init__.py, timeresp.py, wrappers.py - subpackage files + + + **tests/** - unit tests + + * **.github/** - GitHub workflows + + * **benchmarks/** - benchmarking files (not well-maintained) + + * **doc/** - user guide and reference manual + + + index.rst - main documentation index + + + conf.py, Makefile - sphinx configuration files + + + intro.rst, linear.rst, statesp.rst, xferfcn.rst, nonlinear.rst, + flatsys.rst, iosys.rst, nlsys.rst, optimal.rst, phaseplot.rst, + response.rst, descfcn.rst, stochastic.rst, examples.rst - User + Guide + + + functions.rst, classes.rst, config.rst, matlab.rst, develop.rst - + Reference Manual + + + **examples/** + + - \*.py, \*.rst - Python scripts (linked to ../examples/\*.py) + + - \*.ipynb - Jupyter notebooks (linked to ../examples.ipynb) + + + **figures/** + + - \*.pdf, \*.png - Figures for inclusion in documentation + + * **examples/** + + + \*.py - Python scripts + + + \*.ipynb - Jupyter notebooks + + +Naming Conventions +================== + +Generally speaking, standard Python and NumPy naming conventions are +used throughout the package. + +* Python PEP 8 (code style): https://peps.python.org/pep-0008/ + + +Filenames +--------- + +* Source files are lower case, usually less than 10 characters (and 8 + or less is better). + +* Unit tests (in `control/tests/`) are of the form `module_test.py` or + `module_function.py`. + + +Class names +----------- + +* Most class names are in camel case, with long form descriptions of + the object purpose/contents (`TimeResponseData`). + +* Input/output class names are written out in long form as they aren't + too long (`StateSpace`, `TransferFunction`), but for very long names + 'IO' can be used in place of 'InputOutput' (`NonlinearIOSystem`) and + 'IC' can be used in place of 'Interconnected' (`LinearICSystem`). + +* Some older classes don't follow these guidelines (e.g., `LTI` instead + of `LinearTimeInvariantSystem` or `LTISystem`). + + +Function names +-------------- + +* Function names are lower case with words separated by underscores. + +* Function names usually describe what they do + (`create_statefbk_iosystem`, `find_operating_points`) or what they + generate (`input_output_response`, `find_operating_point`). + +* Some abbreviations and shortened versions are used when names get + very long (e.g., `create_statefbk_iosystem` instead of + `create_state_feedback_input_output_system`. + +* Factory functions for I/O systems use short names (partly from MATLAB + conventions, partly because they are pretty frequently used): + `frd`, `flatsys`, `nlsys`, `ss`, and `tf`. + +* Short versions of common commands with longer names are created by + creating an object with the shorter name as a copy of the main + object: `bode = bode_plot`, `step = step_response`, etc. + +* The MATLAB compatibility library (`control.matlab`) uses names that + try to line up with MATLAB (e.g., `lsim` instead of `forced_response`). + + +Parameter names +--------------- + +Parameter names are not (yet) very uniform across the package. A few +general patterns are emerging: + +* Use longer description parameter names that describe the action or + role (e.g., `trajectory_constraints` and `print_summary` in + `optimal.solve_optimal_trajectory`. + +System-creating commands: + +* Commands that create an I/O system should allow the use of the + following standard parameters: + + - `name`: system name + + - `inputs`, `outputs`, `states`: number or names of inputs, outputs, state + + - `input_prefix`, `output_prefix`, `state_prefix`: change the default + prefixes used for naming signals. + + - `dt`: set the timebase. This one takes a bit of care, since if it is + not specified then it defaults to + `config.defaults['control.default_dt']`. This is different than + setting `dt` = None, so `dt` should always be part of `**kwargs`. + + These keywords can be parsed in a consistent way using the + `iosys._process_iosys_keywords` function. + +System arguments: + +* :code:`sys` when an argument is a single input/output system + (e.g. `bandwidth`). + +* `syslist` when an argument is a list of systems (e.g., + `interconnect`). A single system should also be OK. + +* `sysdata` when an argument can either be a system, a list of + systems, or data describing a response (e.g, `nyquist_response`). + + .. todo:: For a future release (v 0.11.x?) we should make this more + consistent across the package. + +Signal arguments: + +* Factory functions use `inputs`, `outputs`, and `states` to provide + either the number of each signal or a list of labels for the + signals. + +Order of arguments for functions taking inputs, outputs, state, time, +frequency, etc: + +* The default order for providing arguments in state space models is + ``(t, x, u, params)``. This is the generic order that should be + used in functions that take signals as parameters, but permuted so + that required arguments go first, common arguments go next (as + keywords, in the order listed above if they also work as positional + arguments), and infrequent arguments go last (in order listed + above). For example:: + + def model_update(t, x, u, params) + resp = initial_response(sys, timepts, x0) # x0 required + resp = input_output_response(sys, timepts, u, x0) # u required + resp = TimeResponseData( + timepts, outputs, states=states, inputs=inputs) + + In the last command, note that states precedes inputs because not + all TimeResponseData elements have inputs (e.g., `initial_response`). + +* The default order for providing arguments in the frequency domain is + system/response first, then frequency:: + + resp = frequency_response(sys, omega) + sys_frd = frd(sys_tf, omega) + sys = frd(response, omega) + +Time and frequency responses: + +* Use `timepts` for lists of times and `omega` for lists of + frequencies at which systems are evaluated. For example:: + + ioresp = ct.input_output_response(sys, timepts, U) + cplt = ct.bode_plot(sys, omega) + +* Use `inputs`, `outputs`, `states`, :code:`time` for time response + data attributes. These should be used as parameter names when + creating `TimeResponseData` objects and also as attributes when + retrieving response data (with dimensions dependent on `squeeze` + processing). These are stored internally in non-squeezed form using + `u`, `y`, `x`, and `t`, but the internal data should generally not + be accessed directly. For example:: + + plt.plot(ioresp.time, ioresp.outputs[0]) + tresp = ct.TimeResponseData(time, outputs, states, ...) # (internal call) + + - Note that the use of `inputs`, `outputs`, and `states` for both + factory function specifications as well as response function + attributes is a bit confusing. + +* Use `frdata`, `omega` for frequency response data attributes. These + should be used as parameter names when creating + `FrequencyResponseData` objects and also as attributes when + retrieving response data. The `frdata` attribute is stored as a 3D + array indexed by outputs, inputs, frequency. + +* Use `complex`, `magnitude`, `phase` for frequency response + data attributes with squeeze processing. For example:: + + ax = plt.subplots(2, 1) + ax[0].loglog(fresp.omega, fresp.magnitude) + ax[1].semilogx(fresp.omega, fresp.phase) + + - The frequency response is stored internally in non-squeezed form + as `fresp`, but this is generally not accessed directly by users. + + - Note that when creating time response data the independent + variable (time) is the first argument whereas for frequency + response data the independent variable (omega) is the second + argument. This is because we also create frequency response data + from a linear system using a call ``frd(sys, omega)``, and + rename frequency response data using a call ``frd(sys, + name='newname')``, so the system/data need to be the first + argument. For time response data we use the convention that we + start with time and then list the arguments in the most frequently + used order. + +* Use `response` or `resp` for generic response objects (time, + frequency, describing function, Nyquist, etc). + + - Note that when responses are evaluated as tuples, the ordering of + the dependent and independent variables switches between time and + frequency domain:: + + t, y = ct.step_response(sys) + mag, phase, omega = ct.frequency_response(sys) + + To avoid confusion, it is better to use response objects:: + + tresp = ct.step_response(sys) + t, y = tresp.time, tresp.outputs + + fresp = ct.frequency_response(sys) + omega, response = fresp.omega, fresp.response + mag, phase, omega = fresp.magnitude, fresp.phase, fresp.omega + + +Parameter aliases +----------------- + +As described above, parameter names are generally longer strings that +describe the purpose of the parameter. Similar to `matplotlib` (e.g., +the use of `lw` as an alias for `linewidth`), some commonly used +parameter names can be specified using an "alias" that allows the use +of a shorter key. + +Named parameter and keyword variable aliases are processed using the +:func:`config._process_kwargs` and :func:`config._process_param` +functions. These functions allow the specification of a list of +aliases and a list of legacy keys for a given named parameter or +keyword. To make use of these functions, the +:func:`~config._process_kwargs` is first called to update the `kwargs` +variable by replacing aliases with the full key:: + + _process_kwargs(kwargs, aliases) + +The values for named parameters can then be assigned to a local +variable using a call to :func:`~config._process_param` of the form:: + + var = _process_param('param', param, kwargs, aliases) + +where `param` is the named parameter used in the function signature +and var is the local variable in the function (may also be `param`, +but doesn't have to be). + +For example, the following structure is used in `input_output_response`:: + + def input_output_response( + sys, timepts=None, inputs=0., initial_state=0., params=None, + ignore_errors=False, transpose=False, return_states=False, + squeeze=None, solve_ivp_kwargs=None, evaluation_times='T', **kwargs): + """Compute the output response of a system to a given input. + + ... rest of docstring ... + + """ + _process_kwargs(kwargs, _timeresp_aliases) + T = _process_param('timepts', timepts, kwargs, _timeresp_aliases) + U = _process_param('inputs', inputs, kwargs, _timeresp_aliases, sigval=0.) + X0 = _process_param( + 'initial_state', initial_state, kwargs, _timeresp_aliases, sigval=0.) + +Note that named parameters that have a default value other than None +must given the signature value (`sigval`) so that +`~config._process_param` can detect if the value has been set (and +issue an error if there is an attempt to set the value multiple times +using alias or legacy keys). + +The alias mapping is a dictionary that returns a tuple consisting of +valid aliases and legacy aliases:: + + alias_mapping = { + 'argument_name_1': (['alias', ...], ['legacy', ...]), + ...} + +If an alias is present in the dictionary of keywords, it will be used +to set the value of the argument. If a legacy keyword is used, a +warning is issued. + +The following tables summarize the aliases that are currently in use +through the python-control package: + +Time response aliases (via `timeresp._timeresp_aliases`): + + .. list-table:: + :header-rows: 1 + + * - Key + - Aliases + - Legacy keys + - Comment + * - evaluation_times + - t_eval + - + - List of times to evaluate the time response (defaults to `timepts`). + * - final_output + - yfinal + - + - Final value of the output (used for :func:`step_info`) + * - initial_state + - X0 + - x0 + - Initial value of the state variable. + * - input_indices + - input + - + - Index(es) to use for the input (used in + :func:`step_response`, :func:`impulse_response`. + * - inputs + - U + - u + - Value(s) of the input variable (time trace or individual point). + * - output_indices + - output + - + - Index(es) to use for the output (used in + :func:`step_response`, :func:`impulse_response`. + * - outputs + - Y + - y + - Value(s) of the output variable (time trace or individual point). + * - return_states + - return_x + - + - Return the state when accessing a response via a tuple. + * - timepts + - T + - + - List of time points for time response functions. + * - timepts_num + - T_num + - + - Number of points to use (e.g., if `timepts` is just the final time). + +Optimal control aliases (via `optimal._optimal_aliases`: + + .. list-table:: + :header-rows: 1 + + * - Key + - Aliases + - Comment + * - final_state + - xf + - Final state for trajectory generation problems (flatsys, optimal). + * - final_input + - uf + - Final input for trajectory generation problems (flatsys). + * - initial_state + - x0, X0 + - Initial state for optimization problems (flatsys, optimal). + * - initial_input + - u0, U0 + - Initial input for trajectory generation problems (flatsys). + * - initial_time + - T0 + - Initial time for optimization problems. + * - integral_cost + - trajectory_cost, cost + - Cost function that is integrated along a trajectory. + * - return_states + - return_x + - Return the state when accessing a response via a tuple. + * - trajectory_constraints + - constraints + - List of constraints that hold along a trajectory (flatsys, optimal) + + +Documentation Guidelines +======================== + +The python-control package is documented using docstrings and Sphinx. +Reference documentation (class and function descriptions, with details +on parameters) should all go in docstrings. User documentation in +more narrative form should be in the `.rst` files in `doc/`, where it +can be incorporated into the User Guide. All significant +functionality should have a narrative description in the User Guide in +addition to docstrings. + +Generally speaking, standard Python and NumPy documentation +conventions are used throughout the package: + +* Python PEP 257 (docstrings): https://peps.python.org/pep-0257/ +* Numpydoc Style guide: https://numpydoc.readthedocs.io/en/latest/format.html + + +General docstring info +---------------------- + +The guiding principle used to guide how docstrings are written is +similar to NumPy (as articulated in the `numpydoc style guide +`_): + + A guiding principle is that human readers of the text are given + precedence over contorting docstrings so our tools produce nice + output. Rather than sacrificing the readability of the docstrings, + we have written pre-processors to assist Sphinx in its task. + +To that end, docstrings in `python-control` should use the following +guidelines: + +* Use single backticks around all Python objects. The Sphinx + configuration file (`doc/conf.py`) defines `default_role` to be + `py:obj`, so everything in a single backtick will be rendered in + code form and linked to the appropriate documentation if it exists. + + - Note: consistent with numpydoc recommendations, parameters names + for functions should be in single backticks, even though they + don't generate a link (but the font will still be OK). + + - The `doc/_static/custom.css` file defines the style for Python + objects and is configured so that linked objects will appear in a + bolder type, so that it is easier to see what things you can click + on to get more information. + + - By default, the string \`sys\` in docstrings would normally + generate a link to the :mod:`sys` Python module. To avoid this, + `conf.py` includes code that converts \`sys\` in docstrings to + \:code\:\`sys`, which renders as :code:`sys` (code style, with no + link). In ``.rst`` files this construction should be done + manually, since ``.rst`` files are not pre-processed as a + docstring. + +* Use double backticks for inline code, such as a Python code fragments. + + - In principle single backticks might actually work OK given the way + that the `py:obj` processing works in Sphinx, but the inclusion of + code is somewhat rare and the extra two backticks seem like a + small sacrifice (and far from a "contortion"). + +* Avoid the use of backticks and \:math\: for simple formulas where + the additional annotation or formatting does not add anything. For + example "-c <= x <= c" (without the double quotes) in + `relay_hysteresis_nonlinearity`. + + - Some of these formulas might be interpreted as Python code + fragments, but they only need to be in double quotes if that makes + the documentation easier to understand. + + - Examples: + + * \`dt\` > 0 not \`\`dt > 0\`\` (`dt` is a parameter) + * \`squeeze\` = True not \`\`squeeze = True\`\` nor squeeze = True. + * -c <= x <= c not \`\`-c <= x <= c\`\` nor \:math\:\`-c \\leq x + \\leq c`. + * \:math\:\`|x| < \\epsilon\` (becomes :math:`|x| < \epsilon`) + +* Built-in Python objects (True, False, None) should be written with no + backticks and should be properly capitalized. + + - Another possibility here is to use a single backtick around + built-in objects, and the `py:obj` processing will then generate a + link back to the primary Python documentation. That seems + distracting for built-ins like `True`, `False` and `None` (written + here in single backticks) and using double backticks looks fine in + Sphinx (``True``, ``False``, ``None``), but seemed to cross the + "contortions" threshold. + +* Strings used as arguments to parameters should be in single + (forward) ticks ('eval', 'rows', etc) and don't need to be rendered + as code if just listed as part of a docstring. + + - The rationale here is similar to built-ins: adding 4 backticks + just to get them in a code font seems unnecessary. + + - Note that if a string is included in Python assignment statement + (e.g., ``method='slycot'``) it looks quite ugly in text form to + have it enclosed in double backticks (\`\`method='slycot'\`\`), so + OK to use method='slycot' (no backticks) or `method` = 'slycot' + (backticks with extra spaces). + +* References to the `defaults` dictionary should be of the form + \`config.defaults['module.param']\` (like a parameter), which + renders as `config.defaults['module.param']` in Sphinx. + + - It would be nice to have the term show up as a link to the + documentation for that parameter (in the + :ref:`package-configuration-parameters` section of the Reference + Manual), but the special processing to do that hasn't been + implemented. + + - Depending on placement, you can end up with lots of white space + around defaults parameters (also true in the docstrings). + +* Math formulas can be written as plain text unless the require + special symbols (this is consistent with numpydoc) or include Python + code. Use the ``:math:`` directive to handle symbols. + +Examples of different styles: + +* Single backticks to a a function: `interconnect` + +* Single backticks to a parameter (no link): `squeeze` + +* Double backticks to a code fragment: ``subsys = sys[i][j]``. + +* Built-in Python objects: True, False, None + +* Defaults parameter: `config.defaults['control.squeeze_time_response']` + +* Inline math: :math:`\eta = m \xi + \beta` + + +Function docstrings +------------------- + +Follow numpydoc format with the following additional details: + +* All functions should have a short (< 64 character) summary line that + starts with a capital letter and ends with a period. + +* All parameter descriptions should start with a capital letter and + end with a period. An exception is parameters that have a list of + possible values, in which case a phrase sending in a colon (:) + followed by a list (without punctuation) is OK. + +* All parameters and keywords must be documented. The + `docstrings_test.py` unit test tries to flag as many of these as + possible. + +* Include an "Examples" section for all non-trivial functions, in a + form that can be checked by running `make doctest` in the `doc` + directory. This is also part of the CI checks. + +For functions that return a named tuple, bundle object, or class +instance, the return documentation should include the primary elements +of the return value:: + + Returns + ------- + resp : `TimeResponseData` + Input/output response data object. When accessed as a tuple, returns + ``time, outputs`` (default) or ``time, outputs, states`` if + `return_states` is True. The `~TimeResponseData.plot` method can be + used to create a plot of the time response(s) (see `time_response_plot` + for more information). + resp.time : array + Time values of the output. + resp.outputs : array + Response of the system. If the system is SISO and `squeeze` is not + True, the array is 1D (indexed by time). If the system is not SISO or + `squeeze` is False, the array is 2D (indexed by output and time). + resp.states : array + Time evolution of the state vector, represented as a 2D array indexed by + state and time. + resp.inputs : array + Input(s) to the system, indexed by input and time. + + +Class docstrings +---------------- + +Follow numpydoc format with the follow additional details: + +* Parameters used in creating an object go in the class docstring and + not in the `__init__` docstring (which is not included in the + Sphinx-based documentation). OK for the `__init__` function to have + no docstring. + +* Parameters that are also attributes only need to be documented once + (in the "Parameters" or "Additional Parameters" section of the class + docstring). + +* Attributes that are created within a class and that might be of + interest to the user should be documented in the "Attributes" + section of the class docstring. + +* Classes should not include a "Returns" section (since they always + return an instance of the class). + +* Functions and attributes that are not intended to be accessed by + users should start with an underscore. + +I/O system classes: + +* Subclasses of `InputOutputSystem` should always have a factory + function that is used to create them. The class documentation only + needs to document the required parameters; the full list of + parameters (and optional keywords) can and should be documented in + the factory function docstring. + + +User Guide +---------- + +The purpose of the User Guide is provide a *narrative* description of +the key functions of the package. It is not expected to cover every +command, but should allow someone who knows about control system +design to get up and running quickly. + +The User Guide consists of chapters that are each their own separate +`.rst` file and each of them generates a separate page. Chapters are +divided into sections whose names appear in the index on the left of +the web page when that chapter is being viewed. In some cases a +section may be in its own file, included in the chapter page by using +the `include` directive (see `nlsys.py` for an example). + +Sphinx files guidelines: + +* Each file should declare the `currentmodule` at or near the top of + the file. Except for subpackages (`control.flatsys`) and modules + that need to be imported separately (`control.optimal`), + `currentmodule` should be set to control. + +* When possible, sample code in the User Guide should use Sphinx + doctest directives so that the code is executed by `make doctest`. + Two styles are possible: doctest-style blocks (showing code with a + prompt and the expected response) and code blocks (using the + `testcode` directive). + +* When referring to the python-control package, several different forms + can be used: + + - Full name: "the Python Control Systems Library (python-control)" + (used sparingly, mainly at the tops of chapters). + + - Adjective form: "the python-control package" or "a python-control + module" (this is the most common form). + + - Noun form: "`python-control`" (only used occasionally). + +* Unlike docstrings, the documentation in the User Guide should use + backticks and \:math\: more liberally when it is appropriate to + highlight/format code properly. However, Python built-ins should + still just be written as True, False, and None (no backticks), for + formatting consistency. + + - The Sphinx documentation is not read in "raw" form, so OK to add + the additional annotations. + + - The Python built-ins occur frequently and are capitalized, and so + the additional formatting doesn't add much and would be + inconsistent if you jump from the User Guide to the Reference + Manual (e.g., to look at a function more closely via a link in the + User Guide). + + +Reference Manual +---------------- + +The Reference Manual should provide a fairly comprehensive description +of every class, function, and configuration variable in the package. +All primary functions and classes bust be included here, since the +Reference Manual generates the stub files used by Sphinx. + + +Modules and subpackages +----------------------- + +When documenting (independent) modules and subpackages (refereed to +here collectively as modules), use the following guidelines for +documentation: + +* In module docstrings, refer to module functions and classes without + including the module prefix. This will let Sphinx set up the links + to the functions in the proper way and has the advantage that it + keeps the docstrings shorter. + +* Objects in the parent (`control`) package should be referenced using + the `~control` prefix, so that Sphinx generates the links properly + (otherwise it only looks within the package). + +* In the User Guide, set ``currentmodule`` to ``control`` and refer to + the module objects using the prefix `~prefix` in the text portions + of the document but `px` (shortened prefix) in the code sections. + This will let users copy and past code from the examples and is + consistent with the use of the `ct` short prefix. Since this is in + the User Guide, the additional characters are not as big an issue. + +* If you include an `autosummary` of functions in the User Guide + section, list the functions using the regular prefix (without ``~``) + to remind everyone the function is in a module. + +* When referring to a module function or class in a docstring or User + Guide section that is not part of the module, use the fully + qualified function or class (\'prefix.function\'). + +The main overarching principle should be to make sure that references +to objects that have more detailed information should show up as a +link, not as code. + + +Utility Functions +================= + +The following utility functions can be used to help with standard +processing and parsing operations: + +.. autosummary:: + :toctree: generated/ + + config._process_legacy_keyword + config._process_kwargs + config._process_param + exception.cvxopt_check + exception.pandas_check + exception.slycot_check + iosys._process_iosys_keywords + mateqn._check_shape + statesp._convert_to_statespace + statesp._ssmatrix + xferfcn._convert_to_transfer_function + + +Sample Files +============ + + +Code template +------------- + +The following file is a template for a python-control module. It can +be found in `python-control/doc/examples/template.py`. + +.. literalinclude:: examples/template.py + :language: python + :linenos: + + +Documentation template +---------------------- + +The following file is a template for a documentation file. It can be +found in `python-control/doc/examples/template.rst`. + +.. literalinclude:: examples/template.rst + :language: text + :linenos: + :lines: 3- diff --git a/doc/examples.rst b/doc/examples.rst index 21364157e..2937fecab 100644 --- a/doc/examples.rst +++ b/doc/examples.rst @@ -1,16 +1,18 @@ -.. _examples: .. currentmodule:: control +.. _examples: + ******** Examples ******** -The source code for the examples below are available in the `examples/` -subdirectory of the source code distribution. They can also be accessed online -via the [python-control GitHub repository](https://github.com/python-control/python-control/tree/master/examples). +The source code for the examples below are available in the +`examples/` subdirectory of the source code distribution. They can +also be accessed online via the `python-control GitHub repository +`_. -Python scripts +Python Scripts ============== The following Python scripts document the use of a variety of methods in the @@ -20,23 +22,25 @@ other sources. .. toctree:: :maxdepth: 1 - secord-matlab - pvtol-nested - pvtol-lqr - rss-balred - phase_plane_plots - robust_siso - robust_mimo - scherer_etal_ex7_H2_h2syn - scherer_etal_ex7_Hinf_hinfsyn - cruise-control - steering-gainsched - steering-optimal - kincar-flatsys - mrac_siso_mit - mrac_siso_lyapunov - -Jupyter notebooks + examples/secord-matlab + examples/pvtol-nested + examples/pvtol-lqr + examples/rss-balred + examples/phase_plane_plots + examples/robust_siso + examples/robust_mimo + examples/scherer_etal_ex7_H2_h2syn + examples/scherer_etal_ex7_Hinf_hinfsyn + examples/cruise-control + examples/steering-gainsched + examples/steering-optimal + examples/kincar-flatsys + examples/mrac_siso_mit + examples/mrac_siso_lyapunov + examples/markov + examples/era_msd + +Jupyter Notebooks ================= The examples below use `python-control` in a Jupyter notebook environment. @@ -49,14 +53,28 @@ online sources. .. toctree:: :maxdepth: 1 - cruise - describing_functions - interconnect_tutorial - kincar-fusion - mhe-pvtol - mpc_aircraft - pvtol-lqr-nested - pvtol-outputfbk - simulating_discrete_nonlinear - steering - stochresp + examples/cruise + examples/describing_functions + examples/interconnect_tutorial + examples/mpc_aircraft + examples/pvtol-lqr-nested + examples/pvtol-outputfbk + examples/simulating_discrete_nonlinear + examples/steering + examples/stochresp + +Google Colab Notebooks +====================== + +A collection of Jupyter notebooks are available on `Google Colab +`_, where they can be executed +through a web browser: + +* `Caltech CDS 110 Google Colab notebooks + `_: + Jupyter notebooks created by Richard Murray for CDS 110 (Analysis + and Design of Feedback Systems) at Caltech. + +Note: in order to execute the Jupyter notebooks in this collection, +you will need a Google account that has access to the Google +Colaboratory application. diff --git a/doc/examples/.gitignore b/doc/examples/.gitignore new file mode 100644 index 000000000..87620ac7e --- /dev/null +++ b/doc/examples/.gitignore @@ -0,0 +1 @@ +.ipynb_checkpoints/ diff --git a/doc/examples/cruise-control.py b/doc/examples/cruise-control.py new file mode 120000 index 000000000..b232fda38 --- /dev/null +++ b/doc/examples/cruise-control.py @@ -0,0 +1 @@ +../../examples/cruise-control.py \ No newline at end of file diff --git a/doc/cruise-control.rst b/doc/examples/cruise-control.rst similarity index 100% rename from doc/cruise-control.rst rename to doc/examples/cruise-control.rst diff --git a/doc/examples/cruise.ipynb b/doc/examples/cruise.ipynb new file mode 120000 index 000000000..4e737aa10 --- /dev/null +++ b/doc/examples/cruise.ipynb @@ -0,0 +1 @@ +../../examples/cruise.ipynb \ No newline at end of file diff --git a/doc/examples/describing_functions.ipynb b/doc/examples/describing_functions.ipynb new file mode 120000 index 000000000..b45877fc1 --- /dev/null +++ b/doc/examples/describing_functions.ipynb @@ -0,0 +1 @@ +../../examples/describing_functions.ipynb \ No newline at end of file diff --git a/doc/examples/era_msd.py b/doc/examples/era_msd.py new file mode 120000 index 000000000..40783be13 --- /dev/null +++ b/doc/examples/era_msd.py @@ -0,0 +1 @@ +../../examples/era_msd.py \ No newline at end of file diff --git a/doc/examples/era_msd.rst b/doc/examples/era_msd.rst new file mode 100644 index 000000000..de702406e --- /dev/null +++ b/doc/examples/era_msd.rst @@ -0,0 +1,15 @@ +ERA example, mass spring damper system +-------------------------------------- + +Code +.... +.. literalinclude:: era_msd.py + :language: python + :linenos: + + +Notes +..... + +1. The environment variable `PYCONTROL_TEST_EXAMPLES` is used for +testing to turn off plotting of the outputs.0 \ No newline at end of file diff --git a/doc/examples/interconnect_tutorial.ipynb b/doc/examples/interconnect_tutorial.ipynb new file mode 120000 index 000000000..69b840e70 --- /dev/null +++ b/doc/examples/interconnect_tutorial.ipynb @@ -0,0 +1 @@ +../../examples/interconnect_tutorial.ipynb \ No newline at end of file diff --git a/doc/examples/kalman-pvtol.ipynb b/doc/examples/kalman-pvtol.ipynb new file mode 100644 index 000000000..cef836d09 --- /dev/null +++ b/doc/examples/kalman-pvtol.ipynb @@ -0,0 +1,625 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c017196f", + "metadata": {}, + "source": [ + "# Extended Kalman filter example (PVTOL)\n", + "\n", + "This notebook illustrates the implementation of an extended Kalman filter and the use of the estimated state for LQR feedback." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "544525ab", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "import control as ct" + ] + }, + { + "cell_type": "markdown", + "id": "859834cf", + "metadata": {}, + "source": [ + "## System definition\n", + "\n", + "We consider the dynamics of a planar vertical takeoff and landing (PVTOL) aircraft model:\n", + "\n", + "![PVTOL diagram](https://murray.cds.caltech.edu/images/murray.cds/7/7d/Pvtol-diagram.png)\n", + "\n", + "The dynamics of the system with disturbances on the $x$ and $y$ variables is given by\n", + "$$\n", + " \\begin{aligned}\n", + " m \\ddot x &= F_1 \\cos\\theta - F_2 \\sin\\theta - c \\dot x + d_x, \\\\\n", + " m \\ddot y &= F_1 \\sin\\theta + F_2 \\cos\\theta - c \\dot y - m g + d_y, \\\\\n", + " J \\ddot \\theta &= r F_1.\n", + " \\end{aligned}\n", + "$$\n", + "The measured values of the system are the position and orientation,\n", + "with added noise $n_x$, $n_y$, and $n_\\theta$:\n", + "$$\n", + " \\vec y = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\end{bmatrix} + \n", + " \\begin{bmatrix} n_x \\\\ n_y \\\\ n_z \\end{bmatrix}.\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ffafed74", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": pvtol\n", + "Inputs (2): ['F1', 'F2']\n", + "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "Parameters: ['m', 'J', 'r', 'g', 'c']\n", + "\n", + "Update: \n", + "Output: \n", + "\n", + "Forward: \n", + "Reverse: \n", + "\n", + ": pvtol_noisy\n", + "Inputs (7): ['F1', 'F2', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", + "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "\n", + "Update: \n", + "Output: \n" + ] + } + ], + "source": [ + "# pvtol = nominal system (no disturbances or noise)\n", + "# noisy_pvtol = pvtol w/ process disturbances and sensor noise\n", + "from pvtol import pvtol, pvtol_noisy, plot_results\n", + "\n", + "# Find the equilibrium point corresponding to the origin\n", + "xe, ue = ct.find_operating_point(\n", + " pvtol, np.zeros(pvtol.nstates),\n", + " np.zeros(pvtol.ninputs), [0, 0, 0, 0, 0, 0],\n", + " iu=range(2, pvtol.ninputs), iy=[0, 1])\n", + "\n", + "x0, u0 = ct.find_operating_point(\n", + " pvtol, np.zeros(pvtol.nstates),\n", + " np.zeros(pvtol.ninputs), np.array([2, 1, 0, 0, 0, 0]),\n", + " iu=range(2, pvtol.ninputs), iy=[0, 1])\n", + "\n", + "# Extract the linearization for use in LQR design\n", + "pvtol_lin = pvtol.linearize(xe, ue)\n", + "A, B = pvtol_lin.A, pvtol_lin.B\n", + "\n", + "print(pvtol, \"\\n\")\n", + "print(pvtol_noisy)" + ] + }, + { + "cell_type": "markdown", + "id": "2b63bf5b", + "metadata": {}, + "source": [ + "We now define the properties of the noise and disturbances. To make things (a bit more) interesting, we include some cross terms between the noise in $\\theta$ and the noise in $x$ and $y$:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1e1ee7c9", + "metadata": {}, + "outputs": [], + "source": [ + "# Disturbance and noise intensities\n", + "Qv = np.diag([1e-2, 1e-2])\n", + "Qw = np.array([[2e-4, 0, 1e-5], [0, 2e-4, 1e-5], [1e-5, 1e-5, 1e-4]])\n", + "Qwinv = np.linalg.inv(Qw)\n", + "\n", + "# Initial state covariance\n", + "P0 = np.eye(pvtol.nstates)" + ] + }, + { + "cell_type": "markdown", + "id": "e4c52c73", + "metadata": {}, + "source": [ + "## Control system design\n", + "\n", + "To design the control system, we first construct an estimator for the state (given the commanded inputs and measured outputs). Since this is a nonlinear system, we use the update law for the nominal system to compute the state update. We also make use of the linearization around the current state for the covariance update (using the function `pvtol.A(x, u)`, which is defined in `pvtol.py`, making this an extended Kalman filter)." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3647bf15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": sys[1]\n", + "Inputs (8): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'F1', 'F2']\n", + "Outputs (6): ['xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "States (42): ['x[0]', 'x[1]', 'x[2]', 'x[3]', 'x[4]', 'x[5]', 'x[6]', 'x[7]', 'x[8]', 'x[9]', 'x[10]', 'x[11]', 'x[12]', 'x[13]', 'x[14]', 'x[15]', 'x[16]', 'x[17]', 'x[18]', 'x[19]', 'x[20]', 'x[21]', 'x[22]', 'x[23]', 'x[24]', 'x[25]', 'x[26]', 'x[27]', 'x[28]', 'x[29]', 'x[30]', 'x[31]', 'x[32]', 'x[33]', 'x[34]', 'x[35]', 'x[36]', 'x[37]', 'x[38]', 'x[39]', 'x[40]', 'x[41]']\n", + "\n", + "Update: \n", + "Output: \n" + ] + } + ], + "source": [ + "# Define the disturbance input and measured output matrices\n", + "F = np.array([[0, 0], [0, 0], [0, 0], [1/pvtol.params['m'], 0], [0, 1/pvtol.params['m']], [0, 0]])\n", + "C = np.eye(3, 6)\n", + "\n", + "# Estimator update law\n", + "def estimator_update(t, x, u, params):\n", + " # Extract the states of the estimator\n", + " xhat = x[0:pvtol.nstates]\n", + " P = x[pvtol.nstates:].reshape(pvtol.nstates, pvtol.nstates)\n", + "\n", + " # Extract the inputs to the estimator\n", + " y = u[0:3] # just grab the first three outputs\n", + " u = u[6:8] # get the inputs that were applied as well\n", + "\n", + " # Compute the linearization at the current state\n", + " A = pvtol.A(xhat, u) # A matrix depends on current state\n", + " # A = pvtol.A(xe, ue) # Fixed A matrix (for testing/comparison)\n", + " \n", + " # Compute the optimal again\n", + " L = P @ C.T @ Qwinv\n", + "\n", + " # Update the state estimate\n", + " xhatdot = pvtol.updfcn(t, xhat, u, params) - L @ (C @ xhat - y)\n", + "\n", + " # Update the covariance\n", + " Pdot = A @ P + P @ A.T - P @ C.T @ Qwinv @ C @ P + F @ Qv @ F.T\n", + "\n", + " # Return the derivative\n", + " return np.hstack([xhatdot, Pdot.reshape(-1)])\n", + "\n", + "def estimator_output(t, x, u, params):\n", + " # Return the estimator states\n", + " return x[0:pvtol.nstates]\n", + "\n", + "estimator = ct.NonlinearIOSystem(\n", + " estimator_update, estimator_output,\n", + " states=pvtol.nstates + pvtol.nstates**2,\n", + " inputs= pvtol_noisy.output_labels \\\n", + " + pvtol_noisy.input_labels[0:pvtol.ninputs],\n", + " outputs=[f'xh{i}' for i in range(pvtol.nstates)],\n", + ")\n", + "print(estimator)" + ] + }, + { + "cell_type": "markdown", + "id": "ba3d2640", + "metadata": {}, + "source": [ + "For the controller, we will use an LQR feedback with physically motivated weights (see OBC, Example 3.5):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9787db61", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": sys[2]\n", + "Inputs (14): ['xd[0]', 'xd[1]', 'xd[2]', 'xd[3]', 'xd[4]', 'xd[5]', 'ud[0]', 'ud[1]', 'xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "Outputs (2): ['F1', 'F2']\n", + "States (0): []\n", + "\n", + "A = []\n", + "\n", + "B = []\n", + "\n", + "C = []\n", + "\n", + "D = [[-3.16227766e+00 -1.31948922e-07 8.67680175e+00 -2.35855555e+00\n", + " -6.98881821e-08 1.91220852e+00 1.00000000e+00 0.00000000e+00\n", + " 3.16227766e+00 1.31948922e-07 -8.67680175e+00 2.35855555e+00\n", + " 6.98881821e-08 -1.91220852e+00]\n", + " [-1.31948921e-06 3.16227766e+00 -2.32324826e-07 -2.36396240e-06\n", + " 4.97998224e+00 7.90913276e-08 0.00000000e+00 1.00000000e+00\n", + " 1.31948921e-06 -3.16227766e+00 2.32324826e-07 2.36396240e-06\n", + " -4.97998224e+00 -7.90913276e-08]] \n", + "\n", + ": sys[3]\n", + "Inputs (13): ['xd[0]', 'xd[1]', 'xd[2]', 'xd[3]', 'xd[4]', 'xd[5]', 'ud[0]', 'ud[1]', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", + "Outputs (14): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'F1', 'F2', 'xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "States (48): ['pvtol_noisy_x0', 'pvtol_noisy_x1', 'pvtol_noisy_x2', 'pvtol_noisy_x3', 'pvtol_noisy_x4', 'pvtol_noisy_x5', 'sys[1]_x[0]', 'sys[1]_x[1]', 'sys[1]_x[2]', 'sys[1]_x[3]', 'sys[1]_x[4]', 'sys[1]_x[5]', 'sys[1]_x[6]', 'sys[1]_x[7]', 'sys[1]_x[8]', 'sys[1]_x[9]', 'sys[1]_x[10]', 'sys[1]_x[11]', 'sys[1]_x[12]', 'sys[1]_x[13]', 'sys[1]_x[14]', 'sys[1]_x[15]', 'sys[1]_x[16]', 'sys[1]_x[17]', 'sys[1]_x[18]', 'sys[1]_x[19]', 'sys[1]_x[20]', 'sys[1]_x[21]', 'sys[1]_x[22]', 'sys[1]_x[23]', 'sys[1]_x[24]', 'sys[1]_x[25]', 'sys[1]_x[26]', 'sys[1]_x[27]', 'sys[1]_x[28]', 'sys[1]_x[29]', 'sys[1]_x[30]', 'sys[1]_x[31]', 'sys[1]_x[32]', 'sys[1]_x[33]', 'sys[1]_x[34]', 'sys[1]_x[35]', 'sys[1]_x[36]', 'sys[1]_x[37]', 'sys[1]_x[38]', 'sys[1]_x[39]', 'sys[1]_x[40]', 'sys[1]_x[41]']\n", + "\n", + "Subsystems (3):\n", + " * ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']>\n", + " * ['F1',\n", + " 'F2']>\n", + " * ['xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']>\n", + "\n", + "Connections:\n", + " * pvtol_noisy.F1 <- sys[2].F1\n", + " * pvtol_noisy.F2 <- sys[2].F2\n", + " * pvtol_noisy.Dx <- Dx\n", + " * pvtol_noisy.Dy <- Dy\n", + " * pvtol_noisy.Nx <- Nx\n", + " * pvtol_noisy.Ny <- Ny\n", + " * pvtol_noisy.Nth <- Nth\n", + " * sys[2].xd[0] <- xd[0]\n", + " * sys[2].xd[1] <- xd[1]\n", + " * sys[2].xd[2] <- xd[2]\n", + " * sys[2].xd[3] <- xd[3]\n", + " * sys[2].xd[4] <- xd[4]\n", + " * sys[2].xd[5] <- xd[5]\n", + " * sys[2].ud[0] <- ud[0]\n", + " * sys[2].ud[1] <- ud[1]\n", + " * sys[2].xh0 <- sys[1].xh0\n", + " * sys[2].xh1 <- sys[1].xh1\n", + " * sys[2].xh2 <- sys[1].xh2\n", + " * sys[2].xh3 <- sys[1].xh3\n", + " * sys[2].xh4 <- sys[1].xh4\n", + " * sys[2].xh5 <- sys[1].xh5\n", + " * sys[1].x0 <- pvtol_noisy.x0\n", + " * sys[1].x1 <- pvtol_noisy.x1\n", + " * sys[1].x2 <- pvtol_noisy.x2\n", + " * sys[1].x3 <- pvtol_noisy.x3\n", + " * sys[1].x4 <- pvtol_noisy.x4\n", + " * sys[1].x5 <- pvtol_noisy.x5\n", + " * sys[1].F1 <- sys[2].F1\n", + " * sys[1].F2 <- sys[2].F2\n", + "\n", + "Outputs:\n", + " * x0 <- pvtol_noisy.x0\n", + " * x1 <- pvtol_noisy.x1\n", + " * x2 <- pvtol_noisy.x2\n", + " * x3 <- pvtol_noisy.x3\n", + " * x4 <- pvtol_noisy.x4\n", + " * x5 <- pvtol_noisy.x5\n", + " * F1 <- sys[2].F1\n", + " * F2 <- sys[2].F2\n", + " * xh0 <- sys[1].xh0\n", + " * xh1 <- sys[1].xh1\n", + " * xh2 <- sys[1].xh2\n", + " * xh3 <- sys[1].xh3\n", + " * xh4 <- sys[1].xh4\n", + " * xh5 <- sys[1].xh5\n" + ] + } + ], + "source": [ + "#\n", + "# LQR design w/ physically motivated weighting\n", + "#\n", + "# Shoot for 1 cm error in x, 10 cm error in y. Try to keep the angle\n", + "# less than 5 degrees in making the adjustments. Penalize side forces\n", + "# due to loss in efficiency.\n", + "#\n", + "\n", + "Qx = np.diag([100, 10, (180/np.pi) / 5, 0, 0, 0])\n", + "Qu = np.diag([10, 1])\n", + "K, _, _ = ct.lqr(A, B, Qx, Qu)\n", + "\n", + "#\n", + "# Control system construction: combine LQR w/ EKF\n", + "#\n", + "# Use the linearization around the origin to design the optimal gains\n", + "# to see how they compare to the final value of P for the EKF\n", + "#\n", + "\n", + "# Construct the state feedback controller with estimated state as input\n", + "statefbk, _ = ct.create_statefbk_iosystem(pvtol, K, estimator=estimator)\n", + "print(statefbk, \"\\n\")\n", + "\n", + "# Reconstruct the control system with the noisy version of the process\n", + "# Create a closed loop system around the controller\n", + "clsys = ct.interconnect(\n", + " [pvtol_noisy, statefbk, estimator],\n", + " inplist = statefbk.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " inputs = statefbk.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " outlist = pvtol.output_labels + statefbk.output_labels + estimator.output_labels,\n", + " outputs = pvtol.output_labels + statefbk.output_labels + estimator.output_labels\n", + ")\n", + "print(clsys)" + ] + }, + { + "cell_type": "markdown", + "id": "5f527f16", + "metadata": {}, + "source": [ + "Note that we have to construct the closed loop system manually since we need to allow the disturbance and noise inputs to be sent to the closed loop system and `create_statefbk_iosystem` does not support this (to be fixed in an upcoming release)." + ] + }, + { + "cell_type": "markdown", + "id": "7bf558a0", + "metadata": {}, + "source": [ + "## Simulations\n", + "\n", + "Finally, we can simulate the system to see how it all works. We start by creating the noise for the system:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c2583a0e", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create the time vector for the simulation\n", + "Tf = 10\n", + "timepts = np.linspace(0, Tf, 1000)\n", + "\n", + "# Create representative process disturbance and sensor noise vectors\n", + "np.random.seed(117) # avoid figures changing from run to run\n", + "V = ct.white_noise(timepts, Qv) # smaller disturbances and noise then design\n", + "W = ct.white_noise(timepts, Qw)\n", + "plt.plot(timepts, V[0], label=\"V[0]\")\n", + "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.xlabel(\"Time $t$ [s]\")\n", + "plt.ylabel(\"Disturbance, sensor noise\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "4d944709", + "metadata": {}, + "source": [ + "### LQR with EKF\n", + "\n", + "We can now feed the desired trajectory plus the noise and disturbances into the system and see how well the controller with a state estimator does in holding the system at an equilibrium point:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "ad7a9750", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Put together the input for the system\n", + "U = [xe, ue, V, W]\n", + "X0 = [x0, xe, P0.reshape(-1)]\n", + "\n", + "# Initial condition response\n", + "resp = ct.input_output_response(clsys, timepts, U, X0)\n", + "\n", + "# Plot the response\n", + "plot_results(timepts, resp.states, resp.outputs[pvtol.nstates:])" + ] + }, + { + "cell_type": "markdown", + "id": "86f10064", + "metadata": {}, + "source": [ + "To see how well the estimtator did, we can compare the estimated position with the actual position:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c5f24119", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Response of the first two states, including internal estimates\n", + "h1, = plt.plot(resp.time, resp.outputs[0], 'b-', linewidth=0.75)\n", + "h2, = plt.plot(resp.time, resp.outputs[1], 'r-', linewidth=0.75)\n", + "\n", + "# Add on the internal estimator states\n", + "xh0 = clsys.find_output('xh0')\n", + "xh1 = clsys.find_output('xh1')\n", + "h3, = plt.plot(resp.time, resp.outputs[xh0], 'k--')\n", + "h4, = plt.plot(resp.time, resp.outputs[xh1], 'k--')\n", + "\n", + "plt.plot([0, 10], [0, 0], 'k--', linewidth=0.5)\n", + "plt.ylabel(r\"Position $x$, $y$ [m]\")\n", + "plt.xlabel(r\"Time $t$ [s]\")\n", + "plt.legend(\n", + " [h1, h2, h3, h4], ['$x$', '$y$', r'$\\hat{x}$', r'$\\hat{y}$'], \n", + " loc='upper right', frameon=False, ncol=2);" + ] + }, + { + "cell_type": "markdown", + "id": "7139202f", + "metadata": {}, + "source": [ + "Note the rapid convergence of the estimate to the proper value, since we are directly measuring the position variables. If we look at the full set of states, we see that other variables have different convergence properties:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "78a61e74", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axs = plt.subplots(2, 3)\n", + "var = ['x', 'y', r'\\theta', r'\\dot x', r'\\dot y', r'\\dot \\theta']\n", + "for i in [0, 1]:\n", + " for j in [0, 1, 2]:\n", + " k = i * 3 + j\n", + " axs[i, j].plot(resp.time, resp.outputs[k], label=f'${var[k]}$')\n", + " axs[i, j].plot(resp.time, resp.outputs[xh0+k], label=f'$\\\\hat {var[k]}$')\n", + " axs[i, j].legend()\n", + " if i == 1:\n", + " axs[i, j].set_xlabel(\"Time $t$ [s]\")\n", + " if j == 0:\n", + " axs[i, j].set_ylabel(\"State\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2039578e", + "metadata": {}, + "source": [ + "Note the (slight) lag in tracking changes in the $\\dot x$ and $\\dot y$ states (varies from simulation to simulation, depending on the specific noise signal)." + ] + }, + { + "cell_type": "markdown", + "id": "0c0d5c99", + "metadata": {}, + "source": [ + "### Full state feedback\n", + "\n", + "To see how the inclusion of the estimator affects the system performance, we compare it with the case where we are able to directly measure the state of the system." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3b6a1f1c", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/python-control/murrayrm/control/statefbk.py:788: UserWarning: cannot verify system output is system state\n", + " warnings.warn(\"cannot verify system output is system state\")\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Compute the full state feedback solution\n", + "lqr_ctrl, _ = ct.create_statefbk_iosystem(pvtol, K)\n", + "\n", + "lqr_clsys = ct.interconnect(\n", + " [pvtol_noisy, lqr_ctrl],\n", + " inplist = lqr_ctrl.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " inputs = lqr_ctrl.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " outlist = pvtol.output_labels + lqr_ctrl.output_labels,\n", + " outputs = pvtol.output_labels + lqr_ctrl.output_labels\n", + ")\n", + "\n", + "# Put together the input for the system (turn off sensor noise)\n", + "U = [xe, ue, V, W*0]\n", + "\n", + "# Run a simulation with full state feedback\n", + "lqr_resp = ct.input_output_response(lqr_clsys, timepts, U, x0)\n", + "\n", + "# Compare the results\n", + "plt.plot(resp.states[0], resp.states[1], 'b-', label=\"Extended KF\")\n", + "plt.plot(lqr_resp.states[0], lqr_resp.states[1], 'r-', label=\"Full state\")\n", + "\n", + "plt.xlabel('$x$ [m]')\n", + "plt.ylabel('$y$ [m]')\n", + "plt.axis('equal')\n", + "plt.legend(frameon=False);" + ] + }, + { + "cell_type": "markdown", + "id": "ffd7d082-2add-4440-99d9-2bab551b51a0", + "metadata": {}, + "source": [ + "The warning here can be ignored. It comes from the way that the `pvtol` dynamics are defined." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/examples/kincar-flatsys.py b/doc/examples/kincar-flatsys.py new file mode 120000 index 000000000..287ee0065 --- /dev/null +++ b/doc/examples/kincar-flatsys.py @@ -0,0 +1 @@ +../../examples/kincar-flatsys.py \ No newline at end of file diff --git a/doc/kincar-flatsys.rst b/doc/examples/kincar-flatsys.rst similarity index 100% rename from doc/kincar-flatsys.rst rename to doc/examples/kincar-flatsys.rst diff --git a/doc/examples/kincar-fusion.ipynb b/doc/examples/kincar-fusion.ipynb new file mode 120000 index 000000000..5e6002937 --- /dev/null +++ b/doc/examples/kincar-fusion.ipynb @@ -0,0 +1 @@ +../../examples/kincar-fusion.ipynb \ No newline at end of file diff --git a/doc/examples/markov.py b/doc/examples/markov.py new file mode 120000 index 000000000..39015d0c9 --- /dev/null +++ b/doc/examples/markov.py @@ -0,0 +1 @@ +../../examples/markov.py \ No newline at end of file diff --git a/doc/examples/markov.rst b/doc/examples/markov.rst new file mode 100644 index 000000000..36e0fd8e5 --- /dev/null +++ b/doc/examples/markov.rst @@ -0,0 +1,15 @@ +Estimation of Makrov parameters +------------------------------- + +Code +.... +.. literalinclude:: markov.py + :language: python + :linenos: + + +Notes +..... + +1. The environment variable `PYCONTROL_TEST_EXAMPLES` is used for +testing to turn off plotting of the outputs.0 \ No newline at end of file diff --git a/doc/examples/mhe-pvtol.ipynb b/doc/examples/mhe-pvtol.ipynb new file mode 120000 index 000000000..fcbf4577b --- /dev/null +++ b/doc/examples/mhe-pvtol.ipynb @@ -0,0 +1 @@ +../../examples/mhe-pvtol.ipynb \ No newline at end of file diff --git a/doc/examples/mpc_aircraft.ipynb b/doc/examples/mpc_aircraft.ipynb new file mode 120000 index 000000000..f5664d841 --- /dev/null +++ b/doc/examples/mpc_aircraft.ipynb @@ -0,0 +1 @@ +../../examples/mpc_aircraft.ipynb \ No newline at end of file diff --git a/doc/examples/mrac_siso_lyapunov.py b/doc/examples/mrac_siso_lyapunov.py new file mode 120000 index 000000000..6dea0c1bb --- /dev/null +++ b/doc/examples/mrac_siso_lyapunov.py @@ -0,0 +1 @@ +../../examples/mrac_siso_lyapunov.py \ No newline at end of file diff --git a/doc/mrac_siso_lyapunov.rst b/doc/examples/mrac_siso_lyapunov.rst similarity index 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a/doc/examples/stochresp.ipynb b/doc/examples/stochresp.ipynb new file mode 120000 index 000000000..56db315a2 --- /dev/null +++ b/doc/examples/stochresp.ipynb @@ -0,0 +1 @@ +../../examples/stochresp.ipynb \ No newline at end of file diff --git a/doc/examples/template.py b/doc/examples/template.py new file mode 100644 index 000000000..77da7e1a1 --- /dev/null +++ b/doc/examples/template.py @@ -0,0 +1,168 @@ +# template.py - template file for python-control module +# RMM, 3 Jan 2024 + +"""Template file for python-control module. + +This file provides a template that can be used when creating a new +file/module in python-control. The key elements of a module are included +in this template, following the suggestions in the Developer Guidelines. + +The first line of a module file should be the name of the file and a short +description. The next few lines can contain information about who created +the file (your name/initials and date). For this file I used the short +version (initials, date), but a longer version would be to do something of +the form:: + + # filename.py - short one line description + # + # Initial author: Full name + # Creation date: date the file was created + +After the header comments, the next item is the module docstring, which +should be a multi-line comment, like this one. The first line of the +comment is a one line summary phrase, starting with a capital letter and +ending in a period (often the same as the line at the very top). The rest +of the docstring is an extended summary (this one is a bit longer than +would be typical). + +After the docstring, you should have the following elements (in Python): + + * Package imports, using the `isort -m2` format (library, standard, custom) + * __all__ command, listing public objects in the file + * Class definitions (if any) + * Public function definitions + * Internal function definitions (starting with '_') + * Function aliases (short = long_name) + +The rest of this file contains examples of these elements. + +""" + +import warnings # Python packages + +import numpy as np # Standard external packages + +from . import config # Other modules/packages in python-control +from .lti import LTI # Public function or class from a module + +__all__ = ['SampleClass', 'sample_function'] + + +class SampleClass(): + """Sample class in the python-control package. + + This is an example of a class definition. The docstring follows + numpydoc format. The first line should be a summary (which will show + up in `autosummary` entries in the Sphinx documentation) and then an + extended summary describing what the class does. Then the usual + sections, per numpydoc. + + Additional guidelines on what should be listed in the various sections + can be found in the 'Class docstrings' section of the Developer + Guidelines. + + Parameters + ---------- + sys : InputOutputSystem + Short description of the parameter. + + Attributes + ---------- + data : array + Short description of an attribute. + + """ + def __init__(self, sys): + # No docstring required here + self.sys = sys # Parameter passed as argument + self.data = sys.name # Attribute created within class + + def sample_method(self, data): + """Sample method within a class. + + This is an example of a method within a class. Document using + numpydoc format. + + """ + return None + + +def sample_function(data, option=False, **kwargs): + """Sample function in the template module. + + This is an example of a public function within the template module. + This function will usually be placed in the `control` namespace by + updating `__init__.py` to import the function (often by importing the + entire module). + + Docstring should be in standard numpydoc format. The extended summary + (this text) should describe the basic operation of the function, with + technical details in the "Notes" section. + + Parameters + ---------- + data : array + Sample parameter for sample function, with short docstring. + option : bool, optional + Optional parameter, with default value `False`. + + Returns + ------- + out : float + Short description of the function output. + + Additional Parameters + --------------------- + inputs : int, str, or list of str + Parameters that are less commonly used, in this case a keyword + parameter. + + See Also + -------- + function1, function2 + + Notes + ----- + This section can contain a more detailed description of how the system + works. OK to include some limited mathematics, either via inline math + directions for a short formula (like this: ..math:`x = \alpha y`) or via a + displayed equation: + + ..math:: + + a = \int_0^t f(t) dt + + The trick in the docstring is to write something that looks good in + pure text format but is also processed by sphinx correctly. + + If you refer to parameters, such as the `data` argument to this + function, but them in single backticks (which will render them in code + style in Sphinx). Strings that should be interpreted as Python code + use double backticks: ``mag, phase, omega = response``. Python + built-in objects, like True, False, and None are written on their own. + + """ + inputs = kwargs['inputs'] + if option is True: + return data + else: + return None + +# +# Internal functions +# +# Functions that are not intended for public use can go anyplace, but I +# usually put them at the bottom of the file (out of the way). Their name +# should start with an underscore. Docstrings are optional, but if you +# don't include a docstring, make sure to include comments describing how +# the function works. +# + + +# Sample internal function to process data +def _internal_function(data): + return None + + +# Aliases (short versions of long function names) +sf = sample_function diff --git a/doc/examples/template.rst b/doc/examples/template.rst new file mode 100644 index 000000000..f9abacede --- /dev/null +++ b/doc/examples/template.rst @@ -0,0 +1,95 @@ +:orphan: remove this line and the next before use (supresses toctree warning) + +.. currentmodule:: control + +************** +Sample Chapter +************** + +This is an example of a top-level documentation file, which serves a +chapter in the User Guide or Reference Manual in the Sphinx +documentation. It is not that likely we will create a lot more files +of this sort, so it is probably the internal structure of the file +that is most useful. + +The file in which a chapter is contained will usual start by declaring +`currentmodule` to be `control`, which will allow text enclosed in +backticks to be searched for class and function names and appropriate +links inserted. The next element of the file is the chapter name, +with asterisks above and below. Chapters should have a capitalized +title and an introductory paragraph. If you need to add a reference +to a chapter, insert a sphinx reference (`.. _ch-sample:`) above +the chapter title. + +.. _sec-sample: + +Sample Section +============== + +A chapter is made of up of multiple sections. Sections use equal +signs below the section title. Following FBS2e, the section title +should be capitalized. If you need to insert a reference to the +section, put that above the section title (`.. _sec-sample:`), as +shown here. + + +Sample subsection +----------------- + +Subsections use dashes below the subsection title. The first word of +the title should be capitalized, but the rest of the subsection title +is lower case (unless it has a proper noun). I usually leave two +blank lines before the start up a subection and one blank line after +the section markers. + + +Mathematics +----------- + +Mathematics can be uncluded using the `math` directive. This can be +done inline using `:math:short formula` (e.g. :math:`a = b`) or as a +displayed equation, using the `.. math::` directive:: + +.. math:: + + a(t) = \int_0^t b(\tau) d\tau + + +Function summaries +------------------ + +Use the `autosummary` directive to include a table with a list of +function sinatures and summary descriptions:: + +.. autosummary:: + + input_output_response + describing_function + some_other_function + + +Module summaries +---------------- + +If you have a docstring at the top of a module that you want to pull +into the documentation, you can do that with the `automodule` +directive: + +.. automodule:: control.optimal + :noindex: + :no-members: + :no-inherited-members: + :no-special-members: + +.. currentmodule:: control + +The `:noindex:` option gets rid of warnings about a module being +indexed twice. The next three options are used to just bring in the +summary and extended summary in the module docstring, without +including all of the documentation of the classes and functions in the +module. + +Note that we `automodule` will set the current module to the one for +which you just generated documentation, so the `currentmodule` should +be reset to control afterwards (otherwise references to functions in +the `control` namespace won't be recognized. diff --git a/doc/examples/vehicle-steering.png b/doc/examples/vehicle-steering.png new file mode 120000 index 000000000..c568707da --- /dev/null +++ b/doc/examples/vehicle-steering.png @@ -0,0 +1 @@ +../../examples/vehicle-steering.png \ No newline at end of file diff --git a/doc/figures/Makefile b/doc/figures/Makefile new file mode 100644 index 000000000..1ca54b372 --- /dev/null +++ b/doc/figures/Makefile @@ -0,0 +1,16 @@ +# Makefile- rules to create figures +# RMM, 26 Dec 2024 + +# List of figures that need to be created (first figure 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Binary files /dev/null and b/doc/figures/xferfcn-delay-compare.png differ diff --git a/doc/flatsys.rst b/doc/flatsys.rst index 2ed873b23..dda35d9a3 100644 --- a/doc/flatsys.rst +++ b/doc/flatsys.rst @@ -1,34 +1,41 @@ +.. currentmodule:: control + .. _flatsys-module: -*************************** -Differentially flat systems -*************************** +Differentially Flat Systems +=========================== + +The `flatsys` subpackage contains a set of classes and functions to +compute trajectories for differentially flat systems. The objects in +this subpackage must be explicitly imported:: + + import control as ct + import control.flatsys as fs -.. automodule:: control.flatsys - :no-members: - :no-inherited-members: - :no-special-members: Overview of differential flatness -================================= +--------------------------------- A nonlinear differential equation of the form .. math:: - \dot x = f(x, u), \qquad x \in R^n, u \in R^m + + \dot x = f(x, u), \qquad x \in R^n, u \in R^m is *differentially flat* if there exists a function :math:`\alpha` such that .. math:: - z = \alpha(x, u, \dot u\, \dots, u^{(p)}) + + z = \alpha(x, u, \dot u\, \dots, u^{(p)}) and we can write the solutions of the nonlinear system as functions of :math:`z` and a finite number of derivatives .. math:: - x &= \beta(z, \dot z, \dots, z^{(q)}) \\ - u &= \gamma(z, \dot z, \dots, z^{(q)}). - :label: flat2state + :label: flat2state + + x &= \beta(z, \dot z, \dots, z^{(q)}) \\ + u &= \gamma(z, \dot z, \dots, z^{(q)}). For a differentially flat system, all of the feasible trajectories for the system can be written as functions of a flat output :math:`z(\cdot)` and @@ -42,13 +49,15 @@ space, and then map these to appropriate inputs. Suppose we wish to generate a feasible trajectory for the nonlinear system .. math:: - \dot x = f(x, u), \qquad x(0) = x_0,\, x(T) = x_f. + + \dot x = f(x, u), \qquad x(0) = x_0,\, x(T) = x_f. If the system is differentially flat then .. math:: - x(0) &= \beta\bigl(z(0), \dot z(0), \dots, z^{(q)}(0) \bigr) = x_0, \\ - x(T) &= \gamma\bigl(z(T), \dot z(T), \dots, z^{(q)}(T) \bigr) = x_f, + + x(0) &= \beta\bigl(z(0), \dot z(0), \dots, z^{(q)}(0) \bigr) = x_0, \\ + x(T) &= \gamma\bigl(z(T), \dot z(T), \dots, z^{(q)}(T) \bigr) = x_f, and we see that the initial and final condition in the full state space depends on just the output :math:`z` and its derivatives at the @@ -58,13 +67,14 @@ system, using equation :eq:`flat2state` to determine the full state space and input trajectories. In particular, given initial and final conditions on :math:`z` and its -derivatives that satisfy the initial and final conditions any curve +derivatives that satisfy the initial and final conditions, any curve :math:`z(\cdot)` satisfying those conditions will correspond to a feasible trajectory of the system. We can parameterize the flat output trajectory using a set of smooth basis functions :math:`\psi_i(t)`: .. math:: - z(t) = \sum_{i=1}^N c_i \psi_i(t), \qquad c_i \in R + + z(t) = \sum_{i=1}^N c_i \psi_i(t), \qquad c_i \in R We seek a set of coefficients :math:`c_i`, :math:`i = 1, \dots, N` such that :math:`z(t)` satisfies the boundary conditions for :math:`x(0)` and @@ -72,121 +82,128 @@ that :math:`z(t)` satisfies the boundary conditions for :math:`x(0)` and the derivatives of the basis functions: .. math:: - \dot z(t) &= \sum_{i=1}^N c_i \dot \psi_i(t) \\ - &\,\vdots \\ - \dot z^{(q)}(t) &= \sum_{i=1}^N c_i \psi^{(q)}_i(t). + + \dot z(t) &= \sum_{i=1}^N c_i \dot \psi_i(t) \\ + &\, \vdots \\ + \dot z^{(q)}(t) &= \sum_{i=1}^N c_i \psi^{(q)}_i(t). We can thus write the conditions on the flat outputs and their derivatives as .. math:: - \begin{bmatrix} - \psi_1(0) & \psi_2(0) & \dots & \psi_N(0) \\ - \dot \psi_1(0) & \dot \psi_2(0) & \dots & \dot \psi_N(0) \\ - \vdots & \vdots & & \vdots \\ - \psi^{(q)}_1(0) & \psi^{(q)}_2(0) & \dots & \psi^{(q)}_N(0) \\[1ex] - \psi_1(T) & \psi_2(T) & \dots & \psi_N(T) \\ - \dot \psi_1(T) & \dot \psi_2(T) & \dots & \dot \psi_N(T) \\ - \vdots & \vdots & & \vdots \\ - \psi^{(q)}_1(T) & \psi^{(q)}_2(T) & \dots & \psi^{(q)}_N(T) \\ - \end{bmatrix} - \begin{bmatrix} c_1 \\ \vdots \\ c_N \end{bmatrix} = - \begin{bmatrix} - z(0) \\ \dot z(0) \\ \vdots \\ z^{(q)}(0) \\[1ex] - z(T) \\ \dot z(T) \\ \vdots \\ z^{(q)}(T) \\ - \end{bmatrix} + + \begin{bmatrix} + \psi_1(0) & \psi_2(0) & \dots & \psi_N(0) \\ + \dot \psi_1(0) & \dot \psi_2(0) & \dots & \dot \psi_N(0) \\ + \vdots & \vdots & & \vdots \\ + \psi^{(q)}_1(0) & \psi^{(q)}_2(0) & \dots & \psi^{(q)}_N(0) \\[1ex] + \psi_1(T) & \psi_2(T) & \dots & \psi_N(T) \\ + \dot \psi_1(T) & \dot \psi_2(T) & \dots & \dot \psi_N(T) \\ + \vdots & \vdots & & \vdots \\ + \psi^{(q)}_1(T) & \psi^{(q)}_2(T) & \dots & \psi^{(q)}_N(T) \\ + \end{bmatrix} + \begin{bmatrix} c_1 \\ \vdots \\ c_N \end{bmatrix} = + \begin{bmatrix} + z(0) \\ \dot z(0) \\ \vdots \\ z^{(q)}(0) \\[1ex] + z(T) \\ \dot z(T) \\ \vdots \\ z^{(q)}(T) \\ + \end{bmatrix} This equation is a *linear* equation of the form .. math:: + M c = \begin{bmatrix} \bar z(0) \\ \bar z(T) \end{bmatrix} where :math:`\bar z` is called the *flat flag* for the system. -Assuming that :math:`M` has a sufficient number of columns and that it is full -column rank, we can solve for a (possibly non-unique) :math:`\alpha` that -solves the trajectory generation problem. +Assuming that :math:`M` has a sufficient number of columns and that it +is full column rank, we can solve for a (possibly non-unique) +:math:`\alpha` that solves the trajectory generation problem. -Module usage -============ +Subpackage usage +---------------- -To create a trajectory for a differentially flat system, a -:class:`~control.flatsys.FlatSystem` object must be created. This is done -using the :func:`~control.flatsys.flatsys` function: +To access the flat system modules, import `control.flatsys`:: - import control.flatsys as fs - sys = fs.flatsys(forward, reverse) + import control.flatsys as fs + +To create a trajectory for a differentially flat system, a +:class:`~flatsys.FlatSystem` object must be created. This is done +using the :func:`~flatsys.flatsys` function:: -The `forward` and `reverse` parameters describe the mappings between the -system state/input and the differentially flat outputs and their -derivatives ("flat flag"). + sys = fs.flatsys(forward, reverse) -The :func:`~control.flatsys.FlatSystem.forward` method computes the -flat flag given a state and input: +The `forward` and `reverse` parameters describe the mappings between +the system state/input and the differentially flat outputs and their +derivatives ("flat flag"). The :func:`~flatsys.FlatSystem.forward` +method computes the flat flag given a state and input:: - zflag = sys.forward(x, u) + zflag = sys.forward(x, u) -The :func:`~control.flatsys.FlatSystem.reverse` method computes the state -and input given the flat flag: +The :func:`~flatsys.FlatSystem.reverse` method computes the state +and input given the flat flag:: - x, u = sys.reverse(zflag) + x, u = sys.reverse(zflag) The flag :math:`\bar z` is implemented as a list of flat outputs :math:`z_i` and their derivatives up to order :math:`q_i`: - zflag[i][j] = :math:`z_i^{(j)}` + ``zflag[i][j]`` = :math:`z_i^{(j)}` The number of flat outputs must match the number of system inputs. For a linear system, a flat system representation can be generated by -passing a :class:`~control.StateSpace` system to the -:func:`~control.flatsys.flatsys` factory function:: +passing a :class:`StateSpace` system to the +:func:`~flatsys.flatsys` factory function:: - sys = fs.flatsys(linsys) + sys = fs.flatsys(linsys) -The :func:`~control.flatsys.flatsys` function also supports the use of +The :func:`~flatsys.flatsys` function also supports the use of named input, output, and state signals:: - sys = fs.flatsys( - forward, reverse, states=['x1', ..., 'xn'], inputs=['u1', ..., 'um']) + sys = fs.flatsys( + forward, reverse, states=['x1', ..., 'xn'], inputs=['u1', ..., 'um']) In addition to the flat system description, a set of basis functions :math:`\phi_i(t)` must be chosen. The `FlatBasis` class is used to represent the basis functions. A polynomial basis function of the form 1, :math:`t`, :math:`t^2`, ... can be computed using the -:class:`~control.flatsys.PolyFamily` class, which is initialized by +:class:`~flatsys.PolyFamily` class, which is initialized by passing the desired order of the polynomial basis set:: - basis = fs.PolyFamily(N) + basis = fs.PolyFamily(N) Additional basis function families include Bezier curves -(:class:`~control.flatsys.BezierFamily`) and B-splines -(:class:`~control.flatsys.BSplineFamily`). +(:class:`~flatsys.BezierFamily`) and B-splines +(:class:`~flatsys.BSplineFamily`). Once the system and basis function have been defined, the -:func:`~control.flatsys.point_to_point` function can be used to compute a +:func:`~flatsys.point_to_point` function can be used to compute a trajectory between initial and final states and inputs:: - traj = fs.point_to_point( - sys, Tf, x0, u0, xf, uf, basis=basis) + traj = fs.point_to_point( + sys, Tf, x0, u0, xf, uf, basis=basis) -The returned object has class :class:`~control.flatsys.SystemTrajectory` and +The returned object has class :class:`~flatsys.SystemTrajectory` and can be used to compute the state and input trajectory between the initial and final condition:: - xd, ud = traj.eval(T) + xd, ud = traj.eval(timepts) -where `T` is a list of times on which the trajectory should be evaluated -(e.g., `T = numpy.linspace(0, Tf, M)`. +where `timepts` is a list of times on which the trajectory should be +evaluated (e.g., `timepts = numpy.linspace(0, Tf, M)`. Alternatively, +the `~flatsys.SystemTrajectory.response` method can be used to return +a `TimeResponseData` object. -The :func:`~control.flatsys.point_to_point` function also allows the +The :func:`~flatsys.point_to_point` function also allows the specification of a cost function and/or constraints, in the same -format as :func:`~control.optimal.solve_ocp`. +format as :func:`optimal.solve_optimal_trajectory`. -The :func:`~control.flatsys.solve_flat_ocp` function can be used to -solve an optimal control problem without a final state:: +The :func:`~flatsys.solve_flat_optimal` function can be used to solve an +optimal control problem for a differentially flat system without a +final state constraint:: - traj = fs.solve_flat_ocp( - sys, timepts, x0, u0, cost, basis=basis) + traj = fs.solve_flat_optimal( + sys, timepts, x0, u0, cost, basis=basis) The `cost` parameter is a function with call signature `cost(x, u)` and should return the (incremental) cost at the given @@ -195,18 +212,25 @@ vector. The `terminal_cost` parameter can be used to specify a cost function for the final point in the trajectory. Example -======= +------- + +To illustrate how we can differential flatness to generate a feasible +trajectory, consider the problem of steering a car to change lanes on +a road. We use the non-normalized form of the dynamics, which are +derived in `Feedback Systems +`, Example 3.11 (Vehicle +Steering). -To illustrate how we can use a two degree-of-freedom design to improve the -performance of the system, consider the problem of steering a car to change -lanes on a road. We use the non-normalized form of the dynamics, which are -derived in *Feedback Systems* by Astrom and Murray, Example 3.11. +.. testsetup:: flatsys -.. code-block:: python + import matplotlib.pyplot as plt + plt.close('all') +.. testcode:: flatsys + + import numpy as np import control as ct import control.flatsys as fs - import numpy as np # Function to take states, inputs and return the flat flag def vehicle_flat_forward(x, u, params={}): @@ -254,17 +278,27 @@ derived in *Feedback Systems* by Astrom and Murray, Example 3.11. return x, u + def vehicle_update(t, x, u, params): + b = params.get('wheelbase', 3.) # get parameter values + dx = np.array([ + np.cos(x[2]) * u[0], + np.sin(x[2]) * u[0], + (u[0]/b) * np.tan(u[1]) + ]) + return dx + vehicle_flat = fs.flatsys( vehicle_flat_forward, vehicle_flat_reverse, + updfcn=vehicle_update, outfcn=None, name='vehicle_flat', inputs=('v', 'delta'), outputs=('x', 'y'), states=('x', 'y', 'theta')) -To find a trajectory from an initial state :math:`x_0` to a final state -:math:`x_\text{f}` in time :math:`T_\text{f}` we solve a point-to-point -trajectory generation problem. We also set the initial and final inputs, which -sets the vehicle velocity :math:`v` and steering wheel angle :math:`\delta` at -the endpoints. +To find a trajectory from an initial state :math:`x_0` to a final +state :math:`x_\text{f}` in time :math:`T_\text{f}` we solve a +point-to-point trajectory generation problem. We also set the initial +and final inputs, which sets the vehicle velocity :math:`v` and +steering wheel angle :math:`\delta` at the endpoints. -.. code-block:: python +.. testcode:: flatsys # Define the endpoints of the trajectory x0 = [0., -2., 0.]; u0 = [10., 0.] @@ -278,14 +312,14 @@ the endpoints. traj = fs.point_to_point(vehicle_flat, Tf, x0, u0, xf, uf, basis=poly) # Create the trajectory - t = np.linspace(0, Tf, 100) - x, u = traj.eval(t) + timepts = np.linspace(0, Tf, 100) + resp_p2p = traj.response(timepts) Alternatively, we can solve an optimal control problem in which we minimize a cost function along the trajectory as well as a terminal -cost:` +cost: -.. code-block:: python +.. testcode:: flatsys # Define the cost along the trajectory: penalize steering angle traj_cost = ct.optimal.quadratic_cost( @@ -296,36 +330,59 @@ cost:` vehicle_flat, np.diag([1e3, 1e3, 1e3]), None, x0=xf) # Use a straight line as the initial guess - timepts = np.linspace(0, Tf, 10) + evalpts = np.linspace(0, Tf, 10) initial_guess = np.array( - [x0[i] + (xf[i] - x0[i]) * timepts/Tf for i in (0, 1)]) + [x0[i] + (xf[i] - x0[i]) * evalpts/Tf for i in (0, 1)]) # Solve the optimal control problem, evaluating cost at timepts bspline = fs.BSplineFamily([0, Tf/2, Tf], 4) - traj = fs.solve_flat_ocp( - vehicle_flat, timepts, x0, u0, traj_cost, + traj = fs.solve_flat_optimal( + vehicle_flat, evalpts, x0, u0, traj_cost, terminal_cost=term_cost, initial_guess=initial_guess, basis=bspline) - x, u = traj.eval(t) + resp_ocp = traj.response(timepts) + +The results of the two approaches can be shown using the +`time_response_plot` function: + +.. testcode:: flatsys -Module classes and functions -============================ + cplt = ct.time_response_plot( + ct.combine_time_responses([resp_p2p, resp_ocp]), + overlay_traces=True, trace_labels=['point_to_point', 'solve_ocp']) + +.. testcode:: flatsys + :hide: + + import matplotlib.pyplot as plt + plt.savefig('figures/flatsys-steering-compare.png') + +.. image:: figures/flatsys-steering-compare.png + :align: center + + +Subpackage classes and functions +-------------------------------- + +The flat systems subpackage `flatsys` utilizes a number of classes to +define the flat system, the basis functions, and the system trajectory: .. autosummary:: - :toctree: generated/ :template: custom-class-template.rst - ~control.flatsys.BasisFamily - ~control.flatsys.BezierFamily - ~control.flatsys.BSplineFamily - ~control.flatsys.FlatSystem - ~control.flatsys.LinearFlatSystem - ~control.flatsys.PolyFamily - ~control.flatsys.SystemTrajectory + flatsys.BasisFamily + flatsys.BezierFamily + flatsys.BSplineFamily + flatsys.FlatSystem + flatsys.LinearFlatSystem + flatsys.PolyFamily + flatsys.SystemTrajectory + +The following functions can be used to define a flat system and +compute trajectories: .. autosummary:: - :toctree: generated/ - ~control.flatsys.flatsys - ~control.flatsys.point_to_point - ~control.flatsys.solve_flat_ocp + flatsys.flatsys + flatsys.point_to_point + flatsys.solve_flat_optimal diff --git a/doc/freqplot-mimo_bode-default.png b/doc/freqplot-mimo_bode-default.png deleted file mode 100644 index 86414d916..000000000 Binary files a/doc/freqplot-mimo_bode-default.png and /dev/null differ diff --git a/doc/freqplot-nyquist-custom.png b/doc/freqplot-nyquist-custom.png deleted file mode 100644 index 06ccda040..000000000 Binary files a/doc/freqplot-nyquist-custom.png and /dev/null differ diff --git a/doc/freqplot-nyquist-default.png b/doc/freqplot-nyquist-default.png deleted file mode 100644 index ede50925b..000000000 Binary files a/doc/freqplot-nyquist-default.png and /dev/null differ diff --git a/doc/freqplot-siso_bode-default.png b/doc/freqplot-siso_bode-default.png deleted file mode 100644 index 3cf235a31..000000000 Binary files a/doc/freqplot-siso_bode-default.png and /dev/null differ diff --git a/doc/freqplot-siso_bode-omega.png b/doc/freqplot-siso_bode-omega.png deleted file mode 100644 index 0240473ad..000000000 Binary files a/doc/freqplot-siso_bode-omega.png and /dev/null differ diff --git a/doc/functions.rst b/doc/functions.rst new file mode 100644 index 000000000..d657fd431 --- /dev/null +++ b/doc/functions.rst @@ -0,0 +1,330 @@ +.. _function-ref: + +****************** +Function Reference +****************** + +.. Include header information from the main control module +.. automodule:: control + :no-members: + :no-inherited-members: + :no-special-members: + + +System Creation +=============== + +Functions that create input/output systems from a description of the +system properties: + +.. autosummary:: + :toctree: generated/ + + ss + tf + frd + nlsys + zpk + pade + rss + drss + +Functions that transform systems from one form to another: + +.. autosummary:: + :toctree: generated/ + + canonical_form + modal_form + observable_form + reachable_form + sample_system + similarity_transform + ss2tf + tf2ss + tfdata + +.. _interconnections-ref: + +System Interconnections +======================= + +.. autosummary:: + :toctree: generated/ + + series + parallel + negate + feedback + interconnect + append + combine_tf + split_tf + summing_junction + connection_table + combine_tf + split_tf + + +Time Response +============= + +.. autosummary:: + :toctree: generated/ + + forced_response + impulse_response + initial_response + input_output_response + step_response + time_response_plot + combine_time_responses + + +Phase plane plots +----------------- + +.. automodule:: control.phaseplot + :no-members: + :no-inherited-members: + :no-special-members: + +.. Reset current module to main package to force reference to use prefix +.. currentmodule:: control + +.. autosummary:: + :toctree: generated/ + + phase_plane_plot + phaseplot.boxgrid + phaseplot.circlegrid + phaseplot.equilpoints + phaseplot.meshgrid + phaseplot.separatrices + phaseplot.streamlines + phaseplot.vectorfield + phaseplot.streamplot + + +Frequency Response +================== + +.. autosummary:: + :toctree: generated/ + + bode_plot + describing_function_plot + describing_function_response + frequency_response + nyquist_response + nyquist_plot + gangof4_response + gangof4_plot + nichols_plot + nichols_grid + + +Control System Analysis +======================= + +Time domain analysis: + +.. autosummary:: + :toctree: generated/ + + damp + step_info + +Frequency domain analysis: + +.. autosummary:: + :toctree: generated/ + + bandwidth + dcgain + linfnorm + margin + stability_margins + system_norm + phase_crossover_frequencies + singular_values_plot + singular_values_response + sisotool + +Pole/zero-based analysis: + +.. autosummary:: + :toctree: generated/ + + poles + zeros + pole_zero_map + pole_zero_plot + pole_zero_subplots + root_locus_map + root_locus_plot + +Passive systems analysis: + +.. autosummary:: + :toctree: generated/ + + get_input_ff_index + get_output_fb_index + ispassive + solve_passivity_LMI + + +Control System Synthesis +======================== + +State space synthesis: + +.. autosummary:: + :toctree: generated/ + + create_statefbk_iosystem + dlqr + lqr + place + place_acker + place_varga + +Frequency domain synthesis: + +.. autosummary:: + :toctree: generated/ + + h2syn + hinfsyn + mixsyn + rootlocus_pid_designer + + +System ID and Model Reduction +============================= +.. autosummary:: + :toctree: generated/ + + minimal_realization + balanced_reduction + hankel_singular_values + model_reduction + eigensys_realization + markov + + +Nonlinear System Support +======================== +.. autosummary:: + :toctree: generated/ + + find_operating_point + linearize + + +Describing functions +-------------------- +.. autosummary:: + :toctree: generated/ + + describing_function + friction_backlash_nonlinearity + relay_hysteresis_nonlinearity + saturation_nonlinearity + + +Differentially flat systems +--------------------------- +.. automodule:: control.flatsys + :no-members: + :no-inherited-members: + :no-special-members: + +.. Reset current module to main package to force reference to use prefix +.. currentmodule:: control + +.. autosummary:: + :toctree: generated/ + + flatsys.flatsys + flatsys.point_to_point + flatsys.solve_flat_optimal + + +Optimal control +--------------- +.. automodule:: control.optimal + :no-members: + :no-inherited-members: + :no-special-members: + +.. Reset current module to main package to force reference to use prefix +.. currentmodule:: control + +.. autosummary:: + :toctree: generated/ + + optimal.create_mpc_iosystem + optimal.disturbance_range_constraint + optimal.gaussian_likelihood_cost + optimal.input_poly_constraint + optimal.input_range_constraint + optimal.output_poly_constraint + optimal.output_range_constraint + optimal.quadratic_cost + optimal.solve_optimal_trajectory + optimal.solve_optimal_estimate + optimal.state_poly_constraint + optimal.state_range_constraint + + +Stochastic System Support +========================= +.. autosummary:: + :toctree: generated/ + + correlation + create_estimator_iosystem + dlqe + lqe + white_noise + + +Matrix Computations +=================== +.. autosummary:: + :toctree: generated/ + + care + ctrb + dare + dlyap + lyap + obsv + gram + +.. _utility-and-conversions: + +Utility Functions +================= +.. autosummary:: + :toctree: generated/ + + augw + bdschur + db2mag + isctime + isdtime + iosys_repr + issiso + mag2db + reset_defaults + reset_rcParams + set_defaults + ssdata + timebase + unwrap + use_fbs_defaults + use_legacy_defaults + use_matlab_defaults diff --git a/doc/index.rst b/doc/index.rst index ec556e7ce..44da952c7 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -2,41 +2,77 @@ Python Control Systems Library ############################## -The Python Control Systems Library (`python-control`) is a Python package that -implements basic operations for analysis and design of feedback control systems. +The Python Control Systems Library (python-control) is a Python +package that implements basic operations for analysis and design of +feedback control systems. .. rubric:: Features -- Linear input/output systems in state-space and frequency domain +- Linear input/output systems in state space and frequency domain - Nonlinear input/output system modeling, simulation, and analysis - Block diagram algebra: serial, parallel, and feedback interconnections -- Time response: initial, step, impulse -- Frequency response: Bode and Nyquist plots -- Control analysis: stability, reachability, observability, stability margins -- Control design: eigenvalue placement, LQR, H2, Hinf -- Model reduction: balanced realizations, Hankel singular values -- Estimator design: linear quadratic estimator (Kalman filter) +- Time response: initial, step, impulse, and forced response +- Frequency response: Bode, Nyquist, and Nichols plots +- Control analysis: stability, reachability, observability, stability + margins, phase plane plots, root locus plots +- Control design: eigenvalue placement, LQR, H2, Hinf, and MPC/RHC +- Trajectory generation: optimal control and differential flatness +- Model reduction: balanced realizations and Hankel singular values +- Estimator design: linear quadratic estimator (Kalman filter), MLE, and MHE + +.. rubric:: Links: + +- GitHub repository: https://github.com/python-control/python-control +- Issue tracker: https://github.com/python-control/python-control/issues +- Mailing list: https://sourceforge.net/p/python-control/mailman/ + +.. rubric:: How to cite + +An `article `_ +about the library is available on IEEE Explore. If the Python Control +Systems Library helped you in your research, please cite:: + + @inproceedings{python-control2021, + title={The Python Control Systems Library (python-control)}, + author={Fuller, Sawyer and Greiner, Ben and Moore, Jason and + Murray, Richard and van Paassen, Ren{\'e} and Yorke, Rory}, + booktitle={60th IEEE Conference on Decision and Control (CDC)}, + pages={4875--4881}, + year={2021}, + organization={IEEE} + } -.. rubric:: Documentation +or the GitHub site: https://github.com/python-control/python-control. .. toctree:: - :maxdepth: 2 + :caption: User Guide + :maxdepth: 1 + :numbered: 2 intro - conventions - control - classes - plotting - matlab - flatsys - iosys - descfcn - optimal + Tutorial + Linear Systems + I/O Response and Plotting + Nonlinear Systems + Interconnected I/O Systems + Stochastic Systems examples + genindex -* :ref:`genindex` +.. toctree:: + :caption: Reference Manual + :maxdepth: 1 -.. rubric:: Development + functions + classes + config + matlab + develop + releases + +*********** +Development +*********** You can check out the latest version of the source code with the command:: @@ -53,16 +89,12 @@ or to test the installed package:: .. _pytest: https://docs.pytest.org/ -Your contributions are welcome! Simply fork the `GitHub repository `_ and send a -`pull request`_. +Your contributions are welcome! Simply fork the `GitHub repository +`_ and send a `pull +request`_. .. _pull request: https://github.com/python-control/python-control/pulls Please see the `Developer's Wiki`_ for detailed instructions. .. _Developer's Wiki: https://github.com/python-control/python-control/wiki - -.. rubric:: Links - -- Issue tracker: https://github.com/python-control/python-control/issues -- Mailing list: http://sourceforge.net/p/python-control/mailman/ diff --git a/doc/interconnect_tutorial.ipynb b/doc/interconnect_tutorial.ipynb deleted file mode 120000 index aa43d9824..000000000 --- a/doc/interconnect_tutorial.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/interconnect_tutorial.ipynb \ No newline at end of file diff --git a/doc/intro.rst b/doc/intro.rst index 2287bbac4..e1e5fb8e6 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -2,37 +2,21 @@ Introduction ************ -Welcome to the Python Control Systems Toolbox (python-control) User's -Manual. This manual contains information on using the python-control +Welcome to the Python Control Systems Library (python-control) User +Guide. This guide contains information on using the python-control package, including documentation for all functions in the package and examples illustrating their use. -Overview of the toolbox -======================= -The python-control package is a set of python classes and functions that -implement common operations for the analysis and design of feedback control -systems. The initial goal is to implement all of the functionality required -to work through the examples in the textbook `Feedback Systems -`_ by Astrom and Murray. A :ref:`matlab-module` is -available that provides many of the common functions corresponding to -commands available in the MATLAB Control Systems Toolbox. +Package Overview +================ -Some differences from MATLAB -============================ -The python-control package makes use of `NumPy `_ and -`SciPy `_. A list of general differences between -NumPy and MATLAB can be found `here -`_. - -In terms of the python-control package more specifically, here are -some things to keep in mind: +.. automodule:: control + :noindex: + :no-members: + :no-inherited-members: + :no-special-members: -* You must include commas in vectors. So [1 2 3] must be [1, 2, 3]. -* Functions that return multiple arguments use tuples. -* You cannot use braces for collections; use tuples instead. -* Time series data have time as the final index (see - :ref:`time-series-convention`). Installation ============ @@ -56,7 +40,7 @@ they are not already present. .. note:: Mixing packages from conda-forge and the default conda channel can sometimes cause problems with dependencies, so it is usually best to - instally NumPy, SciPy, and Matplotlib from conda-forge as well. + install NumPy, SciPy, and Matplotlib from conda-forge as well. To install using pip:: @@ -65,7 +49,7 @@ To install using pip:: .. note:: If you install Slycot using pip you'll need a development - environment (e.g., Python development files, C and Fortran compilers). + environment (e.g., Python development files, C, and FORTRAN compilers). Pip installation can be particularly complicated for Windows. Many parts of `python-control` will work without `slycot`, but some @@ -75,24 +59,136 @@ correctly by running the command:: python -c "import slycot" -and verifying that no error message appears. More information on the +and verifying that no error message appears. More information on the Slycot package can be obtained from the `Slycot project page `_. -Alternatively, to install from source, first `download the source -`_ and unpack it. -To install in your home directory, use:: +Alternatively, to install `python-control` from source, first +`download the source code +`_ and +unpack it. To install in your Python environment, use:: pip install . -Getting started -=============== +The python-control package can also be used with `Google Colab +`_ by including the following lines to import the +control package:: + + %pip install control + import control as ct + +Note that Google Colab does not currently support Slycot, so some +functionality may not be available. + + +Package Conventions +=================== + +The python-control package makes use of a few naming and calling conventions: + +* Function names are written in lower case with underscores between + words (`frequency_response`). + +* Class names use camel case (`StateSpace`, `ControlPlot`, etc) and + instances of the class are created with "factory functions" (`ss`, `tf`) + or as the output of an operation (`bode_plot`, `step_response`). + +* Functions that return multiple values use either objects (with + elements for each return value) or tuples. For those functions that + return tuples, the underscore variable can be used if only some of + the return values are needed:: + + K, _, _ = ct.lqr(sys) + +* Python-control supports both single-input, single-output (SISO) + systems and multi-input, multi-output (MIMO) systems, including + time and frequency responses. By default, SISO systems will + typically generate objects that have the input and output dimensions + suppressed (using the NumPy :func:`numpy.squeeze` function). The + `squeeze` keyword can be set to False to force functions to return + objects that include the input and output dimensions. + + +Some Differences from MATLAB +============================ + +Users familiar with the MATLAB control systems toolbox will find much +of the functionality implemented in `python-control`, though using +Python constructs and coding conventions. The python-control package +makes heavy use of `NumPy `_ and `SciPy +`_ and many differences are reflected in the +use of those . A list of general differences between NumPy and MATLAB +can be found `here +`_. + +In terms of the python-control package more specifically, here are +some things to keep in mind: + +* Vectors and matrices used as arguments to functions can be written + using lists, with commas required between elements and column + vectors implemented as nested list . So [1 2 3] must be written as + [1, 2, 3] and matrices are written using 2D nested lists, e.g., [[1, + 2], [3, 4]]. +* Functions that in MATLAB would return variable numbers of values + will have a parameter of the form `return_\` that is used to + return additional data. (These functions usually return an object of + a class that has attributes that can be used to access the + information and this is the preferred usage pattern.) +* You cannot use braces for collections; use tuples instead. +* Time series data have time as the final index (see + :ref:`time series data conventions `). + + +Documentation Conventions +========================= + +This documentation has a number of notional conventions and functionality: + +* The left panel displays the table of contents and is divided into + two main sections: the User Guide, which contains a narrative + description of the package along with examples, and the Reference + Manual, which contains documentation for all functions, classes, + configurable default parameters, and other detailed information. + +* Class, functions, and methods with additional documentation appear + in a bold, code font that link to the Reference Manual. Example: `ss`. + +* Links to other sections appear in blue. Example: :ref:`nonlinear-systems`. + +* Parameters appear in a (non-bode) code font, as do code fragments. + Example: `omega`. + +* Example code is contained in code blocks that can be copied using + the copy icon in the top right corner of the code block. Code + blocks are of three primary types: summary descriptions, code + listings, and executed commands. + + Summary descriptions show the calling structure of commands but are + not directly executable. Example:: + + resp = ct.frequency_response(sys[, omega]) + + Code listings consist of executable code that can be copied and + pasted into a Python execution environment. In most cases the + objects required by the code block will be present earlier in the + file or, occasionally, in a different section or chapter (with a + reference near the code block). All code listings assume that the + NumPy package is available using the prefix `np` and the python-control + package is available using prefix `ct`. Example: + + .. code:: -There are two different ways to use the package. For the default interface -described in :ref:`function-ref`, simply import the control package as follows:: + sys = ct.rss(4, 2, 1) + resp = ct.frequency_response(sys) + cplt = resp.plot() - >>> import control as ct + Executed commands show commands preceded by a prompt string of the + form ">>> " and also show the output that is obtained when executing + that code. The copy functionality for these blocks is configured to + only copy the commands and not the prompt string or outputs. Example: -If you want to have a MATLAB-like environment, use the :ref:`matlab-module`:: + .. doctest:: - >>> from control.matlab import * + >>> sys = ct.tf([1], [1, 0.5, 1]) + >>> ct.bandwidth(sys) + np.float64(1.4839084518312828) diff --git a/doc/iosys.rst b/doc/iosys.rst index eb4311e05..5e51e7f05 100644 --- a/doc/iosys.rst +++ b/doc/iosys.rst @@ -1,70 +1,158 @@ +.. currentmodule:: control + .. _iosys-module: -******************** -Input/output systems -******************** +************************** +Interconnected I/O Systems +************************** + +Input/output systems can be interconnected in a variety of ways, +including operator overloading, block diagram algebra functions, and +using the :func:`interconnect` function to build a hierarchical system +description. This chapter provides more detailed information on +operator overloading and block diagram algebra, as well as a +description of the :class:`InterconnectedSystem` class, which can be +created using the :func:`interconnect` function. + +Operator Overloading +==================== + +The following operators are defined to operate between I/O systems: + +.. list-table:: + :header-rows: 1 + + * - Operation + - Description + - Equivalent command + * - ``sys1 + sys2`` + - Add the outputs of two systems receiving the same input + - ``parallel(sys1, sys2)`` + * - ``sys1 * sys2`` + - Connect output(s) of sys2 to input(s) of sys1 + - ``series(sys2, sys1)`` + * - ``-sys`` + - Multiply the output(s) of the system by -1 + - ``negate(sys)`` + * - ``tf1 / tf2`` + - Divide one SISO transfer function by another + - N/A + * - ``tf**n`` + - Multiply a transfer function by itself ``n`` times + - N/A + +If either of the systems is a scalar or an array of appropriate +dimension, then the appropriate scalar or matrix operation is +performed. In addition, if a SISO system is combined with a MIMO +system, the SISO system will be broadcast to the appropriate shape. + +Systems of different types can be combined using these operations, +with the following rules: + +* If both systems can be converted into the type of the other, the + leftmost system determines the type of the output. + +* If only one system can be converted into the other, then the more general + system determines the type of the output. In particular: + + - State space and transfer function systems can be converted to + nonlinear systems. + + - Linear systems can be converted to frequency response data (FRD) + systems, using the frequencies of the FRD system. + + - FRD systems can only be combined with FRD systems, constants, + and arrays. + -Module usage -============ +Block Diagram Algebra +===================== -An input/output system is defined as a dynamical system that has a system -state as well as inputs and outputs (either inputs or states can be empty). -The dynamics of the system can be in continuous or discrete time. To simulate -an input/output system, use the :func:`~control.input_output_response` -function:: +Block diagram algebra is implemented using the following functions: - resp = ct.input_output_response(io_sys, T, U, X0, params) - t, y, x = resp.time, resp.outputs, resp.states +.. autosummary:: + + series + parallel + feedback + negate + append + +The :func:`feedback` function implements a standard feedback +interconnection between two systems, as illustrated in the following +diagram: + +.. image:: figures/bdalg-feedback.png + :width: 240 + :align: center + +By default a gain of -1 is applied at the output of the second system, +so the dynamics illustrate above can be created using the command + +.. code:: + + Gyu = ct.feedback(G1, G2) -An input/output system can be linearized around an equilibrium point to obtain -a :class:`~control.StateSpace` linear system. Use the -:func:`~control.find_eqpt` function to obtain an equilibrium point and the -:func:`~control.linearize` function to linearize about that equilibrium point:: +An optional `gain` parameter can be used to change the sign of the gain. - xeq, ueq = ct.find_eqpt(io_sys, X0, U0) - ss_sys = ct.linearize(io_sys, xeq, ueq) +The :func:`feedback` function is also implemented via the +:func:`LTI.feedback` method, so if `G1` is an input/output system then +the following command will also work:: -Input/output systems are automatically created for state space LTI systems -when using the :func:`ss` function. Nonlinear input/output systems can be -created using the :func:`~control.nlsys` function, which requires -the definition of an update function (for the right hand side of the -differential or different equation) and an output function (computes the -outputs from the state):: + Gyu = G1.feedback(G2) - io_sys = ct.nlsys(updfcn, outfcn, inputs=M, outputs=P, states=N) +All block diagram algebra functions allow the name of the system and +labels for signals to be specified using the usual `name`, `inputs`, +and `outputs` keywords, as described in the :class:`InputOutputSystem` +class. For state space systems, the labels for the states can also be +given, but caution should be used since the order of states in the +combined system is not guaranteed. + + +Signal-Based Interconnection +============================ More complex input/output systems can be constructed by using the -:func:`~control.interconnect` function, which allows a collection of +:func:`interconnect` function, which allows a collection of input/output subsystems to be combined with internal connections between the subsystems and a set of overall system inputs and outputs -that link to the subsystems:: +that link to the subsystems. For example, the closed loop dynamics of +a feedback control system using the standard names and labels for +inputs and outputs could be constructed using the command + +.. code:: - steering = ct.interconnect( + clsys = ct.interconnect( [plant, controller], name='system', - connections=[['controller.e', '-plant.y']], - inplist=['controller.e'], inputs='r', + connections=[ + ['controller.u', '-plant.y'], + ['plant.u', 'controller.y']], + inplist=['controller.u'], inputs='r', outlist=['plant.y'], outputs='y') -Interconnected systems can also be created using block diagram manipulations -such as the :func:`~control.series`, :func:`~control.parallel`, and -:func:`~control.feedback` functions. The :class:`~control.InputOutputSystem` -class also supports various algebraic operations such as `*` (series -interconnection) and `+` (parallel interconnection). +The remainder of this section provides a detailed description of the +operation of the :func:`interconnect` function. -Example -======= -To illustrate the use of the input/output systems module, we create a +Illustrative example +-------------------- + +To illustrate the use of the :func:`interconnect` function, we create a model for a predator/prey system, following the notation and parameter -values in FBS2e. +values in `Feedback Systems `_. -We begin by defining the dynamics of the system +We begin by defining the dynamics of the system: -.. code-block:: python +.. testsetup:: predprey + + import matplotlib.pyplot as plt + plt.close('all') + +.. testcode:: predprey - import control as ct - import numpy as np import matplotlib.pyplot as plt + import numpy as np + import control as ct def predprey_rhs(t, x, u, params): # Parameter setup @@ -90,48 +178,50 @@ We begin by defining the dynamics of the system We now create an input/output system using these dynamics: -.. code-block:: python +.. testcode:: predprey - io_predprey = ct.nlsys( - predprey_rhs, None, inputs=('u'), outputs=('H', 'L'), - states=('H', 'L'), name='predprey') + predprey = ct.nlsys( + predprey_rhs, None, inputs=['u'], outputs=['Hares', 'Lynxes'], + states=['H', 'L'], name='predprey') Note that since we have not specified an output function, the entire state will be used as the output of the system. -The `io_predprey` system can now be simulated to obtain the open loop dynamics -of the system: +The `predprey` system can now be simulated to obtain the open loop +dynamics of the system: -.. code-block:: python +.. testcode:: predprey - X0 = [25, 20] # Initial H, L - T = np.linspace(0, 70, 500) # Simulation 70 years of time + X0 = [25, 20] # Initial H, L + timepts = np.linspace(0, 70, 500) # Simulation 70 years of time - # Simulate the system - t, y = ct.input_output_response(io_predprey, T, 0, X0) + # Simulate the system and plots the results + resp = ct.input_output_response(predprey, timepts, 0, X0) + resp.plot(plot_inputs=False, overlay_signals=True, legend_loc='upper left') + +.. testcode:: predprey + :hide: + + plt.savefig('figures/iosys-predprey-open.png') - # Plot the response - plt.figure(1) - plt.plot(t, y[0]) - plt.plot(t, y[1]) - plt.legend(['Hare', 'Lynx']) - plt.show(block=False) +.. image:: figures/iosys-predprey-open.png + :align: center We can also create a feedback controller to stabilize a desired population of the system. We begin by finding the (unstable) equilibrium point for the system and computing the linearization about that point. -.. code-block:: python +.. testcode:: predprey - eqpt = ct.find_eqpt(io_predprey, X0, 0) - xeq = eqpt[0] # choose the nonzero equilibrium point - lin_predprey = ct.linearize(io_predprey, xeq, 0) + xeq, ueq = ct.find_operating_point(predprey, X0, 0) + lin_predprey = ct.linearize(predprey, xeq, ueq) We next compute a controller that stabilizes the equilibrium point using eigenvalue placement and computing the feedforward gain using the number of -lynxes as the desired output (following FBS2e, Example 7.5): +lynxes as the desired output (following `Feedback Systems +`_, Example 7.5): -.. code-block:: python +.. testcode:: predprey K = ct.place(lin_predprey.A, lin_predprey.B, [-0.1, -0.2]) A, B = lin_predprey.A, lin_predprey.B @@ -141,59 +231,137 @@ lynxes as the desired output (following FBS2e, Example 7.5): To construct the control law, we build a simple input/output system that applies a corrective input based on deviations from the equilibrium point. This system has no dynamics, since it is a static (affine) map, and can -constructed using :func:`~control.nlsys` with no update function: +constructed using :func:`nlsys` with no update function: -.. code-block:: python +.. testcode:: predprey - io_controller = ct.nlsys( - None, - lambda t, x, u, params: -K @ (u[1:] - xeq) + kf * (u[0] - xeq[1]), - inputs=('Ld', 'u1', 'u2'), outputs=1, name='control') + def output(t, x, u, params): + Ld, x, ye = u[0], u[1:], xeq[1] + return ueq - K @ (x - xeq) + kf * (Ld - ye) -The input to the controller is `u`, consisting of the vector of hare and lynx -populations followed by the desired lynx population. + controller = ct.nlsys( + None, output, + inputs=['Ld', 'H', 'L'], outputs=1, name='control') + +The input to the controller is `u`, consisting of the desired lynx +population followed by the vector of hare and lynx populations. To connect the controller to the predatory-prey model, we use the -:func:`~control.interconnect` function: +:func:`interconnect` function: -.. code-block:: python +.. testcode:: predprey - io_closed = ct.interconnect( - [io_predprey, io_controller], # systems + closed = ct.interconnect( + [predprey, controller], # systems connections=[ ['predprey.u', 'control.y[0]'], - ['control.u1', 'predprey.H'], - ['control.u2', 'predprey.L'] + ['control.H', 'predprey.Hares'], + ['control.L', 'predprey.Lynxes'] ], - inplist=['control.Ld'], - outlist=['predprey.H', 'predprey.L', 'control.y[0]'] + inplist=['control.Ld'], inputs='Ld', + outlist=['predprey.Hares', 'predprey.Lynxes', 'control.y[0]'], + outputs=['Hares', 'Lynxes', 'u0'], name='closed loop' ) Finally, we simulate the closed loop system: -.. code-block:: python +.. testcode:: predprey # Simulate the system - t, y = ct.input_output_response(io_closed, T, 30, [15, 20]) - - # Plot the response - plt.figure(2) - plt.subplot(2, 1, 1) - plt.plot(t, y[0]) - plt.plot(t, y[1]) - plt.legend(['Hare', 'Lynx']) - plt.subplot(2, 1, 2) - plt.plot(t, y[2]) - plt.legend(['input']) - plt.show(block=False) - -Additional features -=================== - -The I/O systems module has a number of other features that can be used to -simplify the creation and use of interconnected input/output systems. - -Vector elements processing + Ld = 30 + resp = ct.input_output_response( + closed, timepts, inputs=Ld, initial_state=[15, 20]) + cplt = resp.plot( + plot_inputs=False, overlay_signals=True, legend_loc='upper left') + cplt.axes[0, 0].axhline(Ld, linestyle='--', color='black') + +.. testcode:: predprey + :hide: + + plt.savefig('figures/iosys-predprey-closed.png') + +.. image:: figures/iosys-predprey-closed.png + :align: center + +This example shows the standard operations that would be used to build +up an interconnected nonlinear system. The I/O systems module has a +number of other features that can be used to simplify the creation and +use of interconnected input/output systems. + + +Summing junction +---------------- + +The :func:`summing_junction` function can be used to create an +input/output system that takes the sum of an arbitrary number of inputs. For +example, to create an input/output system that takes the sum of three inputs, +use the command + +.. testcode:: summing + + sumblk = ct.summing_junction(3) + +By default, the name of the inputs will be of the form 'u[i]' and the output +will be 'y'. This can be changed by giving an explicit list of names: + +.. testcode:: summing + + sumblk = ct.summing_junction(inputs=['a', 'b', 'c'], output='d') + +A more typical usage would be to define an input/output system that +compares a reference signal to the output of the process and computes +the error: + +.. testcode:: summing + + sumblk = ct.summing_junction(inputs=['r', '-y'], output='e') + +Note the use of the minus sign as a means of setting the sign of the +input 'y' to be negative instead of positive. + +It is also possible to define "vector" summing blocks that take +multi-dimensional inputs and produce a multi-dimensional output. For +example, the command + +.. testcode:: summing + + sumblk = ct.summing_junction(inputs=['r', '-y'], output='e', dimension=2) + +will produce an input/output block that implements ``e[0] = r[0] - y[0]`` and +``e[1] = r[1] - y[1]``. + + +Automatic connections using signal names +---------------------------------------- + +The :func:`interconnect` function allows the interconnection of +multiple systems by using signal names of the form 'sys.signal'. In many +situations, it can be cumbersome to explicitly connect all of the appropriate +inputs and outputs. As an alternative, if the `connections` keyword is +omitted, the :func:`interconnect` function will connect all signals +of the same name to each other. This can allow for simplified methods of +interconnecting systems, especially when combined with the +:func:`summing_junction` function. For example, the following code +will create a unity gain, negative feedback system: + +.. testcode:: autoconnect + + P = ct.tf([1], [1, 0], inputs='u', outputs='y') + C = ct.tf([10], [1, 1], inputs='e', outputs='u') + sumblk = ct.summing_junction(inputs=['r', '-y'], output='e') + T = ct.interconnect([P, C, sumblk], inplist='r', outlist='y') + +If a signal name appears in multiple outputs then that signal will be summed +when it is interconnected. Similarly, if a signal name appears in multiple +inputs then all systems using that signal name will receive the same input. +The :func:`interconnect` function will generate an error if a signal +listed in `inplist` or `outlist` (corresponding to the inputs and outputs +of the interconnected system) is not found, but inputs and outputs of +individual systems that are not connected to other systems are left +unconnected (so be careful!). + + +Vector element processing -------------------------- Several I/O system commands perform processing of vector elements @@ -201,7 +369,7 @@ Several I/O system commands perform processing of vector elements proper shape. For static elements, such as the initial state in a simulation or the -nominal state and input for a linearization), the following processing +nominal state and input for a linearization, the following processing is done: * Scalars are automatically converted to a vector of the appropriate @@ -219,8 +387,8 @@ is done: given vector is non-zero, a warning is issued.) Similar processing is done for input time series, used for the -:func:`~control.input_output_response` and -:func:`~control.forced_response` commands, with the following +:func:`input_output_response` and +:func:`forced_response` commands, with the following additional feature: * Time series elements are broadcast to match the number of time points @@ -251,7 +419,7 @@ In this command, the states and the inputs are broadcast to the size of the state and input vectors, respectively. If we want to linearize the closed loop system around a process state -``x0`` (with two elements) and an estimator state ``0`` (for both states), +`x0` (with two elements) and an estimator state `0` (for both states), we can use the list processing feature:: H = clsys.linearize([x0, 0], 0) @@ -272,78 +440,18 @@ use the list processing feature combined with time series broadcasting:: In this command, the second and third arguments will be broadcast to match the number of time points. -Summing junction ----------------- - -The :func:`~control.summing_junction` function can be used to create an -input/output system that takes the sum of an arbitrary number of inputs. For -example, to create an input/output system that takes the sum of three inputs, -use the command - -.. code-block:: python - - sumblk = ct.summing_junction(3) - -By default, the name of the inputs will be of the form ``u[i]`` and the output -will be ``y``. This can be changed by giving an explicit list of names:: - - sumblk = ct.summing_junction(inputs=['a', 'b', 'c'], output='d') - -A more typical usage would be to define an input/output system that compares a -reference signal to the output of the process and computes the error:: - - sumblk = ct.summing_junction(inputs=['r', '-y'], output='e') - -Note the use of the minus sign as a means of setting the sign of the input 'y' -to be negative instead of positive. - -It is also possible to define "vector" summing blocks that take -multi-dimensional inputs and produce a multi-dimensional output. For example, -the command - -.. code-block:: python - - sumblk = ct.summing_junction(inputs=['r', '-y'], output='e', dimension=2) - -will produce an input/output block that implements ``e[0] = r[0] - y[0]`` and -``e[1] = r[1] - y[1]``. - -Automatic connections using signal names ----------------------------------------- - -The :func:`~control.interconnect` function allows the interconnection of -multiple systems by using signal names of the form ``sys.signal``. In many -situations, it can be cumbersome to explicitly connect all of the appropriate -inputs and outputs. As an alternative, if the ``connections`` keyword is -omitted, the :func:`~control.interconnect` function will connect all signals -of the same name to each other. This can allow for simplified methods of -interconnecting systems, especially when combined with the -:func:`~control.summing_junction` function. For example, the following code -will create a unity gain, negative feedback system:: - - P = ct.tf([1], [1, 0], inputs='u', outputs='y') - C = ct.tf([10], [1, 1], inputs='e', outputs='u') - sumblk = ct.summing_junction(inputs=['r', '-y'], output='e') - T = ct.interconnect([P, C, sumblk], inplist='r', outlist='y') - -If a signal name appears in multiple outputs then that signal will be summed -when it is interconnected. Similarly, if a signal name appears in multiple -inputs then all systems using that signal name will receive the same input. -The :func:`~control.interconnect` function will generate an error if a signal -listed in ``inplist`` or ``outlist`` (corresponding to the inputs and outputs -of the interconnected system) is not found, but inputs and outputs of -individual systems that are not connected to other systems are left -unconnected (so be careful!). Advanced specification of signal names -------------------------------------- In addition to manual specification of signal names and automatic connection of signals with the same name, the -:func:`~control.interconnect` has a variety of other mechanisms +:func:`interconnect` has a variety of other mechanisms available for specifying signal names. The following forms are recognized for the `connections`, `inplist`, and `outlist` -parameters:: +parameters: + +.. code-block:: text (subsys, index, gain) tuple form with integer indices ('sysname', 'signal', gain) tuple form with name lookup @@ -357,40 +465,50 @@ parameters:: For tuple forms, mixed specifications using integer indices and strings are possible. -For the index range form `sysname.signal[i:j]`, if either `i` or `j` +For the index range form ``sysname.signal[i:j]``, if either `i` or `j` is not specified, then it defaults to the minimum or maximum value of the signal range. Note that despite the similarity to slice notation, negative indices and step specifications are not supported. -Using these various forms can simplfy the specification of +Using these various forms can simplify the specification of interconnections. For example, consider a process with inputs 'u' and -'v', each of dimension 2, and two outputs 'w' and 'y', each of -dimension 2:: +'v', each of dimension 2, and two outputs 'w' and 'y', each of +dimension 2: + +.. testcode:: interconnect - P = ct.rss( - states=6, name='P', strictly_proper=True, - inputs=['u[0]', 'u[1]', 'v[0]', 'v[1]'], - outputs=['y[0]', 'y[1]', 'z[0]', 'z[1]']) + P = ct.ss( + np.diag([-1, -2, -3, -4]), np.eye(4), np.eye(4), 0, name='P', + inputs=['u[0]', 'u[1]', 'v[0]', 'v[1]'], + outputs=['y[0]', 'y[1]', 'z[0]', 'z[1]']) Suppose we construct a controller with 2 inputs and 2 outputs that -takes the (2-dimensional) error `e` and outputs and control signal `u`:: +takes the (2-dimensional) error 'e' and outputs and control signal 'u': + +.. testcode:: interconnect - C = ct.rss(4, 2, 2, name='C', input_prefix='e', output_prefix='u') + C = ct.ss( + [], [], [], [[3, 0], [0, 4]], + name='C', input_prefix='e', output_prefix='u') Finally, we include a summing block that will take the difference between -the reference input `r` and the measured output `y`:: +the reference input 'r' and the measured output 'y': + +.. testcode:: interconnect sumblk = ct.summing_junction( inputs=['r', '-y'], outputs='e', dimension=2, name='sum') The closed loop system should close the loop around the process -outputs `y` and inputs `u`, leaving the process inputs `v` and outputs -'w', as well as the reference input `r`. We would like the output of -the closed loop system to consist of all system outputs `y` and `z`, -as well as the controller input `u`. +outputs 'y' and inputs 'u', leaving the process inputs 'v' and outputs +'w', as well as the reference input 'r'. We would like the output of +the closed loop system to consist of all system outputs 'y' and 'z', +as well as the controller input 'u'. This collection of systems can be combined in a variety of ways. The -most explict would specify every signal:: +most explicit would specify every signal: + +.. testcode:: interconnect clsys1 = ct.interconnect( [C, P, sumblk], @@ -403,7 +521,9 @@ most explict would specify every signal:: outlist=['P.y[0]', 'P.y[1]', 'P.z[0]', 'P.z[1]', 'C.u[0]', 'C.u[1]'] ) -This connections can be simplified using signal ranges:: +This connections can be simplified using signal ranges: + +.. testcode:: interconnect clsys2 = ct.interconnect( [C, P, sumblk], @@ -417,7 +537,9 @@ This connections can be simplified using signal ranges:: ) An even simpler form can be used by omitting the range specification -when all signals with the same prefix are used:: +when all signals with the same prefix are used: + +.. testcode:: interconnect clsys3 = ct.interconnect( [C, P, sumblk], @@ -426,17 +548,21 @@ when all signals with the same prefix are used:: ) A further simplification is possible when all of the inputs or outputs -of an individual system are used in a given specification:: +of an individual system are used in a given specification: + +.. testcode:: interconnect clsys4 = ct.interconnect( - [C, P, sumblk], + [C, P, sumblk], name='clsys4', connections=[['P.u', 'C'], ['C', 'sum'], ['sum.y', 'P.y']], inplist=['sum.r', 'P.v'], outlist=['P', 'C.u'] ) -And finally, since we have named the signals throughout the system in -a consistent way, we could let :func:`ct.interconnect` do all of the -work:: +And finally, since we have named the signals throughout the system in a +consistent way, we could let :func:`interconnect` do all of the +work: + +.. testcode:: interconnect clsys5 = ct.interconnect( [C, P, sumblk], inplist=['sum.r', 'P.v'], outlist=['P', 'C.u'] @@ -444,17 +570,90 @@ work:: Various other simplifications are possible, but it can sometimes be complicated to debug error message when things go wrong. Setting -`debug=True` when calling :func:`~control.interconnect` prints out +`debug` = True when calling :func:`interconnect` prints out information about how the arguments are processed that may be helpful in understanding what is going wrong. +If the system is constructed successfully but the system does not seem +to behave correctly, the `print` function can be used to show the +interconnections and outputs: + +.. doctest:: interconnect + + >>> print(clsys4) + : clsys4 + Inputs (4): ['u[0]', 'u[1]', 'u[2]', 'u[3]'] + Outputs (6): ['y[0]', 'y[1]', 'y[2]', 'y[3]', 'y[4]', 'y[5]'] + States (4): ['P_x[0]', 'P_x[1]', 'P_x[2]', 'P_x[3]'] + + Subsystems (3): + * ['u[0]', 'u[1]'], dt=None> + * ['y[0]', 'y[1]', 'z[0]', + 'z[1]']> + * ['e[0]', 'e[1]'], + dt=None> + + Connections: + * C.e[0] <- sum.e[0] + * C.e[1] <- sum.e[1] + * P.u[0] <- C.u[0] + * P.u[1] <- C.u[1] + * P.v[0] <- u[2] + * P.v[1] <- u[3] + * sum.r[0] <- u[0] + * sum.r[1] <- u[1] + * sum.y[0] <- P.y[0] + * sum.y[1] <- P.y[1] + + Outputs: + * y[0] <- P.y[0] + * y[1] <- P.y[1] + * y[2] <- P.z[0] + * y[3] <- P.z[1] + * y[4] <- C.u[0] + * y[5] <- C.u[1] + + A = [[-4. 0. 0. 0.] + [ 0. -6. 0. 0.] + [ 0. 0. -3. 0.] + [ 0. 0. 0. -4.]] + + B = [[3. 0. 0. 0.] + [0. 4. 0. 0.] + [0. 0. 1. 0.] + [0. 0. 0. 1.]] + + C = [[ 1. 0. 0. 0.] + [ 0. 1. 0. 0.] + [ 0. 0. 1. 0.] + [ 0. 0. 0. 1.] + [-3. 0. 0. 0.] + [ 0. -4. 0. 0.]] + + D = [[0. 0. 0. 0.] + [0. 0. 0. 0.] + [0. 0. 0. 0.] + [0. 0. 0. 0.] + [3. 0. 0. 0.] + [0. 4. 0. 0.]] + + Automated creation of state feedback systems --------------------------------------------- +============================================ + +A common architecture in state space feedback control is to use a +linear control law to stabilize a system around a trajectory. The +python-control package can create input/output systems that help +implement this architecture. + -The :func:`~control.create_statefbk_iosystem` function can be used to -create an I/O system consisting of a state feedback gain (with -optional integral action and gain scheduling) and an estimator. A -basic state feedback controller of the form +Standard design patterns +------------------------ + +The :func:`create_statefbk_iosystem` function can be used to create an +I/O system consisting of a state feedback gain (with optional integral +action and gain scheduling) and an estimator. A basic state feedback +controller of the form .. math:: @@ -464,30 +663,191 @@ can be created with the command:: ctrl, clsys = ct.create_statefbk_iosystem(sys, K) -where `sys` is the process dynamics and `K` is the state feedback gain +where :code:`sys` is the process dynamics and `K` is the state feedback gain (e.g., from LQR). The function returns the controller `ctrl` and the closed loop systems `clsys`, both as I/O systems. The input to the controller is the vector of desired states :math:`x_\text{d}`, desired inputs :math:`u_\text{d}`, and system states :math:`x`. +If an `InputOutputSystem` is passed instead of the gain `K`, the error +e = x - xd is passed to the system and the output is used as the +feedback compensation term. + +The above design pattern is referred to as the "trajectory generation" +('trajgen') pattern, since it assumes that the input to the controller is a +feasible trajectory :math:`(x_\text{d}, u_\text{d})`. Alternatively, a +controller using the "reference gain" pattern can be created, which +implements a state feedback controller of the form + +.. math:: + + u = k_\text{f}\, r - K x, + +where :math:`r` is the reference input and :math:`k_\text{f}` is the +feedforward gain (normally chosen so that the steady state output +:math:`y_\text{ss}` will be equal to :math:`r`). + +A reference gain controller can be created with the command:: + + ctrl, clsys = ct.create_statefbk_iosystem( + sys, K, kf, feedfwd_pattern='refgain') + +This reference gain design pattern is described in more detail in +`Feedback Systems `_, Section 7.2 (Stabilization +by State Feedback) and the trajectory generation design pattern is +described in Section 8.5 (State Space Controller Design). + + +Adding state estimation +----------------------- + If the full system state is not available, the output of a state estimator can be used to construct the controller using the command:: ctrl, clsys = ct.create_statefbk_iosystem(sys, K, estimator=estim) -where `estim` is the state estimator I/O system. The controller will +where `estim` is a state estimator I/O system. The controller will have the same form as above, but with the system state :math:`x` replaced by the estimated state :math:`\hat x` (output of `estim`). The closed loop controller will include both the state feedback and the estimator. +An estimator for a linear system should use the process inputs +:math:`u` and outputs :math:`y` to generate an estimate :math:`\hat x` +of the process state. An optimal estimator (Kalman) filter can be +constructed using the :func:`create_estimator_iosystem` command:: + + estim = ct.create_estimator_iosystem(sys, QN, RN) + +where `QN` is covariance matrix for the process disturbances (assumed +by default to enter at the process inputs) and `RN` is the covariance +matrix for the measurement noise. + +As an example, consider a simple double integrator linear system with +an LQR controller: + +.. testsetup:: statefbk + + import numpy as np + import control as ct + +.. testcode:: statefbk + + # System + sys = ct.ss([[0, 1], [0, 0]], [[0], [1]], [[1, 0]], 0, name='sys') + + # Controller + K, _, _ = ct.lqr(sys, np.eye(2), np.eye(1)) + +We construct an estimator for the system assuming disturbance and +noise intensity of 0.01: + +.. testcode:: statefbk + + # Estimator + estim = ct.create_estimator_iosystem(sys, 0.01, 0.01, name='estim') + +resulting in the following dynamics: + +.. doctest:: statefbk + + >>> print(estim) + : estim + Inputs (2): ['y[0]', 'u[0]'] + Outputs (2): ['xhat[0]', 'xhat[1]'] + States (6): ['xhat[0]', 'xhat[1]', 'P[0,0]', 'P[0,1]', 'P[1,0]', 'P[1,1]'] + + Update: ._estim_update at 0x...> + Output: ._estim_output at 0x...> + +The estimator is a nonlinear system with states consisting of the +estimates of the process states (:math:`\hat x`) and the entries of +the covariance of the state error (:math:`P`). The estimator dynamics +are given by + +.. math:: + + \dot {\hat x} &= A \hat x + B u - L (C \hat x - y), \\ + \dot P &= A P + P A^\mathsf{T} + - P C^\mathsf{T} Q_w^{-1} C P + F Q_v F^\mathsf{T}, + +where :math:`L` is the estimator gain and :math:`F` is the mapping +from disturbance signals to the state dynamics (see +`create_estimator_iosystem` and `Optimization-Based Control +`_, Chapter 6 [Kalman Filtering] for more +detailed information). + +We can now create the entire closed loop system using the estimated state: + +.. testcode:: statefbk + + # Estimation-based controller + ctrl, clsys = ct.create_statefbk_iosystem( + sys, K, estimator=estim, name='ctrl') + +The resulting controller is given by + +.. doctest:: statefbk + + >>> print(ctrl) + : ctrl + Inputs (5): ['xd[0]', 'xd[1]', 'ud[0]', 'xhat[0]', 'xhat[1]'] + Outputs (1): ['u[0]'] + States (0): [] + + A = [] + + B = [] + + C = [] + + D = [[ 1. 1.73205081 1. -1. -1.73205081]] + +Note that controller input signals have automatically been named to +match the estimator output signals. The full closed loop system is +given by + +.. doctest:: statefbk + + >>> print(clsys) + : sys_ctrl + Inputs (3): ['xd[0]', 'xd[1]', 'ud[0]'] + Outputs (2): ['y[0]', 'u[0]'] + States (8): ['sys_x[0]', 'sys_x[1]', 'estim_xhat[0]', 'estim_xhat[1]', 'estim_P[0,0]', 'estim_P[0,1]', 'estim_P[1,0]', 'estim_P[1,1]'] + + Subsystems (3): + * ['y[0]']> + * + ['u[0]']> + * ['xhat[0]', 'xhat[1]']> + + Connections: + * sys.u[0] <- ctrl.u[0] + * ctrl.xd[0] <- xd[0] + * ctrl.xd[1] <- xd[1] + * ctrl.ud[0] <- ud[0] + * ctrl.xhat[0] <- estim.xhat[0] + * ctrl.xhat[1] <- estim.xhat[1] + * estim.y[0] <- sys.y[0] + * estim.u[0] <- ctrl.u[0] + + Outputs: + * y[0] <- sys.y[0] + * u[0] <- ctrl.u[0] + +We see that the state of the full closed loop system consists of the +process states as well as the estimated states and the entries of the +covariance matrix. + +Adding integral action +---------------------- + Integral action can be included using the `integral_action` keyword. -The value of this keyword can either be a matrix (ndarray) or a -function. If a matrix :math:`C` is specified, the difference between -the desired state and system state will be multiplied by this matrix -and integrated. The controller gain should then consist of a set of -proportional gains :math:`K_\text{p}` and integral gains -:math:`K_\text{i}` with +The value of this keyword should be a matrix (ndarray). The +difference between the desired state and system state will be +multiplied by this matrix and integrated. The controller gain should +then consist of a set of proportional gains :math:`K_\text{p}` and +integral gains :math:`K_\text{i}` with .. math:: @@ -497,20 +857,112 @@ and the control action will be given by .. math:: - u = u_\text{d} - K\text{p} (x - x_\text{d}) - + u = u_\text{d} - K_\text{p} (x - x_\text{d}) - K_\text{i} \int C (x - x_\text{d}) dt. -If `integral_action` is a function `h`, that function will be called -with the signature `h(t, x, u, params)` to obtain the outputs that -should be integrated. The number of outputs that are to be integrated +.. TODO: If `integral_action` is a function ``h``, that function will + be called with the signature ``h(t, x, u, params)`` to obtain the + outputs that should be integrated. + +The number of outputs that are to be integrated must match the number of additional columns in the `K` matrix. If an estimator is specified, :math:`\hat x` will be used in place of :math:`x`. -Finally, gain scheduling on the desired state, desired input, or -system state can be implemented by setting the gain to a 2-tuple -consisting of a list of gains and a list of points at which the gains -were computed, as well as a description of the scheduling variables:: +As an example, consider the servo-mechanism model `servomech` +described in :ref:`creating nonlinear models `. +We construct a state space controller by linearizing the system around +an equilibrium point, augmenting the model with an integrator, and +computing a state feedback that optimizes a quadratic cost function: + +.. testsetup:: integral_action + + import numpy as np + import control as ct + + # Parameter values + servomech_params = { + 'J': 100, # Moment of inertia of the motor + 'b': 10, # Angular damping of the arm + 'k': 1, # Spring constant + 'r': 1, # Location of spring contact on arm + 'l': 2, # Distance to the read head + } + + # State derivative + def servomech_update(t, x, u, params): + # Extract the configuration and velocity variables from the state vector + theta = x[0] # Angular position of the disk drive arm + thetadot = x[1] # Angular velocity of the disk drive arm + tau = u[0] # Torque applied at the base of the arm + + # Get the parameter values + J, b, k, r = map(params.get, ['J', 'b', 'k', 'r']) + + # Compute the angular acceleration + dthetadot = 1/J * ( + -b * thetadot - k * r * np.sin(theta) + tau) + + # Return the state update law + return np.array([thetadot, dthetadot]) + +.. testcode:: integral_action + + # System dynamics (with full state output) + servomech = ct.nlsys( + servomech_update, None, name='servomech', + params=servomech_params, states=['theta', 'thdot'], + outputs=['theta', 'thdot'], inputs=['tau']) + + # Find operating point with output angle pi/4 + xeq, ueq = ct.find_operating_point( + servomech, [0, 0], 0, y0=[np.pi/4, 0], iy=0) + + # Compute linearization and augment with an integrator on angle + A, B, _, _ = ct.ssdata(servomech.linearize(xeq, ueq)) + C = np.array([[1, 0]]) # theta + A_aug = np.block([ + [A, np.zeros((2, 1))], + [C, np.zeros((1, 1))] + ]) + B_aug = np.block([[B], [0]]) + + # Compute LQR controller + K, _, _ = ct.lqr(A_aug, B_aug, np.diag([1, 1, 0.1]), 1) + + # Create controller with integral action + ctrl, _ = ct.create_statefbk_iosystem( + servomech, K, integral_action=C, name='ctrl') + +The resulting controller now has internal dynamics corresponding to +the integral action: + +.. doctest:: integral_action + + >>> print(ctrl) + : ctrl + Inputs (5): ['xd[0]', 'xd[1]', 'ud[0]', 'theta', 'thdot'] + Outputs (1): ['tau'] + States (1): ['x[0]'] + + A = [[0.]] + + B = [[-1. 0. 0. 1. 0.]] + + C = [[-0.31622777]] + + D = [[ 3.76244547 19.21453568 1. -3.76244547 -19.21453568]] + + +Adding gain scheduling +---------------------- + +Finally, for the trajectory generation design pattern, gain scheduling +on the desired state :math:`x_\text{d}`, desired input +:math:`u_\text{d}`, or current state :math:`x` can be implemented by +setting the gain to a 2-tuple consisting of a list of gains and a list +of points at which the gains were computed, as well as a description +of the scheduling variables:: ctrl, clsys = ct.create_statefbk_iosystem( sys, ([g1, ..., gN], [p1, ..., pN]), gainsched_indices=[s1, ..., sq]) @@ -524,33 +976,79 @@ controller implemented in this case has the form u = u_\text{d} - K(\mu) (x - x_\text{d}) -where :math:`\mu` represents the scheduling variables. See -:ref:`steering-gainsched.py` for an example implementation of a gain -scheduled controller (in the alternative formulation section at the -bottom of the file). +where :math:`\mu` represents the scheduling variables. See :ref:`gain +scheduled control for vehicle steering ` for an +example implementation of a gain scheduled controller (in the +alternative formulation section at the bottom of the file). -Integral action and state estimation can also be used with gain -scheduled controllers. +As an example, consider the following simple model of a mobile robot +("unicycle" model), which has dynamics given by +.. math:: -Module classes and functions -============================ + \frac{dx}{dt} &= v \cos\theta \\ + \frac{dy}{dt} &= v \sin\theta \\ + \frac{d\theta}{dt} &= \omega -.. autosummary:: - :toctree: generated/ - :template: custom-class-template.rst +where :math:`x`, :math:`y` is the position of the robot in the plane, +:math:`\theta` is the angle with respect to the :math:`x` axis, +:math:`v` is the commanded velocity, and :math:`\omega` is the +commanded angular rate. - ~control.InputOutputSystem - ~control.InterconnectedSystem - ~control.LinearICSystem - ~control.NonlinearIOSystem +We define the nonlinear dynamics as follows: -.. autosummary:: - :toctree: generated/ - - ~control.find_eqpt - ~control.interconnect - ~control.input_output_response - ~control.linearize - ~control.nlsys - ~control.summing_junction +.. testsetup:: gainsched + + import itertools + import numpy as np + import control as ct + +.. testcode:: gainsched + + def unicycle_update(t, x, u, params): + return np.array([u[0] * np.cos(x[2]), u[0] * np.sin(x[2]), u[1]]) + + unicycle = ct.nlsys( + unicycle_update, None, name='unicycle', states=3, + inputs=['v', 'omega'], outputs=['x', 'y', 'theta']) + +We construct a gain-scheduled controller by linearizing the dynamics +about a range of different speeds :math:`v` and angles :math:`\theta`: + +.. testcode:: gainsched + + # Speeds and angles at which to compute the gains + speeds = [1, 5, 10] + angles = np.linspace(0, np.pi/2, 4) + points = list(itertools.product(speeds, angles)) + + # Gains for each speed (using LQR controller) + Q = np.identity(unicycle.nstates) + R = np.identity(unicycle.ninputs) + gains = [np.array(ct.lqr(unicycle.linearize( + [0, 0, angle], [speed, 0]), Q, R)[0]) for speed, angle in points] + + # Create gain scheduled controller + ctrl, clsys = ct.create_statefbk_iosystem( + unicycle, (gains, points), gainsched_indices=['v_d', 'th_d'], name='ctrl', + inputs=['x_d', 'y_d', 'th_d', 'v_d', 'omega_d', 'x', 'y', 'theta']) + +The resulting controller has the following structure: + +.. doctest:: gainsched + + >>> print(ctrl) + : ctrl + Inputs (8): ['x_d', 'y_d', 'th_d', 'v_d', 'omega_d', 'x', 'y', 'theta'] + Outputs (2): ['v', 'omega'] + States (0): [] + + Update: ._control_update at 0x...> + Output: ._control_output at 0x...> + +This is a static, nonlinear controller, with the gains scheduled based +on the values of :math:`v_\text{d}` (index 3) and +:math:`\theta_\text{d}` (index 2). + +Integral action and state estimation can also be used with gain +scheduled controllers. diff --git a/doc/kincar-flatsys.py b/doc/kincar-flatsys.py deleted file mode 120000 index 7ef7d684e..000000000 --- a/doc/kincar-flatsys.py +++ /dev/null @@ -1 +0,0 @@ -../examples/kincar-flatsys.py \ No newline at end of file diff --git a/doc/kincar-fusion.ipynb b/doc/kincar-fusion.ipynb deleted file mode 120000 index def600898..000000000 --- a/doc/kincar-fusion.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/kincar-fusion.ipynb \ No newline at end of file diff --git a/doc/linear.rst b/doc/linear.rst new file mode 100644 index 000000000..a9960feca --- /dev/null +++ b/doc/linear.rst @@ -0,0 +1,600 @@ +.. currentmodule:: control + +******************************************** +Linear System Modeling, Analysis, and Design +******************************************** + +Linear time invariant (LTI) systems are represented in `python-control` in +state space, transfer function, or frequency response data (FRD) form. Most +functions in the toolbox will operate on any of these data types, and +functions for converting between compatible types are provided. + + +Creating LTI Systems +==================== + +LTI systems are created using "factory functions" that accept the +parameters required to define the system. Three factory functions are +available for LTI systems: + +.. autosummary:: + + ss + tf + frd + +Each of these functions returns an object of an appropriate class to +represent the system. + + +State space systems +------------------- + +The :class:`StateSpace` class is used to represent state-space realizations +of linear time-invariant (LTI) systems: + +.. math:: + + \frac{dx}{dt} &= A x + B u \\ + y &= C x + D u + +where :math:`u` is the input, :math:`y` is the output, and :math:`x` +is the state. All vectors and matrices must be real-valued. + +To create a state space system, use the :func:`ss` function: + +.. testsetup:: statesp + + A = np.diag([-1, -2]) + B = np.eye(2) + C = np.eye(1, 2) + D = np.zeros((1, 2)) + +.. testcode:: statesp + + sys = ct.ss(A, B, C, D) + +State space systems can be manipulated using standard arithmetic +operations as well as the :func:`feedback`, :func:`parallel`, and +:func:`series` function. A full list of "block diagram algebra" +functions can be found in the :ref:`interconnections-ref` section of +the :ref:`function-ref`. + +Systems, inputs, outputs, and states can be given labels to allow more +customized access to system information: + +.. testcode:: statesp + + sys = ct.ss( + A, B, C, D, name='sys', + states=['x1', 'x2'], inputs=['u1', 'u2'], outputs=['y']) + +The :func:`rss` function can be used to create a random state space +system with a desired number or inputs, outputs, and states: + +.. testcode:: statesp + + sys = ct.rss(states=4, outputs=1, inputs=1, strictly_proper=True) + +The `states`, `inputs`, and `output` parameters can also be +given as lists of strings to create named signals. All systems +generated by :func:`rss` are stable. + + +Transfer functions +------------------ + +The :class:`TransferFunction` class is used to represent input/output +transfer functions + +.. math:: + + G(s) = \frac{\text{num}(s)}{\text{den}(s)} + = \frac{a_0 s^m + a_1 s^{m-1} + \cdots + a_m} + {b_0 s^n + b_1 s^{n-1} + \cdots + b_n}, + +where :math:`n` is greater than or equal to :math:`m` for a proper +transfer function. Improper transfer functions are also allowed. All +coefficients must be real-valued. + +To create a transfer function, use the :func:`tf` function:: + + num = [a0, a1, ..., am] + den = [b0, b1, ..., bn] + + sys = ct.tf(num, den) + +The system name as well as input and output labels can be specified in +the same way as state space systems: + +.. testsetup:: xferfcn + + num = [1, 2] + den = [3, 4] + +.. testcode:: xferfcn + + sys = ct.tf(num, den, name='sys', inputs=['u'], outputs=['y']) + +Transfer functions can be manipulated using standard arithmetic +operations as well as the :func:`feedback`, :func:`parallel`, and +:func:`series` functions. A full list of "block diagram algebra" +functions can be found in the :ref:`interconnections-ref` section of the +:ref:`function-ref`. + +To aid in the construction of transfer functions, the :func:`tf` +factory function can used to create transfer function corresponding +to the derivative or difference operator: + +.. testcode:: xferfcn + + s = ct.tf('s') + +Standard algebraic operations can be used to construct more +complicated transfer functions: + +.. testcode:: xferfcn + + sys = 5 * (s + 10)/(s**2 + 2*s + 1) + +Transfer functions can be evaluated at a point in the complex plane by +calling the transfer function object: + +.. testcode:: xferfcn + + val = sys(1 + 0.5j) + +Discrete time transfer functions (described in more detail below) can +be created using ``z = ct.tf('z')``. + + +Frequency response data (FRD) systems +------------------------------------- + +The :class:`FrequencyResponseData` (FRD) class is used to represent +systems in frequency response data form. The main data attributes are +`omega` and `frdata`, where `omega` is a 1D array of frequencies (in +rad/sec) and `frdata` is the (complex-value) value of the transfer +function at each frequency point. + +FRD systems can be created with the :func:`frd` factory function: + +.. testsetup:: frdata + + sys_lti = ct.rss(2, 2, 2) + lti_resp = ct.frequency_response(sys_lti) + frdata = lti_resp.complex + omega = lti_resp.frequency + +.. testcode:: frdata + + sys = ct.frd(frdata, omega) + +FRD systems can also be created by evaluating an LTI system at a given +set of frequencies: + +.. testcode:: frdata + + frd_sys = ct.frd(sys_lti, omega) + +Frequency response data systems have a somewhat more limited set of +functions that are available, although all of the standard algebraic +manipulations can be performed. + +The FRD class is also used as the return type for the +:func:`frequency_response` function. This object can be assigned to a +tuple using: + +.. testcode:: frdata + + response = ct.frequency_response(sys_lti) + mag, phase, omega = response + +where `mag` is the magnitude (absolute value, not dB or log10) of the +system frequency response, `phase` is the wrapped phase in radians of +the system frequency response, and `omega` is the (sorted) frequencies +at which the response was evaluated. + +Frequency response properties are also available as named attributes of +the `response` object: `response.magnitude`, `response.phase`, +and `response.response` (for the complex response). + + +Multi-input, multi-output (MIMO) systems +---------------------------------------- + +Multi-input, multi-output (MIMO) systems are created by providing +parameters of the appropriate dimensions to the relevant factory +function. For state space systems, the input matrix `B`, output +matrix `C`, and direct term `D` should be 2D matrices of the +appropriate shape. For transfer functions, this is done by providing +a 2D list of numerator and denominator polynomials to the :func:`tf` +function, e.g.: + +.. testsetup:: mimo + + sys = ct.tf(ct.rss(4, 2, 2)) + [[num11, num12], [num21, num22]] = sys.num_list + [[den11, den12], [den21, den22]] = sys.den_list + + A, B, C, D = ct.ssdata(ct.rss(4, 3, 2)) # 3 output, 2 input + +.. testcode:: mimo + + sys = ct.tf( + [[num11, num12], [num21, num22]], + [[den11, den12], [den21, den22]]) + +Similarly, MIMO frequency response data (FRD) systems are created by +providing the :func:`frd` function with a 3D array of response +values,with the first dimension corresponding to the output index of +the system, the second dimension corresponding to the input index, and +the 3rd dimension corresponding to the frequency points in `omega`. + +Signal names for MIMO systems are specified using lists of labels: + +.. testcode:: mimo + + sys = ct.ss(A, B, C, D, inputs=['u1', 'u2'], outputs=['y1', 'y2', 'y3']) + +Signals that are not given explicit labels are given labels of the +form 's[i]' where the default value of 's' is 'x' for states, 'u' for +inputs, and 'y' for outputs, and 'i' ranges over the dimension of the +signal (starting at 0). + +Subsets of input/output pairs for LTI systems can be obtained by +indexing the system using either numerical indices (including slices) +or signal names: + +.. testcode:: mimo + + subsys = sys[[0, 2], 0:2] + subsys = sys[['y1', 'y3'], ['u1', 'u2']] + +Signal names for an indexed subsystem are preserved from the original +system and the subsystem name is set according to the values of +`config.defaults['iosys.indexed_system_name_prefix']` and +`config.defaults['iosys.indexed_system_name_suffix']` (see +:ref:`package-configuration-parameters` for more information). The +default subsystem name is the original system name with '$indexed' +appended. + +For FRD objects, the frequency response properties for MIMO systems +can be accessed using the names of the inputs and outputs: + +.. testcode:: frdata + + response.magnitude['y[0]', 'u[1]'] + +where the signal names are based on the system that generated the frequency +response. + +.. note:: If a system is single-input, single-output (SISO), + `magnitude` and `phase` default to 1D arrays, indexed by + frequency. If the system is not SISO or `squeeze` is set to + False generating the response, the array is 3D, indexed by + the output, input, and frequency. If `squeeze` is True for + a MIMO system then single-dimensional axes are removed. The + processing of the `squeeze` keyword can be changed by + calling the response function with a new argument:: + + mag, phase, omega = response(squeeze=False) + +.. note:: The `frdata` data member is stored as a NumPy array and + cannot be accessed with signal names. Use + `response.complex` to access the complex frequency response + using signal names. + + +.. _discrete_time_systems: + +Discrete Time Systems +===================== + +A discrete-time system is created by specifying a nonzero "timebase" +`dt` when the system is constructed: + +.. testsetup:: dtime + + A, B, C, D = ct.ssdata(ct.rss(2, 1, 1)) + num, den = ct.tfdata(ct.rss(2, 1, 1)) + dt = 0.1 + +.. testcode:: dtime + + sys_ss = ct.ss(A, B, C, D, dt) + sys_tf = ct.tf(num, den, dt) + +The timebase argument is interpreted as follows: + +* `dt` = 0: continuous-time system (default) +* `dt` > 0: discrete-time system with sampling period `dt` +* `dt` = True: discrete time with unspecified sampling period +* `dt` = None: no timebase specified (see below) + +Systems must have compatible timebases in order to be combined. A +discrete-time system with unspecified sampling time (`dt` = True) can +be combined with a system having a specified sampling time; the result +will be a discrete-time system with the sample time of the other +system. Similarly, a system with timebase None can be combined with a +system having a specified timebase; the result will have the timebase +of the other system. For continuous-time systems, the +:func:`sample_system` function or the :meth:`StateSpace.sample` and +:meth:`TransferFunction.sample` methods can be used to create a +discrete-time system from a continuous-time system. The default value +of `dt` can be changed by changing the value of +`config.defaults['control.default_dt']`. + +Functions operating on LTI systems will take into account whether a +system is continuous time or discrete time when carrying out operations +that depend on this difference. For example, the :func:`rss` function +will place all system eigenvalues within the unit circle when called +using `dt` corresponding to a discrete-time system: + +.. testsetup:: + + import random + random.seed(117) + np.random.seed(117) + +.. doctest:: + + >>> sys = ct.rss(2, 1, 1, dt=True) + >>> sys.poles() + array([-0.53807661+0.j, 0.86313342+0.j]) + + +.. include:: statesp.rst + +.. include:: xferfcn.rst + + +Model Conversion and Reduction +============================== + +A variety of functions are available to manipulate LTI systems, +including functions for converting between state space and frequency +domain, sampling systems in time and frequency domain, and creating +reduced order models. + + +Conversion between representations +---------------------------------- + +LTI systems can be converted between representations either by calling +the factory function for the desired data type using the original +system as the sole argument or using the explicit conversion functions +:func:`ss2tf` and :func:`tf2ss`. In most cases these types of +explicit conversions are not necessary, since functions designed to +operate on LTI systems will work on any subclass. + +To explicitly convert a state space system into a transfer function +representation, the state space system can be passed as an argument to +the :func:`tf` factory functions: + +.. testcode:: convert + + sys_ss = ct.rss(4, 2, 2, name='sys_ss') + sys_tf = ct.tf(sys_ss, name='sys_tf') + +The :func:`ss2tf` function can also be used, passing either the state +space system or the matrices that represent the state space systems: + +.. testcode:: convert + :hide: + + A, B, C, D = ct.ssdata(sys_ss) + +.. testcode:: convert + + sys_tf = ct.ss2tf(A, B, C, D) + +In either form, system and signal names can be changed by passing the +appropriate keyword arguments. + +Conversion of transfer functions to state space form is also possible: + +.. testcode:: convert + :hide: + + num, den = ct.tfdata(sys_tf) + +.. testcode:: convert + + sys_ss = ct.ss(sys_tf) + sys_ss = ct.tf2ss(sys_tf) + sys_ss = ct.tf2ss(num, den) + +.. note:: State space realizations of transfer functions are not + unique and the state space representation obtained via these + functions may not match realizations obtained by other + algorithms. + + +Time sampling +------------- + +Continuous time systems can be converted to discrete-time systems using +the :func:`sample_system` function and specifying a sampling time: + +.. doctest:: + + >>> sys_ct = ct.rss(4, 2, 2, name='sys') + >>> sys_dt = ct.sample_system(sys_ct, 0.1, method='bilinear') + >>> print(sys_dt) + : sys$sampled + Inputs (2): ['u[0]', 'u[1]'] + Outputs (2): ['y[0]', 'y[1]'] + States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]'] + dt = 0.1 + + A = [[-0.79324497 -0.51484336 -1.09297036 -0.05363047] + [-3.5428559 -0.9340972 -1.85691838 -0.74843144] + [ 3.90565206 1.9409475 3.21968314 0.48558594] + [ 3.47315264 1.55258121 2.09562768 1.25466845]] + + B = [[-0.01098544 0.00485652] + [-0.41579876 0.02204956] + [ 0.45553908 -0.02459682] + [ 0.50510046 -0.05448362]] + + C = [[-2.74490135 -0.3064149 -2.27909612 -0.64793559] + [ 2.56376145 1.09663807 2.4332544 0.30768752]] + + D = [[-0.34680884 0.02138098] + [ 0.29124186 -0.01476461]] + +Note that the system name for the discrete-time system is the name of +the original system with the string '$sampled' appended. + +Discrete time systems can also be created using the +:func:`StateSpace.sample` or :func:`TransferFunction.sample` methods +applied directly to the system:: + + sys_dt = sys_ct.sample(0.1) + + +Frequency sampling +------------------ + +Transfer functions can be sampled at a selected set of frequencies to +obtain a frequency response data representation of a system by calling +the :func:`frd` factory function with an LTI system and an +array of frequencies: + +.. doctest:: + + >>> sys_ss = ct.rss(4, 1, 1, name='sys_ss') + >>> sys_frd = ct.frd(sys_ss, np.logspace(-1, 1, 5)) + >>> print(sys_frd) + : sys_ss$sampled + Inputs (1): ['u[0]'] + Outputs (1): ['y[0]'] + + Freq [rad/s] Response + ------------ --------------------- + 0.100 -0.2648+0.0006429j + 0.316 -0.2653 +0.003783j + 1.000 -0.2561 +0.008021j + 3.162 -0.2528 -0.001438j + 10.000 -0.2578 -0.002443j + +The :func:`frequency_response` function can also be used for this +purpose, although in that case the output is usually used for plotting +the frequency response, as described in more detail in the +:ref:`frequency_response` section. + + +Model reduction +--------------- + +Reduced order models for LTI systems can be obtained by approximating +the system by a system of lower order that has similar input/output +properties. A variety of functions are available in the +python-control package that perform various types of model +simplification: + +.. autosummary:: + + balanced_reduction + minimal_realization + model_reduction + +The :func:`balanced_reduction` function eliminate states based on the +Hankel singular values of a system. Intuitively, a system (or +subsystem) with small Hankel singular values corresponds to a situation +in which it is difficult to observe a state and/or difficult to +control that state. Eliminating states corresponding to small Hankel +singular values thus represents a good approximation in terms of the +input/output properties of a system. For systems with unstable modes, +:func:`balanced_reduction` first removes the states corresponding to +the unstable subspace from the system, then carries out a balanced +realization on the stable part, and then reinserts the unstable modes. + +The :func:`minimal_realization` function eliminates uncontrollable or +unobservable states in state space models or cancels pole-zero pairs +in transfer functions. The resulting output system has minimal order +and the same input/output response characteristics as the original +model system. Unlike the :func:`balanced_reduction` function, the +:func:`minimal_realization` eliminates all uncontrollable and/or +unobservable modes, so should be used with caution if applied to an +unstable system. + +The :func:`model_reduction` function produces a reduced-order model of +a system by eliminating specified inputs, outputs, and/or states from +the original system. The specific states, inputs, or outputs that are +eliminated can be specified by either listing the states, inputs, or +outputs to be eliminated or those to be kept. Two methods of state +reduction are possible: 'truncate' removes the states marked for +elimination, while 'matchdc' replaces the eliminated states with their +equilibrium values (thereby keeping the input/output gain unchanged at +zero frequency ["DC"]). + + +Displaying LTI System Information +================================= + +Information about an LTI system can be obtained using the Python +`~python.print` function: + +.. doctest:: + + >>> sys = ct.rss(4, 2, 2, name='sys_2x2') + >>> print(sys) + : sys_2x2 + Inputs (2): ['u[0]', 'u[1]'] + Outputs (2): ['y[0]', 'y[1]'] + States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]'] + + A = [[-2.06417506 0.28005277 0.49875395 -0.40364606] + [-0.18000232 -0.91682581 0.03179904 -0.16708786] + [-0.7963147 0.19042684 -0.72505525 -0.52196969] + [ 0.69457346 -0.20403756 -0.59611373 -0.94713748]] + + B = [[-2.3400013 -1.02252469] + [-0.76682007 -0. ] + [ 0.13399373 0.94404387] + [ 0.71412443 -0.45903835]] + + C = [[ 0.62432205 -0.55879494 -0.08717116 1.05092654] + [-0.94352373 0.19332285 1.05341936 0.60141772]] + + D = [[ 0. 0.] + [-0. 0.]] + +A loadable description of a system can be obtained just by displaying +the system object: + +.. doctest:: + + >>> sys = ct.rss(2, 1, 1, name='sys_siso') + >>> sys + StateSpace( + array([[ 0.91008302, -0.87770371], + [ 6.83039608, -5.19117213]]), + array([[0.9810374], + [0.516694 ]]), + array([[1.38255365, 0.96999883]]), + array([[-0.]]), + name='sys_siso', states=2, outputs=1, inputs=1) + +Alternative representations of the system are available using the +:func:`iosys_repr` function and can be configured using +`config.defaults['iosys.repr_format']`. + +Transfer functions are displayed as ratios of polynomials, using +either 's' or 'z' depending on whether the systems is continuous or +discrete time: + +.. doctest:: + + >>> sys_tf = ct.tf([1, 0], [1, 2, 1], 0.1, name='sys') + >>> print(sys_tf) + : sys + Inputs (1): ['u[0]'] + Outputs (1): ['y[0]'] + dt = 0.1 + + z + ------------- + z^2 + 2 z + 1 diff --git a/doc/matlab.rst b/doc/matlab.rst index eac1d157a..42f1e6eb2 100644 --- a/doc/matlab.rst +++ b/doc/matlab.rst @@ -1,7 +1,7 @@ .. _matlab-module: **************************** - MATLAB compatibility module + MATLAB Compatibility Module **************************** .. automodule:: control.matlab @@ -9,7 +9,11 @@ :no-inherited-members: :no-special-members: -Creating linear models +.. warning:: This module is not closely maintained and some + functionality in the main python-control package may not + be be available via the MATLAB compatibility module. + +Creating Linear Models ====================== .. autosummary:: :toctree: generated/ @@ -17,10 +21,9 @@ Creating linear models tf ss frd - rss - drss + zpk -Utility functions and conversions +Utility Functions and Conversions ================================= .. autosummary:: :toctree: generated/ @@ -32,7 +35,7 @@ Utility functions and conversions tf2ss tfdata -System interconnections +System Interconnections ======================= .. autosummary:: :toctree: generated/ @@ -44,7 +47,7 @@ System interconnections connect append -System gain and dynamics +System Gain and Dynamics ======================== .. autosummary:: :toctree: generated/ @@ -55,7 +58,7 @@ System gain and dynamics damp pzmap -Time-domain analysis +Time-Domain Analysis ==================== .. autosummary:: :toctree: generated/ @@ -64,20 +67,22 @@ Time-domain analysis impulse initial lsim + stepinfo -Frequency-domain analysis +Frequency-Domain Analysis ========================= .. autosummary:: :toctree: generated/ bode nyquist - nichols margin + nichols + ngrid freqresp evalfr -Compensator design +Compensator Design ================== .. autosummary:: :toctree: generated/ @@ -90,7 +95,7 @@ Compensator design lqe dlqe -State-space (SS) models +State-space (SS) Models ======================= .. autosummary:: :toctree: generated/ @@ -101,7 +106,7 @@ State-space (SS) models obsv gram -Model simplification +Model Simplification ==================== .. autosummary:: :toctree: generated/ @@ -113,14 +118,14 @@ Model simplification era markov -Time delays +Time Delays =========== .. autosummary:: :toctree: generated/ pade -Matrix equation solvers and linear algebra +Matrix Equation Solvers and Linear Algebra ========================================== .. autosummary:: :toctree: generated/ @@ -130,7 +135,7 @@ Matrix equation solvers and linear algebra care dare -Additional functions +Additional Functions ==================== .. autosummary:: :toctree: generated/ @@ -138,8 +143,8 @@ Additional functions gangof4 unwrap -Functions imported from other modules -===================================== +Functions Imported from Other Packages +====================================== .. autosummary:: ~numpy.linspace diff --git a/doc/mhe-pvtol.ipynb b/doc/mhe-pvtol.ipynb deleted file mode 120000 index 1efa2b5c9..000000000 --- a/doc/mhe-pvtol.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/mhe-pvtol.ipynb \ No newline at end of file diff --git a/doc/mpc_aircraft.ipynb b/doc/mpc_aircraft.ipynb deleted file mode 120000 index 0a3e4df42..000000000 --- a/doc/mpc_aircraft.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/mpc_aircraft.ipynb \ No newline at end of file diff --git a/doc/mrac_siso_lyapunov.py b/doc/mrac_siso_lyapunov.py deleted file mode 120000 index aaccf5585..000000000 --- a/doc/mrac_siso_lyapunov.py +++ /dev/null @@ -1 +0,0 @@ -../examples/mrac_siso_lyapunov.py \ No newline at end of file diff --git a/doc/mrac_siso_mit.py b/doc/mrac_siso_mit.py deleted file mode 120000 index b6a226f7c..000000000 --- a/doc/mrac_siso_mit.py +++ /dev/null @@ -1 +0,0 @@ -../examples/mrac_siso_mit.py \ No newline at end of file diff --git a/doc/nlsys.rst b/doc/nlsys.rst new file mode 100644 index 000000000..31c2656e4 --- /dev/null +++ b/doc/nlsys.rst @@ -0,0 +1,248 @@ +.. currentmodule:: control + +Nonlinear System Models +======================= + +Nonlinear input/output systems are represented as state space systems +of the form + +.. math:: + + \frac{dx}{dt} &= f(t, x, u, \theta), \\ + y &= h(t, x, u, \theta), + +where :math:`t` represents the current time, :math:`x \in +\mathbb{R}^n` is the system state, :math:`u \in \mathbb{R}^m` is the +system input, :math:`y \in \mathbb{R}^p` is the system output, and +:math:`\theta` represents a set of parameters. + +Discrete time systems are also supported and have dynamics of the form + +.. math:: + + x[t+1] &= f(t, x[t], u[t], \theta), \\ + y[t] &= h(t, x[t], u[t], \theta). + +A nonlinear input/output model is said to be "static" if the output +:math:`y(t)` at any given time :math:`t` depends only on the input +:math:`u(t)` at that same time :math:`t` and not on past or future +values of :math:`u`. + + +.. _sec-nonlinear-models: + +Creating nonlinear models +------------------------- + +A nonlinear system is created using the :func:`nlsys` factory function:: + + sys = ct.nlsys( + updfcn[, outfcn], inputs=m, states=n, outputs=p, [, params=params]) + +The `updfcn` argument is a function returning the state update function:: + + updfcn(t, x, u, params) -> array + +where `t` is a float representing the current time, `x` is a 1-D array +with shape (n,), `u` is a 1-D array with shape (m,), and `params` is a +dict containing the values of parameters used by the function. The +dynamics of the system can be in continuous or discrete time (use the +`dt` keyword to create a discrete-time system). + +The output function `outfcn` is used to specify the outputs of the +system and has the same calling signature as `updfcn`. If it is not +specified, then the output of the system is set equal to the system +state. Otherwise, it should return an array of shape (p,). If a +input/output system is static, the state `x` should still be passed to +the output function, but the state is ignored. + +Note that the number of states, inputs, and outputs should generally +be explicitly specified, although some operations can infer the +dimensions if they are not given when the system is created. The +`inputs`, `outputs`, and `states` keywords can also be given as lists +of strings, in which case the various signals will be given the +appropriate names. + +To illustrate the creation of a nonlinear I/O system model, consider a +simple model of a spring loaded arm driven by a motor: + +.. image:: figures/servomech-diagram.png + :width: 240 + :align: center + +The dynamics of this system can be modeled using the following code: + +.. testcode:: + + # Parameter values + servomech_params = { + 'J': 100, # Moment of inertia of the motor + 'b': 10, # Angular damping of the arm + 'k': 1, # Spring constant + 'r': 1, # Location of spring contact on arm + 'l': 2, # Distance to the read head + } + + # State derivative + def servomech_update(t, x, u, params): + # Extract the configuration and velocity variables from the state vector + theta = x[0] # Angular position of the disk drive arm + thetadot = x[1] # Angular velocity of the disk drive arm + tau = u[0] # Torque applied at the base of the arm + + # Get the parameter values + J, b, k, r = map(params.get, ['J', 'b', 'k', 'r']) + + # Compute the angular acceleration + dthetadot = 1/J * ( + -b * thetadot - k * r * np.sin(theta) + tau) + + # Return the state update law + return np.array([thetadot, dthetadot]) + + # System output (tip radial position + angular velocity) + def servomech_output(t, x, u, params): + l = params['l'] + return np.array([l * x[0], x[1]]) + + # System dynamics + servomech = ct.nlsys( + servomech_update, servomech_output, name='servomech', + params=servomech_params, states=['theta', 'thdot'], + outputs=['y', 'thdot'], inputs=['tau']) + +A summary of the model can be obtained using the string representation +of the model (via the Python `~python.print` function): + +.. doctest:: + + >>> print(servomech) + : servomech + Inputs (1): ['tau'] + Outputs (2): ['y', 'thdot'] + States (2): ['theta', 'thdot'] + Parameters: ['J', 'b', 'k', 'r', 'l'] + + Update: + Output: + + +Operating points and linearization +---------------------------------- + +A nonlinear input/output system can be linearized around an equilibrium point +to obtain a :class:`StateSpace` linear system:: + + sys_ss = ct.linearize(sys_nl, xeq, ueq) + +If the equilibrium point is not known, the +:func:`find_operating_point` function can be used to obtain an +equilibrium point. In its simplest form, `find_operating_point` finds +an equilibrium point given either the desired input or desired +output:: + + xeq, ueq = find_operating_point(sys, x0, u0) + xeq, ueq = find_operating_point(sys, x0, u0, y0) + +The first form finds an equilibrium point for a given input `u0` based +on an initial guess `x0`. The second form fixes the desired output +values `y0` and uses `x0` and `u0` as an initial guess to find the +equilibrium point. If no equilibrium point can be found, the function +returns the operating point that minimizes the state update (state +derivative for continuous-time systems, state difference for discrete +time systems). + +More complex operating points can be found by specifying which states, +inputs, or outputs should be used in computing the operating point, as +well as desired values of the states, inputs, outputs, or state +updates. See the :func:`find_operating_point` documentation for more +details. + + +Simulations and plotting +------------------------ + +To simulate an input/output system, use the +:func:`input_output_response` function:: + + resp = ct.input_output_response(sys_nl, timepts, U, x0, params) + t, y, x = resp.time, resp.outputs, resp.states + +Time responses can be plotted using the :func:`time_response_plot` +function or (equivalently) the :func:`TimeResponseData.plot` +method:: + + cplt = ct.time_response_plot(resp) # function call + cplt = resp.plot() # method call + +The resulting :class:`ControlPlot` object can be used to access +different plot elements: + +* `cplt.lines`: Array of `matplotlib.lines.Line2D` objects for each + line in the plot. The shape of the array matches the subplots shape + and the value of the array is a list of Line2D objects in that + subplot. + +* `cplt.axes`: 2D array of `matplotlib.axes.Axes` for the plot. + +* `cplt.figure`: `matplotlib.figure.Figure` containing the plot. + +* `cplt.legend`: legend object(s) contained in the plot. + +The :func:`combine_time_responses` function an be used to combine +multiple time responses into a single `TimeResponseData` object: + +.. testcode:: + + timepts = np.linspace(0, 10) + + U1 = np.sin(timepts) + resp1 = ct.input_output_response(servomech, timepts, U1) + + U2 = np.cos(2*timepts) + resp2 = ct.input_output_response(servomech, timepts, U2) + + resp = ct.combine_time_responses( + [resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]) + resp.plot(legend_loc=False) + +.. testcode:: + :hide: + + import matplotlib.pyplot as plt + plt.savefig('figures/timeplot-servomech-combined.png') + +.. image:: figures/timeplot-servomech-combined.png + :align: center + + +Nonlinear system properties +--------------------------- + +The following basic attributes and methods are available for +:class:`NonlinearIOSystem` objects: + +.. autosummary:: + + ~NonlinearIOSystem.dynamics + ~NonlinearIOSystem.output + ~NonlinearIOSystem.linearize + ~NonlinearIOSystem.__call__ + +The :func:`~NonlinearIOSystem.dynamics` method returns the right hand +side of the differential or difference equation, evaluated at the +current time, state, input, and (optionally) parameter values. The +:func:`~NonlinearIOSystem.output` method returns the system output. +For static nonlinear systems, it is also possible to obtain the value +of the output by directly calling the system with the value of the +input: + +.. doctest:: + + >>> sys = ct.nlsys( + ... None, lambda t, x, u, params: np.sin(u), inputs=1, outputs=1) + >>> sys(1) + np.float64(0.8414709848078965) + +The :func:`NonlinearIOSystem.linearize` method is equivalent to the +:func:`linearize` function. diff --git a/doc/nonlinear.rst b/doc/nonlinear.rst new file mode 100644 index 000000000..66de61c38 --- /dev/null +++ b/doc/nonlinear.rst @@ -0,0 +1,21 @@ +.. _nonlinear-systems: + +*********************************************** +Nonlinear System Modeling, Analysis, and Design +*********************************************** + +The Python Control Systems Library contains a variety of tools for +modeling, analyzing, and designing nonlinear feedback systems, +including support for simulation and optimization. This chapter +describes the primary functionality available, both in the core +python-control package and in specialized modules and subpackages. + +.. include:: nlsys.rst + +.. include:: phaseplot.rst + +.. include:: optimal.rst + +.. include:: descfcn.rst + +.. include:: flatsys.rst diff --git a/doc/optimal.rst b/doc/optimal.rst index 4df8d4861..416256893 100644 --- a/doc/optimal.rst +++ b/doc/optimal.rst @@ -1,16 +1,21 @@ +.. currentmodule:: control + .. _optimal-module: -************************** -Optimization-based control -************************** +Optimization-Based Control +========================== + +The `optimal` module contains a set of classes and functions that can +be used to solve optimal control and optimal estimation problems for +linear or nonlinear systems. The objects in this module must be +explicitly imported:: + + import control as ct + import control.optimal as opt -.. automodule:: control.optimal - :no-members: - :no-inherited-members: - :no-special-members: Optimal control problem setup -============================= +----------------------------- Consider the *optimal control problem*: @@ -65,22 +70,25 @@ can be on the input, the state, or combinations of input and state, depending on the form of :math:`g_i`. Furthermore, these constraints are intended to hold at all instants in time along the trajectory. -For a discrete time system, the same basic formulation applies except +For a discrete-time system, the same basic formulation applies except that the cost function is given by .. math:: J(x, u) = \sum_{k=0}^{N-1} L(x_k, u_k)\, dt + V(x_N). -A common use of optimization-based control techniques is the implementation -of model predictive control (also called receding horizon control). In -model predictive control, a finite horizon optimal control problem is solved, -generating open-loop state and control trajectories. The resulting control -trajectory is applied to the system for a fraction of the horizon -length. This process is then repeated, resulting in a sampled data feedback -law. This approach is illustrated in the following figure: +A common use of optimization-based control techniques is the +implementation of model predictive control (MPC, also called receding +horizon control). In model predictive control, a finite horizon +optimal control problem is solved, generating open-loop state and +control trajectories. The resulting control trajectory is applied to +the system for a fraction of the horizon length. This process is then +repeated, resulting in a sampled data feedback law. This approach is +illustrated in the following figure: -.. image:: mpc-overview.png +.. image:: figures/mpc-overview.png + :width: 640 + :align: center Every :math:`\Delta T` seconds, an optimal control problem is solved over a :math:`T` second horizon, starting from the current state. The first @@ -88,7 +96,7 @@ Every :math:`\Delta T` seconds, an optimal control problem is solved over a x(t))` is then applied to the system. If we let :math:`x_T^{\*}(\cdot; x(t))` represent the optimal trajectory starting from :math:`x(t)` then the system state evolves from :math:`x(t)` at current time :math:`t` to -:math:`x_T^{*}(\delta T, x(t))` at the next sample time :math:`t + \Delta +:math:`x_T^{*}(\Delta T, x(t))` at the next sample time :math:`t + \Delta T`, assuming no model uncertainty. In reality, the system will not follow the predicted path exactly, so that @@ -97,147 +105,46 @@ recompute the optimal path from the new state at time :math:`t + \Delta T`, extending our horizon by an additional :math:`\Delta T` units of time. This approach can be shown to generate stabilizing control laws under suitable conditions (see, for example, the FBS2e supplement on `Optimization-Based -Control `_. - -Optimal estimation problem setup -================================ - -Consider a nonlinear system with discrete time dynamics of the form - -.. math:: - :label: eq_fusion_nlsys-oep - - X[k+1] = f(X[k], u[k], V[k]), \qquad Y[k] = h(X[k]) + W[k], - -where :math:`X[k] \in \mathbb{R}^n`, :math:`u[k] \in \mathbb{R}^m`, and -:math:`Y[k] \in \mathbb{R}^p`, and :math:`V[k] \in \mathbb{R}^q` and -:math:`W[k] \in \mathbb{R}^p` represent random processes that are not -necessarily Gaussian white noise processes. The estimation problem that we -wish to solve is to find the estimate :math:`\hat x[\cdot]` that matches -the measured outputs :math:`y[\cdot]` with "likely" disturbances and -noise. - -For a fixed horizon of length :math:`N`, this problem can be formulated as -an optimization problem where we define the likelihood of a given estimate -(and the resulting noise and disturbances predicted by the model) as a cost -function. Suppose we model the likelihood using a conditional probability -density function :math:`p(x[0], \dots, x[N] \mid y[0], \dots, y[N-1])`. -Then we can pose the state estimation problem as - -.. math:: - :label: eq_fusion_oep - - \hat x[0], \dots, \hat x[N] = - \arg \max_{\hat x[0], \dots, \hat x[N]} - p(\hat x[0], \dots, \hat x[N] \mid y[0], \dots, y[N-1]) - -subject to the constraints given by equation :eq:`eq_fusion_nlsys-oep`. -The result of this optimization gives us the estimated state for the -previous :math:`N` steps in time, including the "current" time -:math:`x[N]`. The basic idea is thus to compute the state estimate that is -most consistent with our model and penalize the noise and disturbances -according to how likely they are (based on the given stochastic system -model for each). - -Given a solution to this fixed-horizon optimal estimation problem, we can -create an estimator for the state over all times by repeatedly applying the -optimization problem :eq:`eq_fusion_oep` over a moving horizon. At each -time :math:`k`, we take the measurements for the last :math:`N` time steps -along with the previously estimated state at the start of the horizon, -:math:`x[k-N]` and reapply the optimization in equation -:eq:`eq_fusion_oep`. This approach is known as a \define{moving horizon -estimator} (MHE). - -The formulation for the moving horizon estimation problem is very general -and various situations can be captured using the conditional probability -function :math:`p(x[0], \dots, x[N] \mid y[0], \dots, y[N-1]`. We start by -noting that if the disturbances are independent of the underlying states of -the system, we can write the conditional probability as +Control `_). -.. math:: - - p \bigl(x[0], \dots, x[N] \mid y[0], \dots, y[N-1]\bigr) = - p_{X[0]}(x[0])\, \prod_{k=0}^{N-1} p_V\bigl(y[k] - h(x[k])\bigr)\, - p\bigl(x[k+1] \mid x[k]\bigr). - -This expression can be further simplified by taking the log of the -expression and maximizing the function - -.. math:: - :label: eq_fusion_log-likelihood - - \log p_{X[0]}(x[0]) + \sum_{k=0}^{N-1} \log - p_W \bigl(y[k] - h(x[k])\bigr) + \log p_V(v[k]). - -The first term represents the likelihood of the initial state, the -second term captures the likelihood of the noise signal, and the final -term captures the likelihood of the disturbances. - -If we return to the case where :math:`V` and :math:`W` are modeled as -Gaussian processes, then it can be shown that maximizing equation -:eq:`eq_fusion_log-likelihood` is equivalent to solving the optimization -problem given by - -.. math:: - :label: eq_fusion_oep-gaussian - - \min_{x[0], \{v[0], \dots, v[N-1]\}} - \|x[0] - \bar x[0]\|_{P_0^{-1}} + \sum_{k=0}^{N-1} - \|y[k] - h(x_k)\|_{R_W^{-1}}^2 + - \|v[k] \|_{R_V^{-1}}^2, - -where :math:`P_0`, :math:`R_V`, and :math:`R_W` are the covariances of the -initial state, disturbances, and measurement noise. - -Note that while the optimization is carried out only over the estimated -initial state :math:`\hat x[0]`, the entire history of estimated states can -be reconstructed using the system dynamics: - -.. math:: - - \hat x[k+1] = f(\hat x[k], u[k], v[k]), \quad k = 0, \dots, N-1. - -In particular, we can obtain the estimated state at the end of the moving -horizon window, corresponding to the current time, and we can thus -implement an estimator by repeatedly solving the optimization of a window -of length :math:`N` backwards in time. Module usage -============ +------------ The optimization-based control module provides a means of computing optimal trajectories for nonlinear systems and implementing -optimization-based controllers, including model predictive control and -moving horizon estimation. It follows the basic problem setups -described above, but carries out all computations in *discrete time* -(so that integrals become sums) and over a *finite horizon*. To local -the optimal control modules, import `control.optimal`: +optimization-based controllers, including model predictive control. +It follows the basic problem setups described above, but carries out +all computations in *discrete time* (so that integrals become sums) +and over a *finite horizon*. To access the optimal control modules, +import `control.optimal`:: - import control.optimal as obc + import control.optimal as opt To describe an optimal control problem we need an input/output system, a time horizon, a cost function, and (optionally) a set of constraints on the -state and/or input, either along the trajectory and at the terminal time. +state and/or input, along the trajectory and/or at the terminal time. The optimal control module operates by converting the optimal control problem into a standard optimization problem that can be solved by :func:`scipy.optimize.minimize`. The optimal control problem can be solved -by using the :func:`~control.obc.solve_ocp` function:: +by using the :func:`~optimal.solve_optimal_trajectory` function:: - res = obc.solve_ocp(sys, timepts, X0, cost, constraints) + res = opt.solve_optimal_trajectory(sys, timepts, X0, cost, constraints) -The `sys` parameter should be an :class:`~control.InputOutputSystem` and the -`timepts` parameter should represent a time vector that gives the list of -times at which the cost and constraints should be evaluated. +The :code:`sys` parameter should be an :class:`InputOutputSystem` and the +`timepts` parameter should represent a time vector that gives the list +of times at which the cost and constraints should be evaluated (the +time points need not be uniformly spaced). -The `cost` function has call signature `cost(t, x, u)` and should return the -(incremental) cost at the given time, state, and input. It will be -evaluated at each point in the `timepts` vector. The `terminal_cost` -parameter can be used to specify a cost function for the final point in the -trajectory. +The `cost` function has call signature ``cost(t, x, u)`` and should +return the (incremental) cost at the given time, state, and input. It +will be evaluated at each point in the `timepts` vector. The +`terminal_cost` parameter can be used to specify a cost function for +the final point in the trajectory. -The `constraints` parameter is a list of constraints similar to that used by -the :func:`scipy.optimize.minimize` function. Each constraint is specified -using one of the following forms:: +The `constraints` parameter is a list of constraints similar to that +used by the :func:`scipy.optimize.minimize` function. Each constraint +is specified using one of the following forms:: LinearConstraint(A, lb, ub) NonlinearConstraint(f, lb, ub) @@ -257,74 +164,58 @@ A nonlinear constraint is satisfied if lb <= f(x, u) <= ub -By default, `constraints` are taken to be trajectory constraints holding at -all points on the trajectory. The `terminal_constraint` parameter can be +The `constraints` are taken as trajectory constraints holding at all +points on the trajectory. The `terminal_constraints` parameter can be used to specify a constraint that only holds at the final point of the trajectory. -The return value for :func:`~control.optimal.solve_ocp` is a bundle object -that has the following elements: +The return value for :func:`~optimal.solve_optimal_trajectory` is a +bundle object that has the following elements: - * `res.success`: `True` if the optimization was successfully solved + * `res.success`: True if the optimization was successfully solved * `res.inputs`: optimal input - * `res.states`: state trajectory (if `return_x` was `True`) - * `res.time`: copy of the time timepts vector + * `res.states`: state trajectory (if `return_x` was True) + * `res.time`: copy of the time `timepts` vector In addition, the results from :func:`scipy.optimize.minimize` are also -available. +available as additional attributes, as described in +`scipy.optimize.OptimizeResult`. To simplify the specification of cost functions and constraints, the -:mod:`~control.ios` module defines a number of utility functions for +:mod:`optimal` module defines a number of utility functions for optimal control problems: .. autosummary:: - ~control.optimal.quadratic_cost - ~control.optimal.input_poly_constraint - ~control.optimal.input_range_constraint - ~control.optimal.output_poly_constraint - ~control.optimal.output_range_constraint - ~control.optimal.state_poly_constraint - ~control.optimal.state_range_constraint - -The optimization-based control module also implements functions for solving -optimal estimation problems. The -:class:`~control.optimal.OptimalEstimationProblem` class is used to define -an optimal estimation problem over a finite horizon:: - - oep = OptimalEstimationProblem(sys, timepts, cost[, constraints]) + optimal.quadratic_cost + optimal.input_poly_constraint + optimal.input_range_constraint + optimal.output_poly_constraint + optimal.output_range_constraint + optimal.state_poly_constraint + optimal.state_range_constraint -Given noisy measurements :math:`y` and control inputs :math:`u`, an -estimate of the states over the time points can be computed using the -:func:`~control.optimal.OptimalEstimationProblem.compute_estimate` method:: - estim = oep.compute_optimal(Y, U[, X0=x0, initial_guess=(xhat, v)]) - xhat, v, w = estim.states, estim.inputs, estim.outputs - -For discrete time systems, the -:func:`~control.optimal.OptimalEstimationProblem.create_mhe_iosystem` -method can be used to generate an input/output system that implements a -moving horizon estimator. +Example +------- -Several functions are available to help set up standard optimal estimation -problems: +Consider the vehicle steering example described in Example 2.3 of +`Optimization-Based Control (OBC) +`_. The +dynamics of the system can be defined as a nonlinear input/output +system using the following code: -.. autosummary:: +.. testsetup:: optimal - ~control.optimal.gaussian_likelihood_cost - ~control.optimal.disturbance_range_constraint + import matplotlib.pyplot as plt + plt.close('all') -Example -======= - -Consider the vehicle steering example described in FBS2e. The dynamics of -the system can be defined as a nonlinear input/output system using the -following code:: +.. testcode:: optimal + import matplotlib.pyplot as plt import numpy as np import control as ct import control.optimal as opt - import matplotlib.pyplot as plt def vehicle_update(t, x, u, params): # Get the parameters for the model @@ -352,39 +243,58 @@ following code:: We consider an optimal control problem that consists of "changing lanes" by moving from the point x = 0 m, y = -2 m, :math:`\theta` = 0 to the point x = 100 m, y = 2 m, :math:`\theta` = 0) over a period of 10 seconds and -with a starting and ending velocity of 10 m/s:: +with a starting and ending velocity of 10 m/s: + +.. testcode:: optimal x0 = np.array([0., -2., 0.]); u0 = np.array([10., 0.]) xf = np.array([100., 2., 0.]); uf = np.array([10., 0.]) Tf = 10 To set up the optimal control problem we design a cost function that -penalizes the state and input using quadratic cost functions:: +penalizes the state and input using quadratic cost functions: + +.. testcode:: optimal Q = np.diag([0, 0, 0.1]) # don't turn too sharply R = np.diag([1, 1]) # keep inputs small P = np.diag([1000, 1000, 1000]) # get close to final point - traj_cost = obc.quadratic_cost(vehicle, Q, R, x0=xf, u0=uf) - term_cost = obc.quadratic_cost(vehicle, P, 0, x0=xf) + traj_cost = opt.quadratic_cost(vehicle, Q, R, x0=xf, u0=uf) + term_cost = opt.quadratic_cost(vehicle, P, 0, x0=xf) We also constrain the maximum turning rate to 0.1 radians (about 6 degrees) -and constrain the velocity to be in the range of 9 m/s to 11 m/s:: +and constrain the velocity to be in the range of 9 m/s to 11 m/s: + +.. testcode:: optimal + + constraints = [ opt.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ] - constraints = [ obc.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ] +Finally, we solve for the optimal inputs: -Finally, we solve for the optimal inputs:: +.. testcode:: optimal timepts = np.linspace(0, Tf, 10, endpoint=True) - result = obc.solve_ocp( + result = opt.solve_optimal_trajectory( vehicle, timepts, x0, traj_cost, constraints, terminal_cost=term_cost, initial_guess=u0) -Plotting the results:: +.. testoutput:: optimal + :hide: + + Summary statistics: + * Cost function calls: ... + * Constraint calls: ... + * System simulations: ... + * Final cost: ... + +Plotting the results: + +.. testcode:: optimal # Simulate the system dynamics (open loop) resp = ct.input_output_response( vehicle, timepts, result.inputs, x0, - t_eval=np.linspace(0, Tf, 100)) + evaluation_times=np.linspace(0, Tf, 100)) t, y, u = resp.time, resp.outputs, resp.inputs plt.subplot(3, 1, 1) @@ -405,21 +315,22 @@ Plotting the results:: plt.xlabel("t [sec]") plt.ylabel("u2 [rad/s]") - plt.suptitle("Lane change manuever") + plt.suptitle("Lane change maneuver") plt.tight_layout() - plt.show() -yields +.. testcode:: optimal + :hide: + + plt.savefig('figures/steering-optimal.png') -.. image:: steering-optimal.png +yields +.. image:: figures/steering-optimal.png + :align: center -An example showing the use of the optimal estimation problem and moving -horizon estimation (MHE) is given in the :doc:`mhe-pvtol Jupyter -notebook `. Optimization Tips -================= +----------------- The python-control optimization module makes use of the SciPy optimization toolbox and it can sometimes be tricky to get the optimization to converge. @@ -442,17 +353,19 @@ solutions do not seem close to optimal, here are a few things to try: `input_output_response` (as done above). * Use a smooth basis: as an alternative to parameterizing the optimal - control inputs using the value of the control at the listed time points, - you can specify a set of basis functions using the `basis` keyword in - :func:`~control.solve_ocp` and then parameterize the controller by linear - combination of the basis functions. The :mod:`!control.flatsys` module - defines several sets of basis functions that can be used. - -* Tweak the optimizer: by using the `minimize_method`, `minimize_options`, - and `minimize_kwargs` keywords in :func:`~control.solve_ocp`, you can - choose the SciPy optimization function that you use and set many - parameters. See :func:`scipy.optimize.minimize` for more information on - the optimizers that are available and the options and keywords that they + control inputs using the value of the control at the listed time + points, you can specify a set of basis functions using the `basis` + keyword in :func:`~optimal.solve_optimal_trajectory` and then + parameterize the controller by linear combination of the basis + functions. The :ref:`flatsys subpackage ` defines + several sets of basis functions that can be used. + +* Tweak the optimizer: by using the `minimize_method`, + `minimize_options`, and `minimize_kwargs` keywords in + :func:`~optimal.solve_optimal_trajectory`, you can choose the SciPy + optimization function that you use and set many parameters. See + :func:`scipy.optimize.minimize` for more information on the + optimizers that are available and the options and keywords that they accept. * Walk before you run: try setting up a simpler version of the optimization, @@ -466,27 +379,30 @@ formulations. Module classes and functions -============================ +---------------------------- + +The following classes and functions are defined in the +`optimal` module: + .. autosummary:: - :toctree: generated/ :template: custom-class-template.rst - ~control.optimal.OptimalControlProblem - ~control.optimal.OptimalControlResult - ~control.optimal.OptimalEstimationProblem - ~control.optimal.OptimalEstimationResult + optimal.OptimalControlProblem + optimal.OptimalControlResult + optimal.OptimalEstimationProblem + optimal.OptimalEstimationResult .. autosummary:: - :toctree: generated/ - - ~control.optimal.create_mpc_iosystem - ~control.optimal.disturbance_range_constraint - ~control.optimal.gaussian_likelihood_cost - ~control.optimal.input_poly_constraint - ~control.optimal.input_range_constraint - ~control.optimal.output_poly_constraint - ~control.optimal.output_range_constraint - ~control.optimal.quadratic_cost - ~control.optimal.solve_ocp - ~control.optimal.state_poly_constraint - ~control.optimal.state_range_constraint + + optimal.create_mpc_iosystem + optimal.disturbance_range_constraint + optimal.gaussian_likelihood_cost + optimal.input_poly_constraint + optimal.input_range_constraint + optimal.output_poly_constraint + optimal.output_range_constraint + optimal.quadratic_cost + optimal.solve_optimal_trajectory + optimal.solve_optimal_estimate + optimal.state_poly_constraint + optimal.state_range_constraint diff --git a/doc/phase_plane_plots.py b/doc/phase_plane_plots.py deleted file mode 120000 index 6076fa4cd..000000000 --- a/doc/phase_plane_plots.py +++ /dev/null @@ -1 +0,0 @@ -../examples/phase_plane_plots.py \ No newline at end of file diff --git a/doc/phaseplot-dampedosc-default.png b/doc/phaseplot-dampedosc-default.png deleted file mode 100644 index da4e24e35..000000000 Binary files a/doc/phaseplot-dampedosc-default.png and /dev/null differ diff --git a/doc/phaseplot-invpend-meshgrid.png b/doc/phaseplot-invpend-meshgrid.png deleted file mode 100644 index 040b45558..000000000 Binary files a/doc/phaseplot-invpend-meshgrid.png and /dev/null differ diff --git a/doc/phaseplot-oscillator-helpers.png b/doc/phaseplot-oscillator-helpers.png deleted file mode 100644 index 0b5ebf43f..000000000 Binary files a/doc/phaseplot-oscillator-helpers.png and /dev/null differ diff --git a/doc/phaseplot.rst b/doc/phaseplot.rst new file mode 100644 index 000000000..d2a3e6353 --- /dev/null +++ b/doc/phaseplot.rst @@ -0,0 +1,140 @@ +.. currentmodule:: control + +.. _phase-plane-plots: + +Phase Plane Plots +================= + +Insight into nonlinear systems can often be obtained by looking at phase +plane diagrams. The :func:`phase_plane_plot` function allows the +creation of a 2-dimensional phase plane diagram for a system. This +functionality is supported by a set of mapping functions that are part of +the `phaseplot` module. + +The default method for generating a phase plane plot is to provide a +2D dynamical system along with a range of coordinates in phase space: + +.. testsetup:: phaseplot + + import matplotlib.pyplot as plt + plt.close('all') + +.. testcode:: phaseplot + + def sys_update(t, x, u, params): + return np.array([[0, 1], [-1, -1]]) @ x + sys = ct.nlsys( + sys_update, states=['position', 'velocity'], + inputs=0, name='damped oscillator') + axis_limits = [-1, 1, -1, 1] + ct.phase_plane_plot(sys, axis_limits) + +.. testcode:: phaseplot + :hide: + + import matplotlib.pyplot as plt + plt.savefig('figures/phaseplot-dampedosc-default.png') + +.. image:: figures/phaseplot-dampedosc-default.png + :align: center + +By default the plot includes streamlines infered from function values +on a grid, equilibrium points and separatrices if they exist. A variety +of options are available to modify the information that is plotted, +including plotting a grid of vectors instead of streamlines, plotting +streamlines from arbitrary starting points and turning on and off +various features of the plot. + +To illustrate some of these possibilities, consider a phase plane plot for +an inverted pendulum system, which is created using a mesh grid: + +.. testcode:: phaseplot + :hide: + + plt.figure() + +.. testcode:: phaseplot + + def invpend_update(t, x, u, params): + m, l, b, g = params['m'], params['l'], params['b'], params['g'] + return [x[1], -b/m * x[1] + (g * l / m) * np.sin(x[0]) + u[0]/m] + invpend = ct.nlsys(invpend_update, states=2, inputs=1, name='invpend') + + ct.phase_plane_plot( + invpend, [-2 * np.pi, 2 * np.pi, -2, 2], + params={'m': 1, 'l': 1, 'b': 0.2, 'g': 1}) + plt.xlabel(r"$\theta$ [rad]") + plt.ylabel(r"$\dot\theta$ [rad/sec]") + +.. testcode:: phaseplot + :hide: + + plt.savefig('figures/phaseplot-invpend-meshgrid.png') + +.. image:: figures/phaseplot-invpend-meshgrid.png + :align: center + +This figure shows several features of more complex phase plane plots: +multiple equilibrium points are shown, with saddle points showing +separatrices, and streamlines generated generated from a rectangular +25x25 grid (default) of function evaluations. Together, the multiple +features in the phase plane plot give a good global picture of the +topological structure of solutions of the dynamical system. + +Phase plots can be built up by hand using a variety of helper +functions that are part of the :mod:`phaseplot` (pp) module. For more +precise control, the streamlines can also generated by integrating the +system forwards or backwards in time from a set of initial +conditions. The initial conditions can be chosen on a rectangular +grid, rectangual boundary, circle or from an arbitrary set of points. + +.. testcode:: phaseplot + :hide: + + plt.figure() + +.. testcode:: phaseplot + + import control.phaseplot as pp + + def oscillator_update(t, x, u, params): + return [x[1] + x[0] * (1 - x[0]**2 - x[1]**2), + -x[0] + x[1] * (1 - x[0]**2 - x[1]**2)] + oscillator = ct.nlsys( + oscillator_update, states=2, inputs=0, name='nonlinear oscillator') + + ct.phase_plane_plot(oscillator, [-1.5, 1.5, -1.5, 1.5], 0.9, + plot_streamlines=True) + pp.streamlines( + oscillator, np.array([[0, 0]]), 1.5, + gridtype='circlegrid', gridspec=[0.5, 6], dir='both') + pp.streamlines( + oscillator, np.array([[1, 0]]), 2 * np.pi, arrows=6, color='b') + plt.gca().set_aspect('equal') + +.. testcode:: phaseplot + :hide: + + plt.savefig('figures/phaseplot-oscillator-helpers.png') + +.. image:: figures/phaseplot-oscillator-helpers.png + :align: center + +The following helper functions are available: + +.. autosummary:: + + phaseplot.equilpoints + phaseplot.separatrices + phaseplot.streamlines + phaseplot.streamplot + phaseplot.vectorfield + +The :func:`phase_plane_plot` function calls these helper functions +based on the options it is passed. + +Note that unlike other plotting functions, phase plane plots do not +involve computing a response and then plotting the result via a +``plot()`` method. Instead, the plot is generated directly be a call +to the :func:`phase_plane_plot` function (or one of the +:mod:`~control.phaseplot` helper functions). diff --git a/doc/phaseplots.py b/doc/phaseplots.py deleted file mode 120000 index 4b0575c0f..000000000 --- a/doc/phaseplots.py +++ /dev/null @@ -1 +0,0 @@ -../examples/phaseplots.py \ No newline at end of file diff --git a/doc/plotting.rst b/doc/plotting.rst deleted file mode 100644 index 2450c576b..000000000 --- a/doc/plotting.rst +++ /dev/null @@ -1,501 +0,0 @@ -.. _plotting-module: - -************* -Plotting data -************* - -The Python Control Systems Toolbox contains a number of functions for -plotting input/output responses in the time and frequency domain, root -locus diagrams, and other standard charts used in control system analysis, -for example:: - - bode_plot(sys) - nyquist_plot([sys1, sys2]) - phase_plane_plot(sys, limits) - pole_zero_plot(sys) - root_locus_plot(sys) - -While plotting functions can be called directly, the standard pattern used -in the toolbox is to provide a function that performs the basic computation -or analysis (e.g., computation of the time or frequency response) and -returns and object representing the output data. A separate plotting -function, typically ending in `_plot` is then used to plot the data, -resulting in the following standard pattern:: - - response = ct.nyquist_response([sys1, sys2]) - count = ct.response.count # number of encirclements of -1 - lines = ct.nyquist_plot(response) # Nyquist plot - -The returned value `lines` provides access to the individual lines in the -generated plot, allowing various aspects of the plot to be modified to suit -specific needs. - -The plotting function is also available via the `plot()` method of the -analysis object, allowing the following type of calls:: - - step_response(sys).plot() - frequency_response(sys).plot() - nyquist_response(sys).plot() - pp.streamlines(sys, limits).plot() - root_locus_map(sys).plot() - -The remainder of this chapter provides additional documentation on how -these response and plotting functions can be customized. - - -Time response data -================== - -Input/output time responses are produced one of several python-control -functions: :func:`~control.forced_response`, -:func:`~control.impulse_response`, :func:`~control.initial_response`, -:func:`~control.input_output_response`, :func:`~control.step_response`. -Each of these return a :class:`~control.TimeResponseData` object, which -contains the time, input, state, and output vectors associated with the -simulation. Time response data can be plotted with the -:func:`~control.time_response_plot` function, which is also available as -the :func:`~control.TimeResponseData.plot` method. For example, the step -response for a two-input, two-output can be plotted using the commands:: - - sys_mimo = ct.tf2ss( - [[[1], [0.1]], [[0.2], [1]]], - [[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="sys_mimo") - response = ct.step_response(sys) - response.plot() - -which produces the following plot: - -.. image:: timeplot-mimo_step-default.png - -The :class:`~control.TimeResponseData` object can also be used to access -the data from the simulation:: - - time, outputs, inputs = response.time, response.outputs, response.inputs - fig, axs = plt.subplots(2, 2) - for i in range(2): - for j in range(2): - axs[i, j].plot(time, outputs[i, j]) - -A number of options are available in the `plot` method to customize -the appearance of input output data. For data produced by the -:func:`~control.impulse_response` and :func:`~control.step_response` -commands, the inputs are not shown. This behavior can be changed -using the `plot_inputs` keyword. It is also possible to combine -multiple lines onto a single graph, using either the `overlay_signals` -keyword (which puts all outputs out a single graph and all inputs on a -single graph) or the `overlay_traces` keyword, which puts different -traces (e.g., corresponding to step inputs in different channels) on -the same graph, with appropriate labeling via a legend on selected -axes. - -For example, using `plot_input=True` and `overlay_signals=True` yields the -following plot:: - - ct.step_response(sys_mimo).plot( - plot_inputs=True, overlay_signals=True, - title="Step response for 2x2 MIMO system " + - "[plot_inputs, overlay_signals]") - -.. image:: timeplot-mimo_step-pi_cs.png - -Input/output response plots created with either the -:func:`~control.forced_response` or the -:func:`~control.input_output_response` functions include the input signals by -default. These can be plotted on separate axes, but also "overlaid" on the -output axes (useful when the input and output signals are being compared to -each other). The following plot shows the use of `plot_inputs='overlay'` -as well as the ability to reposition the legends using the `legend_map` -keyword:: - - timepts = np.linspace(0, 10, 100) - U = np.vstack([np.sin(timepts), np.cos(2*timepts)]) - ct.input_output_response(sys_mimo, timepts, U).plot( - plot_inputs='overlay', - legend_map=np.array([['lower right'], ['lower right']]), - title="I/O response for 2x2 MIMO system " + - "[plot_inputs='overlay', legend_map]") - -.. image:: timeplot-mimo_ioresp-ov_lm.png - -Another option that is available is to use the `transpose` keyword so that -instead of plotting the outputs on the top and inputs on the bottom, the -inputs are plotted on the left and outputs on the right, as shown in the -following figure:: - - U1 = np.vstack([np.sin(timepts), np.cos(2*timepts)]) - resp1 = ct.input_output_response(sys_mimo, timepts, U1) - - U2 = np.vstack([np.cos(2*timepts), np.sin(timepts)]) - resp2 = ct.input_output_response(sys_mimo, timepts, U2) - - ct.combine_time_responses( - [resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]).plot( - transpose=True, - title="I/O responses for 2x2 MIMO system, multiple traces " - "[transpose]") - -.. image:: timeplot-mimo_ioresp-mt_tr.png - -This figure also illustrates the ability to create "multi-trace" plots -using the :func:`~control.combine_time_responses` function. The line -properties that are used when combining signals and traces are set by -the `input_props`, `output_props` and `trace_props` parameters for -:func:`~control.time_response_plot`. - -Additional customization is possible using the `input_props`, -`output_props`, and `trace_props` keywords to set complementary line colors -and styles for various signals and traces:: - - out = ct.step_response(sys_mimo).plot( - plot_inputs='overlay', overlay_signals=True, overlay_traces=True, - output_props=[{'color': c} for c in ['blue', 'orange']], - input_props=[{'color': c} for c in ['red', 'green']], - trace_props=[{'linestyle': s} for s in ['-', '--']]) - -.. image:: timeplot-mimo_step-linestyle.png - -Frequency response data -======================= - -Linear time invariant (LTI) systems can be analyzed in terms of their -frequency response and python-control provides a variety of tools for -carrying out frequency response analysis. The most basic of these is -the :func:`~control.frequency_response` function, which will compute -the frequency response for one or more linear systems:: - - sys1 = ct.tf([1], [1, 2, 1], name='sys1') - sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') - response = ct.frequency_response([sys1, sys2]) - -A Bode plot provide a graphical view of the response an LTI system and can -be generated using the :func:`~control.bode_plot` function:: - - ct.bode_plot(response, initial_phase=0) - -.. image:: freqplot-siso_bode-default.png - -Computing the response for multiple systems at the same time yields a -common frequency range that covers the features of all listed systems. - -Bode plots can also be created directly using the -:meth:`~control.FrequencyResponseData.plot` method:: - - sys_mimo = ct.tf( - [[[1], [0.1]], [[0.2], [1]]], - [[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="sys_mimo") - ct.frequency_response(sys_mimo).plot() - -.. image:: freqplot-mimo_bode-default.png - -A variety of options are available for customizing Bode plots, for -example allowing the display of the phase to be turned off or -overlaying the inputs or outputs:: - - ct.frequency_response(sys_mimo).plot( - plot_phase=False, overlay_inputs=True, overlay_outputs=True) - -.. image:: freqplot-mimo_bode-magonly.png - -The :func:`~control.singular_values_response` function can be used to -generate Bode plots that show the singular values of a transfer -function:: - - ct.singular_values_response(sys_mimo).plot() - -.. image:: freqplot-mimo_svplot-default.png - -Different types of plots can also be specified for a given frequency -response. For example, to plot the frequency response using a a Nichols -plot, use `plot_type='nichols'`:: - - response.plot(plot_type='nichols') - -.. image:: freqplot-siso_nichols-default.png - -Another response function that can be used to generate Bode plots is -the :func:`~control.gangof4` function, which computes the four primary -sensitivity functions for a feedback control system in standard form:: - - proc = ct.tf([1], [1, 1, 1], name="process") - ctrl = ct.tf([100], [1, 5], name="control") - response = rect.gangof4_response(proc, ctrl) - ct.bode_plot(response) # or response.plot() - -.. image:: freqplot-gangof4.png - -Nyquist analysis can be done using the :func:`~control.nyquist_response` -function, which evaluates an LTI system along the Nyquist contour, and -the :func:`~control.nyquist_plot` function, which generates a Nyquist plot:: - - sys = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys') - nyquist_plot(sys) - -.. image:: freqplot-nyquist-default.png - -The :func:`~control.nyquist_response` function can be used to compute -the number of encirclements of the -1 point and can return the Nyquist -contour that was used to generate the Nyquist curve. - -By default, the Nyquist response will generate small semicircles around -poles that are on the imaginary axis. In addition, portions of the Nyquist -curve that are far from the origin are scaled to a maximum value, while the -line style is changed to reflect the scaling, and it is possible to offset -the scaled portions to separate out the portions of the Nyquist curve at -:math:`\infty`. A number of keyword parameters for both are available for -:func:`~control.nyquist_response` and :func:`~control.nyquist_plot` to tune -the computation of the Nyquist curve and the way the data are plotted:: - - sys = ct.tf([1, 0.2], [1, 0, 1]) * ct.tf([1], [1, 0]) - nyqresp = ct.nyquist_response(sys) - nyqresp.plot( - max_curve_magnitude=6, max_curve_offset=1, - arrows=[0, 0.15, 0.3, 0.6, 0.7, 0.925], label='sys') - print("Encirclements =", nyqresp.count) - -.. image:: freqplot-nyquist-custom.png - -All frequency domain plotting functions will automatically compute the -range of frequencies to plot based on the poles and zeros of the frequency -response. Frequency points can be explicitly specified by including an -array of frequencies as a second argument (after the list of systems):: - - sys1 = ct.tf([1], [1, 2, 1], name='sys1') - sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') - omega = np.logspace(-2, 2, 500) - ct.frequency_response([sys1, sys2], omega).plot(initial_phase=0) - -.. image:: freqplot-siso_bode-omega.png - -Alternatively, frequency ranges can be specified by passing a list of the -form ``[wmin, wmax]``, where ``wmin`` and ``wmax`` are the minimum and -maximum frequencies in the (log-spaced) frequency range:: - - response = ct.frequency_response([sys1, sys2], [1e-2, 1e2]) - -The number of (log-spaced) points in the frequency can be specified using -the ``omega_num`` keyword parameter. - - -Pole/zero data -============== - -Pole/zero maps and root locus diagrams provide insights into system -response based on the locations of system poles and zeros in the complex -plane. The :func:`~control.pole_zero_map` function returns the poles and -zeros and can be used to generate a pole/zero plot:: - - sys = ct.tf([1, 2], [1, 2, 3], name='SISO transfer function') - response = ct.pole_zero_map(sys) - ct.pole_zero_plot(response) - -.. image:: pzmap-siso_ctime-default.png - -A root locus plot shows the location of the closed loop poles of a system -as a function of the loop gain:: - - ct.root_locus_map(sys).plot() - -.. image:: rlocus-siso_ctime-default.png - -The grid in the left hand plane shows lines of constant damping ratio as -well as arcs corresponding to the frequency of the complex pole. The grid -can be turned off using the `grid` keyword. Setting `grid` to `False` will -turn off the grid but show the real and imaginary axis. To completely -remove all lines except the root loci, use `grid='empty'`. - -On systems that support interactive plots, clicking on a location on the -root locus diagram will mark the pole locations on all branches of the -diagram and display the gain and damping ratio for the clicked point below -the plot title: - -.. image:: rlocus-siso_ctime-clicked.png - -Root locus diagrams are also supported for discrete time systems, in which -case the grid is show inside the unit circle:: - - sysd = sys.sample(0.1) - ct.root_locus_plot(sysd) - -.. image:: rlocus-siso_dtime-default.png - -Lists of systems can also be given, in which case the root locus diagram -for each system is plotted in different colors:: - - sys1 = ct.tf([1], [1, 2, 1], name='sys1') - sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') - ct.root_locus_plot([sys1, sys2], grid=False) - -.. image:: rlocus-siso_multiple-nogrid.png - - -Phase plane plots -================= -Insight into nonlinear systems can often be obtained by looking at phase -plane diagrams. The :func:`~control.phase_plane_plot` function allows the -creation of a 2-dimensional phase plane diagram for a system. This -functionality is supported by a set of mapping functions that are part of -the `phaseplot` module. - -The default method for generating a phase plane plot is to provide a -2D dynamical system along with a range of coordinates and time limit:: - - sys = ct.nlsys( - lambda t, x, u, params: np.array([[0, 1], [-1, -1]]) @ x, - states=['position', 'velocity'], inputs=0, name='damped oscillator') - axis_limits = [-1, 1, -1, 1] - T = 8 - ct.phase_plane_plot(sys, axis_limits, T) - -.. image:: phaseplot-dampedosc-default.png - -By default, the plot includes streamlines generated from starting -points on limits of the plot, with arrows showing the flow of the -system, as well as any equilibrium points for the system. A variety -of options are available to modify the information that is plotted, -including plotting a grid of vectors instead of streamlines and -turning on and off various features of the plot. - -To illustrate some of these possibilities, consider a phase plane plot for -an inverted pendulum system, which is created using a mesh grid:: - - def invpend_update(t, x, u, params): - m, l, b, g = params['m'], params['l'], params['b'], params['g'] - return [x[1], -b/m * x[1] + (g * l / m) * np.sin(x[0]) + u[0]/m] - invpend = ct.nlsys(invpend_update, states=2, inputs=1, name='invpend') - - ct.phase_plane_plot( - invpend, [-2*pi, 2*pi, -2, 2], 5, - gridtype='meshgrid', gridspec=[5, 8], arrows=3, - plot_equilpoints={'gridspec': [12, 9]}, - params={'m': 1, 'l': 1, 'b': 0.2, 'g': 1}) - plt.xlabel(r"$\theta$ [rad]") - plt.ylabel(r"$\dot\theta$ [rad/sec]") - -.. image:: phaseplot-invpend-meshgrid.png - -This figure shows several features of more complex phase plane plots: -multiple equilibrium points are shown, with saddle points showing -separatrices, and streamlines generated along a 5x8 mesh of initial -conditions. At each mesh point, a streamline is created that goes 5 time -units forward and backward in time. A separate grid specification is used -to find equilibrium points and separatrices (since the course grid spacing -of 5x8 does not find all possible equilibrium points). Together, the -multiple features in the phase plane plot give a good global picture of the -topological structure of solutions of the dynamical system. - -Phase plots can be built up by hand using a variety of helper functions that -are part of the :mod:`~control.phaseplot` (pp) module:: - - import control.phaseplot as pp - - def oscillator_update(t, x, u, params): - return [x[1] + x[0] * (1 - x[0]**2 - x[1]**2), - -x[0] + x[1] * (1 - x[0]**2 - x[1]**2)] - oscillator = ct.nlsys( - oscillator_update, states=2, inputs=0, name='nonlinear oscillator') - - ct.phase_plane_plot(oscillator, [-1.5, 1.5, -1.5, 1.5], 0.9) - pp.streamlines( - oscillator, np.array([[0, 0]]), 1.5, - gridtype='circlegrid', gridspec=[0.5, 6], dir='both') - pp.streamlines( - oscillator, np.array([[1, 0]]), 2*pi, arrows=6, color='b') - plt.gca().set_aspect('equal') - -.. image:: phaseplot-oscillator-helpers.png - -The following helper functions are available: - -.. autosummary:: - ~control.phaseplot.equilpoints - ~control.phaseplot.separatrices - ~control.phaseplot.streamlines - ~control.phaseplot.vectorfield - -The :func:`~control.phase_plane_plot` function calls these helper functions -based on the options it is passed. - -Note that unlike other plotting functions, phase plane plots do not involve -computing a response and then plotting the result via a `plot()` method. -Instead, the plot is generated directly be a call to the -:func:`~control.phase_plane_plot` function (or one of the -:mod:`~control.phaseplot` helper functions. - - -Response and plotting functions -=============================== - -Response functions ------------------- - -Response functions take a system or list of systems and return a response -object that can be used to retrieve information about the system (e.g., the -number of encirclements for a Nyquist plot) as well as plotting (via the -`plot` method). - -.. autosummary:: - :toctree: generated/ - - ~control.describing_function_response - ~control.frequency_response - ~control.forced_response - ~control.gangof4_response - ~control.impulse_response - ~control.initial_response - ~control.input_output_response - ~control.nyquist_response - ~control.pole_zero_map - ~control.root_locus_map - ~control.singular_values_response - ~control.step_response - -Plotting functions ------------------- - -.. autosummary:: - :toctree: generated/ - - ~control.bode_plot - ~control.describing_function_plot - ~control.nichols_plot - ~control.nyquist_plot - ~control.phase_plane_plot - ~control.phaseplot.equilpoints - ~control.phaseplot.separatrices - ~control.phaseplot.streamlines - ~control.phaseplot.vectorfield - ~control.pole_zero_plot - ~control.root_locus_plot - ~control.singular_values_plot - ~control.time_response_plot - - -Utility functions ------------------ - -These additional functions can be used to manipulate response data or -returned values from plotting routines. - -.. autosummary:: - :toctree: generated/ - - ~control.combine_time_responses - ~control.get_plot_axes - ~control.suptitle - - -Response classes ----------------- - -The following classes are used in generating response data. - -.. autosummary:: - :toctree: generated/ - - ~control.DescribingFunctionResponse - ~control.FrequencyResponseData - ~control.FrequencyResponseList - ~control.NyquistResponseData - ~control.PoleZeroData - ~control.TimeResponseData - ~control.TimeResponseList diff --git a/doc/pvtol-lqr-nested.ipynb b/doc/pvtol-lqr-nested.ipynb deleted file mode 120000 index fdc3bcd74..000000000 --- a/doc/pvtol-lqr-nested.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/pvtol-lqr-nested.ipynb \ No newline at end of file diff --git a/doc/pvtol-lqr.py b/doc/pvtol-lqr.py deleted file mode 120000 index a6106b06a..000000000 --- a/doc/pvtol-lqr.py +++ /dev/null @@ -1 +0,0 @@ -../examples/pvtol-lqr.py \ No newline at end of file diff --git a/doc/pvtol-nested.py b/doc/pvtol-nested.py deleted file mode 120000 index f72b7c752..000000000 --- a/doc/pvtol-nested.py +++ /dev/null @@ -1 +0,0 @@ -../examples/pvtol-nested.py \ No newline at end of file diff --git a/doc/pvtol-outputfbk.ipynb b/doc/pvtol-outputfbk.ipynb deleted file mode 120000 index ffcfd5401..000000000 --- a/doc/pvtol-outputfbk.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/pvtol-outputfbk.ipynb \ No newline at end of file diff --git a/doc/pvtol.py b/doc/pvtol.py deleted file mode 120000 index 76dd7bdc0..000000000 --- a/doc/pvtol.py +++ /dev/null @@ -1 +0,0 @@ -../examples/pvtol.py \ No newline at end of file diff --git a/doc/releases.rst b/doc/releases.rst new file mode 100644 index 000000000..88a76775a --- /dev/null +++ b/doc/releases.rst @@ -0,0 +1,64 @@ +************* +Release Notes +************* + +This chapter contains a listing of the major releases of the Python +Control Systems Library (python-control) along with a brief summary of +the significant changes in each release. + +The information listed here is primarily intended for users. More +detailed notes on each release, including links to individual pull +requests and issues, are available on the `python-control GitHub +release page +`_. + + +Version 0.10 +============ + +Version 0.10 of the python-control package introduced the +``_response/_plot`` pattern, described in more detail in +:ref:`response-chapter`, in which input/output system responses +generate an object representing the response that can then be used for +plotting (via the ``.plot()`` method) or other uses. Significant +changes were also made to input/output system functionality, including +the ability to index systems and signal using signal labels. + +.. toctree:: + :maxdepth: 1 + + releases/0.10.1-notes + releases/0.10.0-notes + + +Version 0.9 +=========== + +Version 0.9 of the python-control package included significant +upgrades the the `interconnect` functionality to allow automatic +signal interconnetion and the introduction of an :ref:`optimal control +module ` for optimal trajectory generation. In +addition, the default timebase for I/O systems was set to 0 in Version +0.9 (versus None in previous versions). + +.. toctree:: + :maxdepth: 1 + + releases/0.9.4-notes + releases/0.9.3-notes + releases/0.9.2-notes + releases/0.9.1-notes + releases/0.9.0-notes + + +Earlier Versions +================ + +Summary release notes are included for these collections of early +releases of the python-control package. + +.. toctree:: + :maxdepth: 1 + + releases/0.8.x-notes + releases/0.3-7.x-notes diff --git a/doc/releases/0.10.0-notes.rst b/doc/releases/0.10.0-notes.rst new file mode 100644 index 000000000..360bd9a79 --- /dev/null +++ b/doc/releases/0.10.0-notes.rst @@ -0,0 +1,184 @@ +.. currentmodule:: control + +.. _version-0.10.0: + +Version 0.10.0 Release Notes +---------------------------- + +* Released: 31 March 2024 +* `GitHub release page + `_ + +This release changes the interface for plotting to use a +``_response/_plot`` calling pattern, adds multivariable interconnect +functionality, restructures I/O system classes, and adds the `norm` +(now `system_norm`) function to compute input/output system norms. +Support for the NumPy `~numpy.matrix` class has been removed. + +This version of `python-control` requires Python 3.10 and higher. + + +New classes, functions, and methods +................................... + +The following new classes, functions, and methods have been added in +this release: + +* `time_response_plot`, `TimeResponseData.plot`: Plot simulation + results for time response functions. + +* `InterconnectedSystem.connection_table`: Print out a table of each + signal name, where it comes from (source), and where it goes + (destination), primarily intended for systems that have been + connected implicitly. + +* `nyquist_response`, `NyquistResponseData`: Compute the Nyquist curve + and store in an object that can be used to retrieve information + (e.g., `~NyquistResponseData.count`) or for plotting (via + the `~NyquistResponseData.plot` method). + +* `describing_function_response`, `DescribingFunctionResponse`: Compute + describing functions and store in a form that can be used for + analysis (e.g., `~DescribingFunctionResponse.intersections`) or plotting + (via `describing_function_plot` or the + `~DescribingFunctionResponse.plot` method). + +* `gangof4_response`, `gangof4_plot`: Compute the Gang of Four + response and store in a `FrequencyResponseData` object for plotting. + +* `singular_values_response`: Compute the Gang of Four response and store in a + `FrequencyResponseData` object for plotting. + +* `FrequencyResponseData.plot`: Plot a frequency response using a Bode, + Nichols, or singular values plot. + +* `pole_zero_map`, `PoleZeroData`: New "response" (map) functions for + pole/zero diagrams. The output of `pole_zero_map` can be plotted + using `pole_zero_plot` or the `~PoleZeroData.plot` method. + +* `root_locus_map`: New "response" (map) functions for root locus + diagrams. The output of `root_locus_map` can be plotted using + `root_locus_plot` or the `~PoleZeroData.plot` method. + +* `norm` (now `system_norm`): Compute H2 and H-infinity system norms. + +* `phase_plane_plot`: New implementation of phase + plane plots. See :ref:`phase-plane-plots` for more information. + + +Bug fixes +......... + +The following bugs have been fixed in this release: + +* `sample_system`: Fixed a bug in which the zero frequency (DC) gain + for the 'matched' transformation was being computed incorrectly. + +* `TimeResponseData.to_pandas`: Fixed a bug when the response did not + have state data. + + +Improvements +............ + +The following additional improvements and changes in functionality +were implemented in this release: + +* `interconnect`: Allows a variety of "multivariable" specifications + for connections, inputs, and outputs when systems have variables + with names of the form 'sig[i]'. + +* `nlsys`: Factory function for `NonlinearIOSystem`. + +* Block diagram functions (`series`, `parallel`, `feedback`, `append`, + `negate`) now work on all I/O system classes, including nonlinear + systems. + +* Simulation functions (`initial_response`, `step_response`, + `forced_response`) will now work for nonlinear functions (via an + internal call to `input_output_response`). + +* Bode and Nyquist plots have been significantly enhanced in terms of + functionality for display multiple tracing and other visual + properties. See `bode_plot` and `nyquist_plot` for details, along + with the :ref:`response-chapter` chapter. + +* Properties of frequecy plots can now be set using the + `config.defaults['freqplot.rcParams']` (see + :ref:`package-configuration-parameters` for details). + +* `create_statefbk_iosystem`: Allows passing an I/O system instead of + the a gain (or gain schedule) for the controller. + +* `root_locus_plot`: Interactive mode is now enabled, so clicking on a + location on the root locus curve will generate markers at the + locations on the loci corresponding to that gain and add a message + above the plot giving the frequency and damping ratio for the point + that was clicked. + +* `gram`: Computation of Gramians now supports discrete-time systems. + +* All time response functions now allow the `params` keyword to be + specified (for nonlinear I/O systems) and the parameter values used + for generating a time response are stored in the `TimeResponseData` + object.. + + +Deprecations +............ + +The following functions have been newly deprecated in this release and +generate a warning message when used: + +* `connect`: Use `interconnect`. + +* `ss2io`, `tf2io`: These functions are no longer required since the + `StateSpace` and `TransferFunction` classes are now subclasses of + `NonlinearIOSystem`. + +* `root_locus_plot`, `sisotool`: the `print_gain` keyword has been + replaced `interactive`. + +* In various plotting routines, the (already deprecated) `Plot` + keyword is now the (still deprecated) `plot` keyword. This can be + used to obtain legacy return values from ``_plot`` functions. + +* `phase_plot`: Use `phase_plane_plot` instead. + +The listed items are slated to be removed in future releases (usually +the next major or minor version update). + + +Removals +........ + +The following functions and capabilities have been removed in this release: + +* `use_numpy_matrix`: The `numpy.matrix` class is no longer supported. + +* `NamedIOSystem`: renamed to `InputOutputSystem` + +* `LinearIOSystem`: merged into the `StateSpace` class + +* `pole`: use `poles`. The `matlab.pole` function is still available. + +* `zero`: use `zeros`. The `matlab.zero` function is still available. + +* `timebaseEqual`: use `common_timebase`. + +* The `impulse_response` function no longer accepts the `X0` keyword. + +* The `initial_response` function no longer accepts the :code:`input` + keyword. + +* The deprecated default parameters 'bode.dB', 'bode.deg', + 'bode.grid', and 'bode.wrap_phase' have been removed. They should + be accessed as 'freqplot.dB', 'freqplot.deg', 'freqplot.grid', and + 'freqplot.wrap_phase'. + +* Recalculation of the root locus plot when zooming no longer works + (you can still zoom in and out, you just don't get a recalculated + curve). + +Code that makes use of the functionality listed above will have to be +rewritten to work with this release of the python-control package. diff --git a/doc/releases/0.10.1-notes.rst b/doc/releases/0.10.1-notes.rst new file mode 100644 index 000000000..dd0939021 --- /dev/null +++ b/doc/releases/0.10.1-notes.rst @@ -0,0 +1,200 @@ +.. currentmodule:: control + +.. _version-0.10.1: + +Version 0.10.1 Release Notes (current) +-------------------------------------- + +* Released: 17 Aug 2024 +* `GitHub release page + `_ + +This release provides a number of updates to the plotting functions to +make the interface more uniform between the various types of control +plots (including the use of the `ControlPlot` object as the return +type for all :code:`_plot` functions, adds slice access for state space +models, includes new tools for model identification from data, as well +as compatibility with NumPy 2.0. + +New functions +............. + +The following new functions have been added in this release: + +* `hankel_singular_values`: renamed `hsvd`, with a convenience alias + available for backwards compatibility. + +* `balanced_reduction`: renamed `balred`, with a convenience alias + available for backwards compatibility. + +* `model_reduction`: renamed `modred`, with a convenience alias + available for backwards compatibility. + +* `minimal_realization`: renamed `minreal`, with a convenience alias + available for backwards compatibility. + +* `eigensys_realization`: new system ID method, with a convenience + alias `era` available. + +* All plotting functions now return a `ControlPlot` object with lines, + axes, legend, etc available. Accessing this object as a list is + backward compatible with 10.0 format (with deparecation warning). + + +Bug fixes +......... + +The following bugs have been fixed in this release: + +* Fixed bug in `matlab.rlocus` where `kvect` was being used instead of + `gains`. Also allow `root_locus_plot` to process `kvects` as a + legacy keyword. + +* Fixed a bug in `nyquist_plot` where it generated an error if called + with a `FrequencyResponseData` object. + +* Fixed a bug in processing `indent_radius` keyword when + `nyquist_plot` is passed a system. + +* Fixed a bug in `root_locus_plot` that generated an error when you + clicked on a point outside the border window. + +* Fixed a bug in `interconnect` where specification of a list of + signals as the input was not handled properly (each signal in the + list was treated as a separate input rather than connecting a single + input to the list). + +* Fixed a bug in `impulse_response` where the `input` keyword was not + being handled properly. + +* Fixed bug in `step_info` in computing settling time for a constant + system. + + +Improvements +............ + +The following additional improvements and changes in functionality +were implemented in this release: + +* Added support for NumPy 2. + +* `frequency_response` now properly transfer labels from the system to + the response. + +* I/O systems with no inputs and no outputs are now allowed, mainly + for use by the `phase_plane_plot` function. + +* Improved error messages in `input_output_response` when the number + of states, inputs, or outputs are incompatible with the system size + by telling you which one didn't match. + +* `phase_plane_plot` now generate warnings when simulations fail for + individual initial conditions and drops individual traces (rather + than terminating). + +* Changed the way plot titles are created, using + `matplotlib.axes.set_title` (centers title over axes) instead of + `matplotlib.fig.suptitle` (centers over figure, which is good for + multi-axes plots but otherwise looks funny). + +* Updated arrow placement in `phase_plane_plot` so that very short + lines have zero or one arrows. + +* Subsystem indexing now allows slices as indexing arguments. + +* The `label` keyword is now allowed in frequency response commands to + override default label generation. + +* Restored functionality that allowed omega to be specified as a list + of 2 elements (indicating a range) in all frequency + response/plotting routines. This used to work for + `nyquist_response` but got removed at some point. It now works for + all frequency response commands. + +* Fixed up the `ax` keyword processing to allow arrays or lists + + uniform processing in all frequency plotting routines. + +* Fixed processing of `rcParam` to provide more uniformity. + +* Added new `ControlPlot.set_plot_title` method to set/add titles that are + better centered (on axes instead of figure). + +* Set up `frd` as factory function with keywords, including setting + the signal/system names. + +* Bode and Nyquist plots now allow FRD systems with different omega + vectors as well as mixtures of FRD and other LTI systems. + +* Added unit circle, sensitivity circles, and complementary + sensitivity cicles to `nyquist_plot`. + +* `time_response_plot` improvements: + + - Fixed up the `ax` keyword processing to allow arrays or lists + + uniform processing for all (time and frequency) plot routines. + + - Allow time responses for multiple systems with common time vector + and inputs to find a single time interval. + + - Updated sequential plotting so that different colors are used and + plot title is updated (like Bode and Nyquist). + + - Allow label keyword in various time response commands to override + default label generation. + + - Allow legends to be turned on and off using `show_legend` keyword. + +* `NonlinearIOSystem` improvements: + + - Allow system name to be overridden in `linearize`, even if + `copy_names` is `False`. + + - Allows renaming of system/signal names in bdalg functions + + - New `update_names` method for that allows signal and system names + to be updated. + + - `x0`, `u0` keywords in `linearize` and `input_output_response` + provide common functionality in allowing concatenation of lists + and zero padding ("vector element processing"). + + - Improved error messages when `x0` and `u0` don't match the expected size. + + - If no output function is given in `nlsys`, which provides full + state output, the output signal names are set to match the state + names. + +* `markov` now supports MIMO systems and accepts a `TimeResponseData` + object as input. + +* Processing of the `ax` and `title` keywords is now consistent across + all plotting functions. + +* Set up uniform processing of the `rcParams` keyword argument for + plotting functions (with unit tests). + +* Updated legend processing to be consistent across all plotting + functions, as described in the user documention. + +* Default configuration parameters for plotting are now in + `control.rcParams` and can be reset using `reset_rcParams`. + +* Unified `color` and `*fmt` argument processing code, in addition to + color management for sequential plotting. + + +Deprecations +............ + +The following functions have been newly deprecated in this release and +generate a warning message when used: + +* Assessing the output of a plotting function to a list is now + deprecated. Assign to a `ControlPlot` object and access lines and + other elements via attributes. + +* Deprecated the `relabel` keyword in `time_response_plot`. + +The listed items are slated to be removed in future releases (usually +the next major or minor version update). diff --git a/doc/releases/0.3-7.x-notes.rst b/doc/releases/0.3-7.x-notes.rst new file mode 100644 index 000000000..23b7d03b5 --- /dev/null +++ b/doc/releases/0.3-7.x-notes.rst @@ -0,0 +1,25 @@ +.. currentmodule:: control + +.. _version-0.3-7.x: + +Versions 0.3-0.7 Release Notes +------------------------------ + +* Released: 10 June 2010 - 23 Oct 2015 +* `Detailed release notes `_ + on python-control GitHub wiki. + +[ChatGPT summary] Between versions 0.3d and 0.7.0, the python-control +package underwent significant enhancements and refinements. Key +additions included support for discrete-time systems with a timebase +variable and the introduction of the c2d function for MIMO state-space +systems. New functionality such as rlocus, pade, and nichols was +added, along with minimal realization tools and model reduction +methods like hsvd, modred, and balred. Plotting capabilities were +expanded with more flexible Bode and Nyquist plots, frequency +labeling, and a phase_plot command for 2D nonlinear +systems. Performance improvements included faster versions of freqresp +and forced_response, bug fixes in tools like dare and tf2ss, and +enhanced stability margin and root-locus calculations. Installation +became easier via pip and conda, Python 3 compatibility improved, and +extensive documentation updates ensured a smoother user experience. diff --git a/doc/releases/0.8.x-notes.rst b/doc/releases/0.8.x-notes.rst new file mode 100644 index 000000000..9b6b89742 --- /dev/null +++ b/doc/releases/0.8.x-notes.rst @@ -0,0 +1,32 @@ +.. currentmodule:: control + +.. _version-0.8.x: + +Version 0.8.x Release Notes +---------------------------- + +* Released: 7 Jul 2018 - 28 Dec 2020 +* `Detailed release notes `_ + on python-control GitHub wiki. + +[ChatGPT summary] Between versions 0.8.0 and 0.8.4, the +python-control package introduced significant updates and +enhancements. Notable additions include improved support for nonlinear +systems with a new input/output systems module and functions for +linearization and differential flatness analysis, the ability to +create non-proper transfer functions, and support for dynamic +prewarping during continuous-to-discrete system +conversion. Visualization improvements were made across several +functions, such as enhanced options for Nyquist plots, better +pole-zero mapping compatibility with recent matplotlib updates, and +LaTeX formatting for Jupyter notebook outputs. Bugs were fixed in +critical areas like discrete-time simulations, forced response +computations, and naming conventions for interconnected systems. The +release also focused on expanded configurability with a new +`use_legacy_defaults` function and dict-based configuration handling, +updated unit testing (switching to pytest), and enhanced documentation +and examples, including for `sisotool` and trajectory +planning. Improvements to foundational algorithms, such as pole +placement, transfer function manipulation, and discrete root locus, +rounded out this series of releases, ensuring greater flexibility and +precision for control systems analysis. diff --git a/doc/releases/0.9.0-notes.rst b/doc/releases/0.9.0-notes.rst new file mode 100644 index 000000000..00f20f6df --- /dev/null +++ b/doc/releases/0.9.0-notes.rst @@ -0,0 +1,87 @@ +.. currentmodule:: control + +.. _version-0.9.0: + +Version 0.9.0 Release Notes +---------------------------- + +* Released: 21 Mar 2021 +* `GitHub release page + `_ + +Version 0.9.0 of the Python Control Toolbox (python-control) contains +a number of enhanced features and changes to functions. Some of these +changes may require modifications to existing user code and, in +addition, some default settings have changed that may affect the +appearance of plots or operation of certain functions. + +Significant new additions including improvements in the I/O systems +modules that allow automatic interconnection of signals having the +same name (via the `interconnect` function), generation and plotting +of describing functions for closed loop systems with static +nonlinearities, and a new :ref:`optimal control module +` that allows basic computation of optimal controls +(including model predictive controllers). Some of the changes that may +break use code include the deprecation of the NumPy `~numpy.matrix` +type (2D NumPy arrays are used instead), changes in the return value +for Nyquist plots (now returns number of encirclements rather than the +frequency response), switching the default timebase of systems to be 0 +rather than None (no timebase), and changes in the processing of +return values for time and frequency responses (to make them more +consistent). In many cases, the earlier behavior can be restored by +calling ``use_legacy_defaults('0.8.4')``. + +New features +............ + +* Optimal control module, including rudimentary MPC control +* Describing functions plots +* MIMO impulse and step response +* I/O system improvements: + + - `linearize` retains signal names plus new `interconnect` function + - Add summing junction + implicit signal interconnection + +* Implementation of initial_phase, wrap_phase keywords for bode_plot +* Added IPython LaTeX representation method for StateSpace objects +* New `~StateSpace.dynamics` and `~StateSpace.output` methods in `StateSpace` +* `FRD` systems can now be created from a discrete time LTI system +* Cost and constraints are now allowed for `flatsys.point_to_point` + + +Interface changes +................. + +* Switch default state space matrix type to 'array' (instead of 'matrix') +* Use `~LTI.__call__` instead of `~LTI.evalfr` in LTI system classes +* Default dt is now 0 instead of None +* Change default value of `StateSpace.remove_useless_states` to False +* Standardize time response return values, `return_x`/`squeeze` + keyword processing +* Standardize `squeeze` processing in frequency response functions +* Nyquist plot now returns number of encirclements +* Switch `LTI` class and subclasses to use ninputs, noutputs, nstates +* Use standard time series convention for `markov` input data +* TransferFunction array priority plus system type conversion checking +* Generate error for `tf2ss` of non-proper transfer function +* Updated return values for frequency response evaluated at poles + + +Improvements, bug fixes +....................... + +* Nyquist plot improvements: better arrows, handle poles on imaginary axis +* Sisotool small visual cleanup, new feature to show step response of + different input-output than loop +* Add `bdschur` and fox modal form with repeated eigenvalues +* Fix rlocus timeout due to inefficient _default_wn calculation +* Fix `stability_margins`: finding z for ``|H(z)| = 1`` computed the wrong + polynomials +* Freqplot improvements +* Fix rlocus plotting problem in Jupyter notebooks +* Handle empty pole vector for timevector calculation +* Fix `lqe` docstring and input array type +* Updated `markov` to add tranpose keyword + default warning +* Fix impulse size for discrete-time impulse response +* Extend `returnScipySignalLTI` to handle discrete-time systems +* Bug fixes and extensions for `step_info` diff --git a/doc/releases/0.9.1-notes.rst b/doc/releases/0.9.1-notes.rst new file mode 100644 index 000000000..d0ef8b733 --- /dev/null +++ b/doc/releases/0.9.1-notes.rst @@ -0,0 +1,51 @@ +.. currentmodule:: control + +.. _version-0.9.1: + +Version 0.9.1 Release Notes +---------------------------- + +* Released: 31 Dec 2021 +* `GitHub release page + `_ + +This is a minor release that includes new functionality for discrete +time systems (`dlqr`, `dlqe`, `drss`), flat systems (optimization and +constraints), a new time response data class, and many individual +improvements and bug fixes. + +New features +............ + +* Add optimization to flat systems trajectory generation +* Return a discrete time system with `drss` +* A first implementation of the singular value plot +* Include InfValue into settling min/max calculation for `step_info` +* New time response data class +* Check for unused subsystem signals in `InterconnectedSystem` +* New PID design function built on `sisotool` +* Modify discrete-time contour for Nyquist plots to indent around poles +* Additional I/O system type conversions +* Remove Python 2.7 support and leverage @ operator +* Discrete time LQR and LQE + +Improvements, bug fixes +....................... + +* Change `step_info` undershoot percentage calculation +* IPython LaTeX output only generated for small systems +* Fix warnings generated by `sisotool` +* Discrete time LaTeX repr of `StateSpace` systems +* Updated rlocus.py to remove warning by `sisotool` with `rlocus_grid` = True +* Refine automatic contour determination in Nyquist plot +* Fix `damp` method for discrete time systems with a negative real-valued pole +* Plot Nyquist frequency correctly in Bode plot in Hz +* Return frequency response for 0 and 1-state systems directly +* Fixed prewarp not working in `c2d` and `sample_system`, margin docstring + improvements +* Improved lqe calling functionality +* Vectorize `FRD` feedback function +* BUG: extrapolation in ufun throwing errors +* Allow use of SciPy for LQR, LQE +* Improve `forced_response` and its documentation +* Add documentation about use of axis('equal') in `pzmap`, `rlocus` diff --git a/doc/releases/0.9.2-notes.rst b/doc/releases/0.9.2-notes.rst new file mode 100644 index 000000000..2adec3fb1 --- /dev/null +++ b/doc/releases/0.9.2-notes.rst @@ -0,0 +1,126 @@ +.. currentmodule:: control + +.. _version-0.9.2: + +Version 0.9.2 Release Notes +---------------------------- + +* Released: 28 May 2022 +* `GitHub release page + `_ + +This is a minor release that includes I/O system enhancements, optimal +control enhancements, new functionality for stochastic systems, +updated system class functionality, bug fixes and improvements to +Nyquist plots and Nichols charts, and L-infinity norm for linear +systems. + +New features +............ + +* I/O system enhancements: + + - Modify the `ss`, `rss`, and `drss` functions to return + `LinearIOSystem` objects (instead of `StateSpace` objects). + This makes it easier to create LTI state space systems that can + be combined with other I/O systems without having to add a + conversation step. Since `LinearIOSystem` objects are also + `StateSpace` objects, no functionality is lost. (This change is + implemented through the introduction of a internal + `NamedIOSystem` class, to avoid import cycles.) + + - Added a new function `create_statefbk_iosystem` that creates an + I/O system for implementing a linear state feedback controller + of the form u = ud - Kp(x - xd). The function returns an I/O + system that takes xd, ud, and x as inputs and generates u as an + output. The `integral_action` keyword can be used to define a + set of outputs y = C x for which integral feedback is also + included: u = ud - Kp(x - xd) - Ki(C x - C xd). + + - The `lqr` and `dlqr` commands now accept an `integral_action` + keyword that allows outputs to be specified for implementing + integral action. The resulting gain matrix has the form K = + [Kp, Ki]. (This is useful for combining with the + `integral_action` functionality in `create_statefbk_iosystem`). + +* Optimal control enhancements: + + - Allow `t_eval` keyword in `input_output_response` to allow a + different set of time points to be used for the input vector and + the computed output. + + - The final cost is now saved in optimal control result. + +* Stochastic systems additions: + + - Added two new functions supporting random signals: + `white_noise`, which creates a white noise vector in continuous + or discrete time, and `correlation`, which calculates the + correlation function (or [cross-] correlation matrix), R(tau). + + - Added a new function `create_estimator_iosystem` that matches + the style of `create_statefbk_iosystem` (#710) and creates an + I/O system implementing an estimator (including covariance + update). + + - Added the ability to specify initial conditions for + `input_output_response` as a list of values, so that for + estimators that keep track of covariance you can set the initial + conditions as `[X0, P0]`. In addition, if you specify a fewer + number of initial conditions than the number of states, the + remaining states will be initialized to zero (with a warning if + the last initial condition is not zero). This allows the + initial conditions to be given as `[X0, 0]`. + + - Added the ability to specify inputs for `input_output_response` + as a list of variables. Each element in the list will be + treated as a portion of the input and broadcast (if necessary) + to match the time vector. This allows input for a system with + noise as `[U, V]` and inputs for a system with zero noise as + `[U, np.zero(n)]` (where U is an input signal and `np.zero(n)` + gets broadcast to match the time vector). + + - Added new Jupyter notebooks demonstrate the use of these + functions: `stochresp.ipynb`, `pvtol-outputfbk.ipynb`, + `kincar-fusion.ipynb`. + +* Updated system class functionality: + + - Changed the `LTI` class to use `poles` and `zeros` for + retrieving poles and zeros, with `pole` and `zero` generating a + `PendingDeprecationWarning` (which is ignored by default in + Python). (The MATLAB compatibility module still uses `pole` and + `zero`.) + + - The `TimeResponseData` and `FrequencyResponseData` objects now + implement a `to_pandas` method that creates a simple pandas + dataframe. + + - The `FrequencyResponseData` class is now used as the output for + frequency response produced by `freqresp` and a new function + `frequency_response` has been defined, to be consistent with the + `input_output_response` function. A `FrequencyResponseData` + object can be assigned to a tuple to provide magnitude, phase, + and frequency arrays, mirroring `TimeResponseData` functionality. + + - The `drss`, `rss`, `ss2tf`, `tf2ss`, `tf2io`, and `ss2io` + functions now all accept system and signal name arguments (via + `_process_namedio_keywords`. + + - The `ss` function can now accept function names as arguments, in + which case it creates a `NonlinearIOSystem` (I'm not sure how + useful this is, but `ss` is a sort of wrapper function that + calls the appropriate class constructor, so it was easy enough + to implement.) + +* Added `linform` to compute linear system L-infinity norm. + + +Improvements, bug fixes +....................... + +* Round to nearest integer decade for default omega vector. +* Interpret str-type args to `interconnect` as non-sequence. +* Fixes to various optimization-based control functions. +* Bug fix and improvements to Nyquist plots. +* Improvements to Nichols chart plotting. diff --git a/doc/releases/0.9.3-notes.rst b/doc/releases/0.9.3-notes.rst new file mode 100644 index 000000000..72ff4c8e8 --- /dev/null +++ b/doc/releases/0.9.3-notes.rst @@ -0,0 +1,129 @@ +.. currentmodule:: control + +.. _version-0.9.3: + +Version 0.9.3 Release Notes +---------------------------- + +* Released: date of release +* `GitHub release page + `_ + +This release adds support for collocation in finding optimal +trajectories, adds the ability to compute optimal trajectories for +flat systems, adds support for passivity indices and passivity tests +for discrete time systems, and includes support for gain scheduling +(in `create_statefbk_iosystem`. Setup is now done using setuptools +(`pip install .` instead of `python setup.py install`). + +This release requires Python 3.8 or higher. + + +New classes, functions, and methods +................................... + +The following new classes, functions, and methods have been added in +this release: + +* `ispassive`: check to see if an LTI system is passive (requires + `cvxopt`). + +* `get_output_fb_index`, `get_input_ff_index`: compute passivity indices. + +* `flatsys.BSplineFamily`: new family of basis functions for flat + systems. + +* `flatsys.solve_flat_ocp`: allows solution of optimal control + problems for differentially flat systems with trajectory and + terminal costs and constraints, mirroring the functionality of + `optimal.solve_ocp`. + +* `zpk`: create a transfer funtion from a zero, pole, gain + representation. + +* `find_eqpts` (now `find_operating_system`) now works for + discrete-time systems. + +Bug fixes +......... + +The following bugs have been fixed in this release: + +* Fixed `timebase` bug in `InterconnectedSystem` that gave errors for + discrete-time systems. + +* Fixed incorect dimension check in `matlab.lsim` for discrete-time + systems. + +* Fixed a bug in the computation of derivatives for the Bezier family + of basis functions with rescaled final time, and implemented a final + time rescaling for the polynomial family of basis functions. + +* Fixed bug in the processing of the `params` keyword for systems + without states. + +* Fixed a problem that was identified in PR #785, where + interconnecting a LinearIOSystem with a StateSpace system via the + interconnect function did not work correctly. + +* Fixed an issued regarding the way that `StateSpace._isstatic` was + defining a static system. New version requires nstates == 0. + +* Fixed a bug in which system and system name were not being handled + correctly when a `TransferFunction` system was combined with other + linear systems using interconnect. + +* Fixed a bug in `find_eqpt` where when y0 is None, dy in the root + function could not be calculated (since it tries to subtract + None). + + +Improvements +............ + +The following additional improvements and changes in functionality +were implemented in this release: + +* Handle `t_eval` for static systems in `input_output_response`. + +* Added support for discrete-time passive systems. + +* Added a more descriptive `__repr__` for basis functions (show the + family + information on attributes). + +* `StateSpace.sample` and `TransferFunction.sample` return a system + with the same input and output labels, which is convenient when + constructing interconnected systems using `interconnect`. + +* `optimal.solve_ocp`: add collocation method for solving optimal + control problems. Use `trajectory_method` parameter that be set to + either 'shooting' (default for discrete time systems) or + 'collocation' (default for continuous time systems). When + collocation is used, the `initial_guess` parameter can either be an + input trajectory (as before) or a tuple consisting of a state + trajectory and an input trajectory. + +* `StateSpace` objects can now be divided by a scalar. + +* `rlocus`, `sisotool`: Allow `initial_gain` to be a scalar (instead + of requiring and array). + +* `create_statefbk_iosystem` now supports gain scheduling. + +* `create_estimator_iosystem` now supports continous time systems. + + +Deprecations +............ + +The following functions have been newly deprecated in this release and +generate a warning message when used: + +* In the :ref:`optimal module `, constraints are + specified in the form ``LinearConstraint(A, lb, ub)`` or + ``NonlinearConstraint(fun, lb, ub)`` instead of the previous forms + ``(LinearConstraint, A, lb, ub)`` and ``(NonlinearConstraint, fun, + lb, ub)``. + +The listed items are slated to be removed in future releases (usually +the next major or minor version update). diff --git a/doc/releases/0.9.4-notes.rst b/doc/releases/0.9.4-notes.rst new file mode 100644 index 000000000..6cdff2f42 --- /dev/null +++ b/doc/releases/0.9.4-notes.rst @@ -0,0 +1,137 @@ +.. currentmodule:: control + +.. _version-0.9.4: + +Version 0.9.4 Release Notes +---------------------------- + +* Released: date of release +* `GitHub release page + `_ + +This release adds functions for optimization-based estimation and +moving horizon estimation, better handling of system and signal names, +as well a number of bug fixes, small enhancements, and updated +documentation. + + +New classes, functions, and methods +................................... + +The following new classes, functions, and methods have been added in +this release: + +* Added the `optimal.OptimalEstimationProblem` class, the + `optimal.compute_oep` function, and the + `optimal.create_mhe_iosystem` function, which compute the optimal + estimate for a (nonlinear) I/O system using an explicit cost + function of a fixed window of applied inputs and measured outputs. + +* Added `gaussian_likelyhood_cost` to create cost function + corresponding to Gaussian likelihoods for use in optimal estimation. + +* Added `disturbance_range_constraint` to create a range constraint on + disturbances. + +* Added `LTI.bandwidth` to compute the bandwidth of a linear system. + + +Bug fixes +......... + +The following bugs have been fixed in this release: + +* Fixed a bug in `interconnect` in which the system name was being + clobbered internally. + +* Fixed a bug in `bode_plot` where phase wrapping was not working when + there were multiple systems. + +* Fixed a bug in `root_locus_plot` in which the `ax` parameter was not + being handled correctly. + +* Fixed a bug in `create_statefbk_iosystem` that didn't proper handle + 1D gain schedules. + +* Fixed a bug in `rootlocus_pid_designer` where the Bode plot was + sometimes blank. + +* Fixed a bug in which signal labels for a `StateSpace` system were + lost when computing `forced_response`. + +* Fixed a bug in which the `damp` command was assuming a + continuous-time system when printing out pole locations (but the + return value was correct). + +* Fixed a bug in which signal names could be lost for state transfer + functions when using the `interconnect` function. + +* Fixed a bug in the block-diagonal schur matrix computation used in + `bdschur`. + + +Improvements +............ + +The following additional improvements and changes in functionality +were implemented in this release: + +* Added an `add_unused` keyword parameter to `interconnect` that + allows unused inputs or outputs to be added as inputs or outputs of + the interconnected system (useful for doing a "partial" + interconnection). + +* Added `control_indices` and `state_indices` to + `create_statefbk_iosystem` to allow partial interconnection (e.g., for + inner/outer loop construction). + +* `create_mpc_iosystem` now allows system and signal names to be + specified via appropriate keywords. + +* `TransferFunction` objects can now be displayed either in polynomial + form or in zpk form using the `display_format` parameter when + creating the system. + +* Allow discrete-time Nyquist plots for discrete-time systems with + poles at 0 and 1. + +* Generate a warning if `prewarp_frequency` is used in `sample_system` + for a discretization type that doesn't support it. + +* Converting a system from state space form to transfer function form + (and vice versa) now updates the system name to append "$converted", + removing an issue where two systems might have the same name. + + +Deprecations +............ + +The following functions have been newly deprecated in this release and +generate a warning message when used: + +* Changed `type` keyword for `create_statefbk_iosystem` to + `controller_type` ('linear' or 'nonlinear'). + +* `issys`: use ``isinstance(sys, ct.LTI)``. + +The listed items are slated to be removed in future releases (usually +the next major or minor version update). + + +Removals +........ + +The following functions and capabilities have been removed in this release: + +* `function`: function that was removed. + +* Other functionality that has been removed. + +Code that makes use of the functionality listed above will have to be +rewritten to work with this release of the python-control package. + + +Additional notes +................ + +Anything else that doesn't fit above. diff --git a/doc/releases/template.rst b/doc/releases/template.rst new file mode 100644 index 000000000..6212f410e --- /dev/null +++ b/doc/releases/template.rst @@ -0,0 +1,82 @@ +.. currentmodule:: control + +.. _version-M.nn.p: + +Version M.nn.p Release Notes +---------------------------- + +* Released: date of release +* `GitHub release page + `_ + +Summary of the primary changes for this release. This should be a +paragraph describing the key updates in this release. The individual +subsections below can provide more information, if needed. Any +sections that are empty can be removed. + +This version of `python-control` requires Python 3.x or higher, NumPy +2.y or higher, etc. + + +New classes, functions, and methods +................................... + +The following new classes, functions, and methods have been added in +this release: + +* `function`: what it does + + +Bug fixes +......... + +The following bugs have been fixed in this release: + +* `function`: short description of the bug and what was fixed. + +* Other bug fixes that are not necessarily associated with a specific + function. + + +Improvements +............ + +The following additional improvements and changes in functionality +were implemented in this release: + +* `function`: improvements made that relate to a specific function. + +* Other changes that are not necesarily attached to a specific function. + + +Deprecations +............ + +The following functions have been newly deprecated in this release and +generate a warning message when used: + +* `function`: functions that are newly deprecated. + +* Other calling patterns that will not be supported in the future. + +The listed items are slated to be removed in future releases (usually +the next major or minor version update). + + +Removals +........ + +The following functions and capabilities have been removed in this release: + +* `function`: function that was removed. + +* Other functionality that has been removed. + +Code that makes use of the functionality listed above will have to be +rewritten to work with this release of the python-control package. + + +Additional notes +................ + +Anything else that doesn't fit above. diff --git a/doc/requirements.txt b/doc/requirements.txt index 123dcc0a2..5fdf9113d 100644 --- a/doc/requirements.txt +++ b/doc/requirements.txt @@ -3,6 +3,7 @@ numpy scipy matplotlib sphinx_rtd_theme +sphinx-copybutton numpydoc ipykernel nbsphinx diff --git a/doc/response.rst b/doc/response.rst new file mode 100644 index 000000000..0058a500d --- /dev/null +++ b/doc/response.rst @@ -0,0 +1,1028 @@ +.. _response-chapter: + +.. currentmodule:: control + +********************************** +Input/Output Response and Plotting +********************************** + +The Python Control Systems Toolbox contains a number of functions for +computing and plotting input/output responses in the time and +frequency domain, root locus diagrams, and other standard charts used +in control system analysis, for example:: + + bode_plot(sys) + nyquist_plot([sys1, sys2]) + phase_plane_plot(sys, limits) + pole_zero_plot(sys) + root_locus_plot(sys) + +While plotting functions can be called directly, the standard pattern used +in the toolbox is to provide a function that performs the basic computation +or analysis (e.g., computation of the time or frequency response) and +returns an object representing the output data. A separate plotting +function, typically ending in `_plot`, is then used to plot the data, +resulting in the following standard pattern:: + + response = ct.nyquist_response([sys1, sys2]) + count = ct.response.count # number of encirclements of -1 + cplt = ct.nyquist_plot(response) # Nyquist plot + +Plotting commands return a :class:`ControlPlot` object that +provides access to the individual lines in the generated plot using +`cplt.lines`, allowing various aspects of the plot to be modified to +suit specific needs. + +The plotting function is also available via the ``plot()`` method of the +analysis object, allowing the following type of calls:: + + step_response(sys).plot() + frequency_response(sys).plot() + nyquist_response(sys).plot() + pp.streamlines(sys, limits).plot() + root_locus_map(sys).plot() + +The remainder of this chapter provides additional documentation on how +these response and plotting functions can be customized. + + +Time Response Data +================== + +Time responses are used to provide information on the behavior of a +system in response to a standard input (such as a step function or +impulse function), the initial state with no input, a custom function +of time, or any combination of the above. Time responses are useful +for evaluating system performance of either linear or nonlinear +systems, in continuous or discrete time. The time response for a +linear system to a standard input can be often computed exactly while +the responses of nonlinear systems or linear systems with arbitrary +input signals must be computed numerically. + +Continuous time signals in `python-control` are represented by the +value of the signal at a set of specified time points, with linear +interpolation between the time points. The time points need not be +uniformly spaced. Discrete time signals are represented by the value +of the signal at a uniformly-spaced sequence of times. + + +LTI response functions +---------------------- + +A number of functions are available for computing the output (and +state) response of an LTI systems: + +.. autosummary:: + + initial_response + step_response + impulse_response + forced_response + +Each of these functions returns a :class:`TimeResponseData` object +that contains the data for the time response (described in more detail +in the next section). + +The :func:`forced_response` system is the most general and computes +the response of the system to a given input from a zero or non-zero +initial condition. + +For linear time invariant (LTI) systems, the :func:`impulse_response`, +:func:`initial_response`, and :func:`step_response` functions will +automatically compute the time vector based on the poles and zeros of +the system. If a list of systems is passed, a common time vector will be +computed and a list of responses will be returned in the form of a +:class:`TimeResponseList` object. The :func:`forced_response` function can +also take a list of systems, to which a single common input is applied. +The :class:`TimeResponseList` object has a ``plot()`` method that will plot +each of the responses in turn, using a sequence of different colors with +appropriate titles and legends. + +In addition, the :func:`input_output_response` function, which handles +simulation of nonlinear systems and interconnected systems, can be +used. For an LTI system, results are generally more accurate using +the LTI simulation functions above. The :func:`input_output_response` +function is described in more detail in the :ref:`iosys-module` section. + +.. _time-series-convention: + +Time series data conventions +---------------------------- + +A variety of functions in the library return time series data: sequences of +values that change over time. A common set of conventions is used for +returning such data: columns represent different points in time, rows are +different components (e.g., inputs, outputs or states). For return +arguments, an array of times is given as the first returned argument, +followed by one or more arrays of variable values. This convention is used +throughout the library, for example in the functions +:func:`forced_response`, :func:`step_response`, :func:`impulse_response`, +and :func:`initial_response`. + +.. note:: The convention used by `python-control` is different from + the convention used in the `scipy.signal + `_ + library. In SciPy's convention the meaning of rows and columns is + interchanged. Thus, all 2D values must be transposed when they + are used with functions from `scipy.signal`_. + +The time vector is a 1D array with shape (n, ):: + + T = [t1, t2, t3, ..., tn ] + +Input, state, and output all follow the same convention. Columns are +different points in time, rows are different components:: + + U = [[u1(t1), u1(t2), u1(t3), ..., u1(tn)] + [u2(t1), u2(t2), u2(t3), ..., u2(tn)] + ... + ... + [ui(t1), ui(t2), ui(t3), ..., ui(tn)]] + +(and similarly for `X`, `Y`). So, ``U[:, 2]`` is the system's input +at the third point in time; and ``U[1]`` or ``U[1, :]`` is the +sequence of values for the system's second input. + +When there is only one row, a 1D object is accepted or returned, which adds +convenience for SISO systems: + +The initial conditions are either 1D, or 2D with shape (j, 1):: + + X0 = [[x1] + [x2] + ... + ... + [xj]] + +Functions that return time responses (e.g., :func:`forced_response`, +:func:`impulse_response`, :func:`input_output_response`, +:func:`initial_response`, and :func:`step_response`) return a +:class:`TimeResponseData` object that contains the data for the time +response. These data can be accessed via the +:attr:`~TimeResponseData.time`, :attr:`~TimeResponseData.outputs`, +:attr:`~TimeResponseData.states` and :attr:`~TimeResponseData.inputs` +properties: + +.. testsetup:: time_series, timeplot, freqplot, pzmap, ctrlplot + + import matplotlib.pyplot as plt + import numpy as np + import control as ct + +.. testcode:: time_series + + sys = ct.rss(4, 1, 1) + response = ct.step_response(sys) + plt.plot(response.time, response.outputs) + +The dimensions of the response properties depend on the function being +called and whether the system is SISO or MIMO. In addition, some time +response function can return multiple "traces" (input/output pairs), +such as the :func:`step_response` function applied to a MIMO system, +which will compute the step response for each input/output pair. See +:class:`TimeResponseData` for more details. + +The input, output, and state elements of the response can be accessed using +signal names in place of integer offsets: + +.. testcode:: time_series + + plt.plot(response.time, response.states['x[1]']) + +The time response functions can also be assigned to a tuple, which extracts +the time and output (and optionally the state, if the `return_x` keyword is +used). This allows simple commands for plotting: + +.. testcode:: time_series + + t, y = ct.step_response(sys) + plt.plot(t, y) + +The output of a MIMO LTI system can be plotted like this: + +.. testcode:: time_series + + sys = ct.rss(4, 2, 1) + + timepts = np.linspace(0, 10) + u = np.sin(timepts) + + t, y = ct.forced_response(sys, timepts, u) + plt.plot(t, y[0], label='y_0') + plt.plot(t, y[1], label='y_1') + +For multi-trace systems generated by :func:`step_response` and +:func:`impulse_response`, the input name used to generate the trace can be +used to access the appropriate input output pair: + +.. testcode:: time_series + + response = ct.step_response(sys) + plt.plot(response.time, response.outputs['y[1]', 'u[0]']) + +The convention also works well with the state space form of linear +systems. If `D` is the feedthrough matrix (2D array) of a linear system, +and `U` is its input (array), then the feedthrough part of the system's +response, can be computed like this:: + + ft = D @ U + +Finally, the `~TimeResponseData.to_pandas` method can be used to create +a pandas dataframe:: + + df = response.to_pandas() + +The column labels for the data frame are :code:`time` and the labels +for the input, output, and state signals ('u[i]', 'y[i]', and 'x[i]' +by default, but these can be changed using the `inputs`, `outputs`, +and `states` keywords when constructing the system, as described in +:func:`ss`, :func:`tf`, and other system creation functions. Note +that when exporting to pandas, "rows" in the data frame correspond to +time and "cols" (DataSeries) correspond to signals. + +Time response plots +------------------- + +The input/output time response functions ( :func:`forced_response`, +:func:`impulse_response`, :func:`initial_response`, +:func:`input_output_response`, :func:`step_response`) return a +:class:`TimeResponseData` object, which contains the time, input, +state, and output vectors associated with the simulation, as described +above. Time response data can be plotted with the +:func:`time_response_plot` function, which is also available as the +:func:`TimeResponseData.plot` method. For example, the step response +for a two-input, two-output can be plotted using the commands: + +.. testcode:: timeplot + + sys_mimo = ct.tf2ss( + [[[1], [0.1]], [[0.2], [1]]], + [[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="sys_mimo") + response = ct.step_response(sys_mimo) + response.plot() + +.. testcode:: timeplot + :hide: + + plt.savefig('figures/timeplot-mimo_step-default.png') + plt.close('all') + +which produces the following plot: + +.. image:: figures/timeplot-mimo_step-default.png + :align: center + +A number of options are available in the :func:`time_response_plot` +function (and associated :func:`TimeResponseData.plot` method) to +customize the appearance of input output data. For data produced by +the :func:`impulse_response` and :func:`step_response` commands, the +inputs are not shown. This behavior can be changed using the +`plot_inputs` keyword. It is also possible to combine multiple lines +onto a single graph, using either the `overlay_signals` keyword (which +puts all outputs out a single graph and all inputs on a single graph) +or the `overlay_traces` keyword, which puts different traces (e.g., +corresponding to step inputs in different channels) on the same graph, +with appropriate labeling via a legend on selected axes. + +For example, using `plot_input` = True and `overlay_signals` = True +yields the following plot: + +.. testcode:: timeplot + + ct.step_response(sys_mimo).plot( + plot_inputs=True, overlay_signals=True, + title="Step response for 2x2 MIMO system " + + "[plot_inputs, overlay_signals]") + +.. testcode:: timeplot + :hide: + + plt.savefig('figures/timeplot-mimo_step-pi_cs.png') + plt.close('all') + +.. image:: figures/timeplot-mimo_step-pi_cs.png + :align: center + +Input/output response plots created with either the +:func:`forced_response` or the +:func:`input_output_response` functions include the input signals by +default. These can be plotted on separate axes, but also "overlaid" on the +output axes (useful when the input and output signals are being compared to +each other). The following plot shows the use of `plot_inputs` = 'overlay' +as well as the ability to reposition the legends using the `legend_map` +keyword: + +.. testcode:: timeplot + + timepts = np.linspace(0, 10, 100) + U = np.vstack([np.sin(timepts), np.cos(2*timepts)]) + ct.input_output_response(sys_mimo, timepts, U).plot( + plot_inputs='overlay', + legend_map=np.array([['lower right'], ['lower right']]), + title="I/O response for 2x2 MIMO system " + + "[plot_inputs='overlay', legend_map]") + +.. testcode:: timeplot + :hide: + + plt.savefig('figures/timeplot-mimo_ioresp-ov_lm.png') + +.. image:: figures/timeplot-mimo_ioresp-ov_lm.png + :align: center + +Another option that is available is to use the `transpose` keyword so that +instead of plotting the outputs on the top and inputs on the bottom, the +inputs are plotted on the left and outputs on the right, as shown in the +following figure: + +.. testcode:: timeplot + + U1 = np.vstack([np.sin(timepts), np.cos(2*timepts)]) + resp1 = ct.input_output_response(sys_mimo, timepts, U1) + + U2 = np.vstack([np.cos(2*timepts), np.sin(timepts)]) + resp2 = ct.input_output_response(sys_mimo, timepts, U2) + + ct.combine_time_responses( + [resp1, resp2], trace_labels=["Scenario #1", "Scenario #2"]).plot( + transpose=True, + title="I/O responses for 2x2 MIMO system, multiple traces " + "[transpose]") + +.. testcode:: timeplot + :hide: + + plt.savefig('figures/timeplot-mimo_ioresp-mt_tr.png') + +.. image:: figures/timeplot-mimo_ioresp-mt_tr.png + :align: center + +This figure also illustrates the ability to create "multi-trace" plots +using the :func:`combine_time_responses` function. The line +properties that are used when combining signals and traces are set by +the `input_props`, `output_props` and `trace_props` parameters for +:func:`time_response_plot`. + +Additional customization is possible using the `input_props`, +`output_props`, and `trace_props` keywords to set complementary line colors +and styles for various signals and traces: + +.. testcode:: timeplot + + cplt = ct.step_response(sys_mimo).plot( + plot_inputs='overlay', overlay_signals=True, overlay_traces=True, + output_props=[{'color': c} for c in ['blue', 'orange']], + input_props=[{'color': c} for c in ['red', 'green']], + trace_props=[{'linestyle': s} for s in ['-', '--']]) + +.. testcode:: timeplot + :hide: + + plt.savefig('figures/timeplot-mimo_step-linestyle.png') + +.. image:: figures/timeplot-mimo_step-linestyle.png + :align: center + + +.. _frequency_response: + +Frequency Response Data +======================= + +Linear time invariant (LTI) systems can be analyzed in terms of their +frequency response and `python-control` provides a variety of tools for +carrying out frequency response analysis. The most basic of these is +the :func:`frequency_response` function, which will compute +the frequency response for one or more linear systems: + +.. testcode:: freqplot + + sys1 = ct.tf([1], [1, 2, 1], name='sys1') + sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') + response = ct.frequency_response([sys1, sys2]) + +A Bode plot provide a graphical view of the response an LTI system and can +be generated using the :func:`bode_plot` function: + +.. testcode:: freqplot + + ct.bode_plot(response, initial_phase=0) + +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-siso_bode-default.png') + plt.close('all') + +.. image:: figures/freqplot-siso_bode-default.png + :align: center + +Computing the response for multiple systems at the same time yields a +common frequency range that covers the features of all listed systems. + +Bode plots can also be created directly using the +:meth:`FrequencyResponseData.plot` method: + +.. testcode:: freqplot + + sys_mimo = ct.tf( + [[[1], [0.1]], [[0.2], [1]]], + [[[1, 0.6, 1], [1, 1, 1]], [[1, 0.4, 1], [1, 2, 1]]], name="sys_mimo") + ct.frequency_response(sys_mimo).plot() + +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-mimo_bode-default.png') + plt.close('all') + +.. image:: figures/freqplot-mimo_bode-default.png + :align: center + +A variety of options are available for customizing Bode plots, for +example allowing the display of the phase to be turned off or +overlaying the inputs or outputs: + +.. testcode:: freqplot + + ct.frequency_response(sys_mimo).plot( + plot_phase=False, overlay_inputs=True, overlay_outputs=True) + +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-mimo_bode-magonly.png') + plt.close('all') + +.. image:: figures/freqplot-mimo_bode-magonly.png + :align: center + +The :func:`singular_values_response` function can be used to +generate Bode plots that show the singular values of a transfer +function: + +.. testcode:: freqplot + + ct.singular_values_response(sys_mimo).plot() + +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-mimo_svplot-default.png') + plt.close('all') + +.. image:: figures/freqplot-mimo_svplot-default.png + :align: center + +Different types of plots can also be specified for a given frequency +response. For example, to plot the frequency response using a a Nichols +plot, use `plot_type` = 'nichols': + +.. testcode:: freqplot + + response.plot(plot_type='nichols') + +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-siso_nichols-default.png') + plt.close('all') + +.. image:: figures/freqplot-siso_nichols-default.png + :align: center + +Another response function that can be used to generate Bode plots is the +:func:`gangof4_response` function, which computes the four primary +sensitivity functions for a feedback control system in standard form: + +.. testcode:: freqplot + + proc = ct.tf([1], [1, 1, 1], name="process") + ctrl = ct.tf([100], [1, 5], name="control") + response = ct.gangof4_response(proc, ctrl) + ct.bode_plot(response) # or response.plot() + +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-gangof4.png') + plt.close('all') + +.. image:: figures/freqplot-gangof4.png + :align: center + +Nyquist analysis can be done using the :func:`nyquist_response` +function, which evaluates an LTI system along the Nyquist contour, and +the :func:`nyquist_plot` function, which generates a Nyquist plot: + +.. testcode:: freqplot + + sys = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys') + ct.nyquist_plot(sys) + +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-nyquist-default.png') + plt.close('all') + +.. image:: figures/freqplot-nyquist-default.png + :align: center + +The :func:`nyquist_response` function can be used to compute +the number of encirclements of the -1 point and can return the Nyquist +contour that was used to generate the Nyquist curve. + +By default, the Nyquist response will generate small semicircles around +poles that are on the imaginary axis. In addition, portions of the Nyquist +curve that are far from the origin are scaled to a maximum value, while the +line style is changed to reflect the scaling, and it is possible to offset +the scaled portions to separate out the portions of the Nyquist curve at +:math:`\infty`. A number of keyword parameters for both are available for +:func:`nyquist_response` and :func:`nyquist_plot` to tune +the computation of the Nyquist curve and the way the data are plotted: + +.. testcode:: freqplot + + sys = ct.tf([1, 0.2], [1, 0, 1]) * ct.tf([1], [1, 0]) + nyqresp = ct.nyquist_response(sys) + nyqresp.plot( + max_curve_magnitude=6, max_curve_offset=1, + arrows=[0, 0.15, 0.3, 0.6, 0.7, 0.925], + title='Custom Nyquist plot') + print("Encirclements =", nyqresp.count) + +.. testoutput:: freqplot + :hide: + + Encirclements = 2 + +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-nyquist-custom.png') + plt.close('all') + +.. image:: figures/freqplot-nyquist-custom.png + :align: center + +All frequency domain plotting functions will automatically compute the +range of frequencies to plot based on the poles and zeros of the frequency +response. Frequency points can be explicitly specified by including an +array of frequencies as a second argument (after the list of systems): + +.. testcode:: freqplot + + sys1 = ct.tf([1], [1, 2, 1], name='sys1') + sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') + omega = np.logspace(-2, 2, 500) + ct.frequency_response([sys1, sys2], omega).plot(initial_phase=0) + +.. testcode:: freqplot + :hide: + + plt.savefig('figures/freqplot-siso_bode-omega.png') + plt.close('all') + +.. image:: figures/freqplot-siso_bode-omega.png + :align: center + +Alternatively, frequency ranges can be specified by passing a list of the +form ``[wmin, wmax]``, where `wmin` and `wmax` are the minimum and +maximum frequencies in the (log-spaced) frequency range: + +.. testcode:: freqplot + + response = ct.frequency_response([sys1, sys2], [1e-2, 1e2]) + +The number of (log-spaced) points in the frequency can be specified using +the `omega_num` keyword parameter. + +Frequency response data can also be accessed directly and plotted manually: + +.. testcode:: freqplot + + sys = ct.rss(4, 2, 2, strictly_proper=True) # 2x2 MIMO system + fresp = ct.frequency_response(sys) + plt.loglog(fresp.omega, fresp.magnitude['y[1]', 'u[0]']) + +Access to frequency response data is available via the attributes +`omega`, `magnitude`, `phase`, and `response`, where `response` +represents the complex value of the frequency response at each frequency. +The `magnitude`, `phase`, and `response` arrays can be indexed using +either input/output indices or signal names, with the first index +corresponding to the output signal and the second input corresponding to +the input signal. + +Pole/Zero Data +============== + +Pole/zero maps and root locus diagrams provide insights into system +response based on the locations of system poles and zeros in the complex +plane. The :func:`pole_zero_map` function returns the poles and +zeros and can be used to generate a pole/zero plot: + +.. testcode:: pzmap + :hide: + + plt.close('all') + +.. testcode:: pzmap + + sys = ct.tf([1, 2], [1, 2, 3], name='SISO transfer function') + response = ct.pole_zero_map(sys) + ct.pole_zero_plot(response) + +.. testcode:: pzmap + :hide: + + plt.savefig('figures/pzmap-siso_ctime-default.png') + plt.close('all') + +.. image:: figures/pzmap-siso_ctime-default.png + :align: center + +A root locus plot shows the location of the closed loop poles of a system +as a function of the loop gain: + +.. testcode:: pzmap + + ct.root_locus_map(sys).plot() + +.. testcode:: pzmap + :hide: + + plt.savefig('figures/rlocus-siso_ctime-default.png') + plt.close('all') + +.. image:: figures/rlocus-siso_ctime-default.png + :align: center + +The grid in the left hand plane shows lines of constant damping ratio as +well as arcs corresponding to the frequency of the complex pole. The grid +can be turned off using the `grid` keyword. Setting `grid` to False will +turn off the grid but show the real and imaginary axis. To completely +remove all lines except the root loci, use `grid` = 'empty'. + +On systems that support interactive plots, clicking on a location on the +root locus diagram will mark the pole locations on all branches of the +diagram and display the gain and damping ratio for the clicked point below +the plot title: + +.. testcode:: pzmap + :hide: + + cplt = ct.root_locus_map(sys).plot(initial_gain=3.506) + ax = cplt.axes[0, 0] + freqplot_rcParams = ct.config._get_param('ctrlplot', 'rcParams') + with plt.rc_context(freqplot_rcParams): + ax.set_title( + "Clicked at: -2.729+1.511j gain = 3.506 damping = 0.8748") + + plt.savefig('figures/rlocus-siso_ctime-clicked.png') + plt.close('all') + +.. image:: figures/rlocus-siso_ctime-clicked.png + :align: center + +Root locus diagrams are also supported for discrete-time systems, in which +case the grid is show inside the unit circle: + +.. testcode:: pzmap + + sysd = sys.sample(0.1) + ct.root_locus_plot(sysd) + +.. testcode:: pzmap + :hide: + + plt.savefig('figures/rlocus-siso_dtime-default.png') + plt.close('all') + +.. image:: figures/rlocus-siso_dtime-default.png + :align: center + +Lists of systems can also be given, in which case the root locus diagram +for each system is plotted in different colors: + +.. testcode:: pzmap + + sys1 = ct.tf([1], [1, 2, 1], name='sys1') + sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') + ct.root_locus_plot([sys1, sys2], grid=False) + +.. testcode:: pzmap + :hide: + + plt.savefig('figures/rlocus-siso_multiple-nogrid.png') + plt.close('all') + +.. image:: figures/rlocus-siso_multiple-nogrid.png + :align: center + + +Customizing Control Plots +========================= + +A set of common options are available to customize control plots in +various ways. The following general rules apply: + +* If a plotting function is called multiple times with data that generate + control plots with the same shape for the array of subplots, the new data + will be overlaid with the old data, with a change in color(s) for the + new data (chosen from the standard matplotlib color cycle). If not + overridden, the plot title and legends will be updated to reflect all + data shown on the plot. + +* If a plotting function is called and the shape for the array of subplots + does not match the currently displayed plot, a new figure is created. + Note that only the shape is checked, so if two different types of + plotting commands that generate the same shape of subplots are called + sequentially, the :func:`matplotlib.pyplot.figure` command should be used + to explicitly create a new figure. + +* The `ax` keyword argument can be used to direct the plotting + function to use a specific axes or array of axes. The value of the + `ax` keyword must have the proper number of axes for the plot (so a + plot generating a 2x2 array of subplots should be given a 2x2 array + of axes for the `ax` keyword). + +* The `color`, `linestyle`, `linewidth`, and other matplotlib line + property arguments can be used to override the default line properties. + If these arguments are absent, the default matplotlib line properties are + used and the color cycles through the default matplotlib color cycle. + + The :func:`bode_plot`, :func:`time_response_plot`, + and selected other commands can also accept a matplotlib format + string (e.g., 'r--'). The format string must appear as a positional + argument right after the required data argument. + + Note that line property arguments are the same for all lines generated as + part of a single plotting command call, including when multiple responses + are passed as a list to the plotting command. For this reason it is + often easiest to call multiple plot commands in sequence, with each + command setting the line properties for that system/trace. + +* The `label` keyword argument can be used to override the line labels + that are used in generating the title and legend. If more than one line + is being plotted in a given call to a plot command, the `label` + argument value should be a list of labels, one for each line, in the + order they will appear in the legend. + + For input/output plots (frequency and time responses), the labels that + appear in the legend are of the form ", , , ". The trace name is used only for multi-trace time + plots (for example, step responses for MIMO systems). Common information + present in all traces is removed, so that the labels appearing in the + legend represent the unique characteristics of each line. + + For non-input/output plots (e.g., Nyquist plots, pole/zero plots, root + locus plots), the default labels are the system name. + + If `label` is set to False, individual lines are still given + labels, but no legend is generated in the plot. (This can also be + accomplished by setting `legend_map` to False). + + Note: the `label` keyword argument is not implemented for describing + function plots or phase plane plots, since these plots are primarily + intended to be for a single system. Standard `matplotlib` commands can + be used to customize these plots for displaying information for multiple + systems. + +* The `legend_loc`, `legend_map` and `show_legend` keyword arguments + can be used to customize the locations for legends. By default, a + minimal number of legends are used such that lines can be uniquely + identified and no legend is generated if there is only one line in the + plot. Setting `show_legend` to False will suppress the legend and + setting it to True will force the legend to be displayed even if + there is only a single line in each axes. In addition, if the value of + the `legend_loc` keyword argument is set to a string or integer, it + will set the position of the legend as described in the + :func:`matplotlib.legend` documentation. Finally, `legend_map` can be + set to an array that matches the shape of the subplots, with each item + being a string indicating the location of the legend for that axes (or + None for no legend). + +* The `rcParams` keyword argument can be used to override the default + matplotlib style parameters used when creating a plot. The default + parameters for all control plots are given by the + `config.defaults['ctrlplot.rcParams']` dictionary and have the following + values: + + .. list-table:: + :widths: 50 50 + :header-rows: 1 + + * - Key + - Value + * - 'axes.labelsize' + - 'small' + * - 'axes.titlesize' + - 'small' + * - 'figure.titlesize' + - 'medium' + * - 'legend.fontsize' + - 'x-small' + * - 'xtick.labelsize' + - 'small' + * - 'ytick.labelsize' + - 'small' + + Only those values that should be changed from the default need to be + specified in the `rcParams` keyword argument. To override the + defaults for all control plots, update the + `config.defaults['ctrlplt.rcParams']` dictionary entries. For convenience, + this dictionary can also be accessed as `ct.rcParams`. + + The default values for style parameters for control plots can be restored + using :func:`reset_rcParams`. + +* For multi-input, multi-output time and frequency domain plots, the + `sharex` and `sharey` keyword arguments can be used to determine whether + and how axis limits are shared between the individual subplots. Setting + the keyword to 'row' will share the axes limits across all subplots in a + row, 'col' will share across all subplots in a column, 'all' will share + across all subplots in the figure, and False will allow independent + limits for each subplot. + + For Bode plots, the `share_magnitude` and `share_phase` keyword arguments + can be used to independently control axis limit sharing for the magnitude + and phase portions of the plot, and `share_frequency` can be used instead + of `sharex`. + +* The `title` keyword can be used to override the automatic creation + of the plot title. The default title is a string of the form + " plot for " where is a list of the sys + names contained in the plot (which is updated if the plotting + function is called multiple times). Use `title` = False to suppress + the title completely. The title can also be updated using the + :func:`~ControlPlot.set_plot_title` method for the returned control + plot object. + + The plot title is only generated if `ax` is None. + +The following code illustrates the use of some of these customization +features: + +.. testcode:: ctrlplot + + P = ct.tf([0.02], [1, 0.1, 0.01]) # servomechanism + C1 = ct.tf([1, 1], [1, 0]) # unstable + L1 = P * C1 + C2 = ct.tf([1, 0.05], [1, 0]) # stable + L2 = P * C2 + + plt.rcParams.update(ct.rcParams) + fig = plt.figure(figsize=[7, 4]) + ax_mag = fig.add_subplot(2, 2, 1) + ax_phase = fig.add_subplot(2, 2, 3) + ax_nyquist = fig.add_subplot(1, 2, 2) + + ct.bode_plot( + [L1, L2], ax=[ax_mag, ax_phase], + label=["$L_1$ (unstable)", "$L_2$ (unstable)"], + show_legend=False) + ax_mag.set_title("Bode plot for $L_1$, $L_2$") + ax_mag.tick_params(labelbottom=False) + fig.align_labels() + + ct.nyquist_plot(L1, ax=ax_nyquist, label="$L_1$ (unstable)") + ct.nyquist_plot( + L2, ax=ax_nyquist, label="$L_2$ (stable)", + max_curve_magnitude=22, legend_loc='upper right') + ax_nyquist.set_title("Nyquist plot for $L_1$, $L_2$") + + fig.suptitle("Loop analysis for servomechanism control design") + plt.tight_layout() + +.. testcode:: ctrlplot + :hide: + + plt.savefig('figures/ctrlplot-servomech.png') + plt.close('all') + +.. image:: figures/ctrlplot-servomech.png + :align: center + +As this example illustrates, python-control plotting functions and +Matplotlib plotting functions can generally be intermixed. One type of +plot for which this does not currently work is pole/zero plots with a +continuous-time omega-damping grid (including root locus diagrams), due to +the way that axes grids are implemented. As a workaround, the +:func:`pole_zero_subplots` command can be used to create an array +of subplots with different grid types, as illustrated in the following +example: + +.. testcode:: ctrlplot + + ax_array = ct.pole_zero_subplots(2, 1, grid=[True, False]) + sys1 = ct.tf([1, 2], [1, 2, 3], name='sys1') + sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') + ct.root_locus_plot([sys1, sys2], ax=ax_array[0, 0]) + cplt = ct.root_locus_plot([sys1, sys2], ax=ax_array[1, 0]) + cplt.set_plot_title("Root locus plots (w/ specified axes)") + cplt.figure.tight_layout() + +.. testcode:: ctrlplot + :hide: + + plt.savefig('figures/ctrlplot-pole_zero_subplots.png') + plt.close('all') + +.. image:: figures/ctrlplot-pole_zero_subplots.png + :align: center + +Alternatively, turning off the omega-damping grid (using `grid` = False or +`grid` = 'empty') allows use of Matplotlib layout commands. + + +Response and Plotting Reference +=============================== + +Response functions +------------------ + +Response functions take a system or list of systems and return a response +object that can be used to retrieve information about the system (e.g., the +number of encirclements for a Nyquist plot) as well as plotting (via the +`plot` method). + +.. autosummary:: + + describing_function_response + frequency_response + forced_response + gangof4_response + impulse_response + initial_response + input_output_response + nyquist_response + pole_zero_map + root_locus_map + singular_values_response + step_response + +Plotting functions +------------------ + +Plotting functions take a response or list of responses and return a +`ControlPlot` object that can be used to retrieve information about +the plot. Plotting functions can also be called with a system or list +of systems, in which case the appropriate response will be first +computed and then plotted. + +Note that the `phase_plane_plot` function is part of the +python-control namespace, but the individual functions for customizing +phase plots are contained in the `phaseplot` module, which should be +imported separately using ``import control.phaseplot as pp``. The +phase plane plotting functionality is described in more detail in the +:ref:`phase-plane-plots` section. + +.. autosummary:: + + bode_plot + describing_function_plot + nichols_plot + nyquist_plot + phase_plane_plot + phaseplot.circlegrid + phaseplot.equilpoints + phaseplot.meshgrid + phaseplot.separatrices + phaseplot.streamlines + phaseplot.vectorfield + pole_zero_plot + root_locus_plot + singular_values_plot + time_response_plot + + +Utility functions +----------------- +These additional functions can be used to manipulate response data or +carry out other operations in creating control plots. + + +.. autosummary:: + + phaseplot.boxgrid + combine_time_responses + pole_zero_subplots + reset_rcParams + + +Response and plotting classes +----------------------------- + +The following classes are used in generating response data. + +.. autosummary:: + + ControlPlot + DescribingFunctionResponse + FrequencyResponseData + FrequencyResponseList + NyquistResponseData + PoleZeroData + TimeResponseData + TimeResponseList diff --git a/doc/rlocus-siso_ctime-clicked.png b/doc/rlocus-siso_ctime-clicked.png deleted file mode 100644 index dff339371..000000000 Binary files a/doc/rlocus-siso_ctime-clicked.png and /dev/null differ diff --git a/doc/rlocus-siso_ctime-default.png b/doc/rlocus-siso_ctime-default.png deleted file mode 100644 index 636951ed5..000000000 Binary files a/doc/rlocus-siso_ctime-default.png and /dev/null differ diff --git a/doc/rlocus-siso_dtime-default.png b/doc/rlocus-siso_dtime-default.png deleted file mode 100644 index 301778729..000000000 Binary files a/doc/rlocus-siso_dtime-default.png and /dev/null differ diff --git a/doc/rlocus-siso_multiple-nogrid.png b/doc/rlocus-siso_multiple-nogrid.png deleted file mode 100644 index 07ece6505..000000000 Binary files a/doc/rlocus-siso_multiple-nogrid.png and /dev/null differ diff --git a/doc/robust_mimo.py b/doc/robust_mimo.py deleted file mode 120000 index f49c7abb6..000000000 --- a/doc/robust_mimo.py +++ /dev/null @@ -1 +0,0 @@ -../examples/robust_mimo.py \ No newline at end of file diff --git a/doc/robust_siso.py b/doc/robust_siso.py deleted file mode 120000 index 9d770ea2d..000000000 --- a/doc/robust_siso.py +++ /dev/null @@ -1 +0,0 @@ -../examples/robust_siso.py \ No newline at end of file diff --git a/doc/rss-balred.py b/doc/rss-balred.py deleted file mode 120000 index 04b921134..000000000 --- a/doc/rss-balred.py +++ /dev/null @@ -1 +0,0 @@ -../examples/rss-balred.py \ No newline at end of file diff --git a/doc/scherer_etal_ex7_H2_h2syn.py b/doc/scherer_etal_ex7_H2_h2syn.py deleted file mode 120000 index 527f80144..000000000 --- a/doc/scherer_etal_ex7_H2_h2syn.py +++ /dev/null @@ -1 +0,0 @@ -../examples/scherer_etal_ex7_H2_h2syn.py \ No newline at end of file diff --git a/doc/scherer_etal_ex7_Hinf_hinfsyn.py b/doc/scherer_etal_ex7_Hinf_hinfsyn.py deleted file mode 120000 index 7755a325f..000000000 --- a/doc/scherer_etal_ex7_Hinf_hinfsyn.py +++ /dev/null @@ -1 +0,0 @@ -../examples/scherer_etal_ex7_Hinf_hinfsyn.py \ No newline at end of file diff --git a/doc/secord-matlab.py b/doc/secord-matlab.py deleted file mode 120000 index 988ec5aca..000000000 --- a/doc/secord-matlab.py +++ /dev/null @@ -1 +0,0 @@ -../examples/secord-matlab.py \ No newline at end of file diff --git a/doc/simulating_discrete_nonlinear.ipynb b/doc/simulating_discrete_nonlinear.ipynb deleted file mode 120000 index 1712b729e..000000000 --- a/doc/simulating_discrete_nonlinear.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/simulating_discrete_nonlinear.ipynb \ No newline at end of file diff --git a/doc/statesp.rst b/doc/statesp.rst new file mode 100644 index 000000000..752c488bb --- /dev/null +++ b/doc/statesp.rst @@ -0,0 +1,171 @@ +.. currentmodule:: control + +State Space Analysis and Design +=============================== + +This section describes the functions the are available to analyze +state space systems and design state feedback controllers. The +functionality described here is mainly specific to state space system +representations; additional functions for analysis of linear +input/output systems, including transfer functions and frequency +response data systems, are defined in the next section and can also be +applied to LTI systems in state space form. + + +State space properties +---------------------- + +The following basic attributes and methods are available for +:class:`StateSpace` objects: + +.. autosummary:: + + ~StateSpace.A + ~StateSpace.B + ~StateSpace.C + ~StateSpace.D + ~StateSpace.dt + ~StateSpace.shape + ~StateSpace.nstates + ~StateSpace.poles + ~StateSpace.zeros + ~StateSpace.dcgain + ~StateSpace.sample + ~StateSpace.returnScipySignalLTI + ~StateSpace.__call__ + +A complete list of attributes, methods, and properties is available in +the :class:`StateSpace` class documentation. + + +Similarity transformations and canonical forms +---------------------------------------------- + +State space systems can be transformed into different internal +representations representing a variety of standard canonical forms +that have the same input/output properties. The +:func:`similarity_transform` function allows a change of internal +state variable via similarity transformation and the +:func:`canonical_form` function converts systems into different +canonical forms. Additional information is available on the +documentation pages for the individual functions: + +.. autosummary:: + + canonical_form + observable_form + modal_form + reachable_form + similarity_transform + + +Time domain properties +---------------------- + +The following functions are available to analyze the time domain +properties of a linear system: + +.. autosummary:: + + damp + forced_response + impulse_response + initial_response + ssdata + step_info + step_response + +The time response functions (:func:`impulse_response`, +:func:`initial_response`, :func:`forced_response`, and +:func:`step_response`) are described in more detail in the +:ref:`response-chapter` chapter. + + +State feedback design +--------------------- + +State feedback controllers for a linear system are controllers of the form + +.. math:: + + u = -K x + +where :math:`K \in {\mathbb R}^{m \times n}` is a matrix of feedback +gains. Assuming the systems is controllable, the resulting closed +loop system will have dynamics matrix :math:`A - B K` with stable +eigenvalues. + +Feedback controllers can be designed using one of several +methods: + +.. autosummary:: + + lqr + place + place_acker + place_varga + +The :func:`place`, :func:`place_acker`, and :func:`place_varga` functions +place the eigenvalues of the closed loop system to a desired set of +values. Each takes the `A` and `B` matrices of the state space system +and the desired location of the eigenvalues and returns a gain matrix +`K`:: + + K = ct.place(sys.A, sys.B, E) + +where `E` is a 1D array of desired eigenvalues. + +The :func:`lqr` function computes the optimal state feedback controller +that minimizes the quadratic cost + +.. math:: + + J = \int_0^\infty (x' Q x + u' R u + 2 x' N u) dt + +by solving the appropriate Riccati equation. It returns the gain +matrix `K`, the solution to the Riccati equation `S`, and the location +of the closed loop eigenvalues `E`. It can be called in one of +several forms: + + * ``K, S, E = ct.lqr(sys, Q, R)`` + * ``K, S, E = ct.lqr(sys, Q, R, N)`` + * ``K, S, E = ct.lqr(A, B, Q, R)`` + * ``K, S, E = ct.lqr(A, B, Q, R, N)`` + +If :code:`sys` is a discrete-time system, the first two forms will compute +the discrete-time optimal controller. For the second two forms, the +:func:`dlqr` function can be used to compute the discrete-time optimal +controller. Additional arguments and details are given on the +:func:`lqr` and :func:`dlqr` documentation pages. + +State estimation +---------------- + +State estimators (or observers) are dynamical systems that estimate +the state of a system given a model of the dynamics and the input +and output signals as a function of time. Linear state estimators +have the form + +.. math:: + + \frac{d\hat x}{dt} = A \hat x + B u + L(y - C\hat x - D u), + +where :math:`\hat x` is an estimate of the state and :math:`L \in +{\mathbb R}^{n \times p}` represents the estimator gain. The gain +:math:`L` is chosen such that the eigenvalues of the matrix :math:`A - +L C` are stable, resulting in an estimate that converges to the value +of the system state. + +The gain matrix :math:`L` can be chosen using eigenvalue placement by +calling the :func:`place` function:: + + L = ct.place(sys.A.T, sys.C.T, E).T + +where `E` is the desired location of the eigenvalues and ``.T`` computes +the transpose of a matrix. + +More sophisticated estimators can be constructed by modeling noise and +disturbances as stochastic signals generated by a random process. +Estimators constructed using these models are described in more detail +in the :ref:`kalman-filter` section of the :ref:`stochastic-systems` +chapter. diff --git a/doc/steering-gainsched.py b/doc/steering-gainsched.py deleted file mode 120000 index 200e49543..000000000 --- a/doc/steering-gainsched.py +++ /dev/null @@ -1 +0,0 @@ -../examples/steering-gainsched.py \ No newline at end of file diff --git a/doc/steering-optimal.png b/doc/steering-optimal.png deleted file mode 100644 index 518de89a4..000000000 Binary files a/doc/steering-optimal.png and /dev/null differ diff --git a/doc/steering-optimal.py b/doc/steering-optimal.py deleted file mode 120000 index 506033ec1..000000000 --- a/doc/steering-optimal.py +++ /dev/null @@ -1 +0,0 @@ -../examples/steering-optimal.py \ No newline at end of file diff --git a/doc/steering.ipynb b/doc/steering.ipynb deleted file mode 120000 index a7f083b90..000000000 --- a/doc/steering.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/steering.ipynb \ No newline at end of file diff --git a/doc/stochastic.rst b/doc/stochastic.rst new file mode 100644 index 000000000..881cf234a --- /dev/null +++ b/doc/stochastic.rst @@ -0,0 +1,408 @@ +.. currentmodule:: control + +.. _stochastic-systems: + +****************** +Stochastic Systems +****************** + +The Python Control Systems Library has support for basic operations +involving linear and nonlinear I/O systems with Gaussian white noise +as an input. + + +Stochastic Signals +================== + +A stochastic signal is a representation of the output of a random +process. NumPy and SciPy have a functions to calculate the covariance +and correlation of random signals: + + * :func:`numpy.cov` - with a single argument, returns the sample + variance of a vector random variable :math:`X \in \mathbb{R}^n` + where the input argument represents samples of :math:`X`. With + two arguments, returns the (cross-)covariance of random variables + :math:`X` and :math:`Y` where the input arguments represent + samples of the given random variables. + + * :func:`scipy.signal.correlate` - the "cross-correlation" between two + random (1D) sequences. If these sequences came from a random + process, this is a single sample approximation of the (discrete + time) correlation function. Use the function + :func:`scipy.signal.correlation_lags` to compute the lag + :math:`\tau` and :func:`scipy.signal.correlate` to get the (auto) + correlation function :math:`r_X(\tau)`. + +The python-control package has variants of these functions that do +appropriate processing for continuous-time models. + +The :func:`white_noise` function generates a (multi-variable) white +noise signal of specified intensity as either a sampled continuous-time +signal or a discrete-time signal. A white noise signal along a 1D +array of linearly spaced set of times `timepts` can be computing using + +.. code:: + + V = ct.white_noise(timepts, Q[, dt]) + +where `Q` is a positive definite matrix providing the noise +intensity and `dt` is the sampling time (or 0 for continuous time). + +In continuous time, the white noise signal is scaled such that the +integral of the covariance over a sample period is `Q`, thus +approximating a white noise signal. In discrete time, the white noise +signal has covariance `Q` at each point in time (without any +scaling based on the sample time). + +The python-control :func:`correlation` function computes the +correlation matrix :math:`{\mathbb E}\{X^\mathsf{T}(t+\tau) X(t)\}` or +the cross-correlation matrix :math:`{\mathbb E}\{X^\mathsf{T}(t+\tau) +Y(t)\}`, where :math:`\mathbb{E}` represents expectation: + +.. code:: + + tau, Rtau = ct.correlation(timepts, X[, Y]) + +The signal `X` (and `Y`, if present) represents a continuous or +discrete-time signal sampled at regularly spaced times `timepts`. The +return value provides the correlation :math:`R_\tau` between +:math:`X(t+\tau)` and :math:`X(t)` at a set of time offsets +:math:`\tau` (determined based on the spacing of entries in the +`timepts` vector. + +Note that the computation of the correlation function is based on a +single time signal (or pair of time signals) and is thus a very crude +approximation to the true correlation function between two random +processes. + +To compute the response of a linear (or nonlinear) system to a white +noise input, use the :func:`forced_response` (or +:func:`input_output_response`) function: + +.. testsetup:: + + import matplotlib.pyplot as plt + import numpy as np + import random + import control as ct + + random.seed(71) + np.random.seed(71) + +.. testcode:: + + a, c = 1, 1 + sys = ct.ss([[-a]], [[1]], [[c]], 0, name='sys') + timepts = np.linspace(0, 5, 1000) + Q = np.array([[0.1]]) + V = ct.white_noise(timepts, Q) + resp = ct.forced_response(sys, timepts, V) + resp.plot() + +.. testcode:: + :hide: + + plt.savefig('figures/stochastic-whitenoise-response.png') + plt.close('all') + +.. image:: figures/stochastic-whitenoise-response.png + :align: center + +The correlation function for the output can be computed using the +:func:`correlation` function and compared to the analytical expression: + +.. testcode:: + + tau, r_Y = ct.correlation(timepts, resp.outputs) + plt.plot(tau, r_Y, label='empirical') + plt.plot( + tau, c**2 * Q.item() / (2 * a) * np.exp(-a * np.abs(tau)), + label='approximation') + plt.xlabel(r"$\tau$") + plt.ylabel(r"$r_\tau$") + plt.title(f"Output correlation for {sys.name}") + plt.legend() + +.. testcode:: + :hide: + + plt.savefig('figures/stochastic-whitenoise-correlation.png') + plt.close('all') + +.. image:: figures/stochastic-whitenoise-correlation.png + :align: center + + +.. _kalman-filter: + +Linear Quadratic Estimation (Kalman Filter) +=========================================== + +A standard application of stochastic linear systems is the computation +of the optimal linear estimator under the assumption of white Gaussian +measurement and process noise. This estimator is called the linear +quadratic estimator (LQE) and its gains can be computed using the +:func:`lqe` function. + +We consider a continuous-time, state space system + +.. math:: + + \frac{dx}{dt} &= Ax + Bu + Gw \\ + y &= Cx + Du + v + +with unbiased process noise :math:`w` and measurement noise :math:`v` +with covariances satisfying + +.. math:: + + {\mathbb E}\{w w^T\} = QN,\qquad + {\mathbb E}\{v v^T\} = RN,\qquad + {\mathbb E}\{w v^T\} = NN + +where :math:`{\mathbb E}\{\cdot\}` represents expectation. + +The :func:`lqe` function computes the observer gain matrix :math:`L` +such that the stationary (non-time-varying) Kalman filter + +.. math:: + + \frac{d\hat x}{dt} = A \hat x + B u + L(y - C\hat x - D u), + +produces a state estimate :math:`\hat x` that minimizes the expected +squared error using the sensor measurements :math:`y`. + +As with the :func:`lqr` function, the :func:`lqe` function can be +called in several forms: + + * ``L, P, E = lqe(sys, QN, RN)`` + * ``L, P, E = lqe(sys, QN, RN, NN)`` + * ``L, P, E = lqe(A, G, C, QN, RN)`` + * ``L, P, E = lqe(A, G, C, QN, RN, NN)`` + +where :code:`sys` is an :class:`LTI` object, and `A`, `G`, `C`, `QN`, `RN`, +and `NN` are 2D arrays of appropriate dimension. If :code:`sys` is a +discrete-time system, the first two forms will compute the discrete +time optimal controller. For the second two forms, the :func:`dlqr` +function can be used. Additional arguments and details are given on +the :func:`lqr` and :func:`dlqr` documentation pages. + +.. testsetup:: kalman + + sys = ct.rss(2, 2, 2) + Qu = np.eye(2) + Qv = np.eye(2) + Qw = np.eye(2) + Qx = np.eye(2) + + timepts = np.linspace(0, 10) + U = ct.white_noise(timepts, Qv) + Y = ct.white_noise(timepts, Qw) + + X0 = np.zeros(2) + P0 = np.eye(2) + +The :func:`create_estimator_iosystem` function can be used to create +an I/O system implementing a Kalman filter, including integration of +the Riccati ODE. The command has the form + +.. testcode:: kalman + + estim = ct.create_estimator_iosystem(sys, Qv, Qw) + +The input to the estimator is the measured outputs `Y` and the system +input `U`. To run the estimator on a noisy signal, use the command + +.. testcode:: kalman + + resp = ct.input_output_response(estim, timepts, [Y, U], [X0, P0]) + +If desired, the :func:`correct` parameter can be set to False +to allow prediction with no additional sensor information: + +.. testcode:: kalman + + resp = ct.input_output_response( + estim, timepts, 0, [X0, P0], params={'correct': False}) + +The :func:`create_estimator_iosystem` and +:func:`create_statefbk_iosystem` functions can be used to combine an +estimator with a state feedback controller: + +.. testcode:: kalman + + K, _, _ = ct.lqr(sys, Qx, Qu) + estim = ct.create_estimator_iosystem(sys, Qv, Qw, P0) + ctrl, clsys = ct.create_statefbk_iosystem(sys, K, estimator=estim) + +The controller will have the same form as a full state feedback +controller, but with the system state :math:`x` input replaced by the +estimated state :math:`\hat x` (output of `estim`): + +.. math:: + + u = u_\text{d} - K (\hat x - x_\text{d}). + +The closed loop controller `clsys` includes both the state +feedback and the estimator dynamics and takes as its input the desired +state :math:`x_\text{d}` and input :math:`u_\text{d}`: + +.. testcode:: kalman + :hide: + + Xd = np.zeros((2, timepts.size)) + Ud = np.zeros((2, timepts.size)) + +.. testcode:: kalman + + resp = ct.input_output_response( + clsys, timepts, [Xd, Ud], [X0, np.zeros_like(X0), P0]) + + + +Maximum Likelihood Estimation +============================= + +Consider a *nonlinear* system with discrete-time dynamics of the form + +.. math:: + :label: eq_fusion_nlsys-oep + + X[k+1] = f(X[k], u[k], V[k]), \qquad Y[k] = h(X[k]) + W[k], + +where :math:`X[k] \in \mathbb{R}^n`, :math:`u[k] \in \mathbb{R}^m`, and +:math:`Y[k] \in \mathbb{R}^p`, and :math:`V[k] \in \mathbb{R}^q` and +:math:`W[k] \in \mathbb{R}^p` represent random processes that are not +necessarily Gaussian white noise processes. The estimation problem that we +wish to solve is to find the estimate :math:`\hat x[\cdot]` that matches +the measured outputs :math:`y[\cdot]` with "likely" disturbances and +noise. + +For a fixed horizon of length :math:`N`, this problem can be formulated as +an optimization problem where we define the likelihood of a given estimate +(and the resulting noise and disturbances predicted by the model) as a cost +function. Suppose we model the likelihood using a conditional probability +density function :math:`p(x[0], \dots, x[N] \mid y[0], \dots, y[N-1])`. +Then we can pose the state estimation problem as + +.. math:: + :label: eq_fusion_oep + + \hat x[0], \dots, \hat x[N] = + \arg \max_{\hat x[0], \dots, \hat x[N]} + p(\hat x[0], \dots, \hat x[N] \mid y[0], \dots, y[N-1]) + +subject to the constraints given by equation :eq:`eq_fusion_nlsys-oep`. +The result of this optimization gives us the estimated state for the +previous :math:`N` steps in time, including the "current" time +:math:`x[N]`. The basic idea is thus to compute the state estimate that is +most consistent with our model and penalize the noise and disturbances +according to how likely they are (based on the given stochastic system +model for each). + +Given a solution to this fixed-horizon optimal estimation problem, we +can create an estimator for the state over all times by repeatedly +applying the optimization problem :eq:`eq_fusion_oep` over a moving +horizon. At each time :math:`k`, we take the measurements for the +last :math:`N` time steps along with the previously estimated state at +the start of the horizon, :math:`x[k-N]` and reapply the optimization +in equation :eq:`eq_fusion_oep`. This approach is known as a *moving +horizon estimator* (MHE). + +The formulation for the moving horizon estimation problem is very general +and various situations can be captured using the conditional probability +function :math:`p(x[0], \dots, x[N] \mid y[0], \dots, y[N-1])`. We start by +noting that if the disturbances are independent of the underlying states of +the system, we can write the conditional probability as + +.. math:: + + p \bigl(x[0], \dots, x[N] \mid y[0], \dots, y[N-1]\bigr) = + p_{X[0]}(x[0])\, \prod_{k=0}^{N-1} p_V\bigl(y[k] - h(x[k])\bigr)\, + p\bigl(x[k+1] \mid x[k]\bigr). + +This expression can be further simplified by taking the log of the +expression and maximizing the function + +.. math:: + :label: eq_fusion_log-likelihood + + \log p_{X[0]}(x[0]) + \sum_{k=0}^{N-1} \log + p_W \bigl(y[k] - h(x[k])\bigr) + \log p_V(v[k]). + +The first term represents the likelihood of the initial state, the +second term captures the likelihood of the noise signal, and the final +term captures the likelihood of the disturbances. + +If we return to the case where :math:`V` and :math:`W` are modeled as +Gaussian processes, then it can be shown that maximizing equation +:eq:`eq_fusion_log-likelihood` is equivalent to solving the optimization +problem given by + +.. math:: + :label: eq_fusion_oep-gaussian + + \min_{x[0], \{v[0], \dots, v[N-1]\}} + \|x[0] - \bar x[0]\|_{P_0^{-1}} + \sum_{k=0}^{N-1} + \|y[k] - h(x_k)\|_{R_W^{-1}}^2 + + \|v[k] \|_{R_V^{-1}}^2, + +where :math:`P_0`, :math:`R_V`, and :math:`R_W` are the covariances of the +initial state, disturbances, and measurement noise. + +Note that while the optimization is carried out only over the estimated +initial state :math:`\hat x[0]`, the entire history of estimated states can +be reconstructed using the system dynamics: + +.. math:: + + \hat x[k+1] = f(\hat x[k], u[k], v[k]), \quad k = 0, \dots, N-1. + +In particular, we can obtain the estimated state at the end of the moving +horizon window, corresponding to the current time, and we can thus +implement an estimator by repeatedly solving the optimization of a window +of length :math:`N` backwards in time. + +The :mod:`optimal` module described in the :ref:`optimal-module` +section implements functions for solving optimal estimation problems +using maximum likelihood estimation. The +:class:`optimal.OptimalEstimationProblem` class is used to define an +optimal estimation problem over a finite horizon:: + + oep = opt.OptimalEstimationProblem(sys, timepts, cost[, constraints]) + +Given noisy measurements :math:`y` and control inputs :math:`u`, an +estimate of the states over the time points can be computed using the +:func:`~optimal.OptimalEstimationProblem.compute_estimate` method:: + + estim = oep.compute_optimal( + Y, U[, initial_state=x0, initial_guess=(xhat, v)]) + xhat, v, w = estim.states, estim.inputs, estim.outputs + +For discrete-time systems, the +:func:`~optimal.OptimalEstimationProblem.create_mhe_iosystem` method +can be used to generate an input/output system that implements a +moving horizon estimator. + +Several functions are available to help set up standard optimal estimation +problems: + +.. autosummary:: + + optimal.gaussian_likelihood_cost + optimal.disturbance_range_constraint + +Examples +======== + +The following examples illustrate the use of tools from the stochastic +systems module. Background information for these examples can be +found in the FBS2e supplement on `Optimization-Based Control +`_). + +.. toctree:: + :maxdepth: 1 + + Kalman filter (kinematic car) + (Extended) Kalman filtering (PVTOL) + Moving horizon estimation (PVTOL) diff --git a/doc/stochresp.ipynb b/doc/stochresp.ipynb deleted file mode 120000 index 36190a54c..000000000 --- a/doc/stochresp.ipynb +++ /dev/null @@ -1 +0,0 @@ -../examples/stochresp.ipynb \ No newline at end of file diff --git a/doc/test_sphinxdocs.py b/doc/test_sphinxdocs.py new file mode 100644 index 000000000..1a49f357c --- /dev/null +++ b/doc/test_sphinxdocs.py @@ -0,0 +1,207 @@ +# test_sphinxdocs.py - pytest checks for user guide +# RMM, 23 Dec 2024 +# +# This set of tests is used to make sure that all primary functions are +# referenced in the documentation. + +import inspect +import os +import re +import sys +import warnings +from importlib import resources + +import pytest +import numpydoc.docscrape as npd + +import control +import control.flatsys + +# Location of the documentation and files to check +sphinx_dir = str(resources.files('control')) + '/../doc/generated/' + +# Functions that should not be referenced +legacy_functions = [ + 'acker', # place_acker + 'balred', # balanced_reduction + 'bode', # bode_plot + 'c2d', # sample_system + 'era', # eigensys_realization + 'evalfr', # use __call__() + 'find_eqpt', # find_operating_point + 'FRD', # FrequencyResponseData (or frd) + 'gangof4', # gangof4_plot + 'hsvd', # hankel_singular_values + 'minreal', # minimal_realization + 'modred', # model_reduction + 'nichols', # nichols_plot + 'norm', # system_norm + 'nyquist', # nyquist_plot + 'pzmap', # pole_zero_plot + 'rlocus', # root_locus_plot + 'rlocus', # root_locus_plot + 'root_locus', # root_locus_plot + 'solve_ocp', # solve_optimal_trajectory + 'solve_oep', # solve_optimal_estimate + 'solve_flat_ocp', # solve_flat_optimal +] + +# Functons that we can skip +object_skiplist = [ + control.NamedSignal, # np.ndarray members cause errors + control.FrequencyResponseList, # Use FrequencyResponseData + control.TimeResponseList, # Use TimeResponseData + control.common_timebase, # mainly internal use + control.cvxopt_check, # mainly internal use + control.pandas_check, # mainly internal use + control.slycot_check, # mainly internal use +] + +# Global list of objects we have checked +checked = set() + +# Decide on the level of verbosity (use -rP when running pytest) +verbose = 0 +standalone = False + +control_module_list = [ + control, control.flatsys, control.optimal, control.phaseplot] +@pytest.mark.parametrize("module", control_module_list) +def test_sphinx_functions(module, check_legacy=True): + + # Look through every object in the package + _info(f"Checking module {module}", 1) + + for name, obj in inspect.getmembers(module): + objname = ".".join([module.__name__, name]) + + # Skip anything that is outside of this module + if inspect.getmodule(obj) is not None and \ + not inspect.getmodule(obj).__name__.startswith('control'): + # Skip anything that isn't part of the control package + continue + + elif inspect.isclass(obj) and issubclass(obj, Exception): + continue + + elif inspect.isclass(obj) or inspect.isfunction(obj): + # Skip anything that is inherited, hidden, deprecated, or checked + if inspect.isclass(module) and name not in module.__dict__ \ + or name.startswith('_') or obj in checked: + continue + else: + checked.add(obj) + + # Get the relevant information about this object + exists = os.path.exists(sphinx_dir + objname + ".rst") + deprecated = _check_deprecated(obj) + skip = obj in object_skiplist + referenced = f" {objname} referenced in sphinx docs" + legacy = name in legacy_functions + + _info(f" Checking {objname}", 2) + match exists, skip, deprecated, legacy: + case True, True, _, _: + _info(f"skipped object" + referenced, -1) + case True, _, True, _: + _warn(f"deprecated object" + referenced) + case True, _, _, True: + if check_legacy: + _warn(f"legacy object" + referenced) + case False, False, False, False: + _fail(f"{objname} not referenced in sphinx docs") + + +defaults_skiplist = [] +def test_config_defaults(): + # Keep track of params we found and params we have checked + config_rstdocs = dict() + config_defaults = control.config.defaults + + # Read the documentation file and extract the keys + with open('config.rst', 'r') as file: + for line in file: + if (key_match := re.search(r"py:data:: ([\w]+\.[\w]+)", line)): + if (key := key_match.group(1)) in defaults_skiplist: + _info(f"skipping config param {key}", 2) + continue + else: + _info(f"checking config param {key}", 2) + + if key in config_rstdocs: + _warn(f"config param '{key}' listed multiple times") + + # Get the default value and check it + while not re.match(r"^$|^\.\.", line := next(file)): + if (val_match := re.search(r":value: (.*)", line)): + _info(f"found value for config param {key}", 3) + config_rstdocs[key] = val_match.group(1) + + # Check to make sure (almost) all keys in config.defaults were documented + for key in config_defaults: + if key in defaults_skiplist: + config_rstdocs.pop(key, None) + continue + + if key not in config_rstdocs: + # TODO: change to _fail once everything is set up + _warn(f"config param '{key}' not documented") + continue + + # Make sure the listed default value is correct + try: + if (defval := config_defaults[key]) != eval(config_rstdocs[key]): + _warn(f"config param '{key}' has different default value: " + f"{config_rstdocs[key]} instead of {defval}") + except SyntaxError: + _warn(f"could not evaluate default value for config param '{key}'") + + # Done processing this key + config_rstdocs.pop(key, None) + + if config_rstdocs: + _warn(f"Unknown params in config.rst: {config_rstdocs}") + + +# Test MATLAB library separately (and after config_defaults) +def test_sphinx_matlab(): + import control.matlab + test_sphinx_functions(control.matlab, check_legacy=False) + + +def _check_deprecated(obj): + with warnings.catch_warnings(): + warnings.simplefilter('ignore') # debug via sphinx, not here + doc = npd.FunctionDoc(obj) + + doc_extended = "" if doc is None else "\n".join(doc["Extended Summary"]) + return ".. deprecated::" in doc_extended + + +# Utility function to warn with verbose output +def _info(str, level): + if verbose > level: + print(("INFO: " if level < 0 else " " * level) + str) + +def _warn(str, level=-1): + if verbose > level: + print("WARN: " + " " * level + str) + if not standalone: + warnings.warn(str, stacklevel=2) + +def _fail(str, level=-1): + if verbose > level: + print("FAIL: " + " " * level + str) + if not standalone: + pytest.fail(str) + + +if __name__ == "__main__": + verbose = 0 if len(sys.argv) == 1 else int(sys.argv[1]) + standalone = True + + for module in control_module_list: + test_sphinx_functions(module) + test_config_defaults() + test_sphinx_matlab() + diff --git a/doc/timeplot-mimo_ioresp-mt_tr.png b/doc/timeplot-mimo_ioresp-mt_tr.png deleted file mode 100644 index e4c800086..000000000 Binary files a/doc/timeplot-mimo_ioresp-mt_tr.png and /dev/null differ diff --git a/doc/timeplot-mimo_ioresp-ov_lm.png b/doc/timeplot-mimo_ioresp-ov_lm.png deleted file mode 100644 index 27dd89159..000000000 Binary files a/doc/timeplot-mimo_ioresp-ov_lm.png and /dev/null differ diff --git a/doc/timeplot-mimo_step-default.png b/doc/timeplot-mimo_step-default.png deleted file mode 100644 index 877764fbf..000000000 Binary files a/doc/timeplot-mimo_step-default.png and /dev/null differ diff --git a/doc/timeplot-mimo_step-linestyle.png b/doc/timeplot-mimo_step-linestyle.png deleted file mode 100644 index 9685ea6fa..000000000 Binary files a/doc/timeplot-mimo_step-linestyle.png and /dev/null differ diff --git a/doc/timeplot-mimo_step-pi_cs.png b/doc/timeplot-mimo_step-pi_cs.png deleted file mode 100644 index 6046c8cce..000000000 Binary files a/doc/timeplot-mimo_step-pi_cs.png and /dev/null differ diff --git a/doc/xferfcn.rst b/doc/xferfcn.rst new file mode 100644 index 000000000..627c47c33 --- /dev/null +++ b/doc/xferfcn.rst @@ -0,0 +1,192 @@ +.. currentmodule:: control + +Frequency Domain Analysis and Design +==================================== + +Transfer function properties +---------------------------- + +The following basic attributes and methods are available for +:class:`TransferFunction` objects: + +.. autosummary:: + + ~TransferFunction.num_array + ~TransferFunction.den_array + ~TransferFunction.shape + ~TransferFunction.poles + ~TransferFunction.zeros + ~TransferFunction.dcgain + ~TransferFunction.sample + ~TransferFunction.returnScipySignalLTI + ~TransferFunction.__call__ + +A complete list of attributes, methods, and properties is available in +the :class:`TransferFunction` class documentation. + + +Frequency domain properties +--------------------------- + +The following functions are available to analyze the frequency +domain properties of a linear systems: + +.. autosummary:: + + bandwidth + dcgain + frequency_response + phase_crossover_frequencies + singular_values_response + stability_margins + tfdata + +These functions work on both state space and transfer function models. +The :func:`frequency_response` and :func:`singular_values_response` +functions are described in more detail in the :ref:`response-chapter` +chapter. + + +Input/output norms +------------------ + +Continuous and discrete-time signals can be represented as a normed +linear space with the appropriate choice of signal norm. For +continuous time signals, the three most common norms are the 1-norm, +2-norm, and the :math:`\infty`-norm: + +.. list-table:: + :header-rows: 1 + + * - Name + - Continuous time + - Discrete time + * - 1-norm + - :math:`\int_{-\infty}^\infty |u(\tau)|, d\tau` + - :math:`\sum_k \|x[k]\|` + * - 2-norm + - :math:`\left(\int_{-\infty}^\infty |u(\tau)|^2, d\tau \right)^{1/2}` + - :math:`\left(\sum_k \|x[k]\|^2 \right)^{1/2}` + * - :math:`\infty`-norm + - :math:`\sup_t |u(t)|` + - :math:`\max_k \|x[k]\|` + +Given a norm for input signals and a norm for output signals, we can +define the *induced norm* for an input/output system. The +following table summarizes the induced norms for a transfer function +:math:`G(s)` with impulse response :math:`g(t)`: + +.. list-table:: + :header-rows: 1 + + * - + - :math:`\|u\|_2` + - :math:`\| u \|_\infty` + * - :math:`\| y \|_2` + - :math:`\| G \|_\infty` + - :math:`\infty` + * - :math:`\| y \|_\infty` + - :math:`\| G \|_2` + - :math:`\| g \|_1` + +The system 2-norm and :math:`\infty`-norm can be computed using +:func:`system_norm`:: + + sysnorm = ct.system_norm(sys, p=) + +where `val` is either 2 or 'inf' (the 1-norm is not yet implemented). + + +Stability margins +----------------- + +The stability margin of a system indicates the robustness of a +feedback system to perturbations that might cause the system to become +unstable. Standard measures of robustness include gain margin, phase +margin, and stability margin (distance to the -1 point on the Nyquist +curve). These margins are computed based on the loop transfer +function for a feedback system, assuming the loop will be closed using +negative feedback with gain 1. + +The :func:`stability_margins` function computes all three of these +margins as well as the frequencies at which they occur: + +.. doctest:: + + >>> sys = ct.tf(10, [1, 2, 3, 4]) + >>> gm, pm, sm, wpc, wgc, wms = ct.stability_margins(sys) + >>> print(f"Gain margin: {gm:2.2} at omega = {wpc:2.2} rad/sec") + Gain margin: 0.2 at omega = 1.7 rad/sec + + +Frequency domain synthesis +-------------------------- + +Synthesis of feedback controllers in the frequency domain can be done +using the following functions: + +.. autosummary:: + + h2syn + hinfsyn + mixsyn + +The :func:`mixsyn` function computes a feedback controller +:math:`C(s)` that minimizes the mixed sensitivity gain + +.. math:: + + \| W_1 S \|_\infty + \| W_2 C \|_\infty + \| W_3 T \|_\infty, + +where + +.. math:: + + S = \frac{1}{1 + P C}, \qquad T = \frac{P C}{1 + P C} + +are the sensitivity function and complementary sensitivity function, +and :math:`P(s)` represents the process dynamics. + +The :func:`h2syn` and :func:`hinfsyn` functions compute a feedback +controller :math:`C(s)` that minimizes the 2-norm and the +:math:`\infty`-norm of the sensitivity function for the closed loop +system, respectively. + + +Systems with time delays +------------------------ + +Time delays are not directly representable in `python-control`, but +the :func:`pade` function generates a linear system that approximates +a time delay to a given order: + +.. doctest:: + + >>> num, den = ct.pade(0.1, 3) + >>> delay = ct.tf(num, den, name='delay') + >>> print(delay) + : delay + Inputs (1): ['u[0]'] + Outputs (1): ['y[0]'] + + -s^3 + 120 s^2 - 6000 s + 1.2e+05 + --------------------------------- + s^3 + 120 s^2 + 6000 s + 1.2e+05 + +The plot below shows how the Pade approximation compares to a pure +time delay. + +.. testcode:: + :hide: + + import matplotlib.pyplot as plt + omega = np.logspace(0, 2) + delay_exact = ct.FrequencyResponseData(np.exp(-0.1j * omega ), omega) + cplt = ct.bode_plot( + [delay_exact/0.98, delay*0.98], omega, legend_loc='upper right', + label=['Exact delay', '3rd order Pade approx'], + title="Pade approximation versus pure time delay") + cplt.axes[0, 0].set_ylim([0.1, 10]) + plt.savefig('figures/xferfcn-delay-compare.png') + +.. image:: figures/xferfcn-delay-compare.png diff --git a/examples/.gitignore b/examples/.gitignore new file mode 100644 index 000000000..ad3049346 --- /dev/null +++ b/examples/.gitignore @@ -0,0 +1 @@ +.ipynb-clean diff --git a/examples/Makefile b/examples/Makefile new file mode 100644 index 000000000..554e078ff --- /dev/null +++ b/examples/Makefile @@ -0,0 +1,29 @@ +# Makefile for python-control examples +# RMM, 6 Jul 2024 +# +# This makefile allows cleanup and posting of Jupyter notebooks into +# Google Colab. +# +# Files are copied to Google Colab using rclone. In order to copy files to +# Google Colab, you should edit the GDRIVE variable to use the name of the +# drive you have configured in rclone and the path where you want to place +# the files. The default location is set up for the fbsbook.org@gmail.com +# Google Drive account, currently maintained by Richard Murray. + +NOTEBOOKS = cds110-L*_*.ipynb cds112-L*_*.ipynb +GDRIVE= fbsbook-gdrive:python-control/public/notebooks + +# Clean up notebooks to remove output +clean: .ipynb-clean +.ipynb-clean: $(NOTEBOOKS) + @for i in $?; do \ + echo jupyter nbconvert --clear-output clear-metadata $$i; \ + jupyter nbconvert \ + --ClearMetadataPreprocessor.enabled=True \ + --clear-output $$i; \ + done + touch $@ + +# Post Jupyter notebooks on course website +post: .ipynb-clean + rclone copy . $(GDRIVE) --include /cds110-L\*_\*.ipynb diff --git a/examples/bdalg-matlab.py b/examples/bdalg-matlab.py index 8911d6579..eaafaa59a 100644 --- a/examples/bdalg-matlab.py +++ b/examples/bdalg-matlab.py @@ -1,7 +1,7 @@ -# bdalg-matlab.py - demonstrate some MATLAB commands for block diagram altebra +# bdalg-matlab.py - demonstrate some MATLAB commands for block diagram algebra # RMM, 29 May 09 -from control.matlab import * # MATLAB-like functions +from control.matlab import ss, ss2tf, tf, tf2ss # MATLAB-like functions # System matrices A1 = [[0, 1.], [-4, -1]] diff --git a/examples/cds110-L1_servomech-python.ipynb b/examples/cds110-L1_servomech-python.ipynb new file mode 100644 index 000000000..a4e479492 --- /dev/null +++ b/examples/cds110-L1_servomech-python.ipynb @@ -0,0 +1,571 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "hairy-humidity", + "metadata": { + "id": "hairy-humidity" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 1

\n", + "

Dynamics and Control of a Servomechanism System using Python-Control

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/1GKRYwtbHWSWc21EIYYIZUnbJqUorhY8w)\n", + "\n", + "In this lecture we show how to model an input/output system and design a controller for the system (using eigenvalue placement). This main intent of this lecture is to introduce the Python Control Systems Toolbox ([python-control](https://python-control.org)) and how it can be used to design a control system.\n", + "\n", + "We consider a class of control systems know as *servomechanisms*. Servermechanisms are mechanical systems that use feedback to provide high precision control of position and velocity. Some examples of servomechanisms are shown below:\n", + "\n", + "| | | |\n", + "| -- | -- | -- |\n", + "| Satellite Dish | Disk Drive | Robotics |\n", + "| \"Satellite | \"Disk | \"Disk\n", + "| [YouTube video](https://www.youtube.com/watch?v=HSGfE_sC2hw) | [YouTube video](https://www.youtube.com/watch?v=oQh8KDea6SI) | [YouTube video](https://www.youtube.com/watch?v=hg3TIFIxWCo)\n", + "| | |" + ] + }, + { + "cell_type": "markdown", + "id": "2c284896-bcff-4c06-b80d-d9d6fbc0690f", + "metadata": {}, + "source": [ + "The python-control toolbox can be installed using `pip` over from conda-forge. The code below will import the control toolbox either from your local installation or via pip. We use the prefix `ct` to access control toolbox commands:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "invalid-carnival", + "metadata": {}, + "outputs": [], + "source": [ + "# Import standard packages needed for this exercise\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct" + ] + }, + { + "cell_type": "markdown", + "id": "P7t3Nm4Tre2Z", + "metadata": { + "id": "P7t3Nm4Tre2Z" + }, + "source": [ + "## System dynamics\n", + "\n", + "Consider a simple mechanism consisting of a spring loaded arm that is driven by a motor, as shown below:\n", + "\n", + "
\"servomech-diagram\"
\n", + "\n", + "The motor applies a torque that twists the arm against a linear spring and moves the end of the arm across a rotating platter. The input to the system is the motor torque $\\tau_\\text{m}$. The force exerted by the spring is a nonlinear function of the head position due to the way it is attached.\n", + "\n", + "The equations of motion for the system are given by\n", + "\n", + "$$\n", + "J \\ddot \\theta = -b \\dot\\theta - k r\\sin\\theta + \\tau_\\text{m},\n", + "$$\n", + "\n", + "which can be written in state space form as\n", + "\n", + "$$\n", + "\\frac{d}{dt} \\begin{bmatrix} \\theta \\\\ \\theta \\end{bmatrix} =\n", + " \\begin{bmatrix} \\dot\\theta \\\\ -k r \\sin\\theta / J - b\\dot\\theta / J \\end{bmatrix}\n", + " + \\begin{bmatrix} 0 \\\\ 1/J \\end{bmatrix} \\tau_\\text{m}.\n", + "$$\n", + "\n", + "The system parameters are given by\n", + "\n", + "$$\n", + "k = 1,\\quad J = 100,\\quad b = 10,\n", + "\\quad r = 1,\\quad l = 2,\\quad \\epsilon = 0.01.\n", + "$$\n", + "\n", + "and we assume that time is measured in milliseconds (ms) and distance in centimeters (cm). (The constants here are made up and don't necessarily reflect a real disk drive, though the units and time constants are motivated by computer disk drives.)" + ] + }, + { + "cell_type": "markdown", + "id": "3e476db9", + "metadata": { + "id": "3e476db9" + }, + "source": [ + "The system dynamics can be modeled in python-control using a `NonlinearIOSystem` object, which we create with the `nlsys` function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27bb3c38", + "metadata": {}, + "outputs": [], + "source": [ + "# Parameter values\n", + "servomech_params = {\n", + " 'J': 100, # Moment of inertia of the motor\n", + " 'b': 10, # Angular damping of the arm\n", + " 'k': 1, # Spring constant\n", + " 'r': 1, # Location of spring contact on arm\n", + " 'l': 2, # Distance to the read head\n", + " 'eps': 0.01, # Magnitude of velocity-dependent perturbation\n", + "}\n", + "\n", + "# State derivative\n", + "def servomech_update(t, x, u, params):\n", + " # Extract the configuration and velocity variables from the state vector\n", + " theta = x[0] # Angular position of the disk drive arm\n", + " thetadot = x[1] # Angular velocity of the disk drive arm\n", + " tau = u[0] # Torque applied at the base of the arm\n", + "\n", + " # Get the parameter values\n", + " J, b, k, r = map(params.get, ['J', 'b', 'k', 'r'])\n", + "\n", + " # Compute the angular acceleration\n", + " dthetadot = 1/J * (\n", + " -b * thetadot - k * r * np.sin(theta) + tau)\n", + "\n", + " # Return the state update law\n", + " return np.array([thetadot, dthetadot])\n", + "\n", + "# System output (tip radial position + angular velocity)\n", + "def servomech_output(t, x, u, params):\n", + " l = params['l']\n", + " return np.array([l * x[0], x[1]])\n", + "\n", + "# System dynamics\n", + "servomech = ct.nlsys(\n", + " servomech_update, servomech_output, name='servomech',\n", + " params=servomech_params, states=['theta_', 'thdot_'],\n", + " outputs=['y', 'thdot'], inputs=['tau'])\n", + "\n", + "print(servomech)\n", + "print(\"\\nParams:\", servomech.params)" + ] + }, + { + "cell_type": "markdown", + "id": "competitive-terrain", + "metadata": { + "id": "competitive-terrain" + }, + "source": [ + "### Linearization\n", + "\n", + "To study the open loop dynamics of the system, we compute the linearization of the dynamics about the equilibrium point corresponding to $\\theta_\\text{e} = 15^\\circ$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "senior-carpet", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the equilibrium angle to radians\n", + "theta_e = (15 / 180) * np.pi\n", + "\n", + "# Compute the input required to hold this position\n", + "u_e = servomech.params['k'] * servomech.params['r'] * np.sin(theta_e)\n", + "print(\"Equilibrium torque = %g\" % u_e)\n", + "\n", + "# Linearize the system about the equilibrium point\n", + "P = servomech.linearize([theta_e, 0], u_e)[0, 0]\n", + "# P.update_names(name='linservo')\n", + "print(\"Linearized dynamics:\\n\", P)" + ] + }, + { + "cell_type": "markdown", + "id": "qGtb17lO4PvM", + "metadata": { + "id": "qGtb17lO4PvM" + }, + "source": [ + "We can check the roots of the characteristic equation for this second order system using the `poles` method (we will learn how this works later in the term):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Vkji0Y8FT7oq", + "metadata": {}, + "outputs": [], + "source": [ + "# Check the stability of the equilibrium point\n", + "P.poles()" + ] + }, + { + "cell_type": "markdown", + "id": "naH-Nl7V4c2R", + "metadata": { + "id": "naH-Nl7V4c2R" + }, + "source": [ + "Alternatively, we can look at the eigenvalues of the \"dynamics matrix\" for the linearized system (we will learn about this formulation in [Lecture 3](cds110-L3_lti-systems.ipynb)):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aKxayyiK4NLj", + "metadata": {}, + "outputs": [], + "source": [ + "evals, evecs = np.linalg.eig(P.A)\n", + "print(evals)" + ] + }, + { + "cell_type": "markdown", + "id": "AYQlD5v9GcK4", + "metadata": { + "id": "AYQlD5v9GcK4" + }, + "source": [ + "Both approaches give the same result and we see that the system is stable (negative real part) with an imaginary component (so we can expect some oscillation in the response)." + ] + }, + { + "cell_type": "markdown", + "id": "instant-lancaster", + "metadata": { + "id": "instant-lancaster" + }, + "source": [ + "### Open loop step response\n", + "\n", + "A standard method for understanding the dynamics is to plot output of the system in response to an input that is set to 1 at time $t = 0$ (called the \"step response\").\n", + "\n", + "We use the `step_response` function to plot the step response of the linearized, open-loop system and compute the \"rise time\" and \"settling time\" (we will define these more formally next week)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "african-mauritius", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the step response\n", + "lin_response = ct.step_response(P)\n", + "timepts, output = lin_response.time, lin_response.outputs\n", + "\n", + "# Plot step response (input 0 to output 0)\n", + "plt.plot(timepts, output)\n", + "plt.xlabel(\"Time $t$ [ms]\")\n", + "plt.ylabel(\"Position $y$ [cm]\")\n", + "plt.title(\"Step response for the linearized, open-loop system\")\n", + "\n", + "# Compute and print properties of the step response\n", + "results = ct.step_info(P)\n", + "print(\"Rise time:\", results['RiseTime']) # 10-90% rise time\n", + "print(\"Settling time:\", results['SettlingTime']) # 2% error\n", + "\n", + "# Calculate the rise time start time by hand\n", + "rise_time_start = timepts[np.where(output > 0.1 * output[-1])[0][0]]\n", + "rise_time_stop = rise_time_start + results['RiseTime']\n", + "\n", + "# Add lines for the step response features\n", + "plt.plot([timepts[0], timepts[-1]], [output[-1], output[-1]], 'k--')\n", + "\n", + "plt.plot([rise_time_start, rise_time_start], [0, 2.5], 'k:')\n", + "plt.plot([rise_time_stop, rise_time_stop], [0, 2.5], 'k:')\n", + "plt.arrow(rise_time_start, 0.5, rise_time_stop - rise_time_start, 0)\n", + "plt.text((rise_time_start + rise_time_stop)/2, 0.6, '$T_r$')\n", + "\n", + "plt.plot([0, 0], [0, 2.5], 'k:')\n", + "plt.plot([results['SettlingTime'], results['SettlingTime']], [0, 2.5], 'k:')\n", + "plt.arrow(0, 1.5, results['SettlingTime'], 0)\n", + "plt.text(results['SettlingTime']/2, 1.6, '$T_s$');\n" + ] + }, + { + "cell_type": "markdown", + "id": "DoCK6MWlHaUO", + "metadata": { + "id": "DoCK6MWlHaUO" + }, + "source": [ + "We see that the open loop step response (for the linearized system) is stable, and that the final value is larger than 1 (this value just depends on the parameters in the system)." + ] + }, + { + "cell_type": "markdown", + "id": "nviDlWek9dge", + "metadata": { + "id": "nviDlWek9dge" + }, + "source": [ + "We can also compare the response of the linearized system to the full nonlinear system:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "qwrPhD499jbl", + "metadata": {}, + "outputs": [], + "source": [ + "nl_response = ct.input_output_response(servomech, timepts, U=1)\n", + "\n", + "# Plot step response (input 0 to output 0)\n", + "plt.plot(timepts, output, label=\"linearized\")\n", + "plt.plot(timepts, nl_response.outputs[0], label=\"nonlinear\")\n", + "\n", + "plt.xlabel(\"Time $t$ [ms]\")\n", + "plt.ylabel(\"Position $y$ [cm]\")\n", + "plt.title(\"Step response for the open-loop system\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "7YNmgE2XHmL3", + "metadata": { + "id": "7YNmgE2XHmL3" + }, + "source": [ + "We see that the nonlinear system responds differently. This is because the force exerted by the spring is nonlinear due to the kinematics of the mechanism design." + ] + }, + { + "cell_type": "markdown", + "id": "stuffed-premiere", + "metadata": { + "id": "stuffed-premiere" + }, + "source": [ + "## Feedback control design\n", + "\n", + "We next design a feedback controller for the system that allows the system to track a desired position $y_\\text{d}$ and sets the closed loop eigenvalues of the linearized system to $\\lambda_{1,2} = −10 \\pm 10 i$. We will learn how to do this more formally in later lectures, so if you aren't familiar with these techniques, that's OK.\n", + "\n", + "We make use of full state feedback of the form $u = -K(x - x_\\text{d})$ where $x_\\text{d}$ is the desired state of the system. The python-control `place` command can be used to compute the state feedback gains $K$ that set the closed loop poles at a desired location:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8NK8O6XT7B_a", + "metadata": {}, + "outputs": [], + "source": [ + "# Place the closed loop poles using feedback\n", + "# u = -K (x - xd)\n", + "\n", + "# Find the gains required to place the gains at the desired location\n", + "K = ct.place(P.A, P.B, [-10 + 10*1j, -10 - 10*1j])\n", + "print(f\"{K=}\\n\")\n", + "\n", + "# Implement an I/O system implementing this control law\n", + "def statefbk_output(t, x, u, params):\n", + " l = params.get('l', 2)\n", + " # Create the current and desired state\n", + " x = np.array([u[0] / l, u[1]])\n", + " xd = np.array([u[2] / l, u[3]])\n", + " return -K @ (x - xd)\n", + "\n", + "statefbk = ct.nlsys(\n", + " None, statefbk_output, name='statefbk',\n", + " inputs=['y', 'thdot', 'y_d', 'thdot_d'],\n", + " outputs=['tau']\n", + ")\n", + "print(statefbk)" + ] + }, + { + "cell_type": "markdown", + "id": "v1fb1pJ_zRLk", + "metadata": { + "id": "v1fb1pJ_zRLk" + }, + "source": [ + "Note that this controller has no internal state, but rather is a static input/output function." + ] + }, + { + "cell_type": "markdown", + "id": "ZR8EKtn-H9V7", + "metadata": { + "id": "ZR8EKtn-H9V7" + }, + "source": [ + "We can now connect the controller to the process using the `interconnect` command. Because we have named the signals in a careful way, the `interconnect` command can automatically connect everything together:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "associate-assistant", + "metadata": {}, + "outputs": [], + "source": [ + "clsys = ct.interconnect(\n", + " [servomech, statefbk],\n", + " inputs=['y_d', 'thdot_d'],\n", + " outputs=['y', 'tau']\n", + ")\n", + "print(clsys)" + ] + }, + { + "cell_type": "markdown", + "id": "4o5oy_6N51yf", + "metadata": { + "id": "4o5oy_6N51yf" + }, + "source": [ + "To examine the dynamics of the closed loop system, we plot the step response for the closed loop system and compute the rise time, settling time, and steady state error." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "qIEH3Trn53d4", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the step response of the closed loop system\n", + "timepts = np.linspace(0, 1)\n", + "clsys_resp = ct.input_output_response(clsys, timepts, [1, 0])\n", + "\n", + "plt.plot(clsys_resp.time, clsys_resp.outputs[0])\n", + "plt.xlabel(\"Time $t$ [ms]\")\n", + "plt.ylabel(\"Position $y$ [cm]\")\n", + "plt.title(\"Step response for closed loop, state space controller\")\n", + "\n", + "# Compute and print properties of the step response\n", + "results = ct.step_info(clsys_resp.outputs[0], timepts)\n", + "print(\"\")\n", + "print(f\"Rise time: {results['RiseTime']:.2g} ms\")\n", + "print(f\"Settling time: {results['SettlingTime']:.2g} ms\")\n", + "print(f\"Steady state error: {abs(results['SteadyStateValue'] - 1) * 100:.2g}%\")" + ] + }, + { + "cell_type": "markdown", + "id": "K-ZX_SDmN4rF", + "metadata": { + "id": "K-ZX_SDmN4rF" + }, + "source": [ + "Note the change in timescale (100 ms to 1 ms) and also the fact that the system now goes to the reference value ($y = 1$)." + ] + }, + { + "cell_type": "markdown", + "id": "e0176710", + "metadata": { + "id": "e0176710" + }, + "source": [ + "## Frequency response\n", + "\n", + "Another way to measure the performance of the system is to compute its frequency response.\n", + "\n", + "Roughly speaking, we set the input of the system to be of the form $u(t) = \\sin(\\omega t)$ and then look at the output signal $y(t)$. For a *linear* system, we can show that the output signal will have the form\n", + "\n", + "$$\n", + "y(t) = M \\sin(\\omega t + \\phi)\n", + "$$\n", + "\n", + "where the magnitude $M$ and phase $\\phi$ depend on the input frequency.\n", + "\n", + "We can plot the magnitude (also called the \"gain\") and the phase of the system as a function of the frequency $\\omega$ and plot these values on a log-log and log-linear scale (called a *Bode* plot):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8684cc1", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the linearization of the closed loop system\n", + "G = clsys.linearize([theta_e, 0], [0, 0], name=\"G\")\n", + "\n", + "# Plot the Bode plot (input[0] = yd, outut[0] = y)\n", + "response = ct.frequency_response(G[0, 0])\n", + "cplt = response.plot(title=\"Bode plot for G\", freq_label=\"Frequency [rad/ms]\")" + ] + }, + { + "cell_type": "markdown", + "id": "W_kzSIKGsSka", + "metadata": { + "id": "W_kzSIKGsSka" + }, + "source": [ + "Examination of the frequency response allows us to identify the range of input frequencies over which the control system can accurately track the input ($M(\\omega) \\approx 1$). For this system, we have good tracking up to approximately 10 rad/ms, which corresponds to about 1.6 kHz." + ] + }, + { + "cell_type": "markdown", + "id": "rocky-hobby", + "metadata": { + "id": "rocky-hobby" + }, + "source": [ + "## Trajectory tracking\n", + "\n", + "Another type of analysis we might do is to see how well the system can track a more complicated reference trajectory. For the disk drive example, we might move the system from one point on the disk to a second and then to a third (as we read different portions of the disk).\n", + "\n", + "To explore this, we can create simulations of the full nonlinear system with the linear controllers designed above and plot the response of the system. We do that here for a reference trajectory that has an initial value of 0 cm at $t = 0$, to 1 cm at $t = 0.5$, to 3 cm at $t = 1$, back to 2 cm at $t = 1.5$ ms:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "utility-community", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a reference trajectory to track\n", + "timepts = np.linspace(0, 2.5, 250)\n", + "ref = [\n", + " np.concatenate((\n", + " np.ones(50) * 0,\n", + " np.ones(50) * 1,\n", + " np.ones(50) * 3,\n", + " np.ones(100) * 2,\n", + " )), 0]\n", + "\n", + "# Create the system response and plot the results\n", + "response = ct.input_output_response(clsys, timepts, ref)\n", + "plt.plot(response.time, response.outputs[0])\n", + "\n", + "# Plot the reference trajectory\n", + "plt.plot(timepts, ref[0], 'k--');\n", + "\n", + "# Label the plot\n", + "plt.xlabel(\"Time $t$ [ms]\")\n", + "plt.ylabel(\"Position $y$ [cm]\")\n", + "plt.title(\"Trajectory tracking with full nonlinear dynamics\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "074427a3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds110-L2_invpend-dynamics.ipynb b/examples/cds110-L2_invpend-dynamics.ipynb new file mode 100644 index 000000000..5b1bfc099 --- /dev/null +++ b/examples/cds110-L2_invpend-dynamics.ipynb @@ -0,0 +1,433 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "t0JD8EbaVWg-" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 2

\n", + "

Nonlinear Dynamics (and Control) of an Inverted Pendulum System

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/1is083NiFdHcHX8Hq56oh_AO35nQGO4bh)\n", + "\n", + "In this lecture we investigate the nonlinear dynamics of an inverted pendulum system. More information on this example can be found in [FBS2e](https://fbswiki.org/wiki/index.php?title=FBS), Examples 3.3 and 5.4. This lecture demonstrates how to use [python-control](https://python-control.org) to analyze nonlinear systems, including creating phase plane plots.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the packages needed for the examples included in this notebook\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from math import pi\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P_ZMCccjvHY1" + }, + "source": [ + "## System model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Msad1ficHjtc" + }, + "source": [ + "We consider an invereted pendulum, which is a simplified version of a balance system:\n", + "\n", + "
\"invpend.diagram\"
\n", + "\n", + "The dynamics for an inverted pendulum system can be written as:\n", + "\n", + "$$\n", + " \\dfrac{d}{dt} \\begin{bmatrix} \\theta \\\\ \\dot\\theta\\end{bmatrix} =\n", + " \\begin{bmatrix}\n", + " \\dot\\theta \\\\\n", + " \\dfrac{m g l}{J_\\text{t}} \\sin \\theta\n", + " - \\dfrac{b}{J_\\text{t}} \\dot\\theta\n", + " + \\dfrac{l}{J_\\text{t}} u \\cos\\theta\n", + " \\end{bmatrix}, \\qquad\n", + " y = \\theta,\n", + "$$\n", + "\n", + "where $m$ and $J_t = J + m l^2$ are the mass and (total) moment of inertia of the system to be balanced, $l$ is the distance from the base to the center of mass of the balanced body, $b$ is the coefficient of rotational friction, and $g$ is the acceleration due to gravity.\n", + "\n", + "We begin by creating a nonlinear model of the system:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "invpend_params = {'m': 1, 'l': 1, 'b': 0.5, 'g': 1}\n", + "def invpend_update(t, x, u, params):\n", + " m, l, b, g = params['m'], params['l'], params['b'], params['g']\n", + " umax = params.get('umax', 1)\n", + " usat = np.clip(u[0], -umax, umax)\n", + " return [x[1], -b/m * x[1] + (g * l / m) * np.sin(x[0] + usat/m)]\n", + "invpend = ct.nlsys(\n", + " invpend_update, states=['theta', 'thdot'],\n", + " inputs=['tau'], outputs=['theta', 'thdot'],\n", + " params=invpend_params, name='invpend')\n", + "print(invpend)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IAoQAORFvLj1" + }, + "source": [ + "## Open loop dynamics" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vOALp_IwjVxC" + }, + "source": [ + "The open loop dynamics of the system can be visualized using the `phase_plane_plot` command in python-control:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ct.phase_plane_plot(\n", + " invpend, [-2*pi - 1, 2*pi + 1, -2, 2], 8),\n", + "\n", + "# Draw lines at the downward equilibrium angles\n", + "plt.plot([-pi, -pi], [-2, 2], 'k--')\n", + "plt.plot([pi, pi], [-2, 2], 'k--')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WZuvqNzeJinm" + }, + "source": [ + "We see that the vertical ($\\theta = 0$) equilibrium point is unstable, but the downward equlibrium points ($\\theta = \\pm \\pi$) are stable.\n", + "\n", + "Note also the *separatrices* for the equilibrium point, which gives insights into the regions of attraction (the red dashed line separates the two regions of attraction)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2JibDTJBKHIF" + }, + "source": [ + "## Proportional feedback\n", + "\n", + "We now stabilize the system using a simple proportional feedback controller:\n", + "\n", + "$$u = -k_\\text{p} \\theta.$$\n", + "\n", + "This controller can be designed as an input/output system that has no state dynamics, just a mapping from the inputs to the outputs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the controller\n", + "def propctrl_output(t, x, u, params):\n", + " kp = params.get('kp', 1)\n", + " return -kp * (u[0] - u[1])\n", + "propctrl = ct.nlsys(\n", + " None, propctrl_output, name=\"p_ctrl\",\n", + " inputs=['theta', 'r'], outputs='tau'\n", + ")\n", + "print(propctrl)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AvU35WoBMFjt" + }, + "source": [ + "Note that the input to the controller is the reference value $r$ (which we will always take to be zero), the measured output $y$, which is the angle $\\theta$ for our system. The output of the controller is the system input $u$, corresponding to the force applied to the wheels.\n", + "\n", + "To connect the controller to the system, we use the [`interconnect`](https://python-control.readthedocs.io/en/latest/generated/control.interconnect.html) function, which will connect all signals that have the same names:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the closed loop system\n", + "clsys = ct.interconnect(\n", + " [invpend, propctrl], name='invpend w/ proportional feedback',\n", + " inputs=['r'], outputs=['theta', 'tau'], params={'kp': 1})\n", + "print(clsys)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IIiSaHNuM1u_" + }, + "source": [ + "Note: you will see a warning when you run this command, because the output $\\dot\\theta$ (`thdot`) is not connected to anything. You can ignore this here, but as you get to more complicated examples, you should pay attention to warnings of this sort and make sure they are OK." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now linearize the closed loop system at different gains and compute the eigenvalues to check for stability:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "for kp in [0, 1, 10]:\n", + " print(\"kp = \", kp, \"; poles = \", clsys.linearize([0, 0], [0], params={'kp': kp}).poles())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iV4u31DsNWP9" + }, + "source": [ + "We see that at $k_\\text{p} = 10$ the eigenvalues (poles) of the closed loop system both have negative real part, and so the system is stabilized." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Jg87a3iZP-Qd" + }, + "source": [ + "### Phase portrait\n", + "\n", + "To study the resulting dynamics, we try plotting a phase plot using the same commands as before, but now for the closed loop system (with appropriate proportional gain):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ct.phase_plane_plot(\n", + " clsys, [-2*pi, 2*pi, -2, 2], 8, params={'kp': 10});" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jhU2gidqi-ri" + }, + "source": [ + "This plot is not very useful and has several errors. It shows the limitations of the default parameter values for the `phase_plane_plot` command.\n", + "\n", + "Some things to notice in this plot:\n", + "* Not all of the equilibrium points are showing up (there are two unstable equilibrium points that are missing)\n", + "* There is no detail about what is happening near the origin." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Improved phase portrait\n", + "\n", + "To fix these issues, we can do a couple of things:\n", + "* Restrict the range of the plot from $-3\\pi/2$ to $3\\pi/2$, which means that grid used to calculate the equilibrium point is a bit finer.\n", + "* Reset the grid spacing, so that we have more initial conditions around the edge of the plot and a finer search for equilibrium points.\n", + "\n", + "Here's some improved code:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "kp_params = {'kp': 10}\n", + "ct.phase_plane_plot(\n", + " clsys, [-1.5 * pi, 1.5 * pi, -2, 2], 8,\n", + " gridspec=[13, 7], params=kp_params,\n", + " plot_separatrices={'timedata': 5})\n", + "plt.plot([-pi, -pi], [-2, 2], 'k--', [ pi, pi], [-2, 2], 'k--')\n", + "plt.plot([-pi/2, -pi/2], [-2, 2], 'k:', [ pi/2, pi/2], [-2, 2], 'k:');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Play around with some paramters to see what happens\n", + "fig, axs = plt.subplots(2, 2)\n", + "for i, kp in enumerate([3, 10]):\n", + " for j, umax in enumerate([0.2, 1]):\n", + " ct.phase_plane_plot(\n", + " clsys, [-1.5 * pi, 1.5 * pi, -2, 2], 8,\n", + " gridspec=[13, 7], plot_separatrices={'timedata': 5},\n", + " params={'kp': kp, 'umax': umax}, ax=axs[i, j])\n", + " axs[i, j].set_title(f\"{kp=}, {umax=}\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dYeVbfG4kU-9" + }, + "source": [ + "## State space controller\n", + "\n", + "For the proportional controller, we have limited control over the dynamics of the closed loop system. For example, we see that the solutions near the origin are highly oscillatory in both the $k_\\text{p} = 3$ and $k_\\text{p} = 10$ cases.\n", + "\n", + "An alternative is to use \"full state feedback\", in which we set\n", + "\n", + "$$\n", + "u = -K (x - x_\\text{d}) = -k_1 (\\theta - \\theta_d) - k_2 (\\dot\\theta - \\dot\\theta_d).\n", + "$$\n", + "\n", + "We will learn more about how to design these controllers later, so if you aren't familiar with the idea of eigenvalue placement, just take this as a bit of \"control theory magic\" for now.\n", + "\n", + "To compute the gains, we make use of the `place` command, applied to the linearized system:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Linearize the system\n", + "P = invpend.linearize([0, 0], [0])\n", + "\n", + "# Place the closed loop eigenvalues (poles) at desired locations\n", + "K = ct.place(P.A, P.B, [-1 + 0.1j, -1 - 0.1j])\n", + "print(f\"{K=}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def statefbk_output(t, x, u, params):\n", + " K = params.get('K', np.array([0, 0]))\n", + " return -K @ (u[0:2] - u[2:])\n", + "statefbk = ct.nlsys(\n", + " None, statefbk_output, name=\"k_ctrl\",\n", + " inputs=['theta', 'thdot', 'theta_d', 'thdot_d'], outputs='tau'\n", + ")\n", + "print(statefbk)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "clsys_sf = ct.interconnect(\n", + " [invpend, statefbk], name='invpend w/ state feedback',\n", + " inputs=['theta_d', 'thdot_d'], outputs=['theta', 'tau'], params={'kp': 1})\n", + "print(clsys_sf)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aGm3usQIvmqN" + }, + "source": [ + "### Phase portrait" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ct.phase_plane_plot(\n", + " clsys_sf, [-1.5 * pi, 1.5 * pi, -2, 2], 8,\n", + " gridspec=[13, 7], params={'K': K})\n", + "plt.plot([-pi, -pi], [-2, 2], 'k--', [ pi, pi], [-2, 2], 'k--')\n", + "plt.plot([-pi/2, -pi/2], [-2, 2], 'k:', [ pi/2, pi/2], [-2, 2], 'k:')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "A7UNUtfJwLWQ" + }, + "source": [ + "Note that the closed loop response around the upright equilibrium point is much less oscillatory (consistent with where we placed the closed loop eigenvalues of the system dynamics)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eVSa1Mvqycov" + }, + "source": [ + "## Things to try\n", + "\n", + "Here are some things to try with the above code:\n", + "* Try changing the locations of the closed loop eigenvalues in the `place` command\n", + "* Try resetting the limits of the control action (`umax`)\n", + "* Try leaving the state space controller fixed but changing the parameters of the system dynamics ($m$, $l$, $b$). Does the controller still stabilize the system?\n", + "* Plot the initial condition response of the system and see how to map time traces to phase plot traces." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/cds110-L3_lti-systems.ipynb b/examples/cds110-L3_lti-systems.ipynb new file mode 100644 index 000000000..652bb1216 --- /dev/null +++ b/examples/cds110-L3_lti-systems.ipynb @@ -0,0 +1,515 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "gQZtf4ZqM8HL" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 3

\n", + "

Python Tools for Analyzing Linear Systems

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/164yYvB86c2EvEcIHpUPNXCroiN9nnTAa)\n", + "\n", + "In this lecture we describe tools in the Python Control Systems Toolbox ([python-control](https://python-control.org)) that can be used to analyze linear systems, including some of the options available to present the information in different ways.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "qMVGK15gNQw2" + }, + "source": [ + "## Coupled mass spring system\n", + "\n", + "Consider the spring mass system below:\n", + "\n", + "
\n", + "\n", + "We wish to analyze the time and frequency response of this system using a variety of python-control functions for linear systems analysis.\n", + "\n", + "### System dynamics\n", + "\n", + "The dynamics of the system can be written as\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + " m \\ddot{q}_1 &= -2 k q_1 - c \\dot{q}_1 + k q_2, \\\\\n", + " m \\ddot{q}_2 &= k q_1 - 2 k q_2 - c \\dot{q}_2 + ku\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "or in state space form:\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + " \\dfrac{dx}{dt} &= \\begin{bmatrix}\n", + " 0 & 0 & 1 & 0 \\\\\n", + " 0 & 0 & 0 & 1 \\\\[0.5ex]\n", + " -\\dfrac{2k}{m} & \\dfrac{k}{m} & -\\dfrac{c}{m} & 0 \\\\[0.5ex]\n", + " \\dfrac{k}{m} & -\\dfrac{2k}{m} & 0 & -\\dfrac{c}{m}\n", + " \\end{bmatrix} x\n", + " + \\begin{bmatrix}\n", + " 0 \\\\ 0 \\\\[0.5ex] 0 \\\\[1ex] \\dfrac{k}{m}\n", + " \\end{bmatrix} u.\n", + "\\end{aligned}\n", + "$$\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the parameters for the system\n", + "m, c, k = 1, 0.1, 2\n", + "# Create a linear system\n", + "A = np.array([\n", + " [0, 0, 1, 0],\n", + " [0, 0, 0, 1],\n", + " [-2*k/m, k/m, -c/m, 0],\n", + " [k/m, -2*k/m, 0, -c/m]\n", + "])\n", + "B = np.array([[0], [0], [0], [k/m]])\n", + "C = np.array([[1, 0, 0, 0], [0, 1, 0, 0]])\n", + "D = 0\n", + "\n", + "sys = ct.ss(A, B, C, D, outputs=['q1', 'q2'], name=\"coupled spring mass\")\n", + "print(sys)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kobxJ1yG4v_1" + }, + "source": [ + "Another way to get these same dynamics is to define an input/output system:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coupled_params = {'m': 1, 'c': 0.1, 'k': 2}\n", + "def coupled_update(t, x, u, params):\n", + " m, c, k = params['m'], params['c'], params['k']\n", + " return np.array([\n", + " x[2], x[3],\n", + " -2*k/m * x[0] + k/m * x[1] - c/m * x[2],\n", + " k/m * x[0] -2*k/m * x[1] - c/m * x[3] + k/m * u[0]\n", + " ])\n", + "def coupled_output(t, x, u, params):\n", + " return x[0:2]\n", + "coupled = ct.nlsys(\n", + " coupled_update, coupled_output, inputs=1, outputs=['q1', 'q2'],\n", + " states=['q1', 'q2', 'q1dot', 'q2dot'], name='coupled (nl)',\n", + " params=coupled_params\n", + ")\n", + "print(coupled.linearize([0, 0, 0, 0], [0]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YmH87LEXWo1U" + }, + "source": [ + "### Initial response\n", + "\n", + "The `initial_response` function can be used to compute the response of the system with no input, but starting from a given initial condition. This function returns a response object, which can be used for plotting." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "response = ct.initial_response(sys, X0=[1, 0, 0, 0])\n", + "cplt = response.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y4aAxYvZRBnD" + }, + "source": [ + "If you want to play around with the way the data are plotted, you can also use the response object to get direct access to the states and outputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the outputs of the system on the same graph, in different colors\n", + "t = response.time\n", + "x = response.states\n", + "plt.plot(t, x[0], 'b', t, x[1], 'r')\n", + "plt.legend(['$x_1$', '$x_2$'])\n", + "plt.xlim(0, 50)\n", + "plt.ylabel('States')\n", + "plt.xlabel('Time [s]')\n", + "plt.title(\"Initial response from $x_1 = 1$, $x_2 = 0$\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Cou0QVnkTou9" + }, + "source": [ + "There are also lots of options available in `initial_response` and `.plot()` for tuning the plots that you get." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "for X0 in [[1, 0, 0, 0], [0, 2, 0, 0], [1, 2, 0, 0], [0, 0, 1, 0], [0, 0, 2, 0]]:\n", + " response = ct.initial_response(sys, T=20, X0=X0)\n", + " response.plot(label=f\"{X0=}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "b3VFPUBKT4bh" + }, + "source": [ + "### Step response\n", + "\n", + "Similar to `initial_response`, you can also generate a step response for a linear system using the `step_response` function, which returns a time response object:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cplt = ct.step_response(sys).plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iHZR1Q3IcrFT" + }, + "source": [ + "We can analyze the properties of the step response using the `stepinfo` command:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "step_info = ct.step_info(sys)\n", + "print(\"Input 0, output 0 rise time = \",\n", + " step_info[0][0]['RiseTime'], \"seconds\\n\")\n", + "step_info" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F8KxXwqHWFab" + }, + "source": [ + "Note that by default the inputs are not included in the step response plot (since they are a bit boring), but you can change that:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stepresp = ct.step_response(sys)\n", + "cplt = stepresp.plot(plot_inputs=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the inputs on top of the outputs\n", + "cplt = stepresp.plot(plot_inputs='overlay')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Look at the \"shape\" of the step response\n", + "print(f\"{stepresp.time.shape=}\")\n", + "print(f\"{stepresp.inputs.shape=}\")\n", + "print(f\"{stepresp.states.shape=}\")\n", + "print(f\"{stepresp.outputs.shape=}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "FDfZkyk1ly0T" + }, + "source": [ + "## Forced response\n", + "\n", + "To compute the response to an input, using the convolution equation, we can use the `forced_response` function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "T = np.linspace(0, 50, 500)\n", + "U1 = np.cos(T)\n", + "U2 = np.sin(3 * T)\n", + "\n", + "resp1 = ct.forced_response(sys, T, U1)\n", + "resp2 = ct.forced_response(sys, T, U2)\n", + "resp3 = ct.forced_response(sys, T, U1 + U2)\n", + "\n", + "# Plot the individual responses\n", + "resp1.sysname = 'U1'; resp1.plot(color='b')\n", + "resp2.sysname = 'U2'; resp2.plot(color='g')\n", + "resp3.sysname = 'U1 + U2'; resp3.plot(color='r');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show that the system response is linear\n", + "cplt = resp3.plot()\n", + "cplt.axes[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--')\n", + "cplt.axes[1, 0].plot(resp1.time, resp1.outputs[1] + resp2.outputs[1], 'k--')\n", + "cplt.axes[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show that the forced response from non-zero initial condition is not linear\n", + "X0 = [1, 0, 0, 0]\n", + "resp1 = ct.forced_response(sys, T, U1, X0=X0)\n", + "resp2 = ct.forced_response(sys, T, U2, X0=X0)\n", + "resp3 = ct.forced_response(sys, T, U1 + U2, X0=X0)\n", + "\n", + "cplt = resp3.plot()\n", + "cplt.axes[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--')\n", + "cplt.axes[1, 0].plot(resp1.time, resp1.outputs[1] + resp2.outputs[1], 'k--')\n", + "cplt.axes[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mo7hpvPQkKke" + }, + "source": [ + "### Frequency response" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Manual computation of the frequency response\n", + "resp = ct.input_output_response(sys, T, np.sin(1.35 * T))\n", + "\n", + "cplt = resp.plot(\n", + " plot_inputs='overlay', \n", + " legend_map=np.array([['lower left'], ['lower left']]),\n", + " label=[['q1', 'u[0]'], ['q2', None]])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "muqeLlJJ6s8F" + }, + "source": [ + "The magnitude and phase of the frequency response is controlled by the transfer function,\n", + "\n", + "$$\n", + "G(s) = C (sI - A)^{-1} B + D\n", + "$$\n", + "\n", + "which can be computed using the `ss2tf` function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " G = ct.ss2tf(sys, name='u to q1, q2')\n", + "except ct.ControlMIMONotImplemented:\n", + " # Create SISO transfer functions, in case we don't have slycot\n", + " G = ct.ss2tf(sys[0, 0], name='u to q1')\n", + "print(G)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Gain and phase for the simulation above\n", + "from math import pi\n", + "val = G(1.35j)\n", + "print(f\"{G(1.35j)=}\")\n", + "print(f\"Gain: {np.absolute(val)}\")\n", + "print(f\"Phase: {np.angle(val)}\", \" (\", np.angle(val) * 180/pi, \"deg)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Gain and phase at s = 0 (= steady state step response)\n", + "print(f\"{G(0)=}\")\n", + "print(\"Final value of step response:\", stepresp.outputs[0, 0, -1])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I9eFoXm92Jgj" + }, + "source": [ + "The frequency response across all frequencies can be computed using the `frequency_response` function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "freqresp = ct.frequency_response(sys)\n", + "cplt = freqresp.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pylQb07G2cqe" + }, + "source": [ + "By default, frequency responses are plotted using a \"Bode plot\", which plots the log of the magnitude and the (linear) phase against the log of the forcing frequency.\n", + "\n", + "You can also call the Bode plot command directly, and change the way the data are presented:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cplt = ct.bode_plot(sys, overlay_outputs=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I_LTjP2J6gqx" + }, + "source": [ + "Note the \"dip\" in the frequency response for y[1] at frequency 2 rad/sec, which corresponds to a \"zero\" of the transfer function.\n", + "\n", + "This dip becomes even more pronounced in the case of low damping coefficient $c$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cplt = ct.frequency_response(\n", + " coupled.linearize([0, 0, 0, 0], [0], params={'c': 0.01})\n", + ").plot(overlay_outputs=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c7eWm8LCGh01" + }, + "source": [ + "## Additional resources\n", + "* [Code for FBS2e figures](https://fbswiki.org/wiki/index.php/Category:Figures): Python code used to generate figures in FBS2e\n", + "* [Python-control documentation for plotting time responses](https://python-control.readthedocs.io/en/0.10.0/plotting.html#time-response-data)\n", + "* [Python-control documentation for plotting frequency responses](https://python-control.readthedocs.io/en/0.10.0/plotting.html#frequency-response-data)\n", + "* [Python-control examples](https://python-control.readthedocs.io/en/0.10.0/examples.html): lots of Python and Jupyter examples of control system analysis and design\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/cds110-L4a_predprey-statefbk.ipynb b/examples/cds110-L4a_predprey-statefbk.ipynb new file mode 100644 index 000000000..487a4e40b --- /dev/null +++ b/examples/cds110-L4a_predprey-statefbk.ipynb @@ -0,0 +1,411 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "gQZtf4ZqM8HL" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 4a

\n", + "

Dynamics and State Feedback Control of a Predator-Prey Model

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/1yMOSRNDDNtm-TJGMXX3NS7F4XybOuch-)\n", + "\n", + "In this lecture we describe the use of state space control concepts to analyze and stabilize the dynamics of a nonlinear model of a predator-prey system.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qMVGK15gNQw2" + }, + "source": [ + "## Predator-Prey System Model\n", + "\n", + "We consider a predator-prey system, in which a predator species (lynxes) interacts with a prey species (hares):\n", + "\n", + "
\n", + " \"predprey-photo\"\n", + "   \n", + " \"predprey-photo\"\n", + "
\n", + "\n", + "The graph on the right shows the populations of hares and lynxes between 1845 and 1935 in a section of the Canadian Rockies (MacLulich, 1937)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the dynamics for the predator-prey system (no input)\n", + "predprey_params = {'r': 1.6, 'd': 0.56, 'b': 0.6, 'k': 125, 'a': 3.2, 'c': 50}\n", + "def predprey_update(t, x, u, params):\n", + " \"\"\"Predator prey dynamics\"\"\"\n", + " r, d, b, k, a, c = map(params.get, ['r', 'd', 'b', 'k', 'a', 'c'])\n", + " u = np.clip(u, -r, r)\n", + "\n", + " # Dynamics for the system\n", + " dx0 = (r + u[0]) * x[0] * (1 - x[0]/k) - a * x[1] * x[0]/(c + x[0])\n", + " dx1 = b * a * x[1] * x[0] / (c + x[0]) - d * x[1]\n", + "\n", + " return np.array([dx0, dx1])\n", + "\n", + "# Create a nonlinear I/O system\n", + "predprey = ct.nlsys(\n", + " predprey_update, name='predprey', params=predprey_params,\n", + " states=['H', 'L'], inputs='u', outputs=['H', 'L'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YmH87LEXWo1U" + }, + "source": [ + "### Open loop dynamics\n", + "\n", + "The open loop dynamics of the system are oscillatory, with a period similar to the data shown above:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "T = np.linspace(0, 100, 500)\n", + "response = ct.input_output_response(\n", + " predprey, T, 0, [35, 35]\n", + ")\n", + "ct.time_response_plot(response, plot_inputs=False, overlay_signals=True);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also visualize the data using a phase plane plot:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a simple phase portrait\n", + "ct.phase_plane_plot(predprey, [0, 120, 0, 100], 1, gridtype='meshgrid');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the default parameters give a lot of warning messages and the phase portrait does not convey all of the details in some regions of the state space.\n", + "\n", + "We can make sure of some of the functions in the `phaseplot` module to get a better view of the dynamics:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a phase portrait\n", + "ct.phaseplot.equilpoints(predprey, [-5, 126, -5, 100])\n", + "ct.phaseplot.streamlines(\n", + " predprey, np.array([\n", + " [0, 100], [1, 0],\n", + " ]), 10, color='b')\n", + "ct.phaseplot.streamlines(\n", + " predprey, np.array([[124, 1]]), np.linspace(0, 10, 500), color='b')\n", + "ct.phaseplot.streamlines(\n", + " predprey, np.array([[125, 25], [125, 50], [125, 75]]), 3, color='b')\n", + "ct.phaseplot.streamlines(predprey, np.array([2, 8]), 6, color='b')\n", + "ct.phaseplot.streamlines(\n", + " predprey, np.array([[20, 30]]), np.linspace(0, 65, 500),\n", + " gridtype='circlegrid', gridspec=[2, 1], arrows=10, color='r')\n", + "ct.phaseplot.vectorfield(predprey, [5, 125, 5, 100], gridspec=[20, 20])\n", + "\n", + "# Add the limit cycle\n", + "resp1 = ct.initial_response(predprey, np.linspace(0, 100), [20, 75])\n", + "resp2 = ct.initial_response(\n", + " predprey, np.linspace(0, 20, 500), resp1.states[:, -1])\n", + "plt.plot(resp2.states[0], resp2.states[1], color='k');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KhjlC1258qff" + }, + "source": [ + "### Find the equilibrium points and check stability\n", + "\n", + "We see that there are three equilibrium points in the system. We can test the stability of the center equilibrium point, which from the phase portrait appears to be unstable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xe, ue = ct.find_eqpt(predprey, [20, 30], 0)\n", + "print(f\"{xe=}\")\n", + "print(f\"{ue=}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sys = predprey.linearize(xe, ue)\n", + "print(sys)\n", + "print(\"Poles: \", sys.poles())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sUECx0cz9QpK" + }, + "source": [ + "## Stabilization\n", + "\n", + "Suppose now that we have the ability to modulate the food supply for the hares. We do this by modifying the parameter $r$ in the model (this is the term `u` in the model at the top of the notebook). We can use the `place` command to find a set of gains that stabilize the dynamics around the unstable equilibrium point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "K = ct.place(sys.A, sys.B, [-0.1, -0.2])\n", + "print(f\"{K=}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Design an eigenvalue placement (EP) controller to stabilize the equilibrium point\n", + "epctrl = ct.nlsys(\n", + " None, lambda t, x, u, params: -K @ (u[0:2] - xe),\n", + " inputs=['H', 'L', 'r'], outputs=['u'],\n", + ")\n", + "predprey_ep = ct.interconnect(\n", + " [predprey, epctrl], inputs=['r'], outputs=['H', 'L', 'u'],\n", + " name='predprey w/ eval placement'\n", + ")\n", + "print(predprey_ep)\n", + "\n", + "# Show the connection table, useful for debugging what is connected to what\n", + "predprey_ep.connection_table()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xe_ep, ue_ep = ct.find_eqpt(predprey_ep, [20, 30], [0])\n", + "print(f\"{xe_ep=}\")\n", + "print(f\"{ue_ep=}\")\n", + "print(\"Poles: \", predprey_ep.linearize(xe_ep, ue_ep).poles())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a simple phase portrait\n", + "ct.phase_plane_plot(\n", + " predprey_ep, [0, 120, 0, 100], 1,\n", + " plot_separatrices=False,\n", + " gridtype='meshgrid', gridspec=[8, 5]\n", + " );\n", + "ct.phaseplot.streamlines(\n", + " predprey_ep, np.array([xe_ep]), 20, dir='reverse',\n", + " gridtype='circlegrid', gridspec=[4, 11]);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Simulation from someplace nearby\n", + "T = np.linspace(0, 40)\n", + "response = ct.input_output_response(predprey_ep, T, 0, [35, 35])\n", + "ct.time_response_plot(\n", + " response, plot_inputs=False, overlay_signals=True,\n", + " title=\"I/O response with eval placement, \" +\n", + " f\"r = {predprey.params['r']}\",\n", + " legend_loc='upper right')\n", + "plt.plot([T[0], T[-1]], [0, 0], 'k--')\n", + "plt.plot([T[0], T[-1]], [xe_ep[0], xe_ep[0]], 'k--')\n", + "plt.plot([T[0], T[-1]], [xe_ep[1], xe_ep[1]], 'k--')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zZTBWhlTgSNk" + }, + "source": [ + "## Integral feedback\n", + "\n", + "Another technique that we will learn about later in the class is integral feedback, which can be used to compensate for modeling uncertainty and constant disturbances.\n", + "\n", + "We start by asking what happens if we change the value for the parameter $r$ from its original value of 1.6 to a new value of 1.65 (a change of less than 4%):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate with a change in food for the hares\n", + "T = np.linspace(0, 40)\n", + "response = ct.input_output_response(\n", + " predprey_ep, T, 0, [35, 35], params={'r': 1.65}\n", + ")\n", + "ct.time_response_plot(\n", + " response, plot_inputs=False, overlay_signals=True,\n", + " title=\"I/O response w/ eval placement, \" +\n", + " f\"r = {response.params['r']}\")\n", + "plt.plot([T[0], T[-1]], [0, 0], 'k--')\n", + "plt.plot([T[0], T[-1]], [xe_ep[0], xe_ep[0]], 'k--')\n", + "plt.plot([T[0], T[-1]], [xe_ep[1], xe_ep[1]], 'k--')\n", + "response.sysname" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the controller no longer stabilizes the equilibrium point (shown with the dashed lines). In particular, the steady state value of the lynx population does to almost twice the original value.\n", + "\n", + "This effect is even worse if we increase $r$ just a bit more (from 1.65 to 1.7)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "T = np.linspace(0, 40)\n", + "response = ct.input_output_response(\n", + " predprey_ep, T, 0, xe, params={'r': 1.7}\n", + ")\n", + "ct.time_response_plot(\n", + " response, plot_inputs=False, overlay_signals=True,\n", + " title=\"I/O response for predprey w/ eval placement, \" +\n", + " f\"r = {response.params['r']}\")\n", + "plt.plot([T[0], T[-1]], [0, 0], 'k--')\n", + "plt.plot([T[0], T[-1]], [xe_ep[0], xe_ep[0]], 'k--')\n", + "plt.plot([T[0], T[-1]], [xe_ep[1], xe_ep[1]], 'k--')\n", + "response.sysname" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The system dynamics are now oscillatory, indicating that we are no longer stabilizing the desired equilibrium point. This indicates a lack of robustness in our feedback control system.\n", + "\n", + "We can compensate for the change in the parameter $r$ by making use of integral feedback in our controller. We will learn more about integral feedback in later lectures, but for now we demonstrate its ability to compensate for errors in our system model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Integral feedback\n", + "# Design an eigenvalue placement (EP) controller to stabilize the equilibrium point\n", + "Ki = 0.0001\n", + "pictrl = ct.nlsys(\n", + " lambda t, x, u, params: u[1] - u[2],\n", + " lambda t, x, u, params: -K @ (u[0:2] - xe) - Ki * x[0],\n", + " inputs=['H', 'L', 'r'], outputs=['u'], states=1,\n", + ")\n", + "predprey_pi = ct.interconnect(\n", + " [predprey, pictrl], inputs=['r'], outputs=['H', 'L', 'u'],\n", + " name='predprey_pi'\n", + ")\n", + "print(predprey_pi)\n", + "\n", + "# Simulate with a change in food for the hares\n", + "T = np.linspace(0, 100, 500)\n", + "response = ct.input_output_response(\n", + " predprey_pi, T, xe[1], [25, 25, 0], params={'r': 1.65})\n", + "ct.time_response_plot(\n", + " response, plot_inputs=False, overlay_signals=True,\n", + " title=\"I/O response w/ integral action, \" +\n", + " f\"r = {response.params['r']}\",\n", + " legend_loc='upper right')\n", + "\n", + "plt.plot([T[0], T[-1]], [0, 0], 'k--')\n", + "plt.plot([T[0], T[-1]], [xe_ep[0], xe_ep[0]], 'k--')\n", + "plt.plot([T[0], T[-1]], [xe_ep[1], xe_ep[1]], 'k--')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the system is once again stable at the desired equilibrium point!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/cds110-L4b_lqr-tracking.ipynb b/examples/cds110-L4b_lqr-tracking.ipynb new file mode 100644 index 000000000..f438c692a --- /dev/null +++ b/examples/cds110-L4b_lqr-tracking.ipynb @@ -0,0 +1,930 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "EHq8UWSjXSyz" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 4b

\n", + "

LQR Tracking

\n", + "

Richard M. Murray and Natalie Bernat, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/1Q6hXokOO_e3-wl6_ghigpxGJRUrGcHp3)\n", + "\n", + "This example uses a linear system to show how to implement LQR based tracking and some of the tradeoffs between feedfoward and feedback. Integral action is also implemented." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "a23d6f89" + }, + "source": [ + "# Part I: Second order linear system\n", + "\n", + "We'll use a simple linear system to illustrate the concepts:\n", + "$$\n", + "\\frac{dx}{dt} =\n", + "\\begin{bmatrix}\n", + "0 & 10 \\\\\n", + "-1 & 0\n", + "\\end{bmatrix}\n", + "x +\n", + "\\begin{bmatrix}\n", + "0 \\\\\n", + "1\n", + "\\end{bmatrix}\n", + "u,\n", + "\\qquad\n", + "y = \\begin{bmatrix} 1 & 1 \\end{bmatrix} x.\n", + "$$\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define a simple linear system that we want to control\n", + "A = np.array([[0, 10], [-1, 0]])\n", + "B = np.array([[0], [1]])\n", + "C = np.array([[1, 1]])\n", + "sys = ct.ss(A, B, C, 0, name='sys')\n", + "print(sys)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ja1g1MlbieJy" + }, + "source": [ + "## Linear quadratic regulator (LQR) design\n", + "\n", + "We'll design a controller of the form\n", + "\n", + "$$\n", + "u=-Kx+k_rr\n", + "$$\n", + "\n", + "- For the feedback control gain $K$, we'll use linear quadratic regulator theory. We seek to find the control law that minimizes the cost function:\n", + "\n", + " $$\n", + " J(x(\\cdot), u(\\cdot)) = \\int_0^\\infty x^T(\\tau) Q x(\\tau) + u^T(\\tau) R u(\\tau)\\, d\\tau\n", + " $$\n", + "\n", + " The weighting matrices $Q\\succeq 0 \\in \\mathbb{R}^{n \\times n}$ and $R \\succ 0\\in \\mathbb{R}^{m \\times m}$ should be chosen based on the desired performance of the system (tradeoffs in state errors and input magnitudes). See Example 3.5 in [Optimization Based Control (OBC)](https://fbswiki.org/wiki/index.php/Supplement:_Optimization-Based_Control) for a discussion of how to choose these weights. For now, we just choose identity weights for all states and inputs.\n", + "\n", + "- For the feedforward control gain $k_r$, we derive the feedforward gain from an equilibrium point analysis:\n", + " $$\n", + " y_e = C(A-BK)^{-1}Bk_rr\n", + " \\qquad\\implies\\qquad k_r = \\frac{-1}{C(A-BK)^{-1}B}\n", + " $$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Construct an LQR controller for the system\n", + "Q = np.eye(sys.nstates)\n", + "R = np.eye(sys.ninputs)\n", + "K, _, _ = ct.lqr(sys, Q, R)\n", + "print('K: '+str(K))\n", + "\n", + "# Set the feedforward gain to track the reference\n", + "kr = (-1 / (C @ np.linalg.inv(A - B @ K) @ B))\n", + "print('k_r: '+str(kr))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "99f036ea" + }, + "source": [ + "Now that we have our gains designed, we can simulate the closed loop system:\n", + "$$\n", + "\\frac{dx}{dt} = A_{cl}x + B_{cl} r,\n", + "\\quad A_{cl} = A-BK,\n", + "\\quad B_{cl} = Bk_r\n", + "$$\n", + "Notice that, with a state feedback controller, the new (closed loop) dynamics matrix absorbs the old (open loop) \"input\" $u$, and the new (closed loop) input is our reference signal $r$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create a closed loop system\n", + "A_cl = A - B @ K\n", + "B_cl = B * kr\n", + "clsys = ct.ss(A_cl, B_cl, C, 0)\n", + "print(clsys)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "84422c3f" + }, + "source": [ + "## System simulations\n", + "\n", + "### Baseline controller\n", + "\n", + "To see how the baseline controller performs, we ask it to track a constant reference $r = 2$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the step response with respect to the reference input\n", + "r = 2\n", + "Tf = 8\n", + "tvec = np.linspace(0, Tf, 100)\n", + "\n", + "U = r * np.ones_like(tvec)\n", + "time, output = ct.input_output_response(clsys, tvec, U)\n", + "plt.plot(time, output)\n", + "plt.plot([time[0], time[-1]], [r, r], '--');\n", + "plt.legend(['y', 'r']);\n", + "plt.ylabel(\"Output\")\n", + "plt.xlabel(\"Time $t$ [sec]\")\n", + "plt.title(\"Baseline controller step response\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ea2d1c59" + }, + "source": [ + "Things to try:\n", + "- set $k_r=0$\n", + "- set $k_r \\neq \\frac{-1}{C(A-BK)^{-1}B}$\n", + "- try different LQR weightings" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "84ee7635" + }, + "source": [ + "### Disturbance rejection\n", + "\n", + "To add an input disturbance to the system, we include a second open loop input:\n", + "$$\n", + "\\frac{dx}{dt} =\n", + "\\begin{bmatrix}\n", + "0 & 10 \\\\\n", + "-1 & 0\n", + "\\end{bmatrix}\n", + "x +\n", + "\\begin{bmatrix}\n", + "0 & 0\\\\\n", + "1 & 1\n", + "\\end{bmatrix}\n", + "\\begin{bmatrix}\n", + "u\\\\\n", + "d\n", + "\\end{bmatrix},\n", + "\\qquad\n", + "y = \\begin{bmatrix} 1 & 1 \\end{bmatrix} x.\n", + "$$\n", + "\n", + "Our closed loop system becomes:\n", + "$$\n", + "\\frac{dx}{dt} =\n", + "\\begin{bmatrix}\n", + "0 & 10 \\\\\n", + "-1-K_{1} & 0-K_{2}\n", + "\\end{bmatrix}\n", + "x +\n", + "\\begin{bmatrix}\n", + "0 & 0\\\\\n", + "k_r & 1\n", + "\\end{bmatrix}\n", + "\\begin{bmatrix}\n", + "r\\\\\n", + "d\n", + "\\end{bmatrix},\n", + "\\qquad\n", + "y = \\begin{bmatrix} 1 & 1 \\end{bmatrix} x.\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Resimulate with a disturbance input\n", + "B_ext = np.hstack([B * kr, B])\n", + "clsys = ct.ss(A - B @ K, B_ext, C, 0)\n", + "\n", + "# Construct the inputs for the augmented system\n", + "delta = 0.5\n", + "U = np.vstack([r * np.ones_like(tvec), delta * np.ones_like(tvec)])\n", + "\n", + "time, output = ct.input_output_response(clsys, tvec, U)\n", + "\n", + "plt.plot(time, output[0])\n", + "plt.plot([time[0], time[-1]], [r, r], '--')\n", + "plt.legend(['y', 'r']);\n", + "plt.ylabel(\"Output\")\n", + "plt.xlabel(\"Time $t$ [sec]\")\n", + "plt.title(\"Baseline controller step response with disturbance\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Qis2PP3nd7ua" + }, + "source": [ + "We see that this leads to steady state error, since the feedforward signal didn't include an offset for the disturbance." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "84a9e61c" + }, + "source": [ + "#### Integral feedback\n", + "\n", + "A standard approach to compensate for constant disturbances is to use integral feedback. To do this, we have to keep track of the integral of the error\n", + "\n", + "$$z = \\int_0^\\tau (y - r)\\, d\\tau= \\int_0^\\tau (Cx - r)\\, d\\tau.$$\n", + "\n", + "We do this by creating an augmented system that includes the dynamics of the process ($dx/dt$) along with the dynamics of the integrator state ($dz/dt$):\n", + "\n", + "$$\n", + "\\frac{d}{dt}\\begin{bmatrix}\n", + "x \\\\\n", + "z\n", + "\\end{bmatrix} =\n", + "\\begin{bmatrix}\n", + "A & 0 \\\\\n", + "C & 0\n", + "\\end{bmatrix}\n", + "\\begin{bmatrix}\n", + "x \\\\\n", + "z\n", + "\\end{bmatrix} +\n", + "\\begin{bmatrix}\n", + "B\\\\\n", + "0 \\\\\n", + "\\end{bmatrix}\n", + "u+\n", + "\\begin{bmatrix}\n", + "0\\\\\n", + "-I \\\\\n", + "\\end{bmatrix}\n", + "r,\n", + "\\qquad\n", + "y = \\begin{bmatrix} C \\\\ 0 \\end{bmatrix} \\begin{bmatrix}\n", + "x \\\\\n", + "z\n", + "\\end{bmatrix}.\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define an augmented state space for use with LQR\n", + "A_aug = np.block([[sys.A, np.zeros((sys.nstates, 1))], [C, 0] ])\n", + "B_aug = np.vstack([sys.B, 0])\n", + "print(\"A =\", A_aug, \"\\nB =\", B_aug)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "463d9b85" + }, + "source": [ + "\n", + "Our controller then takes the form:\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "u &= - Kx - k_\\text{i} \\int_0^\\tau (y - r)\\, d\\tau+k_rr \\\\\n", + " &= - (Kx + k_\\text{i}z)+k_rr .\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "This results in the closed loop system:\n", + "$$\n", + "\\frac{dx}{dt} =\n", + "\\begin{bmatrix}\n", + "A-BK & -Bk_i \\\\\n", + "C & 0\n", + "\\end{bmatrix}\n", + "\\begin{bmatrix}\n", + "x \\\\\n", + "z\n", + "\\end{bmatrix} +\n", + "\\begin{bmatrix}\n", + "Bk_r\\\\\n", + "-I \\\\\n", + "\\end{bmatrix}\n", + "r,\n", + "\\qquad\n", + "y = \\begin{bmatrix} C \\\\ 0 \\end{bmatrix} \\begin{bmatrix}\n", + "x \\\\\n", + "z\n", + "\\end{bmatrix}.\n", + "$$\n", + "\n", + "Since z is part of the augmented state space, we can generate an LQR controller for the augmented system to find both the usual gain $K$ and the integral gain $k_i$:\n", + "$$\n", + "\\bar{K} = \\begin{bmatrix} K& k_i\\end{bmatrix}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create an LQR controller for the augmented system\n", + "K_aug, _, _ = ct.lqr(A_aug, B_aug, np.diag([1, 1, 1]), np.eye(sys.ninputs))\n", + "print('K_aug: '+str(K_aug))\n", + "\n", + "K = K_aug[:, 0:2]\n", + "ki = K_aug[:, 2]\n", + "kr = -1 / (C @ np.linalg.inv(A - B * K) @ B)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "19bb6592" + }, + "source": [ + "\n", + "\n", + "\n", + "Notice that the value of $K$ changed, so we needed to recompute $k_r$ too." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zHlf8zoHoqvF" + }, + "source": [ + "To run simulations, we return to our system augmented with a disturbance, but we expand the outputs available to the controller:\n", + "\n", + "$$\n", + "\\frac{dx}{dt} =\n", + "\\begin{bmatrix}\n", + "0 & 10 \\\\\n", + "-1 & 0\n", + "\\end{bmatrix}\n", + "x +\n", + "\\begin{bmatrix}\n", + "0 & 0\\\\\n", + "1 & 1\n", + "\\end{bmatrix}\n", + "\\begin{bmatrix}\n", + "u\\\\\n", + "d\n", + "\\end{bmatrix},\n", + "$$\n", + "\n", + "$$\n", + "\\bar{y} = \\begin{bmatrix} 1 & 0 & 1 \\\\ 0 & 1 & 1 \\end{bmatrix}^T x = \\begin{bmatrix} x_1 & x_2 & y \\end{bmatrix} .\n", + "$$\n", + "\n", + "The controller then constructs its internal state $z$ out of $x$ and $r$.\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Construct a system with disturbance inputs, and full outputs (for the controller)\n", + "A_integral = sys.A\n", + "B_integral = np.hstack([sys.B, sys.B])\n", + "C_integral = [[1, 0], [0, 1], [1, 1]] # outputs for the controller: x1, x2, y\n", + "sys_integral = ct.ss(\n", + " A_integral, B_integral, C_integral, 0,\n", + " inputs=['u', 'd'],\n", + " outputs=['x1', 'x2', 'y']\n", + ")\n", + "print(sys_integral)\n", + "\n", + "# Construct an LQR+integral controller for the system with an internal state z\n", + "A_ctrl = [[0]]\n", + "B_ctrl = [[1, 1, -1]] # z_dot=Cx-r\n", + "C_ctrl = -ki #-ki*z\n", + "D_ctrl = np.hstack([-K, kr]) #-K*x + kr*r\n", + "ctrl_integral=ct.ss(\n", + " A_ctrl, B_ctrl, C_ctrl, D_ctrl, # u = -ki*z - K*x + kr*r\n", + " inputs=['x1', 'x2', 'r'], # system outputs + reference\n", + " outputs=['u'], # controller action\n", + ")\n", + "print(ctrl_integral)\n", + "\n", + "# Create the closed loop system\n", + "clsys_integral = ct.interconnect([sys_integral, ctrl_integral], inputs=['r', 'd'], outputs=['y'])\n", + "print(clsys_integral)\n", + "\n", + "# Resimulate with a disturbance input\n", + "delta = 0.5\n", + "U = np.vstack([r * np.ones_like(tvec), delta * np.ones_like(tvec)])\n", + "time, output, states = ct.input_output_response(clsys_integral, tvec, U, return_x=True)\n", + "plt.plot(time, output[0])\n", + "plt.plot([time[0], time[-1]], [r, r], '--')\n", + "plt.plot(time, states[2])\n", + "plt.legend(['y', 'r', 'z']);\n", + "plt.ylabel(\"Output\")\n", + "plt.xlabel(\"Time $t$ [sec]\")\n", + "plt.title(\"LQR+integral controller step response with disturbance\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "M9nXbITrhYg7" + }, + "source": [ + "Notice that the steady state value of $z=\\int(y-r)$ is not zero, but rather settles to whatever value makes $y-r$ zero!\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f8bfc15c" + }, + "source": [ + "# Part II: PVTOL Linear Quadratic Regulator Example\n", + "\n", + "Natalie Bernat, 26 Apr 2024
\n", + "Richard M. Murray, 25 Jan 2022\n", + "\n", + "This notebook contains an example of LQR control applied to the PVTOL system. It demonstrates how to construct an LQR controller by linearizing the system, and provides an alternate view of the feedforward component of the controller." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "77e2ed47" + }, + "source": [ + "## System description\n", + "\n", + "We use the PVTOL dynamics from [Feedback Systems (FBS2e)](https://fbswiki.org/wiki/index.php/Feedback_Systems:_An_Introduction_for_Scientists_and_Engineers), which can be found in Example 3.12}\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "
\n", + "$$\n", + "\\begin{aligned}\n", + " m \\ddot x &= F_1 \\cos\\theta - F_2 \\sin\\theta - c \\dot x, \\\\\n", + " m \\ddot y &= F_1 \\sin\\theta + F_2 \\cos\\theta - m g - c \\dot y, \\\\\n", + " J \\ddot \\theta &= r F_1.\n", + "\\end{aligned}\n", + "$$\n", + " \n", + "$$\n", + "\\frac{dz}{dt} =\n", + "\\begin{bmatrix}\n", + "z_4 \\\\\n", + "z_5 \\\\\n", + "z_6 \\\\\n", + "-\\frac{c}{m}z_4 \\\\\n", + "-g-\\frac{c}{m}z_5 \\\\\n", + "0\n", + "\\end{bmatrix} +\n", + "\\begin{bmatrix}\n", + "0 \\\\\n", + "0 \\\\\n", + "0 \\\\\n", + "\\frac{F_1}{m}cos\\theta -\\frac{F_2}{m}sin\\theta \\\\\n", + "\\frac{F_1}{m}sin\\theta +\\frac{F_2}{m}cos\\theta \\\\\n", + "-\\frac{r}{J}F_1\n", + "\\end{bmatrix}\n", + "$$\n", + "
\n", + "\n", + "The state space variables for this system are:\n", + "\n", + "$z=(x,y,\\theta, \\dot x,\\dot y,\\dot \\theta), \\quad u=(F_1,F_2)$\n", + "\n", + "Notice that the x and y positions ($z_1$ and $z_2$) do not actually appear in the dynamics-- this makes sense, since the aircraft should hypothetically fly the same way no matter where in the air it is (neglecting effects near the ground)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# PVTOL dynamics\n", + "def pvtol_update(t, x, u, params):\n", + " from math import cos, sin\n", + " \n", + " # Get the parameter values\n", + " m, J, r, g, c = map(params.get, ['m', 'J', 'r', 'g', 'c'])\n", + "\n", + " # Get the inputs and states\n", + " x, y, theta, xdot, ydot, thetadot = x\n", + " F1, F2 = u\n", + "\n", + " # Constrain the inputs\n", + " F2 = np.clip(F2, 0, 1.5 * m * g)\n", + " F1 = np.clip(F1, -0.1 * F2, 0.1 * F2)\n", + "\n", + " # Dynamics\n", + " xddot = (F1 * cos(theta) - F2 * sin(theta) - c * xdot) / m\n", + " yddot = (F1 * sin(theta) + F2 * cos(theta) - m * g - c * ydot) / m\n", + " thddot = (r * F1) / J\n", + "\n", + " return np.array([xdot, ydot, thetadot, xddot, yddot, thddot])\n", + "\n", + "def pvtol_output(t, x, u, params):\n", + " return x\n", + "\n", + "pvtol = ct.nlsys(\n", + " pvtol_update, pvtol_output, name='pvtol',\n", + " states = [f'x{i}' for i in range(6)],\n", + " inputs = ['F1', 'F2'],\n", + " outputs=[f'x{i}' for i in range(6)],\n", + " # outputs = ['x', 'y', 'theta', 'xdot', 'ydot', 'thdot'],\n", + " params = {\n", + " 'm': 4., # mass of aircraft\n", + " 'J': 0.0475, # inertia around pitch axis\n", + " 'r': 0.25, # distance to center of force\n", + " 'g': 9.8, # gravitational constant\n", + " 'c': 0.05, # damping factor (estimated)\n", + " }\n", + ")\n", + "\n", + "print(pvtol)\n", + "print(pvtol.params)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YZiISLS-qMS_" + }, + "source": [ + "Next, we'll linearize the system around the equilibrium points. As discussed in FBS2e (example 7.9), the linearization around this equilibrium point has the form:\n", + "$$\n", + "A =\n", + "\\begin{bmatrix}\n", + "0 & 0 & 0 & 1 & 0 & 0\\\\\n", + "0 & 0 & 0 & 0 & 1 & 0 \\\\\n", + "0 & 0 & 0 & 0 & 0 & 1 \\\\\n", + "0 & 0 & -g & -c/m & 0 & 0 \\\\\n", + "0 & 0 & 0 & 0 & -c/m & 0 \\\\\n", + "0 & 0 & 0 & 0 & 0 & 0\n", + "\\end{bmatrix}\n", + ", \\quad B=\n", + "\\begin{bmatrix}\n", + "0 & 0 \\\\\n", + "0 & 0 \\\\\n", + "0 & 0 \\\\\n", + "1/m & 0 \\\\\n", + "0 & 1/m \\\\\n", + "r/J & 0\n", + "\\end{bmatrix}\n", + ".\n", + "$$\n", + "(note that here $r$ is a system parameter, not the same as the reference $r$ we've been using elsewhere in this notebook)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To compute this linearization in python-control, we start by computing the equilibrium point. We do this using the `find_eqpt` function, which can be used to find equilibrium points satisfying varioius conditions. For this system, we wish to find the state $x_\\text{e}$ and input $u_\\text{e}$ that holds the $x, y$ position of the aircraft at the point $(0, 0)$. The `find_eqpt` function performs a numerical optimization to find the values of $x_\\text{e}$ and $u_\\text{e}$ corresponding to an equilibrium point with the desired values for the outputs. We pass the function initial guesses for the state and input as well the values of the output and the indices of the output that we wish to constrain:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Find the equilibrium point corresponding to hover\n", + "xeq, ueq = ct.find_eqpt(pvtol, np.zeros(6), np.zeros(2), y0=np.zeros(6), iy=[0, 1])\n", + "print(f\"{xeq=}, {ueq=}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using these values, we compute the linearization:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "linsys = pvtol.linearize(xeq, ueq)\n", + "print(linsys)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7cb8840b" + }, + "source": [ + "## Linear quadratic regulator (LQR) design\n", + "\n", + "Now that we have a linearized model of the system, we can compute a controller using linear quadratic regulator theory. We wish to minimize the following cost function\n", + "\n", + "$$\n", + "J(\\phi(\\cdot), \\nu(\\cdot)) = \\int_0^\\infty \\phi^T(\\tau) Q \\phi(\\tau) + \\nu^T(\\tau) R \\nu(\\tau)\\, d\\tau,\n", + "$$\n", + "\n", + "where we have changed to our linearized coordinates:\n", + "\n", + "$$\\phi=z-z_e, \\quad \\nu = u-u_e$$\n", + "\n", + "Using the standard approach for finding K, we obtain a feedback controller for the system:\n", + "$$\\nu=-K\\phi$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Start with a diagonal weighting\n", + "Q1 = np.diag([1, 1, 1, 1, 1, 1])\n", + "R1 = np.diag([1, 1])\n", + "K, X, E = ct.lqr(linsys, Q1, R1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "863d07de" + }, + "source": [ + "To create a controller for the system, we have to apply a control signal $u$, so we change back from the relative coordinates to the absolute coordinates:\n", + "\n", + "$$u=u_e - K(z - z_e)$$\n", + "\n", + "Notice that, since $(Kz_e+u_e)$ is completely determined by (user-defined) inputs to the system, this term is a type of feedforward control signal.\n", + "\n", + "To create a controller for the system, we can use the function [`create_statefbk_iosystem()`](https://python-control.readthedocs.io/en/latest/generated/control.create_statefbk_iosystem.html), which creates an I/O system that takes in a desired trajectory $(x_\\text{d}, u_\\text{d})$ and the current state $x$ and generates a control law of the form:\n", + "\n", + "$$\n", + "u = u_\\text{d} - K (x - x_\\text{d})\n", + "$$\n", + "\n", + "Note that this is slightly different than the first equation: here we are using $x_\\text{d}$ instead of $x_\\text{e}$ and $u_\\text{d}$ instead of $u_\\text{e}$. This is because we want our controller to track a desired trajectory $(x_\\text{d}(t), u_\\text{d}(t))$ rather than just stabilize the equilibrium point $(x_\\text{e}, u_\\text{e})$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "control, pvtol_closed = ct.create_statefbk_iosystem(pvtol, K)\n", + "print(control, \"\\n\")\n", + "print(pvtol_closed)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This command will usually generate a warning saying that python control \"cannot verify system output is system state\". This happens because we specified an output function `pvtol_output` when we created the system model, and python-control does not have a way of checking that the output function returns the entire state (which is needed if we are going to do full-state feedback).\n", + "\n", + "This warning could be avoided by passing the argument `None` for the system output function, in which case python-control returns the full state as the output (and it knows that the full state is being returned as the output)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "bedcb0c0" + }, + "source": [ + "## Closed loop system simulation\n", + "\n", + "For this simple example, we set the target for the system to be a \"step\" input that moves the system 1 meter to the right.\n", + "\n", + "We start by defining a short function to visualize the output using a collection of plots:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Utility function to plot the results in a useful way\n", + "def plot_results(t, x, u, fig=None):\n", + " # Set the size of the figure\n", + " if fig is None:\n", + " fig = plt.figure(figsize=(10, 6))\n", + "\n", + " # Top plot: xy trajectory\n", + " plt.subplot(2, 1, 1)\n", + " lines = plt.plot(x[0], x[1])\n", + " plt.xlabel('x [m]')\n", + " plt.ylabel('y [m]')\n", + " plt.axis('equal')\n", + "\n", + " # Mark starting and ending points\n", + " color = lines[0].get_color()\n", + " plt.plot(x[0, 0], x[1, 0], 'o', color=color, fillstyle='none')\n", + " plt.plot(x[0, -1], x[1, -1], 'o', color=color, fillstyle='full')\n", + "\n", + "\n", + " # Time traces of the state and input\n", + " plt.subplot(2, 4, 5)\n", + " plt.plot(t, x[1])\n", + " plt.xlabel('Time t [sec]')\n", + " plt.ylabel('y [m]')\n", + "\n", + " plt.subplot(2, 4, 6)\n", + " plt.plot(t, x[2])\n", + " plt.xlabel('Time t [sec]')\n", + " plt.ylabel('theta [rad]')\n", + "\n", + " plt.subplot(2, 4, 7)\n", + " plt.plot(t, u[0])\n", + " plt.xlabel('Time t [sec]')\n", + " plt.ylabel('$F_1$ [N]')\n", + "\n", + " plt.subplot(2, 4, 8)\n", + " plt.plot(t, u[1])\n", + " plt.xlabel('Time t [sec]')\n", + " plt.ylabel('$F_2$ [N]')\n", + " plt.tight_layout()\n", + "\n", + " return fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we generate a step response and plot the results. Because our closed loop system takes as inputs $x_\\text{d}$ and $u_\\text{d}$, we need to set those variable to values that would correspond to our step input. In this case, we are taking a step in the $x$ coordinate, so we set $x_\\text{d}$ to be $1$ in that coordinate starting at $t = 0$ and continuing for some sufficiently long period of time ($15$ seconds):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a step response by setting xd, ud\n", + "Tf = 15\n", + "T = np.linspace(0, Tf, 100)\n", + "xd = np.outer(np.array([1, 0, 0, 0, 0, 0]), np.ones_like(T))\n", + "ud = np.outer(ueq, np.ones_like(T))\n", + "ref = np.vstack([xd, ud])\n", + "\n", + "response = ct.input_output_response(pvtol_closed, T, ref, xeq)\n", + "fig = plot_results(response.time, response.states, response.outputs[6:])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f014e660" + }, + "source": [ + "This controller does a pretty good job. We see in the top plot the $x$, $y$ projection of the trajectory, with the open circle indicating the starting point and the closed circle indicating the final point. The bottom set of plots show the altitude and pitch as functions of time, as well as the input forces. All of the signals look reasonable.\n", + "\n", + "The limitations of the linear controller can be seen if we take a larger step, say 10 meters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "xd = np.outer(np.array([10, 0, 0, 0, 0, 0]), np.ones_like(T))\n", + "ref = np.vstack([xd, ud])\n", + "response = ct.input_output_response(pvtol_closed, T, ref, xeq)\n", + "fig = plot_results(response.time, response.states, response.outputs[6:])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4luxppVpm6Xo" + }, + "source": [ + "We now see that the trajectory looses significant altitude ($> 2.5$ meters). This is because the linear controller sees a large initial error and so it applies very large input forces to correct for the error ($F_1 \\approx -10$ N at $t = 0$. This causes the aircraft to pitch over to a large angle (almost $-60$ degrees) and this causes a large loss in altitude.\n", + "\n", + "We will see in the [Lecture 6](cds110-L6a_kincar-trajgen) how to remedy this problem by making use of feasible trajectory generation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/cds110-L5_kincar-estimation.ipynb b/examples/cds110-L5_kincar-estimation.ipynb new file mode 100644 index 000000000..6eea0a1f0 --- /dev/null +++ b/examples/cds110-L5_kincar-estimation.ipynb @@ -0,0 +1,815 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "-cop8q3CTs-G" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 5

\n", + "

State Estimation for a Kinematic Car Model

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/1TESB0NzWS3XBxJa_hdOXMifICbBEDRz8)\n", + "\n", + "In this lecture, we will show how to construct an observer for a system in the presence of noise and disturbances.\n", + "\n", + "Recall that an observer is a system that takes as input the (noisy) measured output of a system along with the applied input to the system, and produces as estimate $\\hat x$ of the current state:\n", + "\n", + "
\n", + "\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Import the various Python packages that we require\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from math import pi, sin, cos, tan\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct\n", + "import control.flatsys as fs" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c5UGnS73sH4c" + }, + "source": [ + "## White noise\n", + "\n", + "A white noise process $W(t)$ is a signal that has the property that the mean of the signal is 0 and the value of the signal at any point in time $t$ is uncorrelated to the value of the signal at a point in time $s$, but that has a fixed amount of variance. Mathematically, a white noise process $W\n", + "(t) \\in \\mathbb{R}^k$ satisfies\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "\\mathbb{E}\\{W(t)\\} &= 0, &&\\text{for all $t$} \\\\\n", + "\\mathbb{E}\\{W^\\mathtt{T}(t) W(s)\\} &= Q\\, \\delta(t-s) && \\text{for all $s, t$},\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "where $Q \\in \\mathbb{R}^{k \\times k}$ is the \"intensity\" of the white noise process.\n", + "\n", + "The python-control function `white_noise` can be used to create an instantiation of a white noise process:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Create the time vector that we want to use\n", + "Tf = 5\n", + "T = np.linspace(0, Tf, 1000)\n", + "dt = T[1] - T[0]\n", + "\n", + "# Create a white noise signal\n", + "?ct.white_noise\n", + "Q = np.array([[0.1]])\n", + "W = ct.white_noise(T, Q)\n", + "\n", + "plt.figure(figsize=[5, 3])\n", + "plt.plot(T, W[0])\n", + "plt.xlabel('Time [s]')\n", + "plt.ylabel('$V$');" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MtAPkkCd14_g" + }, + "source": [ + "To confirm this is a white noise signal, we can compute the correlation function\n", + "\n", + "$$\n", + "\\rho(\\tau) = \\mathbb{E}\\{V^\\mathtt{T}(t) V(t + \\tau)\\} = Q\\, \\delta(\\tau),\n", + "$$\n", + "\n", + "where $\\delta(\\tau)$ is the unit impulse function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Correlation function for the input\n", + "tau, r_W = ct.correlation(T, W)\n", + "\n", + "plt.plot(tau, r_W, 'r-')\n", + "plt.xlabel(r'$\\tau$')\n", + "plt.ylabel(r'$r_W(\\tau)$')\n", + "\n", + "# Compute out the area under the peak\n", + "print(\"Signal covariance: \", Q.item())\n", + "print(\"Area under impulse: \", np.max(W) * dt)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1eN_MZ94tQ9v" + }, + "source": [ + "## System definition: kinematic car\n", + "\n", + "We make use of a simple model for a vehicle navigating in the plane, known as the \"bicycle model\". The kinematics of this vehicle can be written in terms of the contact point $(x, y)$ and the angle $\\theta$ of the vehicle with respect to the horizontal axis:\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
\n", + "$$\n", + "\\large\\begin{aligned}\n", + " \\dot x &= \\cos\\theta\\, v \\\\\n", + " \\dot y &= \\sin\\theta\\, v \\\\\n", + " \\dot\\theta &= \\frac{v}{l} \\tan \\delta\n", + "\\end{aligned}\n", + "$$\n", + "
\n", + "\n", + "The input $v$ represents the velocity of the vehicle and the input $\\delta$ represents the turning rate. The parameter $l$ is the wheelbase." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# System definition\n", + "# Function to compute the RHS of the system dynamics\n", + "def kincar_update(t, x, u, params):\n", + " # Get the parameters for the model\n", + " l = params['wheelbase'] # vehicle wheelbase\n", + " deltamax = params['maxsteer'] # max steering angle (rad)\n", + "\n", + " # Saturate the steering input\n", + " delta = np.clip(u[1], -deltamax, deltamax)\n", + "\n", + " # Return the derivative of the state\n", + " return np.array([\n", + " np.cos(x[2]) * u[0], # xdot = cos(theta) v\n", + " np.sin(x[2]) * u[0], # ydot = sin(theta) v\n", + " (u[0] / l) * np.tan(delta) # thdot = v/l tan(delta)\n", + " ])\n", + "\n", + "kincar_params={'wheelbase': 3, 'maxsteer': 0.5}\n", + "\n", + "# Create nonlinear input/output system\n", + "kincar = ct.nlsys(\n", + " kincar_update, None, name=\"kincar\", params=kincar_params,\n", + " inputs=('v', 'delta'), outputs=('x', 'y', 'theta'),\n", + " states=('x', 'y', 'theta'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Utility function to plot lane change manuever\n", + "def plot_lanechange(t, y, u, figure=None, yf=None, label=None):\n", + " # Plot the xy trajectory\n", + " plt.subplot(3, 1, 1, label='xy')\n", + " plt.plot(y[0], y[1], label=label)\n", + " plt.xlabel(\"x [m]\")\n", + " plt.ylabel(\"y [m]\")\n", + " if yf is not None:\n", + " plt.plot(yf[0], yf[1], 'ro')\n", + "\n", + " # Plot x and y as functions of time\n", + " plt.subplot(3, 2, 3, label='x')\n", + " plt.plot(t, y[0])\n", + " plt.ylabel(\"$x$ [m]\")\n", + "\n", + " plt.subplot(3, 2, 4, label='y')\n", + " plt.plot(t, y[1])\n", + " plt.ylabel(\"$y$ [m]\")\n", + "\n", + " # Plot the inputs as a function of time\n", + " plt.subplot(3, 2, 5, label='v')\n", + " plt.plot(t, u[0])\n", + " plt.xlabel(\"Time $t$ [sec]\")\n", + " plt.ylabel(\"$v$ [m/s]\")\n", + "\n", + " plt.subplot(3, 2, 6, label='delta')\n", + " plt.plot(t, u[1])\n", + " plt.xlabel(\"Time $t$ [sec]\")\n", + " plt.ylabel(\"$\\\\delta$ [rad]\")\n", + "\n", + " plt.subplot(3, 1, 1)\n", + " plt.title(\"Lane change manuever\")\n", + " if label:\n", + " plt.legend()\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5F-40uInyvQr" + }, + "source": [ + "We next define a desired trajectory for the vehicle. For simplicity, we use a piecewise linear trajectory and then stabilize the system around that trajectory. We will learn in a later lecture how to do this is in more rigorous way. For now, it is enough to know that this generates a feasible trajectory for the vehicle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a trajectory for the vehicle\n", + "# Define the endpoints of the trajectory\n", + "x0 = np.array([0., -4., 0.]); u0 = np.array([10., 0.])\n", + "xf = np.array([40., 4., 0.]); uf = np.array([10., 0.])\n", + "Tf = 4\n", + "Ts = Tf / 100\n", + "\n", + "# First 0.6 seconds: drive straight\n", + "T1 = np.linspace(0, 0.6, 15, endpoint=False)\n", + "x1 = np.array([6, -4, 0])\n", + "xd1 = np.array([x0 + (x1 - x0) * (t - T1[0]) / (T1[-1] - T1[0]) for t in T1]).transpose()\n", + "\n", + "# Next 2.8 seconds: change to the other lane\n", + "T2 = np.linspace(0.6, 3.4, 70, endpoint=False)\n", + "x2 = np.array([35, 4, 0])\n", + "xd2 = np.array([x1 + (x2 - x1) * (t - T2[0]) / (T2[-1] - T2[0]) for t in T2]).transpose()\n", + "\n", + "# Final 0.6 seconds: drive straight\n", + "T3 = np.linspace(3.4, Tf, 15, endpoint=False)\n", + "xd3 = np.array([x2 + (xf - x2) * (t - T3[0]) / (T3[-1] - T3[0]) for t in T3]).transpose()\n", + "\n", + "T = np.hstack([T1, T2, T3])\n", + "xr = np.hstack([xd1, xd2, xd3])\n", + "ur = np.array([u0 for t in T]).transpose()\n", + "\n", + "# Now create a simple controller to stabilize the trajectory\n", + "P = kincar.linearize(x0, u0)\n", + "K, _, _ = ct.lqr(\n", + " kincar.linearize(x0, u0),\n", + " np.diag([10, 100, 1]), np.diag([10, 10])\n", + ")\n", + "\n", + "# Construct a closed loop controller for the system\n", + "ctrl, clsys = ct.create_statefbk_iosystem(kincar, K)\n", + "resp = ct.input_output_response(clsys, T, [xr, ur], x0)\n", + "\n", + "xd = resp.states\n", + "ud = resp.outputs[kincar.nstates:]\n", + "\n", + "plot_lanechange(T, xd, ud, label='feasible')\n", + "plot_lanechange(T, xr, ur, label='reference')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Simulation of the open loop trajectory\n", + "sys_resp = ct.input_output_response(kincar, T, ud, xd[:, 0])\n", + "plt.plot(sys_resp.states[0], sys_resp.states[1])\n", + "plt.axis([0, 40, -5, 5])\n", + "plt.xlabel(\"$x$ [m]\")\n", + "plt.ylabel(\"$y$ [m]\")\n", + "plt.gca().set_aspect('equal')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7V81jzfZtiRe" + }, + "source": [ + "## State estimation\n", + "\n", + "To illustrate how we can estimate the state of the trajectory, we construct an observer that takes the measured inputs and outputs to the system and computes an estimate of the state, using a estimator with dynamics\n", + "\n", + "$$\n", + "\\dot{\\hat x} = f(\\hat x, u) - L(C \\hat x - y)\n", + "$$\n", + "\n", + "Note that we go ahead and use the nonlinear dynamics for the prediction term, but the linearization for the correction term.\n", + "\n", + "We can determine the estimator gain $L$ via multiple methods:\n", + "* Eigenvalue placement\n", + "* Optimal estimation (Kalman filter)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Jt_5SUTBuN7-" + }, + "source": [ + "### Eigenvalue placement" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Define the outputs to use for measurements\n", + "C = np.eye(2, 3)\n", + "\n", + "# Compute the linearization of the nonlinear dynamics\n", + "P = kincar.linearize([0, 0, 0], [10, 0])\n", + "\n", + "# Compute the gains via eigenvalue placement\n", + "L = ct.place(P.A.T, C.T, [-1, -2, -3]).T\n", + "\n", + "# Estimator update law\n", + "def estimator_update(t, xhat, u, params):\n", + " # Extract the inputs to the estimator\n", + " y = u[0:2] # first two system outputs\n", + " u = u[2:4] # inputs that were applied\n", + "\n", + " # Update the state estimate\n", + " xhatdot = kincar.updfcn(t, xhat, u, kincar_params) \\\n", + " - params['L'] @ (C @ xhat - y)\n", + "\n", + " # Return the derivative\n", + " return xhatdot\n", + "\n", + "estimator = ct.nlsys(\n", + " estimator_update, None, name='estimator',\n", + " states=kincar.nstates, params={'L': L},\n", + " inputs= kincar.state_labels[0:2] + kincar.input_labels,\n", + " outputs=[f'xh{i}' for i in range(kincar.nstates)],\n", + ")\n", + "print(estimator)\n", + "print(estimator.params)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the estimator from a different initial condition\n", + "estresp = ct.input_output_response(\n", + " estimator, T, [xd[0:2], ud], [0, -3, 0])\n", + "\n", + "fig, axs = plt.subplots(3, 1, figsize=[5, 4])\n", + "\n", + "axs[0].plot(estresp.time, estresp.outputs[0], 'b-', T, xd[0], 'r--')\n", + "axs[0].set_ylabel(\"$x$\")\n", + "axs[0].legend([r\"$\\hat x$\", \"$x$\"])\n", + "\n", + "axs[1].plot(estresp.time, estresp.outputs[1], 'b-', T, xd[1], 'r--')\n", + "axs[1].set_ylabel(\"$y$\")\n", + "\n", + "axs[2].plot(estresp.time, estresp.outputs[2], 'b-', T, xd[2], 'r--')\n", + "axs[2].set_ylabel(r\"$\\theta$\")\n", + "axs[2].set_xlabel(\"Time $t$ [s]\")\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KPkD-wSXt8d0" + }, + "source": [ + "### Kalman filter\n", + "\n", + "An alternative mechanism for creating an estimator is through the use of optimal estimation (Kalman filtering).\n", + "\n", + "Suppose that we have (very) noisy measurements of the system position, and also have disturbances taht are applied to our control signal." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Disturbance and noise covariances\n", + "Qv = np.diag([0.1**2, 0.01**2])\n", + "Qw = np.eye(2) * 0.1**2\n", + "\n", + "u_noisy = ud + ct.white_noise(T, Qv)\n", + "sys_resp = ct.input_output_response(kincar, T, u_noisy, xd[:, 0])\n", + "\n", + "# Create noisy version of the measurements\n", + "y_noisy = sys_resp.outputs[0:2] + ct.white_noise(T, Qw)\n", + "\n", + "plt.plot(y_noisy[0], y_noisy[1], 'k-')\n", + "plt.plot(sys_resp.outputs[0], sys_resp.outputs[1], 'b-')\n", + "plt.axis([0, 40, -5, 5])\n", + "plt.xlabel(\"$x$ [m]\")\n", + "plt.ylabel(\"$y$ [m]\")\n", + "plt.legend(['measured', 'actual'])\n", + "plt.gca().set_aspect('equal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Kalman filter allows us to estimate the optimal state given measurements of the inputs and outputs, as well as knowledge of the covariance of the signals." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the Kalman gains (linear quadratic estimator)\n", + "L_kf, _, _ = ct.lqe(P.A, P.B, C, Qv, Qw)\n", + "\n", + "kfresp = ct.input_output_response(\n", + " estimator, T, [y_noisy, ud], [0, -3, 0],\n", + " params={'L': L_kf})\n", + "\n", + "fig, axs = plt.subplots(3, 1, figsize=[5, 4])\n", + "\n", + "axs[0].plot(T, y_noisy[0], 'k-')\n", + "axs[0].plot(kfresp.time, kfresp.outputs[0], 'b-', T, sys_resp.outputs[0], 'r--')\n", + "axs[0].set_ylabel(\"$x$\")\n", + "axs[0].legend([r\"$\\hat x$\", \"$x$\"])\n", + "\n", + "axs[1].plot(T, y_noisy[1], 'k-')\n", + "axs[1].plot(kfresp.time, kfresp.outputs[1], 'b-', T, sys_resp.outputs[1], 'r--')\n", + "axs[1].set_ylabel(\"$y$\")\n", + "\n", + "axs[2].plot(kfresp.time, kfresp.outputs[2], 'b-', T, sys_resp.outputs[2], 'r--')\n", + "axs[2].set_ylabel(r\"$\\theta$\")\n", + "axs[2].set_xlabel(\"Time $t$ [s]\")\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pMfHmzsW0Dqh" + }, + "source": [ + "We can get a better view of the convergence by plotting the errors:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(3, 1, figsize=[5, 4])\n", + "\n", + "axs[0].plot(kfresp.time, kfresp.outputs[0] - sys_resp.outputs[0])\n", + "axs[0].plot([T[0], T[-1]], [0, 0], 'k--')\n", + "axs[0].set_ylabel(\"$x$ error\")\n", + "axs[0].set_ylim([-1, 1])\n", + "\n", + "axs[1].plot(kfresp.time, kfresp.outputs[1] - sys_resp.outputs[1])\n", + "axs[1].plot([T[0], T[-1]], [0, 0], 'k--')\n", + "axs[1].set_ylabel(\"$y$ error\")\n", + "axs[1].set_ylim([-1, 1])\n", + "\n", + "axs[2].plot(kfresp.time, kfresp.outputs[2] - sys_resp.outputs[2])\n", + "axs[2].plot([T[0], T[-1]], [0, 0], 'k--')\n", + "axs[2].set_ylabel(r\"$\\theta$ error\")\n", + "axs[2].set_xlabel(\"Time $t$ [s]\")\n", + "axs[2].set_ylim([-0.2, 0.2])\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nccW48C5tns9" + }, + "source": [ + "## Output feedback control\n", + "\n", + "We next construct a controller that makes use of the estimated state. We will attempt to control the longitudinal position using the steering angle as an input, with the velocity set to the desired velocity (no tracking of the longitudinal position)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the linearization of the nonlinear dynamics\n", + "P = kincar.linearize([0, 0, 0], [10, 0])\n", + "\n", + "# Extract out the linearized dynamics from delta to y\n", + "Alat = P.A[1:3, 1:3]\n", + "Blat = P.B[1:3, 1:2]\n", + "Clat = P.C[1:2, 1:3]\n", + "\n", + "sys = ct.ss(Alat, Blat, Clat, 0)\n", + "print(sys)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Construct a state space controller, using LQR\n", + "Qx = np.diag([1, 10])\n", + "Qu = np.diag([1])\n", + "\n", + "K, _, _ = ct.lqr(Alat, Blat, Qx, Qu)\n", + "print(f\"{K=}\")\n", + "\n", + "kf = -1 / (Clat @ np.linalg.inv(Alat - Blat @ K) @ Blat)\n", + "print(f\"{kf=}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v5oHK9-XMrEv" + }, + "source": [ + "### Direct state space feedback\n", + "\n", + "We start by checking the response of the system assuming that we measure the state directly.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Construct a controller for the full system\n", + "def ctrl_output(t, x, u, params):\n", + " r_v, r_y = u[0:2]\n", + " x = u[3:5] # y, theta\n", + " return np.vstack([r_v, -K @ x + kf * r_y])\n", + "ctrl = ct.nlsys(\n", + " None, ctrl_output, name='ctrl',\n", + " inputs=['r_v', 'r_y', 'x', 'y', 'theta'],\n", + " outputs=['v', 'delta']\n", + ")\n", + "print(ctrl)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Direct state feedback\n", + "clsys_direct = ct.interconnect(\n", + " [kincar, ctrl],\n", + " inputs=['r_v', 'r_y'],\n", + " outputs=['x', 'y', 'theta', 'v', 'delta'],\n", + ")\n", + "print(clsys_direct)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run a simulation\n", + "clresp_direct = ct.input_output_response(\n", + " clsys_direct, T, [10, xd[1]], X0=[0, -3, 0])\n", + "\n", + "plt.plot(clresp_direct.outputs[0], clresp_direct.outputs[1])\n", + "plt.plot(xd[0], xd[1], 'r--')\n", + "# plt.plot(clresp.time, clresp.outputs[1])\n", + "plt.xlabel(\"$x$ [m]\")\n", + "plt.ylabel(\"$y$ [m]\")\n", + "plt.gca().set_aspect('equal')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J0iS9V8YT4Ox" + }, + "source": [ + "Note the \"lag\" in the $x$ coordinate. This comes from the fact that we did not use feedback to maintain the longitudinal position as a function of time, compared with the desired trajectory. To see this, we can look at the commanded speed ($v$) versus the desired speed:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_lanechange(T, xd, ud, label=\"desired\")\n", + "plot_lanechange(T, clresp_direct.outputs[0:2], clresp_direct.outputs[-2:], label=\"actual\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SDrkfC_LUPDu" + }, + "source": [ + "From this plot we can also see that there is a very large input $\\delta$ applied at $t=0$. This is something we would have to fix if we were to implement this on a physical system (-1 rad $\\approx -60^\\circ$!)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KS0E2g6aMgC0" + }, + "source": [ + "### Estimator-based control\n", + "\n", + "We now consider the case were we cannot directly measure the state, but instead have to estimate the state from the commanded input and measured output. We can insert the estimator into the system model by reconnecting the inputs and outputs. The `ct.interconnect` function provides the needed flexibility:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "?ct.interconnect" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rgI9QjBMAy7b" + }, + "source": [ + "We now create the system model that includes the estimator (observer). Here is the system we are trying to construct:\n", + "\n", + "\n", + "\n", + "\n", + "(Be careful with the notation: in the diagram above $y$ is the measured outputs, which for our system are the $x$ and $y$ position of the vehicle, so overusing the symbol $y$.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Connect the system, estimator, and controller\n", + "clsys_estim = ct.interconnect(\n", + " [kincar, estimator, ctrl],\n", + " inplist=['ctrl.r_v', 'ctrl.r_y', 'estimator.x', 'estimator.y'],\n", + " inputs=['r_v', 'r_y', 'noise_x', 'noise_y'],\n", + " outlist=[\n", + " 'kincar.x', 'kincar.y', 'kincar.theta',\n", + " 'estimator.xh0', 'estimator.xh1', 'estimator.xh2',\n", + " 'ctrl.v', 'ctrl.delta'\n", + " ],\n", + " outputs=['x', 'y', 'theta', 'xhat', 'yhat', 'thhat', 'v', 'delta'],\n", + " connections=[\n", + " ['kincar.v', 'ctrl.v'],\n", + " ['kincar.delta', 'ctrl.delta'],\n", + " ['estimator.x', 'kincar.x'],\n", + " ['estimator.y', 'kincar.y'],\n", + " ['estimator.delta', 'ctrl.delta'],\n", + " ['estimator.v', 'ctrl.v'],\n", + " ['ctrl.x', 'estimator.xh0'],\n", + " ['ctrl.y', 'estimator.xh1'],\n", + " ['ctrl.theta', 'estimator.xh2'],\n", + " ],\n", + ")\n", + "print(clsys_estim)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Run a simulation with no noise first\n", + "clresp_nonoise = ct.input_output_response(\n", + " clsys_estim, T, [10, xd[1], 0, 0], X0=[0, -3, 0, 0, -5, 0])\n", + "\n", + "plt.plot(clresp_nonoise.outputs[0], clresp_nonoise.outputs[1])\n", + "plt.plot(xd[0], xd[1], 'r--')\n", + "\n", + "plt.xlabel(\"$x$ [m]\")\n", + "plt.ylabel(\"$y$ [m]\")\n", + "plt.gca().set_aspect('equal')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Add some noise\n", + "Qv = np.diag([0.1**2, 0.01**2])\n", + "Qw = np.eye(2) * 0.1**2\n", + "\n", + "u_noise = ct.white_noise(T, Qv)\n", + "y_noise = ct.white_noise(T, Qw)\n", + "\n", + "# Run a simulation\n", + "clresp_noisy = ct.input_output_response(\n", + " clsys_estim, T, [10, xd[1], y_noise], X0=[0, -3, 0, 0, -5, 0])\n", + "\n", + "plt.plot(clresp_direct.outputs[0], clresp_direct.outputs[1], label='direct')\n", + "plt.plot(clresp_nonoise.outputs[0], clresp_nonoise.outputs[1], label='nonoise')\n", + "plt.plot(clresp_noisy.outputs[0], clresp_noisy.outputs[1], label='noisy')\n", + "plt.legend()\n", + "plt.plot(xd[0], xd[1], 'r--')\n", + "\n", + "plt.xlabel(\"$x$ [m]\")\n", + "plt.ylabel(\"$y$ [m]\")\n", + "plt.gca().set_aspect('equal')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the differences in y to make differences more clear\n", + "plt.plot(\n", + " clresp_nonoise.time, clresp_nonoise.outputs[1] - clresp_direct.outputs[1],\n", + " label='nonoise')\n", + "plt.plot(\n", + " clresp_noisy.time, clresp_noisy.outputs[1] - clresp_direct.outputs[1],\n", + " label='noisy')\n", + "plt.legend()\n", + "plt.plot([clresp_nonoise.time[0], clresp_nonoise.time[-1]], [0, 0], 'r--')\n", + "\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(\"$y$ [m]\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Show the control inputs as well as the final trajectory\n", + "plot_lanechange(T, xd, ud, label=\"desired\")\n", + "plot_lanechange(T, clresp_noisy.outputs[0:2], clresp_noisy.outputs[-2:], label=\"actual\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZfxhaU9p_W4w" + }, + "source": [ + "### Things to try\n", + "\n", + "* Wrap a controller around the velocity (or $x$ position) in addition to the lateral ($y$) position\n", + "* Change the amounts of noise in the sensor signal\n", + "* Add disturbances to the dynamics (corresponding to wind, hills, etc)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/cds110-L6a_kincar-trajgen.ipynb b/examples/cds110-L6a_kincar-trajgen.ipynb new file mode 100644 index 000000000..e139272bd --- /dev/null +++ b/examples/cds110-L6a_kincar-trajgen.ipynb @@ -0,0 +1,533 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "edb7e2c6", + "metadata": { + "id": "edb7e2c6" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 6a

\n", + "

Trajectory Generation for a Kinematic Car Model

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/1vBFjCU2W6fSavy8loL0JfgZyO6UC46m3)\n", + "\n", + "This notebook contains an example of using (optimal) trajectory generation for a vehicle steering system. It illustrates different methods of setting up optimal control problems and solving them using python-control." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7066eb69", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct\n", + "import control.optimal as opt" + ] + }, + { + "cell_type": "markdown", + "id": "4afb09dd", + "metadata": { + "id": "4afb09dd" + }, + "source": [ + "## Vehicle steering dynamics\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
\n", + "$$\n", + "\\large\\begin{aligned}\n", + " \\dot x &= \\cos\\theta\\, v \\\\\n", + " \\dot y &= \\sin\\theta\\, v \\\\\n", + " \\dot\\theta &= \\frac{v}{l} \\tan \\delta\n", + "\\end{aligned}\n", + "$$\n", + "
\n", + "\n", + "The vehicle dynamics are given by a simple bicycle model. We take the state of the system as $(x, y, \\theta)$ where $(x, y)$ is the position of the vehicle in the plane and $\\theta$ is the angle of the vehicle with respect to horizontal. The vehicle input is given by $(v, \\delta)$ where $v$ is the forward velocity of the vehicle and $\\delta$ is the angle of the steering wheel. The model includes saturation of the vehicle steering angle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6143a8a", + "metadata": {}, + "outputs": [], + "source": [ + "# Code to model vehicle steering dynamics\n", + "\n", + "# Function to compute the RHS of the system dynamics\n", + "def kincar_update(t, x, u, params):\n", + " # Get the parameters for the model\n", + " l = params['wheelbase'] # vehicle wheelbase\n", + " deltamax = params['maxsteer'] # max steering angle (rad)\n", + "\n", + " # Saturate the steering input\n", + " delta = np.clip(u[1], -deltamax, deltamax)\n", + "\n", + " # Return the derivative of the state\n", + " return np.array([\n", + " np.cos(x[2]) * u[0], # xdot = cos(theta) v\n", + " np.sin(x[2]) * u[0], # ydot = sin(theta) v\n", + " (u[0] / l) * np.tan(delta) # thdot = v/l tan(delta)\n", + " ])\n", + "\n", + "kincar_params={'wheelbase': 3, 'maxsteer': 0.5}\n", + "\n", + "# Create nonlinear input/output system\n", + "kincar = ct.nlsys(\n", + " kincar_update, None, name=\"kincar\", params=kincar_params,\n", + " inputs=('v', 'delta'), outputs=('x', 'y', 'theta'),\n", + " states=('x', 'y', 'theta'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c2bf8d6-7580-4712-affc-928a8b046d8a", + "metadata": {}, + "outputs": [], + "source": [ + "# Utility function to plot lane change manuever\n", + "def plot_lanechange(t, y, u, figure=None, yf=None, label=None):\n", + " # Plot the xy trajectory\n", + " plt.subplot(3, 1, 1, label='xy')\n", + " plt.plot(y[0], y[1], label=label)\n", + " plt.xlabel(\"x [m]\")\n", + " plt.ylabel(\"y [m]\")\n", + " if yf is not None:\n", + " plt.plot(yf[0], yf[1], 'ro')\n", + "\n", + " # Plot x and y as functions of time\n", + " plt.subplot(3, 2, 3, label='x')\n", + " plt.plot(t, y[0])\n", + " plt.ylabel(\"$x$ [m]\")\n", + "\n", + " plt.subplot(3, 2, 4, label='y')\n", + " plt.plot(t, y[1])\n", + " plt.ylabel(\"$y$ [m]\")\n", + "\n", + " # Plot the inputs as a function of time\n", + " plt.subplot(3, 2, 5, label='v')\n", + " plt.plot(t, u[0])\n", + " plt.xlabel(\"Time $t$ [sec]\")\n", + " plt.ylabel(\"$v$ [m/s]\")\n", + "\n", + " plt.subplot(3, 2, 6, label='delta')\n", + " plt.plot(t, u[1])\n", + " plt.xlabel(\"Time $t$ [sec]\")\n", + " plt.ylabel(\"$\\\\delta$ [rad]\")\n", + "\n", + " plt.subplot(3, 1, 1)\n", + " plt.title(\"Lane change manuever\")\n", + " if label:\n", + " plt.legend()\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "64bd3c3b", + "metadata": { + "id": "64bd3c3b" + }, + "source": [ + "## Optimal trajectory generation\n", + "\n", + "The general problem we are solving is of the form:\n", + "\n", + "$$\n", + "\\min_{u(\\cdot)}\n", + " \\int_0^T L(x,u)\\, dt + V \\bigl( x(T) \\bigr)\n", + "$$\n", + "subject to\n", + "$$\n", + " \\dot x = f(x, u), \\qquad x\\in \\mathcal{X} \\subset \\mathbb{R}^n,\\, u\\in \\mathcal{U} \\subset \\mathbb{R}^m\n", + "$$\n", + "\n", + "We consider the problem of changing from one lane to another over a perod of 10 seconds while driving at a forward speed of 10 m/s." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42dcbd79", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial and final conditions\n", + "x0 = np.array([ 0., -2., 0.]); u0 = np.array([10., 0.])\n", + "xf = np.array([100., 2., 0.]); uf = np.array([10., 0.])\n", + "Tf = 10" + ] + }, + { + "cell_type": "markdown", + "id": "5ff2e044", + "metadata": { + "id": "5ff2e044" + }, + "source": [ + "An important part of the optimization procedure is to give a good initial guess. Here are some possibilities:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "650d321a", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the time horizon (and spacing) for the optimization\n", + "# timepts = np.linspace(0, Tf, 5, endpoint=True) # Try using this and see what happens\n", + "# timepts = np.linspace(0, Tf, 10, endpoint=True) # Try using this and see what happens\n", + "timepts = np.linspace(0, Tf, 20, endpoint=True)\n", + "\n", + "# Compute some initial guesses to use\n", + "bend_left = [10, 0.01] # slight left veer (will extend over all timepts)\n", + "straight_line = ( # straight line from start to end with nominal input\n", + " np.array([x0 + (xf - x0) * t/Tf for t in timepts]).transpose(),\n", + " u0\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4e75a2c4", + "metadata": { + "id": "4e75a2c4" + }, + "source": [ + "### Approach 1: standard quadratic cost\n", + "\n", + "We can set up the optimal control problem as trying to minimize the distance from the desired final point while at the same time as not exerting too much control effort to achieve our goal.\n", + "\n", + "$$\n", + "\\min_{u(\\cdot)}\n", + " \\int_0^T \\left[(x(\\tau) - x_\\text{f})^T Q_x (x(\\tau) - x_\\text{f}) + (u(\\tau) - u_\\text{f})^T Q_u (u(\\tau) - u_\\text{f})\\right] \\, d\\tau\n", + "$$\n", + "subject to\n", + "$$\n", + " \\dot x = f(x, u), \\qquad x \\in \\mathbb{R}^n,\\, u \\in \\mathbb{R}^m\n", + "$$\n", + "\n", + "The optimization module solves optimal control problems by choosing the values of the input at each point in the time horizon to try to minimize the cost:\n", + "\n", + "$$\n", + "u_i(t_j) = \\alpha_{i, j}, \\qquad\n", + "u_i(t) = \\frac{t_{i+1} - t}{t_{i+1} - t_i} \\alpha_{i, j} + \\frac{t - t_i}{t_{i+1} - t_i} \\alpha_{{i+1},j}\n", + "$$\n", + "\n", + "This means that each input generates a parameter value at each point in the time horizon, so the more refined your time horizon, the more parameters the optimizer has to search over." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "984c2f0b", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the cost functions\n", + "Qx = np.diag([.1, 10, .1]) # keep lateral error low\n", + "Qu = np.diag([.1, 1]) # minimize applied inputs\n", + "quad_cost = opt.quadratic_cost(kincar, Qx, Qu, x0=xf, u0=uf)\n", + "\n", + "# Compute the optimal control, setting step size for gradient calculation (eps)\n", + "start_time = time.process_time()\n", + "result1 = opt.solve_ocp(\n", + " kincar, timepts, x0, quad_cost,\n", + " initial_guess=straight_line,\n", + " # initial_guess= bend_left,\n", + " # initial_guess=u0,\n", + " # minimize_method='trust-constr',\n", + " # minimize_options={'finite_diff_rel_step': 0.01},\n", + " # trajectory_method='shooting'\n", + " # solve_ivp_method='LSODA'\n", + ")\n", + "print(\"* Total time = %5g seconds\\n\" % (time.process_time() - start_time))\n", + "\n", + "# Plot the results from the optimization\n", + "plot_lanechange(timepts, result1.states, result1.inputs, xf)\n", + "print(\"Final computed state: \", result1.states[:,-1])\n", + "\n", + "# Simulate the system and see what happens\n", + "t1, u1 = result1.time, result1.inputs\n", + "t1, y1 = ct.input_output_response(kincar, timepts, u1, x0)\n", + "plot_lanechange(t1, y1, u1, yf=xf[0:2])\n", + "print(\"Final simulated state:\", y1[:,-1])\n", + "\n", + "# Label the different lines\n", + "plt.subplot(3, 1, 1)\n", + "plt.legend(['desired', 'simulated', 'endpoint'])\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "b7cade52", + "metadata": { + "id": "b7cade52" + }, + "source": [ + "Note the amount of time required to solve the problem and also any warning messages about to being able to solve the optimization (mainly in earlier versions of python-control). You can try to adjust a number of factors to try to get a better solution:\n", + "* Try changing the number of points in the time horizon\n", + "* Try using a different initial guess\n", + "* Try changing the optimization method (see commented out code)" + ] + }, + { + "cell_type": "markdown", + "id": "6a9f9d9b", + "metadata": { + "id": "6a9f9d9b" + }, + "source": [ + "### Approach 2: input cost, input constraints, terminal cost\n", + "\n", + "The previous solution integrates the position error for the entire horizon, and so the car changes lanes very quickly (at the cost of larger inputs). Instead, we can penalize the final state and impose a higher cost on the inputs, resulting in a more gradual lane change.\n", + "\n", + "$$\n", + "\\min_{u(\\cdot)}\n", + " \\int_0^T \\underbrace{\\left[x(\\tau)^T Q_x x(\\tau) + (u(\\tau) - u_\\text{f})^T Q_u (u(\\tau) - u_\\text{f})\\right]}_{L(x, u)} \\, d\\tau + \\underbrace{(x(T) - x_\\text{f})^T Q_\\text{f} (x(T) - x_\\text{f})}_{V\\left(x(T)\\right)}\n", + "$$\n", + "subject to\n", + "$$\n", + " \\dot x = f(x, u), \\qquad x \\in \\mathbb{R}^n,\\, u \\in \\mathbb{R}^m\n", + "$$\n", + "\n", + "We can also try using a different solver for this example. You can pass the solver using the `minimize_method` keyword and send options to the solver using the `minimize_options` keyword (which should be set to a dictionary of options)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a201e33c", + "metadata": {}, + "outputs": [], + "source": [ + "# Add input constraint, input cost, terminal cost\n", + "constraints = [ opt.input_range_constraint(kincar, [8, -0.1], [12, 0.1]) ]\n", + "traj_cost = opt.quadratic_cost(kincar, None, np.diag([0.1, 1]), u0=uf)\n", + "term_cost = opt.quadratic_cost(kincar, np.diag([1, 10, 100]), None, x0=xf)\n", + "\n", + "# Compute the optimal control\n", + "start_time = time.process_time()\n", + "result2 = opt.solve_ocp(\n", + " kincar, timepts, x0, traj_cost, constraints, terminal_cost=term_cost,\n", + " initial_guess=straight_line,\n", + " # minimize_method='trust-constr',\n", + " # minimize_options={'finite_diff_rel_step': 0.01},\n", + " # minimize_method='SLSQP', minimize_options={'eps': 0.01},\n", + " # log=True,\n", + ")\n", + "print(\"* Total time = %5g seconds\\n\" % (time.process_time() - start_time))\n", + "\n", + "# Plot the results from the optimization\n", + "plot_lanechange(timepts, result2.states, result2.inputs, xf)\n", + "print(\"Final computed state: \", result2.states[:,-1])\n", + "\n", + "# Simulate the system and see what happens\n", + "t2, u2 = result2.time, result2.inputs\n", + "t2, y2 = ct.input_output_response(kincar, timepts, u2, x0)\n", + "plot_lanechange(t2, y2, u2, yf=xf[0:2])\n", + "print(\"Final simulated state:\", y2[:,-1])\n", + "\n", + "# Label the different lines\n", + "plt.subplot(3, 1, 1)\n", + "plt.legend(['desired', 'simulated', 'endpoint'], loc='upper left')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "3d2ccf97", + "metadata": { + "id": "3d2ccf97" + }, + "source": [ + "### Approach 3: terminal constraints\n", + "\n", + "We can also remove the cost function on the state and replace it with a terminal *constraint* on the state as well as bounds on the inputs. If a solution is found, it guarantees we get to exactly the final state:\n", + "\n", + "$$\n", + "\\min_{u(\\cdot)}\n", + " \\int_0^T \\underbrace{(u(\\tau) - u_\\text{f})^T Q_u (u(\\tau) - u_\\text{f})}_{L(x, u)} \\, d\\tau\n", + "$$\n", + "subject to\n", + "$$\n", + " \\begin{aligned}\n", + " \\dot x &= f(x, u), & \\qquad &x \\in \\mathbb{R}^n,\\, u \\in \\mathbb{R}^m \\\\\n", + " x(T) &= x_\\text{f} & &u_\\text{lb} \\leq u(t) \\leq u_\\text{ub},\\, \\text{for all $t$}\n", + " \\end{aligned}\n", + "$$\n", + "\n", + "Note that trajectory and terminal constraints can be very difficult to satisfy for a general optimization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc77a856", + "metadata": {}, + "outputs": [], + "source": [ + "# Input cost and terminal constraints\n", + "R = np.diag([1, 1]) # minimize applied inputs\n", + "cost3 = opt.quadratic_cost(kincar, np.zeros((3,3)), R, u0=uf)\n", + "constraints = [\n", + " opt.input_range_constraint(kincar, [8, -0.1], [12, 0.1]) ]\n", + "terminal = [ opt.state_range_constraint(kincar, xf, xf) ]\n", + "\n", + "# Compute the optimal control\n", + "start_time = time.process_time()\n", + "result3 = opt.solve_ocp(\n", + " kincar, timepts, x0, cost3, constraints,\n", + " terminal_constraints=terminal, initial_guess=straight_line,\n", + "# solve_ivp_kwargs={'atol': 1e-3, 'rtol': 1e-2},\n", + "# minimize_method='trust-constr',\n", + "# minimize_options={'finite_diff_rel_step': 0.01},\n", + ")\n", + "print(\"* Total time = %5g seconds\\n\" % (time.process_time() - start_time))\n", + "\n", + "# Plot the results from the optimization\n", + "plot_lanechange(timepts, result3.states, result3.inputs, xf)\n", + "print(\"Final computed state: \", result3.states[:,-1])\n", + "\n", + "# Simulate the system and see what happens\n", + "t3, u3 = result3.time, result3.inputs\n", + "t3, y3 = ct.input_output_response(kincar, timepts, u3, x0)\n", + "plot_lanechange(t3, y3, u3, yf=xf[0:2])\n", + "print(\"Final state: \", y3[:,-1])\n", + "\n", + "# Label the different lines\n", + "plt.subplot(3, 1, 1)\n", + "plt.legend(['desired', 'simulated', 'endpoint'], loc='upper left')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "9e744463", + "metadata": { + "id": "9e744463" + }, + "source": [ + "### Approach 4: terminal constraints w/ basis functions (if time)\n", + "\n", + "As a final example, we can use a basis function to reduce the size of the problem and get faster answers with more temporal resolution:\n", + "\n", + "$$\n", + "\\min_{u(\\cdot)}\n", + " \\int_0^T L(x, u) \\, d\\tau + V\\left(x(T)\\right)\n", + "$$\n", + "subject to\n", + "$$\n", + " \\begin{aligned}\n", + " \\dot x &= f(x, u), \\qquad x \\in \\mathcal{X} \\subset \\mathbb{R}^n,\\, u \\in \\mathcal{U} \\subset \\mathbb{R}^m \\\\\n", + " u(t) &= \\sum_i \\alpha_i \\phi^i(t),\n", + " \\end{aligned}\n", + "$$\n", + "where $\\phi^i(t)$ are a set of basis functions.\n", + "\n", + "Here we parameterize the input by a set of 4 Bezier curves but solve for a much more time resolved set of inputs. Note that while we are using the `control.flatsys` module to define the basis functions, we are not exploiting the fact that the system is differentially flat." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee82aa25", + "metadata": {}, + "outputs": [], + "source": [ + "# Get basis functions for flat systems module\n", + "import control.flatsys as flat\n", + "\n", + "# Compute the optimal control\n", + "start_time = time.process_time()\n", + "result4 = opt.solve_ocp(\n", + " kincar, timepts, x0, quad_cost, constraints,\n", + " terminal_constraints=terminal,\n", + " initial_guess=straight_line,\n", + " basis=flat.PolyFamily(4, T=Tf),\n", + " # solve_ivp_kwargs={'method': 'RK45', 'atol': 1e-2, 'rtol': 1e-2},\n", + " # solve_ivp_kwargs={'atol': 1e-3, 'rtol': 1e-2},\n", + " # minimize_method='trust-constr', minimize_options={'disp': True},\n", + " log=False\n", + ")\n", + "print(\"* Total time = %5g seconds\\n\" % (time.process_time() - start_time))\n", + "\n", + "# Plot the results from the optimization\n", + "plot_lanechange(timepts, result4.states, result4.inputs, xf)\n", + "print(\"Final computed state: \", result3.states[:,-1])\n", + "\n", + "# Simulate the system and see what happens\n", + "t4, u4 = result4.time, result4.inputs\n", + "t4, y4 = ct.input_output_response(kincar, timepts, u4, x0)\n", + "plot_lanechange(t4, y4, u4, yf=xf[0:2])\n", + "print(\"Final simulated state: \", y4[:,-1])\n", + "\n", + "# Label the different lines\n", + "plt.subplot(3, 1, 1)\n", + "plt.legend(['desired', 'simulated', 'endpoint'], loc='upper left')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2a74388e", + "metadata": { + "id": "2a74388e" + }, + "source": [ + "Note how much smoother the inputs look, although the solver can still have a hard time satisfying the final constraints, resulting in longer computation times." + ] + }, + { + "cell_type": "markdown", + "id": "1465d149", + "metadata": { + "id": "1465d149" + }, + "source": [ + "### Additional things to try\n", + "\n", + "* Compare the results here with what we go last week exploiting the property of differential flatness (computation time, in particular)\n", + "* Try using different weights, solvers, initial guess and other properties and see how things change.\n", + "* Try using different values for `initial_guess` to get faster convergence and/or different classes of solutions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02bad3d5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds110-L6b_kincar-tracking.ipynb b/examples/cds110-L6b_kincar-tracking.ipynb new file mode 100644 index 000000000..9f4cbb475 --- /dev/null +++ b/examples/cds110-L6b_kincar-tracking.ipynb @@ -0,0 +1,509 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "exempt-legislation", + "metadata": { + "id": "exempt-legislation" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 6b

\n", + "

Trajectory Tracking for a Kinematic Car

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/12VSFMqM6HVyj8TY_3zb0AnsJrG6UeLKF)\n", + "\n", + "This notebook contains an example of using trajectory tracking for a (nonlinear) state space system. The controller is of the form\n", + "\n", + "$$\n", + " u = u_\\text{d} − K (x − x_\\text{d}),\n", + "$$\n", + "\n", + "where $x_\\text{d}, u_\\text{d}$ is a feasible trajectory, and $K$ is a feedback gain first computed around a nominal condition and then computed using gain scheduling." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "corresponding-convenience", + "metadata": {}, + "outputs": [], + "source": [ + "# Import the packages needed for the examples included in this notebook\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import itertools\n", + "from cmath import sqrt\n", + "from math import pi\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct\n", + "import control.optimal as opt\n", + "import control.flatsys as fs" + ] + }, + { + "cell_type": "markdown", + "id": "corporate-sense", + "metadata": { + "id": "corporate-sense" + }, + "source": [ + "## Vehicle Steering Dynamics\n", + "\n", + "The vehicle dynamics are given by a simple bicycle model:\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
\n", + "$$\\large\n", + "\\begin{aligned}\n", + " \\dot x &= \\cos\\theta\\, v \\\\\n", + " \\dot y &= \\sin\\theta\\, v \\\\\n", + " \\dot\\theta &= \\frac{v}{l} \\tan \\delta\n", + "\\end{aligned}\n", + "$$\n", + "
\n", + "\n", + "We take the state of the system as $(x, y, \\theta)$ where $(x, y)$ is the position of the vehicle in the plane and $\\theta$ is the angle of the vehicle with respect to horizontal. The vehicle input is given by $(v, \\delta)$ where $v$ is the forward velocity of the vehicle and $\\delta$ is the angle of the steering wheel. The model includes saturation of the vehicle steering angle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "naval-pizza", + "metadata": {}, + "outputs": [], + "source": [ + "# Code to model vehicle steering dynamics\n", + "\n", + "# Function to compute the RHS of the system dynamics\n", + "def kincar_update(t, x, u, params):\n", + " # Get the parameters for the model\n", + " l = params['wheelbase'] # vehicle wheelbase\n", + " deltamax = params['maxsteer'] # max steering angle (rad)\n", + "\n", + " # Saturate the steering input\n", + " delta = np.clip(u[1], -deltamax, deltamax)\n", + "\n", + " # Return the derivative of the state\n", + " return np.array([\n", + " np.cos(x[2]) * u[0], # xdot = cos(theta) v\n", + " np.sin(x[2]) * u[0], # ydot = sin(theta) v\n", + " (u[0] / l) * np.tan(delta) # thdot = v/l tan(delta)\n", + " ])\n", + "\n", + "kincar_params={'wheelbase': 3, 'maxsteer': 0.5}\n", + "\n", + "# Create nonlinear input/output system\n", + "kincar = ct.nlsys(\n", + " kincar_update, None, name=\"kincar\", params=kincar_params,\n", + " inputs=('v', 'delta'), outputs=('x', 'y', 'theta'),\n", + " states=('x', 'y', 'theta'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6340dbd4-7867-47ad-aefb-1bea7f6ad566", + "metadata": {}, + "outputs": [], + "source": [ + "# Utility function to plot lane change manuever\n", + "def plot_lanechange(t, y, u, figure=None, yf=None, label=None):\n", + " # Plot the xy trajectory\n", + " plt.subplot(3, 1, 1, label='xy')\n", + " plt.plot(y[0], y[1], label=label)\n", + " plt.xlabel(\"x [m]\")\n", + " plt.ylabel(\"y [m]\")\n", + " if yf is not None:\n", + " plt.plot(yf[0], yf[1], 'ro')\n", + "\n", + " # Plot x and y as functions of time\n", + " plt.subplot(3, 2, 3, label='x')\n", + " plt.plot(t, y[0])\n", + " plt.ylabel(\"$x$ [m]\")\n", + "\n", + " plt.subplot(3, 2, 4, label='y')\n", + " plt.plot(t, y[1])\n", + " plt.ylabel(\"$y$ [m]\")\n", + "\n", + " # Plot the inputs as a function of time\n", + " plt.subplot(3, 2, 5, label='v')\n", + " plt.plot(t, u[0])\n", + " plt.xlabel(\"Time $t$ [sec]\")\n", + " plt.ylabel(\"$v$ [m/s]\")\n", + "\n", + " plt.subplot(3, 2, 6, label='delta')\n", + " plt.plot(t, u[1])\n", + " plt.xlabel(\"Time $t$ [sec]\")\n", + " plt.ylabel(\"$\\\\delta$ [rad]\")\n", + "\n", + " plt.subplot(3, 1, 1)\n", + " plt.title(\"Lane change manuever\")\n", + " if label:\n", + " plt.legend()\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "BAsKLMWWK3W2", + "metadata": { + "id": "BAsKLMWWK3W2" + }, + "source": [ + "## State feedback controller\n", + "\n", + "We start by designing a state feedback controller that can be used to stabilize the system. We design the controller around a nominal forward speed of 10 m/s and then apply this to the vehicle at different speeds." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "g7DztIjmK2K_", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the linearization of the dynamics at a nominal point\n", + "x_nom = np.array([0, 0, 0])\n", + "u_nom = np.array([5, 0])\n", + "P = ct.linearize(kincar, x_nom, u_nom) # Linearized systems\n", + "print(P)\n", + "\n", + "Qx = np.diag([1, 10, 0.1])\n", + "Qu = np.diag([1, 1])\n", + "K, _, _ = ct.lqr(P.A, P.B, Qx, Qu)\n", + "print(K)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "szvKKh6rLgkt", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the closed loop system using create_statefbk_iosystem\n", + "?ct.create_statefbk_iosystem\n", + "ctrl, clsys = ct.create_statefbk_iosystem(\n", + " kincar, K, xd_labels=['xd', 'yd', 'thetad'], ud_labels=['vd', 'deltad'])\n", + "print(clsys)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "gow-ZEerMCw7", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a trajectory corresponding to a slow lane change\n", + "x0 = np.array([0, -2, 0]); u0 = [10, 0]\n", + "xf = np.array([100, 2, 0])\n", + "Tf = 10\n", + "timepts = np.linspace(0, Tf, 20)\n", + "\n", + "straight_line = ( # straight line from start to end with nominal input\n", + " np.array([x0 + (xf - x0) * t/Tf for t in timepts]).transpose(),\n", + " u0\n", + ")\n", + "\n", + "desired = opt.solve_ocp(\n", + " kincar, timepts, x0,\n", + " cost=opt.quadratic_cost(kincar, None, Qu, u0=u0),\n", + " terminal_constraints=opt.state_range_constraint(kincar, xf, xf),\n", + " initial_guess=straight_line)\n", + "\n", + "plot_lanechange(desired.time, desired.states, desired.inputs, yf=xf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "NLa4dbI8PWhY", + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate the system with an initial condition error\n", + "# Use t_eval to evaluate at points between inputs\n", + "actual = ct.input_output_response(\n", + " clsys, timepts, [desired.states, desired.inputs],\n", + " X0=[-3, -5, 0], t_eval=np.linspace(0, Tf, 500))\n", + "\n", + "plot_lanechange(actual.time, actual.states, actual.outputs[3:])\n", + "plot_lanechange(desired.time, desired.states, desired.inputs, yf=xf)" + ] + }, + { + "cell_type": "markdown", + "id": "TKyc2jOiWJBe", + "metadata": { + "id": "TKyc2jOiWJBe" + }, + "source": [ + "Note that the value of $\\delta$ is very large at the start. This is truncated in the model so that it does not exceed $\\pm 0.5$ rad." + ] + }, + { + "cell_type": "markdown", + "id": "6c6c4b9b", + "metadata": { + "id": "6c6c4b9b" + }, + "source": [ + "## Reference trajectory subsystem\n", + "\n", + "In addition to generating a trajectory for the system, we can also create $x_\\text{d}$ and $u_\\text{d}$ corresponding to reference inputs $r_y$ and $r_v$.\n", + "\n", + "The reference trajectory block below generates a simple trajectory for the system given the desired speed (vref) and lateral position (yref). The trajectory consists of a straight line of the form (vref * t, yref, 0) with nominal\n", + "input (vref, 0)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "significant-november", + "metadata": {}, + "outputs": [], + "source": [ + "# System state: none\n", + "# System input: vref, yref\n", + "# System output: xd, yd, thetad, vd, deltad\n", + "# System parameters: none\n", + "#\n", + "def trajgen_output(t, x, u, params):\n", + " vref, yref = u\n", + " return np.array([vref * t, yref, 0, vref, 0])\n", + "\n", + "# Define the trajectory generator as an input/output system\n", + "trajgen = ct.nlsys(\n", + " None, trajgen_output, name='trajgen',\n", + " inputs=('vref', 'yref'),\n", + " outputs=('xd', 'yd', 'thetad', 'vd', 'deltad'))\n", + "\n", + "print(trajgen)" + ] + }, + { + "cell_type": "markdown", + "id": "0w5s56uUWw-v", + "metadata": { + "id": "0w5s56uUWw-v" + }, + "source": [ + "## Step responses\n", + "\n", + "To explore the dynamics of the system, we create a set of lane changes at different forward speeds. Since the linearization depends on the speed, this means that the closed loop performance of the system will vary." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "mtGLwMQkXEzw", + "metadata": {}, + "outputs": [], + "source": [ + "steering_fixed = ct.interconnect(\n", + " [kincar, ctrl, trajgen],\n", + " inputs=['vref', 'yref'],\n", + " outputs=kincar.output_labels + kincar.input_labels\n", + ")\n", + "print(steering_fixed)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "sz7NaJTGXua1", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the simulation conditions\n", + "yref = 1\n", + "T = np.linspace(0, 5, 100)\n", + "\n", + "# Do an iteration through different speeds\n", + "for vref in [2, 5, 20]:\n", + " # Simulate the closed loop controller response\n", + " tout, yout = ct.input_output_response(\n", + " steering_fixed, T, [vref * np.ones(len(T)), yref * np.ones(len(T))],\n", + " params={'maxsteer': 1})\n", + "\n", + " # Plot the results\n", + " plot_lanechange(tout, yout, yout[3:])\n", + "\n", + "# Label the different curves\n", + "plt.subplot(3, 1, 1)\n", + "plt.legend([\"$v_d$ = \" + f\"{vref}\" for vref in [2, 10, 20]])\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "3cc26675", + "metadata": { + "id": "3cc26675" + }, + "source": [ + "## Gain scheduled controller\n", + "\n", + "For this system we use a simple schedule on the forward vehicle velocity and\n", + "place the poles of the system at fixed values. The controller takes the\n", + "current and desired vehicle position and orientation plus the velocity\n", + "velocity as inputs, and returns the velocity and steering commands.\n", + "\n", + "Linearizing the system about the desired trajectory, we obtain\n", + "\n", + "$$\n", + " \\begin{aligned}\n", + " A(x_\\text{d}) &= \\left. \\frac{\\partial f}{\\partial x} \\right|_{(x_\\text{d}, u_\\text{d})}\n", + " = \\left.\n", + " \\begin{bmatrix}\n", + " 0 & 0 & -\\sin\\theta_\\text{d}\\, v_\\text{d} \\\\ 0 & 0 & \\cos\\theta_\\text{d}\\, v_\\text{d} \\\\ 0 & 0 & 0\n", + " \\end{bmatrix}\n", + " \\right|_{(x_\\text{d}, u_\\text{d})}\n", + " = \\begin{bmatrix}\n", + " 0 & 0 & 0 \\\\ 0 & 0 & v_\\text{d} \\\\ 0 & 0 & 0\n", + " \\end{bmatrix}, \\\\\n", + " B(x_\\text{d}) &= \\left. \\frac{\\partial f}{\\partial u} \\right|_{(x_\\text{d}, u_\\text{d})}\n", + " = \\begin{bmatrix}\n", + " 1 & 0 \\\\ 0 & 0 \\\\ 0 & v_\\text{d}/l\n", + " \\end{bmatrix}.\n", + " \\end{aligned}\n", + "$$\n", + "\n", + "We see that these matrices depend only on $\\theta_\\text{d}$ and $v_\\text{d}$, so we choose these as the scheduling variables and design a controller of the form\n", + "\n", + "$$\n", + "u = u_\\text{d} - K(\\mu) (x - x_\\text{d})\n", + "$$\n", + "\n", + "where $\\mu = (\\theta_\\text{d}, v_\\text{d})$ and we interpolate the gains based on LQR controllers computed at a fixed set of points $\\mu_i$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "another-milwaukee", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the points for the scheduling variables\n", + "gs_speeds = [2, 10, 20]\n", + "gs_angles = np.linspace(-pi, pi, 4)\n", + "\n", + "# Create controllers at each scheduling point (\n", + "points = [np.array([speed, angle])\n", + " for speed in gs_speeds for angle in gs_angles]\n", + "gains = [np.array(ct.lqr(kincar.linearize(\n", + " [0, 0, angle], [speed, 0]), Qx, Qu)[0])\n", + " for speed in gs_speeds for angle in gs_angles]\n", + "print(f\"{points=}\")\n", + "print(f\"{gains=}\")\n", + "\n", + "# Create the gain scheduled system\n", + "ctrl_gs, _ = ct.create_statefbk_iosystem(\n", + " kincar, (gains, points), name='controller',\n", + " xd_labels=['xd', 'yd', 'thetad'], ud_labels=['vd', 'deltad'],\n", + " gainsched_indices=['vd', 'theta'], gainsched_method='linear')\n", + "print(ctrl_gs)" + ] + }, + { + "cell_type": "markdown", + "id": "4ca5ab53", + "metadata": { + "id": "4ca5ab53" + }, + "source": [ + "## System construction\n", + "\n", + "The input to the full closed loop system is the desired lateral position and the desired forward velocity. The output for the system is taken as the full vehicle state plus the velocity of the vehicle.\n", + "\n", + "We construct the system using the `ct.interconnect` function and use signal labels to keep track of everything. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "editorial-satisfaction", + "metadata": {}, + "outputs": [], + "source": [ + "steering_gainsched = ct.interconnect(\n", + " [trajgen, ctrl_gs, kincar], name='steering',\n", + " inputs=['vref', 'yref'],\n", + " outputs=kincar.output_labels + kincar.input_labels\n", + ")\n", + "print(steering_gainsched)" + ] + }, + { + "cell_type": "markdown", + "id": "47f5d528", + "metadata": { + "id": "47f5d528" + }, + "source": [ + "## System simulation\n", + "\n", + "We now simulate the gain scheduled controller for a step input in the $y$ position, using a range of vehicle speeds $v_\\text{d}$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "smoking-trail", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the reference trajectory for the y position\n", + "# plt.plot([0, 5], [yref, yref], 'k-', linewidth=0.6)\n", + "\n", + "# Find the signals we want to plot\n", + "y_index = steering_gainsched.find_output('y')\n", + "v_index = steering_gainsched.find_output('v')\n", + "\n", + "# Do an iteration through different speeds\n", + "for vref in [2, 5, 20]:\n", + " # Simulate the closed loop controller response\n", + " tout, yout = ct.input_output_response(\n", + " steering_gainsched, T, [vref * np.ones(len(T)), yref * np.ones(len(T))],\n", + " X0=[0, 0, 0], params={'maxsteer': 0.5}\n", + " )\n", + "\n", + " # Plot the results\n", + " plot_lanechange(tout, yout, yout[3:])\n", + "\n", + "# Label the different curves\n", + "plt.subplot(3, 1, 1)\n", + "plt.legend([\"$v_d$ = \" + f\"{vref}\" for vref in [2, 10, 20]])\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f571b2b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds110-L6c_doubleint-rhc.ipynb b/examples/cds110-L6c_doubleint-rhc.ipynb new file mode 100644 index 000000000..2999ff3ef --- /dev/null +++ b/examples/cds110-L6c_doubleint-rhc.ipynb @@ -0,0 +1,651 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9d41c333", + "metadata": { + "id": "9d41c333" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 6c

\n", + "

Receding Horizon Control of a Double Integrator with Bounded Input

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/1AufRjpbdKcOEoWO5NEiczF3C8Rc4JuTL)\n", + "\n", + "To illustrate the implementation of a receding horizon controller, we consider a linear system corresponding to a double integrator with bounded input:\n", + "\n", + "$$\n", + " \\dot x = \\begin{bmatrix} 0 & 1 \\\\ 0 & 0 \\end{bmatrix} x + \\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix} \\text{clip}(u)\n", + " \\qquad\\text{where}\\qquad\n", + " \\text{clip}(u) = \\begin{cases}\n", + " -1 & u < -1, \\\\\n", + " u & -1 \\leq u \\leq 1, \\\\\n", + " 1 & u > 1.\n", + " \\end{cases}\n", + "$$\n", + "\n", + "We implement a model predictive controller by choosing\n", + "\n", + "$$\n", + " Q_x = \\begin{bmatrix} 1 & 0 \\\\ 0 & 0 \\end{bmatrix}, \\qquad\n", + " Q_u = \\begin{bmatrix} 1 \\end{bmatrix}, \\qquad\n", + " P_1 = \\begin{bmatrix} 0.1 & 0 \\\\ 0 & 0.1 \\end{bmatrix}.\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fe0af7f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy as sp\n", + "import matplotlib.pyplot as plt\n", + "import time\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct\n", + "import control.optimal as opt\n", + "import control.flatsys as fs" + ] + }, + { + "cell_type": "markdown", + "id": "4c695f81", + "metadata": { + "id": "4c695f81" + }, + "source": [ + "## System definition\n", + "\n", + "The system is defined as a double integrator with bounded input." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c01f571", + "metadata": {}, + "outputs": [], + "source": [ + "def doubleint_update(t, x, u, params):\n", + " # Get the parameters\n", + " lb = params.get('lb', -1)\n", + " ub = params.get('ub', 1)\n", + " assert lb < ub\n", + "\n", + " # bound the input\n", + " u_clip = np.clip(u, lb, ub)\n", + "\n", + " return np.array([x[1], u_clip[0]])\n", + "\n", + "proc = ct.nlsys(\n", + " doubleint_update, None, name=\"double integrator\",\n", + " inputs = ['u'], outputs=['x[0]', 'x[1]'], states=2)" + ] + }, + { + "cell_type": "markdown", + "id": "6c2f0d00", + "metadata": { + "id": "6c2f0d00" + }, + "source": [ + "## Receding horizon controller\n", + "\n", + "To define a receding horizon controller, we create an optimal control problem (using the `OptimalControlProblem` class) and then use the `compute_trajectory` method to solve for the trajectory from the current state.\n", + "\n", + "We start by defining the cost functions, which consists of a trajectory cost and a terminal cost:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a501efef", + "metadata": {}, + "outputs": [], + "source": [ + "Qx = np.diag([1, 0]) # state cost\n", + "Qu = np.diag([1]) # input cost\n", + "traj_cost=opt.quadratic_cost(proc, Qx, Qu)\n", + "\n", + "P1 = np.diag([0.1, 0.1]) # terminal cost\n", + "term_cost = opt.quadratic_cost(proc, P1, None)" + ] + }, + { + "cell_type": "markdown", + "id": "c5470629", + "metadata": { + "id": "c5470629" + }, + "source": [ + "We also set up a set of constraints the correspond to the fact that the input should have magnitude 1. This can be done using either the [`input_range_constraint`](https://python-control.readthedocs.io/en/0.9.3.post2/generated/control.optimal.input_range_constraint.html) function or the [`input_poly_constraint`](https://python-control.readthedocs.io/en/0.9.3.post2/generated/control.optimal.input_poly_constraint.html) function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb4c511a", + "metadata": {}, + "outputs": [], + "source": [ + "traj_constraints = opt.input_range_constraint(proc, -1, 1)\n", + "# traj_constraints = opt.input_poly_constraint(\n", + "# proc, np.array([[1], [-1]]), np.array([1, 1]))" + ] + }, + { + "cell_type": "markdown", + "id": "a5568374", + "metadata": { + "id": "a5568374" + }, + "source": [ + "We define the horizon for evaluating finite-time, optimal control by setting up a set of time points across the designed horizon. The input will be computed at each time point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9edec673", + "metadata": {}, + "outputs": [], + "source": [ + "Th = 5\n", + "timepts = np.linspace(0, Th, 11, endpoint=True)\n", + "print(timepts)" + ] + }, + { + "cell_type": "markdown", + "id": "cb8fcecc", + "metadata": { + "id": "cb8fcecc" + }, + "source": [ + "Finally, we define the optimal control problem that we want to solve (without actually solving it)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9f31be6", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the optimal control problem\n", + "ocp = opt.OptimalControlProblem(\n", + " proc, timepts, traj_cost,\n", + " terminal_cost=term_cost,\n", + " trajectory_constraints=traj_constraints,\n", + " # terminal_constraints=term_constraints,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ee9a39dd", + "metadata": { + "id": "ee9a39dd" + }, + "source": [ + "To make sure that the problem is properly defined, we solve the problem for a specific initial condition. We also compare the amount of time required to solve the problem from a \"cold start\" (no initial guess) versus a \"warm start\" (use the previous solution, shifted forward on point in time)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "887295eb", + "metadata": {}, + "outputs": [], + "source": [ + "X0 = np.array([1, 1])\n", + "\n", + "start_time = time.process_time()\n", + "res = ocp.compute_trajectory(X0, initial_guess=0, return_states=True)\n", + "stop_time = time.process_time()\n", + "print(f'* Cold start: {stop_time-start_time:.3} sec')\n", + "\n", + "# Resolve using previous solution (shifted forward) as initial guess to compare timing\n", + "start_time = time.process_time()\n", + "u = res.inputs\n", + "u_shift = np.hstack([u[:, 1:], u[:, -1:]])\n", + "ocp.compute_trajectory(X0, initial_guess=u_shift, print_summary=False)\n", + "stop_time = time.process_time()\n", + "print(f'* Warm start: {stop_time-start_time:.3} sec')" + ] + }, + { + "cell_type": "markdown", + "id": "115dec26", + "metadata": { + "id": "115dec26" + }, + "source": [ + "(In this case the timing is not that different since the system is very simple.)\n", + "\n", + "Plotting the result, we see that the solution is properly computed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b98e773", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(res.time, res.states[0], 'k-', label='$x_1$')\n", + "plt.plot(res.time, res.inputs[0], 'b-', label='u')\n", + "plt.xlabel('Time [s]')\n", + "plt.ylabel('$x_1$, $u$')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "0e85981a", + "metadata": { + "id": "0e85981a" + }, + "source": [ + "We implement the receding horizon controller using a function that we can use with different versions of the problem." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb2e8126", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a figure to use for plotting\n", + "def run_rhc_and_plot(\n", + " proc, ocp, X0, Tf, print_summary=False, verbose=False, ax=None, plot=True):\n", + " # Start at the initial point\n", + " x = X0\n", + "\n", + " # Initialize the axes\n", + " if plot and ax is None:\n", + " ax = plt.axes()\n", + "\n", + " # Initialize arrays to store the final trajectory\n", + " time_, inputs_, outputs_, states_ = [], [], [], []\n", + "\n", + " # Generate the individual traces for the receding horizon control\n", + " for t in ocp.timepts:\n", + " # Compute the optimal trajectory over the horizon\n", + " start_time = time.process_time()\n", + " res = ocp.compute_trajectory(x, print_summary=print_summary)\n", + " if verbose:\n", + " print(f\"{t=}: comp time = {time.process_time() - start_time:0.3}\")\n", + "\n", + " # Simulate the system for the update time, with higher res for plotting\n", + " tvec = np.linspace(0, res.time[1], 20)\n", + " inputs = res.inputs[:, 0] + np.outer(\n", + " (res.inputs[:, 1] - res.inputs[:, 0]) / (tvec[-1] - tvec[0]), tvec)\n", + " soln = ct.input_output_response(proc, tvec, inputs, x)\n", + "\n", + " # Save this segment for later use (final point will appear in next segment)\n", + " time_.append(t + soln.time[:-1])\n", + " inputs_.append(soln.inputs[:, :-1])\n", + " outputs_.append(soln.outputs[:, :-1])\n", + " states_.append(soln.states[:, :-1])\n", + "\n", + " if plot:\n", + " # Plot the results over the full horizon\n", + " h3, = ax.plot(t + res.time, res.states[0], 'k--', linewidth=0.5)\n", + " ax.plot(t + res.time, res.inputs[0], 'b--', linewidth=0.5)\n", + "\n", + " # Plot the results for this time segment\n", + " h1, = ax.plot(t + soln.time, soln.states[0], 'k-')\n", + " h2, = ax.plot(t + soln.time, soln.inputs[0], 'b-')\n", + "\n", + " # Update the state to use for the next time point\n", + " x = soln.states[:, -1]\n", + "\n", + " # Append the final point to the response\n", + " time_.append(t + soln.time[-1:])\n", + " inputs_.append(soln.inputs[:, -1:])\n", + " outputs_.append(soln.outputs[:, -1:])\n", + " states_.append(soln.states[:, -1:])\n", + "\n", + " # Label the plot\n", + " if plot:\n", + " # Adjust the limits for consistency\n", + " ax.set_ylim([-4, 3.5])\n", + "\n", + " # Add reference line for input lower bound\n", + " ax.plot([0, 7], [-1, -1], 'k--', linewidth=0.666)\n", + "\n", + " # Label the results\n", + " ax.set_xlabel(\"Time $t$ [sec]\")\n", + " ax.set_ylabel(\"State $x_1$, input $u$\")\n", + " ax.legend(\n", + " [h1, h2, h3], ['$x_1$', '$u$', 'prediction'],\n", + " loc='lower right', labelspacing=0)\n", + " plt.tight_layout()\n", + "\n", + " # Append\n", + " return ct.TimeResponseData(\n", + " np.hstack(time_), np.hstack(outputs_), np.hstack(states_), np.hstack(inputs_))" + ] + }, + { + "cell_type": "markdown", + "id": "be13e00a", + "metadata": { + "id": "be13e00a" + }, + "source": [ + "Finally, we call the controller and plot the response. The solid lines show the portions of the trajectory that we follow. The dashed lines are the trajectory over the full horizon, but which are not followed since we update the computation at each time step. (To get rid of the statistics of each optimization call, use `print_summary=False`.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "305a1127", + "metadata": {}, + "outputs": [], + "source": [ + "Tf = 10\n", + "rhc_resp = run_rhc_and_plot(proc, ocp, X0, Tf, verbose=True, print_summary=False)\n", + "print(f\"xf = {rhc_resp.states[:, -1]}\")" + ] + }, + { + "cell_type": "markdown", + "id": "6005bfb3", + "metadata": { + "id": "6005bfb3" + }, + "source": [ + "## RHC vs LQR vs LQR terminal cost\n", + "\n", + "In the example above, we used a receding horizon controller with the terminal cost as $P_1 = \\text{diag}(0.1, 0.1)$. An alternative is to set the terminal cost to be the LQR terminal cost that goes along with the trajectory cost, which then provides a \"cost to go\" that matches the LQR \"cost to go\" (but keeping in mind that the LQR controller does not necessarily respect the constraints).\n", + "\n", + "The following code compares the original RHC formulation with a receding horizon controller using an LQR terminal cost versus an LQR controller." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea2de1f3", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the LQR solution\n", + "K, P_lqr, E = ct.lqr(proc.linearize(0, 0), Qx, Qu)\n", + "print(f\"P_lqr = \\n{P_lqr}\")\n", + "\n", + "# Create an LQR controller (and run it)\n", + "lqr_ctrl, lqr_clsys = ct.create_statefbk_iosystem(proc, K)\n", + "lqr_resp = ct.input_output_response(lqr_clsys, rhc_resp.time, 0, X0)\n", + "\n", + "# Create a new optimal control problem using the LQR terminal cost\n", + "# (need use more refined time grid as well, to approximate LQR rate)\n", + "lqr_timepts = np.linspace(0, Th, 25, endpoint=True)\n", + "lqr_term_cost=opt.quadratic_cost(proc, P_lqr, None)\n", + "ocp_lqr = opt.OptimalControlProblem(\n", + " proc, lqr_timepts, traj_cost, terminal_cost=lqr_term_cost,\n", + " trajectory_constraints=traj_constraints,\n", + ")\n", + "\n", + "# Create the response for the new controller\n", + "rhc_lqr_resp = run_rhc_and_plot(\n", + " proc, ocp_lqr, X0, 10, plot=False, print_summary=False)\n", + "\n", + "# Plot the different responses to compare them\n", + "fig, ax = plt.subplots(2, 1)\n", + "ax[0].plot(rhc_resp.time, rhc_resp.states[0], label='RHC + P_1')\n", + "ax[0].plot(rhc_lqr_resp.time, rhc_lqr_resp.states[0], '--', label='RHC + P_lqr')\n", + "ax[0].plot(lqr_resp.time, lqr_resp.outputs[0], ':', label='LQR')\n", + "ax[0].legend()\n", + "\n", + "ax[1].plot(rhc_resp.time, rhc_resp.inputs[0], label='RHC + P_1')\n", + "ax[1].plot(rhc_lqr_resp.time, rhc_lqr_resp.inputs[0], '--', label='RHC + P_lqr')\n", + "ax[1].plot(lqr_resp.time, lqr_resp.outputs[2], ':', label='LQR')" + ] + }, + { + "cell_type": "markdown", + "id": "9497530b", + "metadata": { + "id": "9497530b" + }, + "source": [ + "## Discrete time RHC\n", + "\n", + "Many receding horizon control problems are solved based on a discrete-time model. We show here how to implement this for a \"double integrator\" system, which in discrete time has the form\n", + "\n", + "$$\n", + " x[k+1] = \\begin{bmatrix} 1 & 1 \\\\ 0 & 1 \\end{bmatrix} x[k] + \\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix} \\text{clip}(u[k])\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae7cefa5", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# System definition\n", + "#\n", + "\n", + "def doubleint_update(t, x, u, params):\n", + " # Get the parameters\n", + " lb = params.get('lb', -1)\n", + " ub = params.get('ub', 1)\n", + " assert lb < ub\n", + "\n", + " # Get the sampling time\n", + " dt = params.get('dt', 1)\n", + "\n", + " # bound the input\n", + " u_clip = np.clip(u, lb, ub)\n", + "\n", + " return np.array([x[0] + dt * x[1], x[1] + dt * u_clip[0]])\n", + "\n", + "proc = ct.nlsys(\n", + " doubleint_update, None, name=\"double integrator\",\n", + " inputs = ['u'], outputs=['x[0]', 'x[1]'], states=2,\n", + " params={'dt': 1}, dt=1)\n", + "\n", + "#\n", + "# Linear quadratic regulator\n", + "#\n", + "\n", + "# Define the cost functions to use\n", + "Qx = np.diag([1, 0]) # state cost\n", + "Qu = np.diag([1]) # input cost\n", + "P1 = np.diag([0.1, 0.1]) # terminal cost\n", + "\n", + "# Get the LQR solution\n", + "K, P, E = ct.dlqr(proc.linearize(0, 0), Qx, Qu)\n", + "\n", + "# Test out the LQR controller, with no constraints\n", + "linsys = proc.linearize(0, 0)\n", + "clsys_lin = ct.ss(linsys.A - linsys.B @ K, linsys.B, linsys.C, 0, dt=proc.dt)\n", + "\n", + "X0 = np.array([2, 1]) # initial conditions\n", + "Tf = 10 # simulation time\n", + "res = ct.initial_response(clsys_lin, Tf, X0=X0)\n", + "\n", + "# Plot the results\n", + "plt.figure(1); plt.clf(); ax = plt.axes()\n", + "ax.plot(res.time, res.states[0], 'k-', label='$x_1$')\n", + "ax.plot(res.time, (-K @ res.states)[0], 'b-', label='$u$')\n", + "\n", + "# Test out the LQR controller with constraints\n", + "clsys_lqr = ct.feedback(proc, -K, 1)\n", + "tvec = np.arange(0, Tf, proc.dt)\n", + "res_lqr_const = ct.input_output_response(clsys_lqr, tvec, 0, X0)\n", + "\n", + "# Plot the results\n", + "ax.plot(res_lqr_const.time, res_lqr_const.states[0], 'k--', label='constrained')\n", + "ax.plot(res_lqr_const.time, (-K @ res_lqr_const.states)[0], 'b--')\n", + "ax.plot([0, 7], [-1, -1], 'k--', linewidth=0.75)\n", + "\n", + "# Adjust the limits for consistency\n", + "ax.set_ylim([-4, 3.5])\n", + "\n", + "# Label the results\n", + "ax.set_xlabel(\"Time $t$ [sec]\")\n", + "ax.set_ylabel(\"State $x_1$, input $u$\")\n", + "ax.legend(loc='lower right', labelspacing=0)\n", + "plt.title(\"Linearized LQR response from x0\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13cfc5d8", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# Receding horizon controller\n", + "#\n", + "\n", + "# Create the constraints\n", + "traj_constraints = opt.input_range_constraint(proc, -1, 1)\n", + "term_constraints = opt.state_range_constraint(proc, [0, 0], [0, 0])\n", + "\n", + "# Define the optimal control problem we want to solve\n", + "T = 5\n", + "timepts = np.arange(0, T * proc.dt, proc.dt)\n", + "\n", + "# Set up the optimal control problems\n", + "ocp_orig = opt.OptimalControlProblem(\n", + " proc, timepts,\n", + " opt.quadratic_cost(proc, Qx, Qu),\n", + " trajectory_constraints=traj_constraints,\n", + " terminal_cost=opt.quadratic_cost(proc, P1, None),\n", + ")\n", + "\n", + "ocp_lqr = opt.OptimalControlProblem(\n", + " proc, timepts,\n", + " opt.quadratic_cost(proc, Qx, Qu),\n", + " trajectory_constraints=traj_constraints,\n", + " terminal_cost=opt.quadratic_cost(proc, P, None),\n", + ")\n", + "\n", + "ocp_low = opt.OptimalControlProblem(\n", + " proc, timepts,\n", + " opt.quadratic_cost(proc, Qx, Qu),\n", + " trajectory_constraints=traj_constraints,\n", + " terminal_cost=opt.quadratic_cost(proc, P/10, None),\n", + ")\n", + "\n", + "ocp_high = opt.OptimalControlProblem(\n", + " proc, timepts,\n", + " opt.quadratic_cost(proc, Qx, Qu),\n", + " trajectory_constraints=traj_constraints,\n", + " terminal_cost=opt.quadratic_cost(proc, P*10, None),\n", + ")\n", + "weight_list = [P1, P, P/10, P*10]\n", + "ocp_list = [ocp_orig, ocp_lqr, ocp_low, ocp_high]\n", + "\n", + "# Do a test run to figure out how long computation takes\n", + "start_time = time.process_time()\n", + "ocp_lqr.compute_trajectory(X0)\n", + "stop_time = time.process_time()\n", + "print(\"* Process time: %0.2g s\\n\" % (stop_time - start_time))\n", + "\n", + "# Create a figure to use for plotting\n", + "fig, [[ax_orig, ax_lqr], [ax_low, ax_high]] = plt.subplots(2, 2)\n", + "ax_list = [ax_orig, ax_lqr, ax_low, ax_high]\n", + "ax_name = ['orig', 'lqr', 'low', 'high']\n", + "\n", + "# Generate the individual traces for the receding horizon control\n", + "for ocp, ax, name, Pf in zip(ocp_list, ax_list, ax_name, weight_list):\n", + " x, t = X0, 0\n", + " for i in np.arange(0, Tf, proc.dt):\n", + " # Calculate the optimal trajectory\n", + " res = ocp.compute_trajectory(x, print_summary=False)\n", + " soln = ct.input_output_response(proc, res.time, res.inputs, x)\n", + "\n", + " # Plot the results for this time instant\n", + " ax.plot(res.time[:2] + t, res.inputs[0, :2], 'b-', linewidth=1)\n", + " ax.plot(res.time[:2] + t, soln.outputs[0, :2], 'k-', linewidth=1)\n", + "\n", + " # Plot the results projected forward\n", + " ax.plot(res.time[1:] + t, res.inputs[0, 1:], 'b--', linewidth=0.75)\n", + " ax.plot(res.time[1:] + t, soln.outputs[0, 1:], 'k--', linewidth=0.75)\n", + "\n", + " # Update the state to use for the next time point\n", + " x = soln.states[:, 1]\n", + " t += proc.dt\n", + "\n", + " # Adjust the limits for consistency\n", + " ax.set_ylim([-1.5, 3.5])\n", + "\n", + " # Label the results\n", + " ax.set_xlabel(\"Time $t$ [sec]\")\n", + " ax.set_ylabel(\"State $x_1$, input $u$\")\n", + " ax.set_title(f\"MPC response for {name}\")\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "015dc953", + "metadata": { + "id": "015dc953" + }, + "source": [ + "We can also implement a receding horizon controller for a discrete-time system using `opt.create_mpc_iosystem`. This creates a controller that accepts the current state as the input and generates the control to apply from that state." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f8bb594", + "metadata": {}, + "outputs": [], + "source": [ + "# Construct using create_mpc_iosystem\n", + "clsys = opt.create_mpc_iosystem(\n", + " proc, timepts, opt.quadratic_cost(proc, Qx, Qu), traj_constraints,\n", + " terminal_cost=opt.quadratic_cost(proc, P1, None),\n", + ")\n", + "print(clsys)" + ] + }, + { + "cell_type": "markdown", + "id": "f1b08fb4", + "metadata": { + "id": "f1b08fb4" + }, + "source": [ + "(This function needs some work to be more user-friendly, e.g. renaming of the inputs and outputs.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2afd287", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds110-L7_bode-nyquist.ipynb b/examples/cds110-L7_bode-nyquist.ipynb new file mode 100644 index 000000000..6e9f63337 --- /dev/null +++ b/examples/cds110-L7_bode-nyquist.ipynb @@ -0,0 +1,856 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "8c577d78-3e4a-4f08-93ed-5c60867b9a3b", + "metadata": { + "id": "hairy-humidity" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 7

\n", + "

Frequency Domain Analysis using Bode/Nyquist plots

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/1-BIaln1nF41fGqavzliuWT74nBkAnM3x)\n", + "\n", + "The purpose of this lecture is to introduce tools that can be used for frequency domain modeling and analysis of linear systems. It illustrates the use of a variety of frequency domain analysis and plotting tools." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "invalid-carnival", + "metadata": {}, + "outputs": [], + "source": [ + "# Import standard packages needed for this exercise\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import math\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct\n", + "\n", + "# Use ctrlplot defaults for matplotlib\n", + "plt.rcParams.update(ct.rcParams)" + ] + }, + { + "cell_type": "markdown", + "id": "P7t3Nm4Tre2Z", + "metadata": { + "id": "P7t3Nm4Tre2Z" + }, + "source": [ + "## Stable system: servomechanism\n", + "\n", + "We start with a simple example a stable system for which we wish to design a simple controller and analyze its performance, demonstrating along the way the basic frequency domain analysis functions in the Python control toolbox (python-control).\n", + "\n", + "Consider a simple mechanism for positioning a mechanical arm whose equations of motion are given by\n", + "\n", + "$$\n", + "J \\ddot \\theta = -b \\dot\\theta - k r\\sin\\theta + \\tau_\\text{m},\n", + "$$\n", + "\n", + "which can be written in state space form as\n", + "\n", + "$$\n", + "\\frac{d}{dt} \\begin{bmatrix} \\theta \\\\ \\theta \\end{bmatrix} =\n", + " \\begin{bmatrix} \\dot\\theta \\\\ -k r \\sin\\theta / J - b\\dot\\theta / J \\end{bmatrix}\n", + " + \\begin{bmatrix} 0 \\\\ 1/J \\end{bmatrix} \\tau_\\text{m}.\n", + "$$\n", + "\n", + "The system consists of a spring loaded arm that is driven by a motor, as shown below.\n", + "\n", + "
\"servomech-diagram\"
\n", + "\n", + "The motor applies a torque that twists the arm against a linear spring and moves the end of the arm across a rotating platter. The input to the system is the motor torque $\\tau_\\text{m}$. The force exerted by the spring is a nonlinear function of the head position due to the way it is attached.\n", + "\n", + "The system parameters are given by\n", + "\n", + "$$\n", + "k = 1,\\quad J = 100,\\quad b = 10,\n", + "\\quad r = 1,\\quad l = 2,\\quad \\epsilon = 0.01,\n", + "$$\n", + "\n", + "and we assume that time is measured in msec and distance in cm. (The constants here are made up and don't necessarily reflect a real disk drive, though the units and time constants are motivated by computer disk drives.)" + ] + }, + { + "cell_type": "markdown", + "id": "3e476db9", + "metadata": { + "id": "3e476db9" + }, + "source": [ + "The system dynamics can be modeled in python-control using a `NonlinearIOSystem` object, which we create with the `nlsys` function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "27bb3c38", + "metadata": {}, + "outputs": [], + "source": [ + "# Parameter values\n", + "servomech_params = {\n", + " 'J': 100, # Moment of inertia of the motor\n", + " 'b': 10, # Angular damping of the arm\n", + " 'k': 1, # Spring constant\n", + " 'r': 1, # Location of spring contact on arm\n", + " 'l': 2, # Distance to the read head\n", + " 'eps': 0.01, # Magnitude of velocity-dependent perturbation\n", + "}\n", + "\n", + "# State derivative\n", + "def servomech_update(t, x, u, params):\n", + " # Extract the configuration and velocity variables from the state vector\n", + " theta = x[0] # Angular position of the disk drive arm\n", + " thetadot = x[1] # Angular velocity of the disk drive arm\n", + " tau = u[0] # Torque applied at the base of the arm\n", + "\n", + " # Get the parameter values\n", + " J, b, k, r = map(params.get, ['J', 'b', 'k', 'r'])\n", + "\n", + " # Compute the angular acceleration\n", + " dthetadot = 1/J * (\n", + " -b * thetadot - k * r * np.sin(theta) + tau)\n", + "\n", + " # Return the state update law\n", + " return np.array([thetadot, dthetadot])\n", + "\n", + "# System output (end of arm)\n", + "def servomech_output(t, x, u, params):\n", + " l = params['l']\n", + " return np.array([l * x[0]])\n", + "\n", + "# System dynamics\n", + "servomech = ct.nlsys(\n", + " servomech_update, servomech_output, name='servomech',\n", + " params=servomech_params,\n", + " states=['theta_', 'thdot_'],\n", + " outputs=['y'], inputs=['tau'])\n", + "\n", + "print(servomech)\n", + "print(\"\\nParams:\", servomech.params)" + ] + }, + { + "cell_type": "markdown", + "id": "competitive-terrain", + "metadata": { + "id": "competitive-terrain" + }, + "source": [ + "### Linearization\n", + "\n", + "To study the open loop dynamics of the system, we compute the linearization of the dynamics about the equilibrium point corresponding to $\\theta_\\text{e} = 15^\\circ$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "senior-carpet", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the equilibrium angle to radians\n", + "theta_e = (15 / 180) * np.pi\n", + "\n", + "# Compute the input required to hold this position\n", + "u_e = servomech.params['k'] * servomech.params['r'] * np.sin(theta_e)\n", + "print(\"Equilibrium torque = %g\" % u_e)\n", + "\n", + "# Linearize the system about the equilibrium point\n", + "P = servomech.linearize([theta_e, 0], u_e, name='P_ss')\n", + "P.name = 'P_ss' # TODO: fix in nlsys_improvements\n", + "print(\"Linearized dynamics:\", P)\n", + "print(\"Zeros: \", P.zeros())\n", + "print(\"Poles: \", P.poles())\n", + "print(\"\")\n", + "\n", + "# Transfer function representation\n", + "P_tf = ct.tf(P, name='P_tf')\n", + "print(P_tf)" + ] + }, + { + "cell_type": "markdown", + "id": "instant-lancaster", + "metadata": { + "id": "instant-lancaster" + }, + "source": [ + "### Open loop frequency response\n", + "\n", + "A standard method for understanding the dynamics is to plot the output of the system in response to sinusoids with unit magnitude at different frequencies.\n", + "\n", + "We use the `frequency_response` function to plot the step response of the linearized, open-loop system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "RxXFTpwO5bGI", + "metadata": {}, + "outputs": [], + "source": [ + "# Reset the frequency response label to correspond to a time unit of ms\n", + "ct.set_defaults('freqplot', freq_label=\"Frequency [rad/ms]\")\n", + "\n", + "# Frequency response\n", + "freqresp = ct.frequency_response(P, np.logspace(-2, 0))\n", + "freqresp.plot()\n", + "\n", + "# Equivalent command\n", + "ct.bode_plot(P_tf, np.logspace(-2, 0), '--')" + ] + }, + { + "cell_type": "markdown", + "id": "stuffed-premiere", + "metadata": { + "id": "stuffed-premiere" + }, + "source": [ + "### Feedback control design\n", + "\n", + "We next design a feedback controller for the system using a proportional integral controller, which has transfer function\n", + "\n", + "$$\n", + "C(s) = \\frac{k_\\text{p} s + k_\\text{i}}{s}\n", + "$$\n", + "\n", + "We will learn how to choose $k_\\text{p}$ and $k_\\text{i}$ more formally in W9. For now we just pick different values to see how the dynamics are impacted." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8NK8O6XT7B_a", + "metadata": {}, + "outputs": [], + "source": [ + "kp = 1\n", + "ki = 1\n", + "\n", + "# Create tf from numerator/denominator coefficients\n", + "C = ct.tf([kp, ki], [1, 0], name='C')\n", + "print(C)\n", + "\n", + "# Alternative method: define \"s\" and use algebra\n", + "s = ct.tf('s')\n", + "C = ct.tf(kp + ki/s, name='C')\n", + "print(C)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "074427a3", + "metadata": {}, + "outputs": [], + "source": [ + "# Loop transfer function\n", + "L = P * C\n", + "cplt = ct.bode_plot([P, C, L], label=['P', 'C', 'L'])\n", + "cplt.set_plot_title(\"PI controller for servomechanism\")" + ] + }, + { + "cell_type": "markdown", + "id": "Bg5ga11VuRtI", + "metadata": { + "id": "Bg5ga11VuRtI" + }, + "source": [ + "Note that L = P * C corresponds to addition in both the magnitude and the phase." + ] + }, + { + "cell_type": "markdown", + "id": "UmYmSzx2rTfg", + "metadata": { + "id": "UmYmSzx2rTfg" + }, + "source": [ + "### Nyquist analysis\n", + "\n", + "To check stability (and eventually robustness), we use the Nyquist criterion." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "Qmp59pmS9GLj", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[7, 4])\n", + "ax1 = plt.subplot(2, 2, 1)\n", + "ax2 = plt.subplot(2, 2, 3)\n", + "ct.bode_plot(L, ax=[ax1, ax2])\n", + "\n", + "# Tidy up the figure a bit\n", + "fig.align_labels()\n", + "ax1.set_title(\"Bode plot for L\")\n", + "\n", + "ax2 = plt.subplot(1, 2, 2)\n", + "ct.nyquist_plot(L, ax=ax2, title=\"\")\n", + "plt.title(\"Nyquist plot for L\")\n", + "\n", + "plt.suptitle(\"Loop analysis for (unstable) servomechanism\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "s4dDf4PrZqU3", + "metadata": { + "id": "s4dDf4PrZqU3" + }, + "source": [ + "We see from this plot that the loop transfer function encircles the -1 point => closed loop system should be unstable. We can check this by making use of additional features of Nyquist analysis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "K7ifUBL0Z3xN", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the Nyquist *response*, so that we can get back encirclements\n", + "nyqresp = ct.nyquist_response(L)\n", + "print(\"N = encirclements: \", nyqresp.count)\n", + "print(\"P = RHP poles of L: \", np.sum(np.real(L.poles()) > 0))\n", + "print(\"Z = N + P = RHP zeros of 1 + L:\", np.sum(np.real((1 + L).zeros()) > 0))\n", + "print(\"Zeros of (1 + L) = \", (1 + L).zeros())\n", + "print(\"\")\n", + "\n", + "T = ct.feedback(L)\n", + "ct.step_response(T).plot(\n", + " title=\"Step response for (unstable) servomechanism\",\n", + " time_label=\"Time [ms]\");" + ] + }, + { + "cell_type": "markdown", + "id": "p3JxLilMxdOE", + "metadata": { + "id": "p3JxLilMxdOE" + }, + "source": [ + "### Poles on the $j\\omega$ axis\n", + "\n", + "Note that we have a pole at 0 (due to the integrator in the controller). How is this handled?\n", + "\n", + "A: use a small loop to the right around poles on the $j\\omega$ axis => not inside the contour.\n", + "\n", + "To see this, we use the `nyquist_response` function, which returns the contour used to compute the Nyquist curve. If we zoom in on the contour near the origin, we see how the outer edge of the Nyquist curve is computed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "R5IBk3Ai9Slk", + "metadata": {}, + "outputs": [], + "source": [ + "fig = plt.figure(figsize=[7, 5.8])\n", + "\n", + "# Plot the D contour\n", + "ax1 = plt.subplot(2, 2, 1)\n", + "plt.plot(np.real(nyqresp.contour), np.imag(nyqresp.contour))\n", + "plt.axis([-1e-4, 4e-4, 0, 4e-4])\n", + "plt.xlabel('Real axis')\n", + "plt.ylabel('Imaginary axis')\n", + "plt.title(\"Zoom on D-contour\")\n", + "\n", + "# Clean up the display of the units\n", + "from matplotlib import ticker\n", + "ax1.xaxis.set_major_formatter(ticker.StrMethodFormatter(\"{x:.0e}\"))\n", + "ax1.yaxis.set_major_formatter(ticker.StrMethodFormatter(\"{x:.0e}\"))\n", + "\n", + "ax2 = plt.subplot(2, 2, 2)\n", + "ct.nyquist_plot(L, ax=ax2)\n", + "plt.title(\"Nyquist curve\")\n", + "\n", + "plt.suptitle(\"Nyquist contour for pole at the origin\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "h20JRZ_r4fGy", + "metadata": { + "id": "h20JRZ_r4fGy" + }, + "source": [ + "### Second iteration feedback control design\n", + "\n", + "We now redesign the control system to give something that is stable. We can do this by moving the zero for the controller to a lower frequency, so that the phase lag from the integrator does not overlap with the phase lag from the system dynamics." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "YsM8SnXz_Kaj", + "metadata": {}, + "outputs": [], + "source": [ + "# Change the frequency response to avoid crossing over -180 with large gain\n", + "Cnew = ct.tf(kp + (ki/200)/s, name='C_new')\n", + "Lnew = ct.tf(P * Cnew, name='L_new')\n", + "\n", + "plt.figure(figsize=[7, 4])\n", + "ax1 = plt.subplot(2, 2, 1)\n", + "ax2 = plt.subplot(2, 2, 3)\n", + "ct.bode_plot([Lnew, L], ax=[ax1, ax2], label=['L_new', 'L_old'])\n", + "\n", + "# Clean up the figure a bit\n", + "ax1.loglog([1e-3, 1e1], [1, 1], 'k', linewidth=0.5)\n", + "ax1.set_title(\"Bode plot for L_new, L_old\", size='medium')\n", + "\n", + "ax3=plt.subplot(1, 2, 2)\n", + "ct.nyquist_plot(Lnew, max_curve_magnitude=5, ax=ax3)\n", + "ax3.set_title(\"Nyquist plot for Lnew\", size='medium')\n", + "\n", + "plt.suptitle(\"Loop analysis for (stable) servomechanism\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "kFjeGXzDvucx", + "metadata": { + "id": "kFjeGXzDvucx" + }, + "source": [ + "We see now that we have no encirclements, and so the system should be stable.\n", + "\n", + "Note however that the Nyquist curve is close to the -1 point => not *that* stable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "GGfJwG716jU2", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the transfer function from r to y\n", + "Tnew = ct.feedback(Lnew)\n", + "cplt = ct.step_response(Tnew).plot(time_label=\"Time [ms]\")\n", + "cplt.set_plot_title(\"Step response for (stable) spring-mass system\")" + ] + }, + { + "cell_type": "markdown", + "id": "b5114fa7-6924-47d7-8dd2-f12060152edd", + "metadata": {}, + "source": [ + "### Third iteration feedback control design (via loop shaping)\n", + "\n", + "To get a better design, we use a PID controller to shape the frequency response so that we get high gain at low frequency and low phase at crossover." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e6da93a4-5202-45d7-9e5a-697848f4ba71", + "metadata": {}, + "outputs": [], + "source": [ + "# Design parameters\n", + "Td = 1 # Set to gain crossover frequency\n", + "Ti = Td * 10 # Set to low frequency region\n", + "kp = 500 # Tune to get desired bandwith\n", + "\n", + "# Updated gains\n", + "kp = 150\n", + "Ti = Td * 5; kp = 150\n", + "\n", + "# Compute controller parmeters\n", + "ki = kp/Ti\n", + "kd = kp * Td\n", + "\n", + "# Controller transfer function\n", + "ctrl_shape = kp + ki / s + kd * s\n", + "\n", + "# Frequency response (open loop) - use this to help tune your design\n", + "ltf_shape = ct.tf(P_tf * ctrl_shape, name='L_shape')\n", + "\n", + "cplt = ct.frequency_response([P, ctrl_shape]).plot(label=['P', 'C_shape'])\n", + "cplt = ct.frequency_response(ltf_shape).plot(margins=True)\n", + "\n", + "cplt.set_plot_title(\"Loop shaping design for servomechanism controller\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d731f372-4992-464c-9ca5-49cc1d554799", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the transfer function from r to y\n", + "T_shape = ct.feedback(ltf_shape)\n", + "cplt = ct.step_response(T_shape).plot(\n", + " time_label=\"Time [ms]\",\n", + " title = \"Step response for servomechanism with PID controller\")" + ] + }, + { + "cell_type": "markdown", + "id": "JL99vo4trep5", + "metadata": { + "id": "JL99vo4trep5" + }, + "source": [ + "### Closed loop frequency response\n", + "\n", + "We can also look at the closed loop frequency response to understand how different inputs affect different outputs. The `gangof4` function computes the standard transfer functions:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ceqcg3oM619g", + "metadata": {}, + "outputs": [], + "source": [ + "cplt = ct.gangof4(P_tf, ctrl_shape)" + ] + }, + { + "cell_type": "markdown", + "id": "gel18-iqwYYs", + "metadata": { + "id": "gel18-iqwYYs" + }, + "source": [ + "### Stability margins\n", + "\n", + "Another standard set of analysis tools is to identify the gain, phase, and stability margins for the system:\n", + "\n", + "* **Gain margin:** the maximimum amount of additional gain that we can put into the loop and still maintain stability.\n", + "* **Phase margin:** the maximum amount of additional phase (lag) that we can put into the loop and still maintain stability.\n", + "* **Stability margin:** the maximum amount of combined gain and phase at the critical frequency that can be put into the loop and still maintain stability.\n", + "\n", + "The first two of the items can be computed either by looking at the frequency response or by using the `margin` command.\n", + "\n", + "The stabilty margin is the minimum distance between -1 and $L(jw)$, which is just the minimum value of $|1 - L(j\\omega)|$.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "m-8ItbHwxLrv", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=[7, 4])\n", + "\n", + "# Gain and phase margin on Bode plot\n", + "ax1 = plt.subplot(2, 2, 1)\n", + "plt.title(\"Bode plot for Lnew, with margins\")\n", + "ax2 = plt.subplot(2, 2, 3)\n", + "ct.bode_plot(Lnew, ax=[ax1, ax2], margins=True)\n", + "\n", + "# Compute gain and phase margin\n", + "gm, pm, wpc, wgc = ct.margin(Lnew)\n", + "print(f\"Gm = {gm:2.2g} (at {wpc:.2g} rad/ms)\")\n", + "print(f\"Pm = {pm:3.2g} deg (at {wgc:.2g} rad/ms)\")\n", + "\n", + "# Compute the stability margin\n", + "resp = ct.frequency_response(1 + Lnew)\n", + "sm = np.min(resp.magnitude)\n", + "wsm = resp.omega[np.argmin(resp.magnitude)]\n", + "print(f\"Sm = {sm:2.2g} (at {wsm:.2g} rad/ms)\")\n", + "\n", + "# Plot the Nyquist curve\n", + "ax3 = plt.subplot(1, 2, 2)\n", + "ct.nyquist_plot(Lnew, ax=ax3)\n", + "plt.title(\"Nyquist plot for Lnew [zoomed]\")\n", + "plt.axis([-2, 3, -2.6, 2.6])\n", + "\n", + "#\n", + "# Annotate it to see the margins\n", + "#\n", + "\n", + "# Gain margin (special case here, since infinite)\n", + "Lgm = 0\n", + "plt.plot([-1, Lgm], [0, 0], 'k-', linewidth=0.5)\n", + "plt.text(-0.9, 0.1, \"1/gm\")\n", + "\n", + "# Phase margin\n", + "theta = np.linspace(0, 2 * math.pi)\n", + "plt.plot(np.cos(theta), np.sin(theta), 'k--', linewidth=0.5)\n", + "plt.text(-1.3, -0.8, \"pm\")\n", + "\n", + "# Stability margin\n", + "Lsm = Lnew(wsm * 1j)\n", + "plt.plot([-1, Lsm.real], [0, Lsm.imag], 'k-', linewidth=0.5)\n", + "plt.text(-0.4, -0.5, \"sm\")\n", + "\n", + "plt.suptitle(\"\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "WsOzQST9rFC-", + "metadata": { + "id": "WsOzQST9rFC-" + }, + "source": [ + "## Unstable system: inverted pendulum\n", + "\n", + "When we have a system that is open loop unstable, the Nyquist curve will need to have encirclements to be stable. In this case, the interpretation of the various characteristics can be more complicated.\n", + "\n", + "To explore this, we consider a simple model for an inverted pendulum, which has (normalized) dynamics:\n", + "\n", + "$$\n", + "\\dot x = \\begin{bmatrix} 0 & 1 & \\\\ -1 & 0.1 \\end{bmatrix} x + \\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix} u, \\qquad\n", + "y = \\begin{bmatrix} 1 & 0 \\end{bmatrix} x\n", + "$$\n", + "\n", + "Transfer function for the system can be shown to be\n", + "\n", + "$$\n", + "P(s) = \\frac{1}{s^2 + 0.1 s - 1}.\n", + "$$\n", + "\n", + "This system is unstable, with poles $\\sim\\pm 1$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ZbPzrlPIrHnp", + "metadata": {}, + "outputs": [], + "source": [ + "P = ct.tf([1], [1, 0.1, -1])\n", + "P.poles()" + ] + }, + { + "cell_type": "markdown", + "id": "W-sBWxKi6SPx", + "metadata": { + "id": "W-sBWxKi6SPx" + }, + "source": [ + "### PD controller\n", + "\n", + "We construct a proportional-derivative (PD) controller for the system,\n", + "\n", + "$$\n", + "u = k_\\text{p} e + k_\\text{d} \\dot{e}\n", + "$$\n", + "\n", + "which is roughly the equivalent of using state feedback (since the system states are $\\theta$ and $\\dot\\theta$)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "hjQS_dED7yJE", + "metadata": {}, + "outputs": [], + "source": [ + "# Transfer function for a PD controller\n", + "kp = 10\n", + "kd = 2\n", + "C = ct.tf([kd, kp], [1])\n", + "\n", + "# Loop transfer function\n", + "L = P * C\n", + "L.name = 'L'\n", + "print(L)\n", + "print(\"Zeros: \", L.zeros())\n", + "print(\"Poles: \", L.poles())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "YI_KJo0E9pFd", + "metadata": {}, + "outputs": [], + "source": [ + "# Bode and Nyquist plots\n", + "plt.figure(figsize=[7, 4])\n", + "ax1 = plt.subplot(2, 2, 1)\n", + "plt.title(\"Bode plot for L\", size='medium')\n", + "ax2 = plt.subplot(2, 2, 3)\n", + "ct.bode_plot(L, ax=[ax1, ax2])\n", + "\n", + "ax3 = plt.subplot(1, 2, 2)\n", + "ct.nyquist_plot(L, ax=ax3)\n", + "plt.title(\"Nyquist plot for L\", size='medium')\n", + "\n", + "plt.suptitle(\"Loop analysis for inverted pendulum\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dH03kv9-Da8", + "metadata": {}, + "outputs": [], + "source": [ + "# Check the Nyquist criterion\n", + "nyqresp = ct.nyquist_response(L)\n", + "print(\"N = encirclements: \", nyqresp.count)\n", + "print(\"P = RHP poles of L: \", np.sum(np.real(L.poles()) > 0))\n", + "print(\"Z = N + P = RHP zeros of 1 + L:\", np.sum(np.real((1 + L).zeros()) >= 0))\n", + "print(\"Poles of L = \", L.poles())\n", + "print(\"Zeros of 1 + L = \", (1 + L).zeros())\n", + "print(\"\")\n", + "\n", + "T = ct.feedback(L)\n", + "ct.initial_response(T, X0=[0.1, 0]).plot();" + ] + }, + { + "cell_type": "markdown", + "id": "7bb03f68-0c99-40e9-86cd-a9f2816b4096", + "metadata": {}, + "source": [ + "Note that we get a warning when we set the initial condition. This is because `T` is a transfer function and so it doesn't have a unique state space realization. If the initial state is zero this doesn't matter, but if the initial state is nonzero then the assignment of states is not well defined." + ] + }, + { + "cell_type": "markdown", + "id": "VXlYhs8X7DuN", + "metadata": { + "id": "VXlYhs8X7DuN" + }, + "source": [ + "### Gang of 4\n", + "\n", + "Another useful thing to look at is the transfer functions from noise and disturbances to the system outputs and inputs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "oTmOun41_opt", + "metadata": {}, + "outputs": [], + "source": [ + "ct.gangof4(P, C);" + ] + }, + { + "cell_type": "markdown", + "id": "U41ve1zh7XPh", + "metadata": { + "id": "U41ve1zh7XPh" + }, + "source": [ + "We see that the response from the input $r$ (or equivalently noise $n$) to the process input is very large for large frequencies. This means that we are amplifying high frequency noise (and comes from the fact that we used derivative feedback)." + ] + }, + { + "cell_type": "markdown", + "id": "YROqmZTd8WYs", + "metadata": { + "id": "YROqmZTd8WYs" + }, + "source": [ + "### High frequency rolloff\n", + "\n", + "We can attempt to resolve this by \"rolling off\" the derivative action at high frequencies:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "vhKi_L-F_6Ws", + "metadata": {}, + "outputs": [], + "source": [ + "Cnew = (kp + kd * s) / (s/20 + 1)**2\n", + "Cnew.name = 'Cnew'\n", + "print(Cnew)\n", + "\n", + "Lnew = P * Cnew\n", + "Lnew.name = 'Lnew'\n", + "\n", + "plt.figure(figsize=[7, 4])\n", + "ax1 = plt.subplot(2, 2, 1)\n", + "ax2 = plt.subplot(2, 2, 3)\n", + "ct.bode_plot([Lnew, L], ax=[ax1, ax2])\n", + "ax1.loglog([1e-1, 1e2], [1, 1], 'k', linewidth=0.5)\n", + "ax1.set_title(\"Bode plot for L, Lnew\", size='medium')\n", + "\n", + "ax3 = plt.subplot(1, 2, 2)\n", + "ct.nyquist_plot(Lnew, ax=ax3)\n", + "ax3.set_title(\"Nyquist plot for Lnew\", size='medium')\n", + "\n", + "plt.suptitle(\"Stability analysis for inverted pendulum\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "WgrAE9XE7_nJ", + "metadata": { + "id": "WgrAE9XE7_nJ" + }, + "source": [ + "While not (yet) a very high performing controller, this change does get rid of the issues with the high frequency noise:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "FknwW6GkBLLU", + "metadata": {}, + "outputs": [], + "source": [ + "# Check the gang of 4\n", + "ct.gangof4(P, Cnew);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "wJHJLjXwCNz-", + "metadata": {}, + "outputs": [], + "source": [ + "# See what the step response looks like\n", + "Tnew = ct.feedback(Lnew)\n", + "ct.step_response(Tnew, 10).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "WUhz529a-w3q", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds110-L8a_maglev-limits.ipynb b/examples/cds110-L8a_maglev-limits.ipynb new file mode 100644 index 000000000..5a7473ade --- /dev/null +++ b/examples/cds110-L8a_maglev-limits.ipynb @@ -0,0 +1,278 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "gToHma1nvZxz", + "metadata": { + "id": "gToHma1nvZxz" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 8a

\n", + "

Fundamental Limits for Control of a Magnetic Levitation System

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/1MuDZfw72UkI4_Ji_AsEDTPi7IaSURsYP)\n", + "\n", + "This notebook contains the code used to create the magnetic levitation example in Lecture 8-1 of CDS 110, Winter 2024." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc288b3e-60cc-4a75-8af5-81f9d1eede41", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy as sp\n", + "import matplotlib.pyplot as plt\n", + "from math import pi\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct\n", + "import control.optimal as opt\n", + "import control.flatsys as fs" + ] + }, + { + "cell_type": "markdown", + "id": "RFi9litmZKT2", + "metadata": { + "id": "RFi9litmZKT2" + }, + "source": [ + "The magnetic leviation system consists of a metal ball, an electromagnet, and an IR sensor:\n", + "\n", + "
\"maglev-diagram\"
\n", + "\n", + "It is governed by following equation:\n", + "\n", + "$$ \\ddot{z} = g - \\frac{k_mk_A^2}{m}\\frac{u^2}{z^2} - \\frac{c}{m}\\dot{z},$$\n", + "\n", + "where $z$ is the vertical height of the ball and $u$ is the input current applied to the electromagnet. The output is given by $v_{ir}$, which is the voltage measured at the IR sensor:\n", + "\n", + "$$v_{ir} = k_T z + v_0 $$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80da9750-1a34-4a54-ab3a-ff37ea7be0f6", + "metadata": {}, + "outputs": [], + "source": [ + "# System dynamics\n", + "maglev_params = {\n", + " 'kT': 613.65, # gain between position and voltage\n", + " 'v0': -16.18,\t # voltage offset at zero position\n", + " 'm': 0.2,\t # mass of ball, kg\n", + " 'g': 9.81, # gravitational constant\n", + " 'kA': 1,\t # electromagnet conductance\n", + " 'c': 1 # damping (added to improve visualization)\n", + "}\n", + "# gain on magnetic attractive force\n", + "maglev_params['km'] = 3.13e-3 * (maglev_params['m']/2) / maglev_params['kA']**2\n", + "\n", + "def maglev_update(t, x, u, params):\n", + " m, g, kA, km, c = map(params.get, ['m', 'g', 'kA', 'km', 'c'])\n", + " return np.array([\n", + " x[1],\n", + " g - km/m * (kA * u[0])**2 / x[0]**2 - c * x[1]\n", + " ])\n", + "\n", + "def maglev_output(t, x, u, params):\n", + " kT, v0 = map(params.get, ['kT', 'v0'])\n", + " return np.array([kT * x[0] + v0])\n", + "\n", + "maglev = ct.nlsys(\n", + " maglev_update, maglev_output, params=maglev_params, name='maglev',\n", + " inputs='Vu', outputs='Vy', states=['pos', 'vel']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5c56e04-03b7-4c18-be3c-3f4308aedb98", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the equilibrium point that holds the ball at the origin\n", + "xeq, ueq = ct.find_eqpt(maglev, [0.02, 0], 0.2, y0=0)\n", + "print(f\"{xeq=}, {ueq=}\", end='\\n----\\n')\n", + "\n", + "# Compute the linearization at that point\n", + "magP = ct.linearize(maglev, xeq, ueq, name='sys')\n", + "print(magP, end='\\n----\\n')\n", + "\n", + "print(\"Poles:\", magP.poles())\n", + "print(\"Zeros:\", magP.zeros())" + ] + }, + { + "cell_type": "markdown", + "id": "22a2766f-217a-4213-ba19-c11485cc42cc", + "metadata": {}, + "source": [ + "The controller for this system is implemented via an electrical circuit consisting of resistors and capacitors. We don't show the circuit here, but just write down the model for the transfer function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4741e88-bedd-4ef0-b8b9-9deb5fa93d5d", + "metadata": {}, + "outputs": [], + "source": [ + "# Controller (analog circuit)\n", + "k1 = 0.5\t\t\t\t# gain set by gain pot\n", + "R1 = 22000\t\t\t\t# Internal resistor\n", + "R2 = 22000\t\t\t\t# Resistor plug-in\n", + "R = 2000; C = 1e-6\t\t# RC plug-in\n", + "\n", + "# Controller based on analog circuit\n", + "magC1 = -ct.tf([(R1 + R) * C, 1], [R * C, 1]) * k1 * R2/R1\n", + "magL1 = magP * magC1" + ] + }, + { + "cell_type": "markdown", + "id": "641c0df2-90f6-4573-af7f-41a305337e77", + "metadata": {}, + "source": [ + "We can now use a Nyquist plot to see if the controller is stabilizing:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "378b14b8-f8e4-4ed6-b09d-cdf577ea47d1", + "metadata": {}, + "outputs": [], + "source": [ + "# Nyquist plot\n", + "cplt = ct.nyquist_plot([magP, magL1], label=[\"sys\", \"sys * ctrl\"])" + ] + }, + { + "cell_type": "markdown", + "id": "HKGSdW5f91mZ", + "metadata": { + "id": "HKGSdW5f91mZ" + }, + "source": [ + "We see that the controller causes the system to have clockwise net encircelement of the origin. Since the open loop system has one unstable pole, this gives $Z = N + P = 0$ and so the closed loop system is stable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7850f14d-79ab-4250-a0c7-8ddc10ebb977", + "metadata": {}, + "outputs": [], + "source": [ + "# Bode plots\n", + "magC1.name = \"ctrl\"\n", + "cplt = ct.bode_plot(\n", + " [magP, magC1, magL1], np.logspace(0, 4), initial_phase=0,\n", + " label=['P', 'C', 'L'])\n", + "cplt.axes[0, 0].set_ylim(0.06, 1.5e1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d83c5d5c-238a-45a1-9a81-a3779e7f7bc3", + "metadata": {}, + "outputs": [], + "source": [ + "# Sensitivity function for closed loop system/.\n", + "magS1 = ct.feedback(1, magL1, name=\"S1\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bdcb116-02fd-46d9-ab4d-5b25511d0b21", + "metadata": {}, + "outputs": [], + "source": [ + "# Step response\n", + "magT1 = ct.feedback(magL1, name=\"T1\")\n", + "ct.step_response(magT1).plot(title=\"Step response for closed loop system\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2ddb53c-023b-466b-ac15-221c22befd6d", + "metadata": {}, + "outputs": [], + "source": [ + "# Try to improve performance by increasing DC gain\n", + "# System with gain increased\n", + "magC2 = magC1*5 \t\t\t # increased gain\n", + "magL2 = magP * magC2 \t\t\t # loop transfer function\n", + "magS2 = ct.feedback(1, magP * magC2, name=\"S2\") \t# sensitivity function\n", + "magT2 = ct.feedback(magP * magC2, 1, name=\"T2\") \t# closed loop response\n", + "\n", + "# System with gain increased even more\n", + "magC3 = magC1*20\t\t\t # increased gain\n", + "magL3 = magP*magC3\t\t\t # loop transfer function\n", + "magS3 = ct.feedback(1, magP * magC3, name=\"S3\")\t # sensitivity function\n", + "magT3 = ct.feedback(magP * magC3, 1, name=\"T3\")\t # closed loop response\n", + "\n", + "# Plot step responses for different systems\n", + "colors = ['b', 'g', '#FF7F50']\n", + "for sys in [magT1, magT2, magT3]:\n", + " ct.step_response(sys).plot(color=colors.pop())\n", + "\n", + "# Bode plot for sensitivity function\n", + "plt.figure()\n", + "cplt = ct.bode_plot([magS1, magS2, magS3], plot_phase=False)\n", + "\n", + "# Add magnitude of 1\n", + "xdata = cplt.lines[0][0][0].get_xdata()\n", + "ydata = np.ones_like(xdata)\n", + "plt.plot(xdata, ydata, color='k', linestyle='--');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4df561a2-16aa-41b0-9971-f8c151467730", + "metadata": {}, + "outputs": [], + "source": [ + "# Bode integral calculation\n", + "omega = np.linspace(0, 1e6, 100000)\n", + "for name, sys in zip(['C1', 'C2', 'C3'], [magS1, magS2, magS3]):\n", + " freqresp = ct.frequency_response(sys, omega)\n", + " bodeint = np.trapz(np.log(freqresp.magnitude), omega)\n", + " print(\"Bode integral for\", name, \"=\", bodeint)\n", + "\n", + "print(\"pi * sum[ Re(pk) ]\", pi * np.sum(magP.poles()[magP.poles().real > 0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "M2EvTYHq8yRb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds110-L8b_pvtol-complete-limits.ipynb b/examples/cds110-L8b_pvtol-complete-limits.ipynb new file mode 100644 index 000000000..0b482c865 --- /dev/null +++ b/examples/cds110-L8b_pvtol-complete-limits.ipynb @@ -0,0 +1,1032 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "659a189e-33c9-426f-b318-7cb2f433ae4a", + "metadata": { + "id": "659a189e-33c9-426f-b318-7cb2f433ae4a" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 8b

\n", + "

Full Controller Stack for a Planar Vertical Take-Off and Landing (PVTOL) System

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/1XulsQqbthMkr3g58OTctIYKYpqirOgns)\n", + "\n", + "The purpose of this lecture is to introduce tools that can be used for frequency domain modeling and analysis of linear systems." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1be7545a", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy as sp\n", + "import matplotlib.pyplot as plt\n", + "from math import sin, cos, pi\n", + "from scipy.optimize import NonlinearConstraint\n", + "import time\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct\n", + "import control.optimal as opt\n", + "import control.flatsys as fs\n", + "\n", + "# Use control parameters for plotting\n", + "plt.rcParams.update(ct.rcParams)" + ] + }, + { + "cell_type": "markdown", + "id": "c5a1858a", + "metadata": { + "id": "c5a1858a" + }, + "source": [ + "## System definition\n", + "\n", + "Consider the PVTOL system `pvtol_noisy`, defined in `pvtol.py`:\n", + "\n", + "$$\n", + " \\begin{aligned}\n", + " m \\ddot x &= F_1 \\cos\\theta - F_2 \\sin\\theta - c \\dot x + D_x, \\\\\n", + " m \\ddot y &= F_1 \\sin\\theta + F_2 \\cos\\theta - c \\dot y - m g + D_y, \\\\\n", + " J \\ddot \\theta &= r F_1,\n", + " \\end{aligned} \\qquad\n", + " \\vec Y =\n", + " \\begin{bmatrix} x \\\\ y \\\\ \\theta \\end{bmatrix} +\n", + " \\begin{bmatrix} N_x \\\\ N_y \\\\ N_z \\end{bmatrix}.\n", + "$$\n", + "\n", + "Assume that the input disturbances are modeled by independent, first\n", + "order Markov (Ornstein-Uhlenbeck) processes with\n", + "$Q_D = \\text{diag}(0.01, 0.01)$ and $\\omega_0 = 1$ and that the noise\n", + "is modeled as white noise with covariance matrix\n", + "\n", + "$$\n", + " Q_N = \\begin{bmatrix}\n", + " 2 \\times 10^{-4} & 0 & 1 \\times 10^{-5} \\\\\n", + " 0 & 2 \\times 10^{-4} & 1 \\times 10^{-5} \\\\\n", + " 1 \\times 10^{-5} & 1 \\times 10^{-5} & 1 \\times 10^{-4}\n", + " \\end{bmatrix}.\n", + "$$\n", + "\n", + "We will design a controller consisting of a trajectory generation module, a\n", + "gain-scheduled, trajectory tracking module, and a state estimation\n", + "module the moves the system from the origin to the equilibrum point\n", + "point $x_\\text{f}$, $y_\\text{f}$ = 10, 0 while satisfying the\n", + "constraint $0.5 \\sin(\\pi x / 10) - 0.1 \\leq y \\leq 1$." + ] + }, + { + "cell_type": "markdown", + "id": "D1aFeNuglL4a", + "metadata": { + "id": "D1aFeNuglL4a" + }, + "source": [ + "We start by creating the PVTOL system without noise or disturbances." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c32ec3f8", + "metadata": {}, + "outputs": [], + "source": [ + "# STANDARD PVTOL DYNAMICS\n", + "def _pvtol_update(t, x, u, params):\n", + "\n", + " # Get the parameter values\n", + " m = params.get('m', 4.) # mass of aircraft\n", + " J = params.get('J', 0.0475) # inertia around pitch axis\n", + " r = params.get('r', 0.25) # distance to center of force\n", + " g = params.get('g', 9.8) # gravitational constant\n", + " c = params.get('c', 0.05) # damping factor (estimated)\n", + "\n", + " # Get the inputs and states\n", + " x, y, theta, xdot, ydot, thetadot = x\n", + " F1, F2 = u\n", + "\n", + " # Constrain the inputs\n", + " F2 = np.clip(F2, 0, 1.5 * m * g)\n", + " F1 = np.clip(F1, -0.1 * F2, 0.1 * F2)\n", + "\n", + " # Dynamics\n", + " xddot = (F1 * cos(theta) - F2 * sin(theta) - c * xdot) / m\n", + " yddot = (F1 * sin(theta) + F2 * cos(theta) - m * g - c * ydot) / m\n", + " thddot = (r * F1) / J\n", + "\n", + " return np.array([xdot, ydot, thetadot, xddot, yddot, thddot])\n", + "\n", + "# Define pvtol output function to only be x, y, and theta\n", + "def _pvtol_output(t, x, u, params):\n", + " return x[0:3]\n", + "\n", + "# Create nonlinear input-output system of nominal pvtol system\n", + "pvtol_nominal = ct.nlsys(\n", + " _pvtol_update, _pvtol_output, name=\"pvtol_nominal\",\n", + " states = [f'x{i}' for i in range(6)],\n", + " inputs = ['F1', 'F2'],\n", + " outputs = [f'x{i}' for i in range(3)]\n", + ")\n", + "\n", + "print(pvtol_nominal)" + ] + }, + { + "cell_type": "markdown", + "id": "TTMQAAhFldW7", + "metadata": { + "id": "TTMQAAhFldW7" + }, + "source": [ + "Next, we create a PVTOL system with noise and disturbances. This system will use the nominal PVTOL system and add disturbances as inputs to the state dynamics and noise to the system output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "tqSvuzvOkps1", + "metadata": {}, + "outputs": [], + "source": [ + "# Add wind and noise to system dynamics\n", + "def _noisy_update(t, x, u, params):\n", + " # Get the inputs\n", + " F1, F2, Dx, Dy = u[:4]\n", + " if u.shape[0] > 4:\n", + " Nx, Ny, Nth = u[4:]\n", + " else:\n", + " Nx, Ny, Nth = 0, 0, 0\n", + "\n", + " # Get the system response from the original dynamics\n", + " xdot, ydot, thetadot, xddot, yddot, thddot = \\\n", + " _pvtol_update(t, x, [F1, F2], params)\n", + "\n", + " # Get the parameter values we need\n", + " m = params.get('m', 4.) # mass of aircraft\n", + " J = params.get('J', 0.0475) # inertia around pitch axis\n", + "\n", + " # Now add the disturbances\n", + " xddot += Dx / m\n", + " yddot += Dy / m\n", + "\n", + " return np.array([xdot, ydot, thetadot, xddot, yddot, thddot])\n", + "\n", + "# Define pvtol_noisy output function to only be x, y, and theta\n", + "def _noisy_output(t, x, u, params):\n", + " F1, F2, Dx, Dy, Nx, Ny, Nth = u\n", + " return x[0:3] + np.array([Nx, Ny, Nth])\n", + "\n", + "# CREATE NONLINEAR INPUT-OUTPUT SYSTEM\n", + "pvtol_noisy = ct.nlsys(\n", + " _noisy_update, _noisy_output, name=\"pvtol_noisy\",\n", + " states = [f'x{i}' for i in range(6)],\n", + " inputs = ['F1', 'F2'] + ['Dx', 'Dy'] + ['Nx', 'Ny', 'Nth'],\n", + " outputs = ['x', 'y', 'theta'],\n", + " params = {\n", + " 'm': 4., # mass of aircraft\n", + " 'J': 0.0475, # inertia around pitch axis\n", + " 'r': 0.25, # distance to center of force\n", + " 'g': 9.8, # gravitational constant\n", + " 'c': 0.05, # damping factor (estimated)\n", + " }\n", + ")\n", + "\n", + "print(pvtol_noisy)" + ] + }, + { + "cell_type": "markdown", + "id": "057cba8f-79bd-4a45-a184-2424c569785d", + "metadata": { + "id": "057cba8f-79bd-4a45-a184-2424c569785d" + }, + "source": [ + "Note that the outputs of `pvtol_noisy` are not the full set of states, but rather the states we can measure: $x$, $y$, and $\\theta$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ce469b3-faa0-4bac-b9d4-02e4dae7a2da", + "metadata": {}, + "outputs": [], + "source": [ + "# Utility function tlot the trajectory in xy coordinates\n", + "def plot_results(t, x, u, fig=None):\n", + " # Set the size of the figure\n", + " if fig is None:\n", + " fig = plt.figure(figsize=(10, 6))\n", + "\n", + " # Top plot: xy trajectory\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(x[0], x[1])\n", + " plt.xlabel('x [m]')\n", + " plt.ylabel('y [m]')\n", + " plt.axis('equal')\n", + "\n", + " # Time traces of the state and input\n", + " plt.subplot(2, 4, 5)\n", + " plt.plot(t, x[1])\n", + " plt.xlabel('Time t [sec]')\n", + " plt.ylabel('y [m]')\n", + "\n", + " plt.subplot(2, 4, 6)\n", + " plt.plot(t, x[2])\n", + " plt.xlabel('Time t [sec]')\n", + " plt.ylabel('theta [rad]')\n", + "\n", + " plt.subplot(2, 4, 7)\n", + " plt.plot(t, u[0])\n", + " plt.xlabel('Time t [sec]')\n", + " plt.ylabel('$F_1$ [N]')\n", + "\n", + " plt.subplot(2, 4, 8)\n", + " plt.plot(t, u[1])\n", + " plt.xlabel('Time t [sec]')\n", + " plt.ylabel('$F_2$ [N]')\n", + " plt.tight_layout()\n", + "\n", + " return fig\n" + ] + }, + { + "cell_type": "markdown", + "id": "081764e0", + "metadata": { + "id": "081764e0" + }, + "source": [ + "## Estimator\n", + "\n", + "We start by designing an optimal estimator for the system. We choose the noise intensities\n", + "based on knowledge of the modeling errors, disturbances, and sensor characteristics:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "778fb908", + "metadata": {}, + "outputs": [], + "source": [ + "# Disturbance and noise intensities\n", + "Qv = np.diag([1e-2, 1e-2])\n", + "Qw = np.array([[2e-4, 0, 1e-5], [0, 2e-4, 1e-5], [1e-5, 1e-5, 1e-4]])\n", + "Qwinv = np.linalg.inv(Qw)\n", + "\n", + "# Initial state covariance\n", + "P0 = np.eye(pvtol_noisy.nstates)" + ] + }, + { + "cell_type": "markdown", + "id": "1Q55PHN1omJs", + "metadata": { + "id": "1Q55PHN1omJs" + }, + "source": [ + "We will use a linear quadratic estimator (Kalman filter) to design an optimal estimator for the system. Recall that the `ct.lqe` function takes in a linear system as input, so we first linear our `pvtol_noisy` system around its equilibrium point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "WADb1-VcuR5t", + "metadata": {}, + "outputs": [], + "source": [ + "# Find the equilibrium point corresponding to the origin\n", + "xe, ue = ct.find_eqpt(\n", + " sys = pvtol_noisy,\n", + " x0 = np.zeros(pvtol_noisy.nstates),\n", + " u0 = np.zeros(pvtol_noisy.ninputs),\n", + " y0 = [0, 0, 0],\n", + " iu=range(2, pvtol_noisy.ninputs),\n", + " iy=[0, 1]\n", + ")\n", + "print(f\"{xe=}\")\n", + "print(f\"{ue=}\")\n", + "\n", + "# Linearize system for Kalman filter\n", + "pvtol_noisy_lin = pvtol_noisy.linearize(xe, ue)\n", + "\n", + "# Extract the linearization for use in LQR design\n", + "A, B, C = pvtol_noisy_lin.A, pvtol_noisy_lin.B, pvtol_noisy_lin.C" + ] + }, + { + "cell_type": "markdown", + "id": "6E9s147Cpppr", + "metadata": { + "id": "6E9s147Cpppr" + }, + "source": [ + "We want to define an estimator that takes in the measured states $x$, $y$, and $\\theta$, as well as applied inputs $F_1$ and $F_2$. As the estimator doesn't have any measurement of the noise/disturbances applied to the system, we will design our controller with only these inputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "nvZHm0Ooqkj_", + "metadata": {}, + "outputs": [], + "source": [ + "# use ct.lqe to create an L matrix, using only measured inputs F1 and F2\n", + "L, Pf, _ = ct.lqe(A, B[:,:2], C, Qv, Qw)" + ] + }, + { + "cell_type": "markdown", + "id": "KXVetnCUrHvs", + "metadata": { + "id": "KXVetnCUrHvs" + }, + "source": [ + "We now create our estimator." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "M77vo5PgrIEv", + "metadata": {}, + "outputs": [], + "source": [ + "# Create standard (optimal) estimator update function\n", + "def estimator_update(t, xhat, u, params):\n", + "\n", + " # Extract the inputs to the estimator\n", + " y = u[0:3] # just grab the first three outputs\n", + " u_cmd = u[3:5] # get the inputs that were applied as well\n", + "\n", + " # Update the state estimate using PVTOL (non-noisy) dynamics\n", + " return _pvtol_update(t, xhat, u_cmd, params) - L @ (C @ xhat - y)\n", + "\n", + "# Create estimator\n", + "estimator = ct.nlsys(\n", + " estimator_update, None,\n", + " name = 'Estimator',\n", + " states=pvtol_noisy.nstates,\n", + " inputs= pvtol_noisy.output_labels \\\n", + " + pvtol_noisy.input_labels[0:2],\n", + " outputs=[f'xh{i}' for i in range(pvtol_noisy.nstates)],\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1JOPx1TXrnr-", + "metadata": {}, + "outputs": [], + "source": [ + "print(estimator)" + ] + }, + { + "cell_type": "markdown", + "id": "46d8463d", + "metadata": { + "id": "46d8463d" + }, + "source": [ + "## Gain scheduled controller\n", + "\n", + "We next design our (gain scheduled) controller for the system. Here, as in the case of the estimator, we will create the controller using the nominal PVTOL system, so that the applied inputs to the system are only $F_1$ and $F_2$. If we were to make a controller using the noisy PVTOL system, then the inputs applied via control action would include noise and disturbances, which is incorrect." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e5fbef3", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the weights for the LQR problem\n", + "Qx = np.diag([100, 10, (180/np.pi) / 5, 0, 0, 0])\n", + "# Qx = np.diag([10, 100, (180/np.pi) / 5, 0, 0, 0]) # Try this out to see what changes\n", + "Qu = np.diag([10, 1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5cc3cc0", + "metadata": {}, + "outputs": [], + "source": [ + "# Construct the array of gains and the gain scheduled controller\n", + "import itertools\n", + "import math\n", + "\n", + "# Set up points around which to linearize (control-0.9.3: must be 2D or greater)\n", + "angles = np.linspace(-math.pi/3, math.pi/3, 10)\n", + "speeds = np.linspace(-10, 10, 3)\n", + "points = list(itertools.product(angles, speeds))\n", + "\n", + "# Compute the gains at each design point of angles and speeds\n", + "gains = []\n", + "\n", + "# Iterate through points\n", + "for point in points:\n", + "\n", + " # Compute the state that we want to linearize about\n", + " xgs = xe.copy()\n", + " xgs[2], xgs[4] = point[0], point[1]\n", + "\n", + " # Linearize the system and compute the LQR gains\n", + " linsys = pvtol_noisy.linearize(xgs, ue)\n", + " A = linsys.A\n", + " B = linsys.B[:,:2]\n", + " K, X, E = ct.lqr(A, B, Qx, Qu)\n", + " gains.append(K)\n", + "\n", + "# Construct the controller\n", + "gs_ctrl, gs_clsys = ct.create_statefbk_iosystem(\n", + " sys = pvtol_nominal,\n", + " gain = (gains, points),\n", + " gainsched_indices=['xh2', 'xh4'],\n", + " estimator=estimator\n", + ")\n", + "\n", + "print(gs_ctrl)" + ] + }, + { + "cell_type": "markdown", + "id": "ecd28a73", + "metadata": { + "id": "ecd28a73" + }, + "source": [ + "## Trajectory generation\n", + "\n", + "Finally, we need to design the trajectory that we want to follow. We consider a situation with state constraints that represent the specific experimental conditions for this system (at Caltech):\n", + "* `ceiling`: The system has limited vertical travel, so we constrain the vertical position to lie between $-0.5$ and $2$ meters.\n", + "* `nicolas`: When testing, we placed a person in between the initial and final position, and we need to avoid hitting him as we move from start to finish.\n", + "\n", + "The code below defines the initial conditions, final conditions, and constraints." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5eb12bfa", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the initial and final conditions\n", + "x_delta = np.array([10, 0, 0, 0, 0, 0])\n", + "x0, u0 = ct.find_eqpt(\n", + " sys = pvtol_nominal,\n", + " x0 = np.zeros(6),\n", + " u0 = np.zeros(2),\n", + " y0 = np.zeros(3),\n", + " iy=[0, 1]\n", + ")\n", + "xf, uf = ct.find_eqpt(\n", + " sys = pvtol_nominal,\n", + " x0 = x0 + x_delta,\n", + " u0 = u0,\n", + " y0 = (x0 + x_delta)[:3],\n", + " iy=[0, 1]\n", + ")\n", + "\n", + "# Define the time horizon for the manuever\n", + "Tf = 5\n", + "timepts = np.linspace(0, Tf, 100, endpoint=False)\n", + "\n", + "# Create a constraint corresponding to the obstacle\n", + "ceiling = (NonlinearConstraint, lambda x, u: x[1], [-0.5], [2])\n", + "nicolas = (NonlinearConstraint,\n", + " lambda x, u: x[1] - (0.5 * sin(pi * x[0] / 10) - 0.1), [0], [1])\n", + "\n", + "# # Reset the nonlinear constraint to give some extra room\n", + "# nicolas = (NonlinearConstraint,\n", + "# lambda x, u: x[1] - (0.8 * sin(pi * x[0] / 10) - 0.1), [0], [1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "610aa247", + "metadata": {}, + "outputs": [], + "source": [ + "# Re-define the time horizon for the manuever\n", + "Tf = 5\n", + "timepts = np.linspace(0, Tf, 20, endpoint=False)\n", + "\n", + "# We provide a tent shape as an intial guess\n", + "xm = (x0 + xf) / 2 + np.array([0, 0.5, 0, 0, 0, 0])\n", + "tm = int(len(timepts)/2)\n", + "# straight line from start to midpoint to end with nominal input\n", + "tent = (\n", + " np.hstack([\n", + " np.array([x0 + (xm - x0) * t/(Tf/2) for t in timepts[0:tm]]).transpose(),\n", + " np.array([xm + (xf - xm) * t/(Tf/2) for t in timepts[0:tm]]).transpose()\n", + " ]),\n", + " u0\n", + ")\n", + "\n", + "# terminal constraint\n", + "term_constraints = opt.state_range_constraint(pvtol_nominal, xf, xf)\n", + "\n", + "# trajectory cost\n", + "traj_cost = opt.quadratic_cost(pvtol_nominal, None, Qu, x0=xf, u0=uf)\n", + "\n", + "# find optimal trajectory\n", + "start_time = time.process_time()\n", + "traj = opt.solve_ocp(\n", + " sys = pvtol_nominal,\n", + " timepts = timepts,\n", + " initial_guess=tent,\n", + " X0=x0,\n", + " cost = traj_cost,\n", + " trajectory_constraints=[ceiling, nicolas],\n", + " terminal_constraints=term_constraints,\n", + ")\n", + "print(\"* Total time = %5g seconds\\n\" % (time.process_time() - start_time))\n", + "\n", + "# Create the desired trajectory\n", + "xd, ud = traj.states, traj.inputs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e59ddc29", + "metadata": {}, + "outputs": [], + "source": [ + "# Extend the trajectory to hold the final position for Tf seconds\n", + "holdpts = np.arange(Tf, Tf + Tf, timepts[1]-timepts[0])\n", + "xd = np.hstack([xd, np.outer(xf, np.ones_like(holdpts))])\n", + "ud = np.hstack([ud, np.outer(uf, np.ones_like(holdpts))])\n", + "timepts = np.hstack([timepts, holdpts])\n", + "\n", + "# Plot the desired trajectory\n", + "plot_results(timepts, xd, ud)\n", + "plt.suptitle('Desired Trajectory')\n", + "\n", + "# Add the constraints to the plot\n", + "plt.subplot(2, 1, 1)\n", + "\n", + "plt.plot([0, 10], [2, 2], 'r--')\n", + "plt.text(5, 1.8, 'Ceiling', ha='center')\n", + "\n", + "x_nic = np.linspace(0, 10, 50)\n", + "y_nic = 0.5 * np.sin(pi * x_nic / 10) - 0.1\n", + "plt.plot(x_nic, y_nic, 'r--')\n", + "plt.text(5, 0, 'Nicolas Petit', ha='center')\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "affe55fa", + "metadata": { + "id": "affe55fa" + }, + "source": [ + "## Final Control System Implementation\n", + "\n", + "We now put together the final control system and simulate it. If you have named your inputs and outputs to each of the subsystems properly, the code below should connect everything up correctly. If you get errors about inputs or outputs that are not connected to anything, check the names of your inputs and outputs in the various\n", + "systems above and make sure everything lines up as it should." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50dff557", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the interconnected system\n", + "clsys = ct.interconnect(\n", + " [pvtol_noisy, gs_ctrl, estimator],\n", + " inputs=gs_clsys.input_labels[:8] + pvtol_noisy.input_labels[2:],\n", + " outputs=pvtol_noisy.output_labels + pvtol_noisy.input_labels[:2]\n", + ")\n", + "print(clsys)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f24e6f5", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate disturbance and noise vectors\n", + "V = ct.white_noise(timepts, Qv)\n", + "W = ct.white_noise(timepts, Qw)\n", + "for i in range(V.shape[0]):\n", + " plt.subplot(2, 3, i+1)\n", + " plt.plot(timepts, V[i])\n", + " plt.ylabel(f'V[{i}]')\n", + "\n", + "for i in range(W.shape[0]):\n", + " plt.subplot(2, 3, i+4)\n", + " plt.plot(timepts, W[i])\n", + " plt.ylabel(f'W[{i}]')\n", + " plt.xlabel('Time $t$ [s]')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f63091cf", + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate the open loop system and plot the results (+ state trajectory)\n", + "resp = ct.input_output_response(\n", + " sys = clsys,\n", + " T = timepts,\n", + " U = [xd, ud, V, W],\n", + " X0 = np.zeros(12))\n", + "\n", + "plot_results(resp.time, resp.outputs[0:3], resp.outputs[3:5])\n", + "\n", + "# Add the constraints to the plot\n", + "plt.subplot(2, 1, 1)\n", + "plt.plot([0, 10], [1, 1], 'r--')\n", + "x_nic = np.linspace(0, 10, 50)\n", + "y_nic = 0.5 * np.sin(pi * x_nic / 10) - 0.1\n", + "plt.plot(x_nic, y_nic, 'r--')\n", + "plt.text(5, 0, 'Nicolas Petit', ha='center')\n", + "plt.suptitle(\"Measured Trajectory\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "89221230", + "metadata": { + "id": "89221230" + }, + "source": [ + "We see that with the addition of disturbances and noise, we sometimes violate the constraint 'nicolas' (if your plot doesn't show an intersection with the bottom dashed curve, try regenerating the noise and running the simulation again). This can be fixed by establishing a more conservative constraint (see commented out constraint in code block above)." + ] + }, + { + "cell_type": "markdown", + "id": "3f2e9776-0ba9-4295-9473-a17cb4854836", + "metadata": { + "id": "3f2e9776-0ba9-4295-9473-a17cb4854836" + }, + "source": [ + "## Small signal analysis\n", + "\n", + "We next look at the properties of the system using the small signal (linearized) dynamics. This analysis is useful to check the robustness and performance of the controller around trajectories and equilibrium points.\n", + "\n", + "We will carry out the analysis around the initial condition." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "JgZyPyMkcoOl", + "metadata": {}, + "outputs": [], + "source": [ + "## Small signal analysis\n", + "X0 = np.hstack([x0, x0]) # system state, estim state\n", + "U0 = np.hstack([x0, u0, np.zeros(5)]) # xd, ud, dist, noise\n", + "G = clsys.linearize(X0, U0)\n", + "print(clsys)\n", + "\n", + "# Get input/output dictionaries: inp['sig'] = index for 'sig'\n", + "inp = clsys.input_index\n", + "out = clsys.output_index\n", + "\n", + "fig, axs = plt.subplots(2, 3, figsize=[9, 6])\n", + "omega = np.logspace(-2, 2)\n", + "\n", + "# Complementary sensitivity\n", + "G_x_xd = ct.tf(G[out['x'], inp['xd[0]']])\n", + "G_y_yd = ct.tf(G[out['y'], inp['xd[1]']])\n", + "ct.bode_plot(\n", + " [G_x_xd, G_y_yd], omega,\n", + " plot_phase=False, ax=np.array([[axs[0, 0]]]))\n", + "axs[0, 0].legend(['F T_x', 'F T_y'])\n", + "axs[0, 0].loglog([omega[0], omega[-1]], [1, 1], 'k', linewidth=0.5)\n", + "axs[0, 0].set_title(\"From xd, yd\", fontsize=9)\n", + "axs[0, 0].set_ylabel(\"To x, y\")\n", + "axs[0, 0].set_xlabel(\"\")\n", + "\n", + "# Load (or input) sensitivity\n", + "G_x_dx = ct.tf(G[out['x'], inp['Dx']])\n", + "G_y_dy = ct.tf(G[out['y'], inp['Dy']])\n", + "ct.bode_plot(\n", + " [G_x_dx, G_y_dy], omega,\n", + " plot_phase=False, ax=np.array([[axs[0, 1]]]))\n", + "axs[0, 1].legend(['PS_x', 'PS_y'])\n", + "axs[0, 1].loglog([omega[0], omega[-1]], [1, 1], 'k', linewidth=0.5)\n", + "axs[0, 1].set_title(\"From Dx, Dy\", fontsize=9)\n", + "axs[0, 1].set_xlabel(\"\")\n", + "axs[0, 1].set_ylabel(\"\")\n", + "\n", + "# Sensitivity\n", + "G_x_Nx = ct.tf(G[out['x'], inp['Nx']])\n", + "G_y_Ny = ct.tf(G[out['y'], inp['Ny']])\n", + "ct.bode_plot(\n", + " [G_x_Nx, G_y_Ny], omega,\n", + " plot_phase=False, ax=np.array([[axs[0, 2]]]))\n", + "axs[0, 2].legend(['S_x', 'S_y'])\n", + "axs[0, 2].set_title(\"From Nx, Ny\", fontsize=9)\n", + "axs[0, 2].loglog([omega[0], omega[-1]], [1, 1], 'k', linewidth=0.5)\n", + "axs[0, 2].set_xlabel(\"\")\n", + "axs[0, 2].set_ylabel(\"\")\n", + "\n", + "# Noise (or output) sensitivity\n", + "G_F1_xd = ct.tf(G[out['F1'], inp['xd[0]']])\n", + "G_F2_yd = ct.tf(G[out['F2'], inp['xd[1]']])\n", + "ct.bode_plot(\n", + " [G_F1_xd, G_F2_yd], omega,\n", + " plot_phase=False, ax=np.array([[axs[1, 0]]]))\n", + "axs[1, 0].legend(['FCS_x', 'FCS_y'])\n", + "axs[1, 0].loglog([omega[0], omega[-1]], [1, 1], 'k', linewidth=0.5)\n", + "axs[1, 0].set_ylabel(\"To F1, F2\")\n", + "\n", + "G_F1_dx = ct.tf(G[out['F1'], inp['Dx']])\n", + "G_F2_dy = ct.tf(G[out['F2'], inp['Dy']])\n", + "ct.bode_plot(\n", + " [G_F1_dx, G_F2_dy], omega,\n", + " plot_phase=False, ax=np.array([[axs[1, 1]]]))\n", + "axs[1, 1].legend(['~T_x', '~T_y'])\n", + "axs[1, 1].loglog([omega[0], omega[-1]], [1, 1], 'k', linewidth=0.5)\n", + "axs[1, 1].set_ylabel(\"\")\n", + "\n", + "# Sensitivity\n", + "G_F1_Nx = ct.tf(G[out['F1'], inp['Nx']])\n", + "G_F1_Ny = ct.tf(G[out['F1'], inp['Ny']])\n", + "ct.bode_plot(\n", + " [G_F1_Nx, G_F1_Ny], omega,\n", + " plot_phase=False, ax=np.array([[axs[1, 2]]]))\n", + "axs[1, 2].legend(['C S_x', 'C S_y'])\n", + "axs[1, 2].loglog([omega[0], omega[-1]], [1, 1], 'k', linewidth=0.5)\n", + "axs[1, 2].set_ylabel(\"\")\n", + "\n", + "plt.suptitle(\"Gang of Six for PVTOL\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "xfi1mXJTe3Gm", + "metadata": {}, + "outputs": [], + "source": [ + "# Solve for the loop transfer function horizontal direction\n", + "# S = 1 / (1 + L) => S + SL = 1 => L = (1 - S)/S\n", + "Lx = (1 - G_x_Nx) / G_x_Nx; Lx.name = 'Lx'\n", + "Ly = (1 - G_y_Ny) / G_y_Ny; Ly.name = 'Ly'\n", + "\n", + "# Create Nyquist plot\n", + "ct.nyquist_plot([Lx, Ly], max_curve_magnitude=5, max_curve_offset=0.2);" + ] + }, + { + "cell_type": "markdown", + "id": "L7L6UZTn_Qtn", + "metadata": { + "id": "L7L6UZTn_Qtn" + }, + "source": [ + "### Gain Margins of $L_x$, $L_y$\n", + "\n", + "We can zoom in on the plot to see the gain, phase, and stability margins:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3FX7YXrR2cuQ", + "metadata": {}, + "outputs": [], + "source": [ + "cplt = ct.nyquist_plot([Lx, Ly])\n", + "lower_upper_bound = 1.1\n", + "cplt.axes[0, 0].set_xlim([-lower_upper_bound, lower_upper_bound])\n", + "cplt.axes[0, 0].set_ylim([-lower_upper_bound, lower_upper_bound])\n", + "cplt.axes[0, 0].set_aspect('equal')\n", + "\n", + "# Gain margin for Lx\n", + "neg1overgm_x = -0.67 # vary this manually to find intersection with curve\n", + "color = cplt.lines[0][0].get_color()\n", + "plt.plot(neg1overgm_x, 0, color=color, marker='o', fillstyle='none')\n", + "gm_x = -1/neg1overgm_x\n", + "\n", + "# Gain margin for Ly\n", + "neg1overgm_y = -0.32 # vary this manually to find intersection with curve\n", + "color = cplt.lines[1][0].get_color()\n", + "plt.plot(neg1overgm_y, 0, color=color, marker='o', fillstyle='none')\n", + "gm_y = -1/neg1overgm_y\n", + "\n", + "print('Margins obtained visually:')\n", + "print('Gain margin of Lx: '+str(gm_x))\n", + "print('Gain margin of Ly: '+str(gm_y))\n", + "print('\\n')\n", + "\n", + "# get gain margin computationally\n", + "gm_xc, pm_xc, wpc_xc, wgc_xc = ct.margin(Lx)\n", + "gm_yc, pm_yc, wpc_yc, wgc_yc = ct.margin(Ly)\n", + "\n", + "print('Margins obtained computationally:')\n", + "print('Gain margin of Lx: '+str(gm_xc))\n", + "print('Gain margin of Ly: '+str(gm_yc))\n", + "\n", + "print('\\n')" + ] + }, + { + "cell_type": "markdown", + "id": "VnrVNvhz_Zi2", + "metadata": { + "id": "VnrVNvhz_Zi2" + }, + "source": [ + "### Phase Margins of $L_x$, $L_y$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "zKb_o9ZN_ffF", + "metadata": {}, + "outputs": [], + "source": [ + "# add customizations to Nyquist plot\n", + "cplt = ct.nyquist_plot(\n", + " [Lx, Ly], max_curve_magnitude=5, max_curve_offset=0.2,\n", + " unit_circle=True)\n", + "lower_upper_bound = 2\n", + "cplt.axes[0, 0].set_xlim([-lower_upper_bound, lower_upper_bound])\n", + "cplt.axes[0, 0].set_ylim([-lower_upper_bound, lower_upper_bound])\n", + "cplt.axes[0, 0].set_aspect('equal')\n", + "\n", + "# Phase margin of Lx:\n", + "th_pm_x = 0.14*np.pi\n", + "th_plt_x = np.pi + th_pm_x\n", + "color = cplt.lines[0][0].get_color()\n", + "plt.plot(np.cos(th_plt_x), np.sin(th_plt_x), color=color, marker='o')\n", + "\n", + "# Phase margin of Ly\n", + "th_pm_y = 0.19*np.pi\n", + "th_plt_y = np.pi + th_pm_y\n", + "color = cplt.lines[1][0].get_color()\n", + "plt.plot(np.cos(th_plt_y), np.sin(th_plt_y), color=color, marker='o')\n", + "\n", + "print('Margins obtained visually:')\n", + "print('Phase margin: '+str(float(th_pm_x)))\n", + "print('Phase margin: '+str(float(th_pm_y)))\n", + "print('\\n')\n", + "\n", + "# get margin computationally\n", + "gm_xc, pm_xc, wpc_xc, wgc_xc = ct.margin(Lx)\n", + "gm_yc, pm_yc, wpc_yc, wgc_yc = ct.margin(Ly)\n", + "\n", + "print('Margins obtained computationally:')\n", + "print('Phase margin of Lx: '+str(np.deg2rad(pm_xc)))\n", + "print('Phase margin of Ly: '+str(np.deg2rad(pm_yc)))\n", + "\n", + "print('\\n')" + ] + }, + { + "cell_type": "markdown", + "id": "dF0BIq5BDXII", + "metadata": { + "id": "dF0BIq5BDXII" + }, + "source": [ + "### Stability Margins of $L_x$, $L_y$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "XQPB_h6Y1cAW", + "metadata": {}, + "outputs": [], + "source": [ + "# add customizations to Nyquist plot\n", + "cplt = ct.nyquist_plot([Lx, Ly], max_curve_magnitude=5, max_curve_offset=0.2)\n", + "lower_upper_bound = 2\n", + "cplt.axes[0, 0].set_xlim([-lower_upper_bound, lower_upper_bound])\n", + "cplt.axes[0, 0].set_ylim([-lower_upper_bound, lower_upper_bound])\n", + "cplt.axes[0, 0].set_aspect('equal')\n", + "\n", + "# Stability margin:\n", + "sm_x = 0.3 # vary this manually to find min which intersects\n", + "color = cplt.lines[0][0].get_color()\n", + "sm_circle = plt.Circle((-1, 0), sm_x, color=color, fill=False, ls=':')\n", + "cplt.axes[0, 0].add_patch(sm_circle)\n", + "\n", + "sm_y = 0.5 # vary this manually to find min which intersects\n", + "color = cplt.lines[1][0].get_color()\n", + "sm_circle = plt.Circle((-1, 0), sm_y, color=color, fill=False, ls=':')\n", + "cplt.axes[0, 0].add_patch(sm_circle)\n", + "\n", + "print('Margins obtained visually:')\n", + "print('* Stability margin of Lx: '+str(sm_x))\n", + "print('* Stability margin of Ly: '+str(sm_y))\n", + "\n", + "# Compute the stability margin computationally\n", + "print('') # blank line\n", + "print('Margins obtained computationally:')\n", + "resp = ct.frequency_response(1 + Lx)\n", + "sm = np.min(resp.magnitude)\n", + "wsm = resp.omega[np.argmin(resp.magnitude)]\n", + "\n", + "print(f\"* Stability margin of Lx = {sm:2.2g} (at {wsm:.2g} rad/s)\")\n", + "resp = ct.frequency_response(1 + Ly)\n", + "sm = np.min(resp.magnitude)\n", + "wsm = resp.omega[np.argmin(resp.magnitude)]\n", + "print(f\"* Stability margin of Ly = {sm:2.2g} (at {wsm:.2g} rad/s)\")\n", + "print('')" + ] + }, + { + "cell_type": "markdown", + "id": "boAjWk56GXYZ", + "metadata": { + "id": "boAjWk56GXYZ" + }, + "source": [ + "We see that the frequencies at which the stability margins are found corresponds to the peak of the magnitude of the sensitivity functions for $L_x$ and $L_y$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "JkbMn8pif7Ub", + "metadata": {}, + "outputs": [], + "source": [ + "# Confirm stability using Nyquist criterion\n", + "nyqresp_x = ct.nyquist_response(Lx)\n", + "nyqresp_y = ct.nyquist_response(Ly)\n", + "\n", + "print(\"Nx =\", nyqresp_x.count, \"; Px =\", np.sum(np.real(Lx.poles()) > 0))\n", + "print(\"Ny =\", nyqresp_y.count, \"; Py =\", np.sum(np.real(Ly.poles()) > 0))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d038db9-f671-4f0f-82db-51096e8272b7", + "metadata": {}, + "outputs": [], + "source": [ + "# Take a look at the locations of the poles\n", + "np.real(Ly.poles())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9dd57510-4b03-4c0a-90ae-35011f90c41b", + "metadata": {}, + "outputs": [], + "source": [ + "# See what happened in the contour\n", + "plt.plot(np.real(nyqresp_y.contour), np.imag(nyqresp_y.contour))\n", + "plt.axis([-1e-4, 4e-4, 0, 4e-4])\n", + "plt.title(\"Zoom on D-contour\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e7b9a2f9-f40f-4090-ae69-6bf53fea54a9", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds110-L9_servomech-pid.ipynb b/examples/cds110-L9_servomech-pid.ipynb new file mode 100644 index 000000000..3c8f5df5a --- /dev/null +++ b/examples/cds110-L9_servomech-pid.ipynb @@ -0,0 +1,635 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "FAZsjB3IN9JN" + }, + "source": [ + "
\n", + "

CDS 110, Lecture 9

\n", + "

PID Control of a Servomechanism

\n", + "

Richard M. Murray, Winter 2024

\n", + "
\n", + "\n", + "[Open in Google Colab](https://colab.research.google.com/drive/1BP0DFHh94tSxAyQetvOEbBEHKrSoVGQW)\n", + "\n", + "In this lecture we will use a variety of methods to design proportional (P), proportional-integral (PI), and proportional-integral-derivative (PID) controllers for a cart pendulum system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from math import pi\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " !pip install control\n", + " import control as ct" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T0rjwp1mONm1" + }, + "source": [ + "## System dynamics\n", + "\n", + "Consider a simple mechanism consisting of a spring loaded arm that is driven by a motor, as shown below:\n", + "\n", + "
\"servomech-diagram\"
\n", + "\n", + "The motor applies a torque that twists the arm against a linear spring and moves the end of the arm across a rotating platter. The input to the system is the motor torque $\\tau_\\text{m}$. The force exerted by the spring is a nonlinear function of the head position due to the way it is attached.\n", + "\n", + "The equations of motion for the system are given by\n", + "\n", + "$$\n", + "J \\ddot \\theta = -b \\dot\\theta - k r\\sin\\theta + \\tau_\\text{m},\n", + "$$\n", + "\n", + "which can be written in state space form as\n", + "\n", + "$$\n", + "\\frac{d}{dt} \\begin{bmatrix} \\theta \\\\ \\theta \\end{bmatrix} =\n", + " \\begin{bmatrix} \\dot\\theta \\\\ -k r \\sin\\theta / J - b\\dot\\theta / J \\end{bmatrix}\n", + " + \\begin{bmatrix} 0 \\\\ 1/J \\end{bmatrix} \\tau_\\text{m}.\n", + "$$\n", + "\n", + "The system parameters are given by\n", + "\n", + "$$\n", + "k = 1,\\quad J = 100,\\quad b = 10,\n", + "\\quad r = 1,\\quad l = 2,\\quad \\epsilon = 0.01.\n", + "$$\n", + "\n", + "and we assume that time is measured in milliseconds (ms) and distance in centimeters (cm). (The constants here are made up and don't necessarily reflect a real disk drive, though the units and time constants are motivated by computer disk drives.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Parameter values\n", + "servomech_params = {\n", + " 'J': 100, # Moment of inertia of the motor\n", + " 'b': 10, # Angular damping of the arm\n", + " 'k': 1, # Spring constant\n", + " 'r': 1, # Location of spring contact on arm\n", + " 'l': 2, # Distance to the read head\n", + " 'eps': 0.01, # Magnitude of velocity-dependent perturbation\n", + "}\n", + "\n", + "# State derivative\n", + "def servomech_update(t, x, u, params):\n", + " # Extract the configuration and velocity variables from the state vector\n", + " theta = x[0] # Angular position of the disk drive arm\n", + " thetadot = x[1] # Angular velocity of the disk drive arm\n", + " tau = u[0] # Torque applied at the base of the arm\n", + "\n", + " # Get the parameter values\n", + " J, b, k, r = map(params.get, ['J', 'b', 'k', 'r'])\n", + "\n", + " # Compute the angular acceleration\n", + " dthetadot = 1/J * (\n", + " -b * thetadot - k * r * np.sin(theta) + tau)\n", + "\n", + " # Return the state update law\n", + " return np.array([thetadot, dthetadot])\n", + "\n", + "# System output (full state)\n", + "def servomech_output(t, x, u, params):\n", + " l = params['l']\n", + " return l * x[0]\n", + "\n", + "# System dynamics\n", + "servomech = ct.nlsys(\n", + " servomech_update, servomech_output, name='servomech',\n", + " params=servomech_params,\n", + " states=['theta_', 'thdot_'],\n", + " outputs=['y'], inputs=['tau'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n4bQu0e2_aBT" + }, + "source": [ + "In addition to the system dynamics, we assume there are actuator dynamics that limit the performance of the system. We take these as first order dynamics with saturation:\n", + "\n", + "$$\n", + "\\tau = \\text{sat} \\left(\\frac{\\alpha}{s + \\alpha} u\\right)\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "actuator_params = {\n", + " 'umax': 5, # Saturation limits\n", + " 'alpha': 10, # Actuator time constant\n", + "}\n", + "\n", + "def actuator_update(t, x, u, params):\n", + " # Get parameter values\n", + " alpha = params['alpha']\n", + " umax = params['umax']\n", + "\n", + " # Clip the input\n", + " u_clip = np.clip(u, -umax, umax)\n", + "\n", + " # Actuator dynamics\n", + " return -alpha * x + alpha * u_clip\n", + "\n", + "actuator = ct.nlsys(\n", + " actuator_update, None, params=actuator_params,\n", + " inputs='u', outputs='tau', states=1, name='actuator')\n", + "\n", + "system = ct.series(actuator, servomech)\n", + "system.name = 'system' # missing feature\n", + "print(system)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8HYyndF_saE0" + }, + "source": [ + "### Linearization\n", + "\n", + "To study the open loop dynamics of the system, we compute the linearization of the dynamics about the equilibrium point corresponding to $\\theta_\\text{e} = 15^\\circ$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the equilibrium angle to radians\n", + "theta_e = (15 / 180) * np.pi\n", + "\n", + "# Compute the input required to hold this position\n", + "u_e = servomech.params['k'] * servomech.params['r'] * np.sin(theta_e)\n", + "print(\"Equilibrium torque = %g\" % u_e)\n", + "\n", + "# Linearize the system dynamics about the equilibrium point\n", + "P = ct.tf(\n", + " system.linearize([0, theta_e, 0], u_e, copy_names=True)[0, 0])\n", + "P.name = 'P' # bug\n", + "print(P, end=\"\\n\\n\")\n", + "\n", + "ct.bode_plot(P)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "J1dwXObJSKp-" + }, + "source": [ + "## Ziegler-Nichols tuning\n", + "\n", + "Ziegler-Nichols tuning provides a method for choosing the gains of a PID controller that give reasonable closed loop response. More information can be found in [Feedback Systems](https://fbswiki.org/wiki/index.php/Feedback_Systems:_An_Introduction_for_Scientists_and_Engineers) (FBS2e), Section 11.3.\n", + "\n", + "We show here the figures and tables that we will use (from FBS2e):\n", + "\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "To use the Ziegler-Nichols turning rules, we plot the step response, compute the parameters (shown in the figure), and then apply the formulas in the table:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the step response\n", + "resp = ct.step_response(P)\n", + "resp.plot()\n", + "\n", + "# Find the point of the steepest slope\n", + "slope = np.diff(resp.outputs) / np.diff(resp.time)\n", + "mxi = np.argmax(slope)\n", + "mx_time = resp.time[mxi]\n", + "mx_out= resp.outputs[mxi]\n", + "plt.plot(resp.time[mxi], resp.outputs[mxi], 'ro')\n", + "\n", + "# Draw a line going through the point of max slope\n", + "mx_slope = slope[mxi]\n", + "timepts = np.linspace(0, mx_time*2)\n", + "plt.plot(timepts, mx_out + mx_slope * (timepts - mx_time), 'r-')\n", + "\n", + "# Solve for the Ziegler-Nichols parameters\n", + "a = -(mx_out - mx_slope * mx_time) # Find the value of the line at t = 0\n", + "tau = a / mx_slope # Solve a + mx_slope * tau = 0\n", + "print(f\"{a=}, {tau=}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then construct a controller using the parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s = ct.tf('s')\n", + "\n", + "# Proportional controller\n", + "kp = 1/a\n", + "ctrl_zn_P = kp\n", + "\n", + "# PI controller\n", + "kp = 0.9/a\n", + "Ti = tau/0.3; ki = kp/Ti\n", + "ctrl_zn_PI = kp + ki / s\n", + "\n", + "# PID controller\n", + "kp = 1.2/a\n", + "Ti = tau/0.5; ki = kp/Ti\n", + "Td = 0.5 * tau; kd = kp * Td\n", + "ctrl_zn_PID = kp + ki / s + kd * s\n", + "\n", + "print(ctrl_zn_PID)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the closed loop systems and plots the step and\n", + "# frequency responses.\n", + "\n", + "clsys_zn_P = ct.feedback(P * ctrl_zn_P)\n", + "clsys_zn_P.name = 'P'\n", + "\n", + "clsys_zn_PI = ct.feedback(P * ctrl_zn_PI)\n", + "clsys_zn_PI.name = 'PI'\n", + "\n", + "clsys_zn_PID = ct.feedback(P * ctrl_zn_PID)\n", + "clsys_zn_PID.name = 'PID'\n", + "\n", + "# Plot the step responses\n", + "resp.sysname = 'open_loop'\n", + "resp.plot(color='k')\n", + "\n", + "stepresp_zn_P = ct.step_response(clsys_zn_P)\n", + "stepresp_zn_P.plot(color='b')\n", + "\n", + "stepresp_zn_PI = ct.step_response(clsys_zn_PI)\n", + "stepresp_zn_PI.plot(color='r')\n", + "\n", + "stepresp_zn_PID = ct.step_response(clsys_zn_PID)\n", + "stepresp_zn_PID.plot(color='g')\n", + "plt.legend()\n", + "\n", + "plt.figure()\n", + "ct.bode_plot([clsys_zn_P, clsys_zn_PI, clsys_zn_PID]);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6iZwB2WEeg8S" + }, + "source": [ + "## Loop shaping\n", + "\n", + "A better design can be obtained by looking at the loop transfer function and adjusting the controller parameters to give a loop shape that will give closed loop properties. We show the steps for such a design here:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Design parameters\n", + "Td = 1 # Set to gain crossover frequency\n", + "Ti = Td * 10 # Set to low frequency region\n", + "kp = 500 # Tune to get desired bandwith\n", + "\n", + "# Updated gains\n", + "kp = 150\n", + "Ti = Td * 5; kp = 150\n", + "\n", + "# Compute controller parmeters\n", + "ki = kp/Ti\n", + "kd = kp * Td\n", + "\n", + "# Controller transfer function\n", + "ctrl_shape = kp + ki / s + kd * s\n", + "ctrl_shape.name = 'C'\n", + "\n", + "# Frequency response (open loop) - use this to help tune your design\n", + "ltf_shape = P * ctrl_shape\n", + "ltf_shape.name = 'L'\n", + "\n", + "ct.frequency_response([P, ctrl_shape]).plot()\n", + "ct.frequency_response(ltf_shape).plot(margins=True);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the closed loop systemsand plot the step response\n", + "# and Nyquist plot (to make sure margins look OK)\n", + "\n", + "# Create the closed loop systems\n", + "clsys_shape = ct.feedback(P * ctrl_shape)\n", + "clsys_shape.name = 'loopshape'\n", + "\n", + "# Step response\n", + "plt.subplot(2, 1, 1)\n", + "stepresp_shape = ct.step_response(clsys_shape)\n", + "stepresp_shape.plot(color='b')\n", + "plt.plot([0, stepresp_shape.time[-1]], [1, 1], 'k--')\n", + "\n", + "# Compare to the ZN controller\n", + "ax = plt.subplot(2, 1, 2)\n", + "ct.step_response(clsys_shape, stepresp_zn_PID.time).plot(\n", + " color='b', ax=np.array([[ax]]))\n", + "stepresp_zn_PID.plot(color='g', ax=np.array([[ax]]))\n", + "ax.plot([0, stepresp_shape.time[-1]], [1, 1], 'k--')\n", + "\n", + "# Nyquist plot\n", + "plt.figure()\n", + "ct.nyquist([ltf_shape])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the loop shaping controller has better step response (faster rise and settling time, less overshoot)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GyXQXykafzWs" + }, + "source": [ + "### Gang of Four\n", + "\n", + "When designing a controller, it is important to look at all of the input/output responses, not just the response from reference to output (which is what the step response above focuses on). \n", + "\n", + "In the frequency domain, the Gang of 4 plots provide useful information on all (important) input/output pairs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ct.gangof4(P, ctrl_shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These all look pretty resonable, except that the transfer function from the reference $r$ to the system input $u$ is getting large at high frequency. This occurs because we did not filter the derivative on the PID controller, so high frequency components of the reference signal (or the measurement noise!) get amplified. We will fix this in the more advanced controller below." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uFO3wiWXhBAK" + }, + "source": [ + "## Anti-windup + derivative filtering\n", + "\n", + "In addition to the amplification of high frequency signals due to the derivative term, another practical consideration in the use of PID controllers is integrator windup. Integrator windup occurs when there are limits on the control inputs so that the error signal may not descrease quickly. This causes the integral term in the PID controller to see an error for a long period of time, and the resulting integration of the error must be offset by making the error have opposite sign for some period of time. This is often undesireable.\n", + "\n", + "To see how to address both amplification of noise due to the derivative term and integrator windup effects in the presence of input constraints, we now implement PID controller with anti-windup and derivative filtering, as shown in the following figure (see also Figure 11.11 in [FBS2e](https://fbswiki.org/wiki/index.php/Feedback_Systems:_An_Introduction_for_Scientists_and_Engineers)):\n", + "\n", + "
\n", + "\n", + "
\n", + "\n", + "### Low pass filter\n", + "\n", + "The low pass filtered derivative has transfer function\n", + "\n", + "$$\n", + "G(s) = \\frac{a\\, s}{s + a}.\n", + "$$\n", + "\n", + "This can be implemented using the differential equation\n", + "\n", + "$$\n", + "\\dot \\xi = -a \\xi + a y, \\qquad\n", + "\\eta = -a \\xi + a y.\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ctrl_params = {'kaw': 5 * ki, 'a': 10/Td}\n", + "\n", + "def ctrl_update(t, x, u, params):\n", + " # Get the parameter values\n", + " kaw = params['kaw']\n", + " a = params['a']\n", + " umax_ctrl = params.get('umax_ctrl', actuator.params['umax'])\n", + "\n", + " # Extract the signals into more familiar variable names\n", + " r, y = u[0], u[1]\n", + " z = x[0] # integral error\n", + " xi = x[1] # filtered derivative\n", + "\n", + " # Compute the controller components\n", + " u_prop = kp * (r - y)\n", + " u_int = z\n", + " ydt_f = -a * xi + a * (-y)\n", + " u_der = kd * ydt_f\n", + "\n", + " # Compute the commanded and saturated outputs\n", + " u_cmd = u_prop + u_int + u_der\n", + " u_sat = np.clip(u_cmd, -umax_ctrl, umax_ctrl)\n", + "\n", + " dz = ki * (r - y) + kaw * (u_sat - u_cmd)\n", + " dxi = -a * xi + a * (-y)\n", + " return np.array([dz, dxi])\n", + "\n", + "def ctrl_output(t, x, u, params):\n", + " # Get the parameter values\n", + " kaw = params['kaw']\n", + " a = params['a']\n", + " umax_ctrl = params.get('umax_ctrl', params['umax'])\n", + "\n", + " # Extract the signals into more familiar variable names\n", + " r, y = u[0], u[1]\n", + " z = x[0] # integral error\n", + " xi = x[1] # filtered derivative\n", + "\n", + " # Compute the controller components\n", + " u_prop = kp * (r - y)\n", + " u_int = z\n", + " ydt_f = -a * xi + a * (-y)\n", + " u_der = kd * ydt_f\n", + "\n", + " # Compute the commanded and saturated outputs\n", + " u_cmd = u_prop + u_int + u_der\n", + " u_sat = np.clip(u_cmd, -umax_ctrl, umax_ctrl)\n", + "\n", + " return u_cmd\n", + "\n", + "ctrl = ct.nlsys(\n", + " ctrl_update, ctrl_output, name='ctrl', params=ctrl_params,\n", + " inputs=['r', 'y'], outputs=['u'], states=2)\n", + "\n", + "clsys = ct.interconnect(\n", + " [servomech, actuator, ctrl], name='clsys',\n", + " inputs=['r'], outputs=['y', 'tau'])\n", + "print(clsys)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the step responses for the following cases:\n", + "#\n", + "# 'linear': the original linear step response (no actuation limits)\n", + "# 'clipped': PID controller with input limits, but not anti-windup\n", + "# 'anti-windup': PID controller with anti-windup compensation\n", + "\n", + "# Use more time points to get smoother response curves\n", + "timepts = np.linspace(0, 2*stepresp_shape.time[-1], 500)\n", + "\n", + "# Compute the response for the individual cases\n", + "stepsize = theta_e / 2\n", + "resp_ln = ct.input_output_response(\n", + " clsys, timepts, stepsize, params={'umax': np.inf, 'kaw': 0, 'a': 1e3})\n", + "resp_cl = ct.input_output_response(\n", + " clsys, timepts, stepsize, params={'umax': 5, 'kaw': 0, 'a': 100})\n", + "resp_aw = ct.input_output_response(\n", + " clsys, timepts, stepsize, params={'umax': 5, 'kaw': 2*ki, 'a': 100})\n", + "\n", + "# Plot the time responses in a single plot\n", + "ct.time_response_plot(resp_ln, color='b', plot_inputs=False, label=\"linear\")\n", + "ct.time_response_plot(resp_cl, color='r', plot_inputs=False, label=\"clipped\")\n", + "ct.time_response_plot(resp_aw, color='g', plot_inputs=False, label=\"anti-windup\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DZS7v0EmdK3H" + }, + "source": [ + "The response of the anti-windup compensator is very sluggish, indicating that we may be setting $k_\\text{aw}$ too high." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "resp_aw = ct.input_output_response(\n", + " clsys, timepts, stepsize, params={'umax': 5, 'kaw': 0.05 * ki, 'a': 100})\n", + "\n", + "# Plot the time responses in a single plot\n", + "ct.time_response_plot(resp_ln, color='b', plot_inputs=False, label=\"linear\")\n", + "ct.time_response_plot(resp_cl, color='r', plot_inputs=False, label=\"clipped\")\n", + "ct.time_response_plot(resp_aw, color='g', plot_inputs=False, label=\"anti-windup\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pCp_pu0Kh62b" + }, + "source": [ + "This gives a much better response, though the value of $k_\\text{aw}$ falls well outside the range of [2, 10]. One reason for this is that $k_\\text{aw}$ acts on the inputs, $\\tau$, which are roughly 100 larger than the size of the outputs, $y$, as seen in the above plots." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1FVGh3k0Y7vB" + }, + "source": [ + "We can now see if this affects the Gang of Four in the expected way:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "C = ctrl.linearize([0, 0], 0, params=resp_aw.params)[0, 1]\n", + "ct.gangof4(P, C);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vT1WfhRHb2ZU" + }, + "source": [ + "Note that in the transfer function from $r$ to $u$ (which is the same as the transfer function from $e$ to $u$, the high frequency gain is now bounded. (We could make it go back down by using a second order filter.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/cds110_bode-nyquist.ipynb b/examples/cds110_bode-nyquist.ipynb deleted file mode 100644 index eb0988e1c..000000000 --- a/examples/cds110_bode-nyquist.ipynb +++ /dev/null @@ -1,1254 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "8c577d78-3e4a-4f08-93ed-5c60867b9a3b", - "metadata": { - "id": "hairy-humidity" - }, - "source": [ - "# Frequency domain analysis using Bode/Nyquist plots\n", - "\n", - "**CDS 110, Winter 2024**
\n", - "Richard M. Murray\n", - "\n", - "\n", - "The purpose of this lecture is to introduce tools that can be used for frequency domain modeling and analysis of linear systems. It illustrates the use of a variety of frequency domain analysis and plotting tools." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "invalid-carnival", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "python-control 0.10.1.dev32+gdbc998de\n" - ] - } - ], - "source": [ - "# Import standard packages needed for this exercise\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import math\n", - "\n", - "from math import pi, sin, cos\n", - "\n", - "import control as ct\n", - "print(\"python-control\", ct.__version__)" - ] - }, - { - "cell_type": "markdown", - "id": "P7t3Nm4Tre2Z", - "metadata": { - "id": "P7t3Nm4Tre2Z" - }, - "source": [ - "## Stable system: servomechanism\n", - "\n", - "We start with a simple example a stable system for which we wish to design a simple controller and analyze its performance, demonstrating along the way the basic frequency domain analysis functions in the Python control toolbox (python-control).\n", - "\n", - "Consider a simple mechanism for positioning a mechanical arm whose equations of motion are given by\n", - "\n", - "$$\n", - "J \\ddot \\theta = -b \\dot\\theta - k r\\sin\\theta + \\tau_\\text{m},\n", - "$$\n", - "\n", - "which can be written in state space form as\n", - "\n", - "$$\n", - "\\frac{d}{dt} \\begin{bmatrix} \\theta \\\\ \\theta \\end{bmatrix} =\n", - " \\begin{bmatrix} \\dot\\theta \\\\ -k r \\sin\\theta / J - b\\dot\\theta / J \\end{bmatrix}\n", - " + \\begin{bmatrix} 0 \\\\ 1/J \\end{bmatrix} \\tau_\\text{m}.\n", - "$$\n", - "\n", - "The system consists of a spring loaded arm that is driven by a motor, as shown below.\n", - "\n", - "
\"servomech-diagram\"
\n", - "\n", - "The motor applies a torque that twists the arm against a linear spring and moves the end of the arm across a rotating platter. The input to the system is the motor torque $\\tau_\\text{m}$. The force exerted by the spring is a nonlinear function of the head position due to the way it is attached.\n", - "\n", - "The system parameters are given by\n", - "\n", - "$$\n", - "k = 1,\\quad J = 100,\\quad b = 10,\n", - "\\quad r = 1,\\quad l = 2,\\quad \\epsilon = 0.01,\n", - "$$\n", - "\n", - "and we assume that time is measured in msec and distance in cm. (The constants here are made up and don't necessarily reflect a real disk drive, though the units and time constants are motivated by computer disk drives.)" - ] - }, - { - "cell_type": "markdown", - "id": "3e476db9", - "metadata": { - "id": "3e476db9" - }, - "source": [ - "The system dynamics can be modeled in python-control using a `NonlinearIOSystem` object, which we create with the `nlsys` function:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "27bb3c38", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": servomech\n", - "Inputs (1): ['tau']\n", - "Outputs (1): ['y']\n", - "States (2): ['theta_', 'thdot_']\n", - "\n", - "Update: \n", - "Output: \n", - "\n", - "Params: {'J': 100, 'b': 10, 'k': 1, 'r': 1, 'l': 2, 'eps': 0.01}\n" - ] - } - ], - "source": [ - "# Parameter values\n", - "servomech_params = {\n", - " 'J': 100, # Moment of inertial of the motor\n", - " 'b': 10, # Angular damping of the arm\n", - " 'k': 1, # Spring constant\n", - " 'r': 1, # Location of spring contact on arm\n", - " 'l': 2, # Distance to the read head\n", - " 'eps': 0.01, # Magnitude of velocity-dependent perturbation\n", - "}\n", - "\n", - "# State derivative\n", - "def servomech_update(t, x, u, params):\n", - " # Extract the configuration and velocity variables from the state vector\n", - " theta = x[0] # Angular position of the disk drive arm\n", - " thetadot = x[1] # Angular velocity of the disk drive arm\n", - " tau = u[0] # Torque applied at the base of the arm\n", - "\n", - " # Get the parameter values\n", - " J, b, k, r = map(params.get, ['J', 'b', 'k', 'r'])\n", - "\n", - " # Compute the angular acceleration\n", - " dthetadot = 1/J * (\n", - " -b * thetadot - k * r * np.sin(theta) + tau)\n", - "\n", - " # Return the state update law\n", - " return np.array([thetadot, dthetadot])\n", - "\n", - "# System output (end of arm)\n", - "def servomech_output(t, x, u, params):\n", - " l = params['l']\n", - " return np.array([l * x[0]])\n", - "\n", - "# System dynamics\n", - "servomech = ct.nlsys(\n", - " servomech_update, servomech_output, name='servomech',\n", - " params=servomech_params,\n", - " states=['theta_', 'thdot_'],\n", - " outputs=['y'], inputs=['tau'])\n", - "\n", - "print(servomech)\n", - "print(\"\\nParams:\", servomech.params)" - ] - }, - { - "cell_type": "markdown", - "id": "competitive-terrain", - "metadata": { - "id": "competitive-terrain" - }, - "source": [ - "### Linearization\n", - "\n", - "To study the open loop dynamics of the system, we compute the linearization of the dynamics about the equilibrium point corresponding to $\\theta_\\text{e} = 15^\\circ$." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "senior-carpet", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Equilibrium torque = 0.258819\n", - "Linearized dynamics: : P_ss\n", - "Inputs (1): ['u[0]']\n", - "Outputs (1): ['y[0]']\n", - "States (2): ['x[0]', 'x[1]']\n", - "\n", - "A = [[ 0. 1. ]\n", - " [-0.00965926 -0.1 ]]\n", - "\n", - "B = [[0. ]\n", - " [0.01]]\n", - "\n", - "C = [[2. 0.]]\n", - "\n", - "D = [[0.]]\n", - "\n", - "Zeros: []\n", - "Poles: [-0.05+0.08461239j -0.05-0.08461239j]\n", - "\n", - ": P_tf\n", - "Inputs (1): ['u[0]']\n", - "Outputs (1): ['y[0]']\n", - "\n", - "\n", - " 0.02\n", - "----------------------\n", - "s^2 + 0.1 s + 0.009659\n", - "\n" - ] - } - ], - "source": [ - "# Convert the equilibrium angle to radians\n", - "theta_e = (15 / 180) * np.pi\n", - "\n", - "# Compute the input required to hold this position\n", - "u_e = servomech.params['k'] * servomech.params['r'] * np.sin(theta_e)\n", - "print(\"Equilibrium torque = %g\" % u_e)\n", - "\n", - "# Linearize the system about the equilibrium point\n", - "P = servomech.linearize([theta_e, 0], u_e, name='P_ss')\n", - "P.name = 'P_ss' # TODO: fix in nlsys_improvements\n", - "print(\"Linearized dynamics:\", P)\n", - "print(\"Zeros: \", P.zeros())\n", - "print(\"Poles: \", P.poles())\n", - "print(\"\")\n", - "\n", - "# Transfer function representation\n", - "P_tf = ct.tf(P, name='P_tf')\n", - "print(P_tf)" - ] - }, - { - "cell_type": "markdown", - "id": "instant-lancaster", - "metadata": { - "id": "instant-lancaster" - }, - "source": [ - "### Open loop frequency response\n", - "\n", - "A standard method for understanding the dynamics is to plot the output of the system in response to sinusoids with unit magnitude at different frequencies.\n", - "\n", - "We use the `frequency_response` function to plot the step response of the linearized, open-loop system." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "RxXFTpwO5bGI", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[list([])],\n", - " [list([])]],\n", - " dtype=object)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Reset the frequency response label to correspond to a time unit of ms\n", - "ct.set_defaults('freqplot', freq_label=\"Frequency [rad/ms]\")\n", - "\n", - "# Frequency response\n", - "freqresp = ct.frequency_response(P, np.logspace(-2, 0))\n", - "freqresp.plot()\n", - "\n", - "# Equivalent command\n", - "ct.bode_plot(P_tf, np.logspace(-2, 0), '--')" - ] - }, - { - "cell_type": "markdown", - "id": "stuffed-premiere", - "metadata": { - "id": "stuffed-premiere" - }, - "source": [ - "### Feedback control design\n", - "\n", - "We next design a feedback controller for the system using a proportional integral controller, which has transfer function\n", - "\n", - "$$\n", - "C(s) = \\frac{k_\\text{p} s + k_\\text{i}}{s}\n", - "$$\n", - "\n", - "We will learn how to choose $k_\\text{p}$ and $k_\\text{i}$ more formally in W9. For now we just pick different values to see how the dynamics are impacted." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "8NK8O6XT7B_a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": C\n", - "Inputs (1): ['u[0]']\n", - "Outputs (1): ['y[0]']\n", - "\n", - "\n", - "s + 1\n", - "-----\n", - " s\n", - "\n", - ": C\n", - "Inputs (1): ['u[0]']\n", - "Outputs (1): ['y[0]']\n", - "\n", - "\n", - "s + 1\n", - "-----\n", - " s\n", - "\n" - ] - } - ], - "source": [ - "kp = 1\n", - "ki = 1\n", - "\n", - "# Create tf from numerator/denominator coefficients\n", - "C = ct.tf([kp, ki], [1, 0], name='C')\n", - "print(C)\n", - "\n", - "# Alternative method: define \"s\" and use algebra\n", - "s = ct.tf('s')\n", - "C = ct.tf(kp + ki/s, name='C')\n", - "print(C)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "074427a3", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Loop transfer function\n", - "L = P * C\n", - "ct.bode_plot([P, C, L], label=['P', 'C', 'L'])\n", - "ct.suptitle(\"PI controller for servomechanism\")" - ] - }, - { - "cell_type": "markdown", - "id": "Bg5ga11VuRtI", - "metadata": { - "id": "Bg5ga11VuRtI" - }, - "source": [ - "Note that L = P * C corresponds to addition in both the magnitude and the phase." - ] - }, - { - "cell_type": "markdown", - "id": "UmYmSzx2rTfg", - "metadata": { - "id": "UmYmSzx2rTfg" - }, - "source": [ - "### Nyquist analysis\n", - "\n", - "To check stability (and eventually robustness), we use the Nyquist criterion." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "Qmp59pmS9GLj", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=[7, 4])\n", - "ax1 = plt.subplot(2, 2, 1)\n", - "ax2 = plt.subplot(2, 2, 3)\n", - "ct.bode_plot(L, ax=[ax1, ax2])\n", - "\n", - "# Tidy up the figure a bit\n", - "fig.align_labels()\n", - "ax1.set_title(\"Bode plot for L\", fontsize='medium')\n", - "\n", - "ax2 = plt.subplot(1, 2, 2)\n", - "ct.nyquist_plot(L, ax=ax2, title=\"\")\n", - "plt.title(\"Nyquist plot for L\", fontsize='medium')\n", - "\n", - "ct.suptitle(\"Loop analysis for (unstable) servomechanism\")" - ] - }, - { - "cell_type": "markdown", - "id": "s4dDf4PrZqU3", - "metadata": { - "id": "s4dDf4PrZqU3" - }, - "source": [ - "We see from this plot that the loop transfer function encircles the -1 point => closed loop system should be unstable. We can check this by making use of additional features of Nyquist analysis." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "K7ifUBL0Z3xN", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N = encirclements: 2\n", - "P = RHP poles of L: 0\n", - "Z = N + P = RHP zeros of 1 + L: 2\n", - "Zeros of (1 + L) = [-0.26792107+0.j 0.08396054+0.259999j 0.08396054-0.259999j]\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Get the Nyquist *response*, so that we can get back encirclements\n", - "nyqresp = ct.nyquist_response(L)\n", - "print(\"N = encirclements: \", nyqresp.count)\n", - "print(\"P = RHP poles of L: \", np.sum(np.real(L.poles()) > 0))\n", - "print(\"Z = N + P = RHP zeros of 1 + L:\", np.sum(np.real((1 + L).zeros()) > 0))\n", - "print(\"Zeros of (1 + L) = \", (1 + L).zeros())\n", - "print(\"\")\n", - "\n", - "T = ct.feedback(L)\n", - "ct.step_response(T).plot(\n", - " title=\"Step response for (unstable) servomechanism\",\n", - " time_label=\"Time [ms]\");" - ] - }, - { - "cell_type": "markdown", - "id": "p3JxLilMxdOE", - "metadata": { - "id": "p3JxLilMxdOE" - }, - "source": [ - "### Poles on the $j\\omega$ axis\n", - "\n", - "Note that we have a pole at 0 (due to the integrator in the controller). How is this handled?\n", - "\n", - "A: use a small loop to the right around poles on the $j\\omega$ axis => not inside the contour.\n", - "\n", - "To see this, we use the `nyquist_response` function, which returns the contour used to compute the Nyquist curve. If we zoom in on the contour near the origin, we see how the outer edge of the Nyquist curve is computed." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "R5IBk3Ai9Slk", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=[7, 5.8])\n", - "\n", - "# Plot the D contour\n", - "ax1 = plt.subplot(2, 2, 1)\n", - "plt.plot(np.real(nyqresp.contour), np.imag(nyqresp.contour))\n", - "plt.axis([-1e-4, 4e-4, 0, 4e-4])\n", - "plt.xlabel('Real axis')\n", - "plt.ylabel('Imaginary axis')\n", - "plt.title(\"Zoom on D-contour\", size='medium')\n", - "\n", - "# Clean up the display of the units\n", - "from matplotlib import ticker\n", - "ax1.xaxis.set_major_formatter(ticker.StrMethodFormatter(\"{x:.0e}\"))\n", - "ax1.yaxis.set_major_formatter(ticker.StrMethodFormatter(\"{x:.0e}\"))\n", - "\n", - "ax2 = plt.subplot(2, 2, 2)\n", - "ct.nyquist_plot(L, ax=ax2)\n", - "plt.title(\"Nyquist curve\", size='medium')\n", - "\n", - "ct.suptitle(\"Nyquist contour for pole at the origin\")" - ] - }, - { - "cell_type": "markdown", - "id": "h20JRZ_r4fGy", - "metadata": { - "id": "h20JRZ_r4fGy" - }, - "source": [ - "### Second iteration feedback control design\n", - "\n", - "We now redesign the control system to give something that is stable. We can do this by moving the zero for the controller to a lower frequency, so that the phase lag from the integrator does not overlap with the phase lag from the system dynamics." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "YsM8SnXz_Kaj", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Change the frequency response to avoid crossing over -180 with large gain\n", - "Cnew = ct.tf(kp + (ki/200)/s, name='C_new')\n", - "Lnew = ct.tf(P * Cnew, name='L_new')\n", - "\n", - "plt.figure(figsize=[7, 4])\n", - "ax1 = plt.subplot(2, 2, 1)\n", - "ax2 = plt.subplot(2, 2, 3)\n", - "ct.bode_plot([Lnew, L], ax=[ax1, ax2], label=['L_new', 'L_old'])\n", - "\n", - "# Clean up the figure a bit\n", - "ax1.loglog([1e-3, 1e1], [1, 1], 'k', linewidth=0.5)\n", - "ax1.set_title(\"Bode plot for L_new, L_old\", size='medium')\n", - "\n", - "ax3=plt.subplot(1, 2, 2)\n", - "ct.nyquist_plot(Lnew, max_curve_magnitude=5, ax=ax3)\n", - "ax3.set_title(\"Nyquist plot for Lnew\", size='medium')\n", - "\n", - "plt.suptitle(\"Loop analysis for (stable) servomechanism\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "kFjeGXzDvucx", - "metadata": { - "id": "kFjeGXzDvucx" - }, - "source": [ - "We see now that we have no encirclements, and so the system should be stable.\n", - "\n", - "Note however that the Nyquist curve is close to the -1 point => not *that* stable." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "GGfJwG716jU2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Step response for (stable) spring-mass system')" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Compute the transfer function from r to y\n", - "Tnew = ct.feedback(Lnew)\n", - "ct.step_response(Tnew).plot(time_label=\"Time [ms]\")\n", - "plt.suptitle(\"Step response for (stable) spring-mass system\")" - ] - }, - { - "cell_type": "markdown", - "id": "b5114fa7-6924-47d7-8dd2-f12060152edd", - "metadata": {}, - "source": [ - "### Third iteration feedback control design (via loop shaping)\n", - "\n", - "To get a better design, we use a PID controller to shape the frequency response so that we get high gain at low frequency and low phase at crossover." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "e6da93a4-5202-45d7-9e5a-697848f4ba71", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Design parameters\n", - "Td = 1 # Set to gain crossover frequency\n", - "Ti = Td * 10 # Set to low frequency region\n", - "kp = 500 # Tune to get desired bandwith\n", - "\n", - "# Updated gains\n", - "kp = 150\n", - "Ti = Td * 5; kp = 150\n", - "\n", - "# Compute controller parmeters\n", - "ki = kp/Ti\n", - "kd = kp * Td\n", - "\n", - "# Controller transfer function\n", - "ctrl_shape = kp + ki / s + kd * s\n", - "\n", - "# Frequency response (open loop) - use this to help tune your design\n", - "ltf_shape = ct.tf(P_tf * ctrl_shape, name='L_shape')\n", - "\n", - "ct.frequency_response([P, ctrl_shape]).plot(label=['P', 'C_shape'])\n", - "ct.frequency_response(ltf_shape).plot(margins=True)\n", - "\n", - "ct.suptitle(\"Loop shaping design for servomechanism controller\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d731f372-4992-464c-9ca5-49cc1d554799", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 0.98, 'Step response for servomechanism with PID controller')" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Compute the transfer function from r to y\n", - "T_shape = ct.feedback(ltf_shape)\n", - "ct.step_response(T_shape).plot(time_label=\"Time [ms]\")\n", - "plt.suptitle(\"Step response for servomechanism with PID controller\")" - ] - }, - { - "cell_type": "markdown", - "id": "JL99vo4trep5", - "metadata": { - "id": "JL99vo4trep5" - }, - "source": [ - "### Closed loop frequency response\n", - "\n", - "We can also look at the closed loop frequency response to understand how different inputs affect different outputs. The `gangof4` function computes the standard transfer functions:" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "ceqcg3oM619g", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ct.gangof4(P_tf, ctrl_shape);" - ] - }, - { - "cell_type": "markdown", - "id": "gel18-iqwYYs", - "metadata": { - "id": "gel18-iqwYYs" - }, - "source": [ - "### Stability margins\n", - "\n", - "Another standard set of analysis tools is to identify the gain, phase, and stability margins for the sytem:\n", - "\n", - "* **Gain margin:** the maximimum amount of additional gain that we can put into the loop and still maintain stability.\n", - "* **Phase margin:** the maximum amount of additional phase (lag) that we can put into the loop and still maintain stability.\n", - "* **Stability margin:** the maximum amount of combined gain and phase at the critical frequency that can be put into the loop and still maintain stability.\n", - "\n", - "The first two of the items can be computed either by looking at the frequeny response or by using the `margin` command.\n", - "\n", - "The stabilty margin is the minimum distance between -1 and $L(jw)$, which is just the minimum value of $|1 - L(j\\omega)|$.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "m-8ItbHwxLrv", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gm = inf (at nan rad/ms)\n", - "Pm = 47 deg (at 0.15 rad/ms)\n", - "Sm = 0.6 (at 0.19 rad/ms)\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=[7, 4])\n", - "\n", - "# Gain and phase margin on Bode plot\n", - "ax1 = plt.subplot(2, 2, 1)\n", - "plt.title(\"Bode plot for Lnew, with margins\")\n", - "ax2 = plt.subplot(2, 2, 3)\n", - "ct.bode_plot(Lnew, ax=[ax1, ax2], margins=True)\n", - "\n", - "# Compute gain and phase margin\n", - "gm, pm, wpc, wgc = ct.margin(Lnew)\n", - "print(f\"Gm = {gm:2.2g} (at {wpc:.2g} rad/ms)\")\n", - "print(f\"Pm = {pm:3.2g} deg (at {wgc:.2g} rad/ms)\")\n", - "\n", - "# Compute the stability margin\n", - "resp = ct.frequency_response(1 + Lnew)\n", - "sm = np.min(resp.magnitude)\n", - "wsm = resp.omega[np.argmin(resp.magnitude)]\n", - "print(f\"Sm = {sm:2.2g} (at {wsm:.2g} rad/ms)\")\n", - "\n", - "# Plot the Nyquist curve\n", - "ax3 = plt.subplot(1, 2, 2)\n", - "ct.nyquist_plot(Lnew, ax=ax3)\n", - "plt.title(\"Nyquist plot for Lnew [zoomed]\")\n", - "plt.axis([-2, 3, -2.6, 2.6])\n", - "\n", - "#\n", - "# Annotate it to see the margins\n", - "#\n", - "\n", - "# Gain margin (special case here, since infinite)\n", - "Lgm = 0\n", - "plt.plot([-1, Lgm], [0, 0], 'k-', linewidth=0.5)\n", - "plt.text(-0.9, 0.1, \"1/gm\")\n", - "\n", - "# Phase margin\n", - "theta = np.linspace(0, 2 * pi)\n", - "plt.plot(np.cos(theta), np.sin(theta), 'k--', linewidth=0.5)\n", - "plt.text(-1.3, -0.8, \"pm\")\n", - "\n", - "# Stability margin\n", - "Lsm = Lnew(wsm * 1j)\n", - "plt.plot([-1, Lsm.real], [0, Lsm.imag], 'k-', linewidth=0.5)\n", - "plt.text(-0.4, -0.5, \"sm\")\n", - "\n", - "plt.suptitle(\"\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "WsOzQST9rFC-", - "metadata": { - "id": "WsOzQST9rFC-" - }, - "source": [ - "## Unstable system: inverted pendulum\n", - "\n", - "When we have a system that is open loop unstable, the Nyquist curve will need to have encirclements to be stable. In this case, the interpretation of the various characteristics can be more complicated.\n", - "\n", - "To explore this, we consider a simple model for an inverted pendulum, which has (normalized) dynamics:\n", - "\n", - "$$\n", - "\\dot x = \\begin{bmatrix} 0 & 1 & \\\\ -1 & 0.1 \\end{bmatrix} x + \\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix} u, \\qquad\n", - "y = \\begin{bmatrix} 1 & 0 \\end{bmatrix} x\n", - "$$\n", - "\n", - "Transfer function for the system can be shown to be\n", - "\n", - "$$\n", - "P(s) = \\frac{1}{s^2 + 0.1 s - 1}.\n", - "$$\n", - "\n", - "This system is unstable, with poles $\\sim\\pm 1$." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "ZbPzrlPIrHnp", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-1.05124922+0.j, 0.95124922+0.j])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ct.set_defaults('freqplot', freq_label=\"Frequency [{units}]\")\n", - "\n", - "P = ct.tf([1], [1, 0.1, -1])\n", - "P.poles()" - ] - }, - { - "cell_type": "markdown", - "id": "W-sBWxKi6SPx", - "metadata": { - "id": "W-sBWxKi6SPx" - }, - "source": [ - "### PD controller\n", - "\n", - "We construct a proportional-derivative (PD) controller for the system,\n", - "\n", - "$$\n", - "u = k_\\text{p} e + k_\\text{d} \\dot{e}\n", - "$$\n", - "\n", - "which is roughly the equivalent of using state feedback (since the system states are $\\theta$ and $\\dot\\theta$)." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "hjQS_dED7yJE", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": L\n", - "Inputs (1): ['u[0]']\n", - "Outputs (1): ['y[0]']\n", - "\n", - "\n", - " 2 s + 10\n", - "---------------\n", - "s^2 + 0.1 s - 1\n", - "\n", - "Zeros: [-5.+0.j]\n", - "Poles: [-1.05124922+0.j 0.95124922+0.j]\n" - ] - } - ], - "source": [ - "# Transfer function for a PD controller\n", - "kp = 10\n", - "kd = 2\n", - "C = ct.tf([kd, kp], [1])\n", - "\n", - "# Loop transfer function\n", - "L = P * C\n", - "L.name = 'L'\n", - "print(L)\n", - "print(\"Zeros: \", L.zeros())\n", - "print(\"Poles: \", L.poles())" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "YI_KJo0E9pFd", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Bode and Nyquist plots\n", - "plt.figure(figsize=[7, 4])\n", - "ax1 = plt.subplot(2, 2, 1)\n", - "plt.title(\"Bode plot for L\", size='medium')\n", - "ax2 = plt.subplot(2, 2, 3)\n", - "ct.bode_plot(L, ax=[ax1, ax2])\n", - "\n", - "ax3 = plt.subplot(1, 2, 2)\n", - "ct.nyquist_plot(L, ax=ax3)\n", - "plt.title(\"Nyquist plot for L\", size='medium')\n", - "\n", - "ct.suptitle(\"Loop analysis for inverted pendulum\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "8dH03kv9-Da8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "N = encirclements: -1\n", - "P = RHP poles of L: 1\n", - "Z = N + P = RHP zeros of 1 + L: 0\n", - "Poles of L = [-1.05124922+0.j 0.95124922+0.j]\n", - "Zeros of 1 + L = [-1.05+2.8102491j -1.05-2.8102491j]\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/murray/src/python-control/murrayrm/control/timeresp.py:1027: UserWarning: Non-zero initial condition given for transfer function system. Internal conversion to state space used; may not be consistent with given X0.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Check the Nyquist criterion\n", - "nyqresp = ct.nyquist_response(L)\n", - "print(\"N = encirclements: \", nyqresp.count)\n", - "print(\"P = RHP poles of L: \", np.sum(np.real(L.poles()) > 0))\n", - "print(\"Z = N + P = RHP zeros of 1 + L:\", np.sum(np.real((1 + L).zeros()) >= 0))\n", - "print(\"Poles of L = \", L.poles())\n", - "print(\"Zeros of 1 + L = \", (1 + L).zeros())\n", - "print(\"\")\n", - "\n", - "T = ct.feedback(L)\n", - "ct.initial_response(T, X0=[0.1, 0]).plot();" - ] - }, - { - "cell_type": "markdown", - "id": "VXlYhs8X7DuN", - "metadata": { - "id": "VXlYhs8X7DuN" - }, - "source": [ - "### Gang of 4\n", - "\n", - "Another useful thing to look at is the transfer functions from noise and disturbances to the system outputs and inputs:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "oTmOun41_opt", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ct.gangof4(P, C);" - ] - }, - { - "cell_type": "markdown", - "id": "U41ve1zh7XPh", - "metadata": { - "id": "U41ve1zh7XPh" - }, - "source": [ - "We see that the response from the input $r$ (or equivalently noise $n$) to the process input is very large for large frequencies. This means that we are amplifying high frequency noise (and comes from the fact that we used derivative feedback)." - ] - }, - { - "cell_type": "markdown", - "id": "YROqmZTd8WYs", - "metadata": { - "id": "YROqmZTd8WYs" - }, - "source": [ - "### High frequency rolloff\n", - "\n", - "We can attempt to resolve this by \"rolling off\" the derivative action at high frequencies:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "vhKi_L-F_6Ws", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": Cnew\n", - "Inputs (1): ['u[0]']\n", - "Outputs (1): ['y[0]']\n", - "\n", - "\n", - " 800 s + 4000\n", - "----------------\n", - "s^2 + 40 s + 400\n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Cnew = (kp + kd * s) / (s/20 + 1)**2\n", - "Cnew.name = 'Cnew'\n", - "print(Cnew)\n", - "\n", - "Lnew = P * Cnew\n", - "Lnew.name = 'Lnew'\n", - "\n", - "plt.figure(figsize=[7, 4])\n", - "ax1 = plt.subplot(2, 2, 1)\n", - "ax2 = plt.subplot(2, 2, 3)\n", - "ct.bode_plot([Lnew, L], ax=[ax1, ax2])\n", - "ax1.loglog([1e-1, 1e2], [1, 1], 'k', linewidth=0.5)\n", - "ax1.set_title(\"Bode plot for L, Lnew\", size='medium')\n", - "\n", - "ax3 = plt.subplot(1, 2, 2)\n", - "ct.nyquist_plot(Lnew, ax=ax3)\n", - "ax3.set_title(\"Nyquist plot for Lnew\", size='medium')\n", - "\n", - "plt.suptitle(\"Stability analysis for inverted pendulum\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "id": "WgrAE9XE7_nJ", - "metadata": { - "id": "WgrAE9XE7_nJ" - }, - "source": [ - "While not (yet) a very high performing controller, this change does get rid of the issues with the high frequency noise:" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "FknwW6GkBLLU", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Check the gang of 4\n", - "ct.gangof4(P, Cnew);" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "wJHJLjXwCNz-", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[list([])]],\n", - " dtype=object)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# See what the step response looks like\n", - "Tnew = ct.feedback(Lnew)\n", - "ct.step_response(Tnew, 10).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "WUhz529a-w3q", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/cds110_invpend-dynamics.ipynb b/examples/cds110_invpend-dynamics.ipynb deleted file mode 100644 index 0543452dd..000000000 --- a/examples/cds110_invpend-dynamics.ipynb +++ /dev/null @@ -1,610 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "t0JD8EbaVWg-" - }, - "source": [ - "# Inverted Pendulum Dynamics\n", - "\n", - "CDS 110, Winter 2024
\n", - "Richard M. Murray\n", - "\n", - "In this lecture we investigate the nonlinear dynamics of an inverted pendulum system. More information on this example can be found in [FBS2e](https://fbswiki.org/wiki/index.php?title=FBS), Examples 3.3 and 5.4.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# Import the packages needed for the examples included in this notebook\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from math import pi\n", - "\n", - "import control as ct" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "P_ZMCccjvHY1" - }, - "source": [ - "## System model" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Msad1ficHjtc" - }, - "source": [ - "The dynamics for an inverted pendulum system can be written as:\n", - "\n", - "$$\n", - " \\dfrac{d}{dt} \\begin{bmatrix} \\theta \\\\ \\dot\\theta\\end{bmatrix} =\n", - " \\begin{bmatrix}\n", - " \\dot\\theta \\\\\n", - " \\dfrac{m g l}{J_\\text{t}} \\sin \\theta\n", - " - \\dfrac{b}{J_\\text{t}} \\dot\\theta\n", - " + \\dfrac{l}{J_\\text{t}} u \\cos\\theta\n", - " \\end{bmatrix}, \\qquad\n", - " y = \\theta,\n", - "$$\n", - "\n", - "where $m$ and $J_t = J + m l^2$ are the mass and (total) moment of inertia of the system to be balanced, $l$ is the distance from the base to the center of mass of the balanced body, $b$ is the coefficient of rotational friction, and $g$ is the acceleration due to gravity.\n", - "\n", - "We begin by creating a nonlinear model of the system:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": invpend\n", - "Inputs (1): ['tau']\n", - "Outputs (2): ['theta', 'thdot']\n", - "States (2): ['theta', 'thdot']\n", - "\n", - "Update: \n", - "Output: None\n" - ] - } - ], - "source": [ - "invpend_params = {'m': 1, 'l': 1, 'b': 0.5, 'g': 1}\n", - "def invpend_update(t, x, u, params):\n", - " m, l, b, g = params['m'], params['l'], params['b'], params['g']\n", - " umax = params.get('umax', 1)\n", - " usat = np.clip(u[0], -umax, umax)\n", - " return [x[1], -b/m * x[1] + (g * l / m) * np.sin(x[0] + usat/m)]\n", - "invpend = ct.nlsys(\n", - " invpend_update, states=['theta', 'thdot'],\n", - " inputs=['tau'], outputs=['theta', 'thdot'],\n", - " params=invpend_params, name='invpend')\n", - "print(invpend)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IAoQAORFvLj1" - }, - "source": [ - "## Open loop dynamics" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vOALp_IwjVxC" - }, - "source": [ - "The open loop dynamics of the system can be visualized using the `phase_plane_plot` command in python-control:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ct.phase_plane_plot(\n", - " invpend, [-2*pi - 1, 2*pi + 1, -2, 2], 8),\n", - "\n", - "# Draw lines at the downward equilibrium angles\n", - "plt.plot([-pi, -pi], [-2, 2], 'k--')\n", - "plt.plot([pi, pi], [-2, 2], 'k--')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WZuvqNzeJinm" - }, - "source": [ - "We see that the vertical ($\\theta = 0$) equilibrium point is unstable, but the downward equlibrium points ($\\theta = \\pm \\pi$) are stable.\n", - "\n", - "Note also the *separatrices* for the equilibrium point, which gives insights into the regions of attraction (the red dashed line separates the two regions of attraction)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "2JibDTJBKHIF" - }, - "source": [ - "## Proportional feedback\n", - "\n", - "We now stabilize the system using a simple proportional feedback controller:\n", - "\n", - "$$u = -k_\\text{p} \\theta.$$\n", - "\n", - "This controller can be designed as an input/output system that has no state dynamics, just a mapping from the inputs to the outputs:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": p_ctrl\n", - "Inputs (2): ['theta', 'r']\n", - "Outputs (1): ['tau']\n", - "States (0): []\n", - "\n", - "Update: . at 0x13c3c37e0>\n", - "Output: \n" - ] - } - ], - "source": [ - "# Set up the controller\n", - "def propctrl_output(t, x, u, params):\n", - " kp = params.get('kp', 1)\n", - " return -kp * (u[0] - u[1])\n", - "propctrl = ct.nlsys(\n", - " None, propctrl_output, name=\"p_ctrl\",\n", - " inputs=['theta', 'r'], outputs='tau'\n", - ")\n", - "print(propctrl)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "AvU35WoBMFjt" - }, - "source": [ - "Note that the input to the controller is the reference value $r$ (which we will always take to be zero), the measured output $y$, which is the angle $\\theta$ for our system. The output of the controller is the system input $u$, corresponding to the force applied to the wheels.\n", - "\n", - "To connect the controller to the system, we use the [`interconnect`](https://python-control.readthedocs.io/en/latest/generated/control.interconnect.html) function, which will connect all signals that have the same names:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": invpend w/ proportional feedback\n", - "Inputs (1): ['r']\n", - "Outputs (2): ['theta', 'tau']\n", - "States (2): ['invpend_theta', 'invpend_thdot']\n", - "\n", - "Update: .updfcn at 0x13dc72700>\n", - "Output: .outfcn at 0x13dc728e0>\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/murray/src/python-control/murrayrm/control/nlsys.py:1208: UserWarning: Unused output(s) in InterconnectedSystem: (0, 1) : invpend.thdot\n", - " warn(msg)\n" - ] - } - ], - "source": [ - "# Create the closed loop system\n", - "clsys = ct.interconnect(\n", - " [invpend, propctrl], name='invpend w/ proportional feedback',\n", - " inputs=['r'], outputs=['theta', 'tau'], params={'kp': 1})\n", - "print(clsys)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IIiSaHNuM1u_" - }, - "source": [ - "We can now linearize the closed loop system at different gains and compute the eigenvalues to check for stability:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "kp = 0 ; poles = [ 0.78077641+0.j -1.28077641+0.j]\n", - "kp = 1 ; poles = [ 0. +0.j -0.5+0.j]\n", - "kp = 10 ; poles = [-0.25+2.98956519j -0.25-2.98956519j]\n" - ] - } - ], - "source": [ - "# Solution\n", - "for kp in [0, 1, 10]:\n", - " print(\"kp = \", kp, \"; poles = \", clsys.linearize([0, 0], [0], params={'kp': kp}).poles())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iV4u31DsNWP9" - }, - "source": [ - "We see that at $k_\\text{p} = 10$ the eigenvalues (poles) of the closed loop system both have negative real part, and so the system is stabilized." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Jg87a3iZP-Qd" - }, - "source": [ - "### Phase portrait\n", - "\n", - "To study the resulting dynamics, we try plotting a phase plot using the same commands as before, but now for the closed loop system (with appropriate proportional gain):" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ct.phase_plane_plot(\n", - " clsys, [-2*pi, 2*pi, -2, 2], 8, params={'kp': 10});" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5nss-eU_vevc" - }, - "source": [ - "### Improved phase portrait" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jhU2gidqi-ri" - }, - "source": [ - "This plot is not very useful and has several errors. It shows the limitations of the default parameter values for the `phase_plane_plot` command.\n", - "\n", - "Some things to notice in this plot:\n", - "* The equilibrium point at $\\theta = 0$ is not showing up. This happens because the grid spacing is such that we don't find that point.\n", - "\n", - "To fix these issues, we can do a couple of things:\n", - "* Restrict the range of the plot from $-\\pi$ to $\\pi$, which means that grid used to calculate the equilibrium point is a bit finer.\n", - "* Reset the grid spacing, so that we have more initial conditions around the edge of the plot and a finer search for equilibrium points.\n", - "\n", - "Here's some improved code:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kp_params = {'kp': 10}\n", - "ct.phase_plane_plot(\n", - " clsys, [-1.5 * pi, 1.5 * pi, -2, 2], 8,\n", - " gridspec=[13, 7], params=kp_params,\n", - " plot_separatrices={'timedata': 5})\n", - "plt.plot([-pi, -pi], [-2, 2], 'k--', [ pi, pi], [-2, 2], 'k--')\n", - "plt.plot([-pi/2, -pi/2], [-2, 2], 'k:', [ pi/2, pi/2], [-2, 2], 'k:')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Play around with some paramters to see what happens\n", - "fig, axs = plt.subplots(2, 2)\n", - "for i, kp in enumerate([3, 10]):\n", - " for j, umax in enumerate([0.2, 1]):\n", - " ct.phase_plane_plot(\n", - " clsys, [-1.5 * pi, 1.5 * pi, -2, 2], 8,\n", - " gridspec=[13, 7], plot_separatrices={'timedata': 5},\n", - " params={'kp': kp, 'umax': umax}, ax=axs[i, j])\n", - " axs[i, j].set_title(f\"{kp=}, {umax=}\")\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dYeVbfG4kU-9" - }, - "source": [ - "## State space controller\n", - "\n", - "For the proportional controller, we have limited control over the dynamics of the closed loop system. For example, we see that the solutions near the origin are highly oscillatory in both the $k_\\text{p} = 3$ and $k_\\text{p} = 10$ cases.\n", - "\n", - "An alternative is to use \"full state feedback\", in which we set\n", - "\n", - "$$\n", - "u = -K (x - x_\\text{d}) = -k_1 (\\theta - \\theta_d) - k_2 (\\dot\\theta - \\dot\\theta_d).\n", - "$$\n", - "\n", - "To compute the gains, we make use of the `place` command, applied to the linearized system:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "K=array([[2.01, 1.5 ]])\n" - ] - } - ], - "source": [ - "# Linearize the system\n", - "P = invpend.linearize([0, 0], [0])\n", - "\n", - "# Place the closed loop eigenvalues (poles) at desired locations\n", - "K = ct.place(P.A, P.B, [-1 + 0.1j, -1 - 0.1j])\n", - "print(f\"{K=}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": k_ctrl\n", - "Inputs (4): ['theta', 'thdot', 'theta_d', 'thdot_d']\n", - "Outputs (1): ['tau']\n", - "States (0): []\n", - "\n", - "Update: . at 0x13dd50a40>\n", - "Output: \n" - ] - } - ], - "source": [ - "def statefbk_output(t, x, u, params):\n", - " K = params.get('K', np.array([0, 0]))\n", - " return -K @ (u[0:2] - u[2:])\n", - "statefbk = ct.nlsys(\n", - " None, statefbk_output, name=\"k_ctrl\",\n", - " inputs=['theta', 'thdot', 'theta_d', 'thdot_d'], outputs='tau'\n", - ")\n", - "print(statefbk)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": invpend w/ state feedback\n", - "Inputs (2): ['theta_d', 'thdot_d']\n", - "Outputs (2): ['theta', 'tau']\n", - "States (2): ['invpend_theta', 'invpend_thdot']\n", - "\n", - "Update: .updfcn at 0x13dd507c0>\n", - "Output: .outfcn at 0x13dd50860>\n" - ] - } - ], - "source": [ - "clsys_sf = ct.interconnect(\n", - " [invpend, statefbk], name='invpend w/ state feedback',\n", - " inputs=['theta_d', 'thdot_d'], outputs=['theta', 'tau'], params={'kp': 1})\n", - "print(clsys_sf)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "aGm3usQIvmqN" - }, - "source": [ - "### Phase portrait" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "ct.phase_plane_plot(\n", - " clsys_sf, [-1.5 * pi, 1.5 * pi, -2, 2], 8,\n", - " gridspec=[13, 7], params={'K': K})\n", - "plt.plot([-pi, -pi], [-2, 2], 'k--', [ pi, pi], [-2, 2], 'k--')\n", - "plt.plot([-pi/2, -pi/2], [-2, 2], 'k:', [ pi/2, pi/2], [-2, 2], 'k:')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "A7UNUtfJwLWQ" - }, - "source": [ - "Note that the closed loop response around the upright equilibrium point is much less oscillatory (consistent with where we placed the closed loop eigenvalues of the system dynamics)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "eVSa1Mvqycov" - }, - "source": [ - "## Things to try\n", - "\n", - "Here are some things to try with the above code:\n", - "* Try changing the locations of the closed loop eigenvalues in the `place` command\n", - "* Try resetting the limits of the control action (`umax`)\n", - "* Try leaving the state space controller fixed but changing the parameters of the system dynamics ($m$, $l$, $b$). Does the controller still stabilize the system?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/cds110_lti-systems.ipynb b/examples/cds110_lti-systems.ipynb deleted file mode 100644 index 2f28f06c9..000000000 --- a/examples/cds110_lti-systems.ipynb +++ /dev/null @@ -1,827 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "gQZtf4ZqM8HL" - }, - "source": [ - "# Python Tools for Analyzing Linear Systems\n", - "\n", - "CDS 110, Winter 2024
\n", - "Richard M. Murray\n", - "\n", - "In this lecture we describe tools in the Python Control Systems Toolbox (python-control) that can be used to analyze linear systems, including some of the options available to present the information in different ways.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "python-control version: 0.10.1.dev32+gdbc998de\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import control as ct\n", - "print(\"python-control version:\", ct.__version__)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "qMVGK15gNQw2" - }, - "source": [ - "## Coupled mass spring system\n", - "\n", - "Consider the spring mass system below:\n", - "\n", - "\n", - "\n", - "We wish to analyze the time and frequency response of this system using a variety of python-control functions for linear systems analysis.\n", - "\n", - "### System dynamics\n", - "\n", - "The dynamics of the system can be written as\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - " m \\ddot{q}_1 &= -2 k q_1 - c \\dot{q}_1 + k q_2, \\\\\n", - " m \\ddot{q}_2 &= k q_1 - 2 k q_2 - c \\dot{q}_2 + ku\n", - "\\end{aligned}\n", - "$$\n", - "\n", - "or in state space form:\n", - "\n", - "$$\n", - "\\begin{aligned}\n", - " \\dfrac{dx}{dt} &= \\begin{bmatrix}\n", - " 0 & 0 & 1 & 0 \\\\\n", - " 0 & 0 & 0 & 1 \\\\[0.5ex]\n", - " -\\dfrac{2k}{m} & \\dfrac{k}{m} & -\\dfrac{c}{m} & 0 \\\\[0.5ex]\n", - " \\dfrac{k}{m} & -\\dfrac{2k}{m} & 0 & -\\dfrac{c}{m}\n", - " \\end{bmatrix} x\n", - " + \\begin{bmatrix}\n", - " 0 \\\\ 0 \\\\[0.5ex] 0 \\\\[1ex] \\dfrac{k}{m}\n", - " \\end{bmatrix} u.\n", - "\\end{aligned}\n", - "$$\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": coupled spring mass\n", - "Inputs (1): ['u[0]']\n", - "Outputs (2): ['q1', 'q2']\n", - "States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]']\n", - "\n", - "A = [[ 0. 0. 1. 0. ]\n", - " [ 0. 0. 0. 1. ]\n", - " [-4. 2. -0.1 0. ]\n", - " [ 2. -4. 0. -0.1]]\n", - "\n", - "B = [[0.]\n", - " [0.]\n", - " [0.]\n", - " [2.]]\n", - "\n", - "C = [[1. 0. 0. 0.]\n", - " [0. 1. 0. 0.]]\n", - "\n", - "D = [[0.]\n", - " [0.]]\n", - "\n" - ] - } - ], - "source": [ - "# Define the parameters for the system\n", - "m, c, k = 1, 0.1, 2\n", - "# Create a linear system\n", - "A = np.array([\n", - " [0, 0, 1, 0],\n", - " [0, 0, 0, 1],\n", - " [-2*k/m, k/m, -c/m, 0],\n", - " [k/m, -2*k/m, 0, -c/m]\n", - "])\n", - "B = np.array([[0], [0], [0], [k/m]])\n", - "C = np.array([[1, 0, 0, 0], [0, 1, 0, 0]])\n", - "D = 0\n", - "\n", - "sys = ct.ss(A, B, C, D, outputs=['q1', 'q2'], name=\"coupled spring mass\")\n", - "print(sys)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kobxJ1yG4v_1" - }, - "source": [ - "Another way to get these same dynamics is to define and input/output system:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": sys[0]\n", - "Inputs (1): ['u[0]']\n", - "Outputs (2): ['y[0]', 'y[1]']\n", - "States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]']\n", - "\n", - "A = [[ 0. 0. 1. 0. ]\n", - " [ 0. 0. 0. 1. ]\n", - " [-4. 2. -0.1 0. ]\n", - " [ 2. -4. 0. -0.1]]\n", - "\n", - "B = [[0.]\n", - " [0.]\n", - " [0.]\n", - " [2.]]\n", - "\n", - "C = [[1. 0. 0. 0.]\n", - " [0. 1. 0. 0.]]\n", - "\n", - "D = [[0.]\n", - " [0.]]\n", - "\n" - ] - } - ], - "source": [ - "coupled_params = {'m': 1, 'c': 0.1, 'k': 2}\n", - "def coupled_update(t, x, u, params):\n", - " m, c, k = params['m'], params['c'], params['k']\n", - " return np.array([\n", - " x[2], x[3],\n", - " -2*k/m * x[0] + k/m * x[1] - c/m * x[2],\n", - " k/m * x[0] -2*k/m * x[1] - c/m * x[3] + k/m * u[0]\n", - " ])\n", - "def coupled_output(t, x, u, params):\n", - " return x[0:2]\n", - "coupled = ct.nlsys(\n", - " coupled_update, coupled_output, inputs=1, outputs=['q1', 'q2'],\n", - " states=['q1', 'q2', 'q1dot', 'q2dot'], name='coupled (nl)',\n", - " params=coupled_params\n", - ")\n", - "print(coupled.linearize([0, 0, 0, 0], [0]))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YmH87LEXWo1U" - }, - "source": [ - "### Initial response\n", - "\n", - "The `initial_response` function can be used to compute the response of the system with no input, but starting from a given initial condition. This function returns a response object, we can be used for plotting." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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O/OIXv8DmzZulLty+jHyJZtSoUXjnnXcwf/58CIKAv/zlLyFZ9XjfN2vWrMFXX32FM844A9nZ2VizZg2am5sT/gOBkHCUcSOkH6hUKvzf//0fnnvuOeTn50cNZhQKBZYtW4YNGzZg/Pjx+N3vfofHHnvssDz/xIkT8d1336GyshKzZ8/Gcccdh7/85S/Iy8sDII6geOedd3DKKadg7NixePbZZ7F06VKUl5dL57jqqqvgcDgwffp03Hzzzfj1r3+NG264QXr7Sy+9hClTpuC8887DiSeeCMYYPv74417PPzMYDNi9ezd+8pOfYPTo0bjhhhtwyy234MYbbwQAnHnmmfjwww/xxRdfYNq0aZgxYwYef/xxDB8+PKF7ceutt+IPf/gD/vCHP2DChAn49NNPpfESiXr77bcxbdo0XHbZZRg3bhxuv/12Kat6/PHH480338SyZcswfvx43HPPPbj//vtxzTXXSO//j3/8A4WFhZgzZw4uv/xy3HbbbTAYDBHP88gjj+DRRx/FpEmT8P3332P58uXIzMyMek09fa4B8XNVWFiIuXPn4qKLLsINN9yA7OzsmB9nT58bQAxyLrroIpxzzjk444wzMH78+JAg9XAZMWIE3nrrLbzzzjuYOHEinnnmGdx1110AAK1We1if64knnkBaWhpmzpyJ+fPn48wzz8Txxx8vvT3e943ZbMaKFStwzjnnYPTo0bj77rvxj3/8A2efffZhvUZy7BFYbxcHEEKOWfPmzcPkyZNpa6J+FmvHjcHm3nvvxXvvvTdgW7E99NBDePbZZ1FTUzMgz09If6JSKSGEkCFl8eLFmDZtGjIyMvDDDz/gsccewy233DLQl0VIv6DAjRBCyJBSWVmJBx98EG1tbSgqKsIf/vAH3HnnnQN9WYT0CyqVEkIIIYQMEdScQAghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRFDgRgghhBAyRKgG+gIGC7/fj7q6OqSkpEAQhIG+HEIIIYQcQxhj6OrqQn5+PhSK2Hk1CtwC6urqUFhYONCXQQghhJBjWE1NDQoKCmK+nQK3gJSUFADiDTObzQN8NYQQQgg5llitVhQWFkrxSCwUuAXw8qjZbKbAjRBCCCEDoqflWtScQAghhBAyRAz6wG3RokUYN24cFAoFli1bFvM4h8OBK664AikpKSgqKsLSpUv78SoJIYQQQo68QR+4lZaW4sknn8T06dPjHrdo0SK0tbWhtrYWy5Ytwy9/+UtUVFT001USQgghhBx5gz5wu+KKK3D66adDp9PFPW7JkiVYtGgRzGYzZs6cifPPPz9uhq6/7W+xY/nmWjR0Ogf6UgghhBAyRA36wK032tvb0dDQgAkTJkiPTZo0CTt27Ij5Pi6XC1arNeTfkXT7W1vwm2Wb8WNVyxF9HkIIIYQcvY6KwM1ms0GpVMJgMEiPmc1m2Gy2mO/z8MMPw2KxSP+O9Ay38nwLAGB77ZENEAkhhBBy9DoqAjeTyQSfz4fu7m7pMavVCpPJFPN97rzzTnR2dkr/ampqjug1lueLI0Z2N1DgRgghhJDkHBWBW1paGnJzc7Ft2zbpsS1btqC8vDzm+2i1WmlmW3/Mbssxi2v02uzuI/o8hBBCCDl6DfrAzePxwOl0wu/3h7wc7oorrsADDzyArq4urF69Gu+//z4uvfTSAbji6Cx6NQCg0+EZ4CshhBBCyFA16AO366+/Hnq9Ht9//z2uuuoq6PV6rFixAq+99lpIRu3++++HxWJBXl4eLr74YixevBhlZWUDeOWhKHAjhBBCSF8JjDE20BcxGFitVlgsFnR2dh6Rsmm73Y3jHvgCAFD50NlQKwd9zEwIIYSQftLbOISih35iDmTcAMq6EUIIISQ5FLj1E6VCgFGjBAB0Ob0DfDWEEEIIGYoocOtHeo0KAOBw+wb4SgghhBAyFFHg1o8MgYybw0OBGyGEEEISR4FbP9KrA4EbZdwIIYQQkgQK3PqRnjJuhBBCCOkDCtz6Ec+4dbupOYEQQgghiaPArR9Ja9yoVEoIIYSQJFDg1o+oVEoIIYSQvqDArR8FS6UUuBFCCCEkcRS49SOecXNRxo0QQgghSaDArR9pAvuTunz+Ab4SQgghhAxFFLj1I40qELh5KHAjhBBCSOIocOtHWpVYKnVTxo0QQgghSaDArR/xjJvbS4EbIYQQQhJHgVs/kkqlFLgRQgghJAkUuPUjrZRxo65SQgghhCSOArd+RKVSQgghhPQFBW79SEulUkIIIYT0AQVu/UhLGTdCCCGE9AEFbv1IKpXSOBBCCCGEJIECt36kUfItryhwI4QQQkjiKHDrR1o1ZdwIIYQQkjwK3PoR36uU1rgRQgghJBkUuPWj4ADe5Oe4fbOnCa/8eKBP5yCEEELI0KQa6As4lvR1HIjT48N1r6yHz8/AGMM1s0YczssjhBBCyCBHGbd+1NcBvAda7fD5GQBgX4v9sF0XIYQQQoYGCtz6kXyvUsZYwu+/rzkYrNW0dR+26yKEEELI0ECBWz/SqpTSyx5f4oFbk9UpvVzX4YxzJCGEEEKORhS49SO+xg1IbiRIm90tvdzhcMc5khBCCCFHIwrc+hEfBwIALk/iXaFt3bLArdtzWK6JEEIIIUMHBW79SKEQoFYKAJLLuLXbg8Gay+uHM4ngjxBCCCFDFwVu/awvQ3jbu0PLo1YHZd0IIYSQYwkFbv1Mqw7sV5pE4GZ3h2bYOihwI4QQQo4pFLj1s75k3Bxub8jrdpc3xpGEEEIIORpR4NbP+rLtVXdYxs3hpjVuhBBCyLFk0Aduzc3NOPfcc2EwGFBWVoavvvoq6nHXXHMNtFotTCYTTCYTysvL+/lKeye4e0Lic9x4oKZTi+cID+QIIYQQcnQb9IHbzTffjPz8fLS0tODRRx/FxRdfjPb29qjH3nfffbDZbLDZbNixY0c/X2nvqAOlUk8SXaX2QKk0w6gFADioq5QQQgg5pgzqwM1ms2H58uW4//77YTAYsGDBAowfPx4ffPBBn8/tcrlgtVpD/vUHTWAcSKKBm9/P4PSI75Np0gCgUikhhBByrBnUgVtlZSUsFgvy8vKkxyZNmhQzm/bYY48hIyMDM2fOxIoVK+Ke++GHH4bFYpH+FRYWHtZrj4WXShMN3OTZtQwTZdwIIYSQY9GgDtxsNhvMZnPIY2azGTabLeLY3/zmN9i7dy/q6+tx8803Y/78+aipqYl57jvvvBOdnZ3Sv3jHHk68VOpOcK9Svp5NEIBUgzrksSPN52dgLPE1eYQQQgg5vAZ14GYymSJKmFarFSaTKeLY4447DmlpadBoNFi4cCFOPPFEfPHFFzHPrdVqYTabQ/71B2mNW4LjQLoD69v0aiWMGhWAyPEgR0J9pwNTH/wC172yHn4/BW+EEELIQBrUgVtpaSk6OzvR0NAgPbZly5ZedYwqFIPzQwtm3BIN3MTsmkGjhEEjDvHtj1LpFzsb0d7twVe7m7DxYPSmEEIIIYT0j8EZ3QSYTCacf/75WLRoERwOB95//31s374d8+fPjzj27bffht1uh9frxRtvvIGVK1filFNOGYCrjk+jSq45IRi4qaAL7L7QH6XSzTUd0su7G7qO+PMRQgghJLZBHbgBwOLFi1FTU4OMjAzcdtttePPNN5GWlobXXnstJPP2xBNPID8/H5mZmXj88cfx7rvvori4eOAuPAZ1kjsnOAYo41bf4ZRermqOXFtICCGEkP6jGugL6ElWVhY+/vjjiMcXLlyIhQsXSq+vXLmyPy8raRppjluizQmBNW4aJfQ8cOuHjFuDNRi4NcpeJoQQQkj/G/QZt6ONOslxIPI1bvp+KpUyxlDf6ZBeb7K6+nQ+6k4lhBBC+oYCt36W7CbzPEjTq1Uw8K7SI1wqdXn90tBfAGjsSj7jtu1QJ6Y99CV++erGw3FphBBCyDGJArd+pk5y5wReKjVqldBrxE/bkS6VWh2ekNdbutxJn+vpb/aize7Gpzsa0NSHAJAQQgg5llHg1s+SHQfiCCmV9k/GzeoMnRPn8PgSzhRyFU3BjtStNZ19ui5CCCHkWEWBWz9Ldsurbo+8VNo/zQlWp5hxG5aqhyAmCtEZloXrDb+f4WBrt/T6wbbuOEcTQgghJBYK3PpZcOeEBLtKXWL2yyDrKu0+wjsn8FJpqkGNFK2Y5UsmcOtweOCV7bpAgRshhBCSHArc+llwHEiSzQkaJbSqnhsc3F4/Hv5kF15dXZ3klQZLpWadGpbA/qidjsTXuYWvaaM1boQQQkhyBv0ct6MNb05wJVkqNWqU0KrEjJsrTuD24dY6PPfdPgDAjJIMjMqO3N+1JzzjlqJTIVWvQQ0cSWXcmrtCx4i02ZNvciCEEEKOZZRx62fSHLekd05QSevkvH4GX4yN3+X7iia7xyhf42bWq2HR84xb4oEbn//G18lR4EYIIYQkhwK3fqZOslRqdwV3TuClUiB2uXR/i116uSLJPUatDlmplAdu3UkEboGMW1lOCgCgzZ74OQghhBBCgVu/06qS2/KKj/4whAVuLm/0ztKGzuA6stoOR9RjetIlZdxU0hq3jj6USsfkioFbe7ebdlAghBBCkkCBWz9Ldo6bvDlBpVRAESg7xsq4ybenSjZw480JKbq+lUqbbeK1jA4Ebj4/k7J5hBBCCOk9Ctz6WbKlUr7GzRjY7ipeg4LD7UOXKxgY1Xcm18Upb07oS+DG3yc7RQdTYKxIWzetcyOEEEISRYFbP+NdpYnvVRqc4wYEB/lGC9zCg6s2uxv+GE0MvXlOkzYYuIVvg9Ub/H3MOhXSjGrpmgghhBCSGArc+lmyc9zsslIpEFwrF22NG1+bZgwc6/OzqJmyntaZ8XV1eo0SKToxUxa+DVZv8OtJ0amRbtAAoMCNEEIISQYFbv1MnURzgs/PpAydIVAq1cQZwsuDqzSjRsqUtdpDZ6n97o3NGPnnj/HFzsaYzyutq1MrkaITz9OVROAmDfLVq5AaCNzaqVRKCCGEJIwCt37GM26JlErlW1sZIjJukeeRZ7gyTGKg1GILBkotNhfe3VQLPwOWxNlZQb6xvTmQcePnToTUnapTS5k7WxIBIAB8X9mMOX/7Bv9ZuT+p9yeEEEKGMgrc+lkyzQk8gFIIwYBNE2hOiBYA2ly8G1SFTKMWANAqC9w2VgcH8m6v7Yz9vJ7IjFuia9zcXj+cHvEaxcBNHXKNibr3/R042NaN+z/cmfQ5CCGEkKGKArd+plEFmhMSCNy6ZbsmCIHtB+Jn3PjgXJUs4xYslVY1B4fzttnd0nDfWM+rl2XcbC5vQjPY5Bk6k04lZdySydw1d7lCrn3t/taEz0EIIYQMZRS49TMp45ZAqdTuDu6awMVb4yYvlWaaeMYtGLjtb7GFHF/T3h1xjvB1debAWjk/CzZK9AZf32bSqqBUCNI4kGSyZeHZwYpGW4wjD6//+6oS5/9rJXbWWfvl+QghhJBYKHDrZ8FSae+zVvK1Zlz8rtJgqZRn3JplpdJD7aEDeaPNeeNlUkAslWpVCmmUSSLl0uD6NpV0TfJrTERlU+jWXXubjnzgtqehC49/UYGthzrx4Ec7j/jzEUIIIfEctsDN6/Xi2muvPVynO2rJd07obclR3t3JaeNm3IJZroxAxq1N1lXaYBUDNVVg+4U2W2SHp0OWVdOpFRAEIanOUr5DAn9fnnFLJnCrahLLpCWZRgBAbXv8HSH8fganp/fZwWi+3t0kvbx6X2tSJV5CCCHkcDlsgZvP58Mrr7xyuE531NLI9hn19nIoLg/cjIGgB4i/c4JVXio1ihk3eXNCYyDDVp5vFt8WNioECAZuerVSWleXzPo0+X6n/JqA5EqlfOuuKcPTAACNXfF3hLj59Y0Yd8+neGfjoYSfi9vTECyP+pmYgSOEEEIGiqrnQ4LOOeecmG/z+fqW2ThW8HEggNhZqlb2HDuH75oAxF/jZgsplQbWuAUG3nY5PdIatXH5Zmw51BkS1HHyTe05czIZN1kQya+JX0ei6gKB23FFafjfhkMh+7GG21VvxSfbGwAAf/14FxZMHgYF3+A1AXubQ8uxuxu6MLU4PeHzeHx+/PF/W2B1evHIRROQbdYlfA5CCCEkocBtxYoV+POf/4xhw4ZFvM3tduPLL788bBd2tOLrxAAx6ArMo40rXqm0t2vceFdpYyDYSdGqUJBmABAM6kKfUzyHTvacwd0TEsm4BTtcgWCpNNE5bowxKeN2XFGqeA6XFzaXVzqn3Op9wY7TFpsb+1rsGJVtSug5/X4mlWfPGJeDz3c2YndDcg0Kb6yrwXub6wAAj322B49dPCmp8xBCCDm2JRS4TZkyBSUlJfjZz34W8Tan04kbb7zxsF3Y0UqpECAIAGO9HwkSrTkhXsbNLttjlM9x63J64fL60BhY35Zj0UmdotGyX9EybslsexXcqD4s45ZgqbTV7obL64cgACOzTDBpVbC5vGiyOmHKigzIwjtANx1sTzhwq+1wwOHxQa0UcGZ5Lj7f2Zh0qfSDLXXSy5/taMDDF02AqhfZVkIIIUQuod8cDz74IEpLS6O+TavV4ptvvjksF3U0EwQh4c7S4Dw1+Rq32HPcHPL5a3pVsAnB7kZDYH1brlkn2w0hMohyhO2NCkDWnND7jJt8uytAnOUGiNmyRDa+540I2SlaaFQKZKeIAWljjHLpwTZxxEluoCS58WBHr58r/BxF6QaU5aYAAPa3RI5O6YnL68OmmuDzW53efhtlQggh5OiSUMZt9uzZAIA333wz5jHyt11yySVJXtbRTatUwO3193qWW7dHDH6MUTJuUQO3QLZMF2gsyDBp0Gh1odXmljpKc8y6uKM55LsmcIdljZtW/J8xoNvji1rmjIavbxuWqgcAZJu12NdiR1OMBgUedJ0/OR/Pr9iHbbUdvb5mjo9JyU/VoyhDLCu32Fwxy7Ox7GnogtvrR7pRg5FZRqw70I7dDVaMCzSHEEIIIb2VUODGPf3001i1ahVyc3NRUFCAQ4cOoaGhATNnzpQ6EAVBoMAtBrVKAbh6v+1VtyvaHLfYXaV8iykedGUYtWi0utBic0ml0lyLNm4GrTtqxi1QKk1ojhtf4yY+l06tgEohwOtn6HJ6eh0A8fVt+TxwSxEzac1dkRk3p8cnBahnlufi+RX7UNlog9fnT6g82dApPqeYnVQj3ahBm92Ng63dCQVdlYHsWllOCkbnmAKBG3WnEkIISVxSgdvYsWNx8cUX45ZbbpEee/rpp7F9+3Y888wzh+3ijla8QaG3a9yilUrjrXFzyjJuAKQGhVZbsFSaEwhGgOhr1pxx1rglNseNZ9zE9xUEASadCh3dHrFBwdK78/DAbViaGLilGcRrb++ObKyo7XCAMTFDObkwFXq1Eg6PDwdauxNa59YgBblikFiUbhADtzZ7YoFbYFBwaY4JY/LE99tVP7R2Yahp60auRderLmhCCCFHTlI/hZctW4Zf/vKXIY/deOONWLp06WG5qKOdNIS3l6VShydyHEi8rlJnWJlT2vbKHsy4hZZKPRHDgHmwKO8qjdfMEIu8w5VLpskhvFRqCbTjtndHXktNoExamG6AUiFgdGB9WqKNBdJ6wEDgVhwolx5oTWyd297Ajg+jsk3SWrmKxuQybg2dTix8YTV++syPONBij3vs2v1tuGjxD/jdG5v7NIj4zne2YvbfvsHpj3+HtigdyIQQQvpPUoHb8OHDI4btLlmyBIWFhYfloo52mjjNCQ63L6L8F61sGSvj5vH5pcG+OrV4TIZsCG+tLADiAZTHxyJKrtE6WeM1M8TS5Qpd4wYAJm3iQ3ilUqklNOPWESXjxu8fn5U2NhAsyUd51HU4cOWLa3Dr0k0xgxq+xi2PZ9wyxB0bqhMM3HjGbVS2Sdr1odHqkkauJGLR+9vxw95WrK9ux53vbIt5nMvrw61LN2HjwQ68u6kWi7/Zm/BzAcCG6nYsXVsDQAxYn07yPIQQQg6PpAK3F154Affeey9Gjx6NU089FaNHj8aiRYvwn//853Bf31Ep2FUaGix1u704+e/fYvpfv8RmWReitHNCSFdp9DVu8iAkWCoVM26H2h1oCQzbLUwzwKhRIbAkMWI2W3AciDxTpo56bDzhc9wAcYYcEH2WW6fDg9+9sRlPflkZkgWs6xCDqGCpVAxGO6Jk3PhcusxAiXhMIHDbVR/Mct2zfAe+r2zB+1vq8Nqag1GvXZ6dBIDh6WLGrbo1fqZLzunxSY0SpdkpSDVokBoIOhMNAFtsLny1K7gF16p9rTgY4xzf7G6WSr0A8N/V1UkFip/vEIcYWwLZ1nc2Hoqa5e0JYwwPfrgT0x/6Evd9sCOhjmJCCCFBSQVu06ZNQ1VVFV544QXccMMNeOGFF1BVVYVp06Yd7us7KknZsrDAbeuhTjRYnWAM+DQw9R9IbOcE3pggCMFyKl/jxoNBk1YFs14FhUKIuXdovAG8vc24McZkpdJgxi3e7glPfVWJdzfV4okvK/BZIGhwuH1SiY43J6RKa9wiz9ESyLjxEnFZrriubE+jmHFrsjrx9e5G6Xj5jDXO7fVLQS4fKVKcyQO33gdcVc02MCZeLw8kiwOZu3ilTm+U9Y8fbKmD188wqcCC6SPE3RtW7m2J+v5r9osDiK+YUYSidAM6uj34ZFtD1GPj+aFKPP89541DrlmH9m4Pvt3TnPB5XvrhAF5YuR9NXS689MMBvN2HbcgIIeRYlvRKY7VajTlz5uDSSy/FnDlzoFare34nAiDYnBA+DmR7baf08tZDHdLL0Uqlsda48YybVqWQOnz5ujBebixI00tvizXiw+EWr60vW145PD74ApkV+Ro3U5wA8POd8oCqHkBwtEeKTiVlfoIZt8hSKc+48RIxz7jVtDlgc3nx1e4m+BmQFZgFt622E/awsi3faUKlEKTnKkoXA666Tkevs057eZk0yyTd8xGBcun+KJk7xhjufGcbyv7yKX69dJN0/wDgnY21AICLji/AiSUZAMSsWzRbD4lfS1OGp+GnUwoAAO9uqu3VNXNtdjd2BAYZzx6diXMn5gEAPt/RGO/dIjg9PqnEysvO//yyMuRjOxJabC78ZtkmXPzsj/hiZ2LXLNdud+OJLyrw2Ge7pXWPhBAyUAZ9i1hzczPOPfdcGAwGlJWV4auvvop6nMPhwBVXXIGUlBQUFRUN6kaJWAN4eTkQCAYrQPT1ZtoYWbvwxgRALIvK8UAOiJ394g0R+igZN5vL26tfujwwUyqEkGuXsnxhwVJntyfk4169rxWMMak0yTNVQDBwi9ZVyoMunnFLM2qkrNmehi6p3HjVjOHIStHC52cRzQLNsqwd3+M006SBUaMEY2LZuTeqmsVrl3ezxsu4fbWrCUvXHoTPz/DBljq88uMBAGIzw7baTqgUAuZPysfMkRkh90jO6/NjR50YuE0sSMWCyeIWdT9UtUjl395YVdUKxsQxJtkpOpwxLke8xt2NUTOCsbyzsRatdjeGperx5e/nItWgRm2HAz/EyBY6PT4sWXUAL67cL30uObvLi3uWb8ep//gWd76zVfreiHaOy55fjeWb67DuQDuu/+96vB8ls9qTVpsLCxb/gCe/qsTT31ThgqdX9tgUEkuLzYU319Xgw611Ma+bEEJ6MugDt5tvvhn5+floaWnBo48+iosvvhjt7e0Rxy1atAhtbW2ora2Vul4rKioG4Ip7FiyVhv7wlv9SretwSGvguuNseeXyhDUVeCK7QfNSdVDKNlgvSAsGbrEzbrF3TgBC16e12d34cW9LRDDHg0GTViVlm4DY3an7WsTsVIZRA71aiVa7G3sau6TS5PCMYACaahTP4fT4I5oLeImTl4gBSN2cmw62SwHDyWOyMTpHDKh4AwHXJDU4aKXHBEGQNSj07pd3VWCT+pGybbl4yfVAlF0Y3lgvNgLwwPyJLyvQ0e3GWxsOSdecbtRgYkEqlAoBzV0uqYmCq2i0wenxI0WrwogMI4oyDJg6PA2MAcs3i1k3t9ePP7+7Dcfd/zl+uyx6gwYvk84cJQaJU4anId2oQUe3B2sPtPXq42eM4T8/7AcA/HxWMYxaFc4LZO6iZQA9Pj+ue2U9/rJ8Bx74cCfOeGIFNh4Uv9/b7W5c/sIa/HdVNaqa7Vi6tga/f3NzROAKQJzd12RDpkmDs8fnAgD+9PZWVCbYzbvo/R2obu1GvkWHkkwjGq1iFq+3Mxi5FRXNOOOJFbj97a245fVNOPep72OuT/T7Gb7e3Yj/+6oSb284FHVNaXOXC009BOG76q24ccl6nPTo17js+dX4fEdD1HvVk/pOB/726W7cunQTnvqqMqHgP5zN5UVzlyuhwL+vfH6W1MdNyGCV1By3/mKz2bB8+XIcOHAABoMBCxYswOOPP44PPvgAV111VcixS5YswXvvvQez2YyZM2fi/PPPx7Jly3DPPfcM0NXHJmXcvKE/TOQ/EP1MfH1Yql4q4xl61ZwQOnyXP9/onBRpdlj5sODwNGkLqog1bpGZO41KAa1KAZfXD6vTA4tBDb+f4coX12BHnRU3zinBneeMlY63RhkFIn89PFjcF8hOjc5JgUop4PvKFqzb34bqtsiMW4pWJQ3ybe92I88SDEbDM24AMCYvBd9VNONf3+yFw+NDQZoe5flmlGan4Ie9rRG/0PmODHxrLa44w4Bd9daIdW61HQ5Ut9oxY0SGlKEDgKpAQDgyO3jtsUqlPj/Dqiqx9PnWTTPxx7e2YHdDFx7/ogLvBYKcS6aKndt6jVL6nG491Cmt/QOCZfYJBRbpWhYcNwzrq9vxzsZaXD+7BPd/uAOvB5oy3ttchxyzLuRzB0AKcE8alQkAUCkVOHVMNv634RA+39GImSMzpWNdXh/e2VgLp8eHn04pkIL8lXtbsLfJBqNGiUunide+YPIwvLr6IL7Y2QinxxfyR8Zrq6uxcm8LdGoFcsw6VLd247LnV+OaWcX4Ykcj9rXYkWpQ4/rZJXjyy0p8sr0BX+xsxBnludI57C4vXlwpBot/OW8czpuYjytfXIMfq1px06sbsPyWk2DSqtDl9OC9TbXYXmvFCSXpWDB5WMjn7vMdDfhwaz2UCgHPXzUVGSYNznxiBbYc6sTT3+zFb08bHXK/Vu9rxec7GqFVKzCtOA0nlmRCrRTwr2/24smvKsGY+MeH3eXFvmY7rnl5Ld795SxYDME/iNrtbtz46gas3R8MjLXvKjB/Uj6mDE9DRWMXvqtolr5XxuSm4K5zx2J2aVbItXy+owG3Ltsk/Tw41O7Aqn2tmDUqA/edX45R2SlgjOFgWze21XbC52coyTShPN8s3QOr04Nnv63Ciyv3h/yceea7KvzhjDJcM7M45A/CysYufL27CU1dLph1auSl6pBj1qHb5cX66nb8WNWK3Q1WMCY2K506NgeXTS/CtOI06Q87/j3wzsZD+LGqFV4/w4hMccu50TkpMGlVcHh86Oj2oM3uhk6tQHGGEeOHWTAq2wS1UgHGGHbWW/HR1np8sbMR+wMZ0pIsI2aNysQJIzJQnm+GVq1Aq82NVpsbHQ43GBMz+akGNcw6NaxOD2raulHRaENFUxf2NtrAIN6nGSXpmD4iA6OyTdCoFOjodmNfix3VrXapcqJUCNCqFNCplVAIgN3lQ7fbC5vLB4fbi2yzDiOzTBiVbURhugEKQUBdhwNVzTbsb+lGdasd3W5xr+QUnRpmnUr8X6+CQhDgZwxeHwOD+MdxSuDterUSrTYX6jqdqO9woK7TCbfXD6NWiVSDBlkpWmSZtMgwaeDx+tHt9sHt88Pj80t/fAsCoAh8ThxuH2wuL+wuH+xuLwwaJTJNWmSatLDo1TBpVXB5g8fYXB7YXD4wxqBSCFAqBCgV4uB1hUKQPSb+UymEwO8WJTw+PzodHnQ6PLC7vHD7/PD5WMj7qRQClEqF9LpaKUClUEj/e/0MXr8fXh+D0+MT/3n98Pr8UCgEKAXx/RT8f+kx8Y9zxhj8DPAH/meMwe8XXzbr1dLSk4E0qAO3yspKWCwW5OXlSY9NmjQJO3bsCDmuvb0dDQ0NmDBhQshxa9eujXlul8sFlytYhrFa+28gaqwBvM1hZaGmLhcyTVppvIc8ANLGaE7gGTet7JchABxXlCoFbtOK06XHjdpg+VMu2gBeQPzCbe5ySUHX1tpOaR3Uf1dV449nlkm7E0RrTJC/HivjVpJlRFaKFt9XtmBDdbu0Zq1IlnETBAGpBjVabG602z1S4Ob3M6mRQR64zRyZiee+2yd1oZ5VngtBEFAaI+PGS6VZgR0aOH4N8sBtR10nLlr8I1xePy6bXoSHL5ogXQv/pSHPuA0PBKDNXaHbZ+1tssHm8sKoUWJcvhl/PLMMv3hlPf67qjrwfgacOiZbOs+kAksgcOvAWeODgcuWQOA2sSBVeuy8iXm4/4Od2N3QhStfXIuVe1sgCMAlUwrxxvoavLLqAK6fUyLds5q2blS3dkOpEHBCYD0dAJxRnov/bTiEL3Y2YtH8cRAEAW6vH9f8Z5203u7fK/bhv784AaOyTXjphwMAEBLMHV+UhjyLDvWdTny7p1m6drfXj+dX7AMA3HXuOFx03DDcunQTvtrdhOe+Ex/PNeuw5BfTUZqTgm63F09/U4VHPtmNU8ZkS193b6yrQafDg+IMA86bmA+lQsCTPzsO5z31Paqa7bjk2VUYk5uCT3c0SH+gvLG+Bu9srMXTlx8Pi0GNzm4P7n5vOwDghjklGB/4Y+eBBePxm2Wb8a+v9+K0sTkYP8yCbrcXD3+8G0tWV0v36RmIf+iYtCrp6/Gy6UVYNH8cOh0eXPj0D9jXbMevXt+Al38+HWqlAi02F654YQ12N3TBoFHijHE52F5nxd4mG97acEjKugLiL1YBkD6fl00vwl/OGwu9WonnVuzDo5/uBmPA7NJM/HLuSKzc24IXV+7HD3tbcdrjK1CUbkB7tzvij6dMkwYzSjKgVSnx+Y4GaTnDtOI0nDwmG5/vaMTmmg488OFOLF17ED85vgBWpwdf72rCngSymVanF+9uqsW7m2pRmm3CeRPz0Wp34fMdjSHd0ID4h9i6A5FVlnBalQKlOSa02z3Sel65ikYbKhpt0tdksioabfg00DjFAwenp/8yiGTgjMwyUuDWE5vNBrM5dEK92WxGR0dHxHFKpRIGgyHkOJst9kbeDz/8MO67777Der29pQlky8LLLTyoMOtUsDq9aLI6pbKmIISPA4nfnKBXh1bBf33KKOyo7cTs0iwp4wMAJq14LeGL86Nl3AAxeBQDN/Fa18gWxzs8PlQ126WyZJczdNcELtY8uOBfxiaU5YjnWF/dDm9gLWCJ7LoBINWgQUvgr2Wuw+GR/mpMNwZLpSeWZCDfokNdpxMKAfjZdDH7U5otPs/eGKXSrLCM2/D0yFLp3z/bI2Uklq07iF/OHYmiDANqOxxwef3QKBUokK0ztOiD22dVt9pRni8GBZtrxF9OvAx6yphsXHTcMLyzqRYqhYB7zy8PyQhNLEjFsnU1UiMCt6VGfH1SQTCzmmrQ4Ma5JXjq671SJ+ptZ5ThV/NGYleDmLVbuuYgfn1qKYBgtu24wtSQbclml2ZCr1aitsOBHXVWjB9mwTPfVmHVvlbo1UqY9SrUdTpx6XOrcO1JI/D17iYoBOCqmcXSORQKAedOyMMLK/fjo231UuC2fHMt6jqdyErR4uIpBdCplXj+qql4c30Nvt3ThOIMI26aOxJpgc/rTXNH4vU1B7GvxY53NtXikqmFcHv9eOF7Mci7bnaJlBHKStHiuSun4ur/rMXOeit2Bv6IGZVtwrTiNCzfXIeVe1tw0TM/4OmFx+OxT/egqcuFkiwjfhO4JwBw/qR8fLKtAZ/uaMD1/12PX5w0QroGALhgcj70aiW+r2xBbYcDbV430gxq/OW8cbjoePEHvk6txIvXTMNPnvkRP+xtxW3/24KfTSvCXe9tw75mO7JTtHjtuhNQmiNmxTYebMd7m+pwqL0beal6zB6ViZmjMgEmltJf/vEAlq49iK93NyLNoJG2U1t4QhHuO78cKqUCM0dl4mfTivDARzvx5a5GaS2pRqnA2LwU6DVKbK+1osXmxodb66WPd1S2CbefWYbTx+VAEATcNGcklq2rwSOf7MLeJhse/XS3dKxaKWB2aRZGZZtgdYjBU3OXC1q1EmNzUzBrVCZmlGQg3ajBpoPt+N/6Q3h/Sx0qm2x44svgshaLXo35k/Jw7oR8pOhUqGzqwp4GG/Y2dcHl9UOrUiLdqEaaUYNulw8VjV3YWWdFl8uL7bXi51WjUuDksiycOzFfXCYAYEtNB1bubcHG6nbsa7bD7fMjw6iRMkeCIK6Z5Rkfk1aNYak6jMpOQVmuCaU5KVAKArbVdmJVVSu21HSgS7beN8+iw/AMA4alGqRqgMvrg8srZrIMGiVMWhWMWhW0KgXqO53Y22RDVbNN+nmrUSlQkmlESZYRwzOMMOvU8Pj86HJ60OX0wur0wOoQn1OlFKSsWLfbiy6n+M/h8SE9sK43P1WHPIseOrUS3W4v2uxuscze5UJ7txsapQIGjRJalRIqpSB9vzAGMDAwJv4OMAau26hRwh6YNdpqd8Hq8MDm8kKnVsKoUcGkVcGkE49VCIDXz+DzMfgYg88f+c/rF++Ny+uHy+uHSiHAolfDolcjRaeCWqmASinA7w+cy+8P/M/EzFpgbqnHJ2bYPH4xy6dSCFArFVLGU6tWQK1QwBfInvHrYUzM8vqkrBqDUiFAEAQoAllHhSBIGcgcc+jvg4EyqAM3k8kUkQmzWq0wmUwRx/l8PnR3d0vBW7Tj5O688078/ve/Dzlvfw0QlrpKZYGb38+ktSxluSlYd6AdjVaXVMI0aVQhv7RjjwOJXOMGAHkWPZbfclLEtfBg0BY240vaZF4THriFbpMV/lf2jrpOWeAWOcNNPEf0wK02UGIoSNNjcpEYvPAmAIUAjM0LDeJT9XwIbzBz1xrIWlr0aukeAeL9evbKKXjq672YPykfowIBG98Nga8p5GXsJmtgjVuUUikQzLi12Fz4tkIcjzE8w4Dq1m4sWX0Ad507TlrfNiLTGFJS4udps7txoKVbCtw2HewAAEwuSgUgZhX/fvEkXHZCEXLNOhSmhzaZTAwEZlsPdYAxBkEQ4PT4pM/JpMLUkONvOWUUWmwurNnXhkumFeLGOSUQBAHXzCzG79/cgmXravCrk0dBqRDwQ6BkO3NUZsg5dGol5ozOxGc7GvH5jgbo1EqpY/TRn07ESaMypdL5Y5/tAQBcdWJxSMYRAM6blI8XVu7HV7sa4XD7oFUp8Ox3VQCAX5w0Qvr6VSoEXDa9CJdNL0K4FJ0av5o3Cg99vAtPflmJCybn471NYvCXnaKN+Mt4cmEqPv7NbLy1/hAcHh/mlWXhhBHpEAQBV84oxi9eWYeqZjvO+uf3AMSvmScumRzyvSQIAh6+aAIqm7pQ1WzHgx/tAiBmAv9+8SScVCreL8YY9jbZ0GxzYXJhasgyB0D8Wn7i0sn45asbsHxzHZZvFhsn8i06vHb9DOmPK0EQMGV4OqYMT0c0955fjjPG5eAP/9uC+k4nGq0uaFQK3HPeOCw8oShkbWlRhgH/vmoqmrqc2N9sR6pBgxGZxpCfJRsPtmPjwXa4vX4cX5SGk0ZlhvzcUSgEXH5CEc6blIc319Vgy6FO6NUKzCjJwKljckLKvvFMLU7H1OJ03HXeWCzfVIsN1e1INWgwc2QG5pZlSUtBAEjZznj8frHsu7uhCxa9GhMKLBH7IA9L1eOcCXkh76MI+77sjTmjs3DzyaPAGENTlwsujx+ZKZqIz3FvMcbQKW0NqI74WUFIuEEduJWWlqKzsxMNDQ3IzRX/Kt+yZQuuu+66kOPS0tKQm5uLbdu24YQTTpCOKy8vj3lurVYLrXZgomdNlC2vupxe8PWzpTk8cHNG3TIK6HkAb3imLBZeKg3PuDljBG7msC5UvtYmzaBGe7cnpOMumHHrXam02RpcV2bSqjClKE1aBD8m1yxdK5capbOUl5vljQncxIJU/PuqqSGPZaVooVMr4PT4UdvuQHEmL2NGX+PGS6U17d3w+Rm+3t0ExoAJwyy45ZRRuHHJBnywpR53nj02mNXJifwDojjTiI0HO3BAlrnjgdtxsoBLoRBCSttyZbkp0KgUsDq9qG7tRnGmETvqrPD5GTJNWmn0BqdVKfHwRRMjznPOhDzc98FO1HY4sKKiGXNGZ0Wsb5M7Y1wuPtvRiP9tOITPdjTC7fPj5LIszJ+YB0EQ8Pr1M/C7NzZjVVUrTh+Xgz+dPSbiHJMKLChI0+NQuwNf726CUiF24Jp1Kiw8ITJIi+XKE4fjhZX7UNvhwGOf7sHH28Rs0fWzSyL+eAHEX96/Oa004vFx+Wa8d/Ms/Pr1TVh7oA1ZKVo89tOJEcEvIHYpv3njiXjq673YVW/FlOFpuHHOyJCgRSzDp6A0kDmO5szyXLz88+n468e7cKjdgdmlmbhn/riQ9Zq9MXNUJr7+wzx8X9mMbrcPs0ZlRmSK5bJTdMgOWwIAiIHqjJIMzJCVxmMx69S4bnZJQtcZ6zxXnliMK08s7tN5FAoBxZlG6fu3t+/TF4IgSMO5+3oe/rOMkN4Y1IGbyWTC+eefj0WLFuGf//wnvvjiC2zfvh3z58+POPaKK67AAw88gKVLl2LHjh14//33sWbNmgG46p5Je5XKxoHwcp9Ro5TKo41Wl7T2zBQWuPG/knnamP+V5oiyx2g8JilwCy25OmKUSuVdqIwxKas0d3QW3ttcFzImI1bQGS3jxhiTgi6+VdVFxw+TAjc+Q0wuuO2VPOMWub4tHkEQUJRuQEWjDQfbumWBW+i1cHkWPTRKBdw+P+o7HfgyMB/stLE5mDs6CylaFRqsTmw82B41EONGBNa58fKwzeVFRWBPU55x64laqcC4PDM213Rgy6EOFGcapcaESQWWkGxLPDq1Ej+dUoAXV+7Ha2sOwqBRos3uhkWvxnFRruX08hykfaRGfacT9Z1OpBrUeOjCCdLzWfRq/Oea+MO4BUEca/LMt1V44ssK6Q+Fq04sjgj0e7r23542Gne+sw0vBBoShmcYcHkCwR+XY9bhzZtORKfDA4NGKX2fRpNh0uLe82P/Ydhbc0ZnYc7orJ4P7IFeowxp0CCEHL0G/TiQxYsXo6amBhkZGbjtttvw5ptvIi0tDa+99lpIRu3++++XGhkuvvhiLF68GGVlZQN45bFF2/KKp8oterX013BTlzNm1korKwPKM3fOwMu9DdyiNScwxoKl0ihr3ADA6vCgxSYubhYE4KRAV1tNe3DRvtURfY1bSljwB4g7IPC5dlmBoOuSqYX43Wmj8fNZxbhu9oiIa+dr2OQbn/NSaYax93/BFgVKkHzdjzyIDM9cKBUCCtLFwLqisQvfV4qZqVPHZkOnVuL0wKyzD7fWBwO3KMEPDxB5hnJrTQcYEzNC0bIhsUySyqWdIf/LGxN6g5civ97dKI3vOG1sTtTgxaxT4+nLj8ewVD0K0vR49oopIV2tvfWLk0YgRavC3iYbDrU7kJ2ixY1zE8/i/GxaIX41byQUAqTrCc/OJsKiV8cN2gghZCAN6owbAGRlZeHjjz+OeHzhwoVYuHCh9Lper8drr73Wn5eWNJ4tk++cIM+s8QWQ8lJp+HoNTVjgxkuawTVuvfvFY4zSnODy+sFHsoWXSvnC8LZuN/YFsm0FaXqMzBIDkZq2aBm38FKp+LG4feKCVJ1aKY3fSDME16YpFELUshYXrVTKg7hopdJY+I4IPHCLFkTKlWQasa/Zjme+rYLD48OwVHG0CACcNykP72yqxcuBwblqpSCtYZPja5h4qXRTYDuy3mbbuAkFqQCqsS0QsEkdpYU9rwuSG5Vtwgkj0rFmfxs+C+yMMH9SZJaTmzkqEz/86ZSEniNcpkmLR386EXe8tRVatQL/d9lxCWXbOEEQcPtZY3DrqaXQKBV9LoERQshgNugDt6ORJkpzAi9VGjQqad2EvFQanrVSKcSuFz/jnaXiL7xYmbJYTFHWuEXbqJ7LSQkGlXxXgJJMk7RwvrHLCZfXB61KGXOOmymwuT1j4qwonVopawbofbYp3RhZKm0JBG7pxt6vXywKZND4QFReJpUHkXLTitPx5a4maUTB/En5UpnwpFFZSDWopWuaXZoVNfvJM25i1tITt6waD8+4ba/rRHOXS1pzOCnBjBsgNi+s/c9aMCZ24c49DCW8npwzIQ9njMuROrn6ordZZkIIGcqoHjAAoq1x45u6G7VK5ASCl06HRxomGx78CIIQtUGB76TQl1IpD/7USiGiZCQPKvfJdgXgux0wFty6K1aZV6EQYNKErnOLtlNBT/i2V/JSaRvfNSGRUinvFA1k3ILDd6MHkWeGrSW68Lhh0ssalQJ/OF0czKpWCvjlvJFRz2HSqqR1eAdauqVRINHKqvGUZJlg1CjR7fbh1cAcsTG5KSGjUHprdmkW3v3VLDxx6SS8eM3UPgdSvaVSKvrtuQghZKijjNsAUEcZ5SHPuJn1KmmHAp5BiVZC0qoVcHh8IYFbsDmhdzF5tOaEeA0OfLF+Q6cTVbrgwFxBEJCfqkNVsx31HQ6MyDTGbE7gj3W5vLLATQyW4nXDhePByeEqlda0dYst/tbo69u44kxxttdzK6pw09yR0vgT7soTizEu34I0gxolWbFH0hRnGNBic+Hr3U1osYkzlaKVVeNRKgSMH2bBmv1teCYwTuPEkT13BcYyuTAVkxPM+hFCCOk/FLgNgGjNCVLGTaOU2swPtnVLg2HD17gBwbEi8iG8Tm9iXaXRxoHEK7fmWoKNEzxJwjdQz0/Vo6rZLk0t73IFBwqHS9Gpgc5g8wUPlhJpr0+L1pxgF8+TSMaJd/HaXOKASj65PV7273enj8ZvTyuNmSmaMjytx+ctzTFhfXW7NAdt+oj0pMp9U4vTsGZ/m/SHwJzSI1/iJIQQMjCoVDoA4q1x44EUnx/Gx21Ey1pp1Txwi5Zx623gFmhOcAc7PKNtMM/xxfoeH5NGf0iBW2D+FN/0vMMuBmXRZhSFjwSRxm8kknELnLfL6ZXupZRxS2CNm06tRG4gYDzY1o2aQMm0MM0Q7936XN47YYSYGeNbnyW7puzSqUVSEF+cYcDs0sjZa4QQQo4OFLgNAKmrVB64SWvcxICGZ554d6dFH6VUyte4eSLHgSTanOBnwUxbvIybRqVApqwMmWpQS+vJ8lLFa67rcMDl9Un7HGZGCaJSwgb59rSuLBqzXg3eQNjR7YHX50d7oCkg0TVefJ3bwbZuaaRJUXr8wK2vThmbHbIXbPjaud4qyjDgtetPwA1zSvDfa0+Q9uwkhBBy9KGf8AMgWnMCL1XyX+ThZbpoA2WlHRhkAaAzwYybXq2Ugh/eoBAv4waIs8a4UVkmKfPEZ3nVdTqlzJdKIcCsj1EqRd+aE5QKIWQkCA/aBCE4nLe3pFlurd3SWBD5pvZHglmnxiM/mYjROSY8sGB8n55vWnE6/nzO2CN+zYQQQgYWrXEbANIaN698jVugVKoJzbhx0RbKS6VST+QaN72mdzG5IAgwasRGAbvLB6T0PFJkQoEFWwJzw+RruXiptK7DIe1gkGbURC0pSoN8A0N4Y+0N2pNUgxptdjfa7G5py7BUvTrhrBMP3Pa12KWu2COdcQPETcvPn5R/xJ+HEELI0YEybgNAHSVTJmXcAmvOcnqRceO7J0Rd46bq/SL38AaFnvY7PWVMtvSyfJud/ECptL7DIVtrFr1kadYH9yu1ubxSsJhIqRQIrnNrt7uTakzg+EDc7yqa4fMzaFWKqMN3CSGEkIFEGbcBoFFFNidEZNxkAYxGpYgajPA1bqFbXgUCtxhlzmh4gwIvlfJriXWOk8uy8cCC8UjRqkIybnxzbLvbJ+0IEGssR7psBhsvk6ZoVTHLs7HId3LwBhYEJtKYwPGdD3jAWZJlogn8hBBCBh0K3AaAlHGLsuUVz37J934sSNNLm8jLaaJk3Jx8AG8CGbfw3RN49ssQI+MmCAKunDE84nG9Rol0owZtdre0BVOsHQx4QNdqc6MxMH4jK4H1bRwPAMXmBBZy7kQUZxhh0aulPWP5jgSEEELIYEKl0gGg6WGOGwAMzzBI5dKxeeao5wmWSmVr3HpoLIgmfPeEZM7B5QXmvG2rFQO3WKVSXvptsbnQEBgfwtfIJUI+y41vMJ9MqVShEDCvLDiOQ/4yIYQQMlhQxm0AqKVxIPKu0kCWKxBECYKAv144AV/tbsJtZ5RFPU/cUmkvd04A5Gvceh4H0pP8VD121Fmxu6ELQOwgKhi4uaW5b3y4byL4fqWtNhc6AwGxPFuZiN+eNhrbazsxJs+MU8fmJHUOQggh5EiiwG0A9CbjBgCnjs2JG0CEl0q9Pr8UDB6OUmkyU/yHhQVN4a9zfBZcm90l7bSQl0Tglid1sjqhCgw2TuY8gNig8NUf5iX1voQQQkh/oMBtAERb42Z3h2bceiO8VOqUnS+RMiefHRfenJBMqTQ8UIs1VyzdqIEgiIN/d9VbASS23ZX0fIHtqg61d0MbCDSTzbgRQgghgx0FbgNAHcgM8XEgHp9fCuKMCQRLwTlu4vvyUSBAMKjrjfCMGx8HYkgicBuTF7rh+vAYs9BUSgXSDGIjw45aMXBLJlPG9xmttzqhCMyLi5XlI4QQQoY6ak4YAOGbzHe7ggGXQZNAxi1sHpzTE1zflsg+mtIaN3fozgnJlEonDAt2Y+rUCmTHyaLxxgV+/clk3DKNWmhUCjAG+PwMgpDceQghhJChgAK3AaANa07gAZNGqZDWrfXqPOrQvUqdSa5NC3aV9r05IdWgwUXHDQMA/PqU0rjHhjcjDE9iuyaFQgjJsGWnaBO6h4QQQshQQr/hBgDPuPn8DD4/kxoT+K4JvRWxxs2T2AbznCnwvMHmhOTOwz3604n4/vaTcfPJo+IeNzLLJL2ca9ZJ+5cmipdLAaAk0xTnSEIIIWRoo8BtAKhlGSGPzy+N4TAmUCYFgoEbLzUm2w0aPsfNEQgkk2lOAMTAtLAX+3yOyg4GWXzngmRMKkiVXp5YSINzCSGEHL0ocBsAvDkBEIMuXipNtBlAGgdymEql4eNAkg3ceks+5Pa0ccnPTTtrfC74xhLzJ9KG7YQQQo5e1FU6APgcN0AcCRI+fLe3+ABePsdN3pyQiIg5bu6+lUp7qyDNgMULj0dzlwuXTi1M+jzjh1nwxo0notvtw/hhlHEjhBBy9KLAbQAIggCtSgGX1w+nxyetcTMlucaNjxJJtqmAl2il5gReKj3CgRsAnDMh77CcZ1px+mE5DyGEEDKYUal0gPByptMjy7gluMZNE9acwEumiZZK5Rk3n59Jw4BTdBTXE0IIIYMJBW4DhJcz5Rm3RIbvApGlUkeSpVJjINPn8PjQ5fRIj5socCOEEEIGFQrcBoheyrj5kl/jxndOiFjjllxzAgA0WMUN3zVKhRQYEkIIIWRwoMBtgISUSpPMuGmU0de4JRq4aVUKqAJtmQ2dYuBGZVJCCCFk8KHAbYBoQzJufBxIshm3vg3gFQRByro1BjJuVCYlhBBCBh8K3AaIPhB0OTw+dAeaAUzJjgOJmOOW+KeVP3d9IOOW6LUQQggh5MijwG2A6KJk3BIdeCtteRW2yXwyYzx4g4KUcaPAjRBCCBl0KHAbILpAtszpDa5xSzRY0sjmuDHGkl7jBgQbFIJr3JLbN5QQQgghRw4FbgOElzNdHh9szuQCN61sz1OX1y+VXJPZqoo/d4PVBYCaEwghhJDBiAK3AcKDK4fbJ23ubkxyjRsgBm4ONx/km0TGTcMzbg4AVColhBBCBiMK3AaIViqVBue4JZrlCtms3uuXBvkm2p0KBIPG9m5PUtdCCCGEkCOPArcBIp/jlmzGje95Kp4n2J2aTMYtfJ9UGgdCCCGEDD4UuA0Q3vnp8PiCA3gT3GQeCAaALq9Pak5IJnALb0ZIoVIpIYQQMugM6sBt3bp1mDRpEgwGA+bOnYvq6uqYxxYXF8NgMMBkMsFkMuGmm27qxytNHG9OaLO5wZj4WIo28U5OvttCtzuYcdOrEw+6MkyakNczTdqEz0EIIYSQI2vQBm4ulwsXXXQRfvOb36CtrQ0zZszAlVdeGfd9vv76a9hsNthsNjz77LP9dKXJ4ZmyVrvYxakQkhucy5sc7C5fn5oTslN0Ia9npVDgRgghhAw2g7Ye9u2338JkMuHaa68FANxzzz3IyspCdXU1hg8f3ufzu1wuuFwu6XWr1drncyaCB2mtNjcAcX2bIAjx3iUqvi6u2+2VNSckHriFB2oUuBFCCCGDz6DNuO3cuRMTJkyQXjcajRg5ciR27twZ830WLFiAnJwcXHjhhXHLqgDw8MMPw2KxSP8KCwsP27X3Bs+4NdsCc9OSXFPGg7SObg/8gZJrMnPccsyhgRqVSgkhhJDBZ9AGbjabDWazOeQxs9kMm80W9fjXX38dBw4cQGVlJYqKirBgwQIwvngsijvvvBOdnZ3Sv5qamsN6/T3hgVuXM7mOUo6P/mixuSIeS0RBmiHk9WSvhxBCCCFHzoD9dj7jjDOwYsWKqG+7++67YTKZIsqXVqsVJpMp6vvMnDkTAKDT6fD444/DYrFg//79KCkpiXq8VquFVjtwWaXw/USTD9z4Wjmx5KpRKaBUJF5yVSoElGabUNlkw6ljspO6FkIIIYQcWQMWuH3++edx3/7ZZ5/h+eefl1632+2oqqrCuHHjejy3IAhJrRfrT+Fz0pIdeMsDt5YuV8jryXjmiin4aGs9LjxuWNLnIIQQQsiRM2hLpfPmzYPNZsPLL78Ml8uFBx98EFOnTo3amHDw4EGsWrUKHo8Hdrsdf/zjHzF8+HAUFxf3/4X3kjksUDMmUd4EgmVRvlbOkMQG89yobBN+c1opijIMPR9MCCGEkH43aAM3rVaLd955B48//jhSU1Pxww8/YMmSJdLbb7rpJmlWW1dXF2644QakpqaiuLgYe/fuxfLly6FQDNoPD+awgbephsRnuAGyUmmgOzWZxgRCCCGEDA2DegX6tGnTsHXr1qhvk89pKy8vx7Zt2/rrsg4Lsz40UEszamIcGR9fG8ebE5JpTCCEEELI0DB4U1JHOa1KEbJJfLohucCNNznwwI0yboQQQsjRiwK3ASIIQki5NPmMmxio8RluRgrcCCGEkKMWBW4DKNsc3GYqI8nALbw0atEnt1aOEEIIIYMfBW4DaFhqMHBLtpMzfPxH+No5QgghhBw9KHAbQCmyUmlRerKBW2jGLbxblRBCCCFHD2pBHEDXzhqB9dVtOL4oDWplcjG0KWzHBbOePqWEEELI0Yp+yw+gCQUWfH/7KX06R5oxNMNGGTdCCCHk6EWl0iEuwxi632qyg3wJIYQQMvhR4DbE6TXKkA3rc2SdqoQQQgg5ulDgdhRIl40SybVQ4EYIIYQcrShwOwooFcEdGLJM2jhHEkIIIWQoo8DtKDBzZAYAscNUlWR3KiGEEEIGP+oqPQrccdYY6DVKXHVi8UBfCiGEEEKOIArcjgJpRg0WzS8f6MsghBBCyBFGdTVCCCGEkCGCAjdCCCGEkCGCAjdCCCGEkCGCAjdCCCGEkCGCmhMCGGMAAKvVOsBXQgghhJBjDY8/eDwSCwVuAV1dXQCAwsLCAb4SQgghhByrurq6YLFYYr5dYD2FdscIv9+Puro6pKSkQBCEnt8hQVarFYWFhaipqYHZbD7s5x/q6P7ER/cnPro/8dH9iY/uT3x0f2I7nPeGMYauri7k5+dDoYi9ko0ybgEKhQIFBQVH/HnMZjN94cdB9yc+uj/x0f2Jj+5PfHR/4qP7E9vhujfxMm0cNScQQgghhAwRFLgRQgghhAwRFLj1E61Wi0WLFkGr1Q70pQxKdH/io/sTH92f+Oj+xEf3Jz66P7ENxL2h5gRCCCGEkCGCMm6EEEIIIUMEBW6EEEIIIUMEBW6EEEIIIUMEBW6EEEIIIUMEBW79oLm5Geeeey4MBgPKysrw1VdfDfQlDSiXy4Wf//znKCgogMViwbx587Bt2zbp7Y888giysrKQnp6O22+/vcd9245Wq1atgkKhwCOPPCI9RvdG9Mgjj6CwsBApKSmYPHkyOjo6pMeP9fuzceNGzJw5E2azGSUlJXjppZektx2L92fRokUYN24cFAoFli1bFvK2ePdj3bp1mDRpEgwGA+bOnYvq6ur+vvR+Eev+vPzyy5g8eTJSUlJQUlKCZ599NuT9jvX7w3m9XkyYMAFjxowJefyI3h9GjriLL76YXXfddcxut7N3332XpaWlsba2toG+rAFjs9nY/fffz2pqapjX62X/+Mc/WElJCWOMsY8++ogVFRWxqqoqVldXx8aOHctefPHFAb7i/ufz+dgJJ5zApk+fzh5++GHGGN0b7v/+7//YnDlz2IEDB5jf72fbtm1jDoeD7k9AeXk5e/DBB5nP52MbNmxgJpOJ7dmz55i9P0uWLGGff/45O+GEE9jSpUulx+PdD6fTyQoKCtiLL77IHA4Hu/3229ns2bMH6kM4omLdn2effZatWrWKeTwetn37dpadnc2+++47xhjdH7knnniCzZo1i5WVlUmPHen7Q4HbEdbV1cU0Gg2rq6uTHps9ezZ75ZVXBvCqBheXy8UEQWAtLS3sZz/7GXvkkUekt7344ovs5JNPHsCrGxjPPPMMu/XWW9nVV18tBW50bxjzer0sNzeXVVRURLyN7o/IZDKxffv2Sa9PmzaNvf/++8f8/Zk7d27IL9549+PTTz9lY8aMkd5ms9mYXq9nBw4c6L8L7mfh9yfc5Zdfzv7+978zxuj+cA0NDWzs2LHsww8/DAncjvT9oVLpEVZZWQmLxYK8vDzpsUmTJmHHjh0DeFWDy6pVq5CTk4OMjAzs3LkTEyZMkN52LN6rtrY2/POf/8S9994b8jjdG+DQoUNwOBz43//+h5ycHJSVlUklHLo/oltuuQVLliyB1+vF2rVrUVNTgxNOOIHuT5h49yP8bUajESNHjsTOnTv7/ToHA5/Ph7Vr16K8vBwA3R/ujjvuwJ///GcYjcaQx4/0/aFN5o8wm80WsfGs2WyW1uQc6zo7O3HjjTfioYceAhB5v8xmM2w220Bd3oD485//jN/+9rdIS0sLeZzuDVBbW4vOzk5UVVXhwIED2LdvH0477TSUlZXR/Qk466yzcNVVV+H+++8HADz//PPIzs6m+xMm3v2I9XP7WL1fd999N4YNG4YzzzwTAN0fQEw4VFRU4KWXXsJ3330X8rYjfX8ocDvCTCYTrFZryGNWqxUmk2mArmjwcDqdWLBgAc4991xce+21ACLv17F2rzZt2oS1a9fi6aefjnjbsX5vAECv1wMQFwzr9XqUl5fjyiuvxMcff0z3B0Brayvmz5+PV155Beeffz527dqFs846C+Xl5XR/wsS7H/RzO+jZZ5/FO++8gx9++AGCIACg++P3+3Hrrbdi8eLF0j2RO9L3h0qlR1hpaSk6OzvR0NAgPbZlyxYp5Xys8nq9+NnPfob8/Hz8/e9/lx4fN25cSIfpsXavvvvuO1RUVGDYsGHIzc3FG2+8gYceegjXX3/9MX9vAGD06NHQaDQhj7FAJyDdH2Dfvn2wWCy48MILoVQqMX78eMybNw8rVqyg+xMm3v0If5vdbkdVVRXGjRvX79c5kPjPn88++wyZmZnS48f6/bFardi4cSPmz5+P3NxcXHTRRdi7dy9yc3PR3d195O/PYVkpR+L66U9/ym644QbW3d3Nli9ffsx3lTLG2DXXXMPOOOMM5na7Qx7/8MMP2fDhw9m+fftYfX09Ky8vPyY63zi73c7q6+ulf5dccgm76667WHt7+zF/b7jLL7+cXX/99czpdLLdu3ezvLw89vXXX9P9YYx1dHQwi8XC3n//feb3+9muXbtYXl4e++STT47Z++N2u5nD4WCzZ89m//3vf5nD4WA+ny/u/eBdgS+99BJzOp3sT3/601HbNRnr/nz22WcsKyuLbdmyJeJ9jvX74/V6Q35Ov/3222zUqFGsvr6e+f3+I35/KHDrB01NTezss89mer2elZaWsi+++GKgL2lAHThwgAFgOp2OGY1G6d+KFSsYY4z99a9/ZRkZGSw1NZX98Y9/ZH6/f4CveODIu0oZo3vDGGPt7e3soosuYiaTiQ0fPpwtXrxYehvdH7GjbdKkScxkMrHCwkL20EMPSW87Fu/P1VdfzQCE/Pvmm28YY/Hvx9q1a9mECROYTqdjs2fPPmo7JmPdn3nz5jGVShXyM/rGG2+U3u9Yvz9y33zzTUhXKWNH9v4IjB0DExgJIYQQQo4CtMaNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIUA30BQwWfr8fdXV1SElJgSAIA305hBBCCDmGMMbQ1dWF/Px8KBSx82oUuAXU1dWhsLBwoC+DEEIIIcewmpoaFBQUxHw7BW4BKSkpAMQbZjabB/hqCCGEEHIssVqtKCwslOKRWChwC+DlUbPZTIEbIYQQQgZET8u1qDmBEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMCNEEIIIWSIoMBtkGu0OnH+v1bilR8PDPSlEEIIIWSAUeA2yD322R5sPdSJRe/vGOhLIYQQQsgAo8BtkDvU3i293GpzDeCVEEIIIWSgUeA2yDk9funl+k7nAF4JIYQQQgYaBW6DXHu3W3q5ze6OcyQhhBBCjnYUuA1ybbZgsCYP4gghhBBy7KHAbRBze/3ocnml11ttFLgRQgghxzIK3AaxLqcn5PWOPmTcPt/RgLP+uQI/VrX09bIIIYQQMkAocBvE7C5f6OtuX4wje/bkV5XY3dCFy/+9pq+XRQghhJABQoHbIGZ3e0Ne7w57PRE76qzSy4yxpM9DCCGEkIFDgdsgFh6ohWfgesvj84e8bnUkHwASQgghZOBQ4DaI2cICtWQzbh3doWvlajscSV8TIYQQQgYOBW6DWLfr8GTcwseI9KXJgRBCCCEDhwK3QSy8GSHZjFv44N5OhyfGkYQQQggZzChwG8R4oJZmUANIvqu0nQI3Qggh5KhAgdsg5grsU5pu1AAAHEkGbuGBGgVuhBBCyNBEgdsg5g50g6YaxMAtfDxIb9nC1spR4EYIIYQMTRS4DWJurxi48VJpd5LNCeFNDV1OGgdCCCGEDEWDPnBrbm7GueeeC4PBgLKyMnz11VdRj7vmmmug1WphMplgMplQXl7ez1d6+PH5a2a9GLi5fX4pmEuEzRWaYevuww4MhBBCCBk4gz5wu/nmm5Gfn4+WlhY8+uijuPjii9He3h712Pvuuw82mw02mw07duzo5ys9/HjglqrXSI8ls86Nz4PL4GvlPJRxI4QQQoaiQR242Ww2LF++HPfffz8MBgMWLFiA8ePH44MPPhjoS+sXPLtm1CqhUYqfqmTWudkDa9yyUrQAKONGCCGEDFWDOnCrrKyExWJBXl6e9NikSZNiZtMee+wxZGRkYObMmVixYkXcc7tcLlit1pB/g43bJ+4pqlYqYNAqASQ3y40CN0IIIeToMKgDN5vNBrPZHPKY2WyGzWaLOPY3v/kN9u7di/r6etx8882YP38+ampqYp774YcfhsVikf4VFhYe9uvvK55x06gUMGpUAJLbPYFn6TJNYuCW7FgRQgghhAysQR24mUymiEyY1WqFyWSKOPa4445DWloaNBoNFi5ciBNPPBFffPFFzHPfeeed6OzslP7FC/IGCl/jplYqoNeIGbdkSqXOsHlwye7AQAghhJCBNagDt9LSUnR2dqKhoUF6bMuWLb3qGFUo4n9oWq0WZrM55N9gwwM3jUoBvVoM3PhQ3kS4wsaKUMaNEEIIGZoGdeBmMplw/vnnY9GiRXA4HHj//fexfft2zJ8/P+LYt99+G3a7HV6vF2+88QZWrlyJU045ZQCuune+3NmID7fWxT1GKpUqBSlwc3gSD7pcgfdJ4xm3JM5BCCGEkIE3qAM3AFi8eDFqamqQkZGB2267DW+++SbS0tLw2muvhWTennjiCeTn5yMzMxOPP/443n33XRQXFw/chcfR2e3Bdf9dj1te34Sq5sj1epxbVirVBUqlyWTLnIFALd3AS6UUuBFCCCFDkWqgL6AnWVlZ+PjjjyMeX7hwIRYuXCi9vnLlyv68rD5Zs79VenlFRTNGZkWu2QNCmxN0KjHGdnqTyLh5Q7fOcnv98PkZlAoh4XMRQgghZOAM+ozb0ehgW7f0ckOnM+Zx0ZoT+pRxMwYH+VKDAiGEEDL0UOA2AOTBWlOXK+ZxnsAcN3lzgjOJ9WnOQMbNoldDCCTZqEGBEEIIGXoocBsAjbJgrTlO4BZsTlBAl2RzgtcnlkUBQKdWwKDmg3wpcCOEEEKGGgrcBkBzVzDj1mKLl3GTNSdIGbfExoE4ZZvSa1VK6AODfJMN3D7aWo+THv0aP1a1JPX+hBBCCEkeBW4DwOoIri/rcsZea+byRs5xSzTjJi+talUKGPhauSQ2mmeM4ebXN+JQuwN//2xPwu9PCCGEkL6hwG0AdLk8wZednpjHBTNuAvSaQFdpgpkyefCnUAhS4JZMxk2+Hm9PQ1fC708IIYSQvqHAbQDIs2w2lxeMsajH8Tlu2sOQcePjRPR9CNwqG4Mz5+xuH3WmEkIIIf2MArd+xhgLCdz8LHYQ5fEG17hpk+wq5cfz99epAltneRPfOquuwxH3dUIIIYQcWRS49TOHxyd1eXI2V/TMVbRxIIlm3HiAplMrQv5PZqxIozV05lx9nBl0hBBCCDn8KHDrZzzbplQIMOtUgcci17kxxkK2vAoGbgl2lUqlUvH9tTzjlkTgFj5zrs3uTvgchBBCCEkeBW79jAdpJq0KJm3s0Rw82waE7pyQaMDlCgR62oiMW+Kl0vCZc+0UuBFCCCH9igK3fmYNZNxSdKrgUN0ogRvPtgFicwIPuBIvlYZm3HR92IGhwyEGajxT2NYduyP2cNrbZMPqfa09H0gIIYQc5Shw62c2KXBTBxsOojQKeGSPyQfwJrpVFc+s8ffn/yfTnNAZmD83ItMIAOjoPvIZt/pOBy7410r87PnVFLwRQgg55lHg1s+6QjJusRsF+Aw3pUKAUiH0eRyINjAORNuH5gSrQ8ywFaYbAAAd/ZBxW1HRDHsgWP1oa/0Rfz5CCCFkMKPArZ/xNW5mnUoqX0YLolze4PBdALI1bollyoJdpaHNCU5v8oHbsDQ9AMAeoxv2cNpRZ5Ve3t1gjXMkIYQQcvRTDfQFHGu6ZKVSPhUkWjAm36cUCK5Rc/v88Pr8UCl7F3MH57j1rTnB52foCgRqw1LFwK2rHwI3+ciRvU22OEcSQgghRz/KuPUzeVdpvIYD+a4JQDDjBkRfExcLD9B4pi1eli8enm0DgDxL/2Xc5LPj2rs9MWfeEUIIIccCCtz6WUhXaZwgyuMV03E848YDOCCxBgWpq1TKuPHnTCzj1hkI3IwaJdIMagCxBwdzPj+Tnj9ZDWFDfpusyQ397XJ6cNnzq/HbZZvg90ffYowQQggZ7Chw62dd0bpKowRRbp8Y8GgCAZsgBBsUEsmWRXaViudLNKCyBjKFFr0axsD8uXgZN4/Pj8ueX43pD32FmrbuhJ5Lfo5mmzg7LiXwnOFDgHtryepqrNrXivc21+Gr3U1JnYMQQggZaBS49TNeKg3pKo0SRLnDMm5ActtV8XPzjJ00DiTJjJtZr5YGB8v3XA23qqoVaw+0odPhwZLV1Qk9F9fc5QJjYoPG2DwzgOQDt1VVwVEia2isCCGEkCGKArd+Jh8Hoo8zm403J2hkgVsyI0FcnvCu0tjBYjzywC0lMIDX5fVL1xluW22n9PKmg+0JPRfXECiLZqfokGPRAUi+VCrvTt1e1xnnSEIIIWTwosCtn3W5+DgQtWwYbrSMW6CrVLa2TadJfAivU9o5IXyNW3KBm7xUCsQul8o7QHfVd4GxxNeVtQSya1kpWuSkaAEkl3Hr7PaE7Kt6oCW50i0hhBAy0Chw62fRB/DGHgeiCcxxA2QdoQl0lbqkcSCha9ySbU6w6NVQKxVS5i5WuVS+r6nN5ZXePxG8kcOsVyPbLAZujUlk3A602gEEs5cNVmfCO1AQQgghgwEFbv1M3pwQL/vFx4FoZBk3fRIZt+AAXt6dGjvLF481sN2VWacOXH+gQcHdc+AGAIfaHQk9HxC6HjA7hZdKE8+48WBvbL5Zug/JBICEEELIQKPArZ/ZoowDiTrHzRs6gBdAkl2lsTaZTz7jBkAql9piZdwC3aCGQLB5qD3x8iQPcs06FTJMGgAIKXn2VotNfJ8skwY55kAAmGSTAyGEEDKQKHDrR06PT8qkpehU0pq16HuVimvCNCFdpcmPA4ncOSG5AbwWvRiwSZ2lUda4eXx+KcCaXJgKoK8ZNzXSjWLg1ppU4CYGaZkmLbKltXKUcSOEEDL0UODWj3gGSRAAo0YlNQxEneMWKGWGNCfE2WkhFmkAryp0r1KX159Qw4CUcQsM3zXFybjxQEmlEDB+mAVAsoFbIDupVSHDKAZc7d3uhAfohgZuyZdcCSGEkIFGgVs/kra70qigUAhxM2jRMm7JjAMJZtxCmxOA4Pq33uADePkaN14q7Y6yxo2vb8s0aVEY2JC+T4GbToU0o/i8Pj+TrqW3gtejQVYfulMJIYSQgUaBWz+SByKAbBhulADKHW2OGy+tJjIOxBN9AK/8bb0RvsaNX0t3lGtplo3xyA3saxqtGaCnzJlVVirVqpTS7gmJlkuljFuKVupOTbZU6vb68e8V+/Dxtvqk3p8QQgjpCwrc+pF8iC0gK31GCX6Cc9yC40Ck5oRExoF4QwfwqpUKKBVCyNsSuXYeuBnUsQM3vr4tzahBXmBwbkNY4Pb+ljpMuPcz/OPzPTGfMzzQ5Q0KrbZEAzfxeHmpNLzrtbf+9uluPPTxLtz8+kZsr+2fQb5f7WrEL15eRzs+EEIIocCtP3UEgp9UAw/ceCAWZxyIMpgh08bZaSEWqatUViINrq3r3Xn8fiZrTggEbnFGk/ChvCk6ldTF2WJzSbPpGGN44MOdsLt9eOrrvejsjl76lDcnAJAaFNrsiQVdLV2RzQnJBG4+P8NbGw8FPgYx+Iyn1ebCv1fsw75mW9zj4uns9uDm1zfiq91NuGHJhrj7wxJCCDn6UeDWj8KzVrx86Y6S+fLEybj1do0bY0zKqvGmBABxN7ePxu72glc1zVKplK9xi7wWWyC4MGlUyDBqoFYKYCy4rqyq2R4SOK090Bb1ecMzbumBBoVESqVOj0/qfM0yafvUnbqzzooOWZC5Zn/06wbEe3/df9fjoY934WfPr466FrA3PtvZIH2eOh0efL27KanzEEIIOTpQ4NaPOrvFYCFVLwYPmniBW9S9ShPrKpWXQvuSceMBp0alkLKERmmNW2RAYnOJ5zVqxSYMXp5s6BTLpTvC9gqtbOqK+rzBOW5isJjBM24JlEr5+jaNUgGzPjgPrt3uTngbLr7HaW4gi7i3MfZWXnubbNh0sAOAGLB+sq0hoefiNlaH7vO6qg/l0upWO5Zvrk14FAwhhJDBgwK3ftQZVirlWTCvn8EXtlA/XnOCq7eBm0ceuCkjXk40cOOZQvm1RMu48XKeKZApyw2sc+MNChWNoYGafF9TzuPzSwGqlHEzJZ4t4+vbMkwaCIIgZdy8fibtBtFbOwMb1Z8zIQ8qhQC724f6zuhNDhvCAq7vK5sTei5uc00HAGDB5Hzx9UAwmKjqVjvOefJ7/GbZZvzujc1JnYMQQsjAo8CtH/EyGy83yrezCs+6ub1iIBc6xy2xUilfO6cQxJlqnDbBJodogZshTqlUCty04vPwwI0HORWNYqB2XFGq+HhHZPAjnw/HA0Ap45ZI4CZb3waIwbJJ6k5NbJ0bzxROKrSgONMIIDII5TYeFAM3/jH+UNWacIbP4fahMhDUXjWzGACwp7ErqX1WX/h+P+yB9/tkewP2xshyEkIIGdwocOtH4c0JWpV8plroL+NoGTddgs0JPOOmUyshCLLN6gNl095m7nhjgjkQQAGy5gRPZNaKrynjs954aZFn3CoDwc5JozLFx6OM5uBlUr1aKW37lZ5M4GYLjibhkjmPz8+wq1687vJ8C0bnmABEzxYCwJYaMci77qQSaFUKNHe5UNVs7/XzAWJQ6PMzZJq0OK4wFdkpWvj8TCrZJiJ8bdw3u5PLABJCCBlYFLj1o/DMlUohgMdT4Rm3YHNCtL1Ke5cp4xk3eZkUCO6iEC3jtmR1NaY88AX+s3K/9BgvKSZcKg0L3Bo6nXB6fKhuE/ctnRUI3Jqj7GJglW0wz/GAiwdjvRHcNUETcZ5ESq4HWu1weHzQq5UYkWlEUbqYcYs2WJgxhuo2MUgrzzdjUmDbr/D1ar15TgAoyTJCEIK7UOyqtyZ0nkPt3ajtcECpEHDbGaMBAN9V9E/gZnd5k27MkPP5WcIZS0IIORpR4NaP+NgL3pwgCIKUUQufqcabE7TRBvD2tlQaNnyX08bYr5Qxhr99shutdjfu/3CntO4ueqm053EgUuAmlUodqGq2gTHxXOX5ZgBihi78l3t4RykAadurxDJuwRluwfPEz7itrGzB8yuqQj62HYH1bWPyUqBUCNJ8uvrOyMCtze6Wguu8VB2OL0oDECyf9tb+FjFwG5EhBolj81IAJB64rdkndr9OGGbBWeNzAQDrDrTB6+v9HD+5mrZurKhojtpUI/fZjgZMefALTHngS3y5szGp5wKA19ccxLh7PsVJj34jrTMkhJBjFQVu/Si8OQEIBlXhgRsvlcrHgfBMWfgat5q2bnR0RwYh4cN3w88TXiptsDpDNo3n3Z7x1rjZo2RTwkulhekGAMCB1m5pTdjoHBNSdGopAAzfOzR8hhsQHMDb3t37jtBmW+gaNyB+qXR7bSeu+s8a/PXj3bjrvW3S43x927g8MdiUBgtHaU6o7RCDuewULbQqJaYMFwO38IYFr88fd/uuA4HAbXimeP/GBp470eBlbWBsyQkl6SjJNMGkVcHl9SdcugWATQfbcerj3+Gq/6zFz19eG9FUw3W7vbj9ra1wesQmkz/8b0tSu1Xsa7bhnuXb4fL6UdvhwO/f3Jx0wOny+rBmX2vUXTwIIWSooMCtnzDG0BYIruQBkCYQREU2JwQCt5CMW+Q4kH3NNpz2+Hc4+e/fRvxijJVx00kZt9DnrGoK/UXO12/x604zBsuNiWTcSrLEjFFzlwvrDojBy5hcMQjJCVv/xkXLuPGAy+NjsEbZ3D4aqTlBtsYtIxDERduB4X/ra6SZde9uqkVT4Lp4sFSeL5Yr8wJbedVFC9wC5dP8VPEY3qBQ2WSTguDaDgdO/se3OP7+L7BkdXXUaz/QKpaUecaNB417AmvfemvNfnGEyAkj0qFQCBgXyHTG2vnhQIsdd727Df/6ujJk7aXPz3D3e9ulr80f9rbik+3Rt/76YEsdOh0e5Ft0GJObgk6HB09/vbfX18w99fVeeP0ME4ZZkGpQY3dDF97bHH/wcTTtdjfO+7+VuPT51Zj9t2/w7Z7+m4eXbKBJCCHRDPrArbm5Geeeey4MBgPKysrw1VdfRT3O4XDgiiuuQEpKCoqKirB06dJ+vtL4WmxuuL1+CEIwWAHkGbfeNyfIS5yf7miAy+tHe7cHKypaQs4RvsF8+HnCn3N/a2jgxgM5PjctPUrgFn2Nm/gYD9zMOrWU8eJ7fPLsUaxN37vCNrXn183nx/W2XBptjVtGnB0YeGAJiLsjfLytHowxKXDjQQ8v/7bYXBFBN8+4DUvTB55bi+IMMWu2KVAufeST3ahpc8DrZ3j4411Rd4/ga9x4B+vwDCP0aiWcHr9URu1Jo9WJA63dUAjA1OJ0AMD4QPAZrcnB6fFh4Qtr8Nqag/j75xX44/+2StnN19dUY0edFWadCgtPKAIA/HdV9KDz7Y21AMRu2HvOGye+/9qDONTe3avrBsSvAf718sCC8bh+dgkA4LU10Z8zFsbEgJN36Lq9ftzy+qa4GcAWmwsbD7bHXJ/n8vp63Gt308F2nPnECpTe/Qkue361lEFNVEVjF373xmZc8cIaLFl1oMfnJYQc3QZ94HbzzTcjPz8fLS0tePTRR3HxxRejvT1yrdCiRYvQ1taG2tpaLFu2DL/85S9RUVExAFccHf+FlWvWhYwBibV7gkcqlUZvTuA/vHfIymbhGRQemOkiMm7Rmxwaw7JHVc2hGTd54KbXBMu28rKl38+knRN4qRQIZt34SBS+Xis7ZuAWmXEDgrPcervtlbThfZRSaXhzgs3lxe4G8X7eOEcMEj7aVo+Dbd1otbuhVgoYkyted4ZRA41SAcYis4U8cCsIZNwA4PhAuXRjdTsqGrvwgWy7rG63D99WhGaAOrrd0r0aHgj6lAoBZbmJrXPjuzuMyzdLQTBfW7ijNvIc/1tfg9oOB9RKsXHm/S11eH3tQbTaXHjsM3Ff2T+cUYZbThkFQRDLsOEfv9XpkRoxzhmfh5mjMjFzZAY8PoZ/ybJufj/D6n2t2HqoI2rp++Nt9XB5/RiVbcKkAgsunloAlULApoMdCZWL399Sh4+21UOlEPDOr2ZiYoEFNpcXT35ZGfX4l37Yjxl//QoXLf4RMx/5Gq+tqZa+33bVW3HFC2tQdvenmPrQl3h9zcGo56hqtuHKF9diT2MXGBMHJ1+4+AdpLl9v/bC3BfOfWol3N9Vi5d4W/GX5DtywZH3CQ5Tb7G784/M9OP3x7zDvsW9w7/s70BqnyWdvkw3L1h7E8s3BrHOiupwevLq6Gne8tRUPfLgTP+5tSSro9PsZ1uxrxZLV1fh8R0PUZSGJoCYXMtSpej5k4NhsNixfvhwHDhyAwWDAggUL8Pjjj+ODDz7AVVddFXLskiVL8N5778FsNmPmzJk4//zzsWzZMtxzzz0DdPWhdgZ+0fL1Xpy0e0JYOcUTmOOmjZJxA8T1a3qNMuSv+Jq20GyG0xN9jZs2xs4J/BfwpMJUbKnpCJZKAwFOukGecRO/dBgTn0fqMpWd0yQL3EZmmaS1VoIAKQDh2a/2sCCqyxUjcDNqUdPmCClzttndeOyzPTiuKBWXTC2UHnd5fVJJNWSNW4zN6rce6oCfAcNS9bh6ZjGeW7EP66vbpSBrcmGqdC8VCgE5FvFaGqzOkM8rL5XyjBsATC9Oxzsba/HlriZpnt1Z5bkYkWXEM99W4ZvdTbhg8jDpeJ5RyzFrpXsNiJnKzTUd2FlvxfxJ+SHHv7WhBlOGp+GUMTnS43xj+unFGdJjvDt1R10n/H4GhWzG32uBQOSuc8bC42N46ONduP+DnXh9zUFYnV6MyzNj4QlFUCkVmFggfp18t6cZl0wL3vcf97bC62di920g6PzDGaPx4zOr8L8Nh3Dj3JFIN2hw67JNUnfrFTOK8MAF40PG1vCs3UXHD4MgiDtwnFmei4+21ePVNdX464UTpGO73V48+90+bD3UgYnDLLj2pBFINWjQ0OnEPct3AABuOWUUji9Kw13njMWlz6/Gm+trcMspo6SyNwB8ur0e932wE4C4pKCj24O73t2OtzYcQpZJiy93NUql9Da7G39+dxs8Pj+uDszZA8SS8u1vbYXN5cW04jTcde44LFq+HVsOdeLyf6/Gc1dOwezSLDR1OfHR1nrsqrciw6TFT44fhlHZKdJ5NlS34fr/rofL68dJozIxfUQ6nv5mL77c1YRrX16Hf181VfrjyOdnWL65Fh9trUeX04tROSYcX5SGYal6/FjVgv+sDM7xA4CXfzyAz3Y04JkrpmByoOsZEJc5/OPzCrz8437IY6xJBRbpj4+qZjt211thc3lRmGbAuRPzcNn0Iil7zhjDOxtr8fAnu0M6wF9cuR8lWUb8fNYIzB6VCYUg4FBHNw62dqOu0wmLXo3h6QaU5aYg1aBGRaMNn+1owPub69AgCx41KgUunDwMC2cUYcIwCwRBQJfTg221ndhc04HttZ3w+wGzXgWLXg29WolGqwv7W+yobOpCe7cHZp0KkwpTMbs0E/PKslGaLY732d9ix8q9LVhR0YLNNR1wenzINGlQkGZAYboeWSk6uL1+OD0+ONw++BhDVooWhWkGjMg0YliqHt0eL3bXd2HTwXasr27HvmY7vH4/slN0KMkyYkxuCspyzUjRqeD1MdhdXtjdXthdXrh9DBqlAI1KAY1SgbZuD6pb7YF1zB5AEH9mFqQZUJRuQHGmAXkWPWxOL1psLjRaXWiwOuHy+qBRKqBSClApFFArBXj9DG6vHx6fH26vHwpBgFmvhkX2Txv4mm+3u9Fqd6O9240upxdalQJ6tRI6jVL8X62AAAEMDIyJExK0aiW0KgW0KgUYxGU0jsB94v/7/AxmvRpmnQpmvRoqhXhdHh+D1+eHxy/+Lz7mh9fH4PX74fEx+BkDAl+TWrW4i49OHbwehSDA5fXD5fHBGfjfFwjSBQQnOCgEAQpBgEop/q9RClArFdCoFFAqBPj8DF4/C3lun98Pnx/INmtx09yRGGiDOnCrrKyExWJBXl6e9NikSZOwY8eOkOPa29vR0NCACRMmhBy3du3amOd2uVxwuYI/VKzWI9uttjywLueUMdkhj0ul0rDslztKxk0egDk8Pug1ShxsDQZr4eutYneVRt/cnme9TizJwJaaDhxotYMxJgVV6bJyo152LXa3Vwrc+Po2pUII2WZrRkk6lq4Vg4IpRWlSMMLXzbWF/RXNZ8fJmxOAYKAnz5bd98EOLN9ch6VrD6Ik0yiVBHlgplIIIesKY3WVHmgR72VZbgryU/VSAPv3zyuk+yKXZ9Gjps2Buo7QzlKpVCrLuJ01Phd/Wb4dO+utUhB/w9wSeLx+PPNtFb6vbAFjTApcpDJpYH0bx0u18oxbR7cblz63Svr8PXvF8ThrvPg9s0bWmMCNzDJCp1bA7vZhf6sdI7PEX1r1nQ7sbuiCIADnTx6GVL0aK/e24LuKZuyos0KnVuBvP50IVeCPiZPLsrClpgPf7GkKCdxWBHaJmDs6S3psyvB0nFyWhW/2NOO3b2xGu92Ng21iCdfPgFdXH8S4PAsuD5Rga9q6sXZ/GwQBuPC4YEC7cEYRPtpWj/c21eLOs8cgRaeG0+PDVS+uxfpAlu/bPc14dc1BXD+7BB9tE9faTRhmwc0njwrciwxMH5GOtfvb8O8V+3HP/HHSfbzr3e0AgGtnjcCfzxmD/66qxt8+2y1tXwYA507Mw+9OK8XyzXV46uu9eODDnRg/zCI1obzy4wFsqG6HSavCP392HIal6vH69TNw06sb8H1lC67+z1qUZqegsqkrJDh69rsqXDh5GH53+mhUNdtw82sb0e32YXZpJl64eiq0KiWmj0jHL15ehx+rWnHli2vw76umYlttJx79dE/I18TaA20R2cDyfDNunDsSerUSD3+yC/ua7bjkuVV4cMF4/PT4Any5qxH3fbBT+vqdPiIdLo8PWw51Sv/C7Wnswp4vuvCvb/ZiweR8jM0z48Ot9VIjTnGGAedPykej1YWPt9VjX7Mdf3lve8R5epKiU2FacTqqW+2oarbjjfU1eGN9DbJTtFArFajrdCCRRJrV6cX3lS34vrIFf/14N9IManj9TMr0y9lcXmm9aV/UdjhQ2+HA95UtPR8cx75me8iSDtJ/SrKMFLj1xGazwWw2hzxmNpvR0dERcZxSqYTBYAg5zmaLPhwVAB5++GHcd999h/V647nupBHIMGpwvixLAsTOuEVrTlAqxL/E+F983W5vSBdo+GiKmF2lMZoT+F/HkwtToRDEEl5TlwvtUUqlPDBzevwhDQpSmVQTOvT31LE5yLfoUNfpDMlOpMfYfzRmqTQs6LK7vPhke3Af0Hc31UqBG/94MkyakKyS/BzyYKkmUM4uCmTPFkzOxxZZaWu2LBABYneW8l98+bLALdWgwRnlufhoq7hm68SSDBxflAanxweVQkCr3Y26TqcU7PEgckRmWOAWZSTIP7+sDCk137N8B04qzUKnw4O9TTYoBDHjx6mUCozNM2PTQTE7wQO3FYHs16SCVOkeLV54PJ78qhLVrXb84qQSKVsHACeXZeOfX1bi+8oWeHx+qJUKMMak88wZnRly7X84owwrKluke1qYrsdzV0zF95XNePiT3Xjss904Z0IuUg0aLN9cK90neUbsxJIMjMo2YW+TDW+uP4RrZxXjzne2YX11O1J0KtwwuwQfbK1DRaMNj366O3Dv1XjqsuNCvpduOXkUrtq/Fq+vrcbNJ49EhkmLRz/dg1a7G6NzTLjj7DKolApce9IInFGeg4+31cPp8eOUMdnSPfj96aOxr8WOj7bW4+bXNuKDX5+E9m43/vaZ+Lx3njNG+nwatSq8cPVU3P7WVizfXIc9ge7q44pSMac0CzvqrPhyVyPe2VSLdzbVStc5c2QGnrtyirQ93oySDLx2/Qxc/Z+12HiwA1Me/FI6NkWnwi9OGoERmUZsO9SJbbWdaOpyYXiGAZdMLcTZ43Olr/UZJen43Rtb8OWuRtz+1lb8+Z1t8AaiyII0PR66cIIUeDdZnfi2olka5VOcYcSYvBSkGzTYUN2OV9dUY9PBDry5/pB0LXq1Er8+dRSuO6lE+hn3l/nj8L/1NXhvUy12NXRBIYh//BSlGzAsTY9Ohwf7m+3Y22SD2+eHRa/GSaMyMX9SPuaVZUGnVoIxhg3V7fjvqmp8uqMh5Ot+WKoek4tSManAAr1GBavDg06HB91uL7JTdChKN2BUtgk5Zh0arU6s3d+G7yqasXpfK9oDyxI0SgWmDE/D7NGZOLEkAxa9Gk1dLtS0daOm3YFWmwtalRJ6jUL647PJ6kR1WzcOtNhR3+mEVqVAaU4KJgyzYGpxGsrzLVArBTR0OlHZZMOehi5UNHbB5fVDKQgwapUwalUwalTQqBRSRszl88OsU2F4hhFF6QZkGDVgEJd/HGwTM5XVbXY0Wl1I0amQYdQgx6xDjlkHvUYpZrBkWSOlQhw/xbN5PsbQGbhHnQ4PrA4PnB4fUg0apBs0SDeJ/5v1Krh9DA63Fw632CUur9YIgljOdnr8cHl9cHr8UCggZcMMgSydXqOCQhCXUlgdXnQ6PPD5WUhWMPRlBdQK8X+VUoBSELNmjIm/25weX+BaxJd9fgZdIBMnZv6UUCoESPF8ILL3M8DHGPyBTJ/P74fb54fbK94rpUKAWqGAUilArRCgDFyPIAhIM4QmEgbKoA7cTCZTRCbMarXCZDJFHOfz+dDd3S0Fb9GOk7vzzjvx+9//PuS8hYWFMY/vqzPKc3FGeW7E49pYXaVRmhMA8Qei2yt+83i7Qv/E7Oj2SL9AgWDGTZ75AmQDeMNKpXw0RVaKFnkWPWo7HNh6qFPKCqTJSqWAWC51etwhDQp8qyp5mZS//tnv5qCh04nSnGA5KD1Wxi3KOBBAlnELBHpbDnWE3Lsfq4KbsLdEGQUinkN83e3zw+bySs9xMFBqLgiUOC+ZWoiXfjiAg23dmD4iHVMDGRWOBxTy/UrtLq+0Nk1eKgWAReeNg9PtgyAIuP+CcgDiD7ey3BTsqLNi26GOYOAW1pjAlQW6cRutLrTaXNBrlHh7o/gL899XTcWDH+1EdWs3/r1in9RJO2V4WkhHMCA2KGw62IEddVapRPvtnshMmVGrwp/PGYtoJgyzIMOoQavdjfUH2nHiyAwcaO3GoXZxjdyMsAzl+GEWvHTNNCxbdxAjs0y4bnYJLHo1RueY8PbGQ4Fgaw8euKAc/9sgfkwLZNk2QJx9+PNZxbjr3e148ssK7Kyz4t1NtVAqBDyzcApOKs3ETfNG4uUfDuCDrXXIMetwx1ljIu7j7NJMTBhmwbbaTjz9TRXOnpCLZevEDNWDCyZI35cAUJBmwA1zIv/KFgQBj/5kIvY0dGFvkw0XP/sjut3iL5LZpZm4bFpRyPFalRL/vHQyfjlvJGraHBiTmxJSYt9S04FHPtmN1ftboVYocMm0Atx1zjgpm81NLkzFshtm4FevbcT+FjuMGiV+Nr0IN588Svp+kpfdo0nRqfH8lVPwzHdVWPzNXtjd4nDpn88qxi2njAopz2ebdSFLEOSKM434yZQCbKhuwzsba9Fqc6MsNwU/m14YEnAD4s+An88agZ/PGhH32nipTKtShPzxB4j3fGpxOqYWp6PL6UFVsx0+P8PwDEPE93k8WSlajA+U1J0eH6qabVApFBiRaQxZgwwAJVmmiK/lZBSkGaQ/Kgnpi4QDt5UrV2LdunUoLy/HGWecEfK2X/3qV1i8ePFhu7jS0lJ0dnaioaEBubli0LNlyxZcd911IcelpaUhNzcX27ZtwwknnCAdV15eHvPcWq0WWm3vv9GPFE2MrlLenKBRhf7g0qkV6HSI6wc6vGLwMixVL2V5Oh0e6QeYSyqVxuoqDQ0WgzskqFCcaUBth0MaGmvWqUIyFkCwXCrvvAvfYF4uRaeOCMT4urmINW49ZtzEoIyXsGaOzMCPVa3Y32JHl9ODFJ0aLV2Rw3cBsbFCr1bC4fGhze6WromvEeQZN6NWhfdvmYUfq1oxd3RWxC+RaEN4edk0RacK6YgFxF+AL14zLeK+TCywYEedFVsPdUolTr52MbxUatKqMDzDgOrWbuyq70JdhwNdTi+K0g04dUw23F4/bn59I/79/T5pxwr5mjdu/LDQkSAenx8rAyWceWVZEcdHo1AImFuWhXc21uLbPU04cWSGlG2bOjw95Jc/N2d0FuaEZS5VSgUeuGA8Ln1+NZauPQiby4vq1m6kGdQ4Z0JexDkunVqIpWsPYnutVQpaH7hgPE4qFTN8aqUC188pwfWBBpNoBEHA708fjZ+/vA7/+WE/Xl1TDcaAn04pwPQRvf/latKq8NyVU3D5v1dL5bRR2SY8cenkkCyv/HnH5JqlcThykwpTsfSGGbC7vNKam1jG5pnx9R/motXuhkWvjvje7A2FQsDNJ4/CL04agfpOJ/IsuojsfG9NGZ6OKcMPT1AiZlh6Pi5Fpw5Zn5csnVopjfkhZChI6Lv9ueeew09/+lNs2LABt9xyC0455RS0tbVJb3/11VcP68WZTCacf/75WLRoERwOB95//31s374d8+fPjzj2iiuuwAMPPICuri6sXr0a77//Pi699NLDej1HAs+oxZrjplGG/gTTS0GXT+qYzLXopH1EO2RjJYKl0lhz3ILBot/PQkZwDA8EDLw7MD0sYwNEn+UWraM0nrQY6826omx5BchmsAWO5+M1ThmTLQVSfE/R5ij7lHLROkt5xo0vqAfEEuc5E/Kifjy5UUqlh6Ksb+vJhGGpAIBtgSCKMRbcNSEsUwQE57ltOdQhzYC7bHoRFAoB50zIxaTCVHS7fdjXYodKIYSsEeP4L6rttZ1gjGFjdTu6XF6kGdSYWJDa62s/uUxcs8n3Qv2+kpdJexf8cSeUZOCy6WJWhzeD/HLeyIjMLSD+Yn/hqmk4qzwXkwoseOqy46S1cYk4eUw2fnGSmP1xe/2YVJiK+86P/cdeLCOzTPjo1tm4/awy3H3uWLz7q5kJZX/CGbWquEEbJwgCMk3apII2OV1gG7dkgzZCSP9KKOP22GOP4euvv8a4cePg9/tx1113YdasWfj8889RWFh4RNqsFy9ejKuvvhoZGRkoKCjAm2++ibS0NLz22mv461//KjUq3H///bjuuuuQl5eHtLQ0LF68GGVlZYf9eg43vv1UrC2v1BEZNx4s+aXALdOkQXOXBlanF52OYCASLJWGd5XynROCz2l3e6WSqDnQ3QUEF7fLZ89x0Wa52VzRS6WxSF2l3e6QDkeecQvPWoU3FvAF08cVpaI834L6Tid21HVi+oh0KRPGA7qQ85g0qO1wSGvrOh0eKegtTDNEHB9NMOMWDNx4R2lBWiKBmxhEbQsEUe3dHqkbdnhG5LXMHJWJT7Y3SOM5NCoFLg00BwiCgIcWjMeVL65Be7cHfzp7jBRgyo3OSYFGqYDV6cX+Fju+rQgGXMoomaJY5pSKx1c22bC9thM/7BVL1bNLM3t4z0h3nzsOVocXKyqbcd7EfFwbp6SWa9Hh2SunJPwckc85FqeOzUZntwenjM2OyE73VqZJi1/NG9Xn6yGEkJ4kFLg1NTVhzJgxAACFQoGHH34Yw4cPx0knnYSPPvooopR0OGRlZeHjjz+OeHzhwoVYuHCh9Lper8drr7122J//SIu2Vylj4qJJ+ds5+fw0aUZZihYNnU4cbAvNuEkDeGPtnCArz/KJ/hqVuLhzeFiJLny9FhB92yu71JzQuy+t1ECp1M/Ea+AZuN40JzRZnWjuckEhAOPyLCjPN+PLXY3SjC+eCQtfaxN+HiBYJs00aXqdLeQBUbPNJa0tjNaY0JOyXDGI6uj2oKbNIWUK82OUrs4en4sHPtwpZWUvnDwsJCM6fpgFP/zpFDg9/qiZUkD8PB8/PBWr97VhRUUzPgs0eIR3PffEYlBj1qhMrKhoxkXP/Ah3YO4anxWXCKNWhacXHp/w+/WFIAiYOTLxIJMQQgZKQjn2kSNHYv369SGP3XTTTfjb3/6GU089NWS8BukdTZQBvPIOU3V40CXbr1QqBZp0sPC1YiGl0ugZt2g7MPD1bTzDVZwZmukpiBKIRC+VBnZNiLLGLRqNSiEFZ7xBwePzS9t6ha+J4wvum7tcUrZtVLYJeo1SGo7LO/bqpcAtMuMUXirlA5LD5+zFk2nUQq0UwFhwlEpdEqVSjUohDSTefKgD+wKDj8MX1EvPa9Li9jPLoFQIGJllxG1nRmaWDRpVzKCNmxcocy7+tgr7WuzQqBQ4dWzkerieXDurGEDwa/iy6UVH5I84QgghCQZuv/3tb7Fly5aIxy+99FIsWbIEs2bNOmwXdqyQypaywI1n24DYGTdnWMYtNTCnTD5VPPaWV5HjQHgXp1kvBlHFGcaQ55Z3goZfS2ipVDxPb0ulQDCI4g0KNtkspfCMW65ZJw2T/GKnmCXiWzjxob4Vgb08ecYtWqkwfNurg2GNCb2hUAhSCbkhUJaNNny3N/i6sq01HdJaN76WLZrrZpdg419Ox5e/nxt1DV9vnBYI0njQeeqY7IQ+b9zc0Vm4KLCO7oQR6dJ2WIQQQg6/hH5KX3311QCAN998M+rbb7rpppC3XXLJJX24tGND1Iyb7OVYnZwRgVtgvgwveQLBUmjEAF5VsMGB4wNv+aBaPqaCBxGToixYN8jKthzfp9So7f1aoTSDBtWt3VL2i5dJdWpFxMevVAgoTDdgX7NdmolWHlgjNjzDKM2Wq2zqks4XPeMW2uSQTODGz32o3SFl96IN3+2NSYWpWLK6Guur26W5QxN76JiTDxVOxqhsE86dmIePttZDo1Lg16eUJnUeQRDw+KWTce8F5UjRqijbRgghR1BSc9yefvpprFq1Crm5uSgoKMChQ4fQ0NCAmTNnSj+0BUGgwK0Xom0yzxsTlAohYqF4cIeC8MBNzCCFdJXG2PIq2l6lPOCTNwPccsoo3PTqBpwxLiek05Lja9zk40CCzQm9DyrCt72KNcONG5Vlwr5mu7SFz/jAeiqlQkBpthhsfheYSaZXK6MGOOFNDgfbxICrt40JXK5FD6AdDZ1OeHx+aduwRDNus0aJc6Lke1lOTqC7M1mPXzIJ8yfmozTHJA3iTVZ4IwkhhJDDL6nAbezYsbj44otxyy23SI89/fTT2L59O5555pnDdnHHgngZt/AyKRD85djp8ISMu+Cl0nZ5qTTGJvPR9irlXYxmWZBzZnkuVv3pVGSaoq+VkgeRnDTHLZGMW9h6s1iNCdzEAgs+39kIQLxHEwqCM5h4lvDTHWIZtSBNHzUDlGGK3pyQyBo3ILSztK7DAT8T72+mMbHyZZ5Fj/J8M3YEGiuyUrQoTE8s+EuGVqXEWeMjB0MTQggZnJIaALRs2TL88pe/DHnsxhtvxNKlSw/LRR1LtFG2vJL2KVVGBhx8DVpNW7e0Fi7DqIleKo25xi24ro6PcLFKGbewNWUWnbQ3ZTjjYZjjxq8fCGbcunrIuPFF9YA4vkI+6JU3KPDBvKU50bNI6bIdGHx+JjUnRMssxsOH3NZ3OqTZa8MzDFGHr/ZEvh3ahccNo5IjIYSQCEll3IYPH45XXnkF1157rfTYkiVLjuiWUUeraJvMB3dNiAyYeNmPd06mGdTQqZVSkGOVLex3xci4yQfyurx+6NRKqTyZyLopPS+VepKf4wZEbjQfnOEW/Rzjh1lw09yR2Fjdjj+fMybkbeGT1EdHaaoAgttetdpdaLA64fExqJWCFIj1Fl8Tt7+lG9WByfnhux301s9njUCHw4M2mxu/OTW59WaEEEKObkkFbi+88AJ+8pOf4JFHHkFhYSFqamrgdDrx9ttvH+7rO+pF22S+N6XSvU3iyIjcwIwyXlbk2Sqg5zVu/BidWhlc45ZA4BYcBxJly6tEukoNoWXLWLsmyP3p7DFRH59QYJG2swIQc/si3onp9PixIbA7xPAMY0LDZwFxgT8A7Gu29TjGoycalQJ3nBX94yKEEEKAJAO3adOmoaqqCqtWrUJ9fT3y8vJw4oknQq2mxcmJitbhGdw1IXbGjcs1iwFIMHALBlF8DZs2bMsrtVIBpUKAz8/g9PpggTpijltvRN05wZl4qTR8HIi0xi2BBgdOq1LimlnFeObbKpRmmzAtxqbOeo0SeRYd6jud+CKwXm5UEovzC9MN0KgUcHn9WLlX3Osz2YwbIYQQ0pOkAjcAUKvVmDNnzuG8lmNStOYEV5yMW3pYowCfUcYDLmvIGjdeKo1sFNCqFOh2+6Rjwue49UZwk/kopdJeDuAFIpsTgtm/5L48/3hGGeaOzsLonJS4+ziOyDQGAjexkWFkduIBl1IhoCTTiN0NXahqjr2/KCGEEHI49G13YtJn0TaZ500H0YKO8D1Dc81iqZQHbi6vH+5A0wEvF/LMmFz4SJDwOW69wbNqfBwIY0wa0dGXAbwdgWvhI04SpVAImFGS0ePOASVZYoDF7wEveyZKPpxYEIDxwxLf7okQQgjpDQrcBli0TeY93tjNCVmm0DETuRbxdXmGq8vpgcvrlzaN10cL3MJGglijzHHrSfjOCU6PH77AkyZTKrUHMoB8Fl1akoFbb43IDA3USrOjNzL0ZEZJsBw7Pt8SsxuWEEII6SsK3AZYtIwbb1SIVirVqBTS+AwguJm5UiFI4zm6nN6QER36KBuVh+9XGm2OW0/C9yrlZVIAMER5zljMOhVUgaaA9m43Oh1i5o2PODlS5FtKmbQqaZRIos4ZnyfNurtmZvHhuDRCCCEkqqTXuJHDQyubqcYFmxOidzgWZxql9WDl+cHhs2a9Gna3D11Or9TYoFEqos5hkz+v1+eXgq5YIziiMah5qVQM3OQdpYnMMRMEAWlGDZq7XGizu6WMW2oft3TqybTiNBSm61HT5sAFk/NjzqvrSZpRg49unY0Wmyvk80EIIYQcbhS4DTCeVXP1sjkBAM4qz8WG6nZMH5Eeso4rRadCfafYaKDXiO8brUwKyDea94VkyhLJuOlle5X6/Uw2fLf32TYu3SAL3Ph6uyOccVMpFVh2w4lYXdWKcyfm9elcOWZdxPpDQggh5HCjwG2ABbtKo4wDiRG4XXvSCEwssEibq3N8bVWX0yONB4nWmADItr3y+qUuToNGGbcLM5z83A5ZAJjI+jYuzShee5vdjc7uvjUnJGJYqh4/mVJwxJ+HEEIIORwocBtgwU3me9ecAIjr2U4oyYh4nAdrVqcXqYHyZeyMW3CNWzIz3IDQtXPdbp9UKk1JInDjOxkcandIa/yOdKmUEEIIGWqoOWGAyfcq5fuGxmtOiCeYcfPGHQUCBGe7uTy+pGa4AeLYDR68OdyHJ+PG9/vUKBUxr50QQgg5VlHgNsD4zgmMAd7AKI14c9zi4Y0FVodH6vSM1lEKyNe4+ZOa4cZJuyd4vH0K3NIDGTceuFkMatpknRBCCAlDgdsAk5dDebnU1UOpNBZ5xq1bKpVGD6J06uBWW51JzHDj5LPc+lIqTTeE7sFKZVJCCCEkEgVuA0wenPFZbj01J8Qi32ieb/wea56aTrZdVbBUmnzGTSyVisFicqVSsRGBB5HUoUkIIYREouaEAaZUCFApBHj9TNpo3p1kxs0s22ieZ9xirRPjQZ7N5YUiUJJMZIYbxzN6dpc3qQ3mOd6cwGWbtTGOJIQQQo5dFLgNAhqVAl63LyLjplEmtsaLl0qtTo+sVBo/cOtyBme4JbPGzSib5daVZJMDEGxO4HIp40YIIYREoMBtENCqFOiOFrglmnHTB4MxvpVVrOYEKchzeKT9RftSKu12922tXHjGbViaPuFzEEIIIUc7WuM2CGjCZrnx/xNf4xYcwNtTqdQsa2Ro7xa3z0pmU3deKpWvlUsmc5edooVJVmIdnZPcvqGEEELI0YwCt0EgPHBLdhxISpQ1brG6SoPDej3S3qDh5creMEhz3LzBQb5JBG4KhYBhqcEsGwVuhBBCSCQK3AYBPsst2Jwg/t+XcSAOT6CrtBdr3NoCG9Yns8UUX0Nnl5VKk8m4AcCfzhkDjVKBhScUJX0OQggh5GhGa9wGAb5Dgjss45bozgm8K9Tt86OlSwzGTDE6POWNDP7AGrf0JAI3vqF8t8sbHCuSRHcqAJxclo2N95wec4QJIYQQcqyjwG0Q0KrDA7fkmhOMGhUEQdyFobbDASB22dIcpas0mTVufK1ch8MjnSuZUikXK9AkhBBCCJVKBwWeWetrc4JCIUiBjxS4xch+pYR1fiqEYPk0EbykeajdIT2WTFcpIYQQQnpGgdsgwDNrkTsnJL5XZ/jasFjZL51aEXL+NIMGCkXyz1fT1g1AHD+SaKaQEEIIIb1Dv2EHgWBzghiwJbtzAgBkmELnocUK3ARBCMm6pRqSy5JZAu/X1OUSX6emAkIIIeSIocBtENBKGTexmzS4c0Lin54sU+g6tXiNAvLSaDLr24BoGT5ao0YIIYQcKRS4DQJS4OY7DBk32Q4EWpUi7mL/7JTgscnuDZpuDA34UvXJBYCEEEII6RkFboOANIDX07cBvACQmRIMnLLNWghC7HVr+bKBt4XphoSfCwAyTVrInyLHQnuMEkIIIUcKBW6DgCY845bkOBAAKEgLBmA5KfGDqJDALS25wE2tVITMf8tJSS5zRwghhJCeUeA2CGjDukrdSY4DAYDhssxZbg/Zr8mFqdLLU4anJfxcXNZhKLkSQgghpGe0knwQiNyrNPnmhLLc4B6f4/LNcY89uSwbZ4zLQZpBgzG5ye8NOiLTiN0NXQCA4gxj0uchhBBCSHwUuA0Ch3scyB1njcGn2+tx8ZTCuMdqVAo8f9XUhJ8j3PhhFnyyvUF6mRBCCCFHBgVug0Aw4+aD38/g9fPmhMQH4gLAL+eNxC/njTxs19eTi44fhjX72zA2LyVk3RwhhBBCDq9BvcZt3bp1mDRpEgwGA+bOnYvq6uqYxxYXF8NgMMBkMsFkMuGmm27qxyvtG/km87wxAQC0Q2Sz9TyLHv+9djruPHvsQF8KIYQQclQbtIGby+X6//buPbbJ8mHj+NUN6A7dOoSNcdjgHeAGgwwSEYPvBP0ZxBAIEDRkEdG9cogxyD+gIgFDOOwPREMiTCLhFFQ0oCCiYADBkJkZMQgMGQw2poAHBus6tkG3+/0DWtfBCj+g61P6/SRL1udp17uXply57+egCRMm6PXXX1dVVZUee+wxTZ48OeBr9u7dK7fbLbfbrcLCwjYa6b1rfpN573KpdHfHuAEAgAeXZZdKv//+ezkcDuXn50uS5s+fr+TkZFVUVKhnz573/PcbGhrU0NDge+xyue75b96t5jeZb7hx9wSb7e6XSgEAwIPJslM6JSUlGjhwoO9xfHy8evfurZKSklZfM27cOHXp0kXjx48PuKwqSUuXLpXT6fT9pKUFPpA/mLxLolc9Tb6L8NrbRQW8eC4AAIg8li1ubrdbiYn+l7NITEyU2+2+5fM//vhjlZeX6+TJk0pPT9e4ceNkjGn177/11luqrq72/VRWVt7X8f83/p1xa/z34rsskwIAgBZCtlQ6cuRIHThw4Jb75s2bJ4fDcdPypcvlksPhuOVrhg0bJkmKiYnR8uXL5XQ6debMGWVkZNzy+Xa7XXa7NS4W2/xepb4ZtzA5MQEAALSdkBW33bt3B9y/a9curV692ve4trZWZWVl6t+//23/ts1mC6tlxuZ3TvAe42a/i2u4AQCAB5tl28GIESPkdru1bt06NTQ0aNGiRXrkkUdueWLC2bNnVVRUpGvXrqm2tlazZ89Wz5491atXr7Yf+F1ofueEe7n4LgAAeLBZth3Y7XZt3bpVy5cvV1JSkg4ePKiNGzf69s+YMcN3rbaamhpNmzZNSUlJ6tWrl06dOqVt27YpKsqyH8+P984JzS8H4t0GAADgZdnLgUjSkCFD9Ouvv95yX/PrtGVnZ+vIkSNtNaz7rvmM27/FLTxKJwAAaDu0Awvo0OwYN5ZKAQBAa2gHFsDJCQAA4E7QDiygQ7PLgdRf4xg3AABwaxQ3C2g+u+ZuuHbTNgAAAIniZgnNj2dz1XkkUdwAAMDNaAcW0Pz2Vu6GG8WtPf9pAACAP9qBBdhsNt+sm6v++lIp9yoFAAAt0Q4swn6jqPmWSrlXKQAAaIHiZhHepVFOTgAAAK2hHViEd2m0pt7j9xgAAMCLdmARLY9x4+QEAADQEu3AIrwX3PXOuHEBXgAA0BLFzSK8M26+pVKOcQMAAC3QDizCezJCY5PxewwAAOBFO7CIljNsLJUCAICWKG4W0bK4xdspbgAAwB/FzSJaLo3G29uFaCQAAMCqKG4W0aHF0mgsd04AAAAtUNwsghk3AABwOxQ3i7jpGLcOzLgBAAB/FDeLSGgxwxbHjBsAAGiB4mYRibHt/R5zjBsAAGiJ4mYRiTH/zrDFdYhWdJQthKMBAABWRHGziOYzbg/FdwjhSAAAgFVR3CyC4gYAAG6H4mYRzmbFrWMcxQ0AANyM4mYR6Q/F+X6P41IgAADgFihuFtGp2fJoZ4c9hCMBAABWRXGzCJvNpv/73/9RRnK8Zv6nb6iHAwAALMhmjDGhHoQVuFwuOZ1OVVdXKzExMdTDAQAAEeROewgzbgAAAGGC4gYAABAmKG4AAABhguIGAAAQJihuAAAAYaLd7Z8SGbwn17pcrhCPBAAARBpv/7jdxT4objfU1NRIktLS0kI8EgAAEKlqamrkdDpb3c913G5oamrSuXPnlJCQIJvNdt//vsvlUlpamiorK7lO3C2QT2DkExj5BEY+gZFPYOTTuvuZjTFGNTU16tatm6KiWj+SjRm3G6KiotSjR4+gv09iYiL/4wdAPoGRT2DkExj5BEY+gZFP6+5XNoFm2rw4OQEAACBMUNwAAADCBMWtjdjtdi1YsEB2uz3UQ7Ek8gmMfAIjn8DIJzDyCYx8WheKbDg5AQAAIEww4wYAABAmKG4AAABhguIGAAAQJihuAAAAYYLi1gb+/vtvjR49WnFxccrMzNSePXtCPaSQamho0Msvv6wePXrI6XRqxIgROnLkiG9/QUGBkpOT9dBDD2nOnDm3vW/bg6qoqEhRUVEqKCjwbSOb6woKCpSWlqaEhAQNGjRIly9f9m2P9HwOHTqkYcOGKTExURkZGVq7dq1vXyTms2DBAvXv319RUVH69NNP/fYFyuOnn35STk6O4uLiNHz4cFVUVLT10NtEa/msW7dOgwYNUkJCgjIyMlRYWOj3ukjPx8vj8WjgwIHKysry2x7UfAyC7rnnnjOvvPKKqa2tNV988YXp2LGjqaqqCvWwQsbtdpuFCxeayspK4/F4zLvvvmsyMjKMMcZ8/fXXJj093ZSVlZlz586Zfv36mTVr1oR4xG2vsbHRDB061Dz66KNm6dKlxhiy8VqxYoV54oknTHl5uWlqajJHjhwxdXV15HNDdna2WbRokWlsbDQ///yzcTgc5sSJExGbz8aNG83u3bvN0KFDzSeffOLbHiiP+vp606NHD7NmzRpTV1dn5syZY3Jzc0P1EYKqtXwKCwtNUVGRuXbtmjl69KhJSUkx+/fvN8aQT3Pvvfeeefzxx01mZqZvW7DzobgFWU1NjenQoYM5d+6cb1tubq5Zv359CEdlLQ0NDcZms5l//vnHTJo0yRQUFPj2rVmzxjz55JMhHF1orFq1ysycOdNMmTLFV9zIxhiPx2NSU1NNaWnpTfvI5zqHw2FOnz7tezxkyBCzffv2iM9n+PDhfv/wBsrj22+/NVlZWb59brfbxMbGmvLy8rYbcBtrmU9LeXl5ZtmyZcYY8vG6cOGC6devn9mxY4dfcQt2PiyVBtnJkyfldDrVtWtX37acnBwdO3YshKOylqKiInXp0kWdOnVSSUmJBg4c6NsXiVlVVVXp/fff1zvvvOO3nWyk33//XXV1dfr888/VpUsXZWZm+pZwyOe61157TRs3bpTH41FxcbEqKys1dOhQ8mkhUB4t98XHx6t3794qKSlp83FaQWNjo4qLi5WdnS2JfLzeeOMNzZ07V/Hx8X7bg50PN5kPMrfbfdONZxMTE33H5ES66upqTZ8+XYsXL5Z0c16JiYlyu92hGl5IzJ07V7NmzVLHjh39tpON9Mcff6i6ulplZWUqLy/X6dOn9fTTTyszM5N8bhg1apRefPFFLVy4UJK0evVqpaSkkE8LgfJo7Xs7UvOaN2+eunfvrmeeeUYS+UjXJxxKS0u1du1a7d+/329fsPOhuAWZw+GQy+Xy2+ZyueRwOEI0Iuuor6/XuHHjNHr0aOXn50u6Oa9Iy+qXX35RcXGxPvjgg5v2RXo2khQbGyvp+gHDsbGxys7O1uTJk7Vz507ykXTx4kWNGTNG69ev19ixY3X8+HGNGjVK2dnZ5NNCoDz43v5XYWGhtm7dqoMHD8pms0kin6amJs2cOVMrV670ZdJcsPNhqTTI+vbtq+rqal24cMG37fDhw74p50jl8Xg0adIkdevWTcuWLfNt79+/v98ZppGW1f79+1VaWqru3bsrNTVVmzdv1uLFizV16tSIz0aSHn74YXXo0MFvm7lxJiD5SKdPn5bT6dT48eMVHR2tAQMGaMSIETpw4AD5tBAoj5b7amtrVVZWpv79+7f5OEPJ+/2za9cude7c2bc90vNxuVw6dOiQxowZo9TUVE2YMEGnTp1Samqqrly5Evx87suRcgho4sSJZtq0aebKlStm27ZtEX9WqTHGvPTSS2bkyJHm6tWrftt37NhhevbsaU6fPm3Onz9vsrOzI+LMN6/a2lpz/vx538/zzz9v3n77bXPp0qWIz8YrLy/PTJ061dTX15vffvvNdO3a1ezdu5d8jDGXL182TqfTbN++3TQ1NZnjx4+brl27mm+++SZi87l69aqpq6szubm5ZsOGDaaurs40NjYGzMN7VuDatWtNfX29efPNNx/YsyZby2fXrl0mOTnZHD58+KbXRHo+Ho/H73t6y5Ytpk+fPub8+fOmqakp6PlQ3NrAX3/9ZZ599lkTGxtr+vbta7777rtQDymkysvLjSQTExNj4uPjfT8HDhwwxhizZMkS06lTJ5OUlGRmz55tmpqaQjzi0Gl+VqkxZGOMMZcuXTITJkwwDofD9OzZ06xcudK3j3yun9GWk5NjHA6HSUtLM4sXL/bti8R8pkyZYiT5/ezbt88YEziP4uJiM3DgQBMTE2Nyc3Mf2DMmW8tnxIgRpl27dn7f0dOnT/e9LtLzaW7fvn1+Z5UaE9x8bMZEwBUYAQAAHgAc4wYAABAmKG4AAABhguIGAAAQJihuAAAAYYLiBgAAECYobgAAAGGC4gYAABAmKG4AAABhguIGAJLOnj3rdz/GYCgvL5fNZpPD4dCXX34Z8LlbtmyRw+GQzWbzu9cxgMjGnRMARAyHw+H7vba2VnFxcbLZbJKkkpISpaenB/X9y8vLlZWVpfr6+jt+jc1m0/nz55WamhrEkQEIF+1CPQAAaCtut9v3e0xMjI4dO6ZevXqFbkAA8F9iqRQAdH02LCYmxvfYZrNp1apVSk9PV+fOnbV582bt2LFDGRkZSklJ0ebNm33PraqqUl5enlJSUpSRkaH169ff8fv++OOPGjx4sBISEpSamqrly5ff188F4MHCjBsAtOLgwYMqLS3VV199pRkzZmjs2LE6evSo9uzZo/z8fE2cOFHR0dGaPHmyBgwYoMrKSp05c0ZPPfWUBg0apJycnNu+x6xZszR79mzl5eXp0qVLKi8vD/4HAxC2mHEDgFbMmTNHMTExmjBhgi5fvqxXX31VcXFxGjNmjGpqanTu3DlduHBBP/zwg5YsWSK73a6srCzl5eVp69atd/Qe7du314kTJ1RVVaWOHTtq8ODBQf5UAMIZxQ0AWpGSkiJJio6OVvv27ZWcnOzbFxMTo9raWp09e1a1tbXq1KmTkpKSlJSUpA8//FB//vnnHb3HRx99pOPHj6tPnz4aNmyYioqKgvJZADwYWCoFgHvQvXt3JSUl6eLFi3f1+szMTH322WfyeDwqLCzUCy+8oLKysvs8SgAPCmbcAOAedO/eXUOGDNH8+fN15coVeTweHTp0SCUlJXf0+k2bNunixYtq166dEhISFB0dHeQRAwhnFDcAuEebNm1SRUWF74zTWbNmqa6u7o5eu3PnTmVmZiohIUErVqzQ2rVrgzxaAOGMC/ACQBupqKhQVlaW7Ha7NmzYoLFjx7b63K1btyo/P1/19fWqqKhQly5d2nCkAKyK4gYAABAmWCoFAAAIExQ3AACAMEFxAwAACBMUNwAAgDBBcQMAAAgTFDcAAIAwQXEDAAAIExQ3AACAMEFxAwAACBMUNwAAgDDx/3HTKx2ZepXNAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "response = ct.initial_response(sys, X0=[1, 0, 0, 0])\n", - "out = response.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Y4aAxYvZRBnD" - }, - "source": [ - "If you want to play around with the way the data are plotted, you can also use the response object to get direct access to the states and outputs." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot the outputs of the system on the same graph, in different colors\n", - "t = response.time\n", - "x = response.states\n", - "plt.plot(t, x[0], 'b', t, x[1], 'r')\n", - "plt.legend(['$x_1$', '$x_2$'])\n", - "plt.xlim(0, 50)\n", - "plt.ylabel('States')\n", - "plt.xlabel('Time [s]')\n", - "plt.title(\"Initial response from $x_1 = 1$, $x_2 = 0$\");" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Cou0QVnkTou9" - }, - "source": [ - "There are also lots of options available in `initial_response` and `.plot()` for tuning the plots that you get." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Do some Python magic to get different colors\n", - "from itertools import cycle\n", - "prop_cycle = plt.rcParams['axes.prop_cycle']\n", - "colors = cycle(prop_cycle.by_key()['color'])\n", - "\n", - "for X0 in [[1, 0, 0, 0], [0, 2, 0, 0], [1, 2, 0, 0], [0, 0, 1, 0], [0, 0, 2, 0]]:\n", - " response = ct.initial_response(sys, T=20, X0=X0)\n", - " response.plot(color=next(colors), label=f\"{X0=}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "b3VFPUBKT4bh" - }, - "source": [ - "### Step response\n", - "\n", - "Similar to `initial_response`, you can also generate a step response for a linear system using the `step_response` function, which returns a time response object:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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q8Ytf/AKPPfYY1q1bh4MHD2L+/Pk+v2j4i/S1AfhfbB544AFs374de/bswZ133okpU6ZEXXqN1m233QaXy4Vf/epXKC8vx9dff42//vWvACJnwGM1ZMgQfPPNN9i6dSvKy8txzz33oKGhQbw/3L+b48ePY+HChdi2bRtOnDiBtWvXoqKiIuZfLgg5bfFaTEjI2cZisbDf//73bPz48SwpKYlpNBpWUlLCnnzySWYymcTHrVq1ig0ZMoTJZDJWWFgo3v7VV1+xqVOnMrVazXQ6HZs8eTJ78803xfsBsNdee41deeWVTKlUssLCQvb++++HPSf/hfGCiooKdsMNN7Dk5GSmVqvZsGHD2EMPPcRcLhc7dOgQu/rqq1lGRgZTKpVs6NCh7J///Kf43Hnz5rGZM2eyp556iqWlpTGtVst++ctf+nQsm81m9sADD7D09HSmVCrZ+eefz3bs2CHeLzRytLe3i7d5N1pYrVZ26623soKCAqZQKFhubi67//77fRbs79ixg1155ZVMq9WyhIQENmbMGPbcc8+FvBbBGjkYY2zx4sVs0KBBTC6Xs6FDh7L//ve/PvcjSONNMGazmT388MMsJyeHKRQKNmTIEPb222+L92/YsIFNmjSJKRQKlp2dzR5//HFmt9vF+/V6Pbv55puZTqdjBQUFbOnSpUEbOcJ9D/g3cjAW/mvNGGOdnZ1s7ty5TKPRsKysLPbnP/855PcNYyzi12bJkiUsKSmJ/e9//2ODBg1iCoWCXXbZZT4NDqWlpWzs2LEBxw7WyBGpuWXLli1szJgxTKFQsAkTJrD33nuPAWCHDx8Oev6hjuv/dfa/lq2trWzmzJlMq9WyzMxM9uSTT7I77riDzZw5kzHGwv67aWhoYLNmzRK/NwoLC9lTTz0lNvEQ0ls4xrq5qIMQ0qs4jsOnn36KWbNmxfU85s+fj46ODtpVJA76yvdAOEuXLsVDDz2Ejo6OuLz+8uXLceedd0Kv10dca0lIf0M7chBCCPnJ+u9//4tBgwYhLy8P+/fvx+OPP46bb76ZAj5CgqCgjxBCyE9WQ0MDnnrqKTQ0NCAnJwezZ8/Gc889F+/TIqRPovIuIYQQQkg/QN27hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9AAV9hBBCCCH9gCzeJxBPLpcLdXV1SExMBMdx8T4dQgghhJCYMMbQ2dmJ3NxcSCThc3n9Ouirq6tDQUFBvE+DEEIIIeS01NTUID8/P+xj+nXQl5iYCIC/UDqdLs5nQwghhBASG4PBgIKCAjGmCadfB31CSVen01HQRwghhJCfrGiWqVEjByGEEEJIP0BBHyGEEEJIP0BBHyGEEEJIP0BBXx/gdDEcbjCAMRbvUyGEEELIWYqCvj5g6dZqXPO3TXhqZVm8T4UQQgghZykK+vqAj3efAgAs++EEZfsIIYQQckZQ0NcHtHVZxY/bTfY4ngkhhBBCzlYU9MUZYwxtXTbx89p2cxzPhhBCCCFnKwr64sxgccDu9JR06/UU9BFCCCGk51HQF2etRqvP53ozlXcJIYQQ0vMo6Isz79IuQEEfIYQQQs4MCvrizD/oM1DQRwghhJAzgIK+OOuyOXw+p0wfIYQQQs4ECvrizGh1+nxOQR8hhBBCzgQK+uLMZPXN9Bn9PieEEEII6QkU9MVZlzvIS1LLAQAmmzPcwwkhhBBCuqXPBn3Nzc2YNm0aNBoNSkpKsG7duqCPO378OK666iokJycjLy8PL7zwQi+f6ekRyrvpWgUAwGynoI8QQgghPa/PBn0LFixAbm4uWlpa8NJLL2H27Nlob28PeNwDDzyAQYMGobm5GZs3b8Y///nPkAFiX2RyN3Kka5UAADNl+gghhBByBvTJoM9oNGLlypVYtGgRNBoNZs2ahVGjRmH16tUBjz1x4gRuueUWyOVyDBw4EBdccAEOHToUh7PuHmENX3qiO+ijTB8hhBBCzoA+GfRVVlYiKSkJOTk54m1jx45FWVlZwGMXLFiADz74AFarFZWVlfjhhx9wySWXBD2u1WqFwWDw+RNvwhq+DHemj9b0EUIIIeRM6JNBn9FohE6n87lNp9PBaDQGPHbq1KnYtm0bEhISMHToUPziF7/A6NGjgx73hRdeQFJSkvinoKDgjJx/LIRMX4Y702ehoI8QQgghZ0CfDPq0Wm1AFs5gMECr1frc5nQ6cd111+Huu++GxWLB8ePH8dFHH+Hjjz8OetyFCxdCr9eLf2pqas7Ye4iW0L0rNHKY7E4wxuJ5SoQQQgg5C/XJoK+4uBh6vR4NDQ3ibfv378fIkSN9HtfW1oa6ujrcd999kMlkKCoqwqxZs7B+/fqgx1UqldDpdD5/4k0o5wqNHE4Xg83piucpEUIIIeQs1CeDPq1WixkzZqC0tBRmsxmrVq3CwYMHMX36dJ/HZWRkoKCgAG+99RZcLhdOnTqFlStXhizv9kViI4c76AMAi42CPkIIIYT0rD4Z9AHA4sWLUVNTg7S0NDz66KNYsWIFUlJSsHz5cp+M38cff4xly5YhJSUFkyZNwuWXX4677747jmceG6G8m6yRQy7lAAAmO+3KQQghhJCeJYv3CYSSkZGBNWvWBNw+Z84czJkzR/x80qRJ2Lp1a2+eWo9xuZhY3tUoZFDJpbA7HTSrjxBCCCE9rs9m+voDk9dMPq1SBo1Cyt9OQR8hhBBCehgFfXHkndFTySVQy/mgjwY0E0IIIaSnUdAXRxZ3cKeUScBxHNQKvtpO5V1CCCGE9DQK+uLI6uC7dFXuDB+VdwkhhBByplDQF0dCpk8l578MQnnXQuVdQgghhPQwCvriyOoQyrt8sKemTB8hhBBCzhAK+uLIYhfKu76ZPpON5vQRQgghpGdR0BdHQqZPWNOnlPFfDtqGjRBCCCE9jYK+OBIyfUKwpxCCPgcFfYQQQgjpWRT0xZGnkYPP9FHQRwghhJAzhYK+OBJGtgiNHMLfVgr6CCGEENLDKOiLI/+RLZTpI4QQQsiZQkFfHHnW9Pk2cggNHoQQQgghPYWCvjjydO/yXwYlZfoIIYQQcoZQ0BdHnjl9fo0cNLKFEEIIIT2Mgr44Etb0iSNbpO7yrp2CPkIIIYT0LAr64kjo0hWHM8sp00cIIYSQM4OCvjiy+nfvSrs/soUxhr9+fQQvfFkelzWB3x1uxL83HYPLxXr9tQkhhBASmSzeJ9CfWRxCede/ezf2oO27w0341/qjAICCFA3mTinsobOMrKbNhLuW7gIA5CWrce3onF57bUIIIYREhzJ9cWQVGzlOf07fxormoB/3hm1VreLH35Y39eprE0IIISQ6fTboa25uxrRp06DRaFBSUoJ169aFfOySJUtQXFyMhIQEDB8+HBUVFb14pt1ncYTahi32OX37TunFj480dkb9vH9vOoZRpV/jje+rYn5NwaF6g/hxRQyvTQghhJDe02eDvgULFiA3NxctLS146aWXMHv2bLS3twc8bvXq1Xj55Zfx2WefwWg04vPPP0d6enoczjh2nuHMvnP6Yi3vWh1OlNd5Aq9T7WbYo2gGcbkYnv2iHEarAy9+ebjbawGrW7vEj482GcEYresjhBBC+po+GfQZjUasXLkSixYtgkajwaxZszBq1CisXr064LHPPPMMXn31VYwcORIcx2Hw4MFITU2Nw1nHThzZEpDpiy34qmw0wuZ0IVkjh1ImgdPFUNdhjvg872AN6H6Wrr7DIn5stjuhN9u7dRxCCCGEnDl9MuirrKxEUlIScnI8DQFjx45FWVmZz+OcTif27t2LAwcOID8/HwMHDsSiRYtCZpqsVisMBoPPn3gSR7acZiPHsRY+eCvO1CInSQUAaDRYIz7PP+g7WKsP8cjw6vVmv88tIR5JCCGEkHjpk0Gf0WiETqfzuU2n08FoNPrc1tjYCIfDgXXr1uHgwYP4/vvv8eGHH2Lp0qVBj/vCCy8gKSlJ/FNQUHCm3kJULHb/bdj44C/WTF+1O+grSktARqISANDcGTnoO95i8vlcCB5jYbY5YbA4AAAFqWoAgUEgIYQQQuKvTwZ9Wq02IAtnMBig1Wp9blOr+SDj8ccfR3JyMgYMGIAFCxZgzZo1QY+7cOFC6PV68U9NTc2ZeQNR8qzpO71t2ISMXVG6d9AXOdtW7y4ByyQcAH70SqzaTTbxGIPS+a9Pi9EW83EIIYQQcmb1yaCvuLgYer0eDQ0N4m379+/HyJEjfR6XkpKC3Nxcn9vCNREolUrodDqfP/Fkdfhn+vi/nS4GRwyBn0+mT8sHfU1RZPra3AHb2IJkAEBNe+xBX4eJX7+XrJEjLUEBAGjvoqCPEEII6Wv6ZNCn1WoxY8YMlJaWwmw2Y9WqVTh48CCmT58e8Nj58+fjz3/+Mzo7O1FXV4c33ngD06ZNi8NZx84zp8830wfElu070coHa4VpGiRr+MArmmYKITgbm58MAKhpi70s22Hmj5GsUYiv3W6iRg5CCCGkr+mTQR8ALF68GDU1NUhLS8Ojjz6KFStWICUlBcuXL/fJ+JWWliInJwf5+fmYNGkSbrzxRsybNy+OZx4dp4uJgZ2Q4VNIvYK+KNf1GSx2tLqDt6L0BOjUcvftjojPbXMHZ2MLkgDwgWKsnbdipk8tR2oC/9qU6SOEEEL6nj67DVtGRkbQtXlz5szBnDlzxM8VCgXeeustvPXWW715eqfNO6gTMn0yqQRSCQeni0XdwXvC3YyRrlVCq5RBp+K/pIYYMn35KWqkJSjQ2mVDTZsJSXlJUb8PT3lXgRR3eVcoGxNCCCGk7+izmb6zndC5C3gyfYAn2xdtpk9o4hiYrgEAJImZvuiDvhSNAnkpQudtbONWhEaOZI0cqZrTW9N3orULM1/bgkc+3AeXiwY8E0IIIT2Jgr44EbZgk0k4yLzKuooYZ/UJTRyFaQkA4CnvRsj0WR1OdFr5EnBqggLZOn6+X0OM41aEcnCKRi6u6etupu+N749hf00HPtlbix+Ot0Z+AiGEEEKiRkFfnPg3cQg8A5qj23+32t3EUZTGZ/p0qujW9AllWamEg04lF4c6x5rp6zB5GjlS3eXdjm42cmysaBY/3nK0pVvHIIQQQkhwFPTFicVvXIsg1q3YvGf0AYBOHd2avjaxtCuHRMIhO4kv7zbEXN71jGxJcTdydJhscMZYnm01WlHrtXVcWV18d0shhBBCzjYU9MWJ/2BmQazl3ROtnhl9gKe8a3W4fNYN+hPW3Qkl2e5m+vRi964CKe5juVh0I2O8VTX77gZytMkY4pGEEEII6Q4K+uJECMiUfpm+WLZi67TYxd0vCt3lXa1CBo4T7g9d4hXKv0LjR7Y76GswxBb0CQ0jOrUMcqkECQr+/KPpHvYm7AYyKIMPXuv1lpgGVBNCCCEkPAr64kTI5KlCZPqiCfqEoczpWgUS3Wv5JBIOiUp3iTdMB2+Xu4kjwf1YT6bPHHZXE39Gv+MIQWSsmb6T7qBvYmEKFFIJnC4Wc9aREEIIIaFR0BcnQqbPf02fMoby7vEW39KuIJoOXiFY0yr5oDPL3b1rsbtiCti6xOPIfF87ipEx3oQt4ArTErqddSSEEEJIaBT0xYlY3pUF7961OSN37wrr+Qr9gz5V5GybmKFT8MGaSi4V986t64g+2Oqy8ueZ4Bf0xZrpO+XeAq4gVYN0LX8eLVHsH0wIIYSQ6FDQFydiede/ezeG4czH3btxCIOZBULWTQjIgvEv7wLe6/qim9Vnc7jEreS07uBRHBljjrwNnDch01eQoka6VgkAaDFS0EcIIYT0FAr64sQqlnf9Mn3y6Mu7oTJ9GnfJ1mQLHXj5l2WB2Dt4hWMAQIL7Nbuzps/pYmh0l3Jzk9VITxSCPtrOjRBCCOkpFPTFiWdkS/czfZ4t2PyCPoUQ9IXO9Bn9yrKAV6YvyqBPKBErZRJxV5FYtoETtHZZ4WIAxwFpCQqku8vMlOkjhBBCeg4FfXFidQTP9EU7p897XMuANN/yrsZdau2KJtOn8s70xbb/rnB872yhMBw6lkxfs3vtXlqCEjKpxCvTR0EfIYQQ0lMo6IsTS8ht2PjPIwV9wriWtASFuI5OIMzKM4fN9Pl27wKe8m60mT7/Jg6ge+XdJnfQl+kO9jxr+nqvvFtWp8cdb+/AfzYf77XXJIQQQnoTBX1x4une7d42bP7br3lTKyI3cvh37wKe8m69PrpGjmDNIJ5GjhgyfQZ30KfzDfpaeynTxxjDA+/vxcaKZjzz+SEcrNV3+1j/3nQMP3t9K3afaO/BMySEEEJOHwV9cSLsvasMyPQJ5d3wI1uqW4QmDk3AfWKmzx5rI4envBvNgOauINnCpChmBPpr6uQzi55Mn7Cmr3cyfYfqDTjmtQ3cx7tPde84dQY8+0U5dp1ox8Mf7ot5/2FCCCHkTKKgL0485d3uZfqE8q7/YGYAULuDvphHtrgHNJtsTnGbtnD8d+MAvIczRz+yxVPe5V9f2A/YaHXA3gtbsW0/1ubz+baq1m4d5/Mf68SPT7aZsP9Ux+mcFiGEENKjKOiLE3FHjm5uw1bbIQwzVgfcJwRh4Ua2BAvY1AopkjV80BbNur5ggWO31vS5y7sZ7kyfzqu5JNY9fA+c0uPRj/bjL18fFq9xJAfr+HLuHecVAgCONHairSv2LOPekx0+n2892hLzMQghhJAzhYK+OAnVyCGObImQ4apzB325SYFBX6SRLYwxr0YOmc99QrYvmnV9Xe7jaxWB3bsGsz3qPXz9y7syqUTcPziW4PGzvbW4YfEWfLz7FF5bX4VnvzgU1fMqG40AgKmD01GcqQUA7DjeFu4pAZwuhh/dmb2bxucDAMrqDDEdgxBCCDmTKOiLE8/IluB774bL9LlcDHV6zzBjf56RLcGDPqvDBWG5WYLSN+gUjhdNpi9YtlDI9DlcLOycQG/NRt9GDsBTJu6IMuj777ZqPPThPjhcDMNzdACAD3fWRJWxO9km7GySgAmFKQAgBnDRqmzqRJfNiQSFFDPH5QLg1woSQgghfUWfDfqam5sxbdo0aDQalJSUYN26dWEfX11dDbVajXvvvbeXzvD0WELsyBFNebe1ywabwwUJ5+m49eYZ2RK8vOsdjGkUfpk+9/GETGI4wRo51HIp5FIOQHQDmhljYnlXWNMHQCwzR5PpW1vWgKdWlgEA5k8twhcPXICRuTrYnQzfljeGfa7eZBdfoyBVjVF5SQCAAzF28B5t4rOFw3J04jFOtJrQGcOQam8Ha/Uoq+t+FzEhhBDir88GfQsWLEBubi5aWlrw0ksvYfbs2WhvDz0G4+GHH8b48eN78QxPT6RGjnBz+oT1fFk6FeTSwC9hpEYOszvgVMgkkEo4n/sGpPLdwCfc2a9wgmX6OI4Tx7ZEE7AZLA7xvQpr+oDou4BtDheeXsUHfPPOK0Tp9BGQSDhcWpIJIHJThpDlS9cqoVHIMNodsB2s1UddnvY+TmGqBqkJCnHm4ZGGzqiPIfhoVw2u/+dmTPvHZizffiLm5xNCCCHB9FjQ53A4cNddd/XIsYxGI1auXIlFixZBo9Fg1qxZGDVqFFavXh308V9//TUYY7jyyit75PV7Q8hGDin/ebhMn7ieL0hpF4jcyCEMbVb7ZRkBoMg9Aqa6NXLQF6yRA/AO2CJ38Da71/PpVDKfrGe0DSGr9tehTm9BZqISC68bDo7jg1ihTBspWyYEawPcDTEl2YmQSTi0m+xicB2NGvdxCtxB86AMvqs6muvozepw4i9fHxE//+vXR6Lako8QQgiJpMeCPqfTiXfeeadHjlVZWYmkpCTk5OSIt40dOxZlZWUBj7XZbHjsscfw17/+NeJxrVYrDAaDz594CTWnT8z0hWnkaDTwgZLQdOEvUiNH2KDPPexZmAMYjmdHDt/jJMbQwdvoLu1m+b0XIejrMIU/xtdlDQCAOecW+gSNw3ISAQBVzV1hZx56gj4+WFPJpSjJ5p8by5Bm/+MUukfpnGiNfB29ffFjPZo6rchMVCI1QYF2kx2bjzbHdAxCCCEkGFnkh3hcd911Ie9zOqNbtB8No9EInU7nc5tOp0NHR0fAY1955RVcd911GDJkSMTjvvDCC/jTn/7UU6d5Wk5nTp+wJ60wxNifsE7P6nDB6WIBJVyhvCsEh96EoEVvtqPDZBNn5gUTbFcPILYBzWLnrlcTBwAkRbGmz+ZwiWNRLh+e6XNftk6FJLUcerMdlY1GcZ2dvwZ9YNZ0dF4SyuoM+PGUHteMygn6PH9i0OfOlMaSMfUmBLG3Th6A5k4L3t9Rg21VrbhsWFZMx+muQ3UG/Pnrw8hMVOIP00aIX0tCCCE/fTEFfRs3bsQTTzyBvLy8gPtsNhu+/fbbHjkprVYbkIUzGAzQarU+t9XW1uLtt9/G7t27ozruwoUL8cgjj/gcs6Cg4PRPuBtCNXJ4undDB9EtnXxHqrBdmT/vYK7L5gjYm9cc4rX558qQpVOi0WBFdasJ48IEfcF29QA8c/aiyfQFa+IAoivv7j/VgS6bE+laBUbk+P6SwHEchuck4odjbSivN4QM+sTOYa/1hKPykoCdNVE3c9idLtR18MHr6WT6LHYnNlXyQeyVw7NwtLkT7++owY7q3tnSzWRz4Bfv7ES9u3PbYHbgjdsndOtYB07p8fmBOlwwJB0XFmf05GkSQgjpppiCvgkTJmDQoEG49dZbA+6zWCy45557euSkiouLodfr0dDQgOzsbADA/v378ctf/tLncTt37kRNTQ2Ki4sB8BlCl8uF6upqfPXVVwHHVSqVUCqDB0q9zRpqTp8s8pw+MdOXGPy9KGUScBzAGGCxOQODPvdaP3WQTB/A7/LRaLCiuqUL4wqSQ55HpDV9UQV9nYFBV7THOOSegzeuIBkSv2wmAAzJ1OKHY23iPsVBX18cDO0JOsfk+zZzCOsEQ6nvsMDpYlDKJMhwB+LCTinHW7qiOgYAbDvWCpPNiSydEqPydEh1Z3IP1uphsjkCOq397avpwJcH65GiUWDmuFxxW71ofb6/HvV6CyQc4GLAV2UN2FfTEfZ7IJjDDQb87I2tsDpc+L/vj+Ht+RN7LVNJCCEktJjW9D377LNigOVPqVRi/fr1PXJSWq0WM2bMQGlpKcxmM1atWoWDBw9i+vTpPo+79tprcfz4cezbtw/79u3Dvffei9mzZ2P58uU9ch5nitPFxKBOJfMr70qjKO+6Z8+lJQTPwnEcB41c2H83MGMYrrwLeAKWcMESELx7F/Deii36oC8jVNAXZk1fuXsO3nC/LJ+gIIXPutW0hW7ICDYjsCQ7EXJp9M0cJ72aOITgU8j4dVocEdclCta5x8tcPjwLHMchL1mNjEQlnC6GCvcA6VA2VTbjZ69vxf99fwwvfnkYF7y0Hgs/+TGqIduCT/byew7/9qoSccD0ki3Ho36+4KUvD/t0nz+/5jBctA8xIYTEXUyZvgsvvBAAsGLFipCP8b7v5ptv7uZpAYsXL8a8efOQlpaG/Px8rFixAikpKVi+fDmef/55lJWVQalUiplAgA8WjUYj0tLSuv26vcG7sSB0eTdM0NcZPtMH8Fm8LpszeNBnC55lFBSmu9ejhWnmYIx5duQ4jUyf0JSS6dfIkaxWRDxGuXscyrDsEEGfO/CqaQ++rs57RmCGV6lcKZNiaFYiyuoMOFirR747eAzFv4kD4K9/tk6FBoMFJ9pMSAkRoHufy7ryJgDAFV7rE0uyEtHcacWRBkPIjJvTxVC6sgwOF8PUwWlwuBh2HG/D+ztq8MmeWvxh2nDccV5R2NfXm+zY7t6FZNY5eWg1WvG/Pafw1cEG6M32qNf2VTR2Yv2RZkglHD779fm47d8/4GiTERsqmmLO9tmdLnx7qBEtRiuuGZUT8IsBIYSQ2MQU9Alee+01bNu2DdnZ2cjPz8epU6fQ0NCAqVOnimUsjuNOK+jLyMjAmjVrAm6fM2cO5syZE/Q5Tz/9dLdfrzcJTRxA7MOZGWNieTcjxJo+wFO6DdbBKwSCwbp3AWBgWuRxI0KTCBDYvSuUk6Mb2dK98q7LxVAhBH3uTl1/kTJ93kGxf0ARSzNHsKAPAArTNGgwWCKWyQF+y7Z6vQUquQRTB6eLt5dkJ2Lz0RYcaQid6fvmUCOOtXQhSS3Hm3dMhFYpw47jbfjL14exs7odT60sQ5JajpnjAtfiCvacbAdj/K4keclq5CapUJKViCONnVi9vw5zpxSGPX/BZ3trAQCXDcvE6PwkzJ5QgLe3HMeqfXUxBX3NnVbcuXQHDtby2dxXv63Ex/eeh0EZ2gjP5H+pOt7ShWydKmwjUjjl9Qb8/dtKOBnDnHMH4JKSzMhPIoSQPq5bI1uGDx+OV155BSdPnsTWrVtx8uRJvPrqqxg+fDjWr1+P9evX47vvvuvpcz1rCE0ccikX0FkbaU2f0eoZZhyqkQPwBHSWYEGfsKYvVKYvivKuUNoFTrN71+C7767/MUIFfU2dVpjtTkglHApTg2fihCCsxWgNOrNQCDgTFNKAEvXo/Oh35vCf0ScoEps5InfwfnWQ79q9qDjD5xeBkiw+oD3SGHq80Or9dQCAWycXiFnXyQNTseKe83DPRYMAAKWrytBhCr0l3a4TfJZPmG/IcRxmT+RLvB/tPhXx/AH+F5KV+/hzEbaiu34sHzB/c6hR/L6P5GSrCbPf2IqDtQYkqeUYkKpBW5cN9727B44Ie1KvK2/ElOfX4Zq/bcL4Z77BA+/vxeGG2EYz7a/pwKzXtuCrsgZ8c6gR85fsxLIfYh+S3WGy4Q+fHsCU59fh2r9vwuc/1sV8DEII6UndCvo++OAD3HfffT633XPPPXj//fd75KTOdqEGMwOeNX12Jwu6DkrYS1ajkIZsxAA8AV3YTF+oNX3u8m6HyR4yUBCaODQKaUATRVKUa/q6rA6xROxf3hVGtpjtzqBz9oSSbW6yCrIgu5IIx0h0dxKfag/M9jWFKC0DiGlnjpCZPvd1jNTByxjDmgP1AIBpY3yzikPdMwNDZfqsDie+r+Dn+F3rl5HkOA6PXl2CoVladJjsWL79ZMhz2OnuEJ7oDvoA4IZz8iCTcNhf0xHVziJ7TrajtsOMBIUUl7uzeucUJCM3SYUum+c8Ix3jhsVbUN1qQn6KGp8tOB//u28qkjVyHGnsxMdhAtB9NR24993daDfZoZRJ4GJ8QHzN3zZhwfI9qGiM/B4sdiceeH8vrA4XJg9MFdc2Pr2qDJvdndXRaDVaceubP2D59pNoMFhQXm/A/e/txdubY1sjabDYsbmyBa9vqMI/1lXiix/rowqebQ6+NL54w1F8uPMkjjR0xrTDjKCmzYQ3N1bhxS8P47O9tVEH7sGYbU7x/w1CSHx0q7xbWFiId955x2cHjmXLlsVt/MlPjVDe9R/MDHgyfQCf7VNJfB/TaeH/0/TvyPUnBHTh1vSFCvo0ChkyE5Vo6gw9tsUzmDnwW0injm5kS5NXps1/XWCiUiZ2kerNdmQm+p7rSXf2rCDCeru8ZDUON3SitsOMoVm+ZeDmMGXykuxEqOQStJvsqGg0igObgwkV9EXbEHO4oRPHWrqgkElw+XDfEujQLL6c2WK0otVoRZrfue4+0Q6j1YGMRCXGBBlLI5dKcM9Fg/Hbj/Zj2bYTuPfiwQHZZZvDhf01HQCAiUWp4u1pWiWuGJ6Fr8oasGJXDf54/Yiw7+OzvXwm6+qR2eL3FsdxuG50Dv69+TjWHKjH1SOzQz7/q4P1+M0H+2B1uDAqT4e3500SA/L7Lx2CZ78ox5ubjuGWSQUB3dAuF8PCTw7A7mS4akQW/nXbeBxtMuK19UfxxYF6fHGgHmsO1mP2hHw8ef2IkP9+lmypxsk2E7J0Svx73kQkKmVgYPhkTy0WvLcHq++/QJzFGEpblw1z/r0dhxs6kZGoxPM3jMb2Y6349+bjePaLQxiUkRCyXOxwurDpaAu+OdSIncfbcLTZCP9YLSdJhZdvHuuzDMDbvpoOPPzhPhz3W5Obl6zGJSUZuGxYJqYOTg/7S6PD6cKbm47hb99W+iw1Sf9CgV9fMgRzpgyAMsgvrd4YY/jxlB5r3NdeWGZRlKbBzybkY+6UwpDld6eL4XhLF442GWGw2GGyOuBkgIQDJBwHlVyCIZmJGJGjC/k+GGMoqzNg7aFG1HWYoZBJkK1TYVBGAoZkajEwPSHiewD4AH7XiXb8eKoDerMdKRoFitISMCY/CYMytAH/noKx2J1o67KJf4R1srnJahSmaYJup+n9PpqNVhjMdnAcBynHV4jU7v83+WkN4c/B6WJo6rSgrsMCjuMTDkkaOZLVcmgU0qimC3ifj8XuQpfNAZvDBZ1ajoQYjsEYg9PF4GQMLhfgZAxyKQeFNPL7EJ5vc7rAGMBxgJTjIOG4oBMcwj3f6nBBIZVEdf1CcbkYXO5/oFIJ1+3j9KZuBX3//ve/cdNNN+HFF19EQUEBampqYLFY8L///a+nz++sJOzG4T+YGfAN+qwOV8CaPyHo06rCf+nUYbt3w5d3AX5njqZOK060Bl+P1mULPqMPiL68K3SW+u/GAQASCYdEFT9c2WC2B8zxEzJ9/oGWv5wkFQ43dKLBPXvOm2dcS2DQp5RJMWVQGjYcacaGI00hgz69yS4GtwWpviNSCtOETF/48u7/3Nmri4dmBFxPjUKGAakanGwz4UhjJ6b6B33uDN2UQWkh/9O7fmwOnl5dhgaDBbtPtGPywFSf+8vq9LA6XEjRyDHYvX2c4JZJBWLQ95srikMGSzaHSyxfznCXdgXXuoO+deVNsNidQRuIlm45jj99fgiM8esB//nzc3x+obh18gD87dtKHGvuwuajLQGz/9YeakB5vQFapQwv3TQGCpkEI3J1eG3OeDzQYMDfvql0v49T2FXdjiV3ThKXMQiaO614bf1RAMDvrh4mvtfnbxiNY81d2FfTgbv/uwsr7j0vZGNLk8GCuf/ZjopGIzISlfjgV1MwOEOLK4ZnotPiwIe7avCbD/bhiwcvCGgQOnBKj8c+3o/DflnVglQ1xuYnI0Ehw8bKZtTrLZj77+14esbIgAadbw414oH398BidyFdq8CFxRlodH/dazvMWL79JJZvPwmlTILzBqfhsmGZuLQk02dpwqE6Ax7/34/i0obJRakYlpOIdeVNqO0wY9Hnh7Bk63E8cFkxLixOh0YhQ1uXDY0GC062mXCy1YTq1i7sqm5HgyHw3111qwl/XVuB1zdU4fbzivCLCwYiI1EJm8OFH4614suDDVhb1oDWrtDLEQQKqQQTClNwQXE6LhiSjqL0BBxtMmJdeSO+OFAf9t+ehOP//8hIVCJFo0CKRoHkBDlSNAo4nC6cajdjZ3UbqppD/9KmVcowKk+HkblJUMklMNmcaO+yoc1dJWnrsqG9yyZWNIKRSzkMTE9AcWai+O+v2WhDbYcZp9pNqG03h92LXcLxv3yna5VIS1AgXatESoIcRqsTbV1W1LabUdthht0ZPNMrk3BI1siRpJb7BMFC/MIYH7R22RzosvJ/+/8iopBKkKSRI0UjR7JGAcYYzHYnLHYXzDYnLHb+j9nuRKhGfqmEnzqhVkjdlSwZFDIJrHYnTO7112abEyabI+QxJJwn+OKDQf5nicT9sd3JAp4vBMEquQQKmQQc+Md6B3A2pwsOpwt2J4Pd6YLd6Qp6DjIJB5mUg1wigUzKQSaVQC7hMCQrEf+9a3Lwk+5l3Qr6Jk2ahKqqKmzbtg319fXIycnBeeedB7mcpvdHI9RgZsBT3gWCN3N0ukumwYItb2KmL+iavvAjWwC+mWPH8baAbIHAM64l8BjCD8sumxN2pyvkb7G17pJrXkrweXLCjhrBMoYnQ6yj85fj3mmjPsjoFTHTF6Ir9JKhGdhwpBnrDjfhnosHB32MEHyma5UBc/SEoKK1ywaDxR40YDLZHFixqwYA8PPJwTPlQ7O0ONlmQmWjMSC7s+ckH/SNH5Ac9LkAH8BeOSILn+ypxZoD9QFB3+4T/DEmFKYE/KZ68dAMFGdqUdlkxL83HsMjV5UEfY2NFc1oN9mRrlXigiG+53hOQTJyklSo11uwqbIFV47wzWa+vfk4Fn1+CAAwd8oAPD19ZEDJXquU4WcT8rF0azXe2XrCJ+hzuRj+9m0lAODO84sCOqWHZevwxu0TsKu6DQ++vxfHWrow9z/b8b97p/qU9v/69REYrQ6MyU/CDed4ml5Ucin+7/YJuP6fm3GksRM3vb4Vj11dguJMPgtrtjthtDiwt6YD/950DC1GG7J0Siz/JR/wAfwPkEWzRuJwgwH7T+nx6+V78MGvpkCjkMFid+LVbyvw703H4XQxJKnluH5MDi4emoFzBqT4fH9a7E488ekBfLKnFk+tLENtuxm/u2YYJBzw703H8cKX5XAx4JKSDPzj5+eI33NmmxPbjrXgu8NNWH+4GbUdZvcvNM0AylCcqcV5g9NQr7fg2/JGMMYPWX9q+kjcND4PHMfhj9e7sGJXDf72bSVq2sz43cc/Bv1e8KZRSHHpsExMH5OD8wanAwz4trwRb206hsMNnXjj+yr8Z/Mx5CSp0exep+v93CGZWqQlKKBRyCCTcnAxwMUYDGY7Djd0ornTim3HWrHtWKvPntWer50El5ZkYlReEqwOF+o6zKhqNuJokxGdFgeqW01R7ZpTnKnFxKIUZGiVaO2yobLRiAO1ehitDvxwrA0/HGuLeAyZhENKggJpCQro1HLoTXbUtJtgsjlR0WgMO5ZJwvGjsPjMEuBwucSKkYvxyYBOiyPk/9cAHwxl61SQSPjvB4PZwQczLoYWow0txshBdrD35HCPIGvutIrrpLvD6WLotDrQeRpLAFwMcDkZgOiXMjDG/xsOliCJlcPF4HAxWOD7s7u7DWVnQreCPgCQy+W46KKLevJc+g1hMHOwTBvHcVDIJLA5XEGbOYRgKzFipo+/P9ycvlAjWwDv9WjB/0PsCrEFm/+5dVocSA0xrkRYZ5cfJugDgpeJIz1XkJvE/1CvizHTBwBXjszG06sPYcfxNpxqNwUd3eIp7Qaeh9b923eL0YoTLSaxOcTb0q3VMFgcKErT4JKhwUt+Q7MS8W15E474rUlzuRj2nOwA4GnACOW6UTn4ZE8tvjrYgKeuH+GTFdxZLTRxpAY8TyLh8PCVQ/Hr5Xvw+vdVuLgkI+jjhBl/M8flBgRsEgmHa0ZlY8mWanx5oN4n6NtU2YxnvuADvvsvHYLfXjU0ZInk9vMKsXRrNdYdbsSJ1i4xqF57qAGHGzqhVcrwiwsGhrwGE4tS8dn952P2G9twotWEO97egQ9+NQXJGgUOnNJjxW4++C6dPiIga5qlU+GdOydj/pIdONpkxD3LQu8CNDRLi7fumBiQSVTKpHhtznhc/8/N+PGUHrPf2IarRmTjf3tOid9H08fmonT6iJBNWiq5FC/PHotB6Qn469oK/N/GY/i2vBEKmVScW3nrpAI8O2uUz9dBrZDismFZuGxYFhhjqGwy4rvDTfjucBN2n2hHZZMRlU2eoGPa6Bw8NX2ETxZeLpVgzrmFuOGcPCzZUo3V++twpLETjPFLNDISlShI1aAwTYOitAQUZyXi3IGpAf/P3DQhHzeOz8O68ib8c/1R7K/pEN9/ulaJq0Zm4bpROZgyKDXkel2AL9Mda+nClqMt2FzZgm3HWtFpcSBZI8f5g9NxzahsXDYsM+gSFMYYmjutqGru4rNxJhs6TDa0m+xo77JBJuWQmajC2IJkTCxMCTpyyeF04WizEftrOlDZaISTMShlUqQlKJCSoECKRo6UBAVSNfznOpUs6LKEOr0ZlU1GVDUZUdXcJQaH+Slq5KeoUZCiQXaSKuCXZ5eLz6Z1WR0wWBxoNVrR2mVDi9GK9i47EpRSpCYokJusRkGqBlmJSp/rKZRpO8w29/ptO+zunzn+IZNazmfftEoZNEopEhQyqOVScBz/80S4bh0mOzrMNkg5DiqFFCoZn7lTySVQy6VQyqSQSd1ZOAlfppZyHGxOl5jFE7J6JpsTVrvTk/mTy6Bxf6xSSCHlOHeJmA+EnS7Gl44Z/7lQenW6/3YxPqmidq+HV0glsDv54FnIRAoZVeb+5YIB4Nzf+3IpB7mUz+DJpRJI3RlEKceBgQ/2HO5MIP8xnxl0uFw+Fbx463bQR7rPk+kL/o2glLqDviCZvqiDPgV/7KCZvjBBp2Cg144SwRgtoc9DJpVAq5TBaHVAb7aHDPqEwcd5yeGDvmDDjYVybW6I5wqEXSmCDSkOtgWbt7xkNc4blIZtx1rx6Z5aPHB54GDyUOv5BEVpGrQYrahu7QoI+vRmO97YUAUAePDy4pDlWWEtYqVf0HespQt6sx0quSTkgGrBBcXpSFBI0WCw4MdavViyZ4yJmb5JRcEDx2tHZeOakdn4qqwBc/+9A49fU4K5UwrFHyAnWrvE7mOh8cHfdaNzsGRLNb451CjuLtJosOChD/aBMT7LGS7gA4DBGVpcUsJnX9/ceAzP3TAaTr8sX6TfqDMTVVh217m46Y2tONzQidv/swOPXDUUT68qA2PAjLG5QYNaABiRq8NXD12ExeuP4rsjTWgyWCHh+IBKLZdiUIYWlw/PxE3j80P+QpWfosHb8ydh/ts7UFZnQJl7V5ksnRLPzhodkAUNhuM43H9ZMfJTNPjjZwfF8qNCJsET1w7DvKlFYa8jx3EYmpWIoVmJuPfiwdCb7Nh0tBkHTumhU8tx2bDMsN9PGoUMCy4dggWXDoHTxf+QC/cLZKhzuGJEFi4fnolT7WbUdZiRnqhEUVpCVGvkhGMMztBicIYWd5xXFNO5cByHTJ0qaBNXtGRSCYZl60LOCY2GRMIhP0WD/BQNLo1xLJBEwiFBKUOCUoZMHb8DUSw4jnMHQOqYd+/xplHIoFHIQv4/Hg01pHHZ51sllyKx+98CP0kU9MWBZ01f8P+cFDIJYA1V3g29ls5b2DV9Nk/nbSiR9o4NtRuHIEkth9HqCLuu75S7NBpq+HGoTB9jTFwrlB3hP+2cZP7++iCZvuYQu4F4u2lCPrYda8X/9pzC/ZcNCfhhKlyfUEFfYVoCdp1oDzro+q2Nx2CwODA0Sxt2hp4Q9FU0Gn22dNvjDtbG5CeHXQgO8N9rF5dkYM2BBqwrbxSDvhOtJrQYbVBIJSH3J+Y4Dq/cMhZdyxzYVNmCp1cfwtKt1Xj4yqG4bnQOSleViSXFEbnBfwBOGJCCwjQNTrSa8OHOGsydUogFy/egtcuG4Tk6lE4fGdUi6HsvHowNR5rx0e5TePDyYmyqbMHhhk4kqsJn+bwNSNNg+S/PxS3/tw0HavW4c8lOAHyQ//SMkWGfm5qgwJPXj8CTEZpawhk/IAXfPnIxlm6txql2M8YVJOOWSQUh/y2FMuucPFw8NAMbK5vhdDFcWJzRrQHWSRo5rh+Ti+vH5EZ+sB+phINUElvA543jOBSkaiIu0+iNcyGkP+g7Ocd+ROzeDdE5Fm5AsxBsaZWRunfd5d0wI1tUYYI+YWxLu8kedCu0SEGfkAEM18ErZvpClXc1wYM+vdkuXptIP+TETF+HJWBkRXOnMCMwdOB47ahsJCikqG41BV23c8ydZQk1NFj47buiyXe9TqfFjne2VgMAHrmyJGx2Y1BGAiQc/76bvNbMeNbzhS/tCoQxKt+6d/4AgO3HWwHw+w2Hy5BoFDK8c+dkPDNrFNISFKhuNeE3H+zDyKe+xoYjzVDKJPj9tcNCPl8i4XD3hfzMwFe/qcAv39mFXSfakaiUYfGc8VFnis4dmIoJhSmwOVy4a+lO/GlVGQBgwaVDYlo3MzQrEZ/++nxcNiwTSWo5Lh6agQ/vmRIyK93TMnUq/O6aYfjHz8/BXRcMjDngE6QkKDBzXB5uHJ9PO5YQQiKioC8OIpV3PQOaAwM2Y4zdu0Hn9NnC78gB8D/khR8iQgnTm7CmLzFMpg8IPavP5nChvoMPumJd0ydk+VI08ojBQo57TZ/Z7vQ5jsPpErsDw/2wTFDKMMOdhXt/R+CcO6H8XZSeEHAf4NktRFhvJfhgRw06rQ4MydTiqgglPZVcKo5/8Z41J5RlwzVxeLt0WCYkHH8uQpZ1WxUf9E0dHHnrQomEw+1TCrHxd5fisatLkKSWw+Z0QauU4W+3jItY5rp5YgHGD0iGweLA9xX8Vm1/vXksBoa4dsFwHIcXbxwNlVyCsjoDOq0OTCpKwV3nR5fl81aUnoC350/C/tKr8M5dkyNut0cIIT91FPTFgZDpC1nedZfqgrXpGyMEWwKhdBtsmGo0QR/gaYIIth7OGGZOH8B3mgGhM30nWrvgcDEkuPeoDSZk0Ocu1QYb9eJPJZeK2Zu6Dk+Jt7XLBsb4klCk7M5tkwcA4HfNaPcaI2G0OsTM28C04IHLcHcgdKzZKH4tbA4X/uMe0vurCwdFNV9KKPEKQ5I7TDZx4X2kJg5BaoJCfOx3h5vAGMNWd9A3JYqgT5Cg5Nd07fzDFfj2kYux/YnLce3o8FvVAfwvM2/dMRG3nTsAV47Iwnu/PDfs3L5QirMS8f7dU3D9mBzcdf5A/PuOSX1qoTQhhPRV9D9lHESb6QsW9PXMnL7II1sA7yaIwPVwnjJz+ExfqKDvqDtgGZKVGHItV6h5f0LXbXZSdCtwc4IEr43ubGG6VhFx4fjo/CSMzNXB5nThf3s8O0II6/TSEhRiKdpflk6JZI0cLgZUukcyrNpfhwaDBZmJSsw8J7p1VMKQZuEYQml3UHpCwMDmcIRGgc/21mL3iXY0dVqhUUijLhF7U8gkGJKpjak0mablhxW/dcdEnDso+kDT3zkDUvCv28bjqekjQl57QgghvijoiwOxkaNba/qim9MnrNcLtudsNCNbAE9QVRck09cVZdBnMAefuSRkqYaEWAvnfQz/7l2hvJsVZdtVsOBVCBzDrefz9nN3tu/DnTXi2sDKJj7rFq48yXEcxuYnA+DXzzHG8OZGvmP3zvMHRrUjAODZju2Qu0wslnajzPIJZp2TB7mUw56THSh1r4e7dlROzN2XhBBCfnoo6IsDa5Tl3bCNHBEyfRox0+d7DJeLieXlcNswAfy+tgCC7mYhrC0MWd5Vhc/0CWvTirMiB32h1vRlnUamTyjLhhrX4m/GuFwoZRJUNhlxsJYPvH48xe9YEKrrVXBhMT+seGMlPxy3otEIrVKG284dENVrA55mjbI6fiDsriB75UYjM1GFae5SrDAu5OaJwcesEEIIObtQ0BcHwpq6iI0cwYI+S3Rr+oSAzuLXyCFkGYEYyrsdYcq7IYLPJPf+u6EaOYSAaViYPW1DBX1NUY5rEYhjW7zeR5PQuauLLujTqeS4yr3+TCjxHnC/hzFBhi57u3gov3vE1qMtePKzgwCAOVMGxDSXih+wqoaLAesPN2GveyjzpIHBZ8qF8/trh4tbxN127oDTKrMSQgj56aCgLw4izekTSn7hduSIlOkTjm2y+5ZXvUe4hCovC8QMmSFIeVfcezf4MXRh9t9t6uT36OS48OXJSJm+7KToArZcd/BaFyTTlxHDZM4bx/NdvKv218Fid4qZskhB35BMLYbn6OBwMdTrLUhUyXDvRcG3dQtnchEfnJWuKoPN6UJxplbc5isW2UkqsQHj+RtGx/x8QgghP00U9MWB0MihDBn0hc70RTucWSPuvet7DGE9n1Imidg1Kuxb26C3wOW3u7Q4OibEvEChIzbYXoy73aXJkqzEoPvRCoQF+laHy6cLuUEf23o8IXhtCLqmL/omiAuHpCNdq0Rblw3PrymH2e5EklqOgenhAy+O4/DcDaOQopFDo+C30Qq2rVMkV4/kmzDa3B3E08ZE7pgNRS6VRNX9TAgh5OxBO3LEgTiyJcSYiVDlXZvDJXb0JkYaziys6bMFz/RFWs8H8AGRhAPsToaWLqtPkOUZzhz8OEITSFOQoO/7imYAwOQIpUmtQgYJx2+ibTDboZJLYXe60NoVW/eusFVbvd4i7mjhGcwcfdAnk0ow59wB+Pu6Svx32wkAwOXDM6PaNmr8gBRsf+IKSDiE3U80nMuGZWJUng4Haw1I0cgx59zCbh2HEEJI/0SZvjgQsm2hAi+xkcOvvCt0zAKhgy2BmOmzO312ohDHtUTRrSmXSsTBxd5ZMrvTE3yGyjgK6+3aumyweq0jtDlc+NK9T+u1o8JnqiQSTiwTd7hLvM2dVjAGyKUcUqPcgUFYt2d1uMQsmdjIEWO26+6LBonXRMIhpqHACpmk2wEfwAeLy38xBS/cOBqr7r+AdmAghBASEwr64kAYoxKq8zXUnD4hu6aWSyMGD8LIFhfzDR7FJpIoMn2Ap5nDe7Cxb/AZemSL8D6EUioAbD7aDL3ZjsxEZcRMHwAk+41tEdbzZSaqohpqDPBrJNPds+zq3aVqoeycFWUjh0CrlOGDX03BPRcNwrJfnBuxc7enJWnk+PnkAT2yVykhhJD+hYK+ODAJu1kowgd9/uXdaAczA767bXg3b5iiHMwsEIIioRwKeIJPpUwCeYjgk+M4MdsnDEIGgFX76gDw69GiKYsKawOFDF2juBtHbMGaMH6mrsOMdpMNDhcDx0EMBmMxOEOLhdcNx/lD0mN+LiGEEBIvfTboa25uxrRp06DRaFBSUoJ169YFfdwjjzyCQYMGITExERMnTsTGjRt7+UxjJ3S+hgq8PJk+33Er0W7BBvClWbmUD6q8d+WwRLkFmyBLDNw82TrxPCIEn0JgJmTnzDYnvjnUCACYPja6nSiE3SZajFafY0W7nk8gNnMYLOJ7SdUoQgathBBCyNmmz/7EW7BgAXJzc9HS0oKXXnoJs2fPRnt7e8DjkpKSsHbtWuj1ejz++OOYNWsWOjs7gxyx7zBF2Lc21HBmcTeOKDJ9gGdsi0+mzxbdbhyCrCDZui5r+PK0/3OF9YDrjzShy+ZEXrIa5xQkR/X66Vo+09dq5DN93uXdWHiXqU+28dunUYmUEEJIf9Ingz6j0YiVK1di0aJF0Gg0mDVrFkaNGoXVq1cHPLa0tBRDhgyBRCLB7NmzoVarUVFREfS4VqsVBoPB509vY4yJmb6ECJm+kOXdKPc6FbJ5Jq+gL9p9dwVCd2ujVxeucB6hytMCoWv2VDs/H2/1fr60O31sbsj9dv2lJfCvL3TsxrrvrsB7V47qVhMAiAOKCSGEkP6gTwZ9lZWVSEpKQk6Op7tz7NixKCsrC/u86upqtLW1YciQIUHvf+GFF5CUlCT+KSgo6NHzjobV4YIw8k4TIngT5/Q5gzdyRBv0CYGd94w74eNYy7tNPpk+/hiRMo7CnrTHWrrQabHju8NNAIDpY6OfLydk+sTyrj623TgEwszB+g4LTrTymb7CtNB75hJCCCFnmz4Z9BmNRuh0Op/bdDodjEZjyOfY7XbMmzcPjz32GJKSgndULly4EHq9XvxTU1PTo+cdDe/O11CBV6jhzMYYGjkAr/KuPbC8G82cPiB4eVcsM0cIPoXdIo42duKbQ42wOlwYlJGAETm6sM/z5lnTZ/M5j1gHC+e5GzlOtZtQ3cJn+ooo00cIIaQf6ZPDmbVabUDp1WAwQKsNvvMBYwzz589HZmYmnn766ZDHVSqVUCrjO9vM5NVIEap7NVR5N5ZGDsAT2AUr76rl0R1DaMZoN9lhdTihlElhjLAmUTA8JxEcB9TpLfjnd0cBADNiKO0CQJq4ps8Kxpi4pi/W7t0hGfwev3V6Cwzu4JnKu4QQQvqTPpnpKy4uhl6vR0NDg3jb/v37MXLkyKCPf+CBB1BXV4d3330XEkmffEsicT1fmOHKihDl3VhGtgDBy7ueHTmiu07B5u0Je+EmqcOfR6JKjuHZfFbveEsXpBIOt0yKraQujFRp7bLBYHaIAWysa/qSNHLkuUu8QvAsBIKEEEJIf9AnIyStVosZM2agtLQUZrMZq1atwsGDBzF9+vSAx5aWlmLLli1YuXJl3LN40RDWw2nCNEEopHywFmo4c2KY/Wq9BW3kiHFkC8dxYlatyT2rr8PEl1pTotgR44Zz8sSPrx+TI3bRRksI+jpMdlQ08V3ZGYnKsNcvlElFKeLHQzK14t6+hBBCSH/QJ4M+AFi8eDFqamqQlpaGRx99FCtWrEBKSgqWL1/uk/FbtGgRysvLkZubC61WC61Wi+XLl8fxzMMzRZjRB4Tr3o1uLZ1A7Q6MzMHKuzEETVmJvrP6hN0xktSRg6Z5U4swf2oRbplYgEUzRkX9moIUjRwqOX89th5tBQAM7GYDxtUjs8WPr/H6mBBCCOkP+uSaPgDIyMjAmjVrAm6fM2cO5syZI37uva/sT0FXFOvhIq7pi7K8q3YHS96NHOYYu3eBwGaO9hgyfQqZBE/PCF6WjwbHcchP0eBokxFbjrYAAIrSu7cW7+qR2bjnokHotDrw60sHd/ucCCGEkJ+iPhv0na2iyvRJQ+zI0c05feZg5d0o1/QBQKa7vNvot6YvuZfKo/kpahxtMmJHdRsAoCi9e5k+iYTDwuuG9+SpEUIIIT8Zfba8e7bqsoXfdxcI08gR45w+sbwbNNMXfbwv7H4hrOkTMn3JUWT6esKQDN+u7e6WdwkhhJD+jIK+XmZyB26aMN27PTWnT90Dc/oAz3gUoXu3o4vP9KX0UqavJNu3y3ZUXvA5jIQQQggJjYK+XhbNrhohgz5xTl+U3bvuEq53edcS4zZsgO+aPrvTJWYceyvTd97gNPHjnCQV8lNi6wAmhBBCCAV9vU5cDxem8zVYI4fTxcQsXdSZvmDduzGObAE8mb5Gg0U8fyC67t2ekJ+iwa3u+X6/vaokpuHOhBBCCOFRI0cvE8edhMmSBVvTZ/Tavi3cYGdv4pw+n/IufxxVDEFfpjvTZ7A4xL1vdSpZyB1FzoQXbxqDp2eMjOm8CSGEEOJBmb5e1hFNps/dvWt3Mrhc/EgaIehTyCRQymIL+iw+5V0+kIylvJuolInHOlTPb4+XmtA7pV1vFPARQggh3UdBXy/Ti52vkcu7gCfbJzRxRLvvLuAJ7IRGDofTJR4vlvIux3HISeazfbur2wF41vkRQggh5KeBgr5e1hHFjLugQZ/VvRtHlOv5AE9mTCjpenfxxtK9CwCD0vmxKZvdA5JzYtz7lhBCCCHxRUFfL/NsYRZmTZ/U82WxusuxnTEOZgY8mT6hpCsEfRzn6RCO1pBMPuir7TADALIo6COEEEJ+Uijo60VOF4PBEjnTx3GcGJQJu3JEM+rFn9qvvGuxeUq7sXbADs7wHYhcRAOSCSGEkJ8UCvp6UafFDmGr4EjjTtRils4d9Fli23cX8OredZd3TXaHz+2xEDJ9gmK/zwkhhBDSt1HQ14uE0m6CQgq5NPyl9+ybK6zp636mz2J3weViXvvuxh70Dc3y3RVjqN8uGYQQQgjp2yjo60WeJo7I4078t1DrjHELNu9jAIDF4ezWYGZBglKGWeNyAQDTRudAp+qdwcyEEEII6Rk0nLkXtXTye9dGM+NO5Rf0eTJ90Qdb3nPtzDaneKxYZvR5+/PPxuL284owPIeyfIQQQshPDQV9vajBwO9mEc2MO7EJw9b9NX1SCd8QYnW4YLY70XUa5V2AHyUzoTClW88lhBBCSHxRebcXCVuYRTPjTijBnk73LuAbPBotsWcLCSGEEHJ2oKCvF9Xp+Rl32VEEfSo5/6URMn2d3Qz6NF5lYmHAcyzZQkIIIYScHSjo60XHW7oARDfjLmBNnyX2HTkAQBU000dBHyGEENLfUNDXSxhjONbMB32DMiIHff7du0J5N9YsnTirz+70ZAsp00cIIYT0O3026Gtubsa0adOg0WhQUlKCdevWBX2c2WzG3LlzkZiYiAEDBuD999/v5TONzsk2E/RmO+RSDgPTowj6hBl7/o0cMa7H03hl+rq6WSImhBBCyE9fn/3pv2DBAuTm5qKlpQVr167F7NmzUVVVhZQU3+7R0tJStLW1oba2FgcPHsR1112HCRMmYOjQoXE68+A2VjQDAMbkJ/uMUgklYE5fN7N0Ce4Az2h1dDtbSAghhJCfvj6Z6TMajVi5ciUWLVoEjUaDWbNmYdSoUVi9enXAY5ctW4bS0lLodDpMnToVM2bMwAcffBD0uFarFQaDwefPmbb9WCtuXLwFz60pBwBcOyo7qud5r+ljjHW7e1cYomww2z0DninTRwghhPQ7fTLoq6ysRFJSEnJycsTbxo4di7KyMp/Htbe3o6GhAaNHjw77OMELL7yApKQk8U9BQcGZeQNezHYn9pzsgMXuwshcHW47d0BUz/OMWnGhy+YU9+yNNUsn7PFrMNu7HTgSQggh5KevT/70NxqN0Ol0PrfpdDp0dHQEPE4qlUKj0fg8zmg0Bj3uwoUL8cgjj4ifGwyGMx74jcpLwhtzJyBZI8f4ASlQyKKLs4XyrsXu6bqVuYctx0II+vRmu6d7l8q7hBBCSL/TJ3/6a7XagNKrwWCAVqsNeJzT6YTJZBIDv2CPEyiVSiiVyjNz0iGka5W4JsqSrjd1kPl6WpUMHMfFdBydmv8S670yfbE2gxBCCCHkp69PlneLi4uh1+vR0NAg3rZ//36MHDnS53EpKSnIzs7GgQMHwj7up8h7vp7e3P0GDLG8a3FQpo8QQgjpx/pk0KfVajFjxgyUlpbCbDZj1apVOHjwIKZPnx7w2Llz5+KZZ55BZ2cnfvjhB6xatQq33HJLHM66Z6ncZVyLwwmDezCzEMDFQnhOh8kGo43W9BFCCCH9VZ8M+gBg8eLFqKmpQVpaGh599FGsWLECKSkpWL58uU8mb9GiRWLTx+zZs7F48WKUlJTE8cx7hveeuQYzH/QJnbix0LmDvnq9pdvNIIQQQgj56euzP/0zMjKwZs2agNvnzJmDOXPmiJ+r1WosX768N0+tV3iv6dObu5/pEwLFer0FAKCQSmJuBiGEEELITx/99O+jhHV3RosDetPpl3cFqQmKmJtBCCGEEPLTR0FfHyWsu+u0Ok4r05ek8X1OmlZx+idHCCGEkJ8cCvr6qER3WdbmcKHFaAXgWZ8XC61CBolXYi81gYI+QgghpD+ioK+P8u6wrevg1+N1J+iTSDhkJqrEz9Mo6COEEEL6JQr6+iiphEOCu4P3VLsJQPfKuwCQm+wJ+vJTNGEeSQghhJCzFQV9fZjQzFHn7rztbtCX5xXoFaZR0EcIIYT0RxT09WGJfnP5uhv0DctOFD8ekhl8izpCCCGEnN0o6OvD/Jsuuhv0zRibiwSFFJMHpmJcQXIPnBkhhBBCfmr67HBmAqT7jVfJSFR26zgFqRpsXXg5VHIJzegjhBBC+ikK+vow70yfVik7rT1zu5slJIQQQsjZgcq7fVhqgiezl6nrXpaPEEIIIQSgoK9Py09Rix/nJqnDPJIQQgghJDwK+vqwoVmertuRubo4ngkhhBBCfuoo6OvDSrISxXV9FxSnx/lsCCGEEPJTRo0cfZhaIcW3j1yMFqPVJ+tHCCGEEBIrCvr6uNQERcC8PkIIIYSQWFF5lxBCCCGkH6CgjxBCCCGkH6CgjxBCCCGkH6CgjxBCCCGkH+jXjRyMMQCAwWCI85kQQgghhMROiGGEmCacfh30dXZ2AgAKCgrifCaEEEIIId3X2dmJpKSksI/hWDSh4VnK5XKhrq4OiYmJ4DjujL2OwWBAQUEBampqoNPRzhqh0HWKDl2n6NB1ig5dp+jQdYoOXafo9dS1Yoyhs7MTubm5kEjCr9rr15k+iUSC/Pz8Xns9nU5H/wiiQNcpOnSdokPXKTp0naJD1yk6dJ2i1xPXKlKGT0CNHIQQQggh/QAFfYQQQggh/QAFfb1AqVSitLQUSqUy3qfSp9F1ig5dp+jQdYoOXafo0HWKDl2n6MXjWvXrRg5CCCGEkP6CMn2EEEIIIf0ABX2EEEIIIf0ABX2EEEIIIf0ABX2EEEIIIf0ABX1nWHNzM6ZNmwaNRoOSkhKsW7cu3qcUd1arFXfeeSfy8/ORlJSESy65BAcOHBDvf/HFF5GRkYHU1FT87ne/i2o/wbPdtm3bIJFI8OKLL4q30XXy9eKLL6KgoACJiYkYN24cOjo6xNvpOnns2bMHU6dOhU6nw6BBg7BkyRLxvv58rUpLSzFixAhIJBJ88MEHPveFuy47d+7E2LFjodFocPHFF+PEiRO9feq9KtR1Wrp0KcaNG4fExEQMGjQIb7zxhs/z6Dr5cjgcGD16NIYNG+Zz+5m+ThT0nWELFixAbm4uWlpa8NJLL2H27Nlob2+P92nFlcPhwKBBg/DDDz+gra0NM2bMwKxZswAAa9asweuvv47t27ejrKwMn3/+uc8Ppf7I5XLh4YcfxqRJk8Tb6Dr5+uc//4kvv/wSmzdvhsFgwLvvvguVSkXXKYg77rgD06ZNQ0dHBz7++GM8+OCDqKio6PfXqri4GH//+98xefJkn9vDXRer1Yobb7wRv/nNb9DW1oYpU6bg9ttvj8fp95pQ18lqteKNN95Ae3s7Vq9ejdLSUmzcuFG8j66Tr3/9618Bu2j0ynVi5Izp7OxkCoWC1dXVibddeOGF7J133onjWfU9VquVcRzHWlpa2K233spefPFF8b7//Oc/7NJLL43j2cXf66+/zh588EE2b9489sILLzDGGF0nLw6Hg2VnZ7OKioqA++g6BdJqtezYsWPi55MmTWKrVq2ia+V28cUXs/fff1/8PNx1+eqrr9iwYcPE+4xGI1Or1ay6urr3TjhO/K+Tv9tuu4399a9/ZYzRdfK/Tg0NDWz48OHs888/ZyUlJeLtvXGdKNN3BlVWViIpKQk5OTnibWPHjkVZWVkcz6rv2bZtG7KyspCWloZDhw5h9OjR4n39/Xq1tbXhb3/7G55++mmf2+k6eZw6dQpmsxkfffQRsrKyUFJSIpaW6DoFuv/++7Fs2TI4HA7s2LEDNTU1OPfcc+lahRDuuvjfl5CQgMGDB+PQoUO9fp59idPpxI4dOzBy5EgAdJ38Pf7443jiiSeQkJDgc3tvXCdZjx2JBDAajQGbKOt0OnGtEQH0ej3uuecePPfccwACr5lOp4PRaIzX6cXdE088gYceeggpKSk+t9N18qitrYVer0dVVRWqq6tx7NgxXHHFFSgpKaHrFMQ111yDO+64A4sWLQIAvPnmm8jMzKRrFUK46xLq//j+ft2efPJJ5OXl4eqrrwZA18nbtm3bUFFRgSVLluD777/3ua83rhMFfWeQVquFwWDwuc1gMECr1cbpjPoWi8WCWbNmYdq0abjrrrsABF6z/ny99u7dix07duC1114LuI+uk4darQbAL5xWq9UYOXIkbr/9dqxZs4auk5/W1lZMnz4d77zzDmbMmIHy8nJcc801GDlyJF2rEMJdF/o/PtAbb7yBTz75BFu2bAHHcQDoOglcLhcefPBBLF68WLw23nrjOlF59wwqLi6GXq9HQ0ODeNv+/fvFlHd/5nA4cOuttyI3Nxd//etfxdtHjBjh08nbn6/X999/j4qKCuTl5SE7OxsffvghnnvuOdx99910nbwMHToUCoXC5zbm7q6k6+Tr2LFjSEpKwg033ACpVIpRo0bhkksuwcaNG+lahRDuuvjf19XVhaqqKowYMaLXz7MvEP6P+vrrr5Geni7eTteJZzAYsGfPHkyfPh3Z2dm48cYbcfToUWRnZ8NkMvXOdeqx1YEkqJ/97GfsV7/6FTOZTGzlypUsJSWFtbW1xfu04m7+/PnsqquuYjabzef2zz//nBUWFrJjx46x+vp6NnLkSPaf//wnTmcZX11dXay+vl78c/PNN7M//OEPrL29na6Tn9tuu43dfffdzGKxsMOHD7OcnBz23Xff0XXy09HRwZKSktiqVauYy+Vi5eXlLCcnh3355Zf9/lrZbDZmNpvZhRdeyP773/8ys9nMnE5n2OtisVhYfn4+W7JkCbNYLOz3v/89u/DCC+P8Ts6sUNfp66+/ZhkZGWz//v0Bz6HrxF8nh8Ph83/6//73PzZkyBBWX1/PXC5Xr1wnCvrOsKamJnbttdcytVrNiouL2TfffBPvU4q76upqBoCpVCqWkJAg/tm4cSNjjLHnn3+epaWlseTkZPbYY48xl8sV5zPuG7y7dxmj6+Stvb2d3XjjjUyr1bLCwkK2ePFi8T66Tr6++uorNnbsWKbVallBQQF77rnnxPv687WaN28eA+DzZ/369Yyx8Ndlx44dbPTo0UylUrELL7zwrO9IDXWdLrnkEiaTyXz+T7/nnnvE59F18nw/CdavX+/TvcvYmb9OHGP9aPomIYQQQkg/RWv6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AQr6CCGEEEL6AVm8TyCeXC4X6urqkJiYCI7j4n06hBBCCCExYYyhs7MTubm5kEjC5/L6ddBXV1eHgoKCeJ8GIYQQQshpqampQX5+ftjH9OugLzExEQB/oXQ6XZzPhhBCCCEkNgaDAQUFBWJME06/DvqEkq5Op6OgjxBCCCE/WdEsU6NGDkIIIYSQfoCCPkIIIYSQfoCCPkIIIYSQfoCCvrPApspmrN5fF+/TIIQQQkgf1q8bOc4GR5uMuP0/OwAAI3J1GJyhjfMZEUIIIaQvokzfT9y2qhbx400VzXE8E0IIIYT0ZRT0/cQda+kK+jEhhBBCiDcK+n7iTrSaxI/rOsxxPBNCCCGE9GUU9P3ENXdaxY/rOixxPBNCCCGE9GUU9P3EtXXZxI+bvAJAQgghhBBvFPTFUU2bCa9+U4FT7abIDw6htcsT6BnMdjDGeuLUCCGEEHKWoZEtcfSLd3aiotGI/ac6sPTOyTE/32RzwGJ3iZ/bnC6Y7U5oFPRlJYQQQogvyvTFidHqQEWjEQCw4UgzrA5nzMfoMNkBAHIpB5mE32hZb7b33EkSQggh5KxBQV+cVDR2+nxe3RJ7iddodQAAtEoZktRyABT0EUIIISQ4CvripEHv22nrHwRGQwj6EpQyJGn4oE/I/hFCCCGEeKPFX3FS7xf0nWqPfcZel1emT62QAqBMHyGEEEKCo6AvThoNvkFfgz72oM9o8QR9WhX/paSgjxBCCCHBUHk3TlqM/KiVvGQ1gMDMXzS8y7vJ7jV9Bgr6CCGEEBIEBX1xonevvRueowPQvaCvK0gjB63pI4QQQkgwcQv6SktLMWLECEgkEnzwwQchH2c2mzF37lwkJiZiwIABeP/9933uX7p0KfLz86HT6XDnnXfCZrOFOFLf0mEWgr5EAN0M+mz8mJee6N7dX9OBi/68Hk+tPNit5xNCCCGkb4tb0FdcXIy///3vmDw5/FDi0tJStLW1oba2Fh988AHuu+8+VFRUAAAOHDiARx55BJ999hlqampQXV2NZ599tjdO/7R1mPjgVMj0tRitsDlc4Z4SoNPiKe/qTjPoW7LlOE62mfDfbSd89vMlhBBCyNkhbkHf3LlzceWVV0KlUoV93LJly1BaWgqdToepU6dixowZYmbwvffewy233IKJEyciKSkJf/zjH/Huu+/2xumfNiE4K0pLgFLGfxn8mzsi8ZR3pdAq+UYOky32Ic8AsOtEu/jxbq+PCSGEEHJ26NNr+trb29HQ0IDRo0eLt40dOxZlZWUAgEOHDgXcd/z4cZjNwTthrVYrDAaDz594EYK+JI0cmTolAKCps3tBX4JSBo0Y9DliPheL3YnaDs81q27tivkYhBBCCOnb+nTQZzQaIZVKodFoxNt0Oh2MRqN4v06n87lPuD2YF154AUlJSeKfgoKCM3j2odkcLtidDACgVciQmchnO5sMsZVVxR05VDIkuOf0CYFgLE61m8CY5/PabswMJIQQQkjf1qeDPq1WC6fTCZPJs0WZwWCAVqsV7/fO1gkfC/f7W7hwIfR6vfinpqbmDJ59aN7ZOLVCigwtn+lrNnYz6FPKoFHwmb6ubpR3azv8B0XHviUcIYQQQvq2Ph30paSkIDs7GwcOHBBv279/P0aOHAkAGDFiRMB9AwcOhFqtDno8pVIJnU7n8ycehHV3CqkECpnEU96NMdMnlncVMiQo+UyfqRuZvla/YLPF+NPogCaEEEJI9OIW9NntdlgsFrhcLp+P/c2dOxfPPPMMOjs78cMPP2DVqlW45ZZbAAC33XYbVqxYgT179kCv1+O5557D3Llze/utxEzI9GncgZqY6Yuxa9Z7OPPpZPpa3UFeQSofLLebKOgjhBBCzjZxC/ruvvtuqNVqbNq0CXfccQfUajU2btyI5cuXi5k8AFi0aBGSkpKQk5OD2bNnY/HixSgpKQEAjB49Gi+//DKmT5+O/Px8FBQU4A9/+EO83lLUuqx8YJbgDtS628hhdgd4GoV3927smb6WLj7YLM7kZwbSgGdCCCHk7BO3vXeXLl2KpUuXBr1vzpw54sdqtRrLly8PeZz58+dj/vz5PXx2Z1aXkOlzN1+IjRwxZvos7rl+KrlUzBranQw2hwsKWfTxfEsnn9kbkqnFd4ebYLQ6Yj4GIYQQQvo2+qkeByZ3pk8Ys5KR2L3yrsXOH0cll0Ajl3qOH2O2r9Wd6RuYngCO42/rMFOJlxBCCDmbUNAXB0KmL0HM9PFBX4vRCqeLhXyeN8aYGPSp5VLIpBJxyHOs6/qENX2ZiUrPdm5U4iWEEELOKhT0xYFJXIvHZ/pSExTgOMDFPFm3SOxOBiE+VLqzfAnCur4YO3hb3N276VolUjQKAEA7BX2EEELIWYWCvjjw7KTBB2syqQRpCbGVeC0OTzZPJee/jMIawVgyfYwxMdOXmqBAsobP9FEHLyGEEHJ2oaAvDvwzfYCnxBttM4dQ2uU4ft4f4OkGjmVXDovdBZuTbwhJTVCImb4OCvoIIYSQswoFfXHgv6YP8GrmiHJAs9Xu7tyVScG5uy+EDt5Ygj6DhS/jSiUcNAqpV6aPyruEEELI2YSCvjjw794FPJm+aLdi8+7cFQiZPlMM5V2DmQ/udCoZOI7zNHKYKegjhBBCziYU9MWB/5w+wJPpazJEN6DZYvfM6BN41vTFnunTuYO9RGXsJWJCCCGE9H0U9MWBsJOGd3k31kyfWcz0eY4h7sphjSXTxwd3OhUf9AkdwEYLBX2EEELI2YSCvjgQumvV3o0cOveuHFGu6RPKu0qvXTPENX3dyPQlqvhzEYM+yvQRQgghZxUK+uLAe6iyIKOb3bvemb7TW9PnLu+6g79YAkdCCCGE9H0U9MWBVdwz13P5M722YmMs8q4cliDH0HRjZIvBXcbVqd2ZPkXvl3cdThcOnNKf9jrCVqMVu6rborp+hBBCSH9DQV8cWMXSbGCmz2x3RlVaDZrpc5d3TyfTF4/y7h9XHsT0f23GnUt3djtgM1jsuO4fm/CzN7bh3R9O9PAZEkIIIT99FPTFgZDpU/pl6YRGjGhKvELgqJJ5d+92J9Pn172rOr2gjzEW9f7BANBpseP9HTUAgB3H27CvpqNbr7uuvBGN7vWQyyjoI4QQQgJQ0BcH1iBNGIDXgOYogj7PyBavOX3dyvQJ3bu+jRxdMXQAC442deKCl9bjgpe+w9Gmzqie4x/kfV/RHPPrAsAPVW3ixxWNRrRG2QVNCCGE9BcU9MWBmOnzytIBsTVzBCvvCpm+WLJ0/pk+IdvYZXPAFUPGDgCeWlmG2g4z6vUWvLy2IqrnHKjV+35+Sh/ikeFVNRt9Pi+rM3TrOIQQQsjZioK+OPAEfb6XPzOGAc0WR7DuXSHT141GDpVv0McYYLJHn+072WrC1qpW8fP1R5pgc7/PcI43dwEALhqaAQA4WHd6QV9RmgYAUNEYXaaREEII6S8o6IsDa5CADQCy3bP6GvRRBH32wHWB3SnNdpp9M30quQQSfivfmNYGfrybX5d3wZB0pCUoYLG7ArJ4wVS38kHfdaOywXFAo8GKlhhLs21dNnGv4EtKMgEANW2mmI5BCCGEnO0o6OtlTheD3cmXTf0zfTnJagBAfTSZviCNHJ41fd0p7/IBI8dxYravM8qxLS4Xw//21AIAbp5UgFF5SQCAww2RS6x1Hfx7HZqdiIFpCQCAQzGWZo+5s3x5yWoMzUoEAJykoI8QQgjxQUFfLxOyfIBvlg4AcpJiz/QFW9PXFWUjB2MsYBs2wGtdX5SZvq1VrajtMEOnkuGqEVkYls0HXhUN4UusjDE0dfLvNVunQon7eZVNxnBPCyAEeEXpGgxI5cu7Ne3mmI5BCCGEnO0o6OtlVrtnnZtC6nv5s2MJ+sQSsVd51x302Rwu2J2R19NZHS7Y3I8TyrtA7LP6PnKXdmeMy4VKLkWhO2MXKfBqN9nFrGe6VokhmVoAiLrzVyB0O2cmqlCQymdLa9pM3Zr5d7SpE1e88j2u+dtG6gAmhBByVqGgr5cJTRwyCQeZNHimr9FgiTjrzhqke1et8HwczdgWYTCzhPM0gQDeawMjB32dFju+OtgAAJg9oQAAkJvMv4+6jvBBX6O7jJ2WoIBCJhGDvsrG2DJ9whrAdK0CuclqSDj+Oke7pZ23F788jKNNRhxu6MQb31fF/HxCCCGkr6Kgr5eFauIAgAytEhIOcLhYxCxTsDl9CplEzB5GE7B5j2vhOE68PZZ5f9+WN8LqcGFQRgLG5PNr+fLcaxMjBX1CUCaMqinO9JR3Y8nStRht4nHkUonYEBPp9f0ZrQ5srGgRP19zoCGm5xNCCCF9GQV9vSzUuBYAkEklyEzkA5b6CCXeYI0cAKCJoZlDH2Q9H+ApE3dFcYzP99cDAK4fkysGjkJDisHiQKc7sAxGyPRluYO0QRkJ4DhAb7aLgVw0hPJuupYPHjPdx4s107epohk2pwuZiUrIJBxqO8yojTFwJIQQQvoqCvp6mSXEbhwCYV1fxKAvRMZQDNiiGNvi37krHiPK8q7ebMfGSn4HjeljcsTbtUoZktxrBMO9D89aPD5YU8mlYiPG0RiaOTzlXaXP8aKZd+htRzW/q8d1o3MwPEcHADhwqiOmYxBCCCF9VdyCvubmZkybNg0ajQYlJSVYt25d0MeNHDkSWq1W/CORSPDyyy8DADZs2ACJROJz/6ZNm3rzbcTMs+9uYHkX8KyHa9CHzzAFm9MHABr32rxosnTCmr6ATJ87WxgpcPzhWCvsToZBGQkodo9KEQjrE8NlyoSgLFOnFG8bkhF7M4d/0CdkDoW9eKMl7OIxOi8JxWJTSWzrCwkhhJC+Shb5IWfGggULkJubi5aWFqxduxazZ89GVVUVUlJSfB5XVlYmftza2orc3FzMnDlTvG3o0KE4fPhwr5336RK6d0Nm+nTuWX3Rlnfl/uVd/ktqiirTF768G6lEvP0Ynxk7b1BawH25yWocbugM24ksDFRO0SjE24ZkabHucFPUY1scThdauzxr+gAgSydsZxd9ps/lYih3B30jcnVocAekvRn0Negt+PemYxiZp8OscXk+6ywJIYSQ0xWXoM9oNGLlypWorq6GRqPBrFmz8Morr2D16tW44447Qj5vxYoVGD9+PIYMGdKt17VarbBaPdkfg6H392cVGjlCBX050ZZ3hUYOmX95N/ZMX6LK99vAs4dv+MBx+3F+27VzgwR9aQl8INfWFXptnt79+sleQZ/QzBFtsNVmsoExvgM51f2amd3I9NW0m9BpdYhdxMLsv6PNvRP0uVwMdy3diUP1/PckBw6zzsnr1rGOt3Rhc2UzLh+ehVz3+kpCCCEkLuXdyspKJCUlISfHsw5s7NixPlm9YJYvX445c+b43FZdXY3MzEwUFxdj0aJFcDpDByovvPACkpKSxD8FBQWn90a6IVJ5N9pZfZ6RLb5fQmE9XlQjWyy+W7B5jhG5GcTqcOKwe/jyhMKUgPtTtXwA1hqmIaNDCPq8Xl8c2xJl0NfSyR8/NUEBqXv/OGFNX2MMa/qE0m5JViLkUs/4mKqmLrgijM/pCVuqWsSADwAWbzjarTmD9XozZvxrM/64sgyzXtsCvSl0Iw0hhJD+JS5Bn9FohE6n87lNp9PBaAz9g766uho7duzAzTffLN42bNgw7Nu3Dw0NDVi5ciVWrFiBf/zjHyGPsXDhQuj1evFPTU3N6b+ZGEWd6TNEWNMXspFDWI8XfaYvKSDoi9zIcbylC04XQ6JShlz3OXtLT+ADr9au0Nk2vYkP2JI0gUFfc6c1qoDFfz0f4FnTF0v3blkdv0/wyFz++3JAqgYSDjDbnWL5OJIuqwNVzUY0GSxRDcf29nUZPx5m+thcqOQSVDQasbemI6ZjAMDSLdXi9nlNnVYs+6E65mMQQgg5O8Ul6NNqtQGlVYPBAK1WG/I57733Hq644gpkZmaKt2VnZ2PYsGGQSCQYMWIEnnzySXz66achj6FUKqHT6Xz+9DaxAUMWPtPXqLeGzDB5798bak1fNN27+hBBn9gMEuYYwgDl4ixt0LVnqVGUd4Nl+rRKmRj4Hm2O3MzR7DfrD/AEfW1dNtgc0QVfQqZPCPrkUol4nGjGtry5sQrjn/kGl7/8PSY/vw7D//gVfvnOrqhnBe44zq+PnD4mB9eO4jPgq/fXRfVcgdPF8Olefg/kK0dkAQA+/7E+pmMQQgg5e8Ul6CsuLoZer0dDg2f47f79+zFy5MiQz3nvvfcCSrv+JJK+P4FGKMv6d90KMhNV4DjA5nShzRQ8YBKaOABAHSLTF92cvuBBn1YZuZGjspEPyIb6de0K0tzl3VDz9lwuFjLTGMvOHMEyfSkaOeRSPhBtjnIrtUNiE0eSeFtulEOm15U34vk1h2F1uKCWS8UB29+WN+L2/2z32W85mE6LXSxnnzMgBdeOygYArC1rjKnEu/dkO5o6rUhSy/HcDaMgk3A43NCJY91Yl8gYw56T7fi+ojni7jCEEEJ+GuKW6ZsxYwZKS0thNpuxatUqHDx4ENOnTw/6+H379qG6uhqzZs3yuX3Dhg1iibayshLPPvssrr/++jN9+qcl3HBmgN9VQwhgQq3r8w76/I+jiWGwcuhMX+S9d4UgRQjQ/KW5y7ttIcq7nVYHhFjCf03hkBjGpXhvwSbgOE4cch3Nur7mTiuaOq3gOGBYtieIjSboY4zhpa/47vH5U4twaNHVqHzuOnz10IXISFSiqrkLy7adCPv6P57SgzEgP0WNjEQlLizOgEouQW2H2WedXyTCnMHzBqUhM1GFcwelAgA2H20J97SgXvzqMG5cvBXz3t6Be5bt6tb6QkIIIX1L3FJjixcvRk1NDdLS0vDoo49ixYoVSElJwfLlywMyfsuXL8fMmTORkJDgc/vu3bsxZcoUJCQk4KqrrsKsWbPwyCOP9ObbiJkn6Ate3gUgrpELFWxY3MdQyCSQSHxLq2ITRgzl3dCNHKGPcaqdP7fCtISg9wuZvrYuW9CAQVivp5ZLA0rU3tuxRRKsvAt4Zv81RdHBK6znG5ieIK5nBDwzE4X3GsyhegMqGo1QyCR4+Mqh4DgOUgmHYdk6/PbKoQCApVurwwZNP57iX39sQTIAfg/li4ozAABflzVGPH/Brup2AMDEIr6xZnIR31W90317tFbvr8P/fX9M/Pzb8ias7sUy8c7qNtz65jbc/p/tKI8h6CWEEBJe3Ob0ZWRkYM2aNQG3z5kzJ6CM+5e//CXoMX7729/it7/97Rk5vzPFs/du6HhbCGBClUY9W7AFHiOmTJ+p+40cQkCaF2IkiLCmz+5kMFgcAa8RKssI8OsEAU8JORzhGnmXdwGvXTmimNUnZNNGepV2gej2EP7CHQxdPiwz4L3MHJeHP60+hFPtZuw/pcc4d1DnT8hoDvMqlV89MhtrDzVibVkDHnEHj+G4XAy73Jm+yQP5DN+kgXzwt/N4GxhjUc39M1odePaLQwCA+y8dAqVMgpe/qcA/11Vi+picsMewO11YsuU4NlW2IDdJjVsnF+CcAYGd3eFUt3Thjv/sgNn9PT77jW34bMH5ITPKoTicLry+oQqf7q3FwPQEPHvDKOQk0fgaQkj/1vcXwZ1lrBEaOQBPabQ1xHq0UIOZgeiydAC/6L/THdQFBH1i4OgMmqGyeHW0hgr6VHKpuL4w2PvoMPPPT9YEBn3COsE6vUUs34YSbE0f4L0rR+SgT2jiGJHj29gjBn1hdkfZ7m7AuGxYZsB9aoUUlw/nbxe6c4MRZgEO9gpsLhuWCal7Td7JVlPE91DR1AmDxQGNQiq+j3MKUiCXcmgwWMJmK739/dsKNBqsKEzT4P7LhmDe+UVQyiSobDKK1ykYxhge+nAfnl9zGJsqW/DhrhrcsHgr7lyyAz/GsJXdM58fgtnuxPgByThnQDKMVgce/nAfHDF2Q/9x5UG8/E0FjrV0Yd3hJvzs9W3oCLFGlhBC+gsK+nqZJcLIFsBTGg01KkQczBwk6BMzfRFGtnRa7BDiuYA1fe7A0eliYjnam9DNqlXKAvbt9Zbizva1Bxm90hEiyyjcVuIO/HZFKE2GKu/GshVbuV/nrsCzpi/02kohoBGya/4uHsqXaYXuXH+MMVQFWR+ZkqDA5CL+mGsPhQ4YBUIJd/yAFMik/PeWWiHFqLyksK/v7VCdAW9vqQYAPD19JFRyKXQqudgJLHQGB7P6x3p88WM9ZBIOj11dgp9NyIdUwmH9kWbM+NcWPPTB3ohB17FmI9YdboKEA/4yeyzemDsBOpUMB2r1WLq1OuL5C7462ID3d9RAwgG/u6YEhWka1HaY8fSq8HNA/dV2mPHXr4/g4r+sx4RnvsGC5XuiCsBtDhc+21uLF9aU462Nx1Dd0hXT6wq+r2jGfe/uxpx//4DX1h+NqjkrGIfThcrGTlS3dNHaTEL6ubiVd/sra4g9c72laYXybvCAJdRgZsArSxdhTZ9QXlXLpVD4BaDCMQA+Y+gfXArlztxkVdhyX4pGgVPt5qA/7MOVdwFgQlEKjjR2YveJNlzj7mb15/DqcA5V3o2U6euyOnC8lf+hPCJE0NfWZYPJ5hADasGPp/SwOxkyEpUYkKoJevxzB6a5H9sBs80JtcL3WjYarDBaHZBKOBT5rY+8amQWth1rxdpDjfjlhYPCvg+htCus5xNMKkrF3pMd2HWiHTdNyA/5fJvDhYWf/Aini+G60dm41Ctzef2YXHz+Yz2+LmvAk9OGB3zNXS6Gf6yrBAAsuHQIFlzK75hz/6VD8I91lfhsXy0+21eHvTUd+OBXU0KWWT/cyTdlXTw0A4PdezAvvG44Fn5yAK98U4FrR+eEzCwLDBY7SlcdBADcd8lg/PqSIZg6OB03Lt6Cz/bV4dbJAzAlyA4y3u9lS1UL/rvtBNaVN8K7cfmLA/XYfLQFy395rhhM+6tpM+EX7+xEhVfn+XNryjGhMAU3js/D9WNyQ37PCxhjeOHLw3hzo2dd5ZajrfhwZw3+dus4jI+yZG6xO/HuDyfw+oYq8RfIQRkJeHLacFw2LCvi8xlj0JvtaOq0oq3Lhi6rAzaHC5k6FUbm6oL+0unNaHVg+7FWnGg1QSWXoiRbi5G5SRGf56/VaMXmoy1o77IhS6fChKIUsVErVnqzHUarA2kJipjPg5CzAQV9vSyaRo70CLtZhBrMDHiydJHW9IULuqQSDiq5BBa7C11Wh7g+T+AJ+sL/ABZKt8EyfZ4t2IL/AJxUlIL3tp/ErhOhM33BtmATRDtuZf+pDjDGD8X2Dxx1Khm0ShmMVgfqOswYkuk7nuaQuwFkbH5SyOC3IFWNbJ0KDQYL9tV04LzBvgGHsJ6vMFUTEHxfOSILf1p9CLuq29BqtIq/DASz053JE7KDAmG3lN0nwmf6nvn8EPaf0iNRJUPpdN9GqouH8t3Ep9r5bmL/tY9bq1pxtMmIRJUMv7hwoHh7UXoCXrllHG4/rxAPvL8XJ1pNuGfZbnx879SA92pzuPDx7lMAgFsnDxBvv2ViAT7Zcwo7q9vx1GcH8e95E8P+ovHil4fRaLBiYHoCHrisGAAwriAZP588AMu3n8TTq8rw+QMXiNlQb98easSfvz7sE7CdNygNc6YMQLZOJV6ju/+7C188eGHA95zeZMcdb+/A8ZYupGsVuG50Do63dGHL0RbsPtGO3Sfa8afVh3BZSSZumpCPy4dlBjRi+Qd8d5xXiCGZWryxoQon20y4+Y1t+P21w/CLCwaGvA4WuxMf7T6FxeuPits5apUy2JwuHGvuwl1Ld+HmifkonT7Sp3EJ4H/B+WDnSWw40owDp/Ti2kp/GoUU08fk4r5LBqMo3feXlRajFW9uPIb3t58Ul5AI5FIOQ7MSMSY/CUMyEzEgVYMBqRoUpKp9fqnqsjrwbXkjPttbi42VLT5jgzgOmDIwDdeOzsYlQzORn6KGRMLB6WJoNfKd+I0Gi/h3TZsZ1a1dqG7pEoNfqYTDqLwknD84DRcNzcCEwhTI3d8TnRY7Kho7cbihE0caOnG0yQiTzQkXY2CMr9Io5RIoZVIkqeUoSkvAoIwEDEzn/6jlUujN/Bimg7V6HKzT41CdAW1dNrgY//97RqISWToVsnRKZCaqkKCUQSmTQCWXQuU+tsPlQqfFgVajzf1+LGg08O/JYnciO0mF3CQ1fw3TNMjQKiGVcHAyvkJjtTthdbhgsTthsbtgdQT/mwO/g1J6ghLpiQqkJSihlEmgN9vRYbKj3WRDm8mG9i4bHE7mblYTfk5IxWY8tVwKlUIKlUwChUwCuVQCDvw81rYuGzpMNhitDhitTjicLqgV/HM0Cs9z1XIppBIOjAEuxuBi/L8JxgAnY7A5+PPm/3bB5nDBxRiUMqn4dVFIpe6vjwQyqQQOpwsOJ4PN/bfD5YLdyWB3uuB0MXAcP5dVJuEgk/LPk0s5yKX8+5BwHBwuBqfTxf/tYn5/u8AYfz2kEg4yCQeJ+2+pRAIpxydypo/NDfpvqbdR0NfLIu3IAXiPO4lQ3g0SOGqj3IYtUqZNq5TBYrcFDR4b9HwGMifIThzeUtx76gbL9Am3hXr9iYV88HKwVh80QwZ4SrveW7AJsr32MA7XxLDzuNDxGlie5TgOeclqHGnsRF2HJSDoq3AHbCXZwWcVCscYV5CMr8oaUFanDwj6qoKs5xPkp2gwKk+Hg7UGfFXWgDnnFgZ9jdoOM+r0FkglHMYNSPa5Twj6KhqN0JvsPrufCD7ceRLLfjgBjgP+dss4sTQuUCukuLA4A98casTassaAoE8o+84YmwudKvD45wxIwft3T8H0f23Gj6f0eHNjFe53B2SCb8sb0dplQ2ai0md9pETC4fkbRuO6f2zCusNNWLmvLuSexJsrW/De9pMAgOduGOXzS9GjV5XgiwP1ONzQifd2nMQd5xWJ97lcDH9ZewSvb6gCwH/v3zQ+D3OnFKLYq7lm2S/Pxcx/bcHxli48smIf3p43SQzaHE4X7n9/D463dCEvWY3/3TfVM2jdYMHKfbX43+5aHGnsxFdlDfiqrAEjc3V49OoSXDI0AxzHgTGG59eU461NxwEAz98wGredywfAs87Jw8L/HcAXB+rx7BflWHOgHtePyUVKghxGqxONegvq9RbUdZixr6ZDDNZyklR46Ipi3DQ+HxaHC/9YV4m3Nh3Dil18IP2Xn43B+AEp+LFWjw92nMSne2sDlnQkqeVI0yqgVcogl0pwotWEFqMVH+6qwUe7a3Dd6BzMHJeHJLUc6w43Ytm2E+L/PwWpaozOS4LJ5sTBWj1ajDaU1RmCrg9N1ypRkKqG08VQXm8QB9ADwKg8HQakanCi1YSyOgO2HWvFtmOtAMqgkPI/pM12J6IZKSmXcrA7GfbXdGB/TQcWb6iCWi5Flk7JB1lR7sDTXS1Gq7iF5emoau7esgHS+4ZlJ1LQ118J/6GGKy141vSFb+QIViLWRLkNW6Sgj/+t2xa0TCycl39mzF+quKYvdHk3WaMIuA/gZ9YJGbK9Ne2YOjg94DGhOncBINddQjTZnEG7hwW7TggZsuAls9xkFY40dgbdlaOiIfyAasGoPB2+KmvAwVp9wH1HI8w7nDk2DwdrDXhr4zHcMrEgaIZKyPKNytUFlKDTtUoMTE/A8ZYu7DnZ7lO2BfjxKE9+xpdDH75iKC4fHrzsd/XIbHxzqBFflzXgYa9uYrPNKTap3BAiGAOAglQNnp4+Eg99uA+LN1Rh9sQCn+Dy/R18sDZ7Yr6YcREUZyXivosH4x/fHcWjH+1HZVMnrhudg8EZWvHf0cFaPR54fw8A4PYphQHfLykJCvz2qhL88bODeHltBa4fk4vUBAXMNiceWbEPXx7k38MvLxiIBy4vDvr9olPJsXjOeMx6bQs2HGnG39ZV4pErh4IxhqdXl2FTZQvUcinevGOCGPAB/PrSX100GHdfOAjl9Z34dO8pvLf9JMrqDLhzyU6cOzAV14zKxsaKZqw/0gwAeGbmSDHgE177X7edg3N/SMWzX5Rjz8kO7DnZEfJ656eo8csLBuLWyQPEa6SVSvDEdcNx2bBMPPzhPhxv6cLP3tgGjgO8l/qNzkvCrZMLMLkoFQWpmoD/qxhj2Fndjtc3HMX6I834/Mf6gJ1fxuYn4TdXFOOSoZ5sJmMMp9rNYvarusWEk238H73Zjhaj1WdJS2GaBjPH5mLmOXliuR/gS+hrDtRj7aFG7K/pgM3pgvA7roTjv+cz3Rm0LJ0SuUlqFLmzcEXpCUhQSFHbYcb2Y23YfLQFGyua0dplQ7XXes1snQol2YkYlp2I4qxEJKnlEL4thSyT1e5CS5cVx5u7cKzFN5MI8KO3RuTqMCovCaPzkpCl4wfvezJ3VjQZLGg2WtFldXpl4PgsnUTCQauUIV2rQKZOhSz3+8nSqaCUScQgX7iGfCaRQcLxGTghc+j9t5ChVMolULn/Zow/p9YuK1qNNrQYrbA5XEjSyJGkliNVo0BKggIpGjkUMglc7iycw8lgsTthdmcSLXYnzDYnLA4n7E4+m+ZyMSRr5EhxHyNRKUOCUgaphIPF7oTJ/XiLjT+OkFHlOA4SjgPn/ppKOA4cx0Ehk3jeh1Qi7mEvfk0cTvFrY3O6YHe4IJNyUEglkEn5TJ5cwmfxZO7snovxGTubwwW7V1ZQeL6LMcgkEkilQvbOk8UTPgf4a+L0ygC6vDKCwvivvoCCvl7m6d6N3MjR1mWD08UCsljhGjmE9XhWhwsOpytokACEntEnCBc8CmXntITgAZtAKN22dcXWyAHwGbIpg1Lx2b46/FDVGjzoC9HEAfDZqWSNHB0mO+r15qCvY7E7sdO9Fm5SiEaMUGVixhgqIuxKIhjpXv91MEh2Qwj6vH+oebvt3AF44/sqVLea8OamY/j1JUMCHrOtqhVA6GaSCYUpON7ShR3VbT5BX3uXDfe9uwd2J7+O7/5LA48tuGK4bzfxgDR+DeO35Y0wWh3IT1GLWcVQZo7LxX+3VWPPyQ689NVhvHLzOADAyVaTOED6lokDgj73wcuLUdNuxqd7a/Ha+iq8tp7PymXplFDJpTjZZgJjfLCx8LphQY9x2+QBeG/7SZTXG/DkZwfwiwsG4ulVh3CgVg+FVIKXfjYaN5wTet0jAAzP0eH5G0bjtx/txz/WVcLpcqHDZMfy7SfBccCrt4wLyIQKOI7DiFwdRuSOwH2XDMHrG47inW0nsP14m9gFLpdyeGbmKJ8St/fz7zivCNeMzMaKXTU4UKtHl9UJjUKKLJ2KL/clqzA0KxEjcnQhs9tTBqXhy99ciOe+KMfK/XWwuXeSuWpkFm6fUogJhSlhS+gcx2HywFRMHjgZh+oMWL79BHZVt8NodWB0XhJuGJ+Hq0ZkBRyD4zgUpGpQkKrBtaNzfO7Tm+yoaTehps0EmVSCIZlaFKVpgp5HQaoG91w8GPdcPBgOpwsNBgucLga1Qoq0BGXA/5fB5KdokD9Bg5sm5MPlYjje2oVWow2JKhlyk9RBM+LRMFjscDgZ1HJp0OoEIfFGQV8vi6Z7N9Wd/XIxvgzqv5bLHGZki7CmDwBMdid0EYK+cOVdIPhWbMLWZuHWmAERyrsRXh/gfzh9tq8OPxwLvh4t1LgWQU6Smg/6OiwYlh24z/KWoy2w2F3ITVKJ3cL+8lL4oK/Wb+RJo8EKg4VvwBiUEXxAtWCUOwioajYGNIQI41pCZfoSlDL8/tpheOzjH/HK2gqMy0/G1CGeAJgxJgZM5w8JDIwBfl3ax7tPYWNFMx6/xhMQPb+mHC1GK4oztfjr7LEB68u8JWsUOHdgKrZWteLrsgbcfRHfWPKZu7Q7a1xexDmAHMehdPpIzHxtCz7ZU4vbJg/AxKJUvL3lOBgDLhqaIQaT/mRSCV65eSyuHJGFFbtqsLu6HZ1Wh0939rWjsvHCjaMDsp0CqYTDopkjcfP/bcOaAw1Yc4DP7qVo5HjzjomYFKTEH8xNE/JR0dSJ//v+mBh8AnzHc6imI3+pCQr8YdoI3Hn+QCzffgIVjUbkJasx59wBPiXlYDJ1qoDyeKySNQr8ZfZYPHvDKHSY7EhNUARkWKMxIleH524YfVrnAoDPKmmSQjbIhCKTSpCfEvx7JloSCYfBGVoMzjitwwBA0OUNhPQlFPT1Mk/3bujfAmVSCRJVMnRaHOgw2wOCq3DDmRXulLXDxWCyOkP+JxSxvCsOaA5S3o0QbAk8jRxByrum8I0cAMQuy301wTtfhTV93luwectNUqG83hByzt5X7pLeFUGyEgKhW9S/vHvEneUrStOEbcoB4F60rUSjwYryegMmuNcr6s128T0MDhM4/mxCPjYfbcHKfXW4993d+HTB+WJmsKq5C7UdZiikErFT2N8lJRngOH4eYaPBgiydClurWvDR7lPgOODFm8aEDJS8XT0yG1urWrH2EB/0NXda8X0FX46cdU5061XGFiTjlokF+HBXDZ749ABenj1OLO3ec1H4DmWO43Dd6BxcNzoHjDF0mOyobu2Cw8WQn6KOavjypKJUvDF3Ap77ohz1ejMuKclE6fQRMQcOv79mGIozE7FiZw0UMgnuPL8oZGk8nNxkNR67OnhmsjcoZVJk6SgjRUh/QUFfL4umkQPgg7FOi0Msg/ocI0ymj+M4aBRSGCyOsB28hghBnzBYOdgxhHUroYItgbCmL9h7ENf0qUMfozBNg5wkFer1Fuw92e6T4QI8Gcdg5V0AyHGvo6gPMmfPYLGL65Cm+ZWavIUa0CzsFhKuicPbqNwkNBqacLDWE/QJpd1snQqJYTIEHMfhpZvGoKbNhD0nO3DX0p349NfnIzVBgVX7+EzblMFpIctJaVolxuYnY19NB1bvr8PcKYX4w6f8Or455w6IWJYVXDkiC6WryrDrRDuaO634355TcLgYxhYkBzS5hPP4tcPwTXkjKhqNmP6vzQCAC4vTMXVw8KA1GI7j+HVGEZYYBHP1yGxcPTI76l1KQr3+zybk42dhxuAQQkhfQ8OZe5lnZEvkoA/wBGfeLGIzSPBjRLONmmdNXfC4PyFEps/udInPjba8G6wLWdiRI1x5l1/XxwcCPxxrDbhf2Fc31MwuIfMjjK3w9umeWpjtThRnakOuhQM8a/rqOyw+YyOOuJs4iqMMdsR1fV7NHEeb+GNEs8WYSi7Fm3dMRH6K2j36ZBf0Zrs45iRS8HHLpAIAwJIt1ShdWYbjLV3ITFTid9dEn2XKTVZjTH4SGOP35/2ve2DybZMLoj4GwP8y8Pb8SWKWtzBNgz//bEy3A7Du6u3XI4SQeKOgr5dFM6cP8ARD+mBBX5hMH+DdhBF6bIvQjJEeIksmZPr81/S1ec25So4wZFb4od5hsvvsBCDMjQIQccH0lEF8QBZsXZ+wr25mqEyfOLYlsAnj3R9OAOAzXeF++Gcm8gvDHS4mlmIBiE0c0Wf6+DWFB7yCvsNRdv8K0rVKLJk/CYkqGXZWt2PSs9+iTm9BulaJq0aELy3ecE4e0rUK1HaY8eEufgjyopmjYl6DdJ07K7ro80Oo01uQl6zGjLGhu3ZDGVeQjE2/uxSfLTgfax++iPbFJYSQXkBBXy+zuYM+/+G0/oSAqTtBXzSZvkhNEMKaPqPfMYTnpSYowi78Fx4DADany2duoJC9lHBAojL8CgMh07e3pl1834ImdxCWqQv+HoTS7Mk2362zth1rRWWTERqFFDdGyJDJpBJku0eL1Hbwx3G5GCrdpdmhWZGzdAAf5AB8sChcUyFbOCzKwBHgx5csvXMykjVy2JwuSCVcwEy6YFRyKf5x6zlITVBAKZPgmZnRNx14u31Kodi4wnFA6fQR3e5STFTJMa4gOeIvQIQQQnoGrenrZbYYy7vBg77wx0hU8V/WTmvgcwXNxvBNEGL3rl+2sCXKcS2AZ4s3m8OFdpNNDEY7vMbFRAocB6RqkK5VoMVow6F6g7gFlcXuRKeFD54yQpR3hbJpbYfZp2t22TY+y3fDOXlRZbryktWo7TCjtsOCCYXC8ZyQSzkUpoXv3BVk6lTicX6s6cDUIeli0BdttlAwoTAF3z96KTZUNGFEji5it6dg6pB07HjicrhY5F86QklQyvDJfVPx1cEGlGQn4pwotwQjhBASfzH/z79582a8+uqrWLt2bcB9v/71r3vkpM5m0TZy6E6jvCsGjEEaKIRzEAKmkJm+EI0c0XbuAu7F9kIHr9esPmFNYKTysHAMIUu212sYrbCeTymTQKcK/rtLmlaJ1AQFGAOOuafX13WYsfZQIwD47MoQjjC2RZjVV+leizc4QxvTmItz3Ltl7DnZjiaDBa1dNnAcUBxlttBbkkaOmePyog74BDL31kKnI1mjwK2TB1DARwghPzEx/e//f//3f/jZz36G3bt34/7778dll12GtjbPWqt33323x0/wbOJwusRtgiL94A2b6XNnC9WRgj5z8PKusJ5PLuVCd++GKBGLg5kjdO4KhGYO77Et4riYELtx+BOCvn01HeJt4no+nTLsmjwh2ycEau9tPwmni2HKoNSoM2zCNHVhVp+wN2usAZeQpdx7skMcxjs8O3AXDUIIIeRMiCno+8tf/oLvvvsO7777Lg4fPoxzzz0X559/Pmpq+IXh3ov1SSCb07OnZbRBX7BxJ5EyfeGyhIBnXV5aQuiASdjZo8vmX96NPtMHBA/6Iu27629cAR8s7atpF28T1/OFKO0Kit1BX0WjEVaHU5wJF22WDwDykvkZbsLaQHEnjii6br0Jmb5dJ9qxuZIfqHzuoOgGAhNCCCGnK6YUQ1NTE4YN40c8SCQSvPDCCygsLMQFF1yAL774gkYgRCAMZgb4IcrhhBvZ4pnTF/wY4nMt4YO+9MTQmTZhZw//7l1hRl9qlPPRUhICg1fPjL7ogr4xBUngOKCmzYxWoxVpWqVYas3WhQ/6hAn/e0604/P99WjtsiFbp8KVEbpdvQnlVyHYq+xmpm90XhIyEpVo7rSKHbRCowohhBBypsWU6Rs8eDB27drlc9u9996LP//5z7j88sthtVpDPJMAnkyfVMKF3BNXIAwtDtfIETLTp4qU6ROaMUJn67Qh5vS1d0XfyAHw678A31l9kXYD8adTycUdKIQSb40761aQGn4nhXPdM/h2n2jHn78+DAC4/bzCmNbiCSNV6vUWtBqtYqk42s5dgUwqwaxxnp0rtEpZyK3TCCGEkJ4WU9D30EMPYf/+/QG333LLLVi2bBnOP//8Hjuxs5E4riWKgCPcmj5zlJm+UEGfsENFuCyZZ9af35w+d2k22p0QUsRZfYFBX7gt2Pz5r+sTSq0DIgR9A9MTUJKVCIeLodFgRaJShtvPK4z6dQH+egrjXz7ZUwuL3YVkjRxFUXbuept//kCxNP7QFcVicE0IIYScaTH9xJk3bx4AYMWKFUHvv/fee33uu/nmm0/j1M4+YuduiGDNWzTDmUPNNwtXGgY8AVNBauiBuMKaPpMteKYv6vKuuKYvsHs32kwfwAd9H+8+FXPQx3EcHru6BPe8uxtOF8OT1w/v1qboJdmJqO0wY6l7F4qJhakRx80Ek5esxqbfXYoWozVilpIQQgjpSd1KM7z22mvYtm0bsrOzkZ+fj1OnTqGhoQFTp04V1/VxHEdBnx9rNzJ9ZrsTNofLp/Ej2pEtoYK+mvbIpVGxe9fm8NmjVCjTpkTZeRu0kSPG8i7gm+lzuhhq3J20kYI+ALhiRBbW//YS/vFp3Qu0JhSm4LvDTah1ryWcPLD740rUCikFfIQQQnpdt4K+4cOHY/bs2bj//vvF21577TUcPHgQr7/+eo+d3Nkm2t04AH7AMscBjPHZvgyvrcYi7b0bqXv3lDtLlp8SLujjA0rG+MBTo5DB7nTB4J7vF2sjR7CRLclRBo4Av2uFSi5Bp8WBbVWtsDn43SiEcSqRdDfYE1w+PBN/+fqI1+fRN4IQQgghfUG3prR+8MEHuO+++3xuu+eee/D+++/3yEmdraxR7sYBABIJJ25Rpjd7AiaXi4nBY6RMX5fNCbvXmBiADzzrDfyavnDlXbVcCqEZW9g2TCjLclz0WTox0+c1nFnvDgBjWdMnk0owJi8ZAPDeDn5HjYHpCREbYnrKsGwdbp1UAAC447xCsbGEEEII+ano1k/MwsJCvPPOOz63LVu2DAUFBVEfo7m5GdOmTYNGo0FJyf+3d/dBUZ33HsC/u7zssuwLBHkV1BAQAhL0Xm0Se01smibppXCpN9RcfHeapq2tsY6mNSbFUo06TWybtmg7SdVEEqXVCHrN29gkRkdrp6ZeBVOMCiEKiQjssrAsLjz3D/YcWJaXXYRzrPv9zOzMcs7Zs8/+JmN+83vO83vScPjw4QGvW7x4MXQ6HYxGI4xGIzIzMz3O79ixA4mJiTCbzViyZAk6OzsHvM/NoLfS59teo1IlrG/FTkocgaFW7wbLCVvfChvQs6uEED2JZ/QQvfY0Gk3vc33uFbzS1G5EWAiCfHyeTUr6BlrI4c/0LgBMdfe5O3SmAUBPCxQlbfrvu3Dq2a+h+L+mKPq9REREo2FESd9LL72EdevWYfLkyfjqV7+KyZMno6ioCH/84x99vseyZcuQkJCAxsZGbN68GQUFBWhubh7w2p/97Gew2+2w2+2orKyUj585cwYrV67E/v37UVdXh5qaGqxfv34kP0kR/kzvAoA5rCfpsvXZWUN6ng8A9IPcJzhIK7djkbYrk1TV2wD07FQxXF/F/luxyc/z+Ti1C/QmfW2dPc8mdncLv/v0SWbe4dnT7l4Vetz5Oq1NRER0sxnRM30zZszAhQsXcPz4cdTX1yM+Ph733nsvQkJ8+5+43W5HeXk5ampqYDAYkJ+fjy1btuDAgQNYuHChz+N47bXXMHfuXEyfPh0A8Oyzz+Lb3/42iouLB7ze6XR69BK02Ww+f9do8Gd6F+jtt9e3yXKHewVw8DC9/mLNOjTane7tynorYqc/awEA3JUYMez3h+uCgVan3KtPqhre5sezeCZ9MLQaoFv0VPv0oUHyVnRmP5O+/0gZh4lRBtRea4dRF+xXg2UiIqJAN+IHokJCQnDfffdh7ty5uO+++3xO+ADg/PnzsFgsiI+Pl49lZ2d7VPH6+sUvfoGoqCjMnDkTR44ckY9XVVUhKyvL4x6XLl2Cw+EY8D4bN26ExWKRX/5MR4+Gzi6p1Yq/SV/fSt/Qz/NJYt09+D7vV+n7vzorACA7cfipUWkxx41U+rRajVzta2rvhNX9XKA+RDvsb+gvOEiLV5Z+CYtnTsLvF/y7X+MgIiIKdMo8Bd+P3W6H2Wz2OGY2m2G3272uffLJJ/HJJ5+gvr4ey5YtQ25urrzXb//7SO8Hug8ArFmzBlarVX5J91GKP82Zgb7Tu30qfcO0a5HEmr2ndzuud/lV6TP0e6ZP7tHnR6UP6F2w0dx2XV4MIu044q+JUeFYl5fJnSyIiIj8pErSZzQavaZWbTYbjEbvFZHTpk1DZGQkQkNDMW/ePNx777149913B7yP9H6g+wCATqeD2Wz2eClJnt71oTkzAJgGmt4dZjcOSYzJXelr7ZCPvf/Pq2jv7EKCRY/0uOH3je3dis1d6fNzNw5J38UczSNYuUtEREQ3TpWkLzU1FVarFQ0NDfKx06dPe63MHYhW2zvkjIwMnDlzxuMet99+O8LCBm9Foia/K31S0ucY+fTuF7bepO+Njz4DAOTcFe/TbhL9F3L07sbhX8IW0WdXji9aeyqPffsOEhER0dhTrdKXl5eHoqIiOBwOVFRU4OzZs8jNzfW6du/evWhra4PL5cKePXtw9OhRPPDAAwCAwsJClJWV4dSpU7BardiwYQPmz5+v9M/xmXOkq3cHWMgxXKVPmt6tt/YkfZ98Ycc7VZ8DAAqm+/YsY/+t2JrcU7O+7sYhua1Pg+arTPqIiIhUoUrSBwAlJSWoq6tDVFQUVq1ahbKyMkRGRqK0tNSj4vfLX/4SCQkJGDduHLZs2YI33ngDkyZNAgBkZWXhhRdeQG5uLhITE5GUlIS1a9eq9IuG17t617cFDFKlr7XvQg53AqYf5h4T3TtQXGpsgxAC2z64ACGAr2XEYnLs8FO7QO9WbFJzZn/33ZX0NmjudK8m7p1+JiIiImWMqGXLaIiOjsahQ4e8js+bNw/z5s2T/z569OiQ91m8eDEWL1482sMbE/736fPeQ7e30jdc0heOkCAN2ju7cPJSE/Z/dBkA8P3Zd/g83v4LSUayehfwnN51usfPSh8REZGyVKv0BSK/kz79ANO714fed1cSEqSVK3pLd/wNrm6BL6dEYdqESJ/HK1X0pGRvJH36eu7Tk7y2tHfymT4iIiKVMOlTkFTl8rVPn2nAhRzue/jQ405qa9LmnhJe8eBk3weL3mnZprZOdFzvkp/tG2ml71pbJxrdSV8Mkz4iIiJFMelT0Ii3YRuo0ufDc4Hz7p6AMHdyWPDviZgx6Ta/xitV+prbO+VqX7BWI1cgfRXnXklcb3VwIQcREZFKVHumLxA5/W7O3FPp63R1o+N6F/QhQT736QN6nuv73+X/gerPW/Hgnf5vWdZb6buOBpu0AEM37J69/SXd1rOopO/uIFJLGSIiIlIGkz4FdcrNmX1bvWsMDYZGAwjRs4K3b9IX5uM9kqONSI4euFn1cPpW+hrcrV9iLf4na5GGEISHBsnTzNEmndz4mYiIiJTB6V0FdXa5kz4fK31arQYmnecUr9QoOVyBpEnaNaOrW+D85z1b28WNoEKn0WiQ0qdNzO3jwkdngEREROQzJn0KkhZy+PpMH+DdtqXNvQ9uuM63St+N0IcEIdy9K8e5+p4t7kY6LTsloXfLu7vGW258cEREROQXJn0Kkqd3/Uj6evff7anwSfvgGkKVmR6VVupWuZO+uBFM7wLAw5lx8vsHM/x/vpCIiIhuDB+sUpC/q3eBPr363JU+qW2KUs/ERYWH4rNmBz5tagcwsuldAJiVOg6b/zsLJn0I7kmOGs0hEhERkQ+Y9CnI3713gT7Tu+5n+uxypW/sp3cBICEiDKc/s8p/j3R6V6PRYO6MCaM1LCIiIvITp3cV1Onn3ruA9/677Qou5ACACe52K5JJ4wyDXElEREQ3MyZ9ChpZpc9zerd3IYcySd/EqN6VtiZ98Iind4mIiEhdTPoU5G9zZqDvQo5+LVsUmt79t4kR8vvsxAi/GzMTERHRzYFJn4I6pb13fdhNQ9K7kMM9veuu9BkUqvRNjjEh091upfBuPpNHRET0r4oLORQkNWf2p9LXdyFHp6tbvodRoZYtWq0G+5d9GY12J+ItYYp8JxEREY0+VvoUIoSQp3f96dMnLeSwOa7LizgAIEyh6V0ACAnSMuEjIiL6F8ekTyGubgEhet77tXrXvZCjtcMl710bGqT1azEIERERETMHhUjtWgB/mzP3Tu9Ku3EosQUbERER3VqY9CnEeaNJn8Ol+BZsREREdOtg0qcQqdIXrNUgSOt72xNpetdxvQst7l59Sm3BRkRERLcOJn0KGcm+u4Bngne52dFzTM+kj4iIiPzDpE8hTnePPn+TvuAgLUzuxK/2WhsAINIQMrqDIyIiolsekz6FjKRdiyTapAMAVH9uBwBYwkJHb2BEREQUEJj0KURuzDyCpC/GLCV9rQCACFb6iIiIyE+qJX1Xr15FTk4ODAYD0tLScPjw4QGvW7lyJZKTk2EymTB9+nQcOXJEPvf+++9Dq9XCaDTKrw8//FCpn+AX53X/d+OQxJr1AIB6awcAICKMSR8RERH5R7UVAcuWLUNCQgIaGxvxzjvvoKCgABcuXEBkZKTHdRaLBe+88w6Sk5Oxd+9e5Ofno7a2FiaTCQAwefJkfPzxx2r8BL9IlT5/GjNLpKRPwkofERER+UuVSp/dbkd5eTmKi4thMBiQn5+PKVOm4MCBA17XFhUVISUlBVqtFgUFBQgLC0N1dbUKo74xI129CwAx7mf65L/7JYFEREREw1El6Tt//jwsFgvi4+PlY9nZ2aisrBzyczU1NWhqakJKSorHsZiYGKSmpqK4uBhdXV2Dft7pdMJms3m8lDLS1buAd6Uv3sKkj4iIiPyjWqXPbDZ7HDObzbDb7YN+5vr161i0aBFWr14Ni8UCAEhPT8c//vEPNDQ0oLy8HGVlZXjxxRcHvcfGjRthsVjkV1JS0uj8IB903sDq3f5JXxwrfUREROQnVZI+o9HoVWWz2WwwGo0DXi+EwOLFixETE4N169bJx+Pi4pCeng6tVouMjAw888wzeOONNwb93jVr1sBqtcqvurq6Ufk9vriRli2Togzye40GGGfUDXE1ERERkTdVkr7U1FRYrVY0NDTIx06fPo3MzMwBr//hD3+IK1euYNeuXdBqBx/yUOcAQKfTwWw2e7yU0nG9Z3pXH+L/Qo6+z/BlxJuh9WMbNyIiIiJAxUpfXl4eioqK4HA4UFFRgbNnzyI3N9fr2qKiIhw7dgzl5eXQ6TwrXO+//75crTt//jzWr1+Pb3zjG4r8Bn853Elf2AiSPgD4zf9Mw5dTorD2P+8czWERERFRgFCtT19JSQnq6uoQFRWFVatWoaysDJGRkSgtLfWo+BUXF+PcuXNISEiQe/GVlpYCAP7+97/jnnvuQXh4OB566CHk5+dj5cqVav2kIXW4+/SFhY4s6cvNTkDpt+/BzJRxozksIiIiChAaIYRQexBqsdlssFgssFqtYz7V+9yhc/jDkYv4zn3JeJrVOiIiIhoF/uQy3IZNIY7OkT/TR0RERHSjmPQppOMGn+kjIiIiuhFM+hTikFfvMuRERESkPGYgCmGlj4iIiNTEpE8hcsuWEa7eJSIiIroRTPoUIrVs4UIOIiIiUgOTPoVw9S4RERGpiUmfQvhMHxEREamJSZ9CmPQRERGRmpj0KaR3IQdDTkRERMpjBqIQKenTBbPSR0RERMpj0qcAIYS8epctW4iIiEgNTPoU4HR1y+/5TB8RERGpgUmfAqR2LQBbthAREZE6mPQpoMPVk/SFBmkRpNWoPBoiIiIKREz6FNAuN2ZmuImIiEgdzEIUYO9wAQBM+hCVR0JERESBikmfAuzOnqTPqAtWeSREREQUqJj0KaDVXekz6pn0ERERkTqY9CmgteM6AMDEpI+IiIhUwqRPAZzeJSIiIrUx6VNA70IOJn1ERESkDiZ9CrA6pOldrt4lIiIidTDpU0Cj3QkAGGcMVXkkREREFKhUS/quXr2KnJwcGAwGpKWl4fDhwwNe53A4MH/+fJhMJkyYMAGvv/66x/kdO3YgMTERZrMZS5YsQWdnpxLD98tVOenTqTwSIiIiClSqJX3Lli1DQkICGhsbsXnzZhQUFKC5udnruqKiIjQ1NeHy5cvYvXs3vve976G6uhoAcObMGaxcuRL79+9HXV0dampqsH79eqV/yrAaW3sSUSZ9REREpBZVkj673Y7y8nIUFxfDYDAgPz8fU6ZMwYEDB7yuffXVV1FUVASz2YyZM2ciLy8Pu3fvBgC89tprmDt3LqZPnw6LxYJnn30Wu3btUvrnDKmrW+BKiwMAEGNm0kdERETqUCXpO3/+PCwWC+Lj4+Vj2dnZqKys9LiuubkZDQ0NyMrKGvC6qqoqr3OXLl2Cw+EY8HudTidsNpvHa6ydvWxFq9MFkz4YKdHGMf8+IiIiooGoVukzm80ex8xmM+x2u9d1QUFBMBgMA17X/z7S+/73kWzcuBEWi0V+JSUljcrvGcpnzQ4YdcG4NzkKwUFcN0NERETqUKVxnNFo9Kqy2Ww2GI1Gr+u6urrQ3t4uJ359r+t/H+l9//tI1qxZg5UrV3pcP9aJX85d8Xg4MxYt7rYtRERERGpQpfSUmpoKq9WKhoYG+djp06eRmZnpcV1kZCTi4uJw5syZAa/LyMjwOnf77bcjLCxswO/V6XQwm80eLyUEB2m5iIOIiIhUpUrSZzQakZeXh6KiIjgcDlRUVODs2bPIzc31unb+/Pn4+c9/jtbWVpw4cQIVFRWYO3cuAKCwsBBlZWU4deoUrFYrNmzYgPnz5yv9c4iIiIhueqo9ZFZSUoK6ujpERUVh1apVKCsrQ2RkJEpLSz0qfsXFxfKij4KCApSUlCAtLQ0AkJWVhRdeeAG5ublITExEUlIS1q5dq9ZPIiIiIrppaYQQQu1BqMVms8FiscBqtSo21UtEREQ0WvzJZbiclIiIiCgAqLJ692YhFTmV6NdHRERENNqkHMaXiduATvpaW1sBQJF+fURERERjpbW1FRaLZchrAvqZvu7ubly5cgUmkwkajWbMvkfqB1hXV8dnB4fAOPmGcfIN4+Qbxsk3jJNvGCffjVashBBobW1FQkICtNqhn9oL6EqfVqtFYmKiYt+nZG/Af2WMk28YJ98wTr5hnHzDOPmGcfLdaMRquAqfhAs5iIiIiAIAkz4iIiKiAMCkTwE6nQ5FRUXQ6bgV21AYJ98wTr5hnHzDOPmGcfIN4+Q7NWIV0As5iIiIiAIFK31EREREAYBJHxEREVEAYNJHREREFACY9BEREREFACZ9Y+zq1avIycmBwWBAWloaDh8+rPaQVOd0OrFkyRIkJibCYrFg9uzZOHPmjHx+06ZNiI6Oxm233YannnrKp/0Eb3XHjx+HVqvFpk2b5GOMk6dNmzYhKSkJJpMJU6dORUtLi3yccep16tQpzJw5E2azGcnJydi+fbt8LpBjVVRUhIyMDGi1Wuzevdvj3FBx+dvf/obs7GwYDAbcf//9qK2tVXroihosTjt27MDUqVNhMpmQnJyMbdu2eXyOcfLkcrmQlZWF9PR0j+NjHScmfWNs2bJlSEhIQGNjIzZv3oyCggI0NzerPSxVuVwuJCcn48SJE2hqakJeXh7y8/MBAIcOHcLWrVvx17/+FZWVlTh48KDH/5QCUXd3N370ox9hxowZ8jHGydNvfvMbvPnmmzh69ChsNht27doFvV7POA1g4cKFyMnJQUtLC/785z9j+fLlqK6uDvhYpaam4te//jW+9KUveRwfKi5OpxNz5szBk08+iaamJtxzzz1YsGCBGsNXzGBxcjqd2LZtG5qbm3HgwAEUFRXhyJEj8jnGydNvf/tbr100FImToDHT2toqQkNDxZUrV+Rjs2bNEjt37lRxVDcfp9MpNBqNaGxsFI899pjYtGmTfO7ll18WX/nKV1Qcnfq2bt0qli9fLhYtWiQ2btwohBCMUx8ul0vExcWJ6upqr3OMkzej0SguXrwo/z1jxgxRUVHBWLndf//94vXXX5f/Hioub731lkhPT5fP2e12ERYWJmpqapQbsEr6x6m/wsJC8fzzzwshGKf+cWpoaBB33nmnOHjwoEhLS5OPKxEnVvrG0Pnz52GxWBAfHy8fy87ORmVlpYqjuvkcP34csbGxiIqKQlVVFbKysuRzgR6vpqYm/OpXv8K6des8jjNOvT777DM4HA786U9/QmxsLNLS0uSpJcbJ2w9+8AO8+uqrcLlcOHnyJOrq6nD33XczVoMYKi79z4WHh+OOO+5AVVWV4uO8mXR1deHkyZPIzMwEwDj19+Mf/xhPP/00wsPDPY4rEafgUbsTebHb7V6bKJvNZvlZIwKsViueeOIJbNiwAYB3zMxmM+x2u1rDU93TTz+NFStWIDIy0uM449Tr8uXLsFqtuHDhAmpqanDx4kU8+OCDSEtLY5wG8Mgjj2DhwoUoLi4GAPzhD39ATEwMYzWIoeIy2L/xgR63Z555BuPHj8fDDz8MgHHq6/jx46iursb27dvxwQcfeJxTIk5M+saQ0WiEzWbzOGaz2WA0GlUa0c2lo6MD+fn5yMnJwdKlSwF4xyyQ4/XRRx/h5MmT+N3vfud1jnHqFRYWBqDnwemwsDBkZmZiwYIFOHToEOPUz7Vr15Cbm4udO3ciLy8P586dwyOPPILMzEzGahBDxYX/xnvbtm0b9u3bh2PHjkGj0QBgnCTd3d1Yvnw5SkpK5Nj0pUScOL07hlJTU2G1WtHQ0CAfO336tFzyDmQulwuPPfYYEhIS8Pzzz8vHMzIyPFbyBnK8PvjgA1RXV2P8+PGIi4vDnj17sGHDBjz++OOMUx+TJ09GaGioxzHhXl3JOHm6ePEiLBYLvvnNbyIoKAhTpkzB7NmzceTIEcZqEEPFpf+5trY2XLhwARkZGYqP82Yg/Rv19ttvY9y4cfJxxqmHzWbDqVOnkJubi7i4OMyZMweffPIJ4uLi0N7erkycRu3pQBrQo48+Kr7zne+I9vZ2UV5eLiIjI0VTU5Paw1Ld4sWLxUMPPSQ6Ozs9jh88eFBMnDhRXLx4UdTX14vMzEzx8ssvqzRKdbW1tYn6+nr59a1vfUusXbtWNDc3M079FBYWiscff1x0dHSIjz/+WMTHx4u//OUvjFM/LS0twmKxiIqKCtHd3S3OnTsn4uPjxZtvvhnwsers7BQOh0PMmjVLvPLKK8LhcIiurq4h49LR0SESExPF9u3bRUdHh/jJT34iZs2apfIvGVuDxentt98W0dHR4vTp016fYZx64uRyuTz+Td+7d69ISUkR9fX1oru7W5E4MekbY1988YX4+te/LsLCwkRqaqp499131R6S6mpqagQAodfrRXh4uPw6cuSIEEKI5557TkRFRYmIiAixevVq0d3drfKIbw59V+8KwTj11dzcLObMmSOMRqOYOHGiKCkpkc8xTp7eeustkZ2dLYxGo0hKShIbNmyQzwVyrBYtWiQAeLzee+89IcTQcTl58qTIysoSer1ezJo165ZfkTpYnGbPni2Cg4M9/k1/4okn5M8xTr3/PUnee+89j9W7Qox9nDRCBFD3TSIiIqIAxWf6iIiIiAIAkz4iIiKiAMCkj4iIiCgAMOkjIiIiCgBM+oiIiIgCAJM+IiIiogDApI+IiIgoADDpIyIiIgoATPqIiIbw6aefeuwjOhZqamqg0WhgNBqxf//+Ia/du3cvjEYjNBqNx77eRETD4Y4cRBTwjEaj/L6trQ0GgwEajQYAUFVVhQkTJozp99fU1CA9PR0dHR0+f0aj0aC+vh5xcXFjODIiupUEqz0AIiK12e12+b1er0dlZSUmTZqk3oCIiMYAp3eJiIZQU1MDvV4v/63RaLB161ZMmDAB48aNw549e3Dw4EEkJycjJiYGe/bska9tampCYWEhYmJikJycjJ07d/r8vSdOnMC0adNgMpkQFxeHLVu2jOrvIqLAw0ofEZGfjh07hurqahw4cADf/e53kZeXh7Nnz+Lw4cNYunQpHn30UQQFBWHBggWYMmUK6urqcOnSJTzwwAOYOnUqsrOzh/2OFStWYPXq1SgsLERzczNqamrG/ocR0S2NlT4iIj899dRT0Ov1mDNnDlpaWvD9738fBoMBubm5aG1txZUrV9DQ0IAPP/wQzz33HHQ6HdLT01FYWIh9+/b59B0hISH45z//iaamJkRGRmLatGlj/KuI6FbHpI+IyE8xMTEAgKCgIISEhCA6Olo+p9fr0dbWhk8//RRtbW2IiopCREQEIiIi8Pvf/x6ff/65T9/x0ksv4dy5c0hJScHMmTNx/PjxMfktRBQ4OL1LRDQGxo8fj4iICFy7dm1En09LS0NZWRlcLhe2bduG+fPn48KFC6M8SiIKJKz0ERGNgfHjx2PGjBn46U9/ivb2drhcLpw6dQpVVVU+fb60tBTXrl1DcHAwTCYTgoKCxnjERHSrY9JHRDRGSktLUVtbK6/sXbFiBRwOh0+fPXToENLS0mAymfDiiy9i+/btYzxaIrrVsTkzEZHKamtrkZ6eDp1Oh1deeQV5eXmDXrtv3z4sXboUHR0dqK2tRWxsrIIjJaJ/ZUz6iIiIiAIAp3eJiIiIAgCTPiIiIqIAwKSPiIiIKAAw6SMiIiIKAEz6iIiIiAIAkz4iIiKiAMCkj4iIiCgAMOkjIiIiCgBM+oiIiIgCAJM+IiIiogDw/1KCMipYRofeAAAAAElFTkSuQmCC", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "out = ct.step_response(sys).plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iHZR1Q3IcrFT" - }, - "source": [ - "We can analyze the properties of the step response using the `stepinfo` command:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Input 0, output 0 rise time = 0.6153902252990775 seconds\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "[[{'RiseTime': 0.6153902252990775,\n", - " 'SettlingTime': 89.02645259326653,\n", - " 'SettlingMin': -0.13272845655369417,\n", - " 'SettlingMax': 0.9005994876222034,\n", - " 'Overshoot': 170.17984628666102,\n", - " 'Undershoot': 39.81853696610825,\n", - " 'Peak': 0.9005994876222034,\n", - " 'PeakTime': 2.3589958636464634,\n", - " 'SteadyStateValue': 0.33333333333333337}],\n", - " [{'RiseTime': 0.6153902252990775,\n", - " 'SettlingTime': 73.6416969607896,\n", - " 'SettlingMin': 0.2276019820782241,\n", - " 'SettlingMax': 1.13389337710215,\n", - " 'Overshoot': 70.08400656532254,\n", - " 'Undershoot': 0,\n", - " 'Peak': 1.13389337710215,\n", - " 'PeakTime': 6.564162403190159,\n", - " 'SteadyStateValue': 0.6666666666666665}]]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "step_info = ct.step_info(sys)\n", - "print(\"Input 0, output 0 rise time = \",\n", - " step_info[0][0]['RiseTime'], \"seconds\\n\")\n", - "step_info" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "F8KxXwqHWFab" - }, - "source": [ - "Note that by default the inputs are not included in the step response plot (since they are a bit boring), but you can change that:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "stepresp = ct.step_response(sys)\n", - "out = stepresp.plot(plot_inputs=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "out = stepresp.plot(plot_inputs='overlay')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "stepresp.time.shape=(1348,)\n", - "stepresp.inputs.shape=(1, 1, 1348)\n", - "stepresp.states.shape=(4, 1, 1348)\n", - "stepresp.outputs.shape=(2, 1, 1348)\n" - ] - } - ], - "source": [ - "# Look at the \"shape\" of the step response\n", - "print(f\"{stepresp.time.shape=}\")\n", - "print(f\"{stepresp.inputs.shape=}\")\n", - "print(f\"{stepresp.states.shape=}\")\n", - "print(f\"{stepresp.outputs.shape=}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FDfZkyk1ly0T" - }, - "source": [ - "## Forced response\n", - "\n", - "To compute the response to an input, using the convolution equation, we can use the `forced_response` function:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "T = np.linspace(0, 50, 500)\n", - "U1 = np.cos(T)\n", - "U2 = np.sin(3 * T)\n", - "\n", - "resp1 = ct.forced_response(sys, T, U1)\n", - "resp2 = ct.forced_response(sys, T, U2)\n", - "resp3 = ct.forced_response(sys, T, U1 + U2)\n", - "\n", - "# Plot the individual responses\n", - "resp1.sysname = 'U1'; resp1.plot(color='b')\n", - "resp2.sysname = 'U2'; resp2.plot(color='g')\n", - "resp3.sysname = 'U1 + U2'; resp3.plot(color='r');" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Show that the system response is linear\n", - "out = resp3.plot()\n", - "axs = ct.get_plot_axes(out)\n", - "axs[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--')\n", - "axs[1, 0].plot(resp1.time, resp1.outputs[1] + resp2.outputs[1], 'k--')\n", - "axs[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--');" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Show that the forced response from non-zero initial condition is not linear\n", - "X0 = [1, 0, 0, 0]\n", - "resp1 = ct.forced_response(sys, T, U1, X0=X0)\n", - "resp2 = ct.forced_response(sys, T, U2, X0=X0)\n", - "resp3 = ct.forced_response(sys, T, U1 + U2, X0=X0)\n", - "\n", - "out = resp3.plot()\n", - "axs = ct.get_plot_axes(out)\n", - "axs[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--')\n", - "axs[1, 0].plot(resp1.time, resp1.outputs[1] + resp2.outputs[1], 'k--')\n", - "axs[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--');" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mo7hpvPQkKke" - }, - "source": [ - "### Frequency response" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Manual computation of the frequency response\n", - "resp = ct.input_output_response(sys, T, np.sin(1.35 * T))\n", - "\n", - "out = resp.plot(\n", - " plot_inputs='overlay', \n", - " legend_map=np.array([['lower left'], ['lower left']]),\n", - " label=[['q1', 'u[0]'], ['q2', None]])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "muqeLlJJ6s8F" - }, - "source": [ - "The magnitude and phase of the frequency response is controlled by the transfer function,\n", - "\n", - "$$\n", - "G(s) = C (sI - A)^{-1} B + D\n", - "$$\n", - "\n", - "which can be computed using the `ss2tf` function:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - ": u to q1\n", - "Inputs (1): ['u[0]']\n", - "Outputs (2): ['q1', 'q2']\n", - "\n", - "\n", - "Input 1 to output 1:\n", - " 4\n", - "-------------------------------------\n", - "s^4 + 0.2 s^3 + 8.01 s^2 + 0.8 s + 12\n", - "\n", - "Input 1 to output 2:\n", - " 2 s^2 + 0.2 s + 8\n", - "-------------------------------------\n", - "s^4 + 0.2 s^3 + 8.01 s^2 + 0.8 s + 12\n", - "\n" - ] - } - ], - "source": [ - "# Create SISO transfer functions, in case we don't have slycot\n", - "G = ct.ss2tf(sys, name='u to q1')\n", - "print(G)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "G(1.35j)=array([[3.33005647-2.70686327j],\n", - " [3.80831226-2.72231858j]])\n", - "Gain: [[4.29143157]\n", - " [4.681267 ]]\n", - "Phase: [[-0.6825322 ]\n", - " [-0.62061375]] ( [[-39.10621449]\n", - " [-35.55854848]] deg)\n" - ] - } - ], - "source": [ - "# Gain and phase for the simulation above\n", - "from math import pi\n", - "val = G(1.35j)\n", - "print(f\"{G(1.35j)=}\")\n", - "print(f\"Gain: {np.absolute(val)}\")\n", - "print(f\"Phase: {np.angle(val)}\", \" (\", np.angle(val) * 180/pi, \"deg)\")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "G(0)=array([[0.33333333+0.j],\n", - " [0.66666667+0.j]])\n", - "Final value of step response: 0.33297541813724874\n" - ] - } - ], - "source": [ - "# Gain and phase at s = 0 (= steady state step response)\n", - "print(f\"{G(0)=}\")\n", - "print(\"Final value of step response:\", stepresp.outputs[0, 0, -1])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "I9eFoXm92Jgj" - }, - "source": [ - "The frequency response across all frequencies can be computed using the `frequency_response` function:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "freqresp = ct.frequency_response(sys)\n", - "out = freqresp.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pylQb07G2cqe" - }, - "source": [ - "By default, frequency responses are plotted using a \"Bode plot\", which plots the log of the magnitude and the (linear) phase against the log of the forcing frequency.\n", - "\n", - "You can also call the Bode plot command directly, and change the way the data are presented:" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "out = ct.bode_plot(sys, overlay_outputs=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "I_LTjP2J6gqx" - }, - "source": [ - "Note the \"dip\" in the frequency response for y[1] at frequency 2 rad/sec, which corresponds to a \"zero\" of the transfer function.\n", - "\n", - "This dip becomes even more pronounced in the case of low damping coefficient $c$:" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "out = ct.frequency_response(\n", - " coupled.linearize([0, 0, 0, 0], [0], params={'c': 0.01})\n", - ").plot(overlay_outputs=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "c7eWm8LCGh01" - }, - "source": [ - "## Additional resources\n", - "* [Code for FBS2e figures](https://fbswiki.org/wiki/index.php/Category:Figures): Python code used to generate figures in FBS2e\n", - "* [Python-control documentation for plotting time responses](https://python-control.readthedocs.io/en/0.10.0/plotting.html#time-response-data)\n", - "* [Python-control documentation for plotting frequency responses](https://python-control.readthedocs.io/en/0.10.0/plotting.html#frequency-response-data)\n", - "* [Python-control examples](https://python-control.readthedocs.io/en/0.10.0/examples.html): lots of Python and Jupyter examples of control system analysis and design\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.2" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/cds112-L1_python-control.ipynb b/examples/cds112-L1_python-control.ipynb new file mode 100644 index 000000000..140f32074 --- /dev/null +++ b/examples/cds112-L1_python-control.ipynb @@ -0,0 +1,444 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "numerous-rochester", + "metadata": {}, + "source": [ + "# Introduction to the Python Control Systems Library (python-control)\n", + "\n", + "## Input/Output Systems" + ] + }, + { + "cell_type": "markdown", + "id": "69bdd3af", + "metadata": {}, + "source": [ + "Richard M. Murray, 13 Nov 2021 (updated 7 Jul 2024)\n", + "\n", + "This notebook contains an introduction to the basic operations in the Python Control Systems Library (python-control), a Python package for control system design. This notebook is focused on state space control design for a kinematic car, including trajectory generation and gain-scheduled feedback control. This illustrates the use of the input/output (I/O) system class, which can be used to construct models for nonlinear control systems." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "macro-vietnamese", + "metadata": {}, + "outputs": [], + "source": [ + "# Import the packages needed for the examples included in this notebook\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import control as ct\n", + "print(\"python-control version:\", ct.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "distinct-communist", + "metadata": {}, + "source": [ + "### Installation hints\n", + "\n", + "If you get an error importing the `control` package, it may be that it is not in your current Python path. You can fix this by setting the PYTHONPATH environment variable to include the directory where the python-control package is located. If you are invoking Jupyter from the command line, try using a command of the form\n", + "\n", + " PYTHONPATH=/path/to/control jupyter notebook\n", + " \n", + "If you are using [Google Colab](https://colab.research.google.com), use the following command at the top of the notebook to install the `control` package:\n", + "\n", + " !pip install control\n", + " \n", + "For the examples below, you will need version 0.10.0 or higher of the python-control toolbox. You can find the version number using the command\n", + "\n", + " print(ct.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "5dad04d8", + "metadata": {}, + "source": [ + "### More information on Python, NumPy, python-control\n", + "\n", + "* [Python tutorial](https://docs.python.org/3/tutorial/)\n", + "* [NumPy tutorial](https://numpy.org/doc/stable/user/quickstart.html)\n", + "* [NumPy for MATLAB users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html), \n", + "* [Python Control Systems Library (python-control) documentation](https://python-control.readthedocs.io/en/latest/)" + ] + }, + { + "cell_type": "markdown", + "id": "novel-geology", + "metadata": {}, + "source": [ + "## System Definiton\n", + "\n", + "We now define the dynamics of the system that we are going to use for the control design. The dynamics of the system will be of the form\n", + "\n", + "$$\n", + "\\dot x = f(x, u), \\qquad y = h(x, u)\n", + "$$\n", + "\n", + "where $x$ is the state vector for the system, $u$ represents the vector of inputs, and $y$ represents the vector of outputs.\n", + "\n", + "The python-control package allows definition of input/output systems using the `InputOutputSystem` class and its various subclasess, including the `NonlinearIOSystem` class that we use here. A `NonlinearIOSystem` object is created by defining the update law ($f(x, u)$) and the output map ($h(x, u)$), and then calling the factory function `ct.nlsys`.\n", + "\n", + "For the example in this notebook, we will be controlling the steering of a vehicle, using a \"bicycle\" model for the dynamics of the vehicle. A more complete description of the dynamics of this system are available in [Example 3.11](https://fbswiki.org/wiki/index.php/System_Modeling) of [_Feedback Systems_](https://fbswiki.org/wiki/index.php/FBS) by Astrom and Murray (2020)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "sufficient-douglas", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the update rule for the system, f(x, u)\n", + "# States: x, y, theta (postion and angle of the center of mass)\n", + "# Inputs: v (forward velocity), delta (steering angle)\n", + "def vehicle_update(t, x, u, params):\n", + " # Get the parameters for the model\n", + " a = params.get('refoffset', 1.5) # offset to vehicle reference point\n", + " b = params.get('wheelbase', 3.) # vehicle wheelbase\n", + " maxsteer = params.get('maxsteer', 0.5) # max steering angle (rad)\n", + "\n", + " # Saturate the steering input\n", + " delta = np.clip(u[1], -maxsteer, maxsteer)\n", + " alpha = np.arctan2(a * np.tan(delta), b)\n", + "\n", + " # Return the derivative of the state\n", + " return np.array([\n", + " u[0] * np.cos(x[2] + alpha), # xdot = cos(theta + alpha) v\n", + " u[0] * np.sin(x[2] + alpha), # ydot = sin(theta + alpha) v\n", + " (u[0] / a) * np.sin(alpha) # thdot = v sin(alpha) / a\n", + " ])\n", + "\n", + "# Define the readout map for the system, h(x, u)\n", + "# Outputs: x, y (planar position of the center of mass)\n", + "def vehicle_output(t, x, u, params):\n", + " return x\n", + "\n", + "# Default vehicle parameters (including nominal velocity)\n", + "vehicle_params={'refoffset': 1.5, 'wheelbase': 3, 'velocity': 15, \n", + " 'maxsteer': 0.5}\n", + "\n", + "# Define the vehicle steering dynamics as an input/output system\n", + "vehicle = ct.nlsys(\n", + " vehicle_update, vehicle_output, states=3, name='vehicle',\n", + " inputs=['v', 'delta'], outputs=['x', 'y', 'theta'], params=vehicle_params)" + ] + }, + { + "cell_type": "markdown", + "id": "intellectual-democrat", + "metadata": {}, + "source": [ + "## Open loop simulation\n", + "\n", + "After these operations, the `vehicle` object references the nonlinear model for the system. This system can be simulated to compute a trajectory for the system. Here we command a velocity of 10 m/s and turn the wheel back and forth at one Hertz." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "likely-hindu", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the time interval that we want to use for the simualation\n", + "timepts = np.linspace(0, 10, 1000)\n", + "\n", + "# Define the inputs\n", + "U = [\n", + " 10 * np.ones_like(timepts), # velocity\n", + " 0.1 * np.sin(timepts * 2*np.pi) # steering angle\n", + "]\n", + "\n", + "# Simulate the system dynamics, starting from the origin\n", + "response = ct.input_output_response(vehicle, timepts, U, 0)\n", + "time, outputs, inputs = response.time, response.outputs, response.inputs" + ] + }, + { + "cell_type": "markdown", + "id": "dutch-charm", + "metadata": {}, + "source": [ + "We can plot the results using standard `matplotlib` commands:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "piano-algeria", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a figure to plot the results\n", + "fig, ax = plt.subplots(2, 1)\n", + "\n", + "# Plot the results in the xy plane\n", + "ax[0].plot(outputs[0], outputs[1])\n", + "ax[0].set_xlabel(\"$x$ [m]\")\n", + "ax[0].set_ylabel(\"$y$ [m]\")\n", + "\n", + "# Plot the inputs\n", + "ax[1].plot(timepts, U[0])\n", + "ax[1].set_ylim(0, 12)\n", + "ax[1].set_xlabel(\"Time $t$ [s]\")\n", + "ax[1].set_ylabel(\"Velocity $v$ [m/s]\")\n", + "ax[1].yaxis.label.set_color('blue')\n", + "\n", + "rightax = ax[1].twinx() # Create an axis in the right\n", + "rightax.plot(timepts, U[1], color='red')\n", + "rightax.set_ylim(None, 0.5)\n", + "rightax.set_ylabel(r\"Steering angle $\\phi$ [rad]\")\n", + "rightax.yaxis.label.set_color('red')\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "alone-worry", + "metadata": {}, + "source": [ + "Notice that there is a small drift in the $y$ position despite the fact that the steering wheel is moved back and forth symmetrically around zero. Exercise: explain what might be happening." + ] + }, + { + "cell_type": "markdown", + "id": "portable-rubber", + "metadata": {}, + "source": [ + "## Linearize the system around a trajectory\n", + "\n", + "We choose a straight path along the $x$ axis at a speed of 10 m/s as our desired trajectory and then linearize the dynamics around the initial point in that trajectory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "surprising-algorithm", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the desired trajectory \n", + "Ud = np.array([10 * np.ones_like(timepts), np.zeros_like(timepts)])\n", + "Xd = np.array([10 * timepts, 0 * timepts, np.zeros_like(timepts)])\n", + "\n", + "# Now linizearize the system around this trajectory\n", + "linsys = vehicle.linearize(Xd[:, 0], Ud[:, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "protecting-committee", + "metadata": {}, + "outputs": [], + "source": [ + "# Check on the eigenvalues of the open loop system\n", + "np.linalg.eigvals(linsys.A)" + ] + }, + { + "cell_type": "markdown", + "id": "trying-stereo", + "metadata": {}, + "source": [ + "We see that all eigenvalues are zero, corresponding to a single integrator in the $x$ (longitudinal) direction and a double integrator in the $y$ (lateral) direction." + ] + }, + { + "cell_type": "markdown", + "id": "pressed-delta", + "metadata": {}, + "source": [ + "## Compute a state space (LQR) control law\n", + "\n", + "We can now compute a feedback controller around the trajectory. We choose a simple LQR controller here, but any method can be used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "auburn-caribbean", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute LQR controller\n", + "K, S, E = ct.lqr(linsys, np.diag([1, 1, 1]), np.diag([1, 1]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "independent-lafayette", + "metadata": {}, + "outputs": [], + "source": [ + "# Check on the eigenvalues of the closed loop system\n", + "np.linalg.eigvals(linsys.A - linsys.B @ K)" + ] + }, + { + "cell_type": "markdown", + "id": "handmade-moral", + "metadata": {}, + "source": [ + "The closed loop eigenvalues have negative real part, so the closed loop (linear) system will be stable about the operating trajectory." + ] + }, + { + "cell_type": "markdown", + "id": "handy-virgin", + "metadata": {}, + "source": [ + "## Create a controller with feedforward and feedback\n", + "\n", + "We now create an I/O system representing the control law. The controller takes as an input the desired state space trajectory $x_\\text{d}$ and the nominal input $u_\\text{d}$. It outputs the control law\n", + "\n", + "$$\n", + "u = u_\\text{d} - K(x - x_\\text{d}).\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "negative-scope", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the output rule for the controller\n", + "# States: none (=> no update rule required)\n", + "# Inputs: z = [xd, ud, x]\n", + "# Outputs: v (forward velocity), delta (steering angle)\n", + "def control_output(t, x, z, params):\n", + " # Get the parameters for the model\n", + " K = params.get('K', np.zeros((2, 3))) # nominal gain\n", + " \n", + " # Split up the input to the controller into the desired state and nominal input\n", + " xd_vec = z[0:3] # desired state ('xd', 'yd', 'thetad')\n", + " ud_vec = z[3:5] # nominal input ('vd', 'deltad')\n", + " x_vec = z[5:8] # current state ('x', 'y', 'theta')\n", + " \n", + " # Compute the control law\n", + " return ud_vec - K @ (x_vec - xd_vec)\n", + "\n", + "# Define the controller system\n", + "control = ct.nlsys(\n", + " None, control_output, name='control',\n", + " inputs=['xd', 'yd', 'thetad', 'vd', 'deltad', 'x', 'y', 'theta'], \n", + " outputs=['v', 'delta'], params={'K': K})" + ] + }, + { + "cell_type": "markdown", + "id": "affected-motor", + "metadata": {}, + "source": [ + "Because we have named the signals in both the vehicle model and the controller in a compatible way, we can use the autoconnect feature of the `interconnect()` function to create the closed loop system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "stock-regression", + "metadata": {}, + "outputs": [], + "source": [ + "# Build the closed loop system\n", + "vehicle_closed = ct.interconnect(\n", + " (vehicle, control),\n", + " inputs=['xd', 'yd', 'thetad', 'vd', 'deltad'],\n", + " outputs=['x', 'y', 'theta']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "hispanic-monroe", + "metadata": {}, + "source": [ + "## Closed loop simulation\n", + "\n", + "We now command the system to follow in trajectory and use the linear controller to correct for any errors. \n", + "\n", + "The desired trajectory is a given by a longitudinal position that tracks a velocity of 10 m/s for the first 5 seconds and then increases to 12 m/s and a lateral position that varies sinusoidally by $\\pm 0.5$ m around the centerline. The nominal inputs are not modified, so that feedback is required to obtained proper trajectory tracking." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "american-return", + "metadata": {}, + "outputs": [], + "source": [ + "Xd = np.array([\n", + " 10 * timepts + 2 * (timepts-5) * (timepts > 5), \n", + " 0.5 * np.sin(timepts * 2*np.pi), \n", + " np.zeros_like(timepts)\n", + "])\n", + "\n", + "Ud = np.array([10 * np.ones_like(timepts), np.zeros_like(timepts)])\n", + "\n", + "# Simulate the system dynamics, starting from the origin\n", + "resp = ct.input_output_response(\n", + " vehicle_closed, timepts, np.vstack((Xd, Ud)), 0)\n", + "time, outputs = resp.time, resp.outputs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "indirect-longitude", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the results in the xy plane\n", + "plt.plot(Xd[0], Xd[1], 'b--') # desired trajectory\n", + "plt.plot(outputs[0], outputs[1]) # actual trajectory\n", + "plt.xlabel(\"$x$ [m]\")\n", + "plt.ylabel(\"$y$ [m]\")\n", + "plt.ylim(-1, 2)\n", + "\n", + "# Add a legend\n", + "plt.legend(['desired', 'actual'], loc='upper left')\n", + "\n", + "# Compute and plot the velocity\n", + "rightax = plt.twinx() # Create an axis in the right\n", + "rightax.plot(Xd[0, :-1], np.diff(Xd[0]) / np.diff(timepts), 'r--')\n", + "rightax.plot(outputs[0, :-1], np.diff(outputs[0]) / np.diff(timepts), 'r-')\n", + "rightax.set_ylim(0, 13)\n", + "rightax.set_ylabel(\"$x$ velocity [m/s]\")\n", + "rightax.yaxis.label.set_color('red')" + ] + }, + { + "cell_type": "markdown", + "id": "weighted-directory", + "metadata": {}, + "source": [ + "We see that there is a small error in each axis. By adjusting the weights in the LQR controller we can adjust the steady state error (try it!)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f31dd981-161a-49f0-a637-84128f7ec5ff", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds112-L2a_flatness.ipynb b/examples/cds112-L2a_flatness.ipynb new file mode 100644 index 000000000..2b7cfb3a4 --- /dev/null +++ b/examples/cds112-L2a_flatness.ipynb @@ -0,0 +1,490 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "meaning-hypothetical", + "metadata": {}, + "source": [ + "## Differential Flatness\n", + "\n", + "##### Richard M. Murray, 13 Nov 2021 (updated 7 Jul 2024)\n", + "\n", + "This notebook contains an example of using differential flatness as a mechanism for trajectory generation for a nonlinear control system. A differentially flat system is defined by creating an object using the `FlatSystem` class, which has member functions for mapping the system state and input into and out of flat coordinates. The `point_to_point()` function can be used to create a trajectory between two endpoints, written in terms of a set of basis functions defined using the `BasisFamily` class. The resulting trajectory is return as a `SystemTrajectory` object and can be evaluated using the `eval()` member function. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "historic-barbados", + "metadata": {}, + "outputs": [], + "source": [ + "# Import the packages needed for the examples included in this notebook\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import control as ct\n", + "import control.flatsys as fs\n", + "import control.optimal as opt\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "id": "309d3272", + "metadata": {}, + "source": [ + "## Example: bicycle model\n", + "\n", + "To illustrate the methods of generating trajectories using differential flatness, we make use of a simple model for a vehicle navigating in the plane, known as the \"bicycle model\". The kinematics of this vehicle can be written in terms of the contact point $(x, y)$ and the angle $\\theta$ of the vehicle with respect to the horizontal axis:\n", + "\n", + "
\n", + "\n", + "\n", + "\n", + "
\n", + "\n", + " \n", + " \n", + "\n", + "
\n", + "$$\n", + "\\begin{aligned}\n", + " \\dot x &= \\cos\\theta\\, v \\\\\n", + " \\dot y &= \\sin\\theta\\, v \\\\\n", + " \\dot\\theta &= \\frac{v}{l} \\tan \\delta\n", + "\\end{aligned}\n", + "$$\n", + "
\n", + "\n", + "The input $v$ represents the velocity of the vehicle and the input $\\delta$ represents the turning rate. The parameter $l$ is the wheelbase." + ] + }, + { + "cell_type": "markdown", + "id": "35efac80", + "metadata": {}, + "source": [ + "We will generate trajectories for this system that correspond to a \"lane change\", in which we travel longitudinally at a fixed speed for approximately 40 meters, while moving from the right to the left by a distance of 4 meters.\n", + "\n", + "It will be convenient to define a function that we will use to plot the results in a uniform way. In addition to the subplot, we also change the size of the figure to make the figure wider." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "involved-riding", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the trajectory in xy coordinates\n", + "def plot_motion(t, x, ud):\n", + " # Set the size of the figure\n", + " # plt.figure(figsize=(10, 6))\n", + "\n", + " # Top plot: xy trajectory\n", + " plt.subplot(2, 1, 1)\n", + " plt.plot(x[0], x[1])\n", + " plt.xlabel('x [m]')\n", + " plt.ylabel('y [m]')\n", + " plt.axis([x0[0], xf[0], x0[1]-1, xf[1]+1])\n", + "\n", + " # Time traces of the state and input\n", + " plt.subplot(2, 4, 5)\n", + " plt.plot(t, x[1])\n", + " plt.ylabel('y [m]')\n", + "\n", + " plt.subplot(2, 4, 6)\n", + " plt.plot(t, x[2])\n", + " plt.ylabel('theta [rad]')\n", + "\n", + " plt.subplot(2, 4, 7)\n", + " plt.plot(t, ud[0])\n", + " plt.xlabel(\"Time t [sec]\")\n", + " plt.ylabel(\"v [m/s]\")\n", + " plt.axis([0, Tf, u0[0] - 1, uf[0] + 1])\n", + "\n", + " plt.subplot(2, 4, 8)\n", + " plt.plot(t, ud[1])\n", + " plt.xlabel(\"Time t [sec]\")\n", + " plt.ylabel(r\"$\\delta$ [rad]\")\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "3dc0d2bf", + "metadata": {}, + "source": [ + "## Flat system mappings\n", + "\n", + "To define a flat system, we have to define the functions that take the state and compute the flat \"flag\" (flat outputs and their derivatives) and that take the flat flag and return the state and input.\n", + "\n", + "The `forward()` method computes the flat flag given a state and input:\n", + "```\n", + " zflag = sys.forward(x, u)\n", + "```\n", + "The `reverse()` method computes the state and input given the flat flag:\n", + "```\n", + " x, u = sys.reverse(zflag)\n", + "```\n", + "The flag $\\bar z$ is implemented as a list of flat outputs $z_i$ and\n", + "their derivatives up to order $q_i$:\n", + "\n", + "         `zflag[i][j]` = $z_i^{(j)}$\n", + "\n", + "The number of flat outputs must match the number of system inputs.\n", + "\n", + "In addition, a flat system is an input/output system and so we define and update function ($f(x, u)$) and output (use `None` to get the full state)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "above-venezuela", + "metadata": {}, + "outputs": [], + "source": [ + "# Function to take states, inputs and return the flat flag\n", + "def bicycle_flat_forward(x, u, params={}):\n", + " # Get the parameter values\n", + " b = params.get('wheelbase', 3.)\n", + "\n", + " # Create a list of arrays to store the flat output and its derivatives\n", + " zflag = [np.zeros(3), np.zeros(3)]\n", + "\n", + " # Flat output is the x, y position of the rear wheels\n", + " zflag[0][0] = x[0]\n", + " zflag[1][0] = x[1]\n", + "\n", + " # First derivatives of the flat output\n", + " zflag[0][1] = u[0] * np.cos(x[2]) # dx/dt\n", + " zflag[1][1] = u[0] * np.sin(x[2]) # dy/dt\n", + "\n", + " # First derivative of the angle\n", + " thdot = (u[0]/b) * np.tan(u[1])\n", + "\n", + " # Second derivatives of the flat output (setting vdot = 0)\n", + " zflag[0][2] = -u[0] * thdot * np.sin(x[2])\n", + " zflag[1][2] = u[0] * thdot * np.cos(x[2])\n", + "\n", + " return zflag\n", + "\n", + "# Function to take the flat flag and return states, inputs\n", + "def bicycle_flat_reverse(zflag, params={}):\n", + " # Get the parameter values\n", + " b = params.get('wheelbase', 3.)\n", + "\n", + " # Create a vector to store the state and inputs\n", + " x = np.zeros(3)\n", + " u = np.zeros(2)\n", + "\n", + " # Given the flat variables, solve for the state\n", + " x[0] = zflag[0][0] # x position\n", + " x[1] = zflag[1][0] # y position\n", + " x[2] = np.arctan2(zflag[1][1], zflag[0][1]) # tan(theta) = ydot/xdot\n", + "\n", + " # And next solve for the inputs\n", + " u[0] = zflag[0][1] * np.cos(x[2]) + zflag[1][1] * np.sin(x[2])\n", + " thdot_v = zflag[1][2] * np.cos(x[2]) - zflag[0][2] * np.sin(x[2])\n", + " u[1] = np.arctan2(thdot_v, u[0]**2 / b)\n", + "\n", + " return x, u\n", + "\n", + "# Function to compute the RHS of the system dynamics\n", + "def bicycle_update(t, x, u, params):\n", + " b = params.get('wheelbase', 3.) # get parameter values\n", + " dx = np.array([\n", + " np.cos(x[2]) * u[0],\n", + " np.sin(x[2]) * u[0],\n", + " (u[0]/b) * np.tan(u[1])\n", + " ])\n", + " return dx\n", + "\n", + "# Return the entire state as output (instead of default flat outputs)\n", + "def bicycle_output(t, x, u, params):\n", + " return x\n", + "\n", + "# Create differentially flat input/output system\n", + "bicycle_flat = fs.FlatSystem(\n", + " bicycle_flat_forward, bicycle_flat_reverse, \n", + " bicycle_update, bicycle_output,\n", + " inputs=('v', 'delta'), outputs=('x', 'y', 'theta'),\n", + " states=('x', 'y', 'theta'), name='bicycle_model')\n", + "\n", + "print(bicycle_flat)" + ] + }, + { + "cell_type": "markdown", + "id": "75cb8cf6", + "metadata": {}, + "source": [ + "## Point to point trajectory generation\n", + "\n", + "In addition to the flat system description, a set of basis functions\n", + "$\\phi_i(t)$ must be chosen. The `BasisFamily` class is used to\n", + "represent the basis functions. A polynomial basis function of the form\n", + "$1$, $t$, $t^2$, $\\ldots$ can be computed using the `PolyFamily` class,\n", + "which is initialized by passing the desired order of the polynomial\n", + "basis set:\n", + "```\n", + "polybasis = control.flatsys.PolyFamily(N)\n", + "```\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "feef608a", + "metadata": {}, + "outputs": [], + "source": [ + "print(fs.BasisFamily.__doc__)\n", + "print(fs.PolyFamily.__doc__)\n", + "\n", + "# Define a set of basis functions to use for the trajectories\n", + "poly = fs.PolyFamily(6)\n", + "\n", + "# Plot out the basis functions\n", + "t = np.linspace(0, 1.5)\n", + "for k in range(poly.N):\n", + " plt.plot(t, poly(k, t), label=f'k = {k}')\n", + " \n", + "plt.legend()\n", + "plt.title(\"Polynomial basis functions\")\n", + "plt.xlabel(\"Time $t$\")\n", + "plt.ylabel(r\"$\\psi_i(t)$\");" + ] + }, + { + "cell_type": "markdown", + "id": "7aacca93", + "metadata": {}, + "source": [ + "### Approach 1: point to point solution, no cost or constraints\n", + "\n", + "Once the system and basis function have been defined, the\n", + "`point_to_point()` function can be used to compute a trajectory\n", + "between initial and final states and inputs:\n", + "```\n", + "traj = control.flatsys.point_to_point(sys, Tf, x0, u0, xf, uf, basis=polybasis)\n", + "```\n", + "The returned object has class `SystemTrajectory` and can be used\n", + "to compute the state and input trajectory between the initial and final\n", + "condition:\n", + "```\n", + "xd, ud = traj.eval(timepts)\n", + "```\n", + "where `timepts` is a list of times on which the trajectory should be\n", + "evaluated (e.g., `timepts = numpy.linspace(0, Tf, M)`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "surface-piano", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the endpoints of the trajectory\n", + "x0 = np.array([0., -2., 0.]); u0 = np.array([10., 0.])\n", + "xf = np.array([40., 2., 0.]); uf = np.array([10., 0.])\n", + "Tf = 4\n", + "\n", + "# Generate a normalized set of basis functions\n", + "poly = fs.PolyFamily(6, Tf)\n", + "\n", + "# Find a trajectory between the initial condition and the final condition\n", + "traj = fs.point_to_point(bicycle_flat, Tf, x0, u0, xf, uf, basis=poly)\n", + "\n", + "# Create the desired trajectory between the initial and final condition\n", + "timepts = np.linspace(0, Tf, 500)\n", + "xd, ud = traj.eval(timepts)\n", + "\n", + "# Simulation the open system dynamics with the full input\n", + "t, y, x = ct.input_output_response(\n", + " bicycle_flat, timepts, ud, x0, return_x=True)\n", + "\n", + "# Plot the open loop system dynamics\n", + "plt.figure(1)\n", + "plt.suptitle(\"Open loop trajectory for unicycle lane change\")\n", + "plot_motion(t, x, ud)\n", + "\n", + "# Make sure the initial and final points are correct\n", + "print(\"x[0] = \", xd[:, 0])\n", + "print(\"x[T] = \", xd[:, -1])" + ] + }, + { + "cell_type": "markdown", + "id": "82a3318a", + "metadata": {}, + "source": [ + "### A look inside the code\n", + "\n", + "The code to solve this problem is inside the file [flatsys.py](https://github.com/python-control/python-control/blob/main/control/flatsys/flatsys.py) in the python-control package. Here is what operative code inside the `point_to_point()` looks like:\n", + "\n", + " #\n", + " # Map the initial and final conditions to flat output conditions\n", + " #\n", + " # We need to compute the output \"flag\": [z(t), z'(t), z''(t), ...]\n", + " # and then evaluate this at the initial and final condition.\n", + " #\n", + "\n", + " zflag_T0 = sys.forward(x0, u0)\n", + " zflag_Tf = sys.forward(xf, uf)\n", + "\n", + " #\n", + " # Compute the matrix constraints for initial and final conditions\n", + " #\n", + " # This computation depends on the basis function we are using. It\n", + " # essentially amounts to evaluating the basis functions and their\n", + " # derivatives at the initial and final conditions.\n", + "\n", + " # Compute the flags for the initial and final states\n", + " M_T0 = _basis_flag_matrix(sys, basis, zflag_T0, T0)\n", + " M_Tf = _basis_flag_matrix(sys, basis, zflag_Tf, Tf)\n", + "\n", + " # Stack the initial and final matrix/flag for the point to point problem\n", + " M = np.vstack([M_T0, M_Tf])\n", + " Z = np.hstack([np.hstack(zflag_T0), np.hstack(zflag_Tf)])\n", + "\n", + " #\n", + " # Solve for the coefficients of the flat outputs\n", + " #\n", + " # At this point, we need to solve the equation M alpha = zflag, where M\n", + " # is the matrix constrains for initial and final conditions and zflag =\n", + " # [zflag_T0; zflag_tf].\n", + " #\n", + " # If there are no constraints, then we just need to solve a linear\n", + " # system of equations => use least squares. Otherwise, we have a\n", + " # nonlinear optimal control problem with equality constraints => use\n", + " # scipy.optimize.minimize().\n", + " #\n", + "\n", + " # Start by solving the least squares problem\n", + " alpha, residuals, rank, s = np.linalg.lstsq(M, Z, rcond=None)" + ] + }, + { + "cell_type": "markdown", + "id": "f0397b3e", + "metadata": {}, + "source": [ + "### Approach #2: add cost function to make lane change quicker" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "appreciated-baghdad", + "metadata": {}, + "outputs": [], + "source": [ + "# Define timepoints for evaluation plus basis function to use\n", + "timepts = np.linspace(0, Tf, 20)\n", + "basis = fs.PolyFamily(12, Tf)\n", + "\n", + "# Define the cost function (penalize lateral error and steering)\n", + "traj_cost = opt.quadratic_cost(\n", + " bicycle_flat, np.diag([0, 0.1, 0]), np.diag([0.1, 1]), x0=xf, u0=uf)\n", + "\n", + "# Solve for an optimal solution\n", + "start_time = time.process_time()\n", + "traj = fs.point_to_point(\n", + " bicycle_flat, timepts, x0, u0, xf, uf, cost=traj_cost, basis=basis,\n", + ")\n", + "print(\"* Total time = %5g seconds\\n\" % (time.process_time() - start_time))\n", + "\n", + "xd, ud = traj.eval(timepts)\n", + "\n", + "plt.figure(2)\n", + "plt.suptitle(\"Lane change with lateral error + steering penalties\")\n", + "plot_motion(timepts, xd, ud);" + ] + }, + { + "cell_type": "markdown", + "id": "ff7363ca", + "metadata": {}, + "source": [ + "Note that the solution has a very large steering angle (0.2 rad = ~12 degrees)." + ] + }, + { + "cell_type": "markdown", + "id": "3c533abe", + "metadata": {}, + "source": [ + "### Approach #3: optimal cost with trajectory constraints\n", + "\n", + "To get a smaller steering angle, we add constraints on the inputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "stable-network", + "metadata": {}, + "outputs": [], + "source": [ + "constraints = [\n", + " opt.input_range_constraint(bicycle_flat, [8, -0.1], [12, 0.1]) ]\n", + "\n", + "# Solve for an optimal solution\n", + "traj = fs.point_to_point(\n", + " bicycle_flat, timepts, x0, u0, xf, uf, cost=traj_cost,\n", + " trajectory_constraints=constraints, basis=basis,\n", + ")\n", + "xd, ud = traj.eval(timepts)\n", + "\n", + "plt.figure(3)\n", + "plt.suptitle(\"Lane change with penalty + steering constraints\")\n", + "plot_motion(timepts, xd, ud)" + ] + }, + { + "cell_type": "markdown", + "id": "677750b0", + "metadata": {}, + "source": [ + "## Ideas to explore\n", + "* Change the number of basis functions\n", + "* Change the number of time points\n", + "* Change the type of basis functions: BezierFamily" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1622bccd", + "metadata": {}, + "outputs": [], + "source": [ + "# Define a set of basis functions to use for the trajectories\n", + "poly = fs.BezierFamily(6, 2)\n", + "\n", + "# Plot out the basis functions\n", + "t = np.linspace(0, 2)\n", + "for k in range(poly.N):\n", + " plt.plot(t, poly(k, t), label=f'k = {k}')\n", + " \n", + "plt.legend()\n", + "plt.title(\"Bezier basis functions\")\n", + "plt.xlabel(\"Time $t$\")\n", + "plt.ylabel(r\"$\\psi_i(t)$\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc566fb2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds112-L2b_gainsched.ipynb b/examples/cds112-L2b_gainsched.ipynb new file mode 100644 index 000000000..d915f9e3d --- /dev/null +++ b/examples/cds112-L2b_gainsched.ipynb @@ -0,0 +1,408 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "exempt-legislation", + "metadata": {}, + "source": [ + "# Gain Scheduling\n", + "\n", + "##### Richard M. Murray, 19 Nov 2021 (updated 7 Jul 2024)\n", + "\n", + "This notebook contains an example of using gain scheduling for feedback control of a nonlinear system. A gain scheduled controller has feedback gains that depend on a set of measured parameters in the system. For exampe:\n", + "\n", + "$$\n", + " u = u_\\text{d} − K(x_\\text{d}, u_\\text{d}) (x − x_\\text{d}),\n", + "$$\n", + "\n", + "where $K(x_\\text{d}, u_\\text{d})$ depends on the desired system state and input.\n", + "\n", + "In this notebook, we work through the gain scheduled controller in Example 2.1 of OBC." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "corresponding-convenience", + "metadata": {}, + "outputs": [], + "source": [ + "# Import the packages needed for the examples included in this notebook\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from cmath import sqrt\n", + "import control as ct" + ] + }, + { + "cell_type": "markdown", + "id": "corporate-sense", + "metadata": {}, + "source": [ + "## Vehicle Steering Dynamics\n", + "\n", + "The vehicle dynamics are given by a simple bicycle model:\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
\n", + "$$\n", + "\\begin{aligned}\n", + " \\dot x &= \\cos\\theta\\, v \\\\\n", + " \\dot y &= \\sin\\theta\\, v \\\\\n", + " \\dot\\theta &= \\frac{v}{l} \\tan \\delta\n", + "\\end{aligned}\n", + "$$\n", + "
\n", + "\n", + "We take the state of the system as $(x, y, \\theta)$ where $(x, y)$ is the position of the vehicle in the plane and $\\theta$ is the angle of the vehicle with respect to horizontal. The vehicle input is given by $(v, \\delta)$ where $v$ is the forward velocity of the vehicle and $\\delta$ is the angle of the steering wheel. The model includes saturation of the vehicle steering angle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "naval-pizza", + "metadata": {}, + "outputs": [], + "source": [ + "# Bicycle model dynamics\n", + "#\n", + "# System state: x, y, theta\n", + "# System input: v, delta\n", + "# System output: x, y\n", + "# System parameters: wheelbase, maxsteer\n", + "#\n", + "def bicycle_update(t, x, u, params):\n", + " # Get the parameters for the model\n", + " l = params.get('wheelbase', 3.) # vehicle wheelbase\n", + " deltamax = params.get('maxsteer', 0.5) # max steering angle (rad)\n", + "\n", + " # Saturate the steering input\n", + " delta = np.clip(u[1], -deltamax, deltamax)\n", + "\n", + " # Return the derivative of the state\n", + " return np.array([\n", + " np.cos(x[2]) * u[0], # xdot = cos(theta) v\n", + " np.sin(x[2]) * u[0], # ydot = sin(theta) v\n", + " (u[0] / l) * np.tan(delta) # thdot = v/l tan(delta)\n", + " ])\n", + "\n", + "def bicycle_output(t, x, u, params):\n", + " return x # return x, y, theta (full state)\n", + "\n", + "# Define the vehicle steering dynamics as an input/output system\n", + "bicycle = ct.nlsys(\n", + " bicycle_update, bicycle_output, states=3, name='bicycle',\n", + " inputs=('v', 'delta'),\n", + " outputs=('x', 'y', 'theta'))" + ] + }, + { + "cell_type": "markdown", + "id": "3cc26675", + "metadata": {}, + "source": [ + "## Gain scheduled controller\n", + "\n", + "For this system we use a simple schedule on the forward vehicle velocity and\n", + "place the poles of the system at fixed values. The controller takes the\n", + "current and desired vehicle position and orientation plus the velocity\n", + "velocity as inputs, and returns the velocity and steering commands.\n", + "\n", + "Linearizing the system about the desired trajectory, we obtain\n", + "\n", + "$$\n", + " \\begin{aligned}\n", + " A(x_\\text{d}) &= \\left. \\frac{\\partial f}{\\partial x} \\right|_{(x_\\text{d}, u_\\text{d})}\n", + " = \\left.\n", + " \\begin{bmatrix}\n", + " 0 & 0 & -\\sin\\theta_\\text{d}\\, v_\\text{d} \\\\ 0 & 0 & \\cos\\theta_\\text{d}\\, v_\\text{d} \\\\ 0 & 0 & 0\n", + " \\end{bmatrix}\n", + " \\right|_{(x_\\text{d}, u_\\text{d})}\n", + " = \\begin{bmatrix}\n", + " 0 & 0 & 0 \\\\ 0 & 0 & v_\\text{d} \\\\ 0 & 0 & 0\n", + " \\end{bmatrix}, \\\\\n", + " B(x_\\text{d}) &= \\left. \\frac{\\partial f}{\\partial u} \\right|_{(x_\\text{d}, u_\\text{d})}\n", + " = \\begin{bmatrix}\n", + " 1 & 0 \\\\ 0 & 0 \\\\ 0 & v_\\text{d}/l\n", + " \\end{bmatrix}.\n", + " \\end{aligned}\n", + "$$\n", + "\n", + "We form the error dynamics by setting $e = x - x_\\text{d}$ and $w = u -\n", + "u_\\text{d}$:\n", + "$$\n", + " \\dot e_x = w_1, \\qquad \\dot e_y = e_\\theta, \\qquad \\dot e_\\theta =\n", + " \\frac{v_\\text{d}}{l} w_2.\n", + "$$\n", + "We see that the first state is decoupled from the second two states\n", + "and hence we can design a controller by treating these two subsystems\n", + "separately. \n", + "\n", + "Suppose that we wish to place the closed loop eigenvalues\n", + "of the longitudinal dynamics ($e_x$) at $-\\lambda_1$ and place the\n", + "closed loop eigenvalues of the lateral dynamics ($e_y$, $e_\\theta$) at\n", + "the roots of the polynomial equation $s^2 + a_1 s + a_2 = 0$.\n", + "\n", + "This can accomplished by setting\n", + "\n", + "$$\n", + " \\begin{aligned}\n", + " w_1 &= -\\lambda_1 e_x \\\\\n", + " w_2 &= -\\frac{l}{v_\\text{r}}(\\frac{a_2}{v_\\text{r}} e_y + a_1 e_\\theta).\n", + " \\end{aligned}\n", + "$$\n", + "\n", + "Note that the gains depend on the velocity $v_\\text{r}$ (or equivalently on\n", + "the nominal input $u_\\text{d}$), giving us a gain scheduled controller." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "another-milwaukee", + "metadata": {}, + "outputs": [], + "source": [ + "# System state: none\n", + "# System input: x, y, theta, xd, yd, thetad, vd, delta\n", + "# System output: v, delta\n", + "# System parameters: longpole, latomega_c, latzeta_c\n", + "def gainsched_output(t, x, u, params):\n", + " # Get the controller parameters\n", + " longpole = params.get('longpole', -2.)\n", + " latomega_c = params.get('latomega_c', 2)\n", + " latzeta_c = params.get('latzeta_c', 0.5)\n", + " l = params.get('wheelbase', 3)\n", + " vref = params.get('vref', None)\n", + " \n", + " # Extract the system inputs and compute the errors\n", + " x, y, theta, xd, yd, thetad, vd, deltad = u\n", + " ex, ey, etheta = x - xd, y - yd, theta - thetad\n", + "\n", + " # Determine the controller gains\n", + " lambda1 = -longpole\n", + " a1 = 2 * latzeta_c * latomega_c\n", + " a2 = latomega_c**2\n", + " \n", + " # Determine the speed to use for computing the gains\n", + " if vref is None:\n", + " vref = vd\n", + "\n", + " # Compute and return the control law\n", + " v = -lambda1 * ex # leave off feedforward to generate transient\n", + " if vd != 0:\n", + " delta = deltad - ((a2 * l) / vref**2) * ey - ((a1 * l) / vref) * etheta\n", + " else:\n", + " # We aren't moving, so don't turn the steering wheel\n", + " delta = deltad\n", + " \n", + " return np.array([v, delta])\n", + "\n", + "# Define the controller as an input/output system\n", + "gainsched = ct.nlsys(\n", + " None, gainsched_output, name='controller', # static system\n", + " inputs=('x', 'y', 'theta', 'xd', 'yd', 'thetad', # system inputs\n", + " 'vd', 'deltad'),\n", + " outputs=('v', 'delta') # system outputs\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "6c6c4b9b", + "metadata": {}, + "source": [ + "## Reference trajectory subsystem\n", + "\n", + "The reference trajectory block generates a simple trajectory for the system\n", + "given the desired speed (vref) and lateral position (yref). The trajectory\n", + "consists of a straight line of the form (vref * t, yref, 0) with nominal\n", + "input (vref, 0)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "significant-november", + "metadata": {}, + "outputs": [], + "source": [ + "# System state: none\n", + "# System input: vref, yref\n", + "# System output: xd, yd, thetad, vd, deltad\n", + "# System parameters: none\n", + "#\n", + "def trajgen_output(t, x, u, params):\n", + " vref, yref = u\n", + " return np.array([vref * t, yref, 0, vref, 0])\n", + "\n", + "# Define the trajectory generator as an input/output system\n", + "trajgen = ct.nlsys(\n", + " None, trajgen_output, name='trajgen',\n", + " inputs=('vref', 'yref'),\n", + " outputs=('xd', 'yd', 'thetad', 'vd', 'deltad'))\n" + ] + }, + { + "cell_type": "markdown", + "id": "4ca5ab53", + "metadata": {}, + "source": [ + "## System construction\n", + "\n", + "The input to the full closed loop system is the desired lateral position and\n", + "the desired forward velocity. The output for the system is taken as the\n", + "full vehicle state plus the velocity of the vehicle.\n", + "\n", + "We construct the system using the InterconnectedSystem constructor and using\n", + "signal labels to keep track of everything. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "editorial-satisfaction", + "metadata": {}, + "outputs": [], + "source": [ + "steering_gainsched = ct.interconnect(\n", + " # List of subsystems\n", + " (trajgen, gainsched, bicycle), name='steering',\n", + "\n", + " # System inputs\n", + " inplist=['trajgen.vref', 'trajgen.yref'],\n", + " inputs=['yref', 'vref'],\n", + "\n", + " # System outputs\n", + " outlist=['bicycle.x', 'bicycle.y', 'bicycle.theta', 'controller.v',\n", + " 'controller.delta'],\n", + " outputs=['x', 'y', 'theta', 'v', 'delta']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "61fe3404", + "metadata": {}, + "source": [ + "Note the use of signals of the form `sys.sig` to get the signals from a specific subsystem." + ] + }, + { + "cell_type": "markdown", + "id": "47f5d528", + "metadata": {}, + "source": [ + "## System simulation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "smoking-trail", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the simulation conditions\n", + "yref = 1\n", + "T = np.linspace(0, 5, 100)\n", + "\n", + "# Plot the reference trajectory for the y position\n", + "plt.plot([0, 5], [yref, yref], 'k-', linewidth=0.6)\n", + "\n", + "# Find the signals we want to plot\n", + "y_index = steering_gainsched.find_output('y')\n", + "v_index = steering_gainsched.find_output('v')\n", + "\n", + "# Do an iteration through different speeds\n", + "for vref in [5, 10, 15]:\n", + " # Simulate the closed loop controller response\n", + " tout, yout = ct.input_output_response(\n", + " steering_gainsched, T, [vref * np.ones(len(T)), yref * np.ones(len(T))])\n", + "\n", + " # Plot the reference speed\n", + " plt.plot([0, 5], [vref, vref], 'k-', linewidth=0.6)\n", + "\n", + " # Plot the system output\n", + " y_line, = plt.plot(tout, yout[y_index, :], 'r-') # lateral position\n", + " v_line, = plt.plot(tout, yout[v_index, :], 'b--') # vehicle velocity\n", + "\n", + "# Add axis labels\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(r\"$\\dot{x}$ [m/s], $y$ [m]\")\n", + "plt.legend((v_line, y_line), (r\"$\\dot{x}$\", \"$y$\"),\n", + " loc='center right', frameon=False);" + ] + }, + { + "cell_type": "markdown", + "id": "8f31bc48", + "metadata": {}, + "source": [ + "## Comparison to fixed controller" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "homeless-gibson", + "metadata": {}, + "outputs": [], + "source": [ + "# Rerun with no gain-scheduling\n", + "\n", + "# Plot the reference trajectory for the y position\n", + "plt.plot([0, 5], [yref, yref], 'k-', linewidth=0.6)\n", + "\n", + "# Do an iteration through different speeds\n", + "for vref in [5, 10, 15]:\n", + " # Simulate the closed loop controller response\n", + " tout, yout = ct.input_output_response(\n", + " steering_gainsched, T, [vref * np.ones(len(T)), yref * np.ones(len(T))], \n", + " params={'vref': 15})\n", + "\n", + " # Plot the reference speed\n", + " plt.plot([0, 5], [vref, vref], 'k-', linewidth=0.6)\n", + "\n", + " # Plot the system output\n", + " y_line, = plt.plot(tout, yout[y_index, :], 'r-') # lateral position\n", + " v_line, = plt.plot(tout, yout[v_index, :], 'b--') # vehicle velocity\n", + "\n", + "# Add axis labels\n", + "plt.xlabel(\"Time [s]\")\n", + "plt.ylabel(r\"$\\dot{x}$ [m/s], $y$ [m]\")\n", + "plt.legend((v_line, y_line), (r\"$\\dot{x}$\", \"$y$\"),\n", + " loc='center right', frameon=False);" + ] + }, + { + "cell_type": "markdown", + "id": "5811a6e4", + "metadata": {}, + "source": [ + "## Things to try\n", + "* Use different reference trajectories (eg, flatness-based trajectory)\n", + "* Try scheduling on the current state rather than the desired state" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f571b2b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds112-L3a_linquad.ipynb b/examples/cds112-L3a_linquad.ipynb new file mode 100644 index 000000000..11ac54771 --- /dev/null +++ b/examples/cds112-L3a_linquad.ipynb @@ -0,0 +1,399 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "dd522981", + "metadata": {}, + "source": [ + "# Linear quadratic optimal control example\n", + "\n", + "Richard M. Murray, 20 Jan 2022 (updated 7 Jul 2024)\n", + "\n", + "This example works through the linear quadratic finite time optimal control problem. We assume that we have a linear system of the form\n", + "$$\n", + "\\dot x = A x + Bu \n", + "$$\n", + "and that we want to minimize a cost function of the form\n", + "$$\n", + "\\int_0^T (x^T Q_x x + u^T Q_u u) dt + x^T P_1 x.\n", + "$$\n", + "We show how to compute the solution the Riccati ODE and use this to obtain an optimal (time-varying) linear controller." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "866842ea", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy as sp\n", + "import matplotlib.pyplot as plt\n", + "import control as ct\n", + "import control.optimal as opt\n", + "import control.flatsys as fs\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "id": "83a32e85", + "metadata": {}, + "source": [ + "## System dynamics\n", + "\n", + "We use the linearized dynamics of the vehicle steering problem as our linear system. This is mainly for convenient (since we have some intuition about it). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48c1bd7f-0db6-4488-af41-41f685280ec9", + "metadata": {}, + "outputs": [], + "source": [ + "# Vehicle dynamics (bicycle model)\n", + "\n", + "# Function to take states, inputs and return the flat flag\n", + "def _kincar_flat_forward(x, u, params={}):\n", + " # Get the parameter values\n", + " b = params.get('wheelbase', 3.)\n", + " #! TODO: add dir processing\n", + "\n", + " # Create a list of arrays to store the flat output and its derivatives\n", + " zflag = [np.zeros(3), np.zeros(3)]\n", + "\n", + " # Flat output is the x, y position of the rear wheels\n", + " zflag[0][0] = x[0]\n", + " zflag[1][0] = x[1]\n", + "\n", + " # First derivatives of the flat output\n", + " zflag[0][1] = u[0] * np.cos(x[2]) # dx/dt\n", + " zflag[1][1] = u[0] * np.sin(x[2]) # dy/dt\n", + "\n", + " # First derivative of the angle\n", + " thdot = (u[0]/b) * np.tan(u[1])\n", + "\n", + " # Second derivatives of the flat output (setting vdot = 0)\n", + " zflag[0][2] = -u[0] * thdot * np.sin(x[2])\n", + " zflag[1][2] = u[0] * thdot * np.cos(x[2])\n", + "\n", + " return zflag\n", + "\n", + "# Function to take the flat flag and return states, inputs\n", + "def _kincar_flat_reverse(zflag, params={}):\n", + " # Get the parameter values\n", + " b = params.get('wheelbase', 3.)\n", + " dir = params.get('dir', 'f')\n", + "\n", + " # Create a vector to store the state and inputs\n", + " x = np.zeros(3)\n", + " u = np.zeros(2)\n", + "\n", + " # Given the flat variables, solve for the state\n", + " x[0] = zflag[0][0] # x position\n", + " x[1] = zflag[1][0] # y position\n", + " if dir == 'f':\n", + " x[2] = np.arctan2(zflag[1][1], zflag[0][1]) # tan(theta) = ydot/xdot\n", + " elif dir == 'r':\n", + " # Angle is flipped by 180 degrees (since v < 0)\n", + " x[2] = np.arctan2(-zflag[1][1], -zflag[0][1])\n", + " else:\n", + " raise ValueError(\"unknown direction:\", dir)\n", + "\n", + " # And next solve for the inputs\n", + " u[0] = zflag[0][1] * np.cos(x[2]) + zflag[1][1] * np.sin(x[2])\n", + " thdot_v = zflag[1][2] * np.cos(x[2]) - zflag[0][2] * np.sin(x[2])\n", + " u[1] = np.arctan2(thdot_v, u[0]**2 / b)\n", + "\n", + " return x, u\n", + "\n", + "# Function to compute the RHS of the system dynamics\n", + "def _kincar_update(t, x, u, params):\n", + " b = params.get('wheelbase', 3.) # get parameter values\n", + " #! TODO: add dir processing\n", + " dx = np.array([\n", + " np.cos(x[2]) * u[0],\n", + " np.sin(x[2]) * u[0],\n", + " (u[0]/b) * np.tan(u[1])\n", + " ])\n", + " return dx\n", + "\n", + "def _kincar_output(t, x, u, params):\n", + " return x # return x, y, theta (full state)\n", + "\n", + "# Create differentially flat input/output system\n", + "kincar = fs.FlatSystem(\n", + " _kincar_flat_forward, _kincar_flat_reverse, name=\"kincar\",\n", + " updfcn=_kincar_update, outfcn=_kincar_output,\n", + " inputs=('v', 'delta'), outputs=('x', 'y', 'theta'),\n", + " states=('x', 'y', 'theta'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fbdd78c0-30e9-43f7-9e8d-198ae38c2988", + "metadata": {}, + "outputs": [], + "source": [ + "# Utility function to plot lane change manuever\n", + "def plot_lanechange(t, y, u, figure=None, yf=None):\n", + " # Plot the xy trajectory\n", + " plt.subplot(3, 1, 1, label='xy')\n", + " plt.plot(y[0], y[1])\n", + " plt.xlabel(\"x [m]\")\n", + " plt.ylabel(\"y [m]\")\n", + " if yf is not None:\n", + " plt.plot(yf[0], yf[1], 'ro')\n", + "\n", + " # Plot the inputs as a function of time\n", + " plt.subplot(3, 1, 2, label='v')\n", + " plt.plot(t, u[0])\n", + " plt.xlabel(\"Time $t$ [sec]\")\n", + " plt.ylabel(\"$v$ [m/s]\")\n", + "\n", + " plt.subplot(3, 1, 3, label='delta')\n", + " plt.plot(t, u[1])\n", + " plt.xlabel(\"Time $t$ [sec]\")\n", + " plt.ylabel(\"$\\\\delta$ [rad]\")\n", + "\n", + " plt.suptitle(\"Lane change manuever\")\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de9d85f3", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial conditions\n", + "x0 = np.array([-40, -2., 0.])\n", + "u0 = np.array([10, 0]) # only used for linearization\n", + "Tf = 4\n", + "\n", + "# Linearized dynamics\n", + "sys = kincar.linearize(x0, u0)\n", + "print(sys)" + ] + }, + { + "cell_type": "markdown", + "id": "c5c0abe9", + "metadata": {}, + "source": [ + "## Optimal trajectory generation\n", + "\n", + "We generate an trajectory for the system that minimizes the cost function above. Namely, starting from some initial function $x(0) = x_0$, we wish to bring the system toward the origin without using too much control effort." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02e9f87c", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the cost function and the terminal cost\n", + "# (try changing these later to see what happens)\n", + "Qx = np.diag([1, 1, 1]) # state costs\n", + "Qu = np.diag([1, 1]) # input costs\n", + "Pf = np.diag([1, 1, 1]) # terminal costs" + ] + }, + { + "cell_type": "markdown", + "id": "62c76e5e", + "metadata": {}, + "source": [ + "### Finite time, linear quadratic optimization\n", + "\n", + "The optimal solution satisfies the following equations, which follow from the maximum principle:\n", + "\n", + "$$\n", + " \\begin{aligned}\n", + " \\dot x &= \\left(\\frac{\\partial H}{\\partial \\lambda}\\right)^T\n", + " = A x + Bu, \\qquad & x(0) &= x_0, \\\\\n", + " -\\dot \\lambda &= \\left(\\frac{\\partial H}{\\partial x}\\right)^T\n", + " = Q_x x + A^T \\lambda, \\qquad\n", + " & \\lambda(T) &= P_1 x(T), \\\\\n", + " 0 &= \\left(\\frac{\\partial H}{\\partial u}\\right)^T\n", + " = Q_u u + B^T \\lambda. &&\n", + " \\end{aligned}\n", + "$$\n", + "\n", + "The last condition can be solved to obtain the optimal controller\n", + "\n", + "$$\n", + " u = -Q_u^{-1} B^T \\lambda,\n", + "$$\n", + "\n", + "which can be substituted into the equations for the optimal solution.\n", + "\n", + "Given the linear nature of the dynamics, we attempt to find a solution\n", + "by setting $\\lambda(t) = P(t) x(t)$ where $P(t) \\in {\\mathbb R}^{n \\times\n", + "n}$. Substituting this into the necessary condition, we obtain\n", + "\n", + "$$\n", + " \\begin{aligned}\n", + " & \\dot\\lambda =\n", + " \\dot P x + P \\dot x = \\dot P x + P(Ax - BQ_u^{-1} B^T P) x, \\\\\n", + " & \\quad\\implies\\quad\n", + " -\\dot P x - PA x + PBQ_u^{-1}B P x = Q_xx + A^T P x.\n", + " \\end{aligned}\n", + "$$\n", + "\n", + "This equation is satisfied if we can find $P(t)$ such that\n", + "\n", + "$$\n", + " -\\dot P = PA + A^T P - P B Q_u^{-1} B^T P + Q_x,\n", + " \\qquad P(T) = P_1.\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "b63aed88", + "metadata": {}, + "source": [ + "To solve a final value problem with $P(T) = P_1$, we set the \"initial\" condition to $P_1$ and then invert time, so that we solve\n", + "\n", + "$$\n", + "\\frac{dP}{d(-t)} = -\\frac{dP}{dt} = -F(P), \\qquad P(0) = P_1\n", + "$$\n", + "\n", + "Solving this equation from time $t = 0$ to time $t = T$ will give us an solution that goes from $P(T)$ to $P(0)$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02d74789", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the Riccatti ODE\n", + "def Pdot_reverse(t, x):\n", + " # Get the P matrix from the state by resizing\n", + " P = np.reshape(x, (sys.nstates, sys.nstates))\n", + " \n", + " # Compute the right hand side of Riccati ODE\n", + " Prhs = P @ sys.A + sys.A.T @ P + Qx - \\\n", + " P @ sys.B @ np.linalg.inv(Qu) @ sys.B.T @ P\n", + " \n", + " # Return P as a vector, *backwards* in time (no minus sign)\n", + " return Prhs.reshape((-1))\n", + "\n", + "# Solve the Riccati ODE (converting from matrix to vector and back)\n", + "P0 = np.reshape(Pf, (-1))\n", + "Psol = sp.integrate.solve_ivp(Pdot_reverse, (0, Tf), P0)\n", + "Pfwd = np.reshape(Psol.y, (sys.nstates, sys.nstates, -1))\n", + "\n", + "# Reorder the solution in time\n", + "Prev = Pfwd[:, :, ::-1] \n", + "trev = Tf - Psol.t[::-1]\n", + "\n", + "print(\"Trange = \", trev[0], \"to\", trev[-1])\n", + "print(\"P[Tf] =\", Prev[:,:,-1])\n", + "print(\"P[0] =\", Prev[:,:,0])\n", + "\n", + "# Internal comparison: show that initial value is close to algebraic solution\n", + "_, P_lqr, _ = ct.lqr(sys.A, sys.B, Qx, Qu)\n", + "print(\"P_lqr =\", P_lqr)" + ] + }, + { + "cell_type": "markdown", + "id": "f4fb1166", + "metadata": {}, + "source": [ + "For solving the $x$ dynamics, we need a function to evaluate $P(t)$ at an arbitrary time (used by the integrator). We can do this with the SciPy `interp1d` function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "728f675b", + "metadata": {}, + "outputs": [], + "source": [ + "# Define an interpolation function for P\n", + "P = sp.interpolate.interp1d(trev, Prev)\n", + "\n", + "print(\"P(0) =\", P(0))\n", + "print(\"P(3.5) =\", P(3.5))\n", + "print(\"P(4) =\", P(4))" + ] + }, + { + "cell_type": "markdown", + "id": "eb30c3fa", + "metadata": {}, + "source": [ + "We now solve the $\\dot x$ equations *forward* in time, using $P(t)$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "84092dcd", + "metadata": {}, + "outputs": [], + "source": [ + "# Now solve the state forward in time\n", + "def xdot_forward(t, x):\n", + " u = -np.linalg.inv(Qu) @ sys.B.T @ P(t) @ x\n", + " return sys.A @ x + sys.B @ u\n", + "\n", + "# Now simulate from a shifted initial condition\n", + "xsol = sp.integrate.solve_ivp(xdot_forward, (0, Tf), x0)\n", + "tvec = xsol.t\n", + "x = xsol.y\n", + "print(\"x[0] =\", x[:, 0])\n", + "print(\"x[Tf] =\", x[:, -1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8488acad", + "metadata": {}, + "outputs": [], + "source": [ + "# Finally compute the \"desired\" state and input values\n", + "xd = x\n", + "ud = np.zeros((sys.ninputs, tvec.size))\n", + "for i, t in enumerate(tvec):\n", + " ud[:, i] = -np.linalg.inv(Qu) @ sys.B.T @ P(t) @ x[:, i]\n", + "\n", + "plot_lanechange(tvec, xd, ud)" + ] + }, + { + "cell_type": "markdown", + "id": "89483f4b", + "metadata": {}, + "source": [ + "Note here that we are stabilizing the system to the origin (compared to some of other examples where we change langes and so the final $y$ position is $y_\\text{f} = 2$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7ed4c5eb", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds112-L3b_optimal.ipynb b/examples/cds112-L3b_optimal.ipynb new file mode 100644 index 000000000..1c7e0e1c2 --- /dev/null +++ b/examples/cds112-L3b_optimal.ipynb @@ -0,0 +1,461 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "edb7e2c6", + "metadata": {}, + "source": [ + "## Optimal Control\n", + "\n", + "Richard M. Murray, 31 Dec 2021 (updated 7 Jul 2024)\n", + "\n", + "This notebook contains an example of using optimal control for a vehicle steering system. It illustrates different methods of setting up optimal control problems and solving them using python-control." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7066eb69", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import control as ct\n", + "import control.optimal as opt\n", + "import control.flatsys as fs\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "id": "4afb09dd", + "metadata": {}, + "source": [ + "## Vehicle steering dynamics\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
\n", + "$$\n", + "\\begin{aligned}\n", + " \\dot x &= \\cos\\theta\\, v \\\\\n", + " \\dot y &= \\sin\\theta\\, v \\\\\n", + " \\dot\\theta &= \\frac{v}{l} \\tan \\delta, \\qquad |\\delta| \\leq \\delta_\\text{max}\n", + "\\end{aligned}\n", + "$$\n", + "
\n", + "\n", + "The vehicle dynamics are given by a simple bicycle model. We take the state of the system as $(x, y, \\theta)$ where $(x, y)$ is the position of the vehicle in the plane and $\\theta$ is the angle of the vehicle with respect to horizontal. The vehicle input is given by $(v, \\delta)$ where $v$ is the forward velocity of the vehicle and $\\delta$ is the angle of the steering wheel. The model includes saturation of the vehicle steering angle." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a6143a8a", + "metadata": {}, + "outputs": [], + "source": [ + "# Vehicle dynamics (bicycle model)\n", + "\n", + "# Function to take states, inputs and return the flat flag\n", + "def _kincar_flat_forward(x, u, params={}):\n", + " # Get the parameter values\n", + " b = params.get('wheelbase', 3.)\n", + " #! TODO: add dir processing\n", + "\n", + " # Create a list of arrays to store the flat output and its derivatives\n", + " zflag = [np.zeros(3), np.zeros(3)]\n", + "\n", + " # Flat output is the x, y position of the rear wheels\n", + " zflag[0][0] = x[0]\n", + " zflag[1][0] = x[1]\n", + "\n", + " # First derivatives of the flat output\n", + " zflag[0][1] = u[0] * np.cos(x[2]) # dx/dt\n", + " zflag[1][1] = u[0] * np.sin(x[2]) # dy/dt\n", + "\n", + " # First derivative of the angle\n", + " thdot = (u[0]/b) * np.tan(u[1])\n", + "\n", + " # Second derivatives of the flat output (setting vdot = 0)\n", + " zflag[0][2] = -u[0] * thdot * np.sin(x[2])\n", + " zflag[1][2] = u[0] * thdot * np.cos(x[2])\n", + "\n", + " return zflag\n", + "\n", + "# Function to take the flat flag and return states, inputs\n", + "def _kincar_flat_reverse(zflag, params={}):\n", + " # Get the parameter values\n", + " b = params.get('wheelbase', 3.)\n", + " dir = params.get('dir', 'f')\n", + "\n", + " # Create a vector to store the state and inputs\n", + " x = np.zeros(3)\n", + " u = np.zeros(2)\n", + "\n", + " # Given the flat variables, solve for the state\n", + " x[0] = zflag[0][0] # x position\n", + " x[1] = zflag[1][0] # y position\n", + " if dir == 'f':\n", + " x[2] = np.arctan2(zflag[1][1], zflag[0][1]) # tan(theta) = ydot/xdot\n", + " elif dir == 'r':\n", + " # Angle is flipped by 180 degrees (since v < 0)\n", + " x[2] = np.arctan2(-zflag[1][1], -zflag[0][1])\n", + " else:\n", + " raise ValueError(\"unknown direction:\", dir)\n", + "\n", + " # And next solve for the inputs\n", + " u[0] = zflag[0][1] * np.cos(x[2]) + zflag[1][1] * np.sin(x[2])\n", + " thdot_v = zflag[1][2] * np.cos(x[2]) - zflag[0][2] * np.sin(x[2])\n", + " u[1] = np.arctan2(thdot_v, u[0]**2 / b)\n", + "\n", + " return x, u\n", + "\n", + "# Function to compute the RHS of the system dynamics\n", + "def _kincar_update(t, x, u, params):\n", + " b = params.get('wheelbase', 3.) # get parameter values\n", + " #! TODO: add dir processing\n", + " dx = np.array([\n", + " np.cos(x[2]) * u[0],\n", + " np.sin(x[2]) * u[0],\n", + " (u[0]/b) * np.tan(u[1])\n", + " ])\n", + " return dx\n", + "\n", + "def _kincar_output(t, x, u, params):\n", + " return x # return x, y, theta (full state)\n", + "\n", + "# Create differentially flat input/output system\n", + "kincar = fs.FlatSystem(\n", + " _kincar_flat_forward, _kincar_flat_reverse, name=\"kincar\",\n", + " updfcn=_kincar_update, outfcn=_kincar_output,\n", + " inputs=('v', 'delta'), outputs=('x', 'y', 'theta'),\n", + " states=('x', 'y', 'theta'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43377b51-35db-4e8f-9101-b22af1de1cb2", + "metadata": {}, + "outputs": [], + "source": [ + "# Utility function to plot lane change manuever\n", + "def plot_lanechange(t, y, u, figure=None, yf=None):\n", + " # Plot the xy trajectory\n", + " plt.subplot(3, 1, 1, label='xy')\n", + " plt.plot(y[0], y[1])\n", + " plt.xlabel(\"x [m]\")\n", + " plt.ylabel(\"y [m]\")\n", + " if yf is not None:\n", + " plt.plot(yf[0], yf[1], 'ro')\n", + "\n", + " # Plot the inputs as a function of time\n", + " plt.subplot(3, 1, 2, label='v')\n", + " plt.plot(t, u[0])\n", + " plt.xlabel(\"Time $t$ [sec]\")\n", + " plt.ylabel(\"$v$ [m/s]\")\n", + "\n", + " plt.subplot(3, 1, 3, label='delta')\n", + " plt.plot(t, u[1])\n", + " plt.xlabel(\"Time $t$ [sec]\")\n", + " plt.ylabel(\"$\\\\delta$ [rad]\")\n", + "\n", + " plt.suptitle(\"Lane change manuever\")\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "64bd3c3b", + "metadata": {}, + "source": [ + "## Optimal trajectory generation\n", + "\n", + "We consider the problem of changing from one lane to another over a perod of 10 seconds while driving at a forward speed of 10 m/s." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "42dcbd79", + "metadata": {}, + "outputs": [], + "source": [ + "# Initial and final conditions\n", + "x0 = np.array([ 0., -2., 0.]); u0 = np.array([10., 0.])\n", + "xf = np.array([100., 2., 0.]); uf = np.array([10., 0.])\n", + "Tf = 10" + ] + }, + { + "cell_type": "markdown", + "id": "5ff2e044", + "metadata": {}, + "source": [ + "An important part of the optimization procedure is to give a good initial guess. Here are some possibilities:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "650d321a", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the time horizon (and spacing) for the optimization\n", + "# timepts = np.linspace(0, Tf, 5, endpoint=True)\n", + "# timepts = np.linspace(0, Tf, 10, endpoint=True)\n", + "timepts = np.linspace(0, Tf, 20, endpoint=True)\n", + "\n", + "# Compute some initial guesses to use\n", + "bend_left = [10, 0.01] # slight left veer (will extend over all timepts)\n", + "straight_line = ( # straight line from start to end with nominal input\n", + " np.array([x0 + (xf - x0) * t/Tf for t in timepts]).transpose(), \n", + " u0\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "4e75a2c4", + "metadata": {}, + "source": [ + "### Approach 1: standard quadratic cost\n", + "\n", + "We can set up the optimal control problem as trying to minimize the distance form the desired final point while at the same time as not exerting too much control effort to achieve our goal.\n", + "\n", + "(The optimization module solves optimal control problems by choosing the values of the input at each point in the time horizon to try to minimize the cost. This means that each input generates a parameter value at each point in the time horizon, so the more refined your time horizon, the more parameters the optimizer has to search over.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "984c2f0b", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the cost functions\n", + "Qx = np.diag([.1, 10, .1]) # keep lateral error low\n", + "Qu = np.diag([.1, 1]) # minimize applied inputs\n", + "quad_cost = opt.quadratic_cost(kincar, Qx, Qu, x0=xf, u0=uf)\n", + "\n", + "# Compute the optimal control, setting step size for gradient calculation (eps)\n", + "start_time = time.process_time()\n", + "result1 = opt.solve_ocp(\n", + " kincar, timepts, x0, quad_cost, \n", + " initial_guess=straight_line,\n", + " # initial_guess= bend_left,\n", + " # initial_guess=u0,\n", + " # minimize_method='trust-constr',\n", + " # minimize_options={'finite_diff_rel_step': 0.01},\n", + " # trajectory_method='shooting'\n", + " # solve_ivp_method='LSODA'\n", + ")\n", + "print(\"* Total time = %5g seconds\\n\" % (time.process_time() - start_time))\n", + "\n", + "# Plot the results from the optimization\n", + "plot_lanechange(timepts, result1.states, result1.inputs, xf)\n", + "print(\"Final computed state: \", result1.states[:,-1])\n", + "\n", + "# Simulate the system and see what happens\n", + "t1, u1 = result1.time, result1.inputs\n", + "t1, y1 = ct.input_output_response(kincar, timepts, u1, x0)\n", + "plot_lanechange(t1, y1, u1, yf=xf[0:2])\n", + "print(\"Final simulated state:\", y1[:,-1])" + ] + }, + { + "cell_type": "markdown", + "id": "b7cade52", + "metadata": {}, + "source": [ + "Note the amount of time required to solve the problem and also any warning messages about to being able to solve the optimization (mainly in earlier versions of python-control). You can try to adjust a number of factors to try to get a better solution:\n", + "* Try changing the number of points in the time horizon\n", + "* Try using a different initial guess\n", + "* Try changing the optimization method (see commented out code)" + ] + }, + { + "cell_type": "markdown", + "id": "6a9f9d9b", + "metadata": {}, + "source": [ + "### Approach 2: input cost, input constraints, terminal cost\n", + "\n", + "The previous solution integrates the position error for the entire horizon, and so the car changes lanes very quickly (at the cost of larger inputs). Instead, we can penalize the final state and impose a higher cost on the inputs, resuling in a more gradual lane change.\n", + "\n", + "We can also try using a different solver for this example. You can pass the solver using the `minimize_method` keyword and send options to the solver using the `minimize_options` keyword (which should be set to a dictionary of options)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a201e33c", + "metadata": {}, + "outputs": [], + "source": [ + "# Add input constraint, input cost, terminal cost\n", + "constraints = [ opt.input_range_constraint(kincar, [8, -0.1], [12, 0.1]) ]\n", + "traj_cost = opt.quadratic_cost(kincar, None, np.diag([0.1, 1]), u0=uf)\n", + "term_cost = opt.quadratic_cost(kincar, np.diag([1, 10, 100]), None, x0=xf)\n", + "\n", + "# Compute the optimal control\n", + "start_time = time.process_time()\n", + "result2 = opt.solve_ocp(\n", + " kincar, timepts, x0, traj_cost, constraints, terminal_cost=term_cost,\n", + " initial_guess=straight_line, \n", + " # minimize_method='trust-constr',\n", + " # minimize_options={'finite_diff_rel_step': 0.01},\n", + " # minimize_method='SLSQP', minimize_options={'eps': 0.01},\n", + " # log=True,\n", + ")\n", + "print(\"* Total time = %5g seconds\\n\" % (time.process_time() - start_time))\n", + "\n", + "# Plot the results from the optimization\n", + "plot_lanechange(timepts, result2.states, result2.inputs, xf)\n", + "print(\"Final computed state: \", result2.states[:,-1])\n", + "\n", + "# Simulate the system and see what happens\n", + "t2, u2 = result2.time, result2.inputs\n", + "t2, y2 = ct.input_output_response(kincar, timepts, u2, x0)\n", + "plot_lanechange(t2, y2, u2, yf=xf[0:2])\n", + "print(\"Final simulated state:\", y2[:,-1])" + ] + }, + { + "cell_type": "markdown", + "id": "3d2ccf97", + "metadata": {}, + "source": [ + "### Approach 3: terminal constraints\n", + "\n", + "We can also remove the cost function on the state and replace it with a terminal *constraint* on the state. If a solution is found, it guarantees we get to exactly the final state. Note however, that terminal constraints can be very difficult to satisfy for a general optimization (compare the solution times here with what we saw last week when we used differential flatness)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc77a856", + "metadata": {}, + "outputs": [], + "source": [ + "# Input cost and terminal constraints\n", + "R = np.diag([1, 1]) # minimize applied inputs\n", + "cost3 = opt.quadratic_cost(kincar, np.zeros((3,3)), R, u0=uf)\n", + "constraints = [\n", + " opt.input_range_constraint(kincar, [8, -0.1], [12, 0.1]) ]\n", + "terminal = [ opt.state_range_constraint(kincar, xf, xf) ]\n", + "\n", + "# Compute the optimal control\n", + "start_time = time.process_time()\n", + "result3 = opt.solve_ocp(\n", + " kincar, timepts, x0, cost3, constraints,\n", + " terminal_constraints=terminal, initial_guess=straight_line,\n", + "# solve_ivp_kwargs={'atol': 1e-3, 'rtol': 1e-2},\n", + "# minimize_method='trust-constr',\n", + "# minimize_options={'finite_diff_rel_step': 0.01},\n", + ")\n", + "print(\"* Total time = %5g seconds\\n\" % (time.process_time() - start_time))\n", + "\n", + "# Plot the results from the optimization\n", + "plot_lanechange(timepts, result3.states, result3.inputs, xf)\n", + "print(\"Final computed state: \", result3.states[:,-1])\n", + "\n", + "# Simulate the system and see what happens\n", + "t3, u3 = result3.time, result3.inputs\n", + "t3, y3 = ct.input_output_response(kincar, timepts, u3, x0)\n", + "plot_lanechange(t3, y3, u3, yf=xf[0:2])\n", + "print(\"Final state: \", y3[:,-1])" + ] + }, + { + "cell_type": "markdown", + "id": "9e744463", + "metadata": {}, + "source": [ + "### Approach 4: terminal constraints w/ basis functions\n", + "\n", + "As a final example, we can use a basis function to reduce the size\n", + "of the problem and get faster answers with more temporal resolution.\n", + "\n", + "Here we parameterize the input by a set of 4 Bezier curves but solve for a much more time resolved set of inputs. Note that while we are using the `control.flatsys` module to define the basis functions, we are not exploiting the fact that the system is differentially flat." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee82aa25", + "metadata": {}, + "outputs": [], + "source": [ + "# Get basis functions for flat systems module\n", + "import control.flatsys as flat\n", + "\n", + "# Compute the optimal control\n", + "start_time = time.process_time()\n", + "result4 = opt.solve_ocp(\n", + " kincar, timepts, x0, quad_cost, constraints,\n", + " terminal_constraints=terminal,\n", + " initial_guess=straight_line,\n", + " basis=flat.PolyFamily(4, T=Tf),\n", + " # solve_ivp_kwargs={'method': 'RK45', 'atol': 1e-2, 'rtol': 1e-2},\n", + " # solve_ivp_kwargs={'atol': 1e-3, 'rtol': 1e-2},\n", + " # minimize_method='trust-constr', minimize_options={'disp': True},\n", + " log=False\n", + ")\n", + "print(\"* Total time = %5g seconds\\n\" % (time.process_time() - start_time))\n", + "\n", + "# Plot the results from the optimization\n", + "plot_lanechange(timepts, result4.states, result4.inputs, xf)\n", + "print(\"Final computed state: \", result3.states[:,-1])\n", + "\n", + "# Simulate the system and see what happens\n", + "t4, u4 = result4.time, result4.inputs\n", + "t4, y4 = ct.input_output_response(kincar, timepts, u4, x0)\n", + "plot_lanechange(t4, y4, u4, yf=xf[0:2])\n", + "plt.legend(['optimal', 'simulation'])\n", + "print(\"Final simulated state: \", y4[:,-1])" + ] + }, + { + "cell_type": "markdown", + "id": "2a74388e", + "metadata": {}, + "source": [ + "Note how much smoother the inputs look, although the solver can still have a hard time satisfying the final constraints, resulting in longer computation times." + ] + }, + { + "cell_type": "markdown", + "id": "1465d149", + "metadata": {}, + "source": [ + "### Additional things to try\n", + "\n", + "* Compare the results here with what we go last week exploiting the property of differential flatness (computation time, in particular)\n", + "* Try using different weights, solvers, initial guess and other properties and see how things change.\n", + "* Try using different values for `initial_guess` to get faster convergence and/or different classes of solutions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02bad3d5", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds112-L4a_lqr-tracking.ipynb b/examples/cds112-L4a_lqr-tracking.ipynb new file mode 100644 index 000000000..0687f4cc5 --- /dev/null +++ b/examples/cds112-L4a_lqr-tracking.ipynb @@ -0,0 +1,279 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "af1717f2", + "metadata": {}, + "source": [ + "# LQR Tracking Example\n", + "\n", + "Richard M. Murray, 25 Jan 2022\n", + "\n", + "This example uses a linear system to show how to implement LQR based tracking and some of the tradeoffs between feedfoward and feedback. Integral action is also implemented." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50d5c4d3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import control as ct" + ] + }, + { + "cell_type": "markdown", + "id": "a23d6f89", + "metadata": {}, + "source": [ + "## System definition\n", + "\n", + "We use a simple linear system to illustrate the concepts. This system corresponds to the linearized lateral dynamics of a vehicle driving down a road at 10 m/s." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b5923c88", + "metadata": {}, + "outputs": [], + "source": [ + "# Define a simple linear system that we want to control\n", + "sys = ct.ss([[0, 10], [-1, 0]], [[0], [1]], np.eye(2), 0, name='sys')\n", + "print(sys)" + ] + }, + { + "cell_type": "markdown", + "id": "dba5ea2b", + "metadata": {}, + "source": [ + "## Controller design\n", + "\n", + "We start by defining the equilibrium point that we plan to stabilize." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "874c1479", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the desired equilibrium point for the system\n", + "x0 = np.array([2, 0])\n", + "u0 = np.array([2])\n", + "Tf = 4" + ] + }, + { + "cell_type": "markdown", + "id": "99f036ea", + "metadata": {}, + "source": [ + "Then construct a simple LQR controller (gain matrix) and create the controller + closed loop system models:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ce6a230", + "metadata": {}, + "outputs": [], + "source": [ + "# Construct an LQR controller for the system\n", + "K, _, _ = ct.lqr(sys, np.eye(sys.nstates), np.eye(sys.ninputs))\n", + "ctrl, clsys = ct.create_statefbk_iosystem(sys, K)\n", + "print(ctrl)\n", + "print(clsys)" + ] + }, + { + "cell_type": "markdown", + "id": "5c711b56", + "metadata": {}, + "source": [ + "Note that the name of the second system is `u[0]`. This is a bug in control-0.9.3 that will be fixed in a [future release](https://github.com/python-control/python-control/pull/849)." + ] + }, + { + "cell_type": "markdown", + "id": "84422c3f", + "metadata": {}, + "source": [ + "## System simulations\n", + "\n", + "### Baseline controller\n", + "\n", + "To see how the baseline controller performs, we ask it to track a step change in (xd, ud):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b763b91b", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the step response with respect to the reference input\n", + "tvec = np.linspace(0, Tf, 100)\n", + "xd = x0\n", + "ud = u0\n", + "\n", + "# U = np.hstack([xd, ud])\n", + "U = np.outer(np.hstack([xd, ud]), np.ones_like(tvec))\n", + "time, output = ct.input_output_response(clsys, tvec, U)\n", + "plt.plot(time, output[0], time, output[1])\n", + "plt.plot([time[0], time[-1]], [xd[0], xd[0]], '--');\n", + "plt.legend(['x[0]', 'x[1]']);" + ] + }, + { + "cell_type": "markdown", + "id": "84ee7635", + "metadata": {}, + "source": [ + "### Disturbance rejection\n", + "\n", + "We add a disturbance to the system by modifying ud (since this enters directly at the system input u)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ecbb3a0", + "metadata": {}, + "outputs": [], + "source": [ + "# Resimulate with a disturbance input\n", + "delta = 0.5\n", + "U = np.outer(np.hstack([xd, ud + delta]), np.ones_like(tvec))\n", + "time, output = ct.input_output_response(clsys, tvec, U)\n", + "plt.plot(time, output[0], time, output[1])\n", + "plt.plot([time[0], time[-1]], [xd[0], xd[0]], '--')\n", + "plt.legend(['x[0]', 'x[1]']);" + ] + }, + { + "cell_type": "markdown", + "id": "ea2d1c59", + "metadata": {}, + "source": [ + "We see that this leads to steady state error, since some amount of system error is required to generate the force to offset the disturbance." + ] + }, + { + "cell_type": "markdown", + "id": "84a9e61c", + "metadata": {}, + "source": [ + "### Integral feedback\n", + "\n", + "A standard approach to compensate for constant disturbances is to use integral feedback. To do this, we have to decide what output we want to track and create a new controller with integral feedback.\n", + "\n", + "We do this by creating an \"augmented\" system that includes the dynamics of the process along with the dynamics of the controller (= integrators for the errors that we choose):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ee2ecc51", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a controller with integral feedback\n", + "C = np.array([[1, 0]])\n", + "\n", + "# Define an augmented state space for use with LQR\n", + "A_aug = np.block([\n", + " [sys.A, np.zeros((sys.nstates, 1))], \n", + " [C, 0]\n", + "])\n", + "B_aug = np.vstack([sys.B, 0])\n", + "print(\"A =\", A_aug, \"\\nB =\", B_aug)" + ] + }, + { + "cell_type": "markdown", + "id": "463d9b85", + "metadata": {}, + "source": [ + "Now generate an LQR controller for the augmented system:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3dd3479f", + "metadata": {}, + "outputs": [], + "source": [ + "# Create an LQR controller for the augmented system\n", + "K_aug, _, _ = ct.lqr(\n", + " A_aug, B_aug, np.diag([1, 1, 1]), np.eye(sys.ninputs))\n", + "print(K_aug)" + ] + }, + { + "cell_type": "markdown", + "id": "19bb6592", + "metadata": {}, + "source": [ + "We can think about this gain as `K_aug = [K, ki]` and the resulting contoller becomes\n", + "\n", + "$$\n", + "u = u_\\text{d} - K(x - x_\\text{d}) - k_\\text{i} \\int_0^t (y - y_\\text{d})\\, d\\tau.\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e183a822", + "metadata": {}, + "outputs": [], + "source": [ + "# Construct an LQR controller for the system\n", + "integral_ctrl, sys_integral = ct.create_statefbk_iosystem(sys, K_aug, integral_action=C)\n", + "print(integral_ctrl)\n", + "print(sys_integral)\n", + "\n", + "# Resimulate with a disturbance input\n", + "delta = 0.5\n", + "U = np.outer(np.hstack([xd, ud + delta]), np.ones_like(tvec))\n", + "time, output = ct.input_output_response(sys_integral, tvec, U)\n", + "plt.plot(time, output[0], time, output[1])\n", + "plt.plot([time[0], time[-1]], [xd[0], xd[0]], '--')\n", + "plt.legend(['x[0]', 'x[1]']);" + ] + }, + { + "cell_type": "markdown", + "id": "437487da", + "metadata": {}, + "source": [ + "## Things to try\n", + "* Play around with the gains and see whether you can reduce the overshoot (50%!)\n", + "* Try following more complicated trajectories (hint: linear systems are differentially flat...)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99394ace", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds112-L4b_pvtol-lqr.ipynb b/examples/cds112-L4b_pvtol-lqr.ipynb new file mode 100644 index 000000000..b472429e2 --- /dev/null +++ b/examples/cds112-L4b_pvtol-lqr.ipynb @@ -0,0 +1,355 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f8bfc15c", + "metadata": {}, + "source": [ + "# PVTOL Linear Quadratic Regulator Example\n", + "\n", + "Richard M. Murray, 25 Jan 2022\n", + "\n", + "This notebook contains an example of LQR control applied to the PVTOL system. It demonstrates how to construct an LQR controller and also the importance of the feedforward component of the controller. A gain scheduled design is also demonstrated." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c120d65c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import control as ct" + ] + }, + { + "cell_type": "markdown", + "id": "77e2ed47", + "metadata": {}, + "source": [ + "## System description\n", + "\n", + "We use the PVTOL dynamics from the textbook, which are contained in the `pvtol` module. The vehicle model is both an I/O system model and a flat system model (for the case when the viscous damping coefficient $c$ is zero).\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
\n", + "$$\n", + "\\begin{aligned}\n", + " m \\ddot x &= F_1 \\cos\\theta - F_2 \\sin\\theta - c \\dot x, \\\\\n", + " m \\ddot y &= F_1 \\sin\\theta + F_2 \\cos\\theta - m g - c \\dot y, \\\\\n", + " J \\ddot \\theta &= r F_1.\n", + "\\end{aligned}\n", + "$$\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "0a12fc3d", + "metadata": {}, + "source": [ + "The parameter values for the PVTOL system come from the Caltech ducted fan experiment, shown in the video below (the wing forces are not included in the PVTOL model):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7adc6cf1", + "metadata": {}, + "outputs": [], + "source": [ + "from IPython.display import YouTubeVideo\n", + "display(YouTubeVideo('ZFb5kFpgCm4', width=640, height=480))\n", + "\n", + "from pvtol import pvtol, plot_results\n", + "print(pvtol)" + ] + }, + { + "cell_type": "markdown", + "id": "45259984", + "metadata": {}, + "source": [ + "Since we will be creating a linear controller, we need a linear system model. We obtain that model by linearizing the dynamics around an equilibrium point. This can be done in python-control using the `find_eqpt` function. We fix the output of the system to be zero and find the state and inputs that hold us there." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea50d7cd", + "metadata": {}, + "outputs": [], + "source": [ + "# Find the equilibrium point corresponding to hover\n", + "xeq, ueq = ct.find_eqpt(pvtol, np.zeros(6), np.zeros(2), y0=np.zeros(6), iy=[0, 1])\n", + "\n", + "print(\"xeq = \", xeq)\n", + "print(\"ueq = \", ueq)\n", + "\n", + "# Get the linearized dynamics\n", + "linsys = pvtol.linearize(xeq, ueq)\n", + "print(linsys)" + ] + }, + { + "cell_type": "markdown", + "id": "7cb8840b", + "metadata": {}, + "source": [ + "## Linear quadratic regulator (LQR) design\n", + "\n", + "Now that we have a linearized model of the system, we can compute a controller using linear quadratic regulator theory. We seek to find the control law that minimizes the function\n", + "\n", + "$$\n", + "J(x(\\cdot), u(\\cdot)) = \\int_0^\\infty x^T(\\tau) Q_x x(\\tau) + u^T(\\tau) Q_u u(\\tau)\\, d\\tau\n", + "$$\n", + "\n", + "The weighting matrices $Q_x \\in \\mathbb{R}^{n \\times n}$ and $Q_u \\in \\mathbb{R}^{m \\times m}$ should be chosen based on the desired performance of the system (tradeoffs in state errors and input magnitudes). See Example 3.5 in OBC for a discussion of how to choose these weights. For now, we just choose identity weights for all states and inputs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cfa1ba7", + "metadata": {}, + "outputs": [], + "source": [ + "# Start with a diagonal weighting\n", + "Qx1 = np.diag([1, 1, 1, 1, 1, 1])\n", + "Qu1 = np.diag([1, 1])\n", + "K, X, E = ct.lqr(linsys, Qx1, Qu1)" + ] + }, + { + "cell_type": "markdown", + "id": "863d07de", + "metadata": {}, + "source": [ + "To create a controller for the system, we need to create an I/O system that takes in the desired trajectory $(x_\\text{d}, u_\\text{d})$ and the current state $x$ and generates the control law\n", + "\n", + "$$\n", + "u = u_\\text{d} - K (x - x_\\text{d})\n", + "$$\n", + "\n", + "The function `create_statefbk_iosystem()` does this (see [documentation](https://python-control.readthedocs.io/en/0.9.3.post2/generated/control.create_statefbk_iosystem.html) for details)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5db704e6", + "metadata": {}, + "outputs": [], + "source": [ + "control, pvtol_closed = ct.create_statefbk_iosystem(pvtol, K)\n", + "print(control, \"\\n\")\n", + "print(pvtol_closed)" + ] + }, + { + "cell_type": "markdown", + "id": "bedcb0c0", + "metadata": {}, + "source": [ + "## Closed loop system simulation\n", + "\n", + "We now generate a trajectory for the system and track that trajectory.\n", + "\n", + "For this simple example, we take the system input to be a \"step\" input that moves the system 1 meter to the right. More complex trajectories (eg, using the results from HW #3) could also be used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a497aa2c", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a step response by setting xd, ud\n", + "Tf = 15\n", + "T = np.linspace(0, Tf, 100)\n", + "xd = np.outer(np.array([1, 0, 0, 0, 0, 0]), np.ones_like(T))\n", + "ud = np.outer(ueq, np.ones_like(T))\n", + "ref = np.vstack([xd, ud])\n", + "\n", + "response = ct.input_output_response(pvtol_closed, T, ref, xeq)\n", + "plot_results(response.time, response.states, response.outputs[6:])" + ] + }, + { + "cell_type": "markdown", + "id": "f014e660", + "metadata": {}, + "source": [ + "The limitations of the linear controlller can be seen if we take a larger step, say 10 meters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a141f100", + "metadata": {}, + "outputs": [], + "source": [ + "xd = np.outer(np.array([10, 0, 0, 0, 0, 0]), np.ones_like(T))\n", + "ref = np.vstack([xd, ud])\n", + "response = ct.input_output_response(pvtol_closed, T, ref, xeq)\n", + "plot_results(response.time, response.states, response.outputs[6:])" + ] + }, + { + "cell_type": "markdown", + "id": "8adb6ff4", + "metadata": {}, + "source": [ + "We see that the large initial error causes the vehicle to rotate to a very high role angle (almost 1 radian $\\approx 60^\\circ$), at which point the linear model is not very accurate and the controller errors in the $y$ direction get very large.\n", + "\n", + "One way to fix this problem is to change the gains on the controller so that we penalize the $y$ error more and try to keep that error from building up. However, given the fact that we are trying to stabilize a point that is fairly far from our initial condition, it can be difficult to manage the tradesoffs to get good performance.\n", + "\n", + "An alterntaive approach is is to stabilize the system around a trajectory that moves from the initial to final condition. As a very simple approach, we start by using a _nonfeasible_ trajectory that goes from 0 to 10 in 10 seconds." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a075a0a7", + "metadata": {}, + "outputs": [], + "source": [ + "timepts = np.linspace(0, 15, 100)\n", + "xf = np.array([10, 0, 0, 0, 0, 0])\n", + "xd = np.array([xf/10 * t if t < 10 else xf for t in timepts]).T\n", + "ud = np.outer(ueq, np.ones_like(timepts))\n", + "ref = np.vstack([xd, ud])\n", + "response = ct.input_output_response(pvtol_closed, timepts, ref, xeq)\n", + "plot_results(response.time, response.states, response.outputs[6:])" + ] + }, + { + "cell_type": "markdown", + "id": "73d74c23", + "metadata": {}, + "source": [ + "Note that even though the trajectory was not feasible (it asked the system to move sideways while remaining pointed in the vertical ($\\theta = 0$) direction, the controller has very good performance." + ] + }, + { + "cell_type": "markdown", + "id": "b7539806", + "metadata": {}, + "source": [ + "## Gain scheduled controller design" + ] + }, + { + "cell_type": "markdown", + "id": "23d7e21c", + "metadata": {}, + "source": [ + "Another challenge in using linearized models is that they are only accurate near the point in which they were computed. For the PVTOL system, this can be a problem if the roll angle $\\theta$ gets large, since in this case the linearization changes significantly (the forces $F_1$ and $F_2$ are no longer aligned with the horizontal and vertical axes).\n", + "\n", + "One approach to solving this problem is to compute different gains at different points in the operating envelope of the system. The code below illustrates the use of gain scheduling by modifying the system drag to a very high value (so that the vehicle must roll to a large angle in order to move sideways against the high drag) and then demonstrates the difficulty in obtaining good performance while trying to track the (still infeasible) trajectory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4590b138", + "metadata": {}, + "outputs": [], + "source": [ + "# Increase the viscous drag to force larger angles\n", + "linsys = pvtol.linearize(xeq, ueq, params={'c': 20})\n", + "\n", + "# Change to physically motivated gains\n", + "Qx3 = np.diag([10, 100, (180/np.pi) / 5, 0, 0, 0])\n", + "Qu3 = np.diag([10, 1])\n", + "\n", + "# Compute a single gain around hover\n", + "K, X, E = ct.lqr(linsys, Qx3, Qu3)\n", + "control, pvtol_closed = ct.create_statefbk_iosystem(pvtol, K)\n", + "\n", + "# Simulate the response trying to track horizontal trajectory\n", + "response = ct.input_output_response(pvtol_closed, T, ref, xeq, params={'c': 20})\n", + "plot_results(response.time, response.states, response.outputs[6:])" + ] + }, + { + "cell_type": "markdown", + "id": "9e01104a", + "metadata": {}, + "source": [ + "Note that the angle $\\theta$ is quite large (-0.5 rad) during the initla portion of the trajectory, and at this angle (~30$^\\circ$) it is difficult to maintain our altitude while moving sideways. This happens in large part becuase the system model that we used was linearized about the $\\theta = 0$ configuration.\n", + "\n", + "This problem can be addressed by designing a gain scheduled controller in which we compute different system gains at different roll angles. We carry out those computations below, using the `create_statefbk_iosystem` function, but now passing a set of gains and points instead of just a single gain.\n", + "\n", + "(Note: there is a bug in control-0.9.3 that requires gain scheduling to be done on two or more variables, so we also schedule on the horizontal velocity $\\dot x$, even though that doesn't matter that much here.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e427459f", + "metadata": {}, + "outputs": [], + "source": [ + "import itertools\n", + "import math\n", + "\n", + "# Set up points around which to linearize (control-0.9.3: must be 2D or greater)\n", + "angles = np.linspace(-math.pi/3, math.pi/3, 10)\n", + "speeds = np.linspace(-10, 10, 3)\n", + "points = list(itertools.product(angles, speeds))\n", + "\n", + "# Compute the gains at each design point\n", + "gains = []\n", + "for point in points:\n", + " # Compute the state that we want to linearize about\n", + " xgs = xeq.copy()\n", + " xgs[2], xgs[3] = point[0], point[1]\n", + " \n", + " # Linearize the system and compute the LQR gains\n", + " linsys = pvtol.linearize(xgs, ueq, params={'c': 20})\n", + " K, X, E = ct.lqr(linsys, Qx3, Qu3)\n", + " gains.append(K)\n", + " \n", + "# Create a gain scheduled controller off of the current state\n", + "control, pvtol_closed = ct.create_statefbk_iosystem(\n", + " pvtol, (gains, points), gainsched_indices=['x2', 'x3'])\n", + "\n", + "# Simulate the response\n", + "response = ct.input_output_response(pvtol_closed, T, ref, xeq, params={'c': 20})\n", + "plot_results(response.time, response.states, response.outputs[6:])" + ] + }, + { + "cell_type": "markdown", + "id": "7399db70", + "metadata": {}, + "source": [ + "We see that the response is much better, with about 10X less error in the $y$ coordinate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8021347", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds112-L5_rhc-doubleint.ipynb b/examples/cds112-L5_rhc-doubleint.ipynb new file mode 100644 index 000000000..52293b6ff --- /dev/null +++ b/examples/cds112-L5_rhc-doubleint.ipynb @@ -0,0 +1,616 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "9d41c333", + "metadata": {}, + "source": [ + "# RHC Example: Double integrator with bounded input\n", + "\n", + "Richard M. Murray, 3 Feb 2022 (updated 29 Jan 2023)\n", + "\n", + "To illustrate the implementation of a receding horizon controller, we\n", + "consider a linear system corresponding to a double integrator with\n", + "bounded input:\n", + "\n", + "$$\n", + " \\dot x = \\begin{bmatrix} 0 & 1 \\\\ 0 & 0 \\end{bmatrix} x + \\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix} \\text{clip}(u)\n", + " \\qquad\\text{where}\\qquad\n", + " \\text{clip}(u) = \\begin{cases}\n", + " -1 & u < -1, \\\\\n", + " u & -1 \\leq u \\leq 1, \\\\\n", + " 1 & u > 1.\n", + " \\end{cases}\n", + "$$\n", + "\n", + "We implement a model predictive controller by choosing\n", + "\n", + "$$\n", + " Q_x = \\begin{bmatrix} 1 & 0 \\\\ 0 & 0 \\end{bmatrix}, \\qquad\n", + " Q_u = \\begin{bmatrix} 1 \\end{bmatrix}, \\qquad\n", + " P_1 = \\begin{bmatrix} 0.1 & 0 \\\\ 0 & 0.1 \\end{bmatrix}.\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4fe0af7f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy as sp\n", + "import matplotlib.pyplot as plt\n", + "import control as ct\n", + "import control.optimal as opt\n", + "import control.flatsys as fs\n", + "import time" + ] + }, + { + "cell_type": "markdown", + "id": "4c695f81", + "metadata": {}, + "source": [ + "## System definition\n", + "\n", + "The system is defined as a double integrator with bounded input." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c01f571", + "metadata": {}, + "outputs": [], + "source": [ + "def doubleint_update(t, x, u, params):\n", + " # Get the parameters\n", + " lb = params.get('lb', -1)\n", + " ub = params.get('ub', 1)\n", + " assert lb < ub\n", + "\n", + " # bound the input\n", + " u_clip = np.clip(u, lb, ub)\n", + "\n", + " return np.array([x[1], u_clip[0]])\n", + "\n", + "proc = ct.NonlinearIOSystem(\n", + " doubleint_update, None, name=\"double integrator\",\n", + " inputs = ['u'], outputs=['x[0]', 'x[1]'], states=2)" + ] + }, + { + "cell_type": "markdown", + "id": "6c2f0d00", + "metadata": {}, + "source": [ + "## Receding horizon controller\n", + "\n", + "To define a receding horizon controller, we create an optimal control problem (using the `OptimalControlProblem` class) and then use the `compute_trajectory` method to solve for the trajectory from the current state.\n", + "\n", + "We start by defining the cost functions, which consists of a trajectory cost and a terminal cost:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a501efef", + "metadata": {}, + "outputs": [], + "source": [ + "Qx = np.diag([1, 0]) # state cost\n", + "Qu = np.diag([1]) # input cost\n", + "traj_cost=opt.quadratic_cost(proc, Qx, Qu)\n", + "\n", + "P1 = np.diag([0.1, 0.1]) # terminal cost\n", + "term_cost = opt.quadratic_cost(proc, P1, None)" + ] + }, + { + "cell_type": "markdown", + "id": "c5470629", + "metadata": {}, + "source": [ + "We also set up a set of constraints the correspond to the fact that the input should have magnitude 1. This can be done using either the [`input_range_constraint`](https://python-control.readthedocs.io/en/0.9.3.post2/generated/control.optimal.input_range_constraint.html) function or the [`input_poly_constraint`](https://python-control.readthedocs.io/en/0.9.3.post2/generated/control.optimal.input_poly_constraint.html) function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cb4c511a", + "metadata": {}, + "outputs": [], + "source": [ + "traj_constraints = opt.input_range_constraint(proc, -1, 1)\n", + "# traj_constraints = opt.input_poly_constraint(\n", + "# proc, np.array([[1], [-1]]), np.array([1, 1]))" + ] + }, + { + "cell_type": "markdown", + "id": "a5568374", + "metadata": {}, + "source": [ + "We define the horizon for evaluating finite-time, optimal control by setting up a set of time points across the designed horizon. The input will be computed at each time point." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9edec673", + "metadata": {}, + "outputs": [], + "source": [ + "Th = 5\n", + "timepts = np.linspace(0, Th, 11, endpoint=True)\n", + "print(timepts)" + ] + }, + { + "cell_type": "markdown", + "id": "cb8fcecc", + "metadata": {}, + "source": [ + "Finally, we define the optimal control problem that we want to solve (without actually solving it)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9f31be6", + "metadata": {}, + "outputs": [], + "source": [ + "# Set up the optimal control problem\n", + "ocp = opt.OptimalControlProblem(\n", + " proc, timepts, traj_cost,\n", + " terminal_cost=term_cost,\n", + " trajectory_constraints=traj_constraints,\n", + " # terminal_constraints=term_constraints,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ee9a39dd", + "metadata": {}, + "source": [ + "To make sure that the problem is properly defined, we solve the problem for a specific initial condition. We also compare the amount of time required to solve the problem from a \"cold start\" (no initial guess) versus a \"warm start\" (use the previous solution, shifted forward on point in time)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "887295eb", + "metadata": {}, + "outputs": [], + "source": [ + "X0 = np.array([1, 1])\n", + "\n", + "start_time = time.process_time()\n", + "res = ocp.compute_trajectory(X0, initial_guess=0, return_states=True)\n", + "stop_time = time.process_time()\n", + "print(f'* Cold start: {stop_time-start_time:.3} sec')\n", + "\n", + "# Resolve using previous solution (shifted forward) as initial guess to compare timing\n", + "start_time = time.process_time()\n", + "u = res.inputs\n", + "u_shift = np.hstack([u[:, 1:], u[:, -1:]])\n", + "ocp.compute_trajectory(X0, initial_guess=u_shift, print_summary=False)\n", + "stop_time = time.process_time()\n", + "print(f'* Warm start: {stop_time-start_time:.3} sec')" + ] + }, + { + "cell_type": "markdown", + "id": "115dec26", + "metadata": {}, + "source": [ + "(In this case the timing is not that different since the system is very simple.)\n", + "\n", + "Plotting the result, we see that the solution is properly computed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b98e773", + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(res.time, res.states[0], 'k-', label='$x_1$')\n", + "plt.plot(res.time, res.inputs[0], 'b-', label='u')\n", + "plt.xlabel('Time [s]')\n", + "plt.ylabel('$x_1$, $u$')\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "0e85981a", + "metadata": {}, + "source": [ + "We implement the receding horicon controller using a function that we can with different versions of the problem." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb2e8126", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a figure to use for plotting\n", + "def run_rhc_and_plot(\n", + " proc, ocp, X0, Tf, print_summary=False, verbose=False, ax=None, plot=True): \n", + " # Start at the initial point\n", + " x = X0\n", + " \n", + " # Initialize the axes\n", + " if plot and ax is None:\n", + " ax = plt.axes()\n", + " \n", + " # Initialize arrays to store the final trajectory\n", + " time_, inputs_, outputs_, states_ = [], [], [], []\n", + " \n", + " # Generate the individual traces for the receding horizon control\n", + " for t in ocp.timepts:\n", + " # Compute the optimal trajectory over the horizon\n", + " start_time = time.process_time()\n", + " res = ocp.compute_trajectory(x, print_summary=print_summary)\n", + " if verbose:\n", + " print(f\"{t=}: comp time = {time.process_time() - start_time:0.3}\")\n", + "\n", + " # Simulate the system for the update time, with higher res for plotting\n", + " tvec = np.linspace(0, res.time[1], 20)\n", + " inputs = res.inputs[:, 0] + np.outer(\n", + " (res.inputs[:, 1] - res.inputs[:, 0]) / (tvec[-1] - tvec[0]), tvec)\n", + " soln = ct.input_output_response(proc, tvec, inputs, x)\n", + " \n", + " # Save this segment for later use (final point will appear in next segment)\n", + " time_.append(t + soln.time[:-1])\n", + " inputs_.append(soln.inputs[:, :-1])\n", + " outputs_.append(soln.outputs[:, :-1])\n", + " states_.append(soln.states[:, :-1])\n", + "\n", + " if plot:\n", + " # Plot the results over the full horizon\n", + " h3, = ax.plot(t + res.time, res.states[0], 'k--', linewidth=0.5)\n", + " ax.plot(t + res.time, res.inputs[0], 'b--', linewidth=0.5)\n", + "\n", + " # Plot the results for this time segment\n", + " h1, = ax.plot(t + soln.time, soln.states[0], 'k-')\n", + " h2, = ax.plot(t + soln.time, soln.inputs[0], 'b-')\n", + " \n", + " # Update the state to use for the next time point\n", + " x = soln.states[:, -1]\n", + " \n", + " # Append the final point to the response\n", + " time_.append(t + soln.time[-1:])\n", + " inputs_.append(soln.inputs[:, -1:])\n", + " outputs_.append(soln.outputs[:, -1:])\n", + " states_.append(soln.states[:, -1:])\n", + "\n", + " # Label the plot\n", + " if plot:\n", + " # Adjust the limits for consistency\n", + " ax.set_ylim([-4, 3.5])\n", + "\n", + " # Add reference line for input lower bound\n", + " ax.plot([0, 7], [-1, -1], 'k--', linewidth=0.666)\n", + "\n", + " # Label the results\n", + " ax.set_xlabel(\"Time $t$ [sec]\")\n", + " ax.set_ylabel(\"State $x_1$, input $u$\")\n", + " ax.legend(\n", + " [h1, h2, h3], ['$x_1$', '$u$', 'prediction'],\n", + " loc='lower right', labelspacing=0)\n", + " plt.tight_layout()\n", + " \n", + " # Append\n", + " return ct.TimeResponseData(\n", + " np.hstack(time_), np.hstack(outputs_), np.hstack(states_), np.hstack(inputs_))" + ] + }, + { + "cell_type": "markdown", + "id": "be13e00a", + "metadata": {}, + "source": [ + "Finally, we call the controller and plot the response. The solid lines show the portions of the trajectory that we follow. The dashed lines are the trajectory over the full horizon, but which are not followed since we update the computation at each time step. (To get rid of the statistics of each optimization call, use `print_summary=False`.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "305a1127", + "metadata": {}, + "outputs": [], + "source": [ + "Tf = 10\n", + "rhc_resp = run_rhc_and_plot(proc, ocp, X0, Tf, verbose=True, print_summary=False)\n", + "print(f\"xf = {rhc_resp.states[:, -1]}\")" + ] + }, + { + "cell_type": "markdown", + "id": "6005bfb3", + "metadata": {}, + "source": [ + "## RHC vs LQR vs LQR terminal cost\n", + "\n", + "In the example above, we used a receding horizon controller with the terminal cost as $P_1 = \\text{diag}(0.1, 0.1)$. An alternative is to set the terminal cost to be the LQR terminal cost that goes along with the trajectory cost, which then provides a \"cost to go\" that matches the LQR \"cost to go\" (but keeping in mind that the LQR controller does not necessarily respect the constraints).\n", + "\n", + "The following code compares the original RHC formulation with a receding horizon controller using an LQR terminal cost versus an LQR controller." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea2de1f3", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the LQR solution\n", + "K, P_lqr, E = ct.lqr(proc.linearize(0, 0), Qx, Qu)\n", + "print(f\"P_lqr = \\n{P_lqr}\")\n", + "\n", + "# Create an LQR controller (and run it)\n", + "lqr_ctrl, lqr_clsys = ct.create_statefbk_iosystem(proc, K)\n", + "lqr_resp = ct.input_output_response(lqr_clsys, rhc_resp.time, 0, X0)\n", + "\n", + "# Create a new optimal control problem using the LQR terminal cost\n", + "# (need use more refined time grid as well, to approximate LQR rate)\n", + "lqr_timepts = np.linspace(0, Th, 25, endpoint=True)\n", + "lqr_term_cost=opt.quadratic_cost(proc, P_lqr, None)\n", + "ocp_lqr = opt.OptimalControlProblem(\n", + " proc, lqr_timepts, traj_cost, terminal_cost=lqr_term_cost,\n", + " trajectory_constraints=traj_constraints,\n", + ")\n", + "\n", + "# Create the response for the new controller\n", + "rhc_lqr_resp = run_rhc_and_plot(\n", + " proc, ocp_lqr, X0, 10, plot=False, print_summary=False)\n", + "\n", + "# Plot the different responses to compare them\n", + "fig, ax = plt.subplots(2, 1)\n", + "ax[0].plot(rhc_resp.time, rhc_resp.states[0], label='RHC + P_1')\n", + "ax[0].plot(rhc_lqr_resp.time, rhc_lqr_resp.states[0], '--', label='RHC + P_lqr')\n", + "ax[0].plot(lqr_resp.time, lqr_resp.outputs[0], ':', label='LQR')\n", + "ax[0].legend()\n", + "\n", + "ax[1].plot(rhc_resp.time, rhc_resp.inputs[0], label='RHC + P_1')\n", + "ax[1].plot(rhc_lqr_resp.time, rhc_lqr_resp.inputs[0], '--', label='RHC + P_lqr')\n", + "ax[1].plot(lqr_resp.time, lqr_resp.outputs[2], ':', label='LQR')" + ] + }, + { + "cell_type": "markdown", + "id": "9497530b", + "metadata": {}, + "source": [ + "## Discrete time RHC\n", + "\n", + "Many receding horizon control problems are solved based on a discrete-time model. We show here how to implement this for a \"double integrator\" system, which in discrete time has the form\n", + "\n", + "$$\n", + " x[k+1] = \\begin{bmatrix} 1 & 1 \\\\ 0 & 1 \\end{bmatrix} x[k] + \\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix} \\text{clip}(u[k])\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ae7cefa5", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# System definition\n", + "#\n", + "\n", + "def doubleint_update(t, x, u, params):\n", + " # Get the parameters\n", + " lb = params.get('lb', -1)\n", + " ub = params.get('ub', 1)\n", + " assert lb < ub\n", + "\n", + " # Get the sampling time\n", + " dt = params.get('dt', 1)\n", + "\n", + " # bound the input\n", + " u_clip = np.clip(u, lb, ub)\n", + "\n", + " return np.array([x[0] + dt * x[1], x[1] + dt * u_clip[0]])\n", + "\n", + "proc = ct.NonlinearIOSystem(\n", + " doubleint_update, None, name=\"double integrator\",\n", + " inputs = ['u'], outputs=['x[0]', 'x[1]'], states=2,\n", + " params={'dt': 1}, dt=1)\n", + "\n", + "#\n", + "# Linear quadratic regulator\n", + "#\n", + "\n", + "# Define the cost functions to use\n", + "Qx = np.diag([1, 0]) # state cost\n", + "Qu = np.diag([1]) # input cost\n", + "P1 = np.diag([0.1, 0.1]) # terminal cost\n", + "\n", + "# Get the LQR solution\n", + "K, P, E = ct.dlqr(proc.linearize(0, 0), Qx, Qu)\n", + "\n", + "# Test out the LQR controller, with no constraints\n", + "linsys = proc.linearize(0, 0)\n", + "clsys_lin = ct.ss(linsys.A - linsys.B @ K, linsys.B, linsys.C, 0, dt=proc.dt)\n", + "\n", + "X0 = np.array([2, 1]) # initial conditions\n", + "Tf = 10 # simulation time\n", + "res = ct.initial_response(clsys_lin, Tf, X0=X0)\n", + "\n", + "# Plot the results\n", + "plt.figure(1); plt.clf(); ax = plt.axes()\n", + "ax.plot(res.time, res.states[0], 'k-', label='$x_1$')\n", + "ax.plot(res.time, (-K @ res.states)[0], 'b-', label='$u$')\n", + "\n", + "# Test out the LQR controller with constraints\n", + "clsys_lqr = ct.feedback(proc, -K, 1)\n", + "tvec = np.arange(0, Tf, proc.dt)\n", + "res_lqr_const = ct.input_output_response(clsys_lqr, tvec, 0, X0)\n", + "\n", + "# Plot the results\n", + "ax.plot(res_lqr_const.time, res_lqr_const.states[0], 'k--', label='constrained')\n", + "ax.plot(res_lqr_const.time, (-K @ res_lqr_const.states)[0], 'b--')\n", + "ax.plot([0, 7], [-1, -1], 'k--', linewidth=0.75)\n", + "\n", + "# Adjust the limits for consistency\n", + "ax.set_ylim([-4, 3.5])\n", + "\n", + "# Label the results\n", + "ax.set_xlabel(\"Time $t$ [sec]\")\n", + "ax.set_ylabel(\"State $x_1$, input $u$\")\n", + "ax.legend(loc='lower right', labelspacing=0)\n", + "plt.title(\"Linearized LQR response from x0\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "13cfc5d8", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# Receding horizon controller\n", + "#\n", + "\n", + "# Create the constraints\n", + "traj_constraints = opt.input_range_constraint(proc, -1, 1)\n", + "term_constraints = opt.state_range_constraint(proc, [0, 0], [0, 0])\n", + "\n", + "# Define the optimal control problem we want to solve\n", + "T = 5\n", + "timepts = np.arange(0, T * proc.dt, proc.dt)\n", + "\n", + "# Set up the optimal control problems\n", + "ocp_orig = opt.OptimalControlProblem(\n", + " proc, timepts,\n", + " opt.quadratic_cost(proc, Qx, Qu),\n", + " trajectory_constraints=traj_constraints,\n", + " terminal_cost=opt.quadratic_cost(proc, P1, None),\n", + ")\n", + "\n", + "ocp_lqr = opt.OptimalControlProblem(\n", + " proc, timepts,\n", + " opt.quadratic_cost(proc, Qx, Qu),\n", + " trajectory_constraints=traj_constraints,\n", + " terminal_cost=opt.quadratic_cost(proc, P, None),\n", + ")\n", + "\n", + "ocp_low = opt.OptimalControlProblem(\n", + " proc, timepts,\n", + " opt.quadratic_cost(proc, Qx, Qu),\n", + " trajectory_constraints=traj_constraints,\n", + " terminal_cost=opt.quadratic_cost(proc, P/10, None),\n", + ")\n", + "\n", + "ocp_high = opt.OptimalControlProblem(\n", + " proc, timepts,\n", + " opt.quadratic_cost(proc, Qx, Qu),\n", + " trajectory_constraints=traj_constraints,\n", + " terminal_cost=opt.quadratic_cost(proc, P*10, None),\n", + ")\n", + "weight_list = [P1, P, P/10, P*10]\n", + "ocp_list = [ocp_orig, ocp_lqr, ocp_low, ocp_high]\n", + "\n", + "# Do a test run to figure out how long computation takes\n", + "start_time = time.process_time()\n", + "ocp_lqr.compute_trajectory(X0)\n", + "stop_time = time.process_time()\n", + "print(\"* Process time: %0.2g s\\n\" % (stop_time - start_time))\n", + "\n", + "# Create a figure to use for plotting\n", + "fig, [[ax_orig, ax_lqr], [ax_low, ax_high]] = plt.subplots(2, 2)\n", + "ax_list = [ax_orig, ax_lqr, ax_low, ax_high]\n", + "ax_name = ['orig', 'lqr', 'low', 'high']\n", + "\n", + "# Generate the individual traces for the receding horizon control\n", + "for ocp, ax, name, Pf in zip(ocp_list, ax_list, ax_name, weight_list):\n", + " x, t = X0, 0\n", + " for i in np.arange(0, Tf, proc.dt):\n", + " # Calculate the optimal trajectory\n", + " res = ocp.compute_trajectory(x, print_summary=False)\n", + " soln = ct.input_output_response(proc, res.time, res.inputs, x)\n", + "\n", + " # Plot the results for this time instant\n", + " ax.plot(res.time[:2] + t, res.inputs[0, :2], 'b-', linewidth=1)\n", + " ax.plot(res.time[:2] + t, soln.outputs[0, :2], 'k-', linewidth=1)\n", + " \n", + " # Plot the results projected forward\n", + " ax.plot(res.time[1:] + t, res.inputs[0, 1:], 'b--', linewidth=0.75)\n", + " ax.plot(res.time[1:] + t, soln.outputs[0, 1:], 'k--', linewidth=0.75)\n", + " \n", + " # Update the state to use for the next time point\n", + " x = soln.states[:, 1]\n", + " t += proc.dt\n", + "\n", + " # Adjust the limits for consistency\n", + " ax.set_ylim([-1.5, 3.5])\n", + "\n", + " # Label the results\n", + " ax.set_xlabel(\"Time $t$ [sec]\")\n", + " ax.set_ylabel(\"State $x_1$, input $u$\")\n", + " ax.set_title(f\"MPC response for {name}\")\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "015dc953", + "metadata": {}, + "source": [ + "We can also implement a receding horizon controller for a discrete-time system using `opt.create_mpc_iosystem`. This creates a controller that accepts the current state as the input and generates the control to apply from that state." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f8bb594", + "metadata": {}, + "outputs": [], + "source": [ + "# Construct using create_mpc_iosystem\n", + "clsys = opt.create_mpc_iosystem(\n", + " proc, timepts, opt.quadratic_cost(proc, Qx, Qu), traj_constraints,\n", + " terminal_cost=opt.quadratic_cost(proc, P1, None), \n", + ")\n", + "print(clsys)" + ] + }, + { + "cell_type": "markdown", + "id": "f1b08fb4", + "metadata": {}, + "source": [ + "(This function needs some work to be more user-friendly, e.g. renaming of the inputs and outputs.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2afd287", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds112-L6_stochastic-linsys.ipynb b/examples/cds112-L6_stochastic-linsys.ipynb new file mode 100644 index 000000000..3efc158cb --- /dev/null +++ b/examples/cds112-L6_stochastic-linsys.ipynb @@ -0,0 +1,328 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "03aa22e7", + "metadata": {}, + "source": [ + "# Stochastic Response\n", + "Richard M. Murray, 6 Feb 2022 (updated 9 Feb 2023)\n", + "\n", + "This notebook illustrates the implementation of random processes and stochastic response. We focus on a system of the form\n", + "$$\n", + " \\dot X = A X + F V \\qquad X \\in {\\mathbb R}^n\n", + "$$\n", + "\n", + "where $V$ is a white noise process and the system is a first order linear system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "902af902", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy as sp\n", + "import matplotlib.pyplot as plt\n", + "import control as ct\n", + "from math import sqrt, exp" + ] + }, + { + "cell_type": "markdown", + "id": "77d58303", + "metadata": {}, + "source": [ + "## First order linear system\n", + "\n", + "We start by looking at the stochastic response for a first order linear system\n", + "\n", + "$$\n", + "\\begin{gathered}\n", + " \\dot X = -a X + V, \\qquad Y = C X \\\\\n", + " \\mathbb{E}(V) = 0, \\quad \\mathbb{E}(V^\\mathsf{T}(t_1) V(t_2)) = 0.1\\, \\delta(t_1 - t_2)\n", + "\\end{gathered}\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "60192a8c", + "metadata": {}, + "outputs": [], + "source": [ + "# First order system\n", + "a = 1\n", + "c = 1\n", + "sys = ct.tf(c, [1, a])\n", + "\n", + "# Create the time vector that we want to use\n", + "Tf = 5\n", + "T = np.linspace(0, Tf, 1000)\n", + "dt = T[1] - T[0]\n", + "\n", + "# Create the basis for a white noise signal\n", + "# Note: use sqrt(Q/dt) for desired covariance\n", + "Q = np.array([[0.1]])\n", + "# V = np.random.normal(0, sqrt(Q[0,0]/dt), T.shape)\n", + "V = ct.white_noise(T, Q)\n", + "\n", + "plt.plot(T, V[0])\n", + "plt.xlabel('Time [s]')\n", + "plt.ylabel('$V$');" + ] + }, + { + "cell_type": "markdown", + "id": "b4629e2c", + "metadata": {}, + "source": [ + "Note that the magnitude of the signal seems to be much larger than $Q$. This is because we have a Guassian process $\\implies$ the signal consists of a sequence of \"impulse-like\" functions that have magnitude that increases with the time step $dt$ as $1/\\sqrt{dt}$ (this gives covariance $\\mathbb{E}(V(t_1) V^T(t_2)) = Q \\delta(t_2 - t_1)$." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "23319dc6", + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate the sample properties and make sure they match\n", + "print(\"mean(V) [0.0] = \", np.mean(V))\n", + "print(\"cov(V) * dt [%0.3g] = \" % Q, np.round(np.cov(V), decimals=3) * dt)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bdaaccf", + "metadata": {}, + "outputs": [], + "source": [ + "# Response of the first order system\n", + "# Scale white noise by sqrt(dt) to account for impulse\n", + "T, Y = ct.forced_response(sys, T, V)\n", + "plt.plot(T, Y)\n", + "plt.xlabel('Time [s]')\n", + "plt.ylabel('$Y$');" + ] + }, + { + "cell_type": "markdown", + "id": "ead0232e", + "metadata": {}, + "source": [ + "This is a first order system, and so we can use the calculation from the course\n", + "notes to compute the analytical correlation function and compare this to the \n", + "sampled data:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d31ce324", + "metadata": {}, + "outputs": [], + "source": [ + "# Compare static properties to what we expect analytically\n", + "def r(tau):\n", + " return c**2 * Q / (2 * a) * exp(-a * abs(tau))\n", + " \n", + "print(\"* mean(Y) [%0.3g] = %0.3g\" % (0, np.mean(Y).item()))\n", + "print(\"* cov(Y) [%0.3g] = %0.3g\" % (r(0).item(), np.cov(Y).item()))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1cf5a4b1", + "metadata": {}, + "outputs": [], + "source": [ + "# Correlation function for the input\n", + "# Scale by dt to take time step into account\n", + "# r_V = sp.signal.correlate(V, V) * dt / Tf\n", + "# tau = sp.signal.correlation_lags(len(V), len(V)) * dt\n", + "tau, r_V = ct.correlation(T, V)\n", + "\n", + "plt.plot(tau, r_V, 'r-')\n", + "plt.xlabel(r'$\\tau$')\n", + "plt.ylabel(r'$r_V(\\tau)$');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62af90a4", + "metadata": {}, + "outputs": [], + "source": [ + "# Correlation function for the output\n", + "# r_Y = sp.signal.correlate(Y, Y) * dt / Tf\n", + "# tau = sp.signal.correlation_lags(len(Y), len(Y)) * dt\n", + "tau, r_Y = ct.correlation(T, Y)\n", + "plt.plot(tau, r_Y)\n", + "plt.xlabel(r'$\\tau$')\n", + "plt.ylabel(r'$r_Y(\\tau)$')\n", + "\n", + "# Compare to the analytical answer\n", + "plt.plot(tau, [r(t)[0, 0] for t in tau], 'k--');" + ] + }, + { + "cell_type": "markdown", + "id": "2a2785e9", + "metadata": {}, + "source": [ + "The analytical curve may or may not line up that well with the correlation function based on the sample. Try running the code again from the top to see how things change based on the specific random sequence chosen at the start.\n", + "\n", + "Note: the _right_ way to compute the correlation function would be to run a lot of different samples of white noise filtered through the system dynamics and compute $R(t_1, t_2)$ across those samples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd5dfc75", + "metadata": {}, + "outputs": [], + "source": [ + "# As a crude approximation, compute the average correlation\n", + "r_avg = np.zeros_like(r_Y)\n", + "for i in range(100):\n", + " V = ct.white_noise(T, Q)\n", + " _, Y = ct.forced_response(sys, T, V)\n", + " tau, r_Y = ct.correlation(T, Y)\n", + " r_avg = r_avg + r_Y\n", + "r_avg = r_avg / i\n", + "plt.plot(tau, r_avg)\n", + "plt.xlabel(r'$\\tau$')\n", + "plt.ylabel(r'$r_Y(\\tau)$')\n", + "\n", + "# Compare to the analytical answer\n", + "plt.plot(tau, [r(t)[0, 0] for t in tau], 'k--');" + ] + }, + { + "cell_type": "markdown", + "id": "f07ec584", + "metadata": {}, + "source": [ + "## Dryden gust model\n", + "\n", + "Friedland, _Control Systems Design_, Example 10B\n", + "\n", + "Based on experimental data, the power spectral density for the vertical component of random wind velocity in turbulent air can be modeled as\n", + "$$\n", + "S(\\omega) = \\sigma_\\text{z}^2 T \\frac{1 + 3 (\\omega T)^2}{[1 + (\\omega T)^2]^2},\n", + "$$\n", + "where $\\sigma_\\text{z}$ and $T$ are parameters that depend on the wind characteristics.\n", + "\n", + "This power spectral density can be modeled using white noise by running it through a linear system with transfer fucntion\n", + "$$\n", + "H(s) = \\frac{1 + \\sqrt{3} T}{(1 + T s)^2}.\n", + "$$\n", + "A state space realization for this transfer function is given by\n", + "$$\n", + "\\begin{aligned}\n", + " \\dot X &= \\begin{bmatrix} 0 & 1 \\\\ -\\frac{1}{T^2} & -\\frac{2}{T} \\end{bmatrix} X \n", + " + \\begin{bmatrix} 0 \\\\ 1 \\end{bmatrix} V \\\\\n", + " Y &= \\begin{bmatrix} \\frac{1}{T^2} & \\frac{\\sqrt{3}}{T} \\end{bmatrix}\n", + " \\end{aligned}\n", + "$$" + ] + }, + { + "cell_type": "markdown", + "id": "d09fc03a", + "metadata": {}, + "source": [ + "To create a disturbance signal with the characteristics of the Dryden gust model, we create a linear system with the given parameters and computing the input/output response to white noise:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8df16a23", + "metadata": {}, + "outputs": [], + "source": [ + "sigma_z = 1\n", + "T = 1\n", + "filter = ct.ss([[0, 1], [-1/T**2, -2/T]], [[0], [1]], [[1/T**2, sqrt(3)/T]], 0)\n", + "\n", + "timepts = np.linspace(0, 10, 1000)\n", + "V = ct.white_noise(timepts, sigma_z**2)\n", + "resp = ct.input_output_response(filter, timepts, V)\n", + "\n", + "plt.plot(resp.time, resp.outputs);" + ] + }, + { + "cell_type": "markdown", + "id": "4d6604ee", + "metadata": {}, + "source": [ + "We can compute the correlation function and power spectral density to confirm that we match the desired characteristics:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "febc8b80", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the correlation function\n", + "tau, R = ct.correlation(resp.time, resp.outputs)\n", + "\n", + "# Analytical expression for the correlation function (see Friedland)\n", + "def dryden_corrfcn(tau, sigma_z=1, T=1):\n", + " return sigma_z**2 * np.exp(-np.abs(tau)/T) * (1- np.abs(tau)/(2*T))\n", + "\n", + "# Plot the correlation function\n", + "fig, axs = plt.subplots(1, 2)\n", + "axs[0].plot(tau, R)\n", + "axs[0].plot(tau, dryden_corrfcn(tau))\n", + "axs[0].set_xlabel(r\"$\\tau$\")\n", + "axs[0].set_ylabel(r\"$r(\\tau)$\")\n", + "axs[0].set_title(\"Correlation function\")\n", + "\n", + "# Compute the power spectral density\n", + "dt = timepts[1] - timepts[0]\n", + "S = sp.fft.rfft(R) * dt * 2 # rfft returns omega >= 0 => muliple mag by 2\n", + "omega = sp.fft.rfftfreq(R.size, dt)\n", + "\n", + "# Analytical expression for the correlation function (see Friedland)\n", + "def dryden_psd(omega, sigma_z=1., T=1.):\n", + " return sigma_z**2 * T * (1 + 3 * (omega * T)**2) / (1 + (omega * T)**2)**2\n", + "\n", + "# Plot the power spectral density\n", + "axs[1].loglog(omega[1:], np.abs(S[1:]))\n", + "axs[1].loglog(omega[1:], dryden_psd(omega[1:]))\n", + "axs[1].set_xlabel(r\"$\\omega$ [rad/sec]\")\n", + "axs[1].set_ylabel(r\"$S(\\omega)$\")\n", + "axs[1].set_title(\"Power spectral density\")\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1516ff6a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds112-L7_kalman-pvtol.ipynb b/examples/cds112-L7_kalman-pvtol.ipynb new file mode 100644 index 000000000..62270a2d8 --- /dev/null +++ b/examples/cds112-L7_kalman-pvtol.ipynb @@ -0,0 +1,439 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c017196f", + "metadata": {}, + "source": [ + "# PVTOL LQR + EQF example\n", + "RMM, 14 Feb 2022\n", + "\n", + "This notebook illustrates the implementation of an extended Kalman filter and the use of the estimated state for LQR feedback." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "544525ab", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.patches as patches\n", + "import control as ct" + ] + }, + { + "cell_type": "markdown", + "id": "859834cf", + "metadata": {}, + "source": [ + "## System definition\n", + "The dynamics of the system\n", + "with disturbances on the $x$ and $y$ variables is given by\n", + "$$\n", + " \\begin{aligned}\n", + " m \\ddot x &= F_1 \\cos\\theta - F_2 \\sin\\theta - c \\dot x + d_x, \\\\\n", + " m \\ddot y &= F_1 \\sin\\theta + F_2 \\cos\\theta - c \\dot y - m g + d_y, \\\\\n", + " J \\ddot \\theta &= r F_1.\n", + " \\end{aligned}\n", + "$$\n", + "The measured values of the system are the position and orientation,\n", + "with added noise $n_x$, $n_y$, and $n_\\theta$:\n", + "$$\n", + " \\vec y = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\end{bmatrix} + \n", + " \\begin{bmatrix} n_x \\\\ n_y \\\\ n_z \\end{bmatrix}.\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffafed74", + "metadata": {}, + "outputs": [], + "source": [ + "# pvtol = nominal system (no disturbances or noise)\n", + "# noisy_pvtol = pvtol w/ process disturbances and sensor noise\n", + "from pvtol import pvtol, pvtol_noisy, plot_results\n", + "\n", + "# Find the equilibrium point corresponding to the origin\n", + "xe, ue = ct.find_eqpt(\n", + " pvtol, np.zeros(pvtol.nstates),\n", + " np.zeros(pvtol.ninputs), [0, 0, 0, 0, 0, 0],\n", + " iu=range(2, pvtol.ninputs), iy=[0, 1])\n", + "\n", + "x0, u0 = ct.find_eqpt(\n", + " pvtol, np.zeros(pvtol.nstates),\n", + " np.zeros(pvtol.ninputs), np.array([2, 1, 0, 0, 0, 0]),\n", + " iu=range(2, pvtol.ninputs), iy=[0, 1])\n", + "\n", + "# Extract the linearization for use in LQR design\n", + "pvtol_lin = pvtol.linearize(xe, ue)\n", + "A, B = pvtol_lin.A, pvtol_lin.B\n", + "\n", + "print(pvtol, \"\\n\")\n", + "print(pvtol_noisy)" + ] + }, + { + "cell_type": "markdown", + "id": "2b63bf5b", + "metadata": {}, + "source": [ + "We now define the properties of the noise and disturbances. To make things (a bit more) interesting, we include some cross terms between the noise in $\\theta$ and the noise in $x$ and $y$:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e1ee7c9", + "metadata": {}, + "outputs": [], + "source": [ + "# Disturbance and noise intensities\n", + "Qv = np.diag([1e-2, 1e-2])\n", + "Qw = np.array([[2e-4, 0, 1e-5], [0, 2e-4, 1e-5], [1e-5, 1e-5, 1e-4]])\n", + "Qwinv = np.linalg.inv(Qw)\n", + "\n", + "# Initial state covariance\n", + "P0 = np.eye(pvtol.nstates)" + ] + }, + { + "cell_type": "markdown", + "id": "e4c52c73", + "metadata": {}, + "source": [ + "## Control system design\n", + "\n", + "To design the control system, we first construct an estimator for the state (given the commanded inputs and measured outputs. Since this is a nonlinear system, we use the update law for the nominal system to compute the state update. We also make use of the linearization around the current state for the covariance update (using the function `pvtol.A(x, u)`, which is defined in `pvtol.py`, making this an extended Kalman filter)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3647bf15", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the disturbance input and measured output matrices\n", + "F = np.array([[0, 0], [0, 0], [0, 0], [1/pvtol.params['m'], 0], [0, 1/pvtol.params['m']], [0, 0]])\n", + "C = np.eye(3, 6)\n", + "\n", + "# Estimator update law\n", + "def estimator_update(t, x, u, params):\n", + " # Extract the states of the estimator\n", + " xhat = x[0:pvtol.nstates]\n", + " P = x[pvtol.nstates:].reshape(pvtol.nstates, pvtol.nstates)\n", + "\n", + " # Extract the inputs to the estimator\n", + " y = u[0:3] # just grab the first three outputs\n", + " u = u[6:8] # get the inputs that were applied as well\n", + "\n", + " # Compute the linearization at the current state\n", + " A = pvtol.A(xhat, u) # A matrix depends on current state\n", + " # A = pvtol.A(xe, ue) # Fixed A matrix (for testing/comparison)\n", + " \n", + " # Compute the optimal again\n", + " L = P @ C.T @ Qwinv\n", + "\n", + " # Update the state estimate\n", + " xhatdot = pvtol.updfcn(t, xhat, u, params) - L @ (C @ xhat - y)\n", + "\n", + " # Update the covariance\n", + " Pdot = A @ P + P @ A.T - P @ C.T @ Qwinv @ C @ P + F @ Qv @ F.T\n", + "\n", + " # Return the derivative\n", + " return np.hstack([xhatdot, Pdot.reshape(-1)])\n", + "\n", + "def estimator_output(t, x, u, params):\n", + " # Return the estimator states\n", + " return x[0:pvtol.nstates]\n", + "\n", + "estimator = ct.NonlinearIOSystem(\n", + " estimator_update, estimator_output,\n", + " states=pvtol.nstates + pvtol.nstates**2,\n", + " inputs= pvtol_noisy.output_labels \\\n", + " + pvtol_noisy.input_labels[0:pvtol.ninputs],\n", + " outputs=[f'xh{i}' for i in range(pvtol.nstates)],\n", + ")\n", + "print(estimator)" + ] + }, + { + "cell_type": "markdown", + "id": "ba3d2640", + "metadata": {}, + "source": [ + "For the controller, we will use an LQR feedback with physically motivated weights (see OBC, Example 3.5):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9787db61", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# LQR design w/ physically motivated weighting\n", + "#\n", + "# Shoot for 1 cm error in x, 10 cm error in y. Try to keep the angle\n", + "# less than 5 degrees in making the adjustments. Penalize side forces\n", + "# due to loss in efficiency.\n", + "#\n", + "\n", + "Qx = np.diag([100, 10, (180/np.pi) / 5, 0, 0, 0])\n", + "Qu = np.diag([10, 1])\n", + "K, _, _ = ct.lqr(A, B, Qx, Qu)\n", + "\n", + "#\n", + "# Control system construction: combine LQR w/ EKF\n", + "#\n", + "# Use the linearization around the origin to design the optimal gains\n", + "# to see how they compare to the final value of P for the EKF\n", + "#\n", + "\n", + "# Construct the state feedback controller with estimated state as input\n", + "statefbk, _ = ct.create_statefbk_iosystem(pvtol, K, estimator=estimator)\n", + "print(statefbk, \"\\n\")\n", + "\n", + "# Reconstruct the control system with the noisy version of the process\n", + "# Create a closed loop system around the controller\n", + "clsys = ct.interconnect(\n", + " [pvtol_noisy, statefbk, estimator],\n", + " inplist = statefbk.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " inputs = statefbk.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " outlist = pvtol.output_labels + statefbk.output_labels + estimator.output_labels,\n", + " outputs = pvtol.output_labels + statefbk.output_labels + estimator.output_labels\n", + ")\n", + "print(clsys)" + ] + }, + { + "cell_type": "markdown", + "id": "5f527f16", + "metadata": {}, + "source": [ + "Note that we have to construct the closed loop system manually since we need to allow the disturbance and noise inputs to be sent to the closed loop system and `create_statefbk_iosystem` does not support this (to be fixed in an upcoming release)." + ] + }, + { + "cell_type": "markdown", + "id": "7bf558a0", + "metadata": {}, + "source": [ + "## Simulations\n", + "\n", + "Finally, we can simulate the system to see how it all works. We start by creating the noise for the system:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2583a0e", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the time vector for the simulation\n", + "Tf = 10\n", + "timepts = np.linspace(0, Tf, 1000)\n", + "\n", + "# Create representative process disturbance and sensor noise vectors\n", + "np.random.seed(117) # avoid figures changing from run to run\n", + "V = ct.white_noise(timepts, Qv) # smaller disturbances and noise then design\n", + "W = ct.white_noise(timepts, Qw)\n", + "plt.plot(timepts, V[0], label=\"V[0]\")\n", + "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "4d944709", + "metadata": {}, + "source": [ + "### LQR with EKF\n", + "\n", + "We can now feed the desired trajectory plus the noise and disturbances into the system and see how well the controller with a state estimator does in holding the system at an equilibrium point:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad7a9750", + "metadata": {}, + "outputs": [], + "source": [ + "# Put together the input for the system\n", + "U = [xe, ue, V, W]\n", + "X0 = [x0, xe, P0.reshape(-1)]\n", + "\n", + "# Initial condition response\n", + "resp = ct.input_output_response(clsys, timepts, U, X0)\n", + "\n", + "# Plot the response\n", + "plot_results(timepts, resp.states, resp.outputs[pvtol.nstates:])" + ] + }, + { + "cell_type": "markdown", + "id": "86f10064", + "metadata": {}, + "source": [ + "To see how well the estimtator did, we can compare the estimated position with the actual position:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5f24119", + "metadata": {}, + "outputs": [], + "source": [ + "# Response of the first two states, including internal estimates\n", + "h1, = plt.plot(resp.time, resp.outputs[0], 'b-', linewidth=0.75)\n", + "h2, = plt.plot(resp.time, resp.outputs[1], 'r-', linewidth=0.75)\n", + "\n", + "# Add on the internal estimator states\n", + "xh0 = clsys.find_output('xh0')\n", + "xh1 = clsys.find_output('xh1')\n", + "h3, = plt.plot(resp.time, resp.outputs[xh0], 'k--')\n", + "h4, = plt.plot(resp.time, resp.outputs[xh1], 'k--')\n", + "\n", + "plt.plot([0, 10], [0, 0], 'k--', linewidth=0.5)\n", + "plt.ylabel(r\"Position $x$, $y$ [m]\")\n", + "plt.xlabel(r\"Time $t$ [s]\")\n", + "plt.legend(\n", + " [h1, h2, h3, h4], ['$x$', '$y$', r'$\\hat{x}$', r'$\\hat{y}$'], \n", + " loc='upper right', frameon=False, ncol=2);" + ] + }, + { + "cell_type": "markdown", + "id": "7139202f", + "metadata": {}, + "source": [ + "Note the rapid convergence of the estimate to the proper value, since we are directly measuring the position variables. If we look at the full set of states, we see that other variables have different convergence properties:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78a61e74", + "metadata": {}, + "outputs": [], + "source": [ + "fig, axs = plt.subplots(2, 3)\n", + "var = ['x', 'y', r'\\theta', r'\\dot x', r'\\dot y', r'\\dot \\theta']\n", + "for i in [0, 1]:\n", + " for j in [0, 1, 2]:\n", + " k = i * 3 + j\n", + " axs[i, j].plot(resp.time, resp.outputs[k], label=f'${var[k]}$')\n", + " axs[i, j].plot(resp.time, resp.outputs[xh0+k], label=f'$\\\\hat {var[k]}$')\n", + " axs[i, j].legend()\n", + " if i == 1:\n", + " axs[i, j].set_xlabel(\"Time $t$ [s]\")\n", + " if j == 0:\n", + " axs[i, j].set_ylabel(\"State\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "2039578e", + "metadata": {}, + "source": [ + "Note the lag in tracking changes in the $\\dot x$ and $\\dot y$ states (varies from simulation to simulation, depending on the specific noise signal)." + ] + }, + { + "cell_type": "markdown", + "id": "0c0d5c99", + "metadata": {}, + "source": [ + "### Full state feedback\n", + "\n", + "To see how the inclusion of the estimator affects the system performance, we compare it with the case where we are able to directly measure the state of the system." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b6a1f1c", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the full state feedback solution\n", + "lqr_ctrl, _ = ct.create_statefbk_iosystem(pvtol, K)\n", + "\n", + "lqr_clsys = ct.interconnect(\n", + " [pvtol_noisy, lqr_ctrl],\n", + " inplist = lqr_ctrl.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " inputs = lqr_ctrl.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " outlist = pvtol.output_labels + lqr_ctrl.output_labels,\n", + " outputs = pvtol.output_labels + lqr_ctrl.output_labels\n", + ")\n", + "\n", + "# Put together the input for the system (turn off sensor noise)\n", + "U = [xe, ue, V, W*0]\n", + "\n", + "# Run a simulation with full state feedback\n", + "lqr_resp = ct.input_output_response(lqr_clsys, timepts, U, x0)\n", + "\n", + "# Compare the results\n", + "plt.plot(resp.states[0], resp.states[1], 'b-', label=\"Extended KF\")\n", + "plt.plot(lqr_resp.states[0], lqr_resp.states[1], 'r-', label=\"Full state\")\n", + "\n", + "plt.xlabel('$x$ [m]')\n", + "plt.ylabel('$y$ [m]')\n", + "plt.axis('equal')\n", + "plt.legend(frameon=False);" + ] + }, + { + "cell_type": "markdown", + "id": "8c0083cb", + "metadata": {}, + "source": [ + "Things to try:\n", + "* Compute a feasable trajectory and stabilize around that instead of the origin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "777053a4", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds112-L8_fusion-kincar.ipynb b/examples/cds112-L8_fusion-kincar.ipynb new file mode 100644 index 000000000..de4aad5d6 --- /dev/null +++ b/examples/cds112-L8_fusion-kincar.ipynb @@ -0,0 +1,476 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "eec23018", + "metadata": {}, + "source": [ + "# Kinematic car sensor fusion example\n", + "RMM, 24 Feb 2022 (updated 23 Feb 2023)\n", + "\n", + "In this example we work through estimation of the state of a car changing\n", + "lanes with two different sensors available: one with good longitudinal accuracy\n", + "and the other with good lateral accuracy.\n", + "\n", + "All calculations are done in discrete time, using both the form of the Kalman\n", + "filter in Theorem 7.2 and the predictor corrector form." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "107a6613", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy as sp\n", + "import matplotlib.pyplot as plt\n", + "import control as ct\n", + "import control.optimal as opt\n", + "import control.flatsys as fs\n", + "\n", + "# Define some line styles for later use\n", + "ebarstyle = {'elinewidth': 0.5, 'capsize': 2}\n", + "xdstyle = {'color': 'k', 'linestyle': '--', 'linewidth': 0.5, \n", + " 'marker': '+', 'markersize': 4}" + ] + }, + { + "cell_type": "markdown", + "id": "ea8807a4", + "metadata": {}, + "source": [ + "## System definition\n", + "\n", + "We make use of a simple model for a vehicle navigating in the plane, known as the \"bicycle model\". The kinematics of this vehicle can be written in terms of the contact point $(x, y)$ and the angle $\\theta$ of the vehicle with respect to the horizontal axis:\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
\n", + "$$\n", + "\\begin{aligned}\n", + " \\dot x &= \\cos\\theta\\, v \\\\\n", + " \\dot y &= \\sin\\theta\\, v \\\\\n", + " \\dot\\theta &= \\frac{v}{l} \\tan \\delta\n", + "\\end{aligned}\n", + "$$\n", + "
\n", + "\n", + "The input $v$ represents the velocity of the vehicle and the input $\\delta$ represents the turning rate. The parameter $l$ is the wheelbase." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a04106f8", + "metadata": {}, + "outputs": [], + "source": [ + "# Vehicle steering dynamics\n", + "#\n", + "# System state: x, y, theta\n", + "# System input: v, phi\n", + "# System output: x, y\n", + "# System parameters: wheelbase, maxsteer\n", + "#\n", + "from kincar import kincar, plot_lanechange\n", + "print(kincar)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "69c048ed", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a trajectory for the vehicle\n", + "# Define the endpoints of the trajectory\n", + "x0 = [0., -2., 0.]; u0 = [10., 0.]\n", + "xf = [40., 2., 0.]; uf = [10., 0.]\n", + "Tf = 4\n", + "\n", + "# Find a trajectory between the initial condition and the final condition\n", + "traj = fs.point_to_point(kincar, Tf, x0, u0, xf, uf, basis=fs.PolyFamily(6))\n", + "\n", + "# Create the desired trajectory between the initial and final condition\n", + "Ts = 0.1\n", + "# Ts = 0.5\n", + "timepts = np.arange(0, Tf + Ts, Ts)\n", + "xd, ud = traj.eval(timepts)\n", + "\n", + "plot_lanechange(timepts, xd, ud)" + ] + }, + { + "cell_type": "markdown", + "id": "aeeaa39e", + "metadata": {}, + "source": [ + "### Discrete time system model\n", + "\n", + "For the model that we use for the Kalman filter, we take a simple discretization using the approximation that $\\dot x = (x[k+1] - x[k])/T_s$ where $T_s$ is the sampling time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2469c60e", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# Create a discrete-time, linear model\n", + "#\n", + "\n", + "# Linearize about the starting point\n", + "linsys = ct.linearize(kincar, x0, u0)\n", + "\n", + "# Create a discrete-time model by hand\n", + "Ad = np.eye(linsys.nstates) + linsys.A * Ts\n", + "Bd = linsys.B * Ts\n", + "discsys = ct.ss(Ad, Bd, np.eye(linsys.nstates), 0, dt=Ts)\n", + "print(discsys);" + ] + }, + { + "cell_type": "markdown", + "id": "084c5ae8", + "metadata": {}, + "source": [ + "### Sensor model\n", + "\n", + "We assume that we have two sensors: one with good longitudinal accuracy and the other with good lateral accuracy. For each sensor we define the map from the state space to the sensor outputs, the covariance matrix for the measurements, and a white noise signal (now in discrete time).\n", + "\n", + "Note: we pass the keyword `dt` to the `white_noise` function so that the white noise is consistent with a discrete-time model (so the covariance is _not_ rescaled by $\\sqrt{dt}$)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0a19d109", + "metadata": {}, + "outputs": [], + "source": [ + "# Sensor #1: longitudinal\n", + "C_lon = np.eye(2, discsys.nstates)\n", + "Rw_lon = np.diag([0.1 ** 2, 1 ** 2])\n", + "W_lon = ct.white_noise(timepts, Rw_lon, dt=Ts)\n", + "\n", + "# Sensor #2: lateral\n", + "C_lat = np.eye(2, discsys.nstates)\n", + "Rw_lat = np.diag([1 ** 2, 0.1 ** 2])\n", + "W_lat = ct.white_noise(timepts, Rw_lat, dt=Ts)\n", + "\n", + "# Plot the noisy signals\n", + "plt.subplot(2, 1, 1)\n", + "Y = xd[0:2] + W_lon\n", + "plt.plot(Y[0], Y[1])\n", + "plt.plot(xd[0], xd[1], **xdstyle)\n", + "plt.xlabel(\"$x$ position [m]\")\n", + "plt.ylabel(\"$y$ position [m]\")\n", + "plt.title(\"Sensor #1 (longitudinal)\")\n", + " \n", + "plt.subplot(2, 1, 2)\n", + "Y = xd[0:2] + W_lat\n", + "plt.plot(Y[0], Y[1])\n", + "plt.plot(xd[0], xd[1], **xdstyle)\n", + "plt.xlabel(\"$x$ position [m]\")\n", + "plt.ylabel(\"$y$ position [m]\")\n", + "plt.title(\"Sensor #2 (lateral)\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "c3fa1a3d", + "metadata": {}, + "source": [ + "## Linear Quadratic Estimator\n", + "\n", + "We now construct a linear quadratic estimator for the system usign the Kalman filter form. This is idone using the [`create_estimator_iosystem`](https://github.com/python-control/python-control/blob/main/control/stochsys.py#L310-L517) function in python-control." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "993601a2", + "metadata": {}, + "outputs": [], + "source": [ + "# Disturbance and initial condition model\n", + "# Note: multiple by sampling time since we discretized the dynamics\n", + "Rv = np.diag([0.1, 0.01]) * Ts\n", + "# Rv = np.diag([10, 1]) * Ts # Variant: no input information\n", + "P0 = np.diag([1, 1, 0.1])\n", + "\n", + "# Combine the sensors\n", + "# Note: no sampling time here because we are doing discrete-time KF\n", + "C = np.vstack([C_lon, C_lat])\n", + "Rw = sp.linalg.block_diag(Rw_lon, Rw_lat)\n", + "\n", + "estim = ct.create_estimator_iosystem(discsys, Rv, Rw, C=C, P0=P0)\n", + "print(estim)" + ] + }, + { + "cell_type": "markdown", + "id": "0c2e8ab0", + "metadata": {}, + "source": [ + "We can now run the estimator on the noisy signals to see how well it works." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3d02ec33", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute the inputs to the estimator\n", + "Y = np.vstack([xd[0:2] + W_lon, xd[0:2] + W_lat])\n", + "U = np.vstack([Y, ud]) # add input to the Kalman filter\n", + "# U = np.vstack([Y, ud * 0]) # variant: no input information\n", + "X0 = np.hstack([xd[:, 0], P0.reshape(-1)])\n", + "\n", + "# Run the estimator on the trajectory\n", + "estim_resp = ct.input_output_response(estim, timepts, U, X0)\n", + "\n", + "# Run a prediction to see what happens next\n", + "T_predict = np.arange(timepts[-1], timepts[-1] + 4 + Ts, Ts)\n", + "U_predict = np.outer(U[:, -1], np.ones_like(T_predict))\n", + "predict_resp = ct.input_output_response(\n", + " estim, T_predict, U_predict, estim_resp.states[:, -1],\n", + " params={'correct': False})\n", + "\n", + "# Plot the estimated trajectory versus the actual trajectory\n", + "plt.subplot(2, 1, 1)\n", + "plt.errorbar(\n", + " estim_resp.time, estim_resp.outputs[0], \n", + " estim_resp.states[estim.find_state('P[0,0]')], fmt='b-', **ebarstyle)\n", + "plt.errorbar(\n", + " predict_resp.time, predict_resp.outputs[0], \n", + " predict_resp.states[estim.find_state('P[0,0]')], fmt='r-', **ebarstyle)\n", + "plt.plot(timepts, xd[0], 'k--')\n", + "plt.ylabel(\"$x$ position [m]\")\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.errorbar(\n", + " estim_resp.time, estim_resp.outputs[1], \n", + " estim_resp.states[estim.find_state('P[1,1]')], fmt='b-', **ebarstyle)\n", + "plt.errorbar(\n", + " predict_resp.time, predict_resp.outputs[1], \n", + " predict_resp.states[estim.find_state('P[1,1]')], fmt='r-', **ebarstyle)\n", + "# lims = plt.axis(); plt.axis([lims[0], lims[1], -5, 5])\n", + "plt.plot(timepts, xd[1], 'k--');\n", + "plt.ylabel(\"$y$ position [m]\")\n", + "plt.xlabel(\"Time $t$ [s]\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44f69f79", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the estimated errors\n", + "plt.subplot(2, 1, 1)\n", + "plt.errorbar(\n", + " estim_resp.time, estim_resp.outputs[0] - xd[0], \n", + " estim_resp.states[estim.find_state('P[0,0]')], fmt='b-', **ebarstyle)\n", + "plt.errorbar(\n", + " predict_resp.time, predict_resp.outputs[0] - (xd[0] + xd[0, -1]), \n", + " predict_resp.states[estim.find_state('P[0,0]')], fmt='r-', **ebarstyle)\n", + "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", + "# lims = plt.axis(); plt.axis([lims[0], lims[1], -2, 0.2])\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.errorbar(\n", + " estim_resp.time, estim_resp.outputs[1] - xd[1], \n", + " estim_resp.states[estim.find_state('P[1,1]')], fmt='b-', **ebarstyle)\n", + "plt.errorbar(\n", + " predict_resp.time, predict_resp.outputs[1] - xd[1, -1], \n", + " predict_resp.states[estim.find_state('P[1,1]')], fmt='r-', **ebarstyle)\n", + "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2]);" + ] + }, + { + "cell_type": "markdown", + "id": "6f6c1b6f", + "metadata": {}, + "source": [ + "## Things to try\n", + "* Remove the input (and update P0 and Rv)\n", + "* Change the sampling rate" + ] + }, + { + "cell_type": "markdown", + "id": "8f680b92", + "metadata": {}, + "source": [ + "## Predictor-corrector form\n", + "\n", + "Instead of using create_estimator_iosystem, we can also compute out the estimate in a more manual fashion, done here using the predictor-corrector form." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa488d51", + "metadata": {}, + "outputs": [], + "source": [ + "# System matrices\n", + "A, B, F = discsys.A, discsys.B, discsys.B\n", + "\n", + "# Create an array to store the results\n", + "xhat = np.zeros((discsys.nstates, timepts.size))\n", + "P = np.zeros((discsys.nstates, discsys.nstates, timepts.size))\n", + "\n", + "# Update the estimates at each time\n", + "for i, t in enumerate(timepts):\n", + " # Prediction step\n", + " if i == 0:\n", + " # Use the initial condition\n", + " xkkm1 = xd[:, 0]\n", + " Pkkm1 = P0\n", + " else:\n", + " xkkm1 = A @ xkk + B @ ud[:, i-1]\n", + " Pkkm1 = A @ Pkk @ A.T + F @ Rv @ F.T\n", + " \n", + " # Correction step (variant: apply only when sensor data is available)\n", + " L = Pkkm1 @ C.T @ np.linalg.inv(Rw + C @ Pkkm1 @ C.T)\n", + " xkk = xkkm1 - L @ (C @ xkkm1 - Y[:, i])\n", + " Pkk = Pkkm1 - L @ C @ Pkkm1\n", + "\n", + " # Save the state estimate and covariance for later plotting\n", + " xhat[:, i], P[:, :, i] = xkkm1, Pkkm1 # For comparison to Kalman form\n", + " # xhat[:, i], P[:, :, i] = xkk, Pkk # variant: \n", + " \n", + "plt.subplot(2, 1, 1)\n", + "plt.errorbar(timepts, xhat[0], P[0, 0], fmt='b-', **ebarstyle)\n", + "plt.plot(timepts, xd[0], 'k--')\n", + "plt.ylabel(\"$x$ position [m]\")\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.errorbar(timepts, xhat[1], P[1, 1], fmt='b-', **ebarstyle)\n", + "plt.plot(timepts, xd[1], 'k--')\n", + "plt.ylabel(\"$x$ position [m]\")\n", + "plt.xlabel(\"Time $t$ [s]\");" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4eda4729", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the estimated errors (and compare to Kalman form)\n", + "plt.subplot(2, 1, 1)\n", + "plt.errorbar(timepts, xhat[0] - xd[0], P[0, 0], fmt='b-', **ebarstyle)\n", + "plt.plot(estim_resp.time, estim_resp.outputs[0] - xd[0], 'r--', linewidth=3)\n", + "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", + "plt.ylabel(\"x error [m]\")\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.errorbar(timepts, xhat[1] - xd[1], P[1, 1], fmt='b-', **ebarstyle,\n", + " label='predictor/corrector')\n", + "plt.plot(estim_resp.time, estim_resp.outputs[1] - xd[1], 'r--', linewidth=3,\n", + " label='Kalman form')\n", + "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", + "plt.ylabel(\"y error [m]\")\n", + "plt.xlabel(\"Time $t$ [s]\")\n", + "plt.legend(loc='lower right');" + ] + }, + { + "cell_type": "markdown", + "id": "19a673a1", + "metadata": {}, + "source": [ + "## Information filter\n", + "\n", + "An alternative way to implement the computation is using the information filter formulation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36111bc2", + "metadata": {}, + "outputs": [], + "source": [ + "from numpy.linalg import inv\n", + "\n", + "# Update the estimates at each time\n", + "for i, t in enumerate(timepts):\n", + " # Prediction step\n", + " if i == 0:\n", + " # Use the initial condition\n", + " xkkm1 = xd[:, 0]\n", + " Pkkm1 = P0\n", + " else:\n", + " xkkm1 = A @ xkk + B @ ud[:, i-1]\n", + " Pkkm1 = A @ Pkk @ A.T + F @ Rv @ F.T\n", + " \n", + " # Correction step (variant: apply only when sensor data is available)\n", + " Ikk, Zkk = inv(Pkkm1), inv(Pkkm1) @ xkkm1\n", + " \n", + " # Longitudinal sensor update\n", + " Ikk += C_lon.T @ inv(Rw_lon) @ C_lon # Omega_lon\n", + " Zkk += C_lon.T @ inv(Rw_lon) @ Y[:2, i] # Psi_lon\n", + "\n", + " # Lateral sensor update\n", + " Ikk += C_lat.T @ inv(Rw_lat) @ C_lat # Omega_lat\n", + " Zkk += C_lat.T @ inv(Rw_lat) @ Y[2:, i] # Psi_lat\n", + " \n", + " # Compute the updated state and covariance \n", + " Pkk = inv(Ikk)\n", + " xkk = Pkk @ Zkk\n", + "\n", + " # Save the state estimate and covariance for later plotting\n", + " xhat[:, i], P[:, :, i] = xkkm1, Pkkm1\n", + "\n", + "# Plot the estimated errors (and compare to Kalman form)\n", + "plt.subplot(2, 1, 1)\n", + "plt.errorbar(timepts, xhat[0] - xd[0], P[0, 0], fmt='b-', **ebarstyle)\n", + "plt.plot(estim_resp.time, estim_resp.outputs[0] - xd[0], 'r--', linewidth=3)\n", + "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", + "plt.ylabel(\"x error [m]\")\n", + "\n", + "plt.subplot(2, 1, 2)\n", + "plt.errorbar(timepts, xhat[1] - xd[1], P[1, 1], fmt='b-', **ebarstyle,\n", + " label='information filter')\n", + "plt.plot(estim_resp.time, estim_resp.outputs[1] - xd[1], 'r--', linewidth=3,\n", + " label='Kalman form')\n", + "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", + "plt.ylabel(\"y error [m]\")\n", + "plt.xlabel(\"Time $t$ [s]\")\n", + "plt.legend(loc='lower right');" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad5cf57f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/cds112-L9_mhe-pvtol.ipynb b/examples/cds112-L9_mhe-pvtol.ipynb new file mode 100644 index 000000000..be15c4bfa --- /dev/null +++ b/examples/cds112-L9_mhe-pvtol.ipynb @@ -0,0 +1,761 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "baba5fab", + "metadata": {}, + "source": [ + "# Moving Horizon Estimation\n", + "\n", + "Richard M. Murray, 24 Feb 2023\n", + "\n", + "In this notebook we illustrate the implementation of moving horizon estimation (MHE)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36715c5f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy as sp\n", + "import matplotlib.pyplot as plt\n", + "import control as ct\n", + "\n", + "import control.optimal as opt\n", + "import control.flatsys as fs" + ] + }, + { + "cell_type": "markdown", + "id": "d72a155b", + "metadata": {}, + "source": [ + "## System Description\n", + "\n", + "We use the PVTOL dynamics from the textbook, which are contained in the `pvtol` module:\n", + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + "\n", + "
\n", + "$$\n", + "\\begin{aligned}\n", + " m \\ddot x &= F_1 \\cos\\theta - F_2 \\sin\\theta - c \\dot x, \\\\\n", + " m \\ddot y &= F_1 \\sin\\theta + F_2 \\cos\\theta - m g - c \\dot y, \\\\\n", + " J \\ddot \\theta &= r F_1.\n", + "\\end{aligned}\n", + "$$\n", + "
\n", + "\n", + "The measured values of the system are the position and orientation,\n", + "with added noise $n_x$, $n_y$, and $n_\\theta$:\n", + "\n", + "$$\n", + " \\vec y = \\begin{bmatrix} x \\\\ y \\\\ \\theta \\end{bmatrix} + \n", + " \\begin{bmatrix} n_x \\\\ n_y \\\\ n_z \\end{bmatrix}.\n", + "$$\n", + "\n", + "The parameter values for the PVTOL system come from the Caltech ducted fan experiment, described in more detail in [Lecture 4b](cds112-L4b_pvtol-lqr.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08919988", + "metadata": {}, + "outputs": [], + "source": [ + "# pvtol = nominal system (no disturbances or noise)\n", + "# noisy_pvtol = pvtol w/ process disturbances and sensor noise\n", + "from pvtol import pvtol, pvtol_noisy, plot_results\n", + "import pvtol as pvt\n", + "\n", + "# Find the equiblirum point corresponding to the origin\n", + "xe, ue = ct.find_eqpt(\n", + " pvtol, np.zeros(pvtol.nstates),\n", + " np.zeros(pvtol.ninputs), [0, 0, 0, 0, 0, 0],\n", + " iu=range(2, pvtol.ninputs), iy=[0, 1])\n", + "\n", + "# Initial condition = 2 meters right, 1 meter up\n", + "x0, u0 = ct.find_eqpt(\n", + " pvtol, np.zeros(pvtol.nstates),\n", + " np.zeros(pvtol.ninputs), np.array([2, 1, 0, 0, 0, 0]),\n", + " iu=range(2, pvtol.ninputs), iy=[0, 1])\n", + "\n", + "# Extract the linearization for use in LQR design\n", + "pvtol_lin = pvtol.linearize(xe, ue)\n", + "A, B = pvtol_lin.A, pvtol_lin.B\n", + "\n", + "print(pvtol, \"\\n\")\n", + "print(pvtol_noisy)" + ] + }, + { + "cell_type": "markdown", + "id": "5771ab93", + "metadata": {}, + "source": [ + "### Control Design\n", + "\n", + "We begin by designing an LQR conroller than can be used for trajectory tracking, which is described in more detail in [Lecture 4b](cds112-L4b_pvtol-lqr.ipynb):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d2e88938", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", + "# LQR design w/ physically motivated weighting\n", + "#\n", + "# Shoot for 10 cm error in x, 10 cm error in y. Try to keep the angle\n", + "# less than 5 degrees in making the adjustments. Penalize side forces\n", + "# due to loss in efficiency.\n", + "#\n", + "\n", + "Qx = np.diag([100, 10, (180/np.pi) / 5, 0, 0, 0])\n", + "Qu = np.diag([10, 1])\n", + "K, _, _ = ct.lqr(A, B, Qx, Qu)\n", + "\n", + "# Compute the full state feedback solution\n", + "lqr_ctrl, _ = ct.create_statefbk_iosystem(pvtol, K)\n", + "\n", + "# Define the closed loop system that will be used to generate trajectories\n", + "lqr_clsys = ct.interconnect(\n", + " [pvtol_noisy, lqr_ctrl],\n", + " inplist = lqr_ctrl.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " inputs = lqr_ctrl.input_labels[0:pvtol.ninputs + pvtol.nstates] + \\\n", + " pvtol_noisy.input_labels[pvtol.ninputs:],\n", + " outlist = pvtol.output_labels + lqr_ctrl.output_labels,\n", + " outputs = pvtol.output_labels + lqr_ctrl.output_labels\n", + ")\n", + "print(lqr_clsys)" + ] + }, + { + "cell_type": "markdown", + "id": "29f55c0a-8c17-4347-aa46-b1944e700b32", + "metadata": {}, + "source": [ + "(The warning message can be ignored; it is generated because we implement this system as a differentially flat system and hence we require that an output function be explicitly given, rather than using `None`.)" + ] + }, + { + "cell_type": "markdown", + "id": "e9bc481f-7b2f-4b40-89b7-1ef5a35251b7", + "metadata": {}, + "source": [ + "We next define the characteristics of the uncertainty in the system: the disturbance and noise covariances (intensities) as well as the initial condition covariance:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "78853391", + "metadata": {}, + "outputs": [], + "source": [ + "# Disturbance and noise intensities\n", + "Qv = np.diag([1e-2, 1e-2])\n", + "Qw = np.array([[1e-4, 0, 1e-5], [0, 1e-4, 1e-5], [1e-5, 1e-5, 1e-4]])\n", + "\n", + "# Initial state covariance\n", + "P0 = np.eye(pvtol.nstates)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c590fd88", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the time vector for the simulation\n", + "Tf = 6\n", + "timepts = np.linspace(0, Tf, 20)\n", + "\n", + "# Create representative process disturbance and sensor noise vectors\n", + "# np.random.seed(117) # uncomment to avoid figures changing from run to run\n", + "V = ct.white_noise(timepts, Qv)\n", + "W = ct.white_noise(timepts, Qw)\n", + "plt.plot(timepts, V[0], label=\"V[0]\")\n", + "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "markdown", + "id": "7db5188e-03c7-439c-8cf2-47681d3feccf", + "metadata": {}, + "source": [ + "To get a better sense of the size of the disturbances and noise, we simulate the noise-free system with the applied disturbances, and then add in the noise. Note that in this simulation we are still assuming that the controller has access to the noise-free state (not realistic, but used here just to show that the disturbances and noise do not cause large perturbations)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c35fd695", + "metadata": {}, + "outputs": [], + "source": [ + "# Desired trajectory\n", + "xd, ud = xe, ue\n", + "# xd = np.vstack([\n", + "# np.sin(2 * np.pi * timepts / timepts[-1]), \n", + "# np.zeros((5, timepts.size))])\n", + "# ud = np.outer(ue, np.ones_like(timepts))\n", + "\n", + "# Run a simulation with full state feedback (no noise) to generate a trajectory\n", + "uvec = [xd, ud, V, W*0]\n", + "lqr_resp = ct.input_output_response(lqr_clsys, timepts, uvec, x0)\n", + "U = lqr_resp.outputs[6:8] # controller input signals\n", + "Y = lqr_resp.outputs[0:3] + W # noisy output signals (noise in pvtol_noisy)\n", + "\n", + "# Compare to the no noise case\n", + "uvec = [xd, ud, V*0, W*0]\n", + "lqr0_resp = ct.input_output_response(lqr_clsys, timepts, uvec, x0)\n", + "lqr0_fine = ct.input_output_response(lqr_clsys, timepts, uvec, x0, \n", + " t_eval=np.linspace(timepts[0], timepts[-1], 100))\n", + "U0 = lqr0_resp.outputs[6:8]\n", + "Y0 = lqr0_resp.outputs[0:3]\n", + "\n", + "# Compare the results\n", + "# plt.plot(Y0[0], Y0[1], 'k--', linewidth=2, label=\"No disturbances\")\n", + "plt.plot(lqr0_fine.states[0], lqr0_fine.states[1], 'r-', label=\"Actual\")\n", + "plt.plot(Y[0], Y[1], 'b-', label=\"Noisy\")\n", + "\n", + "plt.xlabel('$x$ [m]')\n", + "plt.ylabel('$y$ [m]')\n", + "plt.axis('equal')\n", + "plt.legend(frameon=False)\n", + "\n", + "plt.figure()\n", + "plot_results(timepts, lqr_resp.states, lqr_resp.outputs[6:8])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7f1dec6", + "metadata": {}, + "outputs": [], + "source": [ + "# Utility functions for making plots\n", + "def plot_state_comparison(\n", + " timepts, est_states, act_states=None, estimated_label='$\\\\hat x_{i}$', actual_label='$x_{i}$',\n", + " start=0):\n", + " for i in range(sys.nstates):\n", + " plt.subplot(2, 3, i+1)\n", + " if act_states is not None:\n", + " plt.plot(timepts[start:], act_states[i, start:], 'r--', \n", + " label=actual_label.format(i=i))\n", + " plt.plot(timepts[start:], est_states[i, start:], 'b', \n", + " label=estimated_label.format(i=i))\n", + " plt.legend()\n", + " plt.tight_layout()\n", + " \n", + "# Define a function to plot out all of the relevant signals\n", + "def plot_estimator_response(timepts, estimated, U, V, Y, W, start=0):\n", + " # Plot the input signal and disturbance\n", + " for i in [0, 1]:\n", + " # Input signal (the same across all)\n", + " plt.subplot(4, 3, i+1)\n", + " plt.plot(timepts[start:], U[i, start:], 'k')\n", + " plt.ylabel(f'U[{i}]')\n", + "\n", + " # Plot the estimated disturbance signal\n", + " plt.subplot(4, 3, 4+i)\n", + " plt.plot(timepts[start:], estimated.inputs[i, start:], 'b-', label=\"est\")\n", + " plt.plot(timepts[start:], V[i, start:], 'k', label=\"actual\")\n", + " plt.ylabel(f'V[{i}]')\n", + "\n", + " plt.subplot(4, 3, 6)\n", + " plt.plot(0, 0, 'b', label=\"estimated\")\n", + " plt.plot(0, 0, 'k', label=\"actual\")\n", + " plt.plot(0, 0, 'r', label=\"measured\")\n", + " plt.legend(frameon=False)\n", + " plt.grid(False)\n", + " plt.axis('off')\n", + " \n", + " # Plot the output (measured and estimated) \n", + " for i in [0, 1, 2]:\n", + " plt.subplot(4, 3, 7+i)\n", + " plt.plot(timepts[start:], Y[i, start:], 'r', label=\"measured\")\n", + " plt.plot(timepts[start:], estimated.states[i, start:], 'b', label=\"measured\")\n", + " plt.plot(timepts[start:], Y[i, start:] - W[i, start:], 'k', label=\"actual\")\n", + " plt.ylabel(f'Y[{i}]')\n", + " \n", + " for i in [0, 1, 2]:\n", + " plt.subplot(4, 3, 10+i)\n", + " plt.plot(timepts[start:], estimated.outputs[i, start:], 'b', label=\"estimated\")\n", + " plt.plot(timepts[start:], W[i, start:], 'k', label=\"actual\")\n", + " plt.ylabel(f'W[{i}]')\n", + " plt.xlabel('Time [s]')\n", + "\n", + " plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "73dd9be3", + "metadata": {}, + "source": [ + "## State Estimation\n", + "\n", + "We next consider the problem of only measuring the (noisy) outputs of the system and designing a controller that uses the estimated state as the input to the LQR controller that we designed previously.\n", + "\n", + "We start by using a standard Kalman filter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5a1f32da", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new system with only x, y, theta as outputs\n", + "sys = ct.nlsys(\n", + " pvt._noisy_update, lambda t, x, u, params: x[0:3], name=\"pvtol_noisy\",\n", + " states = [f'x{i}' for i in range(6)],\n", + " inputs = ['F1', 'F2'] + ['Dx', 'Dy'],\n", + " outputs = ['x', 'y', 'theta']\n", + ")\n", + "print(sys)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a0679f4", + "metadata": {}, + "outputs": [], + "source": [ + "# Standard Kalman filter\n", + "linsys = sys.linearize(xe, [ue, V[:, 0] * 0])\n", + "# print(linsys)\n", + "B = linsys.B[:, 0:2]\n", + "G = linsys.B[:, 2:4]\n", + "linsys = ct.ss(\n", + " linsys.A, B, linsys.C, 0,\n", + " states=sys.state_labels, inputs=sys.input_labels[0:2], outputs=sys.output_labels)\n", + "# print(linsys)\n", + "\n", + "estim = ct.create_estimator_iosystem(linsys, Qv, Qw, G=G, P0=P0)\n", + "print(estim)\n", + "print(f'{xe=}, {P0=}')\n", + "\n", + "kf_resp = ct.input_output_response(\n", + " estim, timepts, [Y, U], X0 = [xe, P0.reshape(-1)])\n", + "plot_state_comparison(timepts, kf_resp.outputs, lqr_resp.states)" + ] + }, + { + "cell_type": "markdown", + "id": "654dde1b", + "metadata": {}, + "source": [ + "### Extended Kalman filter\n", + "\n", + "We see that the standard Kalman filter does not do a good job in estimating the $y$ position (state $x_2$) nor the $y$ velocity (state $x_4$).\n", + "\n", + "A better estimate can be obtained using an extended Kalman filter, which uses the linearization of the system around the current state, rather than a fixed linearization." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1f83a335", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the disturbance input and measured output matrices\n", + "F = np.array([[0, 0], [0, 0], [0, 0], [1/pvtol.params['m'], 0], [0, 1/pvtol.params['m']], [0, 0]])\n", + "C = np.eye(3, 6)\n", + "\n", + "Qwinv = np.linalg.inv(Qw)\n", + "\n", + "# Estimator update law\n", + "def estimator_update(t, x, u, params):\n", + " # Extract the states of the estimator\n", + " xhat = x[0:pvtol.nstates]\n", + " P = x[pvtol.nstates:].reshape(pvtol.nstates, pvtol.nstates)\n", + "\n", + " # Extract the inputs to the estimator\n", + " y = u[0:3] # just grab the first three outputs\n", + " u = u[6:8] # get the inputs that were applied as well\n", + "\n", + " # Compute the linearization at the current state\n", + " A = pvtol.A(xhat, u) # A matrix depends on current state\n", + " # A = pvtol.A(xe, ue) # Fixed A matrix (for testing/comparison)\n", + " \n", + " # Compute the optimal \"gain\n", + " L = P @ C.T @ Qwinv\n", + "\n", + " # Update the state estimate\n", + " xhatdot = pvtol.updfcn(t, xhat, u, params) - L @ (C @ xhat - y)\n", + "\n", + " # Update the covariance\n", + " Pdot = A @ P + P @ A.T - P @ C.T @ Qwinv @ C @ P + F @ Qv @ F.T\n", + "\n", + " # Return the derivative\n", + " return np.hstack([xhatdot, Pdot.reshape(-1)])\n", + "\n", + "def estimator_output(t, x, u, params):\n", + " # Return the estimator states\n", + " return x[0:pvtol.nstates]\n", + "\n", + "ekf = ct.NonlinearIOSystem(\n", + " estimator_update, estimator_output,\n", + " states=pvtol.nstates + pvtol.nstates**2,\n", + " inputs= pvtol_noisy.output_labels \\\n", + " + pvtol_noisy.input_labels[0:pvtol.ninputs],\n", + " outputs=[f'xh{i}' for i in range(pvtol.nstates)]\n", + ")\n", + "print(ekf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4caf69b", + "metadata": {}, + "outputs": [], + "source": [ + "ekf_resp = ct.input_output_response(\n", + " ekf, timepts, [lqr_resp.states, lqr_resp.outputs[6:8]],\n", + " X0=[xe, P0.reshape(-1)])\n", + "plot_state_comparison(timepts, ekf_resp.outputs, lqr_resp.states)" + ] + }, + { + "cell_type": "markdown", + "id": "10163c6c-5634-4dbb-ba11-e20fb1e065ed", + "metadata": {}, + "source": [ + "## Maximum Likelihood Estimation\n", + "\n", + "Finally, we illustrate how to set up the problem as maximum likelihood problem, which is described in more detail in the [Optimization-Based Control](https://fbswiki.org/wiki/index.php/Supplement:_Optimization-Based_Control) (OBC) course notes, in Section 7.6.\n", + "\n", + "The basic idea in maximum likelihood estimation is to set up the estimation problem as an optimization problem where we define the likelihood of a given estimate (and the resulting noise and disturbances predicted by the\n", + "model) as a cost function." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1074908c", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the optimal estimation problem\n", + "traj_cost = opt.gaussian_likelihood_cost(sys, Qv, Qw)\n", + "init_cost = lambda xhat, x: (xhat - x) @ P0 @ (xhat - x)\n", + "oep = opt.OptimalEstimationProblem(\n", + " sys, timepts, traj_cost, terminal_cost=init_cost)\n", + "\n", + "# Compute the estimate from the noisy signals\n", + "est = oep.compute_estimate(Y, U, X0=lqr_resp.states[:, 0])\n", + "plot_state_comparison(timepts, est.states, lqr_resp.states)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0c6981b9", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the response of the estimator\n", + "plot_estimator_response(timepts, est, U, V, Y, W)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25b8aa85", + "metadata": {}, + "outputs": [], + "source": [ + "# Noise free and disturbance free => estimation should be near perfect\n", + "noisefree_cost = opt.gaussian_likelihood_cost(sys, Qv, Qw*1e-6)\n", + "oep0 = opt.OptimalEstimationProblem(\n", + " sys, timepts, noisefree_cost, terminal_cost=init_cost)\n", + "est0 = oep0.compute_estimate(Y0, U0, X0=lqr0_resp.states[:, 0],\n", + " initial_guess=(lqr0_resp.states, V * 0))\n", + "plot_state_comparison(\n", + " timepts, est0.states, lqr0_resp.states, estimated_label='$\\\\bar x_{i}$')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a76821f", + "metadata": {}, + "outputs": [], + "source": [ + "plot_estimator_response(timepts, est0, U0, V*0, Y0, W*0)" + ] + }, + { + "cell_type": "markdown", + "id": "6b9031cf", + "metadata": {}, + "source": [ + "### Bounded disturbances\n", + "\n", + "Another situation that the maximum likelihood framework can handle is when input distributions that are bounded. We implement that here by carrying out the optimal estimation problem with constraints." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93482470", + "metadata": {}, + "outputs": [], + "source": [ + "V_clipped = np.clip(V, -0.05, 0.05) \n", + "\n", + "plt.plot(timepts, V[0], label=\"V[0]\")\n", + "plt.plot(timepts, V_clipped[0], label=\"V[0] clipped\")\n", + "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56e186f1", + "metadata": {}, + "outputs": [], + "source": [ + "uvec = [xe, ue, V_clipped, W]\n", + "clipped_resp = ct.input_output_response(lqr_clsys, timepts, uvec, x0)\n", + "U_clipped = clipped_resp.outputs[6:8] # controller input signals\n", + "Y_clipped = clipped_resp.outputs[0:3] + W # noisy output signals\n", + "\n", + "traj_constraint = opt.disturbance_range_constraint(\n", + " sys, [-0.05, -0.05], [0.05, 0.05])\n", + "oep_clipped = opt.OptimalEstimationProblem(\n", + " sys, timepts, traj_cost, terminal_cost=init_cost,\n", + " trajectory_constraints=traj_constraint)\n", + "\n", + "est_clipped = oep_clipped.compute_estimate(\n", + " Y_clipped, U_clipped, X0=lqr0_resp.states[:, 0])\n", + "plot_state_comparison(timepts, est_clipped.states, lqr_resp.states)\n", + "plt.suptitle(\"MHE with constraints\")\n", + "plt.tight_layout()\n", + "\n", + "plt.figure()\n", + "ekf_unclipped = ct.input_output_response(\n", + " ekf, timepts, [clipped_resp.states, clipped_resp.outputs[6:8]],\n", + " X0=[xe, P0.reshape(-1)])\n", + "\n", + "plot_state_comparison(timepts, ekf_unclipped.outputs, lqr_resp.states)\n", + "plt.suptitle(\"EKF w/out constraints\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "108c341a", + "metadata": {}, + "outputs": [], + "source": [ + "plot_estimator_response(timepts, est_clipped, U, V_clipped, Y, W)" + ] + }, + { + "cell_type": "markdown", + "id": "430117ce", + "metadata": {}, + "source": [ + "## Moving Horizon Estimation (MHE)\n", + "\n", + "Finally, we can now move to the implementation of a moving horizon estimator, using our fixed horizon, maximum likelihood, optimal estimator. The details of this implementation are described in more detail in the [Optimization-Based Control](https://fbswiki.org/wiki/index.php/Supplement:_Optimization-Based_Control) (OBC) course notes, in Section 7.6." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "121d67ba", + "metadata": {}, + "outputs": [], + "source": [ + "# Use a shorter horizon\n", + "mhe_timepts = timepts[0:5]\n", + "oep = opt.OptimalEstimationProblem(\n", + " sys, mhe_timepts, traj_cost, terminal_cost=init_cost)\n", + "\n", + "try:\n", + " mhe = oep.create_mhe_iosystem(2)\n", + " \n", + " est_mhe = ct.input_output_response(\n", + " mhe, timepts, [Y, U], X0=resp.states[:, 0], \n", + " params={'verbose': True}\n", + " )\n", + " plot_state_comparison(timepts, est_mhe.states, lqr_resp.states)\n", + "except:\n", + " print(\"MHE for continuous-time systems not implemented\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1914ad96", + "metadata": {}, + "outputs": [], + "source": [ + "# Create discrete-time version of PVTOL\n", + "Ts = 0.1\n", + "print(f\"Sample time: {Ts=}\")\n", + "dsys = ct.nlsys(\n", + " lambda t, x, u, params: x + Ts * sys.updfcn(t, x, u, params),\n", + " sys.outfcn, dt=Ts, states=sys.state_labels,\n", + " inputs=sys.input_labels, outputs=sys.output_labels,\n", + ")\n", + "print(dsys)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "11162130", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a new list of time points\n", + "timepts = np.arange(0, Tf, Ts)\n", + "\n", + "# Create representative process disturbance and sensor noise vectors\n", + "# np.random.seed(117) # avoid figures changing from run to run\n", + "V = ct.white_noise(timepts, Qv)\n", + "# V = np.clip(V0, -0.1, 0.1) # Hold for later\n", + "W = ct.white_noise(timepts, Qw, dt=Ts)\n", + "# plt.plot(timepts, V0[0], 'b--', label=\"V[0]\")\n", + "plt.plot(timepts, V[0], label=\"V[0]\")\n", + "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.legend();" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8a6a693", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate a new trajectory over the longer time vector\n", + "uvec = [xd, ud, V, W*0]\n", + "lqr_resp = ct.input_output_response(lqr_clsys, timepts, uvec, x0)\n", + "U = lqr_resp.outputs[6:8] # controller input signals\n", + "Y = lqr_resp.outputs[0:3] + W # noisy output signals" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d683767f", + "metadata": {}, + "outputs": [], + "source": [ + "mhe_timepts = timepts[0:10]\n", + "oep = opt.OptimalEstimationProblem(\n", + " dsys, mhe_timepts, traj_cost, terminal_cost=init_cost,\n", + " disturbance_indices=[2, 3])\n", + "mhe = oep.create_mhe_iosystem()\n", + " \n", + "mhe_resp = ct.input_output_response(\n", + " mhe, timepts, [Y, U], X0=x0, \n", + " params={'verbose': True}\n", + ")\n", + "plot_state_comparison(timepts, mhe_resp.states, lqr_resp.states)" + ] + }, + { + "cell_type": "markdown", + "id": "ad6aac39-5b55-4ffd-ab21-44385dc11ff5", + "metadata": {}, + "source": [ + "Although this estimator eventually converges to the underlying tate of the system, the initial transient response is quite poor.\n", + "\n", + "One possible explanation is that we are not starting the system at the origin, even though we are penalizing the initial state if it is away from the origin.\n", + "\n", + "To see if this matters, we shift the problem to one in which the system's initial condition is at the origin:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfc68072", + "metadata": {}, + "outputs": [], + "source": [ + "# Resimulate starting at the origin and moving to the \"initial\" condition\n", + "uvec = [x0, ue, V, W*0]\n", + "lqr_resp = ct.input_output_response(lqr_clsys, timepts, uvec, xe)\n", + "U = lqr_resp.outputs[6:8] # controller input signals\n", + "Y = lqr_resp.outputs[0:3] + W # noisy output signals" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49213d04", + "metadata": {}, + "outputs": [], + "source": [ + "mhe_timepts = timepts[0:8]\n", + "oep = opt.OptimalEstimationProblem(\n", + " dsys, mhe_timepts, traj_cost, terminal_cost=init_cost,\n", + " disturbance_indices=[2, 3])\n", + "mhe = oep.create_mhe_iosystem()\n", + " \n", + "mhe_resp = ct.input_output_response(\n", + " mhe, timepts, [Y, U],\n", + " params={'verbose': True}\n", + ")\n", + "plot_state_comparison(timepts, mhe_resp.outputs, lqr_resp.states)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "650a559a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/check-controllability-and-observability.py b/examples/check-controllability-and-observability.py index 67ecdf26c..a8fc5c6ad 100644 --- a/examples/check-controllability-and-observability.py +++ b/examples/check-controllability-and-observability.py @@ -4,8 +4,8 @@ RMM, 6 Sep 2010 """ -import numpy as np # Load the scipy functions -from control.matlab import * # Load the controls systems library +import numpy as np # Load the numpy functions +from control.matlab import ss, ctrb, obsv # Load the controls systems library # Parameters defining the system diff --git a/examples/cruise-control.py b/examples/cruise-control.py index 7c2e562a1..77768aa86 100644 --- a/examples/cruise-control.py +++ b/examples/cruise-control.py @@ -50,7 +50,7 @@ def vehicle_update(t, x, u, params={}): """ from math import copysign, sin sign = lambda x: copysign(1, x) # define the sign() function - + # Set up the system parameters m = params.get('m', 1600.) g = params.get('g', 9.8) @@ -80,13 +80,13 @@ def vehicle_update(t, x, u, params={}): # Letting the slope of the road be \theta (theta), gravity gives the # force Fg = m g sin \theta. - + Fg = m * g * sin(theta) # A simple model of rolling friction is Fr = m g Cr sgn(v), where Cr is # the coefficient of rolling friction and sgn(v) is the sign of v (+/- 1) or # zero if v = 0. - + Fr = m * g * Cr * sign(v) # The aerodynamic drag is proportional to the square of the speed: Fa = @@ -95,11 +95,11 @@ def vehicle_update(t, x, u, params={}): # of the car. Fa = 1/2 * rho * Cd * A * abs(v) * v - + # Final acceleration on the car Fd = Fg + Fr + Fa dv = (F - Fd) / m - + return dv # Engine model: motor_torque @@ -108,7 +108,7 @@ def vehicle_update(t, x, u, params={}): # the rate of fuel injection, which is itself proportional to a control # signal 0 <= u <= 1 that controls the throttle position. The torque also # depends on engine speed omega. - + def motor_torque(omega, params={}): # Set up the system parameters Tm = params.get('Tm', 190.) # engine torque constant @@ -165,8 +165,8 @@ def motor_torque(omega, params={}): for m in (1200, 1600, 2000): # Compute the equilibrium state for the system - X0, U0 = ct.find_eqpt( - cruise_tf, [0, vref[0]], [vref[0], gear[0], theta0[0]], + X0, U0 = ct.find_operating_point( + cruise_tf, [0, vref[0]], [vref[0], gear[0], theta0[0]], iu=[1, 2], y0=[vref[0], 0], iy=[0], params={'m': m}) t, y = ct.input_output_response( @@ -247,7 +247,6 @@ def pi_update(t, x, u, params={}): # Assign variables for inputs and states (for readability) v = u[0] # current velocity vref = u[1] # reference velocity - z = x[0] # integrated error # Compute the nominal controller output (needed for anti-windup) u_a = pi_output(t, x, u, params) @@ -347,9 +346,9 @@ def cruise_plot(sys, t, y, label=None, t_hill=None, vref=20, antiwindup=False, # Compute the equilibrium throttle setting for the desired speed (solve for x # and u given the gear, slope, and desired output velocity) -X0, U0, Y0 = ct.find_eqpt( +X0, U0, Y0 = ct.find_operating_point( cruise_pi, [vref[0], 0], [vref[0], gear[0], theta0[0]], - y0=[0, vref[0]], iu=[1, 2], iy=[1], return_y=True) + y0=[0, vref[0]], iu=[1, 2], iy=[1], return_outputs=True) # Now simulate the effect of a hill at t = 5 seconds plt.figure() @@ -394,7 +393,7 @@ def sf_output(t, z, u, params={}): ud = params.get('ud', 0) # Get the system state and reference input - x, y, r = u[0], u[1], u[2] + x, r = u[0], u[2] return ud - K * (x - xd) - ki * z + kf * (r - yd) @@ -440,13 +439,13 @@ def sf_output(t, z, u, params={}): 4./180. * pi for t in T] t, y = ct.input_output_response( cruise_sf, T, [vref, gear, theta_hill], [X0[0], 0], - params={'K': K, 'kf': kf, 'ki': 0.0, 'kf': kf, 'xd': xd, 'ud': ud, 'yd': yd}) + params={'K': K, 'kf': kf, 'ki': 0.0, 'xd': xd, 'ud': ud, 'yd': yd}) subplots = cruise_plot(cruise_sf, t, y, label='Proportional', linetype='b--') # Response of the system with state feedback + integral action t, y = ct.input_output_response( cruise_sf, T, [vref, gear, theta_hill], [X0[0], 0], - params={'K': K, 'kf': kf, 'ki': 0.1, 'kf': kf, 'xd': xd, 'ud': ud, 'yd': yd}) + params={'K': K, 'kf': kf, 'ki': 0.1, 'xd': xd, 'ud': ud, 'yd': yd}) cruise_plot(cruise_sf, t, y, label='PI control', t_hill=8, linetype='b-', subplots=subplots, legend=True) diff --git a/examples/cruise.ipynb b/examples/cruise.ipynb index 4f1c152f9..08a1583ac 100644 --- a/examples/cruise.ipynb +++ b/examples/cruise.ipynb @@ -154,7 +154,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -357,7 +357,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -420,22 +420,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "system: a = (0.010124405669387215-0j) , b = (1.3203061238159202+0j)\n", - "pzcancel: kp = 0.5 , ki = (0.005062202834693608+0j) , 1/(kp b) = (1.5148002148317266+0j)\n", + "system: a = 0.010124405669387215 , b = 1.3203061238159202\n", + "pzcancel: kp = 0.5 , ki = 0.005062202834693608 , 1/(kp b) = 1.5148002148317266\n", "sfb_int: K = 0.5 , ki = 0.1\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/murray/src/python-control/murrayrm/control/xferfcn.py:1109: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " num[i, j, maxindex+1-len(numpoly):maxindex+1] = numpoly\n" - ] - }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -450,7 +442,7 @@ "\n", "# Construction a controller that cancels the pole\n", "kp = 0.5\n", - "a = -P.poles()[0]\n", + "a = -P.poles()[0].real\n", "b = np.real(P(0)) * a\n", "ki = a * kp\n", "control_pz = ct.TransferFunction(\n", @@ -510,21 +502,9 @@ "execution_count": 9, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/murray/src/python-control/murrayrm/control/xferfcn.py:1109: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " num[i, j, maxindex+1-len(numpoly):maxindex+1] = numpoly\n", - "/Users/murray/src/python-control/murrayrm/control/xferfcn.py:1109: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " num[i, j, maxindex+1-len(numpoly):maxindex+1] = numpoly\n", - "/Users/murray/src/python-control/murrayrm/control/xferfcn.py:1109: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " num[i, j, maxindex+1-len(numpoly):maxindex+1] = numpoly\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -577,21 +557,9 @@ "execution_count": 10, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/murray/src/python-control/murrayrm/control/xferfcn.py:1109: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " num[i, j, maxindex+1-len(numpoly):maxindex+1] = numpoly\n", - "/Users/murray/src/python-control/murrayrm/control/xferfcn.py:1109: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " num[i, j, maxindex+1-len(numpoly):maxindex+1] = numpoly\n", - "/Users/murray/src/python-control/murrayrm/control/xferfcn.py:1109: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " num[i, j, maxindex+1-len(numpoly):maxindex+1] = numpoly\n" - ] - }, { "data": { - "image/png": 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lMVEESleviv+UDKFUioDS11fMzPTxEcXbW7TCFS1ubtb9u0VUlahUwP37pZecnMLtgwfFt0VLTk5hYGTc+f0VV+6g6fvvv8fOnTvh6uqKvXv36owPUigU5QqaVq5cCQDo3r27zvF169Zh5MiRAICpU6ciNzcXb775Ju7du4cOHTogJiZGJxVAcnKyztIunTp1wsaNG/HOO+/g3XffRYMGDbBp0yZ06NChzHWsilauBEaNKnz888/Ap58CRSYkVnoqFbB/vwiUfvwRSE7Wfb5+faB7d6BbN6BLF9GKVBW+vB89EgHRmTPA2bOFJSmpsJtFHzs7oE4dIDBQbOVSu3Zh65uvr2gFIqLKRZJEIJORoVuyskS3ulyysgpLdrbufna2CHLMQR6TK7cou7qKVmTtx9qtzHIBgGXLjFePcudp8vPzw8SJEzF9+nSdAMUWyXmajJXnwRokJSWhfv36AIDExESMGhWMPXsKn7ezA/buFcFFZZeeDqxZA3z+uWghkVWrBvTpAzz3HNCjh/jCt3VqtWg5OnoUOHZMlISEkluN3NyARo2Axo3FtkEDEUzWqyd+XkUamInIAvLzxTjBW7fEOEHtcueOGIupXe7dEwFSBSe567C3F93m7u6iuLkVdqPLj7WLPI7T1VX3sdzVLm/l4uhYvj9ijf39Xe6Wpvz8fAwZMsTmAyYSoqKAPXvEL+6jR+LL94UXRBfXP+PzKxVJAv74A1ixAti8uXB8Uo0awIABwMCBYnyWrbeCPHoEnDgB7NsnyoED4q/HotzdxQD/kBCgZUugRQugWTPRWlQVWtuIKpvc3MJJFPIYwZs3xR+BN28W7t+6pf/ftKEcHcX/i0pl4fhBebKKvO/pWViKji2Ui7Nz1fi/otxB04gRI7Bp0ya8/fbbxqwPVRLu7u6apKLu7u545hnR2vDXX+IfTFaW+Ec8ejTwf/9n4cpqkSRg1y5g1izg0KHC4x06AG+8IQI9Wx6kLUnA6dNATIwoBw8Wbz53dQXatBFZ39u1E6VBg6rxHx6RpRUUiP875UkTN27oL2UNhOztxWxdb+/CWbvyVp7BK5caNQonWbi68t9+WZQ7aFKpVFi4cCF27tyJVq1aFRsIvmjRogpXjizH29sbd+7c0TkWGQm8+aboJ5ZnJPzyi2jB6NrV/HUsat8+4N13xRYQ9Xz5ZREshYVZtm6mdO8e8OuvYkB7TIz4C1RbzZri/nTtKrpTQ0PFDDIiMi5JEq0/V6+KMZMpKYUlOVkESWlpxSedlMTZWbT2yuMDixbtSRXVqxemlyDTKfeYpqeeeqrkkyoU2L17d7krVdlUxTFN+uTkAHXrii/psDDR7QMAnTqJbh9L/bUSHw9MnQr8/rt47OQEjB0LzJhRObsOjeHqVTGDcft2ESQWFBQ+V60a8NRTQO/ewNNPi242/mdKVHFqtfijJClJzCK9cqVwXw6UDJlV6uAgJkvUqSO28uQJuciBklLJVqCKqjRjmvZojwomm6NSqXD48GEAQMeOHWFvbw83NxGM/JPJQePgQdHi1K+feeuYkQG8846Y3adWi/+IRo0CZs4UwZ2tSUkBNm4U5eRJ3edatBA//969RRCrJzcrERlATrVx6RJw+bJIs5GYKPaTkh6fe0yhEEFQYKD4f0i71Kkjtj4+/EPGWpW7pakqqYotTUVnz8mJRq9fFzOnCgqApk2BP/8Ur2/VSrT4mOM/ArUa+OYb4L//FYMgATFWaf58oEg+VKt36xbwww/Ahg2iNU9mZwd07iwGtQ8YIMYkEZFhVCrRMnTxohinKZe//xatRaV1n8mpNoKDxf+F8jYoSBTOKq1cLNrSdPr0aYSEhBg8Y+7cuXNo0qQJHDiAwmbUrg28+CLw7beFA6rt7cXg440bAa0coyZx7hwwZoyYGQeIwG35cpEywFao1aKr8YsvRPebPPNPoRDjkoYOBf71LzHIk4hKlpMj/rD780/gwoXC7d9/Q5OwVx83N6BhQ/HHiJz9X94GBjIoqsrK1NJkb2+PtLQ0eHt7G/R6T09PJCQkaFosrBVbmhJ1lrQ5eVKMabK3183z0aCB+A/JFP+hqFTA4sWiOy4vT/ynNns2MGmSGMNkC27cANauFTmlrlwpPB4WJoLRIUNE0EpEuh4+FGtDnj0r/rA6d048Tkoq+T3OzoU5yJo0EfuNGolgydeXY4lshUVbmiRJwrvvvotqBia3yS8tlCer1aaNyJodFyeaoq9dE3k6Ll8WX/hjxxr38xITgZEjRTZvAHjmGZGo0laSUSYkAJ98Ilrq5AHd1asDr7wiUjqEhlqydkSVhySJIQIJCaKcOSNauf/6q+QuNW9vMRmiWTPRMi2XwECOK6KyK1PQ1LVrV1y8eNHg14eHh8PVlpPiVGGTJomg6f598bhGDZFS/733gOHDjZM0UpKA1auBt94Szezu7qK1adQo6/8rUJJEioCPPwa0J5o++aTofvz3v207nxTR46jVohvtxAnRuh0fLwKlIplQNLy8ChOzahd2Y5MxlSlo2rt3r4mqQdamTx8RGGVkiG665OTCVqdPPhH5kioiORl4/XWRdwgQLVvr14sBl9ZMksTad3PmiL+SAfHzGzxYBIdt21q0ekQWIUni3/yRI2KJHzlQ0rdCvb090Ly5aIENDRWBUsuWzF5P5sER2lQurq5iGZLt20Ugc/myyLp97ZoYa9SkiZjRVlaSJLr4oqJEy5WLi0hxMHGidTelS5LInD57tviLGRAtZ6+/LlrtgoIsWz8ic7p/X6x7ePBgYaBUNCkrIP79P/GEGNfXurXYb9GicCFWInNj0ER6ubu7w8PDQ7Ovz3PPiaBJHodz/rwYz7RqlRiPU706EBFh+GdeuybG8OzcKR6HhwPr1okAzJrFxIgB7MeOicfu7iK7elSU6NYksnXXr4sxiX/8IQKlU6eKLxbr4CBajuTlfcLCRIsSJ19TZcJfR9LL29sbWfraxrU8+6xoDr96Vcxgu3AB+O47kTF80yZg0CCxDlyHDqV/Vk6OSBvw4YeiOd7ZGZg3D5g8WTTFW6uzZ4EpUwqDwGrVRIvZW29xnAXZtitXxJjHffvE9vLl4q+pW1f8YRQeLgKl1q05jo8qPwZNVG6+viIgOnxYzEw5dUokYvz6a+DuXSA2Vsx0O3BAPF9Ubq5olZo/X6zXBIjzrV8vZrdYq5s3xYLBX34pBrM6OgLjxollXXx8LF07IuO7dUtMaNi1S5TERN3n7exE11rnzmKyQ3i4bWbtJ9tX7qApKSlJJ3cP2RaVSoXk5GQAQGBgIOxLaPLp318ETfJ4o40bgQ8+ALZuFQknjx4VY59mzRLBg729KKmpwKJFYguIpHGzZ4sFdq21dSk/X8zumzevcFbhv/4lgsKGDS1bNyJjyssTXW07d4py6pTu8/b2oouta1cxiePJJ8U6akTWrtzLqLi4uGDs2LF45513UMvG+xqY3DKxxAD57Fkxc8XJSQRFOTnAoUNAx47A7dviP80LF0r+nMBAMdNuxAjrzrK7e7doTZKXlWnXTswi7NLFsvUiMpYrV4AdO0SqjN27xb91bS1bAj17ij+WunYVuduILK3SLNi7f/9+vP3222jQoAGmTp2KyZMnG5z0kmxHixZi9tyVKyJQ2rdPrJPWsaMYtxMbK3I3paaKgZ9yUSiAgQNFziVrXlz2xg0xRmnjRvHYxwf46CMxEN6aZ/sRqVSiFfn//k+Us2d1n/f1FRM9evcWrcnseqaqoMIL9sbExGDmzJm4fv065syZg9GjRxu8Np21YEtTyS1NgJgyv3Sp+I8zNlYES9euWXcw9DgqFfDZZ2JWXHa2CJDefBN4/30xa5DIGj14IP4N//gj8PPPuokk7eyATp3EBJDevcVMNxv7r55skLG/vyv8Kx8REYFjx45h8eLF+OSTT9C8eXNs3bq1whUj6/Hcc2KbkAAEBIhuuZ9+smiVTOr8edHtNmmSCJg6dBDpBJYtY8BE1ufePeCrr4Dnnxd/8AwcKCZj3Lkjfp+HDhWzYm/dEmkDpk8XM90YMFFVZLTZc88++yxq166NqVOnYvDgwVAVTcJBNqtLF8DTU/ynOnKk+A/3yy9Flmtbkp8PLFwoWpPy88WYjYULgf/8h18gZF3u3hWtSZs3A7//Djx6VPhcYKAInJ5/XgzgtuaxhkTGVu6gae3atTh37hzOnz+Pc+fO4fr161AoFAgMDES/fv2MWUeq5JycgL59RW4mOc9KbKzI32Qrma5PnABee00sDgqILopVq2xn0WCyfRkZIlDauFGkBZCT0gJASIgIkp5/XqQG4HIkRPqVO2iaMWMGQkJC0LJlS/zrX/9Cy5YtERISAjc3N2PWj6xE//4iaNq/H3j6aTG7Zt06scaaNSsoEMu4vPee2K9VS4zfevFFfrFQ5ZeTIwZxb9gA/PqraCGVhYaKhaH//W/rzotGZE7lDppu6lsoiGyGq6srXP5Z4MnVgDS9ffuK3CxnzwKffloYNL37rvXmXbp0CRg2TKyNBYgvlxUrAG9vy9aLqDQqlWhJ+vZbkS9NOzVA8+ZijNILLwCNG1uujkTWihnBSS8/Pz/k5uYa/HovL5HtNy5OtMjUqCFWLf/9dzHTxppIEvD55yKVwIMHIinfZ58BL73E1iWqvE6dAr75Bvj++8KksQAQHCwCpRdfFLmUiKj8GDSR0Tz3nAiaYmJEnqJly8SAcGsKmm7dAl59FfjlF/H46afFwHYu+UCV0Z07Ikhau1bMXpV5eYkg6ZVXRM40BvtExlHhPE1VQVXM01Qe586JAaWuriLJZbt2YubN9evW0aX1+++iOy4tTeSYWrAAmDCBM+OoclGpxB8ma9eK1B7yOCUnJzG2cNgw0V3u5GTZehJVBpUuTxPZpqSkJCgUCigUCiQlJRn0nubNAT8/sRDv/ftA27ZiKvM335i4shX06JHIPRMRIQKm5s1F3qVJkxgwUeVx7ZqYkBAcLBbC3rxZBEytW4vJCTduiGMDBjBgIjKVcn8ljBw5Evv27TNmXcjKKRSiOwsQA1FHjxb7X34pxglVRpcvi7FYCxaIOo4ZIwImjv2gykClEpm5+/cX6TtmzwZSUsSYwYkTgfh44ORJ0SJas6ala0tk+8odNGVnZyMiIgKNGjXChx9+iOvXrxuzXmSlevQQ2127xODTatXEgr2HD1u2Xvp89534K/3oUZH5eMsWkXuJSyiSpaWni1QXDRqIsYL/93+AWg106yZmxd24ASxZInIqEZH5lDto2rJlC65fv47x48fjhx9+QL169dC3b19s3rwZj7TTy1KVIgdNR4+KrZwVfPFiy9RHn+xsYPhwMUg2O1u0NJ06BQwaZOmaUVUmScChQ+L3sm5d4O23RYJYLy8xk/PPP4G9e4GXXwb+yQZCRGZWoREbNWvWxKRJkxAfH4+jR4+iYcOGGDZsGAICAjB58mRcunTJWPUkKxEUJP46VqnEYHB5XNAPP4i/li3t2DHRuvTNN6Jec+cCe/aIpSOILCE/X7R6tm8vFsT97jtxrH17MXPz2jXg44+BJk0sXVMiMsow19TUVMTExCAmJgb29vZ45plncO7cOTRv3hyLK1MTA5mFdhdd69bir2RAjBfKyLBMneTM3p06iXFMgYEiPcKsWYADE2+QBdy6JdYxrFdPtC4dPy5mbY4cKYL7I0eAESMKlyYiIssrd9D06NEjbNmyBf369UNQUBB++OEHTJ48Gampqfjqq68QExODb775Bu+9954x60tWQDtoAkRrTqNGYhzGf/9r/vpcuiQWFX77bRE8DR4sctp07mz+uhD99RcwdqwI3GfNEoko/f1FAJWSIjLpt21r6VoSkT7l/hvb398farUaQ4cOxdGjR/GEnhGJvXv3RvXq1StQPbIUV1dXOP0zb9mQZVS0PfWU2J45Iwa0+viIGXTduontiy8WBlampFaLZU+mThVpEDw9xdTs4cOZ7I/M748/RDfb9u2Fs0nbtgUmTxZL9DBNAFHlV+7klt988w0GDx6sWZ/MljG5Zdk98YQYXL1hgwiSAGD8eLEcSXCwCKhMubbz1asi5cHvv4vHPXqIZIAcu0TmJEnAjh3Ahx8CBw8WHu/fH5gyRbSAMoAnMp1Kk9yyW7ducHZ2LnZckiQkJydXqFJk/Yp20QFiTFFgIJCUBMycaZrPzcsTX1DNmomAydVVLOcSE8OAicxHpQI2bRJ/PPTrJwImJycRyJ8/LzJ5d+3KgInI2pQ7aAoODsatW7eKHb979y6Cg4MrVCmyfvqCJg8P4IsvxP7SpcDu3cb9zJgYoFUrEZDl5oovpYQE0cLFzN5kDo8eiRbNpk1FC+vp04C7uxjLd+WK+P1v1szStSSi8ir3V4kkSVDo+TPp/v37VaLLztaVZxkVbV27illpSUmiyCIixIK4kiQW8v3oIzH2qCL+/lsM7u7dWwyy9fMTCQD37gUaN67YuYkMIQdLTZoAo0aJ30kvL7HsSXIysHChGOxNRNatzAPBo6KiAAAKhQLvvvsuqmmlT1apVDhy5IjeQeGG2LdvHz766COcOHECqamp2LZtGwYOHKh5/ubNm5g2bRpiYmKQkZGBrl27YtmyZWjUqFGJ51y/fj1effXVYsdzc3MZ3JmQuzvQoYMY/Kq9pAogWpmyskQG7qlTgdhY4Kuvyv6lcuyY+DLaskUEYfb2YjmJOXMApdKol0Ok16NHIkCfNw9ITBTHfHzE7/WYMeLfARHZjjK3NMXHxyM+Ph6SJOHMmTOax/Hx8fjzzz8RGhqK9evXl6syOTk5CA0NxfLly4s9J0kSBg4ciMTERGzfvh3x8fEICgpCz549kZOTU+p5PT09kZqaqlMYMJmevi46QHyR/PADsHq1GHMUGyu61X755fHnfPQI+PVXMUOvfXuxQKkkiQVMT5wQmccZMJGpqdXA99+LrrbXXhMBk48P8MknomX1rbcYMBHZojK3NO3ZswcA8Oqrr2Lp0qXw8PAwWmX69u2Lvn376n3u0qVLOHz4MM6ePYsWLVoAAFasWAEfHx9s2LABo7WbMopQKBTw8/MzWj3JMD16iO6JXbvEl4z2uCKFAnj9dTF7aOhQMfaoXz+gYUMRQLVsKbbBwWLtuqNHRYmPBx4+FOdwcABeeknMQuICu2QOkiQy28+cKWaAAiJYmjZN5F7iuoVEtq1MQVNUVBTef/99uLm5oXr16pg9e3aJr120aFGFK6ctLy8PAHRaiOzt7eHk5IQDBw6UGjTdv38fQUFBUKlUeOKJJ/D++++jdevWpX6W/HmAmLJIZdexo/gSuXULOHtWBEFFNW0qFvOdPl0sQPr336Js3VryeWvUEOOiIiPFGl1E5hAXB8yYIdaHA0SL5tSpYqkgU6bPIKLKo0xBU3x8vGYx3oSEhBJfp2+AeEU1bdoUQUFBmDFjBj7//HO4ublh0aJFSEtLQ2pqaqnvW79+PVq2bImsrCwsWbIETz75JE6dOlXiWKjo6GjMnTvX6NdQ1Tg5iQHhv/0mWpv0BU2AWDpi8WLx1/upU2LG0ZkzYpuUJAbXtmsnuuPatxdr23E2HJnLhQuiJennn8VjV1cRKE2dKgJ4Iqo6yp3c0tQUCkWxgeAnTpzAqFGjcOrUKdjb26Nnz56w++fbc8eOHQadV61Wo02bNujatSuWLl2q9zX6Wprq1q1bpZJbJiUloX79+gCAxMTEcqeR+PhjMd26b1+R5I/IWty8KSYVfPGFyLtkby+6lGfN4kw4Imth7OSWVrVUaVhYGBISEpCZmYn8/Hx4e3ujQ4cOaFuGhZrs7OzQrl07XLp0qcTXODs7603cWZU4OTnB3t5es19effqIoGnPHuDBA475oMrv4UNg0SKRjPX+fXHsueeABQtEdzIRVV3l7uSIjo7G2rVrix1fu3YtFixYUKFKPY5SqYS3tzcuXbqE48ePY8CAAQa/V5IkJCQkwJ9/Kpaqdu3aKCgoQEFBAWrXrl3u87RoITJxP3woAieiykqSRPqKZs1EV/H9+6JbeO9esV4cAyYiKnfQ9Pnnn6Opnv9FWrRogVWrVpXrnPfv30dCQoJmvFRSUhISEhI0y7L88MMP2Lt3rybtQK9evTBw4EBERERozjF8+HDMmDFD83ju3LnYuXMnEhMTkZCQgFGjRiEhIQFjx44tVx2pbBQK4Nlnxb4hKQWILOH0aeDpp8XCuVeuALVri/xLhw+LhaaJiIAKdM+lpaXpba3x9vYudWB2aY4fP46nnnpK81hOpDlixAisX78eqampiIqKws2bN+Hv74/hw4fj3Xff1TlHcnKyZpwTAGRkZOA///kP0tLSoFQq0bp1a+zbtw/t27cvVx2p7J59Fli5UgRNksT1tqjyuHcPeOcdYNUqkRbDxUUM8J46lTPiiKi4cg8Eb9SoEWbPno1XXnlF5/g333yD2bNnI1FOj2sDjD2QzBoYayA4IMYy1awpuujOnAFCQoxVS6LyUauB9evFrLjbt8WxF14QGeaDgixaNSIyokozEHz06NGIjIzEo0eP8PTTTwMAdu3ahalTp+Ktt96qcMXIdlSrJjJ4//qraG1i0ESWdPIk8OabwJEj4nGLFsDy5UD37hatFhFZgXIHTVOnTsXdu3fx5ptvIj8/H4BIPDlt2jSdMUVEgOiik4OmadMsXRuqijIzxQDvFStEN7G7OzB3rliv0NHR0rUjImtQ4TxN9+/fx4ULF+Dq6opGjRrZ5FR9ds9VrHsOEINrg4NFrptbt5gUkMxHksRah5GRgDzc8qWXgI8+AgICLFo1IjIxY39/Vzivsru7O9q1a4eQkBCbDJjIOOrVA5o3F0kCY2IsXRuqKpKSxGLOQ4aIgKlRI+D334HvvmPARERlV6HklhkZGVizZg0uXLgAhUKBZs2aYdSoUVBymXnS49lngfPnRRfdkCGWrg3ZsoICkaByzhwgN1cs6TNjhljjUGv5SiKiMil3S9Px48fRoEEDLF68GHfv3sXt27exePFiNGjQACdPnjRmHclGyPmafv1VtDgRmcLJk2KNwmnTRMD01FMiD9OcOQyYiKhiyh00TZ48Gc899xyuXLmCrVu3Ytu2bUhKSkK/fv0QGRlpxCqSJTg5OcHOzg52dnYVWkZFW6dOYmX427eBY8eMckoijQcPxJI97doB8fFi3Ny6dWKx6CZNLF07IrIFFWppmjZtGhwcCnv4HBwcMHXqVBw/ftwolSPLqV27NlQqFVQqVYWWUdHm6AjIyduZHZyMafduoGVLsUC0Wg28+CJw4QIwciSTqRKR8ZQ7aPL09NQsb6ItJSUFHh4eFaoU2S4uqULGlJUFjBkD9OgBJCYCdeoAP/8MbNgA+PpaunZEZGvKHTQNGTIEo0aNwqZNm5CSkoJr165h48aNGD16NIYOHWrMOpIN6dtX/OUfHw/cuGHp2pA1++03kZhy9WrxeOxY4Nw5oF8/y9aLiGxXuWfPffzxx1AoFBg+fDgKCgogSRKcnJzwxhtvYP78+casI1lAcnIygv5ZT+Lq1asIDAw0ynl9fMSYk6NHxYDwUaOMclqqQu7dA6KixDIoAFC/PrBmDTN6E5HplbulycnJCUuWLMG9e/eQkJCAhIQE3L17F4sXL2a+Jhug0prepjLyVDe5i27bNqOelqqAX38Vy/CsXy9aLCMjxcw4BkxEZA5lammKiooy+LWLFi0qc2Woahg8GJg9G9i5E7hzRyzmS1SarCzRurRmjXjcqJGYGffkk5atFxFVLWUKmuLj4w16nYLTVagUzZoBTzwBJCQAmzeLgbxEJdm1C3jtNSA5WbQuTZoEfPCBWAiaiMicyhQ07dmzx1T1oCrmpZdE0PT99wyaSL+cHJGg8rPPxOPgYNEt17WrRatFRFVYhdeeIyqPoUNFq8G+fUBKiqVrQ5XNkSNA69aFAdMbb4ixSwyYiMiSKhQ07d+/H6+88grCw8Nx/fp1AMA333yDAwcOGKVyZLvq1Cn8Aty40bJ1ocrj0SPg3XdF9vhLl4DatcXYtxUrAHd3S9eOiKq6cgdNW7ZsQe/eveHq6or4+Hjk5eUBALKzs/Hhhx8arYJkGfb29lAoFFAoFLC3tzfJZ7z0kth+/71JTk9W5sIFoGNHYN48kdX7pZeAM2cKs8gTEVlauYOmefPmYdWqVfjiiy/g6OioOd6pUycu2GsDAgMDoVaroVarjZajqah//UssrZKQAJw/b5KPICsgScDy5UCbNmKxXS8vYNMm4LvvxPpxRESVRbmDposXL6KrngEGnp6eyMjIqEidqIqoWRPo00fsb9hg2bqQZaSmAs88A0yYADx8CPTuLVqXXnjB0jUjIiqu3EGTv78//v7772LHDxw4gPr161eoUlR1aHfRSZJl60Lm9eOPYpHd334DXFyApUtF8sqAAEvXjIhIv3IHTWPGjMGkSZNw5MgRKBQK3LhxA9999x2mTJmCN99805h1JAtITk6GnZ0d7Ozs9C7MbCz9+wNubmKx1aNHTfYxVInk5AD/+Q/w/PMiuekTTwAnTojWJqZ4I6LKrNxrz02dOhWZmZl46qmn8PDhQ3Tt2hXOzs6YMmUKxo8fb8w6kgWoVCpI/zT9GHsZFW1ubsDAgWL8yvffAx06mOyjqBI4cUK0Lv71lwiQ/vtf4P33AScnS9eMiOjxytzSlJCQoNn/4IMPcPv2bRw9ehSHDx/GrVu38P777xuzflQFyF10mzYBBQWWrQuZhloNLFwIhIeLgKl2bZHpe8ECBkxEZD3KHDS1adMGYWFhWLlyJTIzM1GtWjW0bdsW7du3hzsTqVA59OolBoXfvAkw6bztuX5d3ONp00QepkGDgFOngKeesnTNiIjKpsxB0x9//IE2bdpg+vTp8Pf3xyuvvMLlVahCHB0LZ0utW2fZupBx/fwzEBoK7N4t1or74gux3iAXaSYia1TmoCk8PBxffPEF0tLSsHLlSly7dg09e/ZEgwYN8MEHH+DatWumqCfZuNGjxfaHH4AbNyxbF6q4hw+BiROB554Tg71btxY5mEaP5mBvIrJe5Z495+rqihEjRmDv3r3466+/MHToUHz++ecIDg7GM888Y8w6UhXQpg3w5JNiTNPnn1u6NlQRFy6IAf3LlonHkycDhw4BTZpYtl5ERBVllAV7GzRogOnTp2PmzJnw9PTEzp07jXFasiDtpVNMtYxKURMniu3nnwP/rMpDVkSSgLVrgbZtxeK63t7AL78AixYBzs6Wrh0RUcVVOGiKi4vDiBEj4Ofnh6lTp2LQoEH4448/jFE3sqDAwEBIkgRJkky2jEpRzz8vZlXdvCm66ch6ZGUBL78MjBoFPHgA9OwpBnuz0ZmIbEm5gqaUlBS8//77aNCgAZ566ilcvnwZy5Ytw40bN/DFF1+gY8eOxq4nVQGOjoCcF3XJEmYItxbHj4vu1Q0bAHt7IDoa2LkT8Pe3dM2IiIxLIUll+2rq1asX9uzZA29vbwwfPhyvvfYamtj4YIWsrCwolUpkZmbC09PT0tWxabduAXXriu65Q4fEqvdUOUmSCG6nThWpBAIDgY0bRS4mIqLKwNjf32VuaXJ1dcWWLVtw7do1LFiwwOYDpqrq+vXrsLe3h729Pa5fv262z/X2BoYOFfvyQGKqfO7eBQYMEIO8Hz0SXasJCQyYiMi2lTlo+umnnzBgwACzDQ4my8jPz4darYZarUZ+fr5ZP3vCBLH93/+YfqAyOnRIrBf3888im/fy5cCWLUCNGpauGRGRaRll9hyRMbVpA3TuzPQDlY28FEqXLkBKCtCwIXD4MDBuHHMvEVHVwKCJKiWmH6hcbt8G+vcXS6GoVKIL9eRJkbSSiKiqYNBEldLAgYXpBzZtsnRtqrYDB0R33I4dgIsLsHo18N13gIeHpWtGRGReDJqoUnJ0BMaPF/vz5onBxmReajUwfz7QvbtYdLdJE+DIEeD119kdR0RVU6UKmvbt24f+/fsjICAACoUCP/74o87zN2/exMiRIxEQEIBq1aqhT58+uHTp0mPPu2XLFjRv3hzOzs5o3rw5tm3bZqIrIGMaP17Mprt0iQv5mtutW8CzzwIzZojuuFdeEfmYWrWydM2IiCynUgVNOTk5CA0NxfLly4s9J0kSBg4ciMTERGzfvh3x8fEICgpCz549kZOTU+I5Dx06hCFDhmDYsGE4deoUhg0bhhdeeAFHjhwx5aWQEbi7A+++K/bnzhWZpsn09u0TY5V++010x61ZA3z9tbgfRERVWZmTW5qLQqHAtm3bMHDgQADAX3/9hSZNmuDs2bNo0aIFAEClUsHHxwcLFizA6NGj9Z5nyJAhyMrKwq+//qo51qdPH9SoUQMbNmwwqC5Mbmk5eXmiW+jqVWDBApFIkUxDpRLZvGfPFl1zTZuKtA8tW1q6ZkRE5WPx5JaWkvfPFCoXFxfNMXt7ezg5OeHAgQMlvu/QoUOIiIjQOda7d28cPHiw1M/KysrSKWQZzs7Ae++J/eho4N49y9bHVqWlAb17i5Y9tRoYPhw4dowBExGRNqsJmpo2bYqgoCDMmDED9+7dQ35+PubPn4+0tDSkpqaW+L60tDT4+vrqHPP19UVaWlqJ74mOjoZSqdSUunXrGu06qOxefhlo0QLIyAA++sjStbE9u3aJ2XG7dgHVqgHr1wNffcXuOCKioqwmaHJ0dMSWLVvw119/wcvLC9WqVcPevXvRt2/fx2YnVxSZ6iNJUrFj2mbMmIHMzExNSUlJMco1WJPr16/DwcEBDg4OZl1GRR97e+DDD8X+kiVAKTEylcGjR8DMmUCvXiK1Q8uWYrD3iBGWrhkRUeVkNUETAISFhSEhIQEZGRlITU3Fb7/9hjt37iA4OLjE9/j5+RVrVUpPTy/W+qTN2dkZnp6eOqWqyc/Ph0qlgkqlMvsyKvr07y/WNXvwQKQgoIq5cgXo1k0Eo5Ik0ggcOQI0a2bpmhERVV5WFTTJlEolvL29cenSJRw/fhwDBgwo8bXh4eGIjY3VORYTE4NOnTqZuppkRAqFyBkEiOSKly9btj7WbPNm0R136BCgVIrB3qtXA66ulq4ZEVHl5mDpCmi7f/8+/v77b83jpKQkJCQkwMvLC4GBgfjhhx/g7e2NwMBAnDlzBpMmTcLAgQN1BnoPHz4ctWvXRnR0NABg0qRJ6Nq1KxYsWIABAwZg+/bt+P3330sdPE6VU9euQJ8+Yir8uHHAr78yyWJZPHgATJ4sAiQA6NgR2LABqFfPotUiIrIalaql6fjx42jdujVa/7OgVVRUFFq3bo1Zs2YBAFJTUzFs2DA0bdoUEydOxLBhw4qlDUhOTtYZGN6pUyds3LgR69atQ6tWrbB+/Xps2rQJHTp0MN+FkdEsXixm1O3cCaxda+naWI+TJ8VCyKtXi0BzxgyRj4kBExGR4SptnqbKpCrmaUpKSkL9+vUBAImJiaWOGzO3Tz4BpkwRa5+dPQsEBlq6RpWXSgV8/LFIJfDoERAQIBJV9uhh6ZoREZlelc3TRCSLjAQ6dQKys4HRo8VAZiouJUUER9Oni4DpX/8CTp9mwEREVF4Mmsjq2NuLtehcXIDYWOCLLyxdo8pFkoBvvhHrxMXFAW5uYimUH34Aata0dO2IiKwXgybSKzg4GJIkQZKkStU1J2vcWGQIB4C33hJT6EnkWxo0SGT0zsgA2rcHEhKA117joHkioopi0ERWa+JEoHNn4P59YNQosfxHVbZ5MxASAvz4I+DoCHzwAfDHH0DDhpauGRGRbWDQRFbLzk5007m6Art3iwHPVdGtW8DQocDgwcDt20BoqFg37u23AYdKlVSEiMi6MWgivdLS0uDs7AxnZ+dS1+mztIYNgUWLxP60aWLcTlUhj11q1gzYuFGM9XrnHeDoURE4ERGRcTFoIr1yc3ORn5+P/Px85ObmWro6pRozBpgwQewPGwYcPGjZ+phDUhLQu7cYu3Tnjhj0fegQ8P77gJOTpWtHRGSbGDSR1VMoRNLL554D8vLEViuxvE159EjkqQoJETMHnZ3FgPjjx4F27SxdOyIi28agiWyCvT3w/fdA27ai5aVvXzG+x5b8/rvodpsyRSyJ0r07cOaMyMPk6Gjp2hER2T4GTWQz3NyA//s/sTTI338DAwYAlbxn0SBXr4rElL16ARcuALVqibxLu3cDjRpZunZERFUHgyayKb6+wI4dQPXqYmxTjx5idpk1ys4G5swBmjYFtm4VrWkTJgB//cW8S0RElsCgiWxOs2aixal6dTE4ukMH0UJjLfLygKVLgQYNgLlzgYcPgW7dgPh4cbxGDUvXkIioamIWFwPIaxpnZWVZuCbmk52drbNvbdfesqUYAzR4sJhp1rGjmJ7fvbula1YylQr43/9EUsqUFHGsfn1g1ixg4EDRsmRlt4GIyKLk7y7JSIuUKiRjncmGJSYmokGDBpauBhEREZXD5cuXUb9+/Qqfhy1NBvDy8gIAJCcnQ6lUWrg25pOVlYW6desiJSUFnp6elq6O2fC6ed1VAa+b110VZGZmIjAwUPM9XlEMmgxgZyeGfimVyir1yybz9PTkdVchvO6qhdddtVTV65a/xyt8HqOchYiIiMjGMWgiIiIiMgCDJgM4Oztj9uzZcHZ2tnRVzIrXzeuuCnjdvO6qgNdtnOvm7DkiIiIiA7CliYiIiMgADJqIiIiIDMCgiYiIiMgADJqIiIiIDMCgyQArVqxAcHAwXFxcEBYWhv3791u6SiY1Z84cKBQKneLn52fpahndvn370L9/fwQEBEChUODHH3/UeV6SJMyZMwcBAQFwdXVF9+7dce7cOctU1oged90jR44sdv87duxomcoaSXR0NNq1awcPDw/4+Phg4MCBuHjxos5rbPF+G3Ldtni/V65ciVatWmkSOYaHh+PXX3/VPG+L9xp4/HXb4r3WJzo6GgqFApGRkZpjxrrnDJoeY9OmTYiMjMTMmTMRHx+PLl26oG/fvkhOTrZ01UyqRYsWSE1N1ZQzZ85YukpGl5OTg9DQUCxfvlzv8wsXLsSiRYuwfPlyHDt2DH5+fujVq5fOYsbW6HHXDQB9+vTRuf87duwwYw2NLy4uDuPGjcPhw4cRGxuLgoICREREICcnR/MaW7zfhlw3YHv3u06dOpg/fz6OHz+O48eP4+mnn8aAAQM0X5K2eK+Bx183YHv3uqhjx45h9erVaNWqlc5xo91ziUrVvn17aezYsTrHmjZtKk2fPt1CNTK92bNnS6GhoZauhlkBkLZt26Z5rFarJT8/P2n+/PmaYw8fPpSUSqW0atUqC9TQNIpetyRJ0ogRI6QBAwZYpD7mkp6eLgGQ4uLiJEmqOve76HVLUtW435IkSTVq1JC+/PLLKnOvZfJ1S5Lt3+vs7GypUaNGUmxsrNStWzdp0qRJkiQZ9983W5pKkZ+fjxMnTiAiIkLneEREBA4ePGihWpnHpUuXEBAQgODgYLz44otITEy0dJXMKikpCWlpaTr33tnZGd26dbP5ew8Ae/fuhY+PDxo3bozXX38d6enplq6SUWVmZgIoXIy7qtzvotcts+X7rVKpsHHjRuTk5CA8PLzK3Oui1y2z5Xs9btw4PPvss+jZs6fOcWPecy7YW4rbt29DpVLB19dX57ivry/S0tIsVCvT69ChA77++ms0btwYN2/exLx589CpUyecO3cONWvWtHT1zEK+v/ru/dWrVy1RJbPp27cvBg8ejKCgICQlJeHdd9/F008/jRMnTthENmFJkhAVFYXOnTsjJCQEQNW43/quG7Dd+33mzBmEh4fj4cOHcHd3x7Zt29C8eXPNl6St3uuSrhuw3XsNABs3bsTJkydx7NixYs8Z8983gyYDKBQKnceSJBU7Zkv69u2r2W/ZsiXCw8PRoEEDfPXVV4iKirJgzcyvqt17ABgyZIhmPyQkBG3btkVQUBB++eUXDBo0yII1M47x48fj9OnTOHDgQLHnbPl+l3Tdtnq/mzRpgoSEBGRkZGDLli0YMWIE4uLiNM/b6r0u6bqbN29us/c6JSUFkyZNQkxMDFxcXEp8nTHuObvnSlGrVi3Y29sXa1VKT08vFrHaMjc3N7Rs2RKXLl2ydFXMRp4tWNXvPQD4+/sjKCjIJu7/hAkT8NNPP2HPnj2oU6eO5rit3++SrlsfW7nfTk5OaNiwIdq2bYvo6GiEhoZiyZIlNn+vS7pufWzlXp84cQLp6ekICwuDg4MDHBwcEBcXh6VLl8LBwUFzX41xzxk0lcLJyQlhYWGIjY3VOR4bG4tOnTpZqFbml5eXhwsXLsDf39/SVTGb4OBg+Pn56dz7/Px8xMXFVal7DwB37txBSkqKVd9/SZIwfvx4bN26Fbt370ZwcLDO87Z6vx933frYwv3WR5Ik5OXl2ey9Lol83frYyr3u0aMHzpw5g4SEBE1p27YtXn75ZSQkJKB+/frGu+cVHq5u4zZu3Cg5OjpKa9askc6fPy9FRkZKbm5u0pUrVyxdNZN56623pL1790qJiYnS4cOHpX79+kkeHh42d83Z2dlSfHy8FB8fLwGQFi1aJMXHx0tXr16VJEmS5s+fLymVSmnr1q3SmTNnpKFDh0r+/v5SVlaWhWteMaVdd3Z2tvTWW29JBw8elJKSkqQ9e/ZI4eHhUu3ata36ut944w1JqVRKe/fulVJTUzXlwYMHmtfY4v1+3HXb6v2eMWOGtG/fPikpKUk6ffq09Pbbb0t2dnZSTEyMJEm2ea8lqfTrttV7XRLt2XOSZLx7zqDJAJ999pkUFBQkOTk5SW3atNGZrmuLhgwZIvn7+0uOjo5SQECANGjQIOncuXOWrpbR7dmzRwJQrIwYMUKSJDFNdfbs2ZKfn5/k7Owsde3aVTpz5oxlK20EpV33gwcPpIiICMnb21tydHSUAgMDpREjRkjJycmWrnaF6LteANK6des0r7HF+/2467bV+/3aa69p/s/29vaWevTooQmYJMk277UklX7dtnqvS1I0aDLWPVdIkiSVs0WMiIiIqMrgmCYiIiIiAzBoIiIiIjIAgyYiIiIiAzBoIiIiIjIAgyYiIiIiAzBoIiIiIjIAgyYiIiIiAzBoIiIiIjKA1QVN+/btQ//+/REQEACFQoEff/zxse+Ji4tDWFgYXFxcUL9+faxatcr0FSUiIiKbYnVBU05ODkJDQ7F8+XKDXp+UlIRnnnkGXbp0QXx8PN5++21MnDgRW7ZsMXFNichYunfvjsjISEtXo0Tdu3eHQqGAQqFAQkKCQe8ZOXKk5j2G/PFHRJZn1cuoKBQKbNu2DQMHDizxNdOmTcNPP/2ECxcuaI6NHTsWp06dwqFDh/S+Jy8vT2dVaLVajbt376JmzZpQKBRGqz8RAUqlstTnhw4dig8//BCOjo7w8PAwU60KTZs2DcnJydiwYUOJr3nmmWfQsGFDzJw5EzVr1oSDg8Njz5uZmYmHDx+icePG+O6779CvXz9jVpuIAEiShOzsbAQEBMDOzgjtRMZZGs8yAEjbtm0r9TVdunSRJk6cqHNs69atkoODg5Sfn6/3PbNnzy5xoUsWFhYWFhYW6yopKSlGiTse/+eQlUtLS4Ovr6/OMV9fXxQUFOD27dvw9/cv9p4ZM2YgKipK8zgzMxOBgYFISUmBp6enyetcGVy5cgWhoaEAgFOnTqFevXqWrRAREVEZZWVloW7dukZrpbb5oAlAsS416Z8eyZK62pydneHs7FzsuKenZ5UJmoKCgtClSxfNflW5biIisj3GGlpj80GTn58f0tLSdI6lp6fDwcEBNWvWtFCtKj+lUol9+/ZZuhpERESVhtXNniur8PBwxMbG6hyLiYlB27Zt4ejoaKFaERERkbWxuqDp/v37SEhI0EzrTUpKQkJCApKTkwGI8UjDhw/XvH7s2LG4evUqoqKicOHCBaxduxZr1qzBlClTLFF9q3H//n289NJLeOmll3D//n1LV4eIiMjirC7lwN69e/HUU08VOz5ixAisX78eI0eOxJUrV7B3717Nc3FxcZg8eTLOnTuHgIAATJs2DWPHjjX4M7OysqBUKpGZmVllxvYkJSWhfv36AIDExEQEBwdbuEZERERlY+zvb6sLmiyBQRODJiIisj7G/v62uu45IiIiIktg0ERERERkAAZNRERERAZg0ERERERkAAZNRERERAaw+YzgVD5eXl5o3bq1Zp+IiKiqY9BEeimVSpw8edLS1SAiIqo02D1HREREZAAGTaRXbm4uxo8fj/HjxyM3N9fS1SEiIrI4ZgQ3ADOCMyM4ERFZH2YEJyIiIrIABk1EREREBmDQRERERGQABk1EREREBmDQRERERGQABk1EREREBmBGcNJLqVSiadOmmn0iIqKqjkET6eXl5YULFy5YuhpERESVBrvniIiIiAzAlibSKzc3F/PnzwcATJ8+Ha6urhauERERkWVxGRUDcBkVLqNCRETWh8uoEBEREVmAVQZNK1asQHBwMFxcXBAWFob9+/eX+vrvvvsOoaGhqFatGvz9/fHqq6/izp07ZqotERER2QKrC5o2bdqEyMhIzJw5E/Hx8ejSpQv69u2L5ORkva8/cOAAhg8fjlGjRuHcuXP44YcfcOzYMYwePdrMNSciIiJrZnVB06JFizBq1CiMHj0azZo1w6effoq6deti5cqVel9/+PBh1KtXDxMnTkRwcDA6d+6MMWPG4Pjx42auOREREVkzqwqa8vPzceLECUREROgcj4iIwMGDB/W+p1OnTrh27Rp27NgBSZJw8+ZNbN68Gc8++2yJn5OXl4esrCydQkRERFWbVQVNt2/fhkqlgq+vr85xX19fpKWl6X1Pp06d8N1332HIkCFwcnKCn58fqlevjmXLlpX4OdHR0VAqlZpSt25do14HERERWR+rCppkCoVC57EkScWOyc6fP4+JEydi1qxZOHHiBH777TckJSVh7NixJZ5/xowZyMzM1JSUlBSj1t8aKJVKBAUFISgoiMuoEBERwcqSW9aqVQv29vbFWpXS09OLtT7JoqOj8eSTT+K///0vAKBVq1Zwc3NDly5dMG/ePPj7+xd7j7OzM5ydnY1/AVbEy8sLV65csXQ1iIiIKg2ramlycnJCWFgYYmNjdY7HxsaiU6dOet/z4MED2NnpXqa9vT0A0UJFREREZAiramkCgKioKAwbNgxt27ZFeHg4Vq9ejeTkZE1324wZM3D9+nV8/fXXAID+/fvj9ddfx8qVK9G7d2+kpqYiMjIS7du3R0BAgCUvpVLLz8/H559/DgAYM2YMnJycLFwjIiIiy7K6oGnIkCG4c+cO3nvvPaSmpiIkJAQ7duxAUFAQACA1NVUnZ9PIkSORnZ2N5cuX46233kL16tXx9NNPY8GCBZa6BKtw/fp1TJw4EQDQr18/LqNCRERVHteeMwDXnrONtedu3QIuXQLu3xclJ0eU/HzA3R3w8Cgs1asDAQFiW8IcA6shSeI679wB7t4FMjOB7GzxM5C3ublAXp5uKSgA1GpRVCqxBcTPQ6EA7OzE1sFBtzg6Ak5OgLOzbnF1FaVatcJ9d3dR3NzEtlo16/95E1HlYezvb6traSLbdvMm8Mcfohw4AJw9C/j5AY0bA40aiW2TJkB4uPiSLcmtW0BMDHDqFHD6tNiWkJWiVK6uIngKCADq1gXq1wcaNCgs/v6W/ZLPywOuXAESE4GUFOD6deDGjcJteroIlvLzLVfHslAoigewnp6iKJWFpXp13VKjRuHW01MEdERExsagiSqFPXuAceOACxeKP5eYKMpvvxUec3QEunQB+vQRJSQEePQI+OUX4KuvxLagQPc8CgUQGCi+XN3cCoujo2iJyc4uLHfvAvfuiRaYy5dF0cfNDWjaFGjWTJTmzUVp0AD4Z76BUdy+LQJIufz1l6hTSopoSTKEkxNQs6a4fjkwkVt6qlUr3jLk4CCCD3t7sZUDEUkqLHIrVEGB+PnL2/x83Varhw/Fz1K7PHggfu5yq598bvkelJednQisatQAvLx0i3ysZs3i2xo1xDUTEZWE3XMGYPecabvnfv0VeP558eWqUIgA6Mkngc6dgTZtRGvJpUsiULh0CUhIEK0r2tzcxBd2Xl7hsSeeADp1Alq1AkJDxXlLa50qKjcXSE0tbLW5erUwgLp8GUhOLuyyKsrFRQRPLVuKEhIitoa0TN2+DRw5IsrRo+J6b94s+fVubiJICwoCatcWrWK1a4vP8vUVAUHNmuJ1lbXrS60WP2+5yzA7G8jKEtvMTLGfmalbMjJ0ixzkVoRSWTygKm1fDsSMGSATkfEY+/ubQZMBGDSZLmjavh0YPFi0Tjz3HLB+vfgSKo0kAX//LVqTPv8c+PNP3eerVQP69wfGjBGtUaZqPcjPFy1gFy6Icv584X5JX95eXoWBVMuWIrBSKoETJ4Ddu0W3ZGKi/vcGBxcGYE2bFnYR+vhU3mDI3PLyRPAkF7nF8O7dwiKP7dLeZmZW7HO1W7a0t0X3ixZ2JRKZFoMmC2DQZJqg6X//A15+WbQQDR4MfPed6CozxOHDIig6fVo8DgkRLSvHjolWB5mXlwigBg4EIiJEQGVqKpUIfM6eBc6cEUXuUiupZaqoOnWAdu2Ap58W2xYtytZKRmVTUFAYXOkLqooGXHKp6LKUCkVhwFV0bJa8lff1FVdXBsxEpWHQZAFVMWi6e/cumjdvDkAsRePl5WXU83/7LTBihAgiXnkFWLfOsBah7Gxg2jRg1SrR4lSzJvDJJ8Dw4eLLIz9ftNj88APw44/ii03m4gL06iXKU0+JQMRcXzjp6cCWLcCGDSLge/TIsPc5O4vWpIYNC7fyfmCg4UEmmcajRyW3bBU9VrRUtCsREPe/enXdwfHag+W1B88XPSYPrmcKNrJlDJosoCoGTaYUGwv07i2CnlGjRBebIWNCMjLE+44eFY9HjAA+/hioVUv/6wsKRHfXjz+KUnQclLe3CJ66dxdjn0JCjDs2JTsb2LwZ+PprIC5Od8B2/fpAv35Az55A69Zi7NS5c8DFi6LI47dKm/VmZydm9AUHi/PVqydKUFDh+CYObK68Hj7UHY8lF+1j8jYzU/e5jAzDWy0fx8WlMICSZyrKj7VnL8r72jMb5WPyhAKO7aLKhkGTBTBoMq6ePYFdu4Bhw8QYJkPGdNy7J7rXjh8XXW4//CC6rgwlSaIrb8cOMVPvwIHif+m7uQHt2wMdOwIdOoiB5IGBZWuNUqlES9dXXwFbt+p+RliY6CYcMEAEaI87r0olBp///XdhuXxZBFNJSeJLtzT29iJwqlNHBFd164p9eZC4XFxdDb8+qhwkSQyaz8jQHRQv7xc9pq/IMxaNSc695eFRmHtLe+vmJrrItWevVqtWWOQ8XkUfy3m9+EcAlRWDJguoikFTfn4+tm/fDgAYMGCA0ZZR+esvkWdJoRDjfurVe/x77t4VXWonT4ruuF27xGy4isjLEy1We/YA+/aJfX3T3D09dWffBQcXtua4uBS+LjMTWLMGWLZMt0WrcWPRIvbyy+I9xqJWixl1iYkigEpMFAHWlStim5xseBegUilm2dWqJVrfvL3FvjxLTHsws9y94+HBAczWrqCgcJaiPENRe19Ogiof157RWLQUTe9hKg4OhQGUq6v4N1jSVl9xdi6+r72VS9HHcnFy4u+9tWHQZAFVMWgy1UDwqChg8WLg2WeB//u/x7/+zh3RMpWQIL7Md+0SM8iMTaUSs94OHxbl2DHxuLTAw99fzFzLyhL5kuQvDjc3UechQ0T3X82a5h97pFaLLr+UFFGuXSvcT00V5caNx7dWlUShEIGT9vgY7a4c7TxQ2vmgSmt5YCuCdZIk0Y0sZ9qXAyk5476ch0s7C7+co0vef/BAN3eXdi4vY4z9MiYHB90gSntffqy9NbTImfTlfbkUfVxakTPya2/lUlUnDDBosgAGTcYJmnJzRdfQvXsiYHr22dJff+cO0KOHyObt4yO6vVq0qHA1DJafL8YXyVnFL1wQLTlJSWXv2vD0LD7dXF+SSbkLQi5yYkm1WjepZNECFC5top2M0tFR/3Imbm5iW1Agfs63b4ss6rduFe7LM8S0BzNnZJguu7ijY2G9ii65oq9FQN+Xhr194fVr/xy0l33RLvLPTd6WdKxoKXpe7c+U9+X7p73V92VW9Bq0v0CNNUZITkSq/ftSlPY1VSaSpD85am5u4XHt5/PyCo/Jx+UEq/JWe187AWvRY3l51pNN/3Hk30l9v5fy721pRft12r/r2qWkfwtFj8vHij5X0uOi/97KciwvLwvjxlnZMip37941+uwrsj7/+5/44g0KElm8SyNJwMiRImDx9RUB0z+T+czGyakwn5Ls/Hlg9mwxwFvWsqUY/1Stmugyu3mzMBC5d09ci9zFcfWqea/BEPb2umNM5JYgOUDx8xP3zNm5sFWh6JeW9hdWXp5ooVOpdIv8ZV002JM9elQ4Dod0FQ3miu4Duj9P7Z91RT9T32eVpuhnmuPP8tJ+Po+ru76fm/a+/H5rb15Qq8W/XVsJAi3FLEFTrVq1UKdOHYSGhuqURo0aQVHZ/qwhk1m5UmzHjHn8X9Bffy1ao5ycgJ07zR8wFXX5sgiWvv++8D/PIUPEsWbNSn6fSiUCpzt3dGdDyTOitBfNlRfOLSgoLHL3oL6WjqLHJKlwYV15eZNHj4ovxJubK1rK5HOrVIVBHVVOxgiCyvuZ5v7c8rDEz4eqJrMETefPn0dCQgLi4+Nx7NgxfP7557h79y5cXV3RokULHDlyxBzVIAuKjxfLgjg6Aq+9Vvprr10DJk0S+3PnVnzQd0U8eAB8+CHw0UeFf6E9/7yolyFjq+ztxaDqktIiWNKjR7pjS7THnOTkFO+6yM8XwVnRZn19g22dnIp3RckDaLW7weQATzvQ017XTt4vqZutpC630uj7YpWkklsr9HXnldTFp+9z9LWw6btGtVp3/T655OcXru8nB8IFBYU/LzlQ1v7Zyfvy+YvWq+jPECjexaL985W32j+noufTd0xfkX939HXDFP0s+fPk6yj6uyIX7T80tJ9XqwvHGhb92ZdWt6I/C3lMkDzurmhdta9b/nnrq7P2fdK+d9r1UqmK/27L90e7nnJ9inYXy3+QFv391Pd7X7Q+2utJylt916Lv38Pjuty0f8/0/XvVTqGh799HQYH++mnXS74ObQ8eiD/UjcUsQVPTpk3RtGlTvPjiiwAASZLw22+/YcKECejRo4c5qkAWJrcyDRokuttKIuduyswU0/+nTDFP/fTVY+tWMXA9OVkc69ULmD9frIdnCxwdCwdyExHZoqws4wZNFpk8qVAo0LdvX3z77be4ceOGJapAZpSZKZZIAYA33ij9tV9+CcTEiBaLr76yzIyqv/4SSTT//W8RMAUFiQBq507bCZiIiKjszBI0qUtIXduxY0fs3bvXHFWgMnJ3d4eXlxe8vLzgXsFFz775RjSRNm8OdO1a8uuuXBEtOwDwwQdiUVpzUquB5cvFoO7YWNHNNGuWGPz9/POVb1YRERGZl1n+jnd3d0dISAieeOIJhIaG4oknnkCTJk1w9OhR3L9/3xxVoDLy9vbGnTt3KnweSSrsmhs7tuTAQ60WY53u3wc6dy4c02Qu164Br74K/P67eNyzp1jfrkED89aDiIgqL7METVu3bsWpU6dw6tQpfPbZZ7h06RLUajUUCgXef/99c1SBLOSPP0RLTbVqYtmUkqxfL7JzV6smFu811xpWkiRmxI0bJ7oRXV3FoO833mDmXyIi0mWWoKlPnz7oo5WY5+HDh7h8+TJq1qwJPz8/c1SBykilUuHw4cMARDeqfTmjmNhYsR04UCRz1KegQHTHAcCcOUDDhuX6qDKTZ1V8+6143K6d6Eps0sQ8n09ERNbFIgsXuLi4oIU5UztTmSUnJ6Nz584AKpYR/PhxsQ0PL/k1W7aItdNq1gTefLNcH1NmSUliJl9CgmjVmjULmDHD/MudEBGR9eBqT2QykgScOCH227Yt+TXR0WJ/4kSRjdrUYmOBF18US4N4e4tM5d27m/5ziYjIunHUBpnM9etiSRF7+5ITVO7cKZZKcXMDxo83bX0kSYxX6tNHBExt24qgjgETEREZwiqDphUrViA4OBguLi4ICwvD/v37S319Xl4eZs6ciaCgIDg7O6NBgwZYu3atmWpbdcldcy1aiAHW+syfL7ZjxgCmXJ7w0SNgxAhg6lQxU+/VV4H9+4G6dU33mUREZFusrntu06ZNiIyMxIoVK/Dkk0/i888/R9++fXH+/HkEBgbqfc8LL7yAmzdvYs2aNWjYsCHS09NRIOfWJ5N5XNfcoUNAXJwYRzR5sunqkZMDDB4M/PqraPVaulTMjmPeJSIiKguztTTt378fr7zyCsLDw3H9+nUAwDfffIMDBw6U6TyLFi3CqFGjMHr0aDRr1gyffvop6tati5VyMqAifvvtN8TFxWHHjh3o2bMn6tWrh/bt26NTp04VviYqndzSFBam/3m5lWn4cKBOHdPU4e5dkXPp119Fa9dPP4nB5gyYiIiorMwSNG3ZsgW9e/eGq6sr4uPjkZeXBwDIzs7Ghx9+aPB58vPzceLECUREROgcj4iIwMGDB/W+56effkLbtm2xcOFC1K5dG40bN8aUKVOQm5tb4ufk5eUhKytLp1DZSFJh0KSvpencORHAKBTAf/9rmjpcuwZ06QIcPgzUqAHs2gU884xpPouIiGyfWYKmefPmYdWqVfjiiy/gqDWnu1OnTjh58qTB57l9+zZUKhV8i6z46uvri7S0NL3vSUxMxIEDB3D27Fls27YNn376KTZv3oxx48aV+DnR0dFQKpWaUrcKDnxxd3eHh4cHPDw8yrWMSkoKcPu2WDuuVavizy9YILaDBpkmL9LFi0CnTiKxZu3aYvxSaWkPiIiIHscsQdPFixfRVc+iY56ensjIyCjz+RRF+lYkSSp2TCZnHv/uu+/Qvn17PPPMM1i0aBHWr19fYmvTjBkzkJmZqSkpKSllrqO18/b21rSyeXt7l/n9citTSIhYfFfb1asiCzcATJ9ewYrq8fffwFNPicCtSRPg4EExGJ2IiKgizBI0+fv74++//y52/MCBA6hfv77B56lVqxbs7e2LtSqlp6cXa33S/uzatWtDqVRqjjVr1gySJOHatWt63+Ps7AxPT0+dQmVTWtfcF18AKhXQo0fJg8TLKzlZnDc1VQRsBw4AJcwPICIiKhOzBE1jxozBpEmTcOTIESgUCty4cQPfffcdpkyZgjfLkALayckJYWFhiJXX5vhHbGxsiQO7n3zySdy4cUNnYeC//voLdnZ2qGOq0cc2QKVSISkpCUlJSVCpVGV+f0kz5yQJ+OEHsf/aaxWsZBGpqSJgSk4GGjcWi+/WqmXczyAioipMMpO3335bcnV1lRQKhaRQKCQXFxfpnXfeKfN5Nm7cKDk6Okpr1qyRzp8/L0VGRkpubm7SlStXJEmSpOnTp0vDhg3TvD47O1uqU6eO9O9//1s6d+6cFBcXJzVq1EgaPXq0wZ+ZmZkpAZAyMzPLXF9rlZiYKAGQAEiJiYlleq9aLUleXpIESNKxY7rPnToljjs7S1JWlvHqm54uSc2bi3MHB0tSSorxzk1ERNbJ2N/fZsvT9MEHH2DmzJk4f/481Go1mjdvXq4BxkOGDMGdO3fw3nvvITU1FSEhIdixYweCgoIAAKmpqUhOTta83t3dHbGxsZgwYQLatm2LmjVr4oUXXsC8efOMdm2k68oVMdXf0RFo2VL3ObmVqU8fwMPDOJ937x4QEVE46HvXLtOlMCAioqpLIUmSZOlKVHZZWVlQKpXIzMysMuObkpKSNOPNyrpg7+bNIplkWFjh2CZAdM01ayZmtn37LfDyyxWvZ36+CJji4gAfH2DfPtPMxiMiIutj7O9vk7U0RUVFGfzaRYsWmaoaZAElJbU8d04ETM7OQP/+Ff8cSRKZvePiRKtVbCwDJiIiMh2TBU3x8fEGva6kVAFkvUqaOSd3zfXuDRijwe7jj4G1awE7O+B//9OfD4qIiMhYTBY07dmzx1SnpkpMkkqeOScHTf/+d8U/Z/t2YNo0sf/pp2KMFBERkSmZJeVAcnIySho6pT1om6xfYiKQkQE4OekmlDx3DrhwQRx/7rmKfUZ8PPDSS4Xdc+PHV+x8REREhjBL0BQcHIxbt24VO37nzp0yDTAm83F1dYWLiwtcXFzg6upq8PvkrrnQUBEgyeRWpogIQCvPaJmlpoqg68EDoFcvYMkSLr5LRETmYZaUA1IJy5zcv38fLkXX2KBKwc/Pr9RFjUvyuK65wYPLX6eCAmDIELEQb9OmYhyT1lKGREREJmXSoEmeQadQKPDuu++iWrVqmudUKhWOHDmCJ554wpRVIDPTN3Pu/HlRHB0r1jX33nti4V13d+Cnn4Dq1StUVSIiojIxadAkz6CTJAlnzpyBk1Z/jZOTE0JDQzFlyhRTVoHMSK3W39K0ebPYRkSUP9DZtQuQ85GuXg00alTuahIREZWLSYMmeQbdq6++iqVLl8KjSApoSZKQkpJiyipQOZUnueXly0BWFuDiAjRvXni8ol1zN2+KRJiSBIweDQwdWr7zEBERVYRZBoJ//fXXesfH3L17lwPBbYjcyhQaWjjW6M8/gbNny981p1YDw4aJwKlFCzHwm4iIyBLMEjSVlG6AA8Fty/nzYqu93tzPP4ttjx5AjRplP+eCBSLTt6urGPitNSyOiIjIrMw2EHzWrFkcCG7j5KBJu2suLk5se/Uq+/kOHgTefVfsL1+ue14iIiJz40BwMpoLF8S2WTOxVamAAwfEfteuZTtXbi4wcqQ4x0svAa++arRqEhERlYvZBoIvWbLEKCsMU+VUUABcuiT25aDp9GkgM1MsplvWBsU5c8T5AgKAzz5jAksiIrI8syS3XLdunTk+hizo8mXg0SMx5qhuXXFM7prr3BlwKMNv2rFjYjFeAFi5kvmYiIiocjBL0AQAGRkZWLNmDS5cuACFQoFmzZph1KhRUFZkTQ0yGVdXV013qiHLqMjjmZo1A+z+mV4gB01l6ZrLzwdGjRKz5oYOrfg6dURERMZiltlzx48fR4MGDbB48WLcvXsXt2/fxuLFi9GgQQOcPHnSHFWgMvLz80NeXh7y8vLg5+f32NcXHc+kVovs3QDQrZvhnxsdDZw5A9SqxfQCRERUuZilpWny5Ml47rnn8MUXX8Dhn36agoICjB49GpGRkdi3b585qkEmVDRoOn8euHNHdNdpL6lSmrNngQ8+EPvLlgHe3savJxERUXmZJWg6fvy4TsAEAA4ODpg6dSraFl3ZlaxS0aBJ7poLDwe0Jk2WqKAAeO01MS7quefEwrxERESViVm65zw9PZGcnFzseEpKSrGlVahySEpKgkKhgEKhQFJSUqmvVasLgyY5l5LceGho19zy5WIAuFIpBn9zthwREVU2ZgmahgwZglGjRmHTpk1ISUnBtWvXsHHjRowePRpDuZCY1UtJAR48EEulNGgg1oiTW5oMCZpu3RIpBgBg4UKRZoCIiKiyMUv33McffwyFQoHhw4ejoKAAAODo6Ig33ngD8+fPN0cVyITkVqZGjURqgYsXxVpxzs5A+/aPf/+774p8Tq1bi5lzRERElZHJW5oePXqE3r17Y9y4cbh37x4SEhIQHx+Pu3fvYvHixXB2di7zOVesWIHg4GC4uLggLCwM++VpWo/xxx9/wMHBgUu3GFnRrjm5lalDB+BxSwueOgV88YXYX7IEsLc3TR2JiIgqyuRBk6OjI86ePQuFQoFq1aqhZcuWaNWqlc46dGWxadMmREZGYubMmYiPj0eXLl3Qt29fvWOmtGVmZmL48OHo0aNHuT6XSqadowkwfDyTJAGRkWJM1AsvAF26mKyKREREFWaWMU3Dhw/HmjVrjHKuRYsWYdSoURg9ejSaNWuGTz/9FHXr1sXKlStLfd+YMWPw0ksvITw83Cj1oELaM+fKMp5p61Zg717RGrVwoUmrSEREVGFmGdOUn5+PL7/8ErGxsWjbti3c3Nx0nl+0aJHB5zlx4gSmT5+uczwiIgIHDx4s8X3r1q3D5cuX8e2332LevHmP/Rw5qaMsKyvLoPpVRZKkGzQlJQHXromxTR07lvy+hw8Bea3m//4XCAoyfV2JiIgqwixB09mzZ9GmTRsAwF9//aXznKIMc8tv374NlUoFX19fneO+vr5IS0vT+55Lly5h+vTp2L9/v06eqNJER0dj7ty5BtfLFjk5OcH+nwFGTqUkWrp1C7h7V6QIaNIE2LhRHG/XDigSG+tYtAi4cgWoXRuYNs2IFSciIjIRswRNe/bsMer5igZakiTpDb5UKhVeeuklzJ07F40bNzb4/DNmzEBUVJTmcVZWFurKq9BWEbVr19bMdCyNPJ4pOBhwdTVsPNONG8CHH4r9hQtLD66IiIgqC7Mt2GsMtWrVgr29fbFWpfT09GKtTwCQnZ2N48ePIz4+HuPHjwcAqNVqSJIEBwcHxMTE4Omnny72Pmdn53LN6quKSsoEXtoive+9B+TkiGzhTNNFRETWwmxB065du7Br1y6kp6dDrVbrPLd27VqDzuHk5ISwsDDExsbi+eef1xyPjY3FgAEDir3e09MTZ86c0Tm2YsUK7N69G5s3b0ZwcHA5roS0aQdNKSliTJOdHfDkk/pff+UKIM8JWLCAmb+JiMh6mCVomjt3Lt577z20bdsW/v7+ZRrHVFRUVBSGDRuGtm3bIjw8HKtXr0ZycjLGjh0LQHStXb9+HV9//TXs7OwQEhKi834fHx+4uLgUO066kpKSUL9+fQBAYmJiiQGmdo4muWuudWvA01P/ed9/X6wz16sXUwwQEZF1MUvQtGrVKqxfvx7Dhg2r8LmGDBmCO3fu4L333kNqaipCQkKwY8cOBP0z/So1NfWxOZvIeLRzNK1bJ/ZLGs/099/AV1+J/ffeM33diIiIjEkhSZJk6g+pWbMmjh49igYNGpj6o0wiKysLSqUSmZmZ8CypCcXGGNLSlJkJVK8u9jMyRAbwixeB7duB554rfs7hw4FvvgGeeQb45RfT1Z2IiAgw/ve3WZJbjh49Gt9//705PorM6M8/xdbfX+RdunhRPO7cWf9rv/tO7LOViYiIrJHJuue0p+yr1WqsXr0av//+O1q1agVHR0ed1xqa3JIqF+3xTPLyfy1bAl5exV87d65YLmXgQCAszGxVJCIiMhqTBU3x8fE6j+VFcs+ePatzvCKDwsmytMczyYPA9aUaOHsW2LRJ7FfxnKFERGTFTBY07dmzB6+99hqWLFkCDw8PU30MWZB2uoHVq8W+vkHgs2eL5VYGDwZatTJf/YiIiIzJpGOavvrqK+Tm5pryI8hEnJycYGdnBzs7uxKXUZGDpjp1gNOnxX7RNAIJCWJhXoVCBE9ERETWyqQpB8wwMY9MpHbt2lCpVCU+//ChSGQJiJlzkgQ0bgz4+em+Tl4f+cUXgRYtTFNXIiIiczD57DmOWbJNf/0lBnbXqFHYylR0PNOFC6KVCQBmzjRv/YiIiIzN5MktGzdu/NjA6e7du6auBhmZPM5fe+Zc0fFM0dGiBWrgQLYyERGR9TN50DR37lwolUpTfwwZWXJysibL+tWrVxEYGKjz/O7dYtuhA7BkidjXbmlKSgLk1FxsZSIiIltg8qDpxRdfhI+Pj6k/hoxMezxT0bFNkgTs2iX2fXwAlQoICgK046qFC8XxiAigbVtz1JiIiMi0TDqmieOZbNPFi8D164CzMyD3rGq3Mt24AaxdK/bZykRERLbCpEETZ8/ZJrmV6ckngUOHxL72eKZPPgHy88XzRVMQEBERWSuTBk1qtZpdczZIDpq6dQOOHBH7ckvT7dvAqlVif+ZMkZ+JiIjIFphlwV6yHSoVsGeP2PfxES1Kfn5Aw4bi2NKlwIMHQJs2QJ8+lqsnERGRsTFoojI5eVIks1QqgZs3xbFu3USLUlYWsGyZOPb222xlIiIi22Ly2XNknezt7TUD+e3t7TXH5a657t2BAwfEvtw19/HHIqBq2hR4/nnz1ZWIiMgc2NJEegUGBkKtVkOtVuvkaNIOmg4eFPtduwLJycBHH4nH8+YBdvzNIiIiG8OWJjLYw4eFrUs+PmLskpeXyAr+yivi+W7dgEGDLFtPIiIiU2B7ABns4EERGPn7AzEx4lhEBHD4MLBhgxjDtHgxxzIREZFtYtBEeiUnJ8POzg52dnZITk4GUNg1Fx5euETKxInA5Mli/7XXgNatLVBZIiIiM2D3HOmlUqk0yUnlZVR+/1089/Ah8OgR0LkzcPkycPQo4O4uxjIRERHZKgZNZJCMDOD4cbG/f7/Yjh8PvPWW2J85U+RrIiIislXsniODxMUBajXg7Q1kZwONGwPnz4s16OrVAyIjLV1DIiIi07LKoGnFihUIDg6Gi4sLwsLCsF9u+tBj69at6NWrF7y9veHp6Ynw8HDs3LnTjLW1DfJ4ptxcsR0+vDDFwEcfAS4ulqkXERGRuVhd0LRp0yZERkZi5syZiI+PR5cuXdC3b1/NYOWi9u3bh169emHHjh04ceIEnnrqKfTv3x/x8fFmrrl1k8cz3b8P1KoFfPutCKC6dgX+9S/L1o2IiMgcFJI82tdKdOjQAW3atMHKlSs1x5o1a4aBAwciOjraoHO0aNECQ4YMwaxZswx6fVZWFpRKJTIzM+Hp6VmuelubpKQk1K9fHwBw6NAVhIcHaZ5r0EAMAA8IEIPAa9e2VC2JiIhKZuzvb6tqacrPz8eJEycQERGhczwiIgIH5fTUj6FWq5GdnQ0vL68SX5OXl4esrCydUtUULp2iwOTJSs1xBwcRMFWrBvz8MwMmIiKqOqwqaLp9+zZUKhV8fX11jvv6+iItLc2gc3zyySfIycnBCy+8UOJroqOjoVQqNaVu3boVqrc1CgwMhEol4YUX1Dh8uDrkGKqgQCSv/P57oE0by9aRiIjInKwqaJIpiqScliSp2DF9NmzYgDlz5mDTpk3w8fEp8XUzZsxAZmampqSkpFS4ztZGkkQ6gf/9T6wj90+qJgBiYd4BAyxXNyIiIkuwqjxNtWrVgr29fbFWpfT09GKtT0Vt2rQJo0aNwg8//ICePXuW+lpnZ2c4OztXuL7WbMEC4NNPxb5aXXh8zJjCDOBERERViVW1NDk5OSEsLAyxsbE6x2NjY9GpU6cS37dhwwaMHDkS33//PZ599llTV9MqSJLoanv4UORdunYNOHIE2LIFGDECmDEjF0A7AIEAbgMAevcGli3j2nJERFQ1WVVLEwBERUVh2LBhaNu2LcLDw7F69WokJydj7NixAETX2vXr1/H1118DEAHT8OHDsWTJEnTs2FHTSuXq6gqlUlni5+jj7S22kqRb5GMy65qPWBI1AJEC3McnGwsW1MKwYdCMbSIiIqpqrC5oGjJkCO7cuYP33nsPqampCAkJwY4dOxAUJKbEp6am6uRs+vzzz1FQUIBx48Zh3LhxmuMjRozA+vXry/TZ+flGuQQr8VCzt3cv0KyZ5WpCRERUGVhdniZLkPM8hIVlwsXFE05OYuq9gwPg5CRaXxwdRZGP29uLIu8rFGJrZ1fYvaVvq1CI12gfl9+jvS16Hu33yMfVatEFV1AgBnLn54s15G7eBNLSRLlxQ7yuWjXAzQ3w8BD7YWFJWLVK5GlKTExEcHCwaX/IRERERmbsPE1W19JkSbt3A1UktyWSkoBVqyxdCyIiosrDqgaCExEREVkKgyYiIiIiAzBoIiIiIjIAxzSRXsHBweAcASIiokJsaSIiIiIyAIMmIiIiIgMwaCK9rl+/DgcHBzg4OOD69euWrg4REZHFcUwT6ZWfnw+VSqXZJyIiqurY0kRERERkAAZNRERERAZg0ERERERkAAZNRERERAZg0ERERERkAAZNRERERAZgygHSi8uoEBER6WJLExEREZEBGDQRERERGYBBE+mVlpYGZ2dnODs7Iy0tzdLVISIisjiOaSK9cnNzNcun5ObmWrg2RERElseWJiIiIiIDMGgiIiIiMgCDJiIiIiIDWGXQtGLFCgQHB8PFxQVhYWHYv39/qa+Pi4tDWFgYXFxcUL9+faxatcpMNSUiIiJbYXVB06ZNmxAZGYmZM2ciPj4eXbp0Qd++fZGcnKz39UlJSXjmmWfQpUsXxMfH4+2338bEiROxZcsWM9eciIiIrJlCsrK0zx06dECbNm2wcuVKzbFmzZph4MCBiI6OLvb6adOm4aeffsKFCxc0x8aOHYtTp07h0KFDej8jLy8PeXl5mseZmZkIDAxESkoKPD09jXg1ldeVK1cQGhoKADh16hTq1atn2QoRERGVUVZWFurWrYuMjAwolcqKn1CyInl5eZK9vb20detWneMTJ06Uunbtqvc9Xbp0kSZOnKhzbOvWrZKDg4OUn5+v9z2zZ8+WALCwsLCwsLDYQLl8+bJR4hCrytN0+/ZtqFQq+Pr66hz39fUtMQFjWlqa3tcXFBTg9u3b8Pf3L/aeGTNmICoqSvM4IyMDQUFBSE5ONk6kaiXkCL0qtbABvG5ed9XA6+Z1VwVyT5GXl5dRzmdVQZNMoVDoPJYkqdixx71e33GZnAm7KKVSWaV+2WSenp687iqE11218Lqrlqp63XZ2xhnCbVUDwWvVqgV7e/tirUrp6enFWpNkfn5+el/v4OCAmjVrmqyuREREZFusKmhycnJCWFgYYmNjdY7HxsaiU6dOet8THh5e7PUxMTFo27YtHB0dTVZXIiIisi1WFTQBQFRUFL788kusXbsWFy5cwOTJk5GcnIyxY8cCEOORhg8frnn92LFjcfXqVURFReHChQtYu3Yt1qxZgylTphj8mc7Ozpg9e7beLjtbxuvmdVcFvG5ed1XA6zbOdVtdygFAJLdcuHAhUlNTERISgsWLF6Nr164AgJEjR+LKlSvYu3ev5vVxcXGYPHkyzp07h4CAAEybNk0TZBEREREZwiqDJiIiIiJzs7ruOSIiIiJLYNBEREREZAAGTUREREQGYNBEREREZAAGTQZYsWIFgoOD4eLigrCwMOzfv9/SVTKpOXPmQKFQ6BQ/Pz9LV8vo9u3bh/79+yMgIAAKhQI//vijzvOSJGHOnDkICAiAq6srunfvjnPnzlmmskb0uOseOXJksfvfsWNHy1TWSKKjo9GuXTt4eHjAx8cHAwcOxMWLF3VeY4v325DrtsX7vXLlSrRq1UqT/To8PBy//vqr5nlbvNfA46/bFu+1PtHR0VAoFIiMjNQcM9Y9Z9D0GJs2bUJkZCRmzpyJ+Ph4dOnSBX379kVycrKlq2ZSLVq0QGpqqqacOXPG0lUyupycHISGhmL58uV6n1+4cCEWLVqE5cuX49ixY/Dz80OvXr2QnZ1t5poa1+OuGwD69Omjc/937NhhxhoaX1xcHMaNG4fDhw8jNjYWBQUFiIiIQE5OjuY1tni/DbluwPbud506dTB//nwcP34cx48fx9NPP40BAwZoviRt8V4Dj79uwPbudVHHjh3D6tWr0apVK53jRrvnRln214a1b99eGjt2rM6xpk2bStOnT7dQjUxv9uzZUmhoqKWrYVYApG3btmkeq9Vqyc/PT5o/f77m2MOHDyWlUimtWrXKAjU0jaLXLUmSNGLECGnAgAEWqY+5pKenSwCkuLg4SZKqzv0uet2SVDXutyRJUo0aNaQvv/yyytxrmXzdkmT79zo7O1tq1KiRFBsbK3Xr1k2aNGmSJEnG/ffNlqZS5Ofn48SJE4iIiNA5HhERgYMHD1qoVuZx6dIlBAQEIDg4GC+++CISExMtXSWzSkpKQlpams69d3Z2Rrdu3Wz+3gPA3r174ePjg8aNG+P1119Henq6patkVJmZmQCgWfm8qtzvotcts+X7rVKpsHHjRuTk5CA8PLzK3Oui1y2z5Xs9btw4PPvss+jZs6fOcWPecwej1NRG3b59GyqVqthiwL6+vsUWAbYlHTp0wNdff43GjRvj5s2bmDdvHjp16oRz585VmUWO5fur795fvXrVElUym759+2Lw4MEICgpCUlIS3n33XTz99NM4ceKETSzBIEkSoqKi0LlzZ4SEhACoGvdb33UDtnu/z5w5g/DwcDx8+BDu7u7Ytm0bmjdvrvmStNV7XdJ1A7Z7rwFg48aNOHnyJI4dO1bsOWP++2bQZACFQqHzWJKkYsdsSd++fTX7LVu2RHh4OBo0aICvvvoKUVFRFqyZ+VW1ew8AQ4YM0eyHhISgbdu2CAoKwi+//IJBgwZZsGbGMX78eJw+fRoHDhwo9pwt3++SrttW73eTJk2QkJCAjIwMbNmyBSNGjEBcXJzmeVu91yVdd/PmzW32XqekpGDSpEmIiYmBi4tLia8zxj1n91wpatWqBXt7+2KtSunp6cUiVlvm5uaGli1b4tKlS5auitnIswWr+r0HAH9/fwQFBdnE/Z8wYQJ++ukn7NmzB3Xq1NEct/X7XdJ162Mr99vJyQkNGzZE27ZtER0djdDQUCxZssTm73VJ162PrdzrEydOID09HWFhYXBwcICDgwPi4uKwdOlSODg4aO6rMe45g6ZSODk5ISwsDLGxsTrHY2Nj0alTJwvVyvzy8vJw4cIF+Pv7W7oqZhMcHAw/Pz+de5+fn4+4uLgqde8B4M6dO0hJSbHq+y9JEsaPH4+tW7di9+7dCA4O1nneVu/3465bH1u43/pIkoS8vDybvdclka9bH1u51z169MCZM2eQkJCgKW3btsXLL7+MhIQE1K9f33j3vMLD1W3cxo0bJUdHR2nNmjXS+fPnpcjISMnNzU26cuWKpatmMm+99Za0d+9eKTExUTp8+LDUr18/ycPDw+auOTs7W4qPj5fi4+MlANKiRYuk+Ph46erVq5IkSdL8+fMlpVIpbd26VTpz5ow0dOhQyd/fX8rKyrJwzSumtOvOzs6W3nrrLengwYNSUlKStGfPHik8PFyqXbu2VV/3G2+8ISmVSmnv3r1Samqqpjx48EDzGlu834+7blu93zNmzJD27dsnJSUlSadPn5befvttyc7OToqJiZEkyTbvtSSVft22eq9Loj17TpKMd88ZNBngs88+k4KCgiQnJyepTZs2OtN1bdGQIUMkf39/ydHRUQoICJAGDRoknTt3ztLVMro9e/ZIAIqVESNGSJIkpqnOnj1b8vPzk5ydnaWuXbtKZ86csWyljaC0637w4IEUEREheXt7S46OjlJgYKA0YsQIKTk52dLVrhB91wtAWrduneY1tni/H3fdtnq/X3vtNc3/2d7e3lKPHj00AZMk2ea9lqTSr9tW73VJigZNxrrnCkmSpHK2iBERERFVGRzTRERERGQABk1EREREBmDQRERERGQABk1EREREBmDQRERERGQABk1EREREBmDQRERERGQABk1EREREBmDQRERERGQABk1EVOl1794dkZGRlq5Gibp37w6FQgGFQoGEhASD3jNy5EjNe3788UeT1o+IjINBExFZlBw4lFRGjhyJrVu34v3337dI/SIjIzFw4MDHvu71119HamoqQkJCDDrvkiVLkJqaWsHaEZE5OVi6AkRUtWkHDps2bcKsWbNw8eJFzTFXV1colUpLVA0AcOzYMTz77LOPfV21atXg5+dn8HmVSqVFr4uIyo4tTURkUX5+fpqiVCqhUCiKHSvaPde9e3dMmDABkZGRqFGjBnx9fbF69Wrk5OTg1VdfhYeHBxo0aIBff/1V8x5JkrBw4ULUr18frq6uCA0NxebNm0us16NHj+Dk5ISDBw9i5syZUCgU6NChQ5mubfPmzWjZsiVcXV1Rs2ZN9OzZEzk5OWX+GRFR5cCgiYis0ldffYVatWrh6NGjmDBhAt544w0MHjwYnTp1wsmTJ9G7d28MGzYMDx48AAC88847WLduHVauXIlz585h8uTJeOWVVxAXF6f3/Pb29jhw4AAAICEhAampqdi5c6fB9UtNTcXQoUPx2muv4cKFC9i7dy8GDRoESZIqfvFEZBHsniMiqxQaGop33nkHADBjxgzMnz8ftWrVwuuvvw4AmDVrFlauXInTp0+jZcuWWLRoEXbv3o3w8HAAQP369XHgwAF8/vnn6NatW7Hz29nZ4caNG6hZsyZCQ0PLXL/U1FQUFBRg0KBBCAoKAgC0bNmyvJdLRJUAgyYiskqtWrXS7Nvb26NmzZo6QYmvry8AID09HefPn8fDhw/Rq1cvnXPk5+ejdevWJX5GfHx8uQImQAR1PXr0QMuWLdG7d29ERETg3//+N2rUqFGu8xGR5TFoIiKr5OjoqPNYoVDoHFMoFAAAtVoNtVoNAPjll19Qu3Ztnfc5OzuX+BkJCQnlDprs7e0RGxuLgwcPIiYmBsuWLcPMmTNx5MgRBAcHl+ucRGRZHNNERDavefPmcHZ2RnJyMho2bKhT6tatW+L7zpw5o9OiVVYKhQJPPvkk5s6di/j4eDg5OWHbtm3lPh8RWRZbmojI5nl4eGDKlCmYPHky1Go1OnfujKysLBw8eBDu7u4YMWKE3vep1WqcPn0aN27cgJubW5lSBBw5cgS7du1CREQEfHx8cOTIEdy6dQvNmjUz1mURkZmxpYmIqoT3338fs2bNQnR0NJo1a4bevXvj559/LrWrbN68edi0aRNq166N9957r0yf5+npiX379uGZZ55B48aN8c477+CTTz5B3759K3opRGQhConzX4mIKqR79+544okn8Omnn5b5vQqFAtu2bTMo6zgRWRZbmoiIjGDFihVwd3fHmTNnDHr92LFj4e7ubuJaEZExsaWJiKiCrl+/jtzcXABAYGAgnJycHvue9PR0ZGVlAQD8/f3h5uZm0joSUcUxaCIiIiIyALvniIiIiAzAoImIiIjIAAyaiIiIiAzAoImIiIjIAAyaiIiIiAzAoImIiIjIAAyaiIiIiAzAoImIiIjIAAyaiIiIiAzw/6vvi1UVy7V/AAAAAElFTkSuQmCC\n", + "image/png": 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", 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" ] @@ -662,7 +630,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -791,7 +759,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -804,7 +772,7 @@ "# Compute the equilibrium throttle setting for the desired speed\n", "X0, U0, Y0 = ct.find_eqpt(\n", " cruise_pi, [vref[0], 0], [vref[0], gear[0], theta0[0]],\n", - " y0=[0, vref[0]], iu=[1, 2], iy=[1], return_y=True)\n", + " y0=[0, vref[0]], iu=[1, 2], iy=[1], return_outputs=True)\n", "\n", "# Now simulate the effect of a hill at t = 5 seconds\n", "plt.figure()\n", @@ -834,7 +802,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -875,7 +843,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -918,9 +886,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.13.1" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/examples/describing_functions.ipynb b/examples/describing_functions.ipynb index fc7185901..617920e8e 100644 --- a/examples/describing_functions.ipynb +++ b/examples/describing_functions.ipynb @@ -46,7 +46,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -69,17 +69,9 @@ "execution_count": 3, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/murray/anaconda3/envs/python3.10-slycot/lib/python3.10/site-packages/matplotlib/cbook/__init__.py:1298: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -90,7 +82,7 @@ ], "source": [ "amp_range = np.linspace(0, 2, 50)\n", - "plt.plot(amp_range, ct.describing_function(saturation, amp_range))\n", + "plt.plot(amp_range, ct.describing_function(saturation, amp_range).real)\n", "plt.xlabel(\"Amplitude A\")\n", "plt.ylabel(\"Describing function, N(A)\")\n", "plt.title(\"Describing function for a saturation nonlinearity\");" @@ -111,7 +103,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -137,7 +129,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", 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" ] @@ -189,7 +181,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -232,7 +224,7 @@ "$$\n", "H(j\\omega) N(a) = -1$$\n", "\n", - "The `describing_function_plot` function plots $H(j\\omega)$ and $-1/N(a)$ and prints out the the amplitudes and frequencies corresponding to intersections of these curves. " + "The `describing_function_plot` function plots $H(j\\omega)$ and $-1/N(a)$ and prints out the amplitudes and frequencies corresponding to intersections of these curves. " ] }, { @@ -243,7 +235,7 @@ { "data": { "text/plain": [ - "[(3.343977839541308, 1.4142156916816762)]" + "" ] }, "execution_count": 7, @@ -252,7 +244,7 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -288,7 +280,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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mkDihbJWlZoqJiLCmZoD8e6GqGoYnii/f1Z/nnBJe5hFpKEKg2g0JEYIgpKV3LNvMrBCc1LyKVUc0FRmC72CVMyG592J8MoFkOtNVtci9YN1YhyQv4U2rGsYmmUeEUjMEQRCWcaJAoyqDRQFYIzCZYRGRYlIzQHYvZE/NsHRKQ5UPPk9xR5uRmonIHREJTSagZexPzPciMiRECIKQlkJLdxkdDmrkxcyqxUZEnNJdlVXMFNPMjOGU7qqjmbRMfaUXngJb3fNA/BUSBEHMQbERkXajkZfchy+Qa1YtVog4IzVTSnt3BvOIyJ6aYUbVfA3cvCEhQhCEtAxkIhptBc7T6HBQI6+sWbW4Q8cpbd5HS0xRAc6pmmHiVAZ/CEBChCAISdG04qsknBIFSKVVo/lWsRERZtBkeykrbB8aKksRIpmISCQOTZO3x4zRVVWCihmAhAhBEJISnkoaVRKF3gWz1uZD0Wmk0qrpa7OLsYwpUVGKD8MzATMWS0jd2pwdvvUlpCOYKJtKphGTuMHbCKVmCIIgrIcZCoMVXvg97oKe21Tlh9etQNWAQYnD8CPRbBTAXWB7dwa7a05L3tp8PFb64Vvl96Aq0+BtSOIqorFY8d12eUBChCAIKRk22nkX/mHrcinGePR+iUt4SzWqAoDX7UJdpsST3UnLCOubUV9CagZwxhRemebMACRECIKQFOZpKLZ5FWvk1SexSZM1MyvVC8CEzIjEPhEzIiJAblMzefdilDwiBEEQ1jNslGsWWbZaJ//k2WylSGkheOaxkVmIjJkkRLKGVXkF6uhEtrmbDJAQIQhCSozUTIn9M/okrpwZLrG9O4M9X9ZGXsm0ish0CoAZQkQXqLLuBZAVZaVeF3ZBQoQgCCkpxSMC5PYSkTciwsyq5qVm5PSIhCaTAPTqoWBFaS3Ns03N5BQiekm3vh8UESEIgrAQwyNSpBDJdleVNyLCzKrFRoUYbA9lTc2wap+6Cm/R1UMM2du8j+eIslKNu3bh4b0AgiCsYXQijvuePoz2YACbuhtwVkew5A9pkSg1ItJuDL6TV4iYZVZtltysyrwypfQQYTRLLkSYOK0voaTbbkiIEIRD+bc/HsCDL/ca/75sdTN++D82cVyRuZSemtEjIiMTcSRSatETW3nCUjMle0Rq5DarmtFVlcHSGazyRDbGJCvdBSg1QxCOZHQijod2nQQAbF7WCJcCPH1gGIeHJzivzBySadU4KIpNS9RXeuHPiI9BCSskNE3L9hEpYeIskOMRiUp6+JrQVZXB5rOMT8rZaXbEpOohOyEhQhAO5Gcv9iCRUrG+M4if3XIBLl3VDAB4+K8nOa/MHFgo3u1Sis6DK4qSrRaRMBIQmUoZLe5Lvftl+zAak3PGitFDxISISH2VbnZNqxoi08mSX89uxkyqpLITEiIE4TASKRU/3nEcAPCRi7qhKAres7ETAPDwrpNS3uWdCkvLNFX74CohD26YNCX0A4xkoiE1fg8C3sJa3J8K85gk0xrCUxIeviw1Y0IDL7/HjRq/7lqQMT0zShERgiB489+v9WE4GkdrrR9Xr20HALx1TStqAh6cDE3hxaNjnFdYOsMTeiqlWH8IQ+aICBNPpaZlAP3wrQ3oh6+MPhEzIyJAVtCMSShEWAm2LF1VARIiBOE4fvPXEwCAD124xDBgBrxuvPPs9hk/l5lSm5kxmplJU0JvxIjJpsQmo1pEvr0Yy5SsmuERAXIMqxL2VTEG3lFEhCAIHqRVDa/2hgEAbz2zdcbPrjtXT888uqcfUxKPOAdy2rtnumAWS7MREZHPrMoOHLNC8DLPm8nOmSmtmRmDHeIyRkRCJosyOyAhQhAO4sjwBCbiKVT63FjZUjPjZ+ctqUdrrR+xRBqvnQjxWaBJlFq6y2iqkbdaxDhwTEpHyNxLxKiaMSs1YwgR+faCeXzqKkiIEATBgd29IQDA2kWnNy9TFAVnd9YBAF7vi9i8MnMptasqo1lijwjroFlnUhRA5sF3Zg28YzRUsSoieQVqXaU514UdkBAhCAfxaibScU5X3aw/X9sRBAC8fjJs04qswfSIiISHb2jS3CiArL1EphJpTCX1VKNZ6QipUzNT+ppLnbljJyRECMJBMH/I+kzk41TWddYCICHCMNIREpbvhqZYasakiIikoox1VfW6FaPstlQaJBUi08k0ppMqAIqIEATBgelkGvv69ZTL+q7grI9hEZHDwxOYTKRsW5vZmFU1ww7fWCIt3X6wAzhokhdAVrNqrj9EUcyZrcLKd2WrmmH+ELdLQbVJoswOSIgQhEPY1x9BStXQVO3DoswclVNpqQ2gucYPVYMhWmQjFk8hlqn6KTUiUuVzI+DVPwZlS0lkzapme0Tk2gdjzoyJVSKypmYMf0iF1zRRZgckRAjCIbyaMaqu76yb90No3SLmE5FTiLDDIeB1oarEuz5FUbLTViUr4TU8IiaX7w5PyNXm3eyKGWBmakamvWDXRFCitAxAQoQgHMOrJ3Tfx9lz+EMYazvk9omwg4cNJysV4wCWKCKiqlpOmaY5hw4TZImUimhcnjTVuAUtzdm1lUirmJBoL0ImXxN2QUKEIByCERGZwx/CWJuJiOyRVYhk7vrMMuPJWMIbmU6CjQyqMykSEPC6UeXTZ9aMS5SSyHZVNe/wrfC5UZGZ3yNTeiZslO7K00MEsFiIbNu2Deeffz5qamrQ0tKCd73rXThw4ICVb0kQZclUIo0jIzEAeUREMkLk0NAEppPydVg1+w64ScLBd6yHSJXPbbTxN4N6Cb0RZs+ZYRht3iXaC1a6SxGRHLZv345PfvKT2LFjB5544gmkUils3boVsVjMyrcliLLj2Kj+N1VX6V3wgG4PBtBY5UNa1XBgIGrH8kzFbE+AjIPvQkZUyJrDlxlAZWDMor1gQ+PGJDLvMrOqbB4RS+t7HnvssRn/fuCBB9DS0oJXXnkFl1xyiZVvTRBlxfGMEFnaWLXgYxVFwVmLgnjm4DD29kWwfo7mZ6JidpVEs4QREau6ZzJxNxZLmvq6VhK2IDUDyNlLJCRhe3fAZo9IOKznpBsaGmb9eTweRyQSmfFFEMTCHB2ZBAAsbazM6/HLm3XBwgSMTLBD0rwZK/rryBQRGTe5qyqDlQLL5BGxqpOojKmZsITt3QEbhYimabj99ttx0UUXYe3atbM+Ztu2bQgGg8ZXV1eXXcsjCKlhgmJJHhERAFjSoAuWYxIKEbMnrTZL2FHUsogIiwJIlJph1UNmC5FGCQffjZts5LYL24TIbbfdhtdeew0///nP53zMnXfeiXA4bHz19vbatTyCkJqjGaNqd1OeQqSRRUQmLVuTVYxZ1T8jKk//jJBFBw4zfMoUEWFRALM6zDJkHHxneEQkM6va0gP2U5/6FH7/+9/jmWeeQWdn55yP8/v98PvN6Q1AEOUEExRL8kzNsMf1jE1C0zSpujCaXSXBhMh0UkUskZaiNfb4pLnpKYZsVTNpVTN6nlgXEZFjL4BsdIjKd3PQNA233XYbHnroITz55JPo7u628u0IoiyZSqQxENG7guYbEemsr4RLASYTaam8EUCOP8KkiEiV34PKTP8MWQyrIYsOHNmqZqLTSbAgllUeEZmEiBEpkywiYqkQ+eQnP4mf/OQn+NnPfoaamhoMDAxgYGAAU1NTVr4tQZQVx8f0tEywwpv3weTzuNAe1OfR9EiUnlFVzYgGmNlJU7YSXqsOnGzVjByHL4sAVHjN7acCyDf4LpFSjRlM5BHJ4f7770c4HMZll12G9vZ24+sXv/iFlW9LEGXFMVYxk2c0hLG0iRlW5REi0ekU0pmWomZ+2MpWwpuNClkTBWBiT3SyqQjzD17ZUjNsLxQFqAnIJUQsTYbKYvwiCJk5ZvQQyc8fwljcUIW/YBQ9ElXOMKNqtd8Dv8dt2uuyQ0eWypmQRa28mbAJTSaQVjW4XWJ7h6yqmAGyomwqmcZUIo0Kn3nXmxWEM2XMtQGv8P/fToVmzRCE5BRaustghlWZIiJWRQKMLpqSNPIKWWVWzbyeqgGRKfH3ggmRWguESLXfA69bP9Bl8MxYVdJtByRECEJysqW7hUVEWATl+JhEQsSCke+5ryfDgZNIZSfCmu0R8bpdqAnogXIZ9sLKclVFUYySYJn2QjajKkBChCCkJ1u6W1hEZHGDfN1VzZ4zw5CpQoJ1ElUUayIBMomysMVj71mn2ZAEnhlWSRWUrHQXICFCEFIzlUijP5wp3S0yNROaTBof6KJj9pwZhkxlq+GcKIAVXoBsLxHxr4mIhR4RICvKpBAikpbuAiRECEJqejJpldqAp+DccJXfY5StylLCa/acGQY7fGUo1Ry3OATfING8GSvNqkB2iq0UAtXCCiKrISFCEBLDhMiSxqqiuqNmDatypGfMnjPDaJAoHTFu0dh7hkzzZqwee59NzcizFxQRIQjCVvrDenPA9mCgqOfntnqXAbPnzDByPSKitx0wxt5bdPjKNG/G6oiIVKkZ8ogQBMGDvpDuD+moqyjq+UsyhtVjI5JFRCwyq8ZTKiYz3SlFxShhtjoiQkIkJzUjgRAhjwhBEDwYKDEi0tWgC5i+sBxjF6yKiFT6si3CRT+Axy1OR0hl3LUpIsKahYkMeUQIguBCX6Zipr3IiEhbRsCwyhvRyXpEzBUiiqJI4xNhh6JlERGJ5s1YLURYdEGOiAgJEYIgOFCqR4QNvhsITwvvjUirmpEHt+IQlqWXiNUHjizzZlLpnMZuFomyOknEKZBNzbAmbDJBQoQgJEVVNQywiEiRQqStVn/eZCKNyHTKtLVZQXgqO/LdikNYFiESmbY2CsAqksTfh+z1WhuwZmwaGyUQFlyUqaqGaEaU1VZYOkLOEkiIEISkjMYSSKY1KArQWlucEKnwuY1DfUDw9Aw7GGsDHnjd5n90yWLSNOarWDRhNeuLSCKVVi15DzNg+1Dt98BjwfUAAHWZ6EJoKil0xHAikTJEulXXhZWQECEISWFpmZYaf0kHM4uK9AtuWLWqqyqjQZLmVZEpa+98cyMtIYE77mZTEdYdvEykp3MiDiLCOsz6PS4EvGJPCZ4NEiIEISmsdLctWJxRlcHSOrJERMyumGE0VPkz7yPu4QtYn5rxuF3Ga4vcyMtqoyoABLxuVGQO9pDA10VWnMoXDQFIiBCEtLAIRkeR/hAGEzKiV85Y3Sch642IW/L6ZqCqmnH3a2UIvkGCeTN2CBEgGxUROVLGxGmNRV4ZqyEhQhCSkjWqlhYR6ZAkIpLtk2BtI69xgQ/fWCIFlXkBLDyAWddWkf0yVg+8Y7DrTeQ0lR3i1EpIiBCEpPSVWDHDMHqJRMQWIqFJi6tFWP8Mge98mRjzWewFqJegbNXq64FRJ0GailUQUWqGIAhb6Q9leojUlSZEsr1ExDarWh2Kb6gWf8aK4QWw+M6XdW0NCxwFsKuTKCvhFfm6iE6ziAilZgiCsJF+k1IzsnRXDVl88OR2VlVVMUs1s0ZVaw8co2xV4P4ZRhkzpWbIrEoQhP2kVQ2DETbwzpzUTHQ6ZXSqFJGwxaF4duComriRAPsOXxYRETcKYJtZ1UjNiHlNAFmBSh4RgiBsY2QijpSqwaUAzdX+kl6r2u8x3PYiG1atDsX7PC5jH0T1idhlSmR7LPLhG7JJiDC/jNAeEUOgUmqGIAib6Mv4Q1prA6Z0lZShl0hoyvpZGsacFUH9AMyUaPXhG5QhCmCTRyRbvivwXlBEhCAIu+k3qWKGke0lIq5h1erUDJC9+x0VVIiEbbrzlcEXYV8fERkiIrpApT4iBEHYRp9RMVOaUZXRXit2RCStakY0wMo7YOEjInalZirYsDcx9wGwfuYOg/VUEVmUGRERMqsSBGEXzKjaVuSwu1MRvZcIK08ErL0DNjqKCnoAW93enVEn+OGbTKuYTKQB2NhZVVBxClBqhiAIDgxF9TbkrbWlGVUZontEmFehyue2ZPIuQ5qIiOWVIvo+TCbSiKfSlr5XMUxMZ6u7rE5HsNRMZDqFtKBl3VHDO0SpGYIgbGI4I0RaasyNiLCUj2jYXSEhqkfEroZmNQEPFEX/bxFLmVkEoMrnNsWsPR+5s41E3AtNs2f+kJWQECEICWERkeYasyIime6qgqZmDGOiRXNmGPWCl63alZpxuRTjPcIC7oWdDbw8bhdq/HqkQcSW97FE2pb5Q1ZCQoQgJCQbETFHiLAUT2gyKWQo3urJuwzRKyTsqpoBchp5CRgFsNsTUVclrkBl0RCf2wW/R84jXc5VE0QZM51MGweSWRGRYIUXXrceix+dEO8Qtm/SqriHL2DvlNVgpbht3u1u4CVyU7NsxYwHCsunSQYJEYKQjJEJPRric7tMO5gVRUFTpkMri7aIBDsM7WpeJeLhm0yriNlUKQKIPXXW7oiIyA3esj1E5EzLACRECEI6cv0hZt4BsegKEzoiYVfzqtw7X9EG30VtrBQBcufNiHz42hMRMfwyQu6F3JN3ARIiBCEdwyYbVRnNIkdEDLOqPa3NVQ2YSIg1AJAdONV+j+WVIoDYw97sbuAltBCRvJkZQEKEIKTD7IoZBns9IYWIDe3dASDgdSPg1T8WQzGxDp2wzXe+hkdEwAm8LDpkd2pGRCFi915YAQkRgpAMsytmGIYQETA1Yww4s3DgHaNe0APY7jtfoSMiNptVRRYisk/eBUiIEIR0DEf1Xh9lFRHJiAKrzaqAuMZEO3tnAIJ7RDiZVWkvrIGECEFIhtldVRkiV83YZVYFshER0ZpX2TXkjSFyBRGJsix274UVkBAhCMmw3CMiYGrGLo8IIO6hk9svwg6CFWKmqAD7owC1UkREKDUzK8888wyuueYadHR0QFEU/Pa3v7Xy7QiiLLDMIyJoRGQ6mUY8pQKwvmoGEDcSYFdTN4ao+wCQRyQXJkSoj8gcxGIxrF+/Ht/61resfBuCKBtUVbOufDfzepOJNGJxcUpX2Ye/26UYMz+spI5SMwCyZtXodAqptGrLe+ZLhKpmDLKpGXkjIpau/Oqrr8bVV19t5VsQRFkRmkoilWm0xTwdZlHl96DS58ZkIo2RiTiqbDj08yG3bNWOFtbsABZt2Jtx+NrcO4O9d0OV9RVL+ZBWNUzE+TQ0S6RUTCfTCHjdtrxvPkTJrGou8XgckUhkxhdBEFmGMhUz9ZVe+CwYcCVi5Uy2vbs9B6Go82bsTs3kTp0Vqc37xIwOs/bsRbXfA7dLF8GiparsFqhWIJQQ2bZtG4LBoPHV1dXFe0kEIRRWVcwwRPSJsEPQvgoJ0VMz9kWqggKKMuaJqPC6LRHjs6EoirHvIqVnNE2zdRCiVQglRO68806Ew2Hjq7e3l/eSiDJgMpHCaydC2HMijMPDE9A0sWaM5DIUscYfwjBKeAWqnAkbzczs9UaIl5qxv5W3UUEk0F6EOTXwYgJVJCEylUwbqVryiJiE3++H32/NByxBzMZ0Mo3r7nse+weixvfuevsa3HLJMo6rmhsmEKwSIiKmZgwhYkPFjP4+YkZE7E7NANlOtiKV8PJq4CViCS8zqnpcCioE8q0UilAREYKwm3sf3Y/9A1H4PS6jHPYrjx/A0ZEY55XNDouImF26yxBZiNh1ANfn9BERZQKvHoK33wsQFLCEl1cDLxErZ3KjZHYYua3CUiEyMTGB3bt3Y/fu3QCAo0ePYvfu3ejp6bHybQkiL7YfHMYPnz8GAPjO/7MRL37+Sly0ognxlIo7H3pNyBSNXRGREYFSM4ZZ1aaDhx1wqgZEBSljjqdUJDIltPZGRAQUIpwaeAkpRKZYDxGhkhsFY6kQ2blzJzZs2IANGzYAAG6//XZs2LABX/jCF6x8W4JYkMlECp/91asAgL/bvASXn9ECRVHwpXevQ8Drwo4jY/jlTvE8SkMRa+bMMEQ0q2Y9AfYcwAGv2whzi1ItwvbApQBVPvtC8CJ2mY3YfD0wghXimVWdMGcGsFiIXHbZZdA07bSvH/7wh1a+LUEsyCN7BjAUjaOzvgJ3Xr3G+P7ixkrc/rZVAIDvbj8iXFSkHD0iIcMjYl8fi3rBUhK5h6+dIfjsAEAxBBmQLVe1OwpgREQE2ovotPzNzADyiBBlCot2vH/TYlSccof5gQuWwOd24chIDG8OTfBY3pwMRywu382ZNyOKCGMf/HamJIKVzKQpiBCZtt+omvt+kWkxUlQAuJWripyaoYgIQUjG0ZEYXjo6BpcCXHfuotN+Xu334KKVTQCAx14fsHt5czKVSBueBasiIo3V+gGcTGvCfODaXTUD5EZExLj7tbu9O0PEwzfKqYEXqyASaS/sbnVvFSREiLLj16/o0ZBLVjWjPVgx62OuOqsNAPDYXnGECEuX+D0uy4x6fo/bOHyGBEnPhGzuIwKIN/CN1zwRIUtWqXzXwO7hf1ZBQoQoK9Kqhl+/cgIA8L7z5u7c+9YzW+FSgL19EfSOTdq1vHkZnsgaVa30CTRloiKjE/yjAaqqcemfEWT9M0QRIpxTM3T4CroXZFYlCPl47s0RDEbiqK/04so1LXM+rqHKh03dDQCAPwoSFbG6hwijsUp//dEY/4hINJ4Ca+VhZyiepWZEaWrGOpvaHgXIvF9EqMOXTzoiK0RE8svwMe6aDQkRoqz40xuDAIB3nN0Ov2f+MkiWnhFFiFhdMcNgPpGxGP9DmB2AAa/L1omnopWt8mjvDmQbmsUzU2dFgFv5bmVWlIli5OZ1XZgNCRGibNA0DU8fHAIAXL567mgIY2tGiOw8Pi7EgTRkccUMg417FyE1k21mZu8IeqO1uSAREXbna3dqptrnQWborDBREd4NzRJpFVOiiTJKzRCEHBwZiaF3bAo+twublzcu+PiOugosbqiEpgGvnQhZv8AFYGZVyyMiTIgIkJqxu707o85IzYhx+PKYvAsALpcilElTVTVMxPlUzVT53HBnVJkIewHwqyAyGxIiRNnw9IFhAMCm7gZU+vL7QN+wuA4AsKsnZNGq8oelZqz2iLCIiAipGTZsLWhj6S4g3qRVniF4wycyzX8vovEUWFbEbl+EoijZyczCXRfkESEIKXj6gJ6WuWx1c97P2dBVBwDY1TNuxZIKYihqbXt3RmOmzbtIqRl+ERH+ewDY3+Y+F5GqRaKZg9fvcS3o8bKCbHdV/nsxYxAipWYIQnymEmm8eHQMQIFCZHE9AGB3b4i7QY2lZqz2iGRTM/wP4TCHHiK57xcRZAIvzzJNkYRItkqEz8ErUpoqdxAipWYIQgJeODKCRErForoKLG+uzvt5a9pr4fO4MD6ZxPFRfv1E0qqGkUyEwuqISINAVTM8uqoCMyfwTiT4l2vyMqvmvqcIUYBsPxU+qQixRBmfQYhWQEKEKAu2Z/whl65uLqgZmM/jwtqOWgDArl5+6ZnxyQTSqgZFyZbXWgXrI8LekydhTqmZgNeNgNc1Yw28UFWNqxeAvacI82Z4le4yhBIimWuiJmDvIEQrICFClAU7juhpmYtXNBX8XCM9w9Gwykp3Gyp98Lqt/bNlzbw0jb9HImtWtbd8FxDn0JlIZA2aPFIzIqUjeM9WCeak7HgT5tT23wpIiBCOJzSZwIHBKADg/Ey31EIwKmd6QyauqjDsamYGAB63y0iF8E7P8CrfBXJ7ifA9dNih5/fY29SNIYogA8SJiIgwldkp7d0BEiJEGbDzmJ5SWdZchabqwg9yFhF5oy/CrbvkUMSeihlGoyBNzbINzcrXpMmzYgYQZx9y12B3PxWGSBGRqEMm7wIkRIgy4KVjelrmgiKiIQDQEQygucaPlKrh9ZNhM5eWN9keItZWzDBEmTfDMyLCepew9BAvsiWafA5fkebN8G5pbggRofwylJohCOF5KVO2e/7S4oSIoihYtygIANg3EDVtXYXAPCJ2RUREaWrGq2oGECcSwGvyLkOUfQD4Vg8B2UNfiL2g1AxByMFkImVEMYoVIgCwuq0GAHBgIGLKugrFrq6qDFbCyzM1k0ipmEzoqTC7Z83o7ylG2aooqRmhIiK8+oiIFB2ackZ7d4CECOFwdvWEkFI1dAQD6KyvKPp1VrfqQuTgwIRZSyuIYZsjIk0CzJthB7Ci8BlzLkokgPdgM1H2AeCfjqg1UjMC7AVFRAhCDlg31fO7G0qqtV+VESIHBqNcOqzaHhERIDUTzngzagNeuFz290lg6SDuVTPTvNMR+vvGEmmkMp08eSFK+a5IooyHSDcb+X8DwnZUVcPBoSheOT6OwUgcF69swsbF9VwOi4V4OSNENhVpVGUsa66C26UgPJXEUDSO1lp7TKMMu6tmGgSYN8PTqAqI0z+DexQg56CLTKcMkcoD3uW77H2nkyriqTSXeTeMiEMm7wIkRIgCeaMvgs/8YhcODmZTFP/nz4fQWuvH59++Btees4jj6maSSKlGN9RNJfhDAL3T5tLGShwejmH/QNRWIRKLpxDLeCVabHrfJgHmzRiluxyMqvr7ZvqIiCJEOEUBPG4Xqv0eTMRTCE8l+QoRIx3B5+iq8XugKHqzv+h0Cv5qfkIkynkvzIRSM0ReaJqGH79wDO+67y84ODiBSp8bb1nRiGvWd6Am4MFgJI6/f3A3vv/sEd5LNdhzMozppIqGKh9WtOQ/X2YuzmjTW70ftLlyhg27q/C6bZspIcK8Gd4REVFMmrz3Ife9eUaHVFXDRJxvFMDlUlDtF6Nyhnd0yEzkl1KELfzHs0fwpUf2AwCuPKMF/3b9euPOKJ5K48uPHcAPnjuKf/3DPsTiafz9W1fyXC4A4OVM/5DzltSbMothVWsN/rCn3+jSaheGP6TWb9tMCfb/ls2bcXNIu4U4zZlhGF00Obe55907A8j6EHiKsmg82+qepy8iWOFFdDrFXaDy9suYCUVEiAX5/at9hgi5422r8P2bzpsRnvV73Pind6zBZ/9mNQDg3/90EC8cHuWy1lxeMskfwljdpkdVDtosRIweIkV0hS2Whkxague8mRDHHiJAtnw3lkgjydGkybt3Ru5784wCsIM/4HVx9WYYJbycm5rx9g6ZCQkRYl52HhvD//zlqwCAj7ylG5+6cuWsd+WKouCTl6/ABy5YDAD47K9fRSzO7w9VVTXsPGauEGGVMwcHo7ZOpR2O6kbVllr7hIgI82YigphVc9fCgzBnjwggiBARpFxVhKZm08k04ik1sx6KiBAOJjyVxN8/uBuJtIqr17bhn96xZsHnfP7ta7CorgInxqew7dF9Nqxydg4MRhGZTqHK58aZ7bWmvOaSxir4PS5MJ1X0jk2a8pr5MBS1PyICZNMzvCpnWEqERzMzAHC7FCMFwNOwmk3N8E1HALwjImJUiYjgHWJzZhQFqPZRRIRwMHf/7nWcDE1hSWMl/u369XmV51b7Pfi3954NAPjJjh68ymliLUvLnLukHh63OZe526VgZauenrHTJ8LMqnZVzDCaOM+bIZMmkExnu8vy3AcRGnnxrphhsIiMCNGhar9HyLYJhUJChJiV3+0+id/u7oPbpeDfbzjHcIrnw5YVTbhug17G+/U/HbRqifPCBt2VWrZ7KkZjMxsrZ3hHRHilZlgUIsjJIwJk/Sm82rzn3nUX8jdoNiJEAUSpEhFClAmQrjMTEiLEaYxOxHH37/cCAG67fAXOXVxf8Gt8+sqVcLsUPHVgGLt6xs1e4rxompYddGeSP4SxskUXIoeH7Wv1ziIizTZ6RAD+82YoIpI1RFb7PaZF9oqB9z4A4lSJZEUZPw9c1EHNzAAq37WFZFrFsZEYDgxGMRSJw+91ocrnwdmdQXQ3VdlWkpkv//qHfQhNJrGmvRa3XbGiqNdY2lSFd29YhF+/cgJf/9Mh/Ogjm0xe5dwcH53EcDQOn9uFc7rqTH3tZc1VAIAjwzFTX3c+uEVEKrMlvDwIc25oBmT9KbxKeHkbdhlCCBFBqkRqBShlFiVNZRbO+C0ERNM0/OXNUfz+1ZN47PWBOUu9Wmv9eMe6Dtx8cTc66oofymYWzxwcxsO7TkJRgHuvWwdvCXdhn7piBR7edRLbDw7jrz3jRUVWioFFQ87uDCLgNbfMb1mTLkSOjsSgaZrlIjKtahiLZfuI2AnP1IymadnUjADeiDCnu1928POeJ8IOf55RABGqhwBRUjMUESHmIa1q+MOeftz31JvYn+MjqPK5sbK1BovqK5BMqRiLJfDaiTAGI3H851+O4scvHMN7N3bic1efYbSWtpupRBp3/XYPAODDW5ZifYnRhCWNVbhuwyL86pUT+MFzR3HuB2wSIiaX7eayuLESLgWYiKcwHI1bbiAdnYhD1QCXAjRW8REiPCIisUTaKJHmVTUD5Ay+m+IUERGgmRkgSEREsL0QIyJCQoQ4hZeOjuGe3+/FG/0RALr4uHbDIvzt+g6cv7ThtO6U08k0nj88gu8/exTPHx7Fgy/34sn9Q/jSu9fhrWe22r7+r//5IHrHptARDOCOratNec2PXNSNX71yAo+9PoCB8DTagtZXfrCOqmb7QwC9eVtnfSV6xiZxeDhmuRBhaZnGar/t3U3rjYiI/R+4LBXi87gQ8JavN0KEZma57y9C+S7vvRBhGKIoaSqzILOqCfSFpvCpn+/C+777At7oj6Am4ME/vHUVnv/clfjSu9fhwmWNsx4iAa8bV5zRip/dciF+fetmLG+uwlA0jpt/vBNfemSfrU2z9vaF8f1njwIA/uXataY59Ne012JTdwPSqoafvXjclNecj8HINI6PTsKlABuXWBOBYT6RoyPW+0SGOflDgKxHZIxD+W6uUZWnh4p1V+VVNSNiOkK18XMpF1GiACJ0VhVlL8yChEgJTCfT+OafD+HKr27Hf73aB0UB3r9pMZ7+n5fh79+6sqCyw/OWNuAPn74YN1/UDQD43jNHcMuPdxoTFq0krWq486E9SKsa3rGu3fRozE2blwIAfvZSD+KptKmvfSrMH7KmvdayP9JlTXovkSM2VM5ke4jYL0Tqq/T9G48loWn2Hj6GUbXMIwEiNDMDsgeepgETCT4HsChRgNzUjN1/FwwWHeLtHTILEiJFoGka/uvVPlz51e346hMHMZVM4/yl9fiv2y7CtuvWobHIu9eA141/eueZ+MaN58DvceHJ/UP4wH+8aLlZ8AfPHcFrJ8KoCXhw9zVnmv76W89qRVttACMTCTy6Z8D018/FKNs1uX9ILt02RkSGMu3deUREmCclkVYRS1grIE9FhNJdINvDhFdnVVH2IeB1w+/Rjwte0aGoIOW7TAilVM1oNmc3ovhlzIKESIG8diKE67/zAj718104GdL9FN+48Rz88mObsXZR0JT3uPacRfjlxzajscqHPSfDuPF7L2AoMm3Ka5/Km0NRfOVxvenYP71jjSWeB6/bhQ9mZtD83x3WpmeYP+QCC/whjOWZypkjdqZmauwXIhU+t+HPGLe5cob3wDsG94iIIKkZQKC94Hz4Vnjd8GRS7bwqZ0QRZWZhixC577770N3djUAggI0bN+LZZ5+1421N5bUTIXzip6/gb7/1F+w8Po4Krxu3v20V/nzHZbj2nEWm57HXd9XhFx/bjNZaPw4OTuB9330BJ8bNnW+SSqu441evIZFScemqZrzvvC5TXz+XG87vgtul4JXj45ZNrw1NJozW6+dZGBFZ1qynZnrGJpFIWTuVlZlVWzgIESDXJ2KzEJkU49BhFWzhST5heOZD4B0RyV0Dj2qRtKohGmeHL990hKIo3JuaiZKmMgvLhcgvfvELfOYzn8Fdd92FXbt24eKLL8bVV1+Nnp4eq9+6ZMJTSTz4Ug/e950X8Lff+gseyaQVrtuwCE/+z0vx6StXosJn3TjqFS3V+NXHtqCroQLHRifxvu+8YGo64L6nD+PV3hBqAh7c+551lpoCW2oDuPKMFgDAgy/1WvIeO4+NQ9N0M6mVEYTWWj8qfW6kVQ29JovDU8lGROydM8Oo59RLhN118yzdBbKHbyKtYjppreicjbAgUYDcNfCIAkzkGENrBIgC8K6cIbNqgXzta1/DRz/6Udx8881Ys2YNvv71r6Orqwv333+/1W89J5qmIZFSMZ1MYzKRwlgsgeOjMbxyfAy/fuUEtj26D+/69l9w7v/3BD730B68dGwMbpeCd29YhMc+czG+dsM5aA/a03xscWMlfvmxzVjWXIW+8DSu/84Lpsw5efbQMP49MwfmnmvOsuX3ef8mPT3z0K4TmE6an1t94cgoAGvTMoB+R9TN0jMWd1gd4mhWBfg1NQtn+nbwTs1U+dxGxRuPXiJRIzXD/86XZ2qGHbwVXjd8Hv6OAt7dVUUpZTYLS6/uRCKBV155BZ/73OdmfH/r1q14/vnnT3t8PB5HPJ4tFYxEIpasa/vBYXz4gZfzeuyq1mq8e0Mn3rWhwzbxcSrtwQr88mOb8aEfvIR9/RHc8L0X8OOPbMLZnXVFvd7J0BQ+/fNd0DTghvO68J6NneYueA4uWdWMjmAAfeFp/HHvAK49Z5Gpr//coREAwFtWNJn6urOxrLkae/siODoyAcCani+apnEt3wX4NTUTxaSpKArqKrwYjSUQnkra/hnADmCeg/8YPIVIWLBUBM/oUCKlYipzI0cRkTwYGRlBOp1Ga+vMD+rW1lYMDJxePbFt2zYEg0Hjq6vLGs+Ca5YURKXPjUV1FdiyvBEfuGAxvnL9ejz3j5fj8X+4FB+/bDk3EcJoqvbjwVsuxDlddQhNJvGB/3gRzx4aLvh1QpMJ3PyjnRifTGLtolr887VnWbDa2XG7FFyf8aH8/CVzU3NDkWkcGIxCUYC3LLdeiNgREYkl0sYHDg+zKgDUc/aI8I6IANkDOGRztYimacL0EQHEiIiIsA8A39RMbkuHagEiZWZgy29xqvdgrhkdd955J26//Xbj35FIxBIxsnl5I169eyvcLgUuBfC5XVwnW+ZLsNKLn9x8AW7+0cvYcWQMH37gZfzTO9bgw1uW5uXvCE0m8MHvv4h9/RE0Vftw/wc3mj6LZSHed34XvvnkIew4MoajIzHjQC+V597UoyFrO4KGr8FKltsw/I5VSlX53KjiNAKed0REBG8Ei0bYfehMJ1Uk07pBVoR9yKYj7DdoijZbhadZNXcis93dlq3C0tO3qakJbrf7tOjH0NDQaVESAPD7/aitrZ3xZQVetwvBCi+q/R5U+viO1y6Uar8HP/rIJrzn3E6kVQ3//F9v4Laf7TL6TczFocEobvzeDuzt00XIz2+5EF0NlTatOsuiugpcuqoZAPDgy+ZFRVha5qKV1kdDgGxE5OiodUIk28yMj1EVyJpVRyc4RUQEOHh4RQJYFMDtUlBloSk+X3hGAUSbNpvtrsovIiLKXpiBpSewz+fDxo0b8cQTT8z4/hNPPIEtW7ZY+daOxu9x4yvXn4273r4GbpeCP+zpx5Vf3Y7vbD98miAZiyXw7afexDv+z3PYPxBFU7UPP7vlQqxsreG0euDGjGn1N6+cMKX8VdM0IyJysQ3+EABYmhEiw9E4JuLW3BUNcfaHANnyXbsjIhFBPCIAvzbv4RyjKs829wyuqRmBImRA1qvCZy/Eig6ZgeWS6vbbb8eHPvQhnHfeedi8eTO+973voaenB7feeqvVb+1oFEXBLZcsw+bljfj8w3vw2okw7n10P7782H6c3VmH2govYvEUdvWMg42GuHx1M7Zdd7Ytg+fm44ozWtBc48dwNI4/7xvE1evaS3q9g4MTGIrGEfC6sHGpPRN+awNeNFb5MBpL4NhIzLRmdrkMhHVR2crx/xePqplkWjV6RvCaRJ0Lt4iIYIcvX4+IWA28ePZUEc0vYwaWC5EbbrgBo6Oj+Jd/+Rf09/dj7dq1eOSRR7BkyRKr37osWLsoiIc/8Rb8amcvfrGzF7t6QtjdG5rxmDXttbj5om5cd675jdeKwet24fqNnbjv6cP4+cu9JQsRZtrd1N0Iv8e+EPbSpipdiIxaJEQyHpF2AYTIuI3RgNwPdxHCz8GMGLK7fNeomBFEiPCsFBGtgRcTAXyjQ2LshRnY8pt84hOfwCc+8Qk73qoscbsU3LhpMW7ctBjHRmLY1x/BZCINDXpPDR5ekIW48fzFuO/pw3j20DB6xyZLWuP2g7oQsSstw1jaWIVXjo/jmEWt3pkQaeXqEWEVIwmkVc0Wcxz7cK/xi+HfykYC7DUmilQxA3COAgi2F1lRxsOsKtZemIFzJBUBQL9LX2pSJYqVLG6sxEUrmvDcmyP45c5e3LF1dVGvE5pM4IXDeiOzK9e0mLnEBelu0sXT0RFruqsOZlIzbTyFSCYaoGr6YWBHRRKbMyNC7wwg6xEJ2e6TYV4AMT6mc1Mzc1U+WoVoQ974ijLneUT4324QZcuNm/TS7F/u7EUqXZxp9Yk3BpFSNZzRVmPMgLELJviOWVQ508+ESJCfWdXrdhmjxkdt8omI0syMwevQEXUfkmnN6G9jF8bhK0gUgGdnVSbKagRIW5oFCRGCG287sxUNVT4MRuJ4+kDhzdkA4JE9/QCAt5foMymGpY0ZIWJBakZVNaMCqo1zM71Gm3uJhAVqZgZk1xHiZVYV5PCtzGl3b3f/DFH9MtF4CmnV3mGIol0XZkBChOCG3+PGe87V27wX01MkPJU0ynbfvq7N1LXlA4uIjMYSphv4xiYTSKY1KAq/ybsMuwffiRoJ4NVHRJQQfO7UWX4VRGJEAXJFwITNPpHotFgpOzMgIUJwhfUUeXL/kFGumi9/3jeIZFrDqtZqrGixvy9Ktd9jtF43OyrC9qKxyg8vZ8Om0UvEJiHCmpkFOU/eZeR2VlVtvPsVqbssg58oEys14/O4UJHpSs1NoAqyF2ZAQoTgyvLmamzqboCqAT978XhBz2VpmavX2p+WYXRn0jNHLRIiPEt3GUZExKbUDCuTFS0iomkw+pvYQdYXIc6dL4/uqqm0ajQNFEmUsYiE3eXMZFYlCAv4H1uWAgAeeP5Y3h9wQ5FpPHOQpWX4CZGlmcqZYyZXzohQusswmprZ1OadXQOieET8Hnf27tfOfiqC+SIAPibN3M7FIhk0eZmYKSJCEBbwN2e1YXVrDaLTKfznc0fzes4Dzx9DIq1i45J6rG7j167eqsqZAQEqZhgNNkdEwgLNmWHwSElQakaHRQAqfW7uacpceDU1E80vYwbi/F8lyhaXS8Gnr1wJAPjPvxxd8A97Ip7CT3boaZyPXbLM8vXNh2WpGaOrKt+KGcB+j4hoZlUguxY7u6uKWB3BRYgIGgHg0Wk2lVYRS+il06LtRymQECGE4Oq1bVjVWp1XVOTBl3oQnU5hWXMV3rrm9CnOdmJVRGRQoNRM1iNizweuaA3NgJmGVTtQVc3wo4goyOyNiIgZAcimZuzzDUVzKnSqBUpTlQoJEUIIXC4Ff3/lKgDA/U8fxr7+yKyPS6ZVQ6jccvEyuGxoOT4frJdIaDJpaufNAQG6qjIaMm3eKSKSreixmmg8BS1ToCOSL4JHFEDYiEjA/gm8bC9ES1OVinN+E0J63r6uDW9d04JEWsWnf74L07N0b9z2yH70hafRVO3Huzcs4rDKmVT43IZYMDM9I5JHhLV5t6OPiKZpOQ3NxCjfBbJ+FbsOHRYF8HtcCHjtG+S4EDwMmqJWifAQZVHBypjNgoQIIQyKouB/v+dsNFX7cWhoAvf8fu+MroW/2tmL//yLHg3513edJcwHtFE5Y1J6JhZPGWF53l1VAb2XCaB7c+Ipa1t7TydVJDLt/kWMiNh1AItYMQPw9oiIExkCeIkyMdNUpUJChBCKxmo/vvq+9QCAB1/uxbu+/Rf84bV+/O/H9uOuh18HAHz6ypW4imPvkFPpbmKGVXNKeJlRtdrvQbWf/wdOTcBjtPa2OjXBzKAel4IqnxhCE8hp825TakbEihmAt0dErL3gUTUjapqqVEiIEMJx6apmfOX69agJeLDnZBif/Nlfcf/Th5FIq9h6Zis+k6mwEQWzZ86wtExrLf+0DKD7d+ozB7HV6ZlsV1WvrdNdF8LuA1jEZmZA9gC006ApWldVRrahGYcmd4KJslIR6yoniAzv3diJS1Y14d5H92NXTwjndNXhklVNeOfZHdwNqqdiduVMtqsq/7QMo77Sh5GJhOWGVfb6rFJHFIIZv4pd5bsRAQ27AO+IiFjHVS2P1IygaapScdZvQziKlpoAvva+c3gvY0GyqZkYNE0r+U5epK6qDNbUbNRqIZKJiDQIZFQFcg9ge+5+RRt4x2D7MJVMI5FS4fNYH1QPC9hPBeCUmhE0TVUqlJohiBJZ3FAJRdEd7WakLkSqmGEwITJucXdV1r1VlPbuDKNqxq55OwJ2lwV0vxDT2balqQQXZfaWMutCWKSSbjMgIUIQJRLwutGRSaOYkZ45Ma6bXhfVVZb8WmZhNDWz2iPCUjPCRkTsNauKlppxuRTDQG2/X0asvWDCaDqpWl5NxhCx264ZkBAhCBNgJbxmVM70jk8BALoaxPGI2NXmnaVmRPOIsAhNLJFGMlNebCXZ7rJi7QNgfyRA1FLmGn82OmSXedcw7gq2F6VCQoQgTMCsyhlN04yISFe9gBERi8tXWeqnXrDUTE3OHagdkQDWpVe01AzAo4JITLOqy6Wgxs8qZ+zdC0rNEARxGoZhtcTUzPBEHNNJFYoCdNSJExFprLIrIiJm1YzbpRiVCnb0EmEHjmheGcDeRl6iD3mr5ZSyq6sQ6++jVEiIEIQJmBUR6R3T0zJttQFbKhLypd6uqhlBPSKAvYPvQoJ6RAB7q0Vyh7yJGAXI9lUpb+9QqYjzSUcQEmP0EsmU8BaLiGkZgINHRMBIALsLDdvQS8SomhFwH+yMiLCUR5XPDY+AQ96yfhl7PCIkRAiCmJPFDZVwKbqZcXgiXvTrnMgYVTsFMqoCQH1mAu/YZKIkobUQoqZmAPu8Eaqq5Rg0BdwHGyNDoncSZb4VO/YinkpjKjMINCigQC0FEiIEYQI+jwuL6jMlvCVUzvSOCRoRyQiDRErFZMKaUsVkWjVC8SKnZqz2iESnU2BaT8Q7XzvNqqLPVrEzNcP2W1FgmGSdAgkRgjAJ5hM5MjxR9Gv0stRMg1hCpNLnQcCrf1xY1UuEHfCKUt4HMGsjX+lzC+UTYjDTrj0RETErZhh2ljLn9hARbcxFqYh3lROEpKxsqQEAvDlUghDJmFW76sVKzQBZn4hVQoSlZYIVXmPar0iwQ8fyCcSCdlVlZGesWO+LED4iYqNfxqn+EICECEGYxsrWagDAwSKFSFrV0BdizczEiogAub1ELBIiAlfMAFlhYPWhExa4mRlgc2pGdI9IJjpkhygT2cBcKiRECMIkVrboQuTNwWhRz+8PTyGlavC6FaEG3jEaLO4lInLFDJATEbE8NcPufMVOR9jrERF0LyrtS81QRIQgiAVhqZm+8DSiRXwwsbTMoroKIVMT9TalZoSNiNhULRI2uqqKuQ92piNEnzZrZ0+VsOB7UQokRAjCJIKVXrTU6BNzDw8X3thMVKMqw+oJvCKX7gL2ddEUPQTP7sij8RTSqnWl3EDObBXyiFBEhCCI/GA+kUNFpGdOZEp3OwUr3WU0WDyBN+sREfODlkUorDarin7g5K6rmMhfIchTNWOjR0TQ66IUSIgQhImw9MyhIgyrIk7dzaXeaiFiRALEjIgYfoCppKVN3bKTd8U8cLxuFyp9bgB2lDILXjWTk5qx8poAsqJMVIFaCiRECMJEVrQUHxERtZkZI9vm3ZrDh0VEGgRNzbA70URaNTpcWkH2zlfMfQDs80YYU4gFFacsUpNWNcsa/TFEj5SVAgkRgjCRVa3FR0SOjepCZLGgHpHcNu9WkDWrivlBW+lzw5MxEVt5AIs8eZcRtKmXSFjwvajwuuF1W39N5L4+CRGCIOaFlfCeGJ/CZCL/D+mxWAIjmRk1LKoiGo1VuhHXqvLdkFG+K+bdr6IoxoFopU+EdVYV+cCxo4RX0zThjbuKotjYcVfslF0pkBAhCBOpr/KhqVo/SA8P5V85c2BAT+V0NVSgStA5EiwiMj6ZsKRaYkzwqhnAnsoZdviKLETs2IdYIo1U5joTOk1lkxChiAhBEHlj+ESG8veJHMx4SlZnUjsiwiIVqmb+h25a1YQPwwNZn4i1ERHx98GOqbPMH+LzuIw5RyJixzUBkBApmi9+8YvYsmULKisrUVdXZ+VbEYQwsMqZg4P5+0QOMCHSJq4Q8bpdxuE4mkkjmYVedaD/t6ipGSDXG2HNoTOdTCORUme8l4jYMewtlNNpV1HEa/DHsPqaAOS5LorFUiGSSCRw/fXX4+Mf/7iVb0MQQsHExBv9kbyfw1IzqwSOiABAYyZtMjJhrk+EGVVr/B543QLf/WZEEvNxmA07fN0uBdWCpugAezwiRoRM4LQMYP01AchzXRSLpb/RP//zPwMAfvjDH1r5NgQhFOs76wAAr/aGoGnagndzmqbh4ID4EREAaKz24/BwDKMxcyMiIQn8IYD1BzA7zOoq5IgC2OKVEThFBdgryoKCXxfFIpS0isfjiMezH3CRSP53lAQhCqvbauDzuBCeSuL46CSWNlXN+/j+8DSi8RQ8LgXLmsSsmGEwI+6oyRGRsZjYA+8YQYv9ALIcvqyPiJXpiPHJrCgTGauvCcDZ/hBAMLPqtm3bEAwGja+uri7eSyKIgvF5XDiroxYA8OqJ0IKPZ/6QZc1V8HmE+pM8DVbCa7ZHZFzwxlUMq+9+ZTlw7PBFyGBeBuyKDul/H04ceAcUIUTuueceKIoy79fOnTuLWsydd96JcDhsfPX29hb1OgTBG5ae2d0bWvCxsvhDgGzX01GTe4mMCd5VlWG5EJFknkjQhknEondVZdgxlTnrlxH7uiiWglMzt912G2688cZ5H7N06dKiFuP3++H3+4t6LkGIxDlddQB0n8hCGP4QCYSIVakZFmFhry8qVh86hkdE8MPXVo+I4Iev3R4RJ1KwEGlqakJTU5MVayEIx7A+I0Re74sgmVbnrQSRoXSX0VidSc2YbFZlVThN1WLfiFjtB5DlwDE8ItOpvAzZxSBDPxUAtnTbdfLAO8Bis2pPTw/GxsbQ09ODdDqN3bt3AwBWrFiB6mqxTXkEUQpLGytRG/AgMp3CgYEo1i4Kzvq4tKoZc2mkECJV1kREWHv7RsGFiOUREcmiAGlVQyyRtqSkNCzB8D/ApuiQw4WIpc64L3zhC9iwYQPuvvtuTExMYMOGDdiwYUPRHhKCkAVFUYyoyHw+kYODUSRSKip9bmGn7ubChMKIyWbVbERE7EOnNqeRl2pBm3tZogABrwu+TJTP+jSV2HsRzAglq64JQB7jbrFYKkR++MMfQtO0074uu+wyK9+WIISAGVZfm6dy5pmDwwCAC7ob4HKJ3x+ACYXIdMro9GgGWY+I2BERdkeqaUB02vzJs2FJIiKKomRnrFhdyiz4Xlh9TQBZIUJVMwRBFASLiPy1JzTnY545pAuRS1Y127Ci0qkNeOHJCKYxkypnVFUzXqtR8IiI3+NGhdcNwJpIgCzVQ4C182Y0TTOiQ6I3ufN5XKj06deEVd1VZfEOFQsJEYKwiPOW1MPtUvDm0ASODJ8+d2YykcLLR8cByCNEXC7FOBjMSs+Ep5LGlFXWp0RkDHOiBYcO66cigxCxct7MdFI1Im4ylKzaVdZNQoQgiIKor/LhLSv0CrNH9vSf9vMXj4whkVaxqK4CyxbovioSzLBqVkSEVeDUBjzCN3QDrDt0NE0z+rOIPPiPYeXhywSZ160Y0QaRsauaijwiBEEUzDvXtQMA/vu104XI9oPZtIxM8yOaTC7hHY5mjKo14kdDAOsOnclEdsKqTBERKzwiWX+IT4q/jWyUzJo0FaVmCIIomq1ntcLjUrB/IIo3h2amZ5g/5FJJ0jKMRpObmjFB0yRBWgawLhLAIkz+HM+ByNRbOHVWlooZhpXRoVgibaQuSYgQBFEwdZU+XLTy9PRM79gkjgzH4HYp2LKikdfyioL5OEZMEiIj0YwQqRE/CgBY10sk1x8iUxRg3IKIiCyt7hms10l40gLf0AyBKtScWtMgIUIQFvOOTHrmDznpmR88dxQAcO7iOqNLpSxkIyLmpGaYL0IGoypgfUREBn8IkBMRseDwlaWfCsPK2TsyGZiLhYQIQVjM1jPb4HUrODAYxVcfP4DHXh/AD58/BgD4+GXL+S6uCIx5MyaZVWVp786os+gAlu3AMSIiMWs9IjJgpVmVCVTR5w+VgjPjPAQhEMFKL+7Yuhr3Prof33zyTbgzfThuvqgbV5zRynl1hdOQiVyYFRHJtneX44O21qJDZywmR98MBjsYxy2JiJBHhMGus4YqOfaiGCgiQhA2cOuly7HtunVwKfp8jvVddfhfV53Be1lFwQSDWR4RWbqqMuotqpBgXoAGSQ7feguHvUnnEbGwaoYiIgRBmMb7Ny1GZ30FHnt9ALddsUKKnhmzwapbzOojIsucGUZDpbl9VBhjmciCLBERS6tmmBCRZC+M5m6WRESYQJVjL4qBhAhB2MjFK5tx8Uq5ynVPhUVEppJpTCZSJTv5ZYuINGR+/3GzhcgEM+3KceCwKMB0UsV0Mo2A17ySY5bukSYiUsF8QxZERJhAlSRSVgxy3pIRBMGNSp8bAa/+0VFqL5GpRBqxRBqAPB6RhhxvhJnTVmWLiFT7PcbcIbN9IrJ1ErW2y6xc3qFiICFCEERBKIqS00ukNMMqe77P40K1X44ALcvVq5q5B0/WIyLHgaMoimWVM0ZqRpaqmcw+TCXTiKfSpr72uGRl3cVAQoQgiIJhfo7haGlChJUAN1f7pWjiBeiiqSagi6YxEyMB45JFRADrSpllq5qp8XvALl/zG91RRIQgCOI0WmoDAIChEoUI66oqS1qG0WDy4D9V1YwDR5Y+IkDWt2Bmd9WpRBrTSX3mTlASIeJyKZbN3slGROTYi2IgIUIQRMG01uqpmaHIdEmvY8yZkcSoyjBbiESnU0hn/CayRAGAnIiIiZUz7JrwuhXUSJKuA6zxiWialo2UUWqGIAgiS2uNHhEZjJTqEZGrUoRhdgkvS/FU+z3we8QfeMeos6C5G9tTWWbuMKzYC91zIs9E5mIhIUIQRMG0ZlIzg9HSIiLMrNpUU94RkdzDVyaYb8HMUubsXsh1TRgdd800MGdEjc8tx0TmYiEhQhBEwbRkUjOlRkRGZY2ImHwAGwPvJNsHKybwjsXkvCasGAJo+EOqvFJFhwqFhAhBEAXDIiKlekQGM89vliwiUm9yRES29u4MKw5fWaNDhji1opLKwf4QgIQIQRBFwITIaCyBZFot+nUGMkKko67ClHXZhZGaMenQka2ZGSNbNWOmWVVOIVJv+IbMjw6RECEIgjiF+kovvG49VFxsLxFN0zAQ1oVIW0bYyILZZlXZmpkxslUzJh6+0qbrWHM380RZyOghIlekrFBIiBAEUTCKoqDFqJwpLj0TmkwaFQHMcyILbN6M2WZV2SIidRZM4DUiIpL1lqk3OUoGUESEIAhiXko1rPZnoiFN1T6pSlaBnHkzZkVEJuVOR4RMnLszlukjIl1ExIKpzCHyiBAEQcwN6yUyVGQJ70BkSn8dydIyQPbuN5ZIYzpZ+mwRWQ2aLCKianpTNjOQtXzXklLmMmjvDpAQIQiiSFqNiEiRQiSs3/m2B+UTIrUBcyfPytjeHQD8HrfR38Isw6qsZtXcqhmzokPZiAh5RAiCIE6DzZspNjUzENYjIm0SChFFUYy7VNYLpRRGM43dZAzB15toWE2kVCOyIltqJjc6FJk2xzMjq3eoUEiIEARRFK0lDr7rl7RihtFoUt+IREpFJHP4yhYFALIzVsyJDOmv4c4ZIicLfo8b1ZnZOGb5RIyqGQkFaiGQECEIoihKHXzHeoi0BeXqIcKoN8mcyNrce92KMa9EJlhpqRlNzVh0qb7SC5dLvk6ibC/MSlONSVrWXSgkRAiCKIpSy3dZDxEZPSKAefNmWB+Wpmq/lIdvnVFBVHo6QvZyVWawNaOp2XQyjamMEbqO+ogQBEGcDouIjE8mEU8VXjnChIiMVTOAefNmmBCRrc09o77SvIjImKRlzAzWot+MyhkWVfG4FNRkUj5OhYQIQRBFEazwwufRP0KGCjSsTsRTiMZ1X4SMZlUgayAcLVWIZFIzLdIKEeaVMSEiktmLRsmamTHMbGrGIkx1lT5HD7wDSIgQBFEkiqJkfSIF9hJh0ZCagMcw+MmGWWZV2SMiZppVZe2nwjCz0V22yZ2z0zIACRGCIErAaGpWYERE1hkzuZg1gdcQItVyChEWvTBDiIxK2syMYeZUZmZillWUFQIJEYIgiqa1tjjDar/EPUQYjWYLEUkjIk0ZATUSNS8iIlsPEUaDSVEyABjJVBA1SSpQC4GECEEQRcPmzfQXKESYcJG1YgYwb+w7S2vJKkQaM9ELdgdfCrJ2VWWYVdINZPeThAhBEMQ8dNZXAgBOjE8V9DyjmZmkPUSAmSmJdAktvZlZVVYh0lSTNWiWsg+AkyIipRt3ZY+UFQIJEYIgimZxgy5EescmC3qeEzwijVU+KAqQVrWi74A1TcvxiMi5Fw2V+j5oWumRAMOsKmnVDDOWmhkRkdU7VAiWCZFjx47hox/9KLq7u1FRUYHly5fj7rvvRiJh3mRCgiD4woTI8dEChYgDUjMet8u4cy92AvFEPIXppApA3jtfj9tlpCRKSc+kVS2nUkROIcL2ITyVRCqtlvRaRmqmRs69KATL6ub2798PVVXx3e9+FytWrMDrr7+OW265BbFYDF/5yleseluCIGykq0FPrYSnkghPJhHMc0qoERGRWIgAenfZkYkEhqJxnFXE81k0pMbvQUVmiq2MNFX7MBZLlDQAMDSZgJbJ7MjaWTVY4TWiQ6GpZEn+Dmb+LQePiGVC5KqrrsJVV11l/HvZsmU4cOAA7r//fhIiBOEQKn0eNFX7MTIRR+/4JIKVwQWfE51OGqbERfXyekQA3az7Rj8wXOQEYqf4AJqq/Tg4OFFSRISlM4IVXnjdcroGPG4XghVehCaTGIslihYRqqphNEZmVUsIh8NoaGiY8+fxeByRSGTGF0EQYrM4ExXpydMnwtI4TdU+1AbkbtbEuqEWO29n2Ai/y33YGCW8JQiRUcmNqowGEypnwlNJJNN6eEjWLrOFYJsQOXz4ML75zW/i1ltvnfMx27ZtQzAYNL66urrsWh5BEEXCfCL5CpEjIzEAQHdTlWVrsgs2+G8oWtwBzBrByR4RYYflSAmpGWP4n+R7UW/CDCIm6IIVXvg98qbs8qVgIXLPPfdAUZR5v3bu3DnjOX19fbjqqqtw/fXX4+abb57zte+8806Ew2Hjq7e3t/DfiCAIW1ncqAuKfIXI0WFdiCxtdIAQKbLFPWPYIZURZkREmJiTdeYOw+glUkJTMyNSVgbREKAIj8htt92GG2+8cd7HLF261Pjvvr4+XH755di8eTO+973vzfs8v98Pv1/ui5Agyo1CS3iPjWYiIs0OECIlRkSc4hFpNkOIROSexsxgJbylRUTKx6gKFCFEmpqa0NTUlNdjT548icsvvxwbN27EAw88AJdLTgMSQRBzU2xqZpkTUjMsIlLmZlWWmimlambQECJy70V23kzxTc2ckqbKF8uqZvr6+nDZZZdh8eLF+MpXvoLh4WHjZ21tbVa9LUEQNsOEyMnxKaTSKjzzVDxomoajwxMAgKVOECKZg2I4GoemaQWPax92SDrC3NSM5BERw6xa/F6UUzMzwEIh8vjjj+PNN9/Em2++ic7Ozhk/07TS2gATBCEOLTV++DwuJFIq+sPT6MoIk9kYn0wiMp0C4AyPCItkJNIqQpNJ4244X2Rv785gd+6jE4miBBmQjYi0SB4RaTREWQmpGYdEyvLFslzJhz/8YWiaNusXQRDOweVS0FWfXwnv0RE9GtIRDCDglb8awO9xoy7TxK1Qn0ha1TDqECHCSm4TadUQmoXC0luye0RYdKtYAzOQO/CuPMyqZNogCKJk8vWJHB3Rf+4Eoyqj1TCsFnbwjMbiUDXApWQn2MpKwOtGjV8PsBeTnplMpBCN6wJG9jRVtpKq+NTMcBlN3gVIiBAEYQL5CxE9IuKEHiIMdvAMFmhYZf6Qhio/3K7CUxmiwdIzI0UcwCwaUulzo9pvmWPAFpgwDU0mEU+li3oN1t5d9khZvpAQIQiiZLryFiLO6SHCaC4yFM/m7cheJcJgaYTRIspWB3NKd4vxl4hEXaUXvoxhe7gIUaZp5dXeHSAhQhCECbCICGtWNhcsNbPMQakZo5dIgRGRk6EpAMCiOrnn7TBYeqmY1IxTmpkBgKIoOeK08L0ot/buAAkRgiBM4MyOWgDAwcEoppOzh6NVVcMxo717tW1rs5rcEt5CODGuC5HO+rmrjGSCjasvJjWTrZiR26jKMIRIEf1l2HVUG/CURXt3gIQIQRAmsKiuAk3VPqRUDW/0zz6scjA6jalkGm6Xgk7Jp+7mwqo8Ck3NnMwIEdknEDOMXiJFpGZY5KDVARERIFecFl4545RBiIVAQoQgiJJRFAXrO+sAAK/2hmZ9zP7+KABgSWOltGPeZ6NYs+qJcT1N5RRRZvTPKMqs6oz27oxSKmdY/5FyaWYGkBAhCMIk1nfVAZhbiOw4OgoAOG9JvU0rsofcvhGF9Elymkek2ZjAW0xqJuMRcYhxt1jfEJAVchQRIQiCKBBDiJwIz/rzl46OAQAu6G60a0m2wA6d6aRq9MJYiKlE2rjz7XKKRyRzB19U1UwmhSF7e3cGE6eDJaRmKCJCEARRIOs7gwD0Et3QKSPQJxMp7MkIlE3dDbavzUoqfG7UBPTeF/neAbNoSI3fg9oKuftmMJgQYXN3CmHY6KrqjMO3lGGIg2FntLovBBIiBEGYQl2lD0sb9bv7106Jivz1eAgpVUNHMOAYT0QuzNvAeoMsBPOHLKqvkL5vBqMtqO/BZCKNyFT+bd5j8Zyuqk7xiBjddgsXIk5L2eUDCRGCIExjLp/ISxl/yAXLGh1z8ObC+qgcH5u/jwqDHTZOEmUBr9toanYiNH9ju1zYYV3lgK6qDBbNGI3FkUqrBT23L0xChCAIomiMyplTIiI7Mv4Qp6VlGEaL+9H8DmDWQ8Rph01H5vfpC+XvjXBaxQygN3dzKYCmFeaZSauaEVXrcNi1MR8kRAiCMA0WEdndGzJ8AtPJNHZnIiQXOFSILMmkpI7nKUROOqyZGaMjyITIVN7PGXTgyHu3SzE8M4X4REYm4kimNbgUZ3SZzRcSIgRBmMZZHbXwe1wYmYjjqQNDAPQ0TSKloqna76hhd7kwIXJsNL/UTK5HxEmw36cQIeLEiAiQ20sk/+gQS9m11QbgcVCvnYUon9+UIAjLCXjd+PCWpQCAL/5hHxIpFT947igAPRriRH8IACzJDPHrGZvMq2LEiR4RIJtOOFGAEHHa8D9GMYZVJuDKKS0DkBAhCMJkPnH5CjRU+XB4OIbrv/sCHn9jED63C7dcsoz30iyjs74CiqJXjLD+IHMRT6WNBl5O84gsqtMP30IiIsczE5uZz8YptBQxb4aECEEQhAkEK7z4h7euBJCtnvnXd6/FORn/iBPxe9yGP+L4AumZ/oyRs8LrRkOVs6arLqrTxUQhQoQZfBc3Oittl9txN1+YyZeECEEQRIm8f9NirGjRJ+x+5C3deN95XZxXZD35GlZP5Ay7c1qqqqMum45IpBYuW9U0DT0OjYg01xafmmGRpXLBGUXbBEEIhcftwo8+sgl/PT6Ot69r570cW1jSWInnD48aqYa5OBly1rC7XBqqfPB7XIinVAyEp7G4cX5xMTwRx1QyDZfivDRVNiJSgBAJU2qGIAjCNBbVVeCa9R1wu5x11z8Xixv01MJCqRkWMXHawQvoU5jZ73Uyj/QMS8t01FXA53HWcZT1iFBqZiGc9X+eIAiCE0vzTM3sH4gCAFa31Vi+Jh6wEt58hAjbqyULRE5khAmywch0XmmqqUQaY5nmZyRECIIgiIJhaYieBVIzb/RFAABnttdaviYeFNLUzKkVM4DeoK3C64aq5SfKWFqm2u9BbaC8XBMkRAiCIEyA9RIZiyUQmU7O+pixWAIDmVD9GU4VInX5C5GeTBqLpbWchKIoBTW6y5buBhxnYl4IEiIEQRAmUO33GEPf5po5s69fj4Ysaax0zIC3UykkNcOiR05MzQA5lVQjhQiR8krLACRECIIgTMOYwjuHEHF6WgbIlvAWIkScmJoBgKWZKNlClVQAcLJMjaoACRGCIAjTYOmZoyMTs/6cRUScLEQW5aRm5mt3PxFPGV1oFyrzlZXFBQxDzPYQISFCEARBFAkTGGza8Km8kREiaxwsRNqCekRkOqlifHJ2rwyQTV/VV3pRG/Dasja7YRGRfDwi/RmzanuwvJqZASRECIIgTGNTdwMA4KWjY0irM6MB8VQabw7pkZIzO5wrRPwet9FDo3eelISRlnFYa/dcmEekd2zytOvhVFjUpLPemdGh+SAhQhAEYRJnddSiyudGZDqFA5l+IYxDgxNIqRqCFV7H3/Uub9bb+x8YjM75mJ4xPUqwxKH+EABoD1bA53YhmdaMiMdsTMRTRuv/lZnRCOUECRGCIAiT8Lhd2LhUj4q8eHR0xs/eyPGHOL0886xMxIeZc2eDRQCcalQFALdLQWcDG4Y4d3ToYEawtdT4Ue+wQYj5QEKEIAjCRC7ISc/kYlTMODgtwzgzDyGSTc04V4gA+flEDjq82+5CkBAhCIIwkVyfSG7VyK6ecQDONqoyDCHSH4E6izdC0zSjgoilcZxKPlOZWQprdSsJEYIgCKJEzu4Mwu9xYTSWwOFh3Zz6+skwXj0Rhtet4JJVTZxXaD3Lm6vh87hmeB9y6R2bwshEAl63YqRxnIrRS2SeiAjzE62iiAhBEARRKn6PGxsW1wEAXsykZ370/DEAwNvXtaOlxtlGVQDwul3G3f3evvBpP3+lR9+XtYuCCHjdtq7NbvLpJcI8ImeQECEIgiDM4ILuRgDAH/cOYjgax+9e7QMA/N3mpRxXZS+spwoz6eby1+MhAMC5i+vtXBIXcj0iszV4G5mIY2QiAUUBVpRhxQxAQoQgCMJ03rqmFQDwzMFhvPObzyKRUrF2US3OzURKyoGzFs1tWP1rxi9TDkJkUV0F3C4F00kVQ9H4aT9nRtXFDZWo9Dlz/tBCkBAhCIIwmXWdQXz7A+fC73FhMKIfPjdtXur4st1cWERk7ylCJBZPYX/m8D13SZ3dy7Idn8dl9EqZLU1V7kZVgIQIQRCEJbzj7Hb84mOb0Vrrx5LGSlyzvoP3kmzljPZaKAowEJnG6EQ2EvDqiRDSqoaOYADtwfKYq8IqqV44PHraz5g/pFxLdwGLhcjf/u3fYvHixQgEAmhvb8eHPvQh9PX1WfmWBEEQwnBOVx2e+V+X40+3X+p4U+apVPs9hj9iX3+2w+qunhAAYMMS56dlGJuX656hF46cLkSMihmKiFjD5Zdfjl/+8pc4cOAAfvOb3+Dw4cN473vfa+VbEgRBCIXf44bXXZ7BZ5aeye0y+9fj5eMPYWxepguRvX0RhCYTxvc1TcPBQb3Eu1wrZgCLhcg//MM/4MILL8SSJUuwZcsWfO5zn8OOHTuQTM49kZEgCIJwBn+ztg2AXr4cmU4ilVZzjKp1HFdmLy21ASxvroKmZUu6AeD1kxFMxFPwe1xY2uTc4X8LYZtMHxsbw09/+lNs2bIFXu/sI5/j8TgikciML4IgCEJO3rGuHStaqhGZTuGB547h3/90EOOTSdQGPDirI8h7ebayZbneyC7XJ/Kbv54AALztzNayjZoBNgiRf/zHf0RVVRUaGxvR09OD3/3ud3M+dtu2bQgGg8ZXV1eX1csjCIIgLMLtUvD3V64EAHxn+2F8+6nDAIAvXbcOPk95HbyGTyQjRBIpFb/bfRIA8N6NndzWJQIFXwn33HMPFEWZ92vnzp3G4z/72c9i165dePzxx+F2u/F3f/d3szZ1AYA777wT4XDY+Ort7S3+NyMIgiC484517VjVWo2pZBoA8KELl+CdZ5dXBREAXJjxiRwYjGJ0Io6nDgxhfDKJlho/Ll7ZzHl1fCm4e8ptt92GG2+8cd7HLF261PjvpqYmNDU1YdWqVVizZg26urqwY8cObN68+bTn+f1++P3+QpdEEARBCIrLpeCOravxsf/7CtYtCuKud6zhvSQuNFT5cEZbDfYPRPH9544ajczefe4iuF3l019mNgoWIkxYFAOLhMTjp3eXIwiCIJzJ35zVhj98+iJ0N1WVXRlzLtes78D+gQO4/+nDxvfee255p2WAIoRIvrz00kt46aWXcNFFF6G+vh5HjhzBF77wBSxfvnzWaAhBEAThXMrNnDobH790ORqqfPjyY/sxPpnE+q46rCzj/iEMy4RIRUUFHnroIdx9992IxWJob2/HVVddhQcffJDSLwRBEETZ4XIpeP+mxXj72nb8954+XFLm3hCGos3lHBWASCSCYDCIcDiM2tpa3sshCIIgCCIPCjm/y6t+iiAIgiAIoSAhQhAEQRAEN0iIEARBEATBDRIiBEEQBEFwg4QIQRAEQRDcICFCEARBEAQ3SIgQBEEQBMENEiIEQRAEQXCDhAhBEARBENwgIUIQBEEQBDdIiBAEQRAEwQ0SIgRBEARBcIOECEEQBEEQ3PDwXsB8sMHAkUiE80oIgiAIgsgXdm6zc3w+hBYi0WgUANDV1cV5JQRBEARBFEo0GkUwGJz3MYqWj1zhhKqq6OvrQ01NDRRFMfW1I5EIurq60Nvbi9raWlNf22nQXuUP7VX+0F7lD+1VYdB+5Y9Ve6VpGqLRKDo6OuByze8CEToi4nK50NnZael71NbW0oWaJ7RX+UN7lT+0V/lDe1UYtF/5Y8VeLRQJYZBZlSAIgiAIbpAQIQiCIAiCG2UrRPx+P+6++274/X7eSxEe2qv8ob3KH9qr/KG9Kgzar/wRYa+ENqsSBEEQBOFsyjYiQhAEQRAEf0iIEARBEATBDRIiBEEQBEFwg4QIQRAEQRDcKEshct9996G7uxuBQAAbN27Es88+y3tJQnLPPfdAUZQZX21tbbyXJQTPPPMMrrnmGnR0dEBRFPz2t7+d8XNN03DPPfego6MDFRUVuOyyy7B3714+i+XMQnv14Q9/+LTr7MILL+SzWI5s27YN559/PmpqatDS0oJ3vetdOHDgwIzH0HWVJZ/9omtL5/7778fZZ59tNC3bvHkzHn30UePnvK+rshMiv/jFL/CZz3wGd911F3bt2oWLL74YV199NXp6engvTUjOOuss9Pf3G1979uzhvSQhiMViWL9+Pb71rW/N+vMvf/nL+NrXvoZvfetbePnll9HW1oa3ve1txvykcmKhvQKAq666asZ19sgjj9i4QjHYvn07PvnJT2LHjh144oknkEqlsHXrVsRiMeMxdF1lyWe/ALq2AKCzsxP33nsvdu7ciZ07d+KKK67Atddea4gN7teVVmZs2rRJu/XWW2d874wzztA+97nPcVqRuNx9993a+vXreS9DeABoDz/8sPFvVVW1trY27d577zW+Nz09rQWDQe073/kOhxWKw6l7pWmadtNNN2nXXnstl/WIzNDQkAZA2759u6ZpdF0txKn7pWl0bc1HfX299v3vf1+I66qsIiKJRAKvvPIKtm7dOuP7W7duxfPPP89pVWJz6NAhdHR0oLu7GzfeeCOOHDnCe0nCc/ToUQwMDMy4zvx+Py699FK6zubg6aefRktLC1atWoVbbrkFQ0NDvJfEnXA4DABoaGgAQNfVQpy6Xwy6tmaSTqfx4IMPIhaLYfPmzUJcV2UlREZGRpBOp9Ha2jrj+62trRgYGOC0KnG54IIL8OMf/xh//OMf8R//8R8YGBjAli1bMDo6yntpQsOuJbrO8uPqq6/GT3/6Uzz55JP46le/ipdffhlXXHEF4vE476VxQ9M03H777bjooouwdu1aAHRdzcds+wXQtZXLnj17UF1dDb/fj1tvvRUPP/wwzjzzTCGuK6Gn71qFoigz/q1p2mnfI/Q/Ysa6deuwefNmLF++HD/60Y9w++23c1yZHNB1lh833HCD8d9r167FeeedhyVLluAPf/gDrrvuOo4r48dtt92G1157Dc8999xpP6Pr6nTm2i+6trKsXr0au3fvRigUwm9+8xvcdNNN2L59u/FzntdVWUVEmpqa4Ha7T1N5Q0NDp6lB4nSqqqqwbt06HDp0iPdShIZVFtF1Vhzt7e1YsmRJ2V5nn/rUp/D73/8eTz31FDo7O43v03U1O3Pt12yU87Xl8/mwYsUKnHfeedi2bRvWr1+Pb3zjG0JcV2UlRHw+HzZu3IgnnnhixvefeOIJbNmyhdOq5CEej2Pfvn1ob2/nvRSh6e7uRltb24zrLJFIYPv27XSd5cHo6Ch6e3vL7jrTNA233XYbHnroITz55JPo7u6e8XO6rmay0H7NRrleW7OhaRri8bgY15UtlliBePDBBzWv16v94Ac/0N544w3tM5/5jFZVVaUdO3aM99KE44477tCefvpp7ciRI9qOHTu0d77znVpNTQ3tlaZp0WhU27Vrl7Zr1y4NgPa1r31N27Vrl3b8+HFN0zTt3nvv1YLBoPbQQw9pe/bs0d7//vdr7e3tWiQS4bxy+5lvr6LRqHbHHXdozz//vHb06FHtqaee0jZv3qwtWrSo7Pbq4x//uBYMBrWnn35a6+/vN74mJyeNx9B1lWWh/aJrK8udd96pPfPMM9rRo0e11157Tfv85z+vuVwu7fHHH9c0jf91VXZCRNM07dvf/ra2ZMkSzefzaeeee+6Mci8iyw033KC1t7drXq9X6+jo0K677jpt7969vJclBE899ZQG4LSvm266SdM0vdTy7rvv1tra2jS/369dcskl2p49e/gumhPz7dXk5KS2detWrbm5WfN6vdrixYu1m266Sevp6eG9bNuZbY8AaA888IDxGLqusiy0X3RtZfnIRz5inHnNzc3alVdeaYgQTeN/XSmapmn2xF4IgiAIgiBmUlYeEYIgCIIgxIKECEEQBEEQ3CAhQhAEQRAEN0iIEARBEATBDRIiBEEQBEFwg4QIQRAEQRDcICFCEARBEAQ3SIgQBEEQBMENEiIEQRAEQXCDhAhBEARBENwgIUIQBEEQBDdIiBAEQRAEwY3/HxNlI/VeMovWAAAAAElFTkSuQmCC", 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" ] @@ -328,8 +320,7 @@ { "data": { "text/plain": [ - "[(0.6260158833534124, 0.3102619497970334),\n", - " (0.8741930326860968, 1.2156410944770426)]" + "" ] }, "execution_count": 9, @@ -338,7 +329,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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" ] @@ -385,7 +376,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.13.1" } }, "nbformat": 4, diff --git a/examples/era_msd.py b/examples/era_msd.py new file mode 100644 index 000000000..101933435 --- /dev/null +++ b/examples/era_msd.py @@ -0,0 +1,63 @@ +# era_msd.py +# Johannes Kaisinger, 4 July 2024 +# +# Demonstrate estimation of State Space model from impulse response. +# SISO, SIMO, MISO, MIMO case + +import numpy as np +import matplotlib.pyplot as plt +import os + +import control as ct + +# set up a mass spring damper system (2dof, MIMO case) +# Mechanical Vibrations: Theory and Application, SI Edition, 1st ed. +# Figure 6.5 / Example 6.7 +# m q_dd + c q_d + k q = f +m1, k1, c1 = 1., 4., 1. +m2, k2, c2 = 2., 2., 1. +k3, c3 = 6., 2. + +A = np.array([ + [0., 0., 1., 0.], + [0., 0., 0., 1.], + [-(k1+k2)/m1, (k2)/m1, -(c1+c2)/m1, c2/m1], + [(k2)/m2, -(k2+k3)/m2, c2/m2, -(c2+c3)/m2] +]) +B = np.array([[0.,0.],[0.,0.],[1/m1,0.],[0.,1/m2]]) +C = np.array([[1.0, 0.0, 0.0, 0.0],[0.0, 1.0, 0.0, 0.0]]) +D = np.zeros((2,2)) + +xixo_list = ["SISO","SIMO","MISO","MIMO"] +xixo = xixo_list[3] # choose a system for estimation +match xixo: + case "SISO": + sys = ct.StateSpace(A, B[:,0], C[0,:], D[0,0]) + case "SIMO": + sys = ct.StateSpace(A, B[:,:1], C, D[:,:1]) + case "MISO": + sys = ct.StateSpace(A, B, C[:1,:], D[:1,:]) + case "MIMO": + sys = ct.StateSpace(A, B, C, D) + + +dt = 0.1 +sysd = sys.sample(dt, method='zoh') +response = ct.impulse_response(sysd) +response.plot() +plt.show() + +sysd_est, _ = ct.eigensys_realization(response,r=4,dt=dt) + +step_true = ct.step_response(sysd) +step_true.sysname="H_true" +step_est = ct.step_response(sysd_est) +step_est.sysname="H_est" + +step_true.plot(title=xixo) +step_est.plot(color='orange',linestyle='dashed') + +plt.show() + +if 'PYCONTROL_TEST_EXAMPLES' not in os.environ: + plt.show() \ No newline at end of file diff --git a/examples/kincar-fusion.ipynb b/examples/kincar-fusion.ipynb index 3444ac95a..062345ad3 100644 --- a/examples/kincar-fusion.ipynb +++ b/examples/kincar-fusion.ipynb @@ -23,10 +23,10 @@ "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", + "\n", "import control as ct\n", "import control.optimal as opt\n", "import control.flatsys as fs\n", - "from IPython.display import Image\n", "\n", "# Define line styles\n", "ebarstyle = {'elinewidth': 0.5, 'capsize': 2}\n", @@ -76,11 +76,11 @@ "Outputs (3): ['x', 'y', 'theta']\n", "States (3): ['x', 'y', 'theta']\n", "\n", - "Update: \n", - "Output: \n", + "Update: \n", + "Output: \n", "\n", - "Forward: \n", - "Reverse: \n" + "Forward: \n", + "Reverse: \n" ] } ], @@ -112,7 +112,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -133,11 +133,10 @@ "\n", "# Create the desired trajectory between the initial and final condition\n", "Ts = 0.1\n", - "# Ts = 0.5\n", - "T = np.arange(0, Tf + Ts, Ts)\n", - "xd, ud = traj.eval(T)\n", + "timepts = np.arange(0, Tf + Ts, Ts)\n", + "xd, ud = traj.eval(timepts)\n", "\n", - "plot_lanechange(T, xd, ud)" + "plot_lanechange(timepts, xd, ud)" ] }, { @@ -160,18 +159,19 @@ "name": "stdout", "output_type": "stream", "text": [ - ": sys[3]\n", + ": sys[0]$sampled\n", "Inputs (2): ['u[0]', 'u[1]']\n", "Outputs (3): ['y[0]', 'y[1]', 'y[2]']\n", "States (3): ['x[0]', 'x[1]', 'x[2]']\n", + "dt = 0.1\n", "\n", "A = [[ 1.0000000e+00 0.0000000e+00 -5.0004445e-07]\n", " [ 0.0000000e+00 1.0000000e+00 1.0000000e+00]\n", " [ 0.0000000e+00 0.0000000e+00 1.0000000e+00]]\n", "\n", - "B = [[0.1 0. ]\n", - " [0. 0. ]\n", - " [0. 0.33333333]]\n", + "B = [[ 9.99999999e-02 -8.33407417e-08]\n", + " [ 0.00000000e+00 1.66666667e-01]\n", + " [ 0.00000000e+00 3.33333333e-01]]\n", "\n", "C = [[1. 0. 0.]\n", " [0. 1. 0.]\n", @@ -179,26 +179,26 @@ "\n", "D = [[0. 0.]\n", " [0. 0.]\n", - " [0. 0.]]\n", - "\n", - "dt = 0.1\n", - "\n" + " [0. 0.]]\n" ] } ], "source": [ "#\n", - "# Create a discrete time, linear model\n", + "# Create a discrete-time, linear model\n", "#\n", "\n", "# Linearize about the starting point\n", - "linsys = ct.linearize(vehicle, x0, u0)\n", + "veh_lin = ct.linearize(vehicle, x0, u0)\n", + "\n", + "# Create a discrete-time model by hand\n", + "veh_lin_dt = ct.sample_system(veh_lin, Ts)\n", "\n", - "# Create a discrete time model by hand\n", - "Ad = np.eye(linsys.nstates) + linsys.A * Ts\n", - "Bd = linsys.B * Ts\n", - "discsys = ct.ss(Ad, Bd, np.eye(linsys.nstates), 0, dt=Ts)\n", - "print(discsys)" + "# Update the model to have full-state output\n", + "# veh_lin_dt = ct.ss(\n", + "# veh_lin_dt.A, veh_lin_dt.B, np.eye(veh_lin_dt.nstates), 0, dt=veh_lin_dt.dt,\n", + "# name=\"vehicle-lin-dt\")\n", + "print(veh_lin_dt)" ] }, { @@ -219,7 +219,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -230,23 +230,24 @@ ], "source": [ "# Sensor #1: longitudinal\n", - "C_lon = np.eye(2, discsys.nstates)\n", + "C_lon = np.eye(2, veh_lin_dt.nstates)\n", "Rw_lon = np.diag([0.1 ** 2, 1 ** 2])\n", - "W_lon = ct.white_noise(T, Rw_lon, dt=Ts)\n", + "W_lon = ct.white_noise(timepts, Rw_lon, dt=Ts)\n", "\n", "# Sensor #2: lateral\n", - "C_lat = np.eye(2, discsys.nstates)\n", + "C_lat = np.eye(2, veh_lin_dt.nstates)\n", "Rw_lat = np.diag([1 ** 2, 0.1 ** 2])\n", - "W_lat = ct.white_noise(T, Rw_lat, dt=Ts)\n", + "W_lat = ct.white_noise(timepts, Rw_lat, dt=Ts)\n", "\n", "# Plot the noisy signals\n", "plt.subplot(2, 1, 1)\n", "Y = xd[0:2] + W_lon\n", - "plt.plot(Y[0], Y[1])\n", - "plt.plot(xd[0], xd[1], **xdstyle)\n", + "plt.plot(Y[0], Y[1], label=\"measured\")\n", + "plt.plot(xd[0], xd[1], **xdstyle, label=\"actual\")\n", "plt.xlabel(\"$x$ position [m]\")\n", "plt.ylabel(\"$y$ position [m]\")\n", "plt.title(\"Sensor #1\")\n", + "plt.legend()\n", " \n", "plt.subplot(2, 1, 2)\n", "Y = xd[0:2] + W_lat\n", @@ -278,13 +279,14 @@ "name": "stdout", "output_type": "stream", "text": [ - ": sys[4]\n", + ": sys[2]\n", "Inputs (6): ['y[0]', 'y[1]', 'y[2]', 'y[3]', 'u[0]', 'u[1]']\n", "Outputs (3): ['xhat[0]', 'xhat[1]', 'xhat[2]']\n", "States (12): ['xhat[0]', 'xhat[1]', 'xhat[2]', 'P[0,0]', 'P[0,1]', 'P[0,2]', 'P[1,0]', 'P[1,1]', 'P[1,2]', 'P[2,0]', 'P[2,1]', 'P[2,2]']\n", + "dt = 0.1\n", "\n", - "Update: ._estim_update at 0x166ac1120>\n", - "Output: ._estim_output at 0x166ac0dc0>\n" + "Update: ._estim_update at 0x141142d40>\n", + "Output: ._estim_output at 0x141142700>\n" ] } ], @@ -302,7 +304,7 @@ "C = np.vstack([C_lon, C_lat])\n", "Rw = sp.linalg.block_diag(Rw_lon, Rw_lat)\n", "\n", - "estim = ct.create_estimator_iosystem(discsys, Rv, Rw, C=C, P0=P0)\n", + "estim = ct.create_estimator_iosystem(veh_lin_dt, Rv, Rw, C=C, P0=P0)\n", "print(estim)" ] }, @@ -311,7 +313,7 @@ "id": "d9e2e618", "metadata": {}, "source": [ - "Finally, we estimate the position of the vehicle based on sensor measurements. We assume that the input to the vehicle (velocity and steering angle) is available, though we can also explore what happens if that information is not available (see commented out code).\n", + "Finally, we estimate the position of the vehicle based on sensor measurements. We assume that the input to the vehicle (velocity and steering angle) is available, though we can also explore what happens if that information is not available (see commented out code below).\n", "\n", "We also carry out a prediction of the position of the vehicle by turning off the correction term in the Kalman filter." ] @@ -324,7 +326,7 @@ "outputs": [ { "data": { - "image/png": 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\n"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+ "image/png": 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", "text/plain": [ "
" ] @@ -341,25 +343,28 @@ "X0 = np.hstack([xd[:, 0], P0.reshape(-1)])\n", "\n", "# Run the estimator on the trajectory\n", - "estim_resp = ct.input_output_response(estim, T, U, X0)\n", + "estim_resp = ct.input_output_response(estim, timepts, U, X0)\n", "\n", "# Run a prediction to see what happens next\n", - "T_predict = np.arange(T[-1], T[-1] + 4 + Ts, Ts)\n", - "U_predict = np.outer(U[:, -1], np.ones_like(T_predict))\n", + "timepts_predict = np.arange(timepts[-1], timepts[-1] + 4 + Ts, Ts)\n", + "U_predict = np.outer(U[:, -1], np.ones_like(timepts_predict))\n", "predict_resp = ct.input_output_response(\n", - " estim, T_predict, U_predict, estim_resp.states[:, -1],\n", + " estim, timepts_predict, U_predict, estim_resp.states[:, -1],\n", " params={'correct': False})\n", "\n", "# Plot the estimated trajectory versus the actual trajectory\n", "plt.subplot(2, 1, 1)\n", "plt.errorbar(\n", " estim_resp.time, estim_resp.outputs[0], \n", - " estim_resp.states[estim.find_state('P[0,0]')], fmt='b-', **ebarstyle)\n", + " estim_resp.states[estim.find_state('P[0,0]')], \n", + " fmt='b-', **ebarstyle, label=\"estimated\")\n", "plt.errorbar(\n", " predict_resp.time, predict_resp.outputs[0], \n", - " predict_resp.states[estim.find_state('P[0,0]')], fmt='r-', **ebarstyle)\n", - "plt.plot(T, xd[0], 'k--')\n", + " predict_resp.states[estim.find_state('P[0,0]')],\n", + " fmt='r-', **ebarstyle, label=\"predicted\")\n", + "plt.plot(timepts, xd[0], 'k--', label=\"actual\")\n", "plt.ylabel(\"$x$ position [m]\")\n", + "plt.legend()\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.errorbar(\n", @@ -369,9 +374,9 @@ " predict_resp.time, predict_resp.outputs[1], \n", " predict_resp.states[estim.find_state('P[1,1]')], fmt='r-', **ebarstyle)\n", "# lims = plt.axis(); plt.axis([lims[0], lims[1], -5, 5])\n", - "plt.plot(T, xd[1], 'k--');\n", + "plt.plot(timepts, xd[1], 'k--');\n", "plt.ylabel(\"$y$ position [m]\")\n", - "plt.xlabel(\"Time $t$ [s]\");" + "plt.xlabel(\"Time $t$ [sec]\");" ] }, { @@ -390,7 +395,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -404,12 +409,16 @@ "plt.subplot(2, 1, 1)\n", "plt.errorbar(\n", " estim_resp.time, estim_resp.outputs[0] - xd[0], \n", - " estim_resp.states[estim.find_state('P[0,0]')], fmt='b-', **ebarstyle)\n", + " estim_resp.states[estim.find_state('P[0,0]')],\n", + " fmt='b-', **ebarstyle, label=\"estimated\")\n", "plt.errorbar(\n", " predict_resp.time, predict_resp.outputs[0] - (xd[0] + xd[0, -1]), \n", - " predict_resp.states[estim.find_state('P[0,0]')], fmt='r-', **ebarstyle)\n", + " predict_resp.states[estim.find_state('P[0,0]')],\n", + " fmt='r-', **ebarstyle, label=\"predicted\")\n", "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", "# lims = plt.axis(); plt.axis([lims[0], lims[1], -2, 0.2])\n", + "plt.ylabel(\"$x$ error [m]\")\n", + "plt.legend(loc='lower right')\n", "\n", "plt.subplot(2, 1, 2)\n", "plt.errorbar(\n", @@ -418,7 +427,9 @@ "plt.errorbar(\n", " predict_resp.time, predict_resp.outputs[1] - xd[1, -1], \n", " predict_resp.states[estim.find_state('P[1,1]')], fmt='r-', **ebarstyle)\n", - "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2]);" + "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", + "plt.ylabel(\"$y$ error [m]\")\n", + "plt.xlabel(\"Time $t$ [sec]\");" ] }, { @@ -429,6 +440,7 @@ "### Things to try\n", "\n", "To gain a bit more insight into sensor fusion, you can try the following:\n", + "\n", "* Remove the input (and update P0)\n", "* Change the sampling rate" ] @@ -451,7 +463,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -462,14 +474,14 @@ ], "source": [ "# System matrices\n", - "A, B, F = discsys.A, discsys.B, discsys.B\n", + "A, B, F = veh_lin_dt.A, veh_lin_dt.B, veh_lin_dt.B\n", "\n", "# Create an array to store the results\n", - "xhat = np.zeros((discsys.nstates, T.size))\n", - "P = np.zeros((discsys.nstates, discsys.nstates, T.size))\n", + "xhat = np.zeros((veh_lin_dt.nstates, timepts.size))\n", + "P = np.zeros((veh_lin_dt.nstates, veh_lin_dt.nstates, timepts.size))\n", "\n", "# Update the estimates at each time\n", - "for i, t in enumerate(T):\n", + "for i, t in enumerate(timepts):\n", " # Prediction step\n", " if i == 0:\n", " # Use the initial condition\n", @@ -489,14 +501,16 @@ " # xhat[:, i], P[:, :, i] = xkkm1, Pkkm1 # For comparison to Kalman form\n", " \n", "plt.subplot(2, 1, 1)\n", - "plt.errorbar(T, xhat[0], P[0, 0], fmt='b-', **ebarstyle)\n", - "plt.plot(T, xd[0], 'k--')\n", + "plt.errorbar(timepts, xhat[0], P[0, 0], fmt='b-', **ebarstyle, label=\"estimated\")\n", + "plt.plot(timepts, xd[0], 'k--', label=\"actual\")\n", "plt.ylabel(\"$x$ position [m]\")\n", + "plt.legend()\n", "\n", "plt.subplot(2, 1, 2)\n", - "plt.errorbar(T, xhat[1], P[1, 1], fmt='b-', **ebarstyle)\n", - "plt.plot(T, xd[1], 'k--')\n", - "plt.ylabel(\"$x$ position [m]\");" + "plt.errorbar(timepts, xhat[1], P[1, 1], fmt='b-', **ebarstyle)\n", + "plt.plot(timepts, xd[1], 'k--')\n", + "plt.ylabel(\"$x$ position [m]\")\n", + "plt.xlabel(\"Time $t$ [sec]\");" ] }, { @@ -515,7 +529,7 @@ "outputs": [ { "data": { - "image/png": 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J8Pe/dZu2XEyCFwlehHB7GbM+xoWglthfsxc+/FA/NqRJE2DhQl5wWQhzEhI4yD1yhO+/+CJnO/b35zjEz89IHHLiBKfxvXCB+/WCg3l7ZCQ3ffn4cGBT/Fa3Li/OpNBqebCSSxfGqnjcLniZN28e3n//faSmpuKOO+7AnDlz0K1bN6NlU1NT8eKLLyI+Ph7Hjh3DuHHjMMeKvNsSvAjh5ohQ+NoMrHv7AOZgPLagh8HD48YZfk4IYU52Nncj/fyz8ccrVeLxMcpPb2/AU6NDZc0N+If4IySEF6uO+PFlNEyNQ12cQ22kwBfFpjTVq1d8dUueMrZ3L4+ubtLE8Na0KXD77Y496QrKms9vL0dXZsWKFZgwYQLmzZuHu+66CwsWLEDv3r2RlJSE+vXrlyqfm5uLmjVrYtq0afhYOnqFqFCupOuwZMBazNv5JE6j0c2thosnejn8XUlUJJUr6xfLNObGDb4Z8gDgX2Lb+wb3agbmoVbVLIT4XkOtgBuo/QqPr/H1BdqcPAOvnBzg8GG+FVenjvEMe8KuHN7y0qlTJ4SHh2P+/PlF21q0aIGBAwciJibG7L733HMP2rVrJy0vQqjcwYPAp3N1+P7rfNwo5FX3alS+gQcfq4RHHjEc0yFjPIS1lFldisJCHqOrNIxkZ3MAk53NY3ZXrSp9jLp1OTA5f77sBTtfmlCA9184y11QxW8nT/KBfv1VX5hIxtBYyG1aXvLy8hAfH48pU6YYbI+KisLO4ikoyyE3Nxe5ublF9zMyMuxy3OJkIJ0Q1iso4ARtn3zCC//yt11ftEUixo28gSFzIytMUjXhWu3a8c0STZtyDruSlPdzIp6Kfe4cMHMm8MsvpcteuurFc9YbN+Y57Kb89huvG/HTT2VMhxLWcmjwkp6ejsLCQoSGhhpsDw0NRVpaml2eIyYmBrNmzbLLsUxZsAAw9hQzZvA/txCCpaYCKSk8C/W77wDlZe6p0eEhWolx3l+g688ToHmwv2srKm5ZZX3p1Gj043TnzeOlGADg1CkeDHz+PLfcDB0KREWZeaLcXODZZ3mHLl04kGnSxK7ncitzylBpTYkmMyIqtc1WU6dOhVarLbolJyfb5bjFjRqlT1XdrZv+91Gj7P5UQqjau+8CHTrw+j7Fv588/ZQOP0WvQ7eN0yVwEaoRFsbZfMPDgf/9j7ununblJIN9+vAq2Cb5+gIbNgD16/OKnZ078+rXwi4cGrwEBwfD09OzVCvLxYsXS7XG2MrX1xcBAQEGN3tT/oHbtePZcco/s3QZCaGXlcULEyr8cR0aFAIAqgR6Ad9+ywv0CKFSwcHAn38Cw4bxuJrnn+ckgoWFJnZo1YqnX4eHA+npPP9/5UpnVrnCcmjw4uPjg4iICMTGxhpsj42NRZcuXRz51HaXkwMMHsxrjeTnW7DD6tWckrqMlqDUVGD//tI3Y2NshHBXly8DPXty07qvL/DptDRcrtMGFweMQvw+wksvubqGQtiHry8vWj17Nt+fOxcYMIBbY4wKCwO2bAH69uUPkkcf5eyLFSvFmvORgy1fvpy8vb1pyZIllJSURBMmTCB/f386ffo0ERFNmTKFoqOjDfZJSEighIQEioiIoKFDh1JCQgIdOnTIoufTarUEgLRarV3PIzGRqFIlIoBo+HAinc5MYZ2OqGZNLhwURGSmLjNmcLGStxkzTOyQkUE0ZgzR11/bfjJC2NGZM0TNm/P/bfXqRDsWHCSqVYs3NG5MdOmSq6sohEP8+CORnx//q7duza8Fk/Lz+b0bIBo5sowPkVuTNZ/fDg9eiIg+//xzatCgAfn4+FB4eDht2bKl6LHhw4dT9+7dDSvFiR8Mbg0aNLDouRwVvBARrVtHpNHw/97UqWYKHjyoj0JGjyYqLDRZdMsWoho1uGjVqkT79hHFxxOlpBgpfP06UbduXNjXlygtrdznJER5HDxIVKcO/0vWrUt06I2fiXx89O/mRv+Rhag4du8mCg3lf/nQUL5vkk5H9NNPHMiIUtwueHEmRwYvRERt2+rjkk8+MVHo7be5QKdOZo919ChRSIhhi8uXX5oIXrKyiO65R19QoyH64Qd7nJIQNtm2jahaNf53bNlCR2eHTdP/fz70ELcSCnELOHOGqE0b/tf38+MWGYvk5xNNmkR07pxD66cW1nx+y8IMFig+LiUggAdpATxt7qefjOywbh3/fPJJ/bacHGDJkqJ+zhMneOzWxYuGuz71FBARwdOzi9y4wZ2qmzfziOGFC3kp2iFD7HWKQlhl7VpOb3HtGs8C3dZ4OOp9e3MQwJtvcq72qlVdWkchnKV+fWD7dp6NmpPDSyd16gRMmcIpAxITTez46qvARx9x8plhw4AdO2QsjKWcEEw5lSNaXkyNSwG4hXzTpmKFL1zQ9y0p0XRhIVHPnrxt1iw6eZKoXj2+26wZ0Z9/cqQOEHl4EK1ZU6Ll5X//4werVCHascNu5yWELRYt4v9TgKhfP24UpD/+4GaYtWtdXT0hXObuu41/TjRoYGI8zIkTRHfeaVj4jjuI5s4lunLF2dV3Oek2snPwkpLCXTnFb3v2EPXpw/9rAQFE//xzs7AyiCU83PAgn39OBNBp1KcGQRkE8CDH4sNWevXi440bV6IC27bxAMhiY4WKnDxpdkyNEPai0xG9+ab+PfapIdmGXffXrrmsbkK4g4QEou+/J5o9mygqiqhyZcOe/l69+ItqTk6xnXQ6Hijz1FOGO1StSpSZ6apTcQkJXhw45qW4Gzf0kXZYGNHNCVTcj5mcXKp88gvvUmMcJ4DottqZpca1bNzIx/L3NxJ0Z2eXrsBTT/FX4HXr7HI+QhiTksLB+mOP6d9Xx7eKpYKAakTHjrm6ekK4rexsoqVLie6917BxJTiYaOJEon//LbHDtWtEn33Gg90fftjwsZUria5edVbVXUKCFycFL0T8v9Sqlb4LKD3deLnz54maNtXx7FEcp2TfJqW6gHQ6/p8FiN7t+DPPzzbnpZe4cI8e9jkZIYwYPbr4G6+Onsdn+g2ffurq6gmhCsePE02bRlS7tmEg07o1UUxMiZmmOp1hq8uxY1y4UiWeZp2a6rLzcCQJXpwYvBBxI4syhiUyUsdjAIpJTeXABiBq2FBHZ+4doU+KkZRkUParxQUEENVBMuXWqk+lDlbc2bNEnp58rP37HXBm4la3fTsPtQKINCikT8B5Kq6jMn3dz9IpFUIIRX4+0a+/EjVpYnx8zKRJRnbavp3HwiiFAgJ4umsFm3Its42crG5dXsKium8Wdu3SYHC3cygo4McuXuTMo0eP8oj0TZs0qL/2Mx6KTgRcvao/UGEhhvz1DGohFedRFz8NXQ1Urmz6ievV42HtAGdsFMJOiHgSxD33ANevA01qZuAf3454AZ8jt04jnF2+C1ELH3V1NYVQHS8vTrY7YIDxxzdvBrTaEhvvugs4eJAz9UZEABkZwLhxwJ133rrrJTkhmHIqV7S8EBGRTkfbQx4iP2QTQDRwIM8iatpUPybm+PFi5S9dMuzwLCwkGsEtMm9pXiOAx/yWmYRx3z5+Ai8vo+NshLDWtWucpkX5kjek21nKhD/f6dnTdN+oEMJixSeCrFvHvf/Kay40lOjbb028/xcUEM2fzy33AE8QuX696OGtW3k45G23EQUG8mv5q694MHEpublEq1dzoerVeZzN+fMOOV9LSLeRK4KXxEQigH72eJQAXammwLFjzexbWKjPnOvpSelLfilaisBgGrYp3btz4Vdesc+5iFtWQoK+OdvHh2jePCLdjRyiyEiil1+ucM3UQriTDRs46FA+N+66y8zQx4sXOUqZP5/y84l++41o0CAijab05w9A1K5dsX337eOlCoKCShe8/XaXzWCV4MUVwcsbbxABdCPqQZpWLNFoUBDRzz+XkSX9r7/0O9zMmvvcc3y3f38LnnvNGmVADUflQthgyRL9Oi0NaufSnp3FAhWDuZ1CCEfJyeEBvMqsaQ8P/vJrbKLRgQM8b0NZSqwor0xIFg0eTDRqFA+PUaZqjx17M/H14MH6wmFh/MVk/Xqijh2tSA9sfxK8uCJ4URINLVpERDwJIzjYyFQ4Y7Zu5daTFSuKNh09qs91d+RIGfsXFhItXnzL5QQQ9pGVVdRjSQBR37Zn6bJPLaLJk11dNSFuWWfPEj36qP51WbMmf8FISyOaM4eoffuS0691NC54KcWjPemgIXr2WaIzZyj98+U0Iuz3onL16xP9Nns/0dCh3NRT/AtvQYFhX9WKFUTvvOO0FlcJXpwdvKSkFP0HpSWkFPVjduum79O0ZX26Bx/kw44ebf8qC0HEQbIyPd/DQ0dvd/qFCnEzan7wQWnJE8LFYmP1YydL3ry9ebjKL7/w8BVKTSWKjjZaeONji6hhQ/2mxx8vY8H3K1f4GzjAkZITZrRK8OLs4GXRIr7Ad95pcimBGTOsP+zmzfqp/Wb/yYrT6WRApTBLGSj4zjucEBEgqlGtgP64fYy+ffmNNyRzsxBuYvx4458rzz1nYoctW/QJyG67jVNjnz5N169zcjxleY/gYE6iZ3RgsE5H9PXX+oHBnp7cGmssYaqdWPP5rSEics08J8fIyMhAYGAgtFotAgICnPOkCQnAV18BLVsidcBopKaWLhIWxjdrEPFMuPh4XuvutdcsqMeTT/L06p07rXsyccsYNw749FP9/QBosQV3ox0OANWqAUuXAn36uKx+QghDL77IqQtKmjTJTJaMwkLg0iUgNBTQaAwe2rMHePpp4N9/+X6fPsD8+ZzOo5QLF/hN48cf+f5ttwGLFgHdu9t8PqZY8/ktwYub++EH4PHH+f/vzBnA19dM4bQ0oEEDIC+Pg5fISKfVU7iZnBzOBaHR8M3DA9BocOSYJ7r38cfFy54AAB/k4DzqIBhXkBLcGrV3reIVboUQbiM1FXb7UqzIywPee4+/GOfl8XfeFi0Ab2/Ax4cXhQ8M5M8cHx/A9/xJeP39ByplX4Y38pERPRaPT6hZ7noUJ8FLBQpe8vOBxo2Bc+eAL7/khhWznnqKW4EeeQT46Sen1FG42PXrwB9/AN26ASEhvE2Jeov5BsPwPOYhG/4I8r+BWe9WQmQkUP2P5QjcsgZ5ny9GrSb+LjgBIYSrjBgBfPNN+Y5htgXICtZ8fnuV/+lucT/9BAQHA127cshqZ97e3GL3yivcbDhiRKkWQEMTJ3LwsmoVcOoU0KiR3esk3MClS8C6dcDq1UBsLJCby+2+o0eXKnod/hiDz/EthgMAeuJPfP/ONdQa8wgXCB8MTBlUxj+WEKIiCgoyvv3ee4F+/fitJTeXW2f++IOHMQAEwLXvF9LyUh5EvDZASgrw++/AAw845GmuXeOVAJQv2FFRZezQqxewcSMwfjwwZ45D6iRcQKvl5rfVq4EdOwCdTv9YkybA1KnckV3MgX8Ijw0Cjh7VwMOD8MaMQkx5qRCevl6Ap6eTT0AI4W6s6ZJyRPdVcdJt5KzgJT4e6NAB8PcH0tMBPz+HPdWECcDcuRyXbNhQRuGNG7lglSpAcjIPwhTuKyuLI1TlptXqf69aFYiO5nJXrwI1a/JAPIDXOBk4kG933GHQckIELFjA/ze5uUCdOtyTdPfdTjwvIYSwgnQbOcu6dfwzKsqhgQvAjSiffsotL//+C7RqZabw/fdzgX//BZYvN9qVIFzoxg2gUiX9/SZNeES/MW3aIG9QNIgA3+rVuXO5Xj1e1c3o1ACOeUaOBH7+me/37Qt8/TX3bgohREUgwUt5KMFL//4Of6pGjYCHH+YPpI8/BpYsMVNYowE++IC/oTuoK0vYID8fmDwZ2LSJZ4MpAUxgILfcVasGVKuGnIAQxGki8fuVTvjrXAf84w8UFPD4p8qV30P16kDoUt6tWjX+6eXFl71SJY5Xz5/nbdOmATNmyHAWIUTFIsGLrc6fB/bv50+Fvn2d8pTDh3Pw8t13wODB+oFWRvsbe/VySp2Ehc6fBx57TJ9/Z/16nhEGIG9PIvYc8MOmzZqiuCY3t/Qh8vO5R0mrBU6fLvspCwqAzEwJXIQQFY8EL7b69Vf+2bmzfnqqg+3bxz/z8w0H7c6YAcycaWbH7Gzu1vLwcGT1bknKALb8fODkSf0Y2uDgYt00u3dzE8i1HOgq342UZ2fi0rUeSBzL1/SffyohJ8fwuLVqAe3a8ViVFi146Et2Nt9CQvhWfHjMmjXKLAAhhKj4JHix1Y4d/NMJXUaKUaN4bPArr/C36fffB3r0KGOU9zvvcBfSQw8BX3whM0zs7IMPjGe+NNQJwJ/8azaAOaVLVKrE0xJ79OBbs2bWtZg884zpWQBCCFHRSPBiq2++4akctWo59Wl79OBWl40befjEW29xd5JJderwLJXFi3mg6Ndf82AIUW6FhZxm25iqVYFquMr9NgDgXwWoXg3XtB5Fm4obPdqSIMg0e01VFEIINZBPMVtpNEB4uFOfcsECYNYs/f3CQk7tsXs3p/4wKjqaczs/8QSvWZOTw3NmfXycUueK6swZYNgwYPt2vn/vvdwzpMxKDwsDwvIzOXnhm28WRZjm8iQIIYSwjOR5sQWRS0ZBFv/g0+mA2bOBX37hqixYwNNjTVqzhgeM5uXxAOOff3b49O6K6ocfgOef5/EmVaoAn3xyM/MxCNi1C+jSRV84N7eMBamEEEIA1n1+ywhOa+l0QNu2/E360iWnPnVYGDf2hIdzbryVK4GxYzmWevZZ4LPPzOw8YACwdi0HLOvX8wCLGzecVveK4No1YOhQXjJIq+V1LxMTeb0pTfolYNAg4K67+O+skMBFCCHsToIXa+3bBxw8yE0egYEurYqHB3/rf+klvv/CCzyI1yQlPa+/P1C7tnywWmHzZqBNG2DZMh7zPOvFDGx9fjmafPAcJwQMCeF1rry8jPcLCSGEsBsZ82ItJTFdr15uMW5Eo+FlzStV4qEVr7zCw1qmTzexQ/fuPMr09ttl6rQF8vKA6a8R3v8AINKgSRPg+yU56Hx/MM+PLq5dO2DePG6SEUII4TASvFjLiVl1LaXRAG+8wQ0pr70GvP46BzBvvWViaE7LlvrfCws5ScwLLzgtX41bI8KF+HNI334YJ3ZfxmvrO+NgJq/MPWQIsHAhUKWKH9CpE3e73X0337p2lfz7QgjhJE756j1v3jw0atQIfn5+iIiIwLZt28yW37JlCyIiIuDn54fGjRvjiy++cEY1y3bmDPDPP9xi0aePq2tTyrRpnHcEAN5+m7uTyhyOPWUKRzndu/Pq2LcKnY6z3oIz8//1F/BRxFIM91mGXndeRvuJ92DA8iE4mNkIQUjHDxiMesE3UKXKzf03b+YuxI8+4oURJXARQgincXjLy4oVKzBhwgTMmzcPd911FxYsWIDevXsjKSkJ9Y0sLHfq1Cn06dMHI0eOxPfff48dO3bg+eefR82aNfG///3P0dU1T8mq26WLPje/m3nxRR6TO3Ysf67m5PCCjiZ7iEaO5MVwjhzhFoS//gIaNHBqnZ0hNRU4ufcyCj+dh1NJ2Th8oQb+odZIrFkbaReU5qnHDfaphGw0xGkEIANP4muM8S42O0uS/QkhhMs4fKp0p06dEB4ejvnz5xdta9GiBQYOHIiYmJhS5SdPnoy1a9fi8OHDRdtGjx6Nf/75B7t27Srz+Rw6VfqBB3hZ53ff5cElbuyDD7iKRMCDDwKvvsoL+xlNZnb6NCcqOXWKVyz+9Vcenaoi168D584Bycl8K/n78f8KkZtvOuBo0gRo2zgTbZvloF7b6qhZywthYYbdbpIITgghHMeaz2+Htrzk5eUhPj4eU6ZMMdgeFRWFncoCdSXs2rULUcUX7gHQq1cvLFmyBPn5+fD29jZ4LDc3F7nFVrHLyMiwU+2NiIzkrhU3Gu9iSmqqvsto7Vr97N1Jk4APPyxRuGFDYNs2oGdP4OhRoGNHXrp69Gi3XdUvNZUvxbJlwJdfchJh8zhwCYAWnihAIbyQiSoYNNgTCxdyRlyg6s2bEEIId+bQ4CU9PR2FhYUIDQ012B4aGoq0tDSj+6SlpRktX1BQgPT0dISV+OobExODWcXTzjrSjBl8U7Fjx0w8UKcOp4sdPhz47Tdee2DAAJ5S7YZiYrg7rKSgIM6BU68eULcuUC8wA/UmD0XdvBOo1q4hLn7wHQqr68enhIUpgYsQQgi1cMpsI02Jb+9EVGpbWeWNbQeAqVOnYtKkSUX3MzIyUK9evfJUt0J46SVOpgZwK8XUqZyeZt06nlj0wQdG0rwEB3OBuXM5aDERuJw/D3z3HReNjOTEvV5eJrpVkpJ4WrayntKVKzwop3Jlm8/t+HHOcmvM8OElW5YCgKBBwNatwOefI8wNprcLIYQoH4fONgoODoanp2epVpaLFy+Wal1R1KpVy2h5Ly8vBBkZJOvr64uAgACDmzDMxtu3LxAfrx+m89lnPOb4+HEjO3p4ABMncrZYRWwsMHs2CvMKsXw5JxieOhXYuZMDhU6dgIgI/UwnANzEM3QoJ3D7/nv99vHjear2mjUWTIUq7ddfuWXl8mVuZVm8mM9Nub30Enj6UFKSfqfoaGDRIrfIyyOEEKL8HBq8+Pj4ICIiArGxsQbbY2Nj0aX4+i/FREZGliq/ceNGdOjQodR4F2E5b28eZ7x+PX/o79/Pgc2PP5axo1aLwieGY9lr/6J19XMYMoQDB2M2bwauJ50Fnn4aaNGCB6QQAQcOFB0LW7fylPOBA3mJAqMRVGk6Haej6d+fD9OlCx/26af1QVp4OBB25RCP2XngAcBE16QQQgiVIwdbvnw5eXt705IlSygpKYkmTJhA/v7+dPr0aSIimjJlCkVHRxeVP3nyJFWuXJkmTpxISUlJtGTJEvL29qaff/7ZoufTarUEgLRarUPOpyJITibq2pWIIwuiUaOIsrNLlysoIPr+Ox01D7taVLa65gpN/d9R2ryZKD6e6I8/iHr10h+rPk7TOvTlO/36Ee3fb3jQ69eJXn2VyNuby/j4EE2fTpSVZbK+V64Q9emjf44xY4hyc40U/PVXoqpVuVDjxkRHjpTvDyWEEMJprPn8dnjwQkT0+eefU4MGDcjHx4fCw8Npy5YtRY8NHz6cunfvblB+8+bN1L59e/Lx8aGGDRvS/PnzLX4uCV4sk5/PMYRGw5/1bdroP+vz84m++47o9tv1AUONwAJ6q9anpMXN4GDSJIMI4vfwV6khThaVf6RHOqWkmKnA0aNEUVH6J2jQgCgxsVSxf/7hOAQg8vMj+uYbI8fS6Yg+/FB/Mt27E6Wnl+fPI4QQwsncLnhxJglerPPHH0RBQfyZX6kS0dNPE9WvXyxoqUE0ezaRVktEN24QjRunfzAigigjgw+0bRtd73QvvTzoDHl68sMBAUTz5xMVFpp4cp2O6OefierVI6pV6+aT6C1dynUCiBo2LN2IQ0REaWlETzyhr9PIkSaaZYQQQrgzaz6/HZ6kztkcmqSugnrxRc7GW1LPnsDq1UamEq9dCzz5JI8E/vZb/XYiQKNBYiLw7LPA3r28uUsXYMECHrtrVFYW8N9/SK3VHqmpQF4u4ctndmBRUlcAwP3V9mBZvckIyjzNs5XmzdNPpVq3jrPweXjw6OHx4902N40QQgjT3CZJnVCH6GheEHnNGh7QO3AgcN99PIPIaA6UBx/kNZ4CAw233wwa2rUDdu3iGEOZldSuHc8SKizkxZh1Ov49JwfIyfFHTk57ZGYqCzVrAHDgMg1vYda1GfC8ptM/T/ERw2FhQOfOnH/ngQfs9ScRQgjhxqTlRWDmTMBYnr8ZM/ix8nj2WZ6lbK0qmiy80Wk9uja7hJrNqqNhu+pA9Zu32rUls5wQQlQw0vIirDJqFDemlGSPdXxMxRgPPcTJ8vz8OFmenx93LX3yCT9+nfwxKe4xIO5mENW7/HURQghRMUjLi3Co1FS+lWQsG681ZYUQQlQs0vIi3IY1gYcEKUIIISzh0Ay7QgghhBD2JsGLEEIIIVRFghchhBBCqIoEL0IIIYRQFQlehBBCCKEqErwIIYQQQlUkeBFCCCGEqkjwIoQQQghVkeBFCCGEEKoiwYsQQgghVEWCFyGEEEKoigQvQgghhFAVCV6EEEIIoSoSvAghhBBCVSR4EUIIIYSqSPAihBBCCFWR4EUIIYQQqiLBixBCCCFURYIXIYQQQqiKBC9CCCGEUBUJXoQQQgihKhK8CCGEEEJVJHgRQgghhKpI8CKEEEIIVZHgRQghhBCq4tDg5erVq4iOjkZgYCACAwMRHR2Na9eumd1n1apV6NWrF4KDg6HRaJCYmOjIKgohhBBCZRwavAwdOhSJiYnYsGEDNmzYgMTERERHR5vdJysrC3fddRfeeecdR1ZNCCGEECrl5agDHz58GBs2bEBcXBw6deoEAFi0aBEiIyNx9OhRNGvWzOh+SnBz+vRpR1VNCCGEECrmsJaXXbt2ITAwsChwAYDOnTsjMDAQO3futNvz5ObmIiMjw+AmhBBCiIrLYcFLWloaQkJCSm0PCQlBWlqa3Z4nJiamaExNYGAg6tWrZ7djCyGEEML9WB28zJw5ExqNxuxt3759AACNRlNqfyIyut1WU6dOhVarLbolJyfb7dhCCCGEcD9Wj3kZO3YsBg8ebLZMw4YNceDAAVy4cKHUY5cuXUJoaKi1T2uSr68vfH197XY8IYQQQrg3q4OX4OBgBAcHl1kuMjISWq0We/bsQceOHQEAu3fvhlarRZcuXayvqRBCCCEEHDjmpUWLFnjggQcwcuRIxMXFIS4uDiNHjkS/fv0MZho1b94cq1evLrp/5coVJCYmIikpCQBw9OhRJCYm2nWcjBBCCCHUy6F5XpYuXYrWrVsjKioKUVFRaNOmDb777juDMkePHoVWqy26v3btWrRv3x59+/YFAAwePBjt27fHF1984ciqCiGEEEIlNERErq6EPWVkZCAwMBBarRYBAQGuro4QQgghLGDN57esbSSEEEIIVZHgRQghhBCqIsGLEEIIIVRFghchhBBCqIoEL0IIIYRQFQlehBBCCKEqErwIIYQQQlUkeBFCCCGEqkjwIoQQQghVkeBFCCGEEKoiwYsQQgghVEWCFyGEEEKoigQvQgghhFAVCV6EEEIIoSoSvAghhBBCVSR4EUIIIYSqSPAihBBCCFWR4EUIIYQQqiLBixBCCCFURYIXIYQQQqiKBC9CCCGEUBUJXoQQQgihKhK8CCGEEEJVJHgRQgghhKpI8CKEEEIIVZHgRQghhBCqIsGLEEIIIVRFghchhBBCqIoEL0IIIYRQFQlehBBCCKEqErwIIYQQQlUcGrxcvXoV0dHRCAwMRGBgIKKjo3Ht2jWT5fPz8zF58mS0bt0a/v7+qF27NoYNG4aUlBRHVlMIIYQQKuLQ4GXo0KFITEzEhg0bsGHDBiQmJiI6Otpk+ezsbOzfvx/Tp0/H/v37sWrVKvz333948MEHHVlNIYQQQqiIhojIEQc+fPgwWrZsibi4OHTq1AkAEBcXh8jISBw5cgTNmjWz6Dh79+5Fx44dcebMGdSvX7/M8hkZGQgMDIRWq0VAQEC5zkEIIYQQzmHN57eXoyqxa9cuBAYGFgUuANC5c2cEBgZi586dFgcvWq0WGo0G1apVM/p4bm4ucnNzDcoD/EcQQgghhDoon9uWtKk4LHhJS0tDSEhIqe0hISFIS0uz6Bg5OTmYMmUKhg4dajIKi4mJwaxZs0ptr1evnnUVFkIIIYTLZWZmIjAw0GwZq4OXmTNnGg0Witu7dy8AQKPRlHqMiIxuLyk/Px+DBw+GTqfDvHnzTJabOnUqJk2aVHRfp9PhypUrCAoKsuh5rJGRkYF69eohOTm5QnZJVfTzAyr+Ocr5qV9FP8eKfn5AxT9HR50fESEzMxO1a9cus6zVwcvYsWMxePBgs2UaNmyIAwcO4MKFC6Ueu3TpEkJDQ83un5+fj8ceewynTp3C33//bfaP4+vrC19fX4NtprqY7CUgIKBC/kMqKvr5ARX/HOX81K+in2NFPz+g4p+jI86vrBYXhdXBS3BwMIKDg8ssFxkZCa1Wiz179qBjx44AgN27d0Or1aJLly4m91MCl2PHjmHTpk0ICgqytopCCCGEqMAcNlW6RYsWeOCBBzBy5EjExcUhLi4OI0eORL9+/QwG6zZv3hyrV68GABQUFOCRRx7Bvn37sHTpUhQWFiItLQ1paWnIy8tzVFWFEEIIoSIOzfOydOlStG7dGlFRUYiKikKbNm3w3XffGZQ5evRo0Qyhc+fOYe3atTh37hzatWuHsLCwotvOnTsdWVWL+Pr6YsaMGaW6qSqKin5+QMU/Rzk/9avo51jRzw+o+OfoDufnsDwvQgghhBCOIGsbCSGEEEJVJHgRQgghhKpI8CKEEEIIVZHgRQghhBCqIsFLCfPmzUOjRo3g5+eHiIgIbNu2zWz5LVu2ICIiAn5+fmjcuDG++OILJ9XUNtac3+bNm6HRaErdjhw54sQaW27r1q3o378/ateuDY1Gg19++aXMfdR2/aw9RzVdw5iYGNx5552oWrUqQkJCMHDgQBw9erTM/dR0DW05RzVdw/nz56NNmzZFycsiIyPx+++/m91HTdcPsP4c1XT9jImJiYFGo8GECRPMlnP2dZTgpZgVK1ZgwoQJmDZtGhISEtCtWzf07t0bZ8+eNVr+1KlT6NOnD7p164aEhAS8+uqrGDduHFauXOnkmlvG2vNTHD16FKmpqUW32267zUk1tk5WVhbatm2Lzz77zKLyart+gPXnqFDDNdyyZQvGjBmDuLg4xMbGoqCgAFFRUcjKyjK5j9quoS3nqFDDNaxbty7eeecd7Nu3D/v27cO9996LAQMG4NChQ0bLq+36Adafo0IN16+kvXv3YuHChWjTpo3Zci65jiSKdOzYkUaPHm2wrXnz5jRlyhSj5V955RVq3ry5wbZRo0ZR586dHVbH8rD2/DZt2kQA6OrVq06onX0BoNWrV5sto7brV5Il56jma3jx4kUCQFu2bDFZRu3X0JJzVPM1JCKqXr06LV682Ohjar9+CnPnqNbrl5mZSbfddhvFxsZS9+7dafz48SbLuuI6SsvLTXl5eYiPj0dUVJTB9qioKJMJ8nbt2lWqfK9evbBv3z7k5+c7rK62sOX8FO3bt0dYWBh69uyJTZs2ObKaTqWm61dearyGSvLKGjVqmCyj9mtoyTkq1HYNCwsLsXz5cmRlZSEyMtJoGbVfP0vOUaG26zdmzBj07dsX9913X5llXXEdJXi5KT09HYWFhaUWjQwNDUVaWprRfdLS0oyWLygoQHp6usPqagtbzi8sLAwLFy7EypUrsWrVKjRr1gw9e/bE1q1bnVFlh1PT9bOVWq8hEWHSpEno2rUrWrVqZbKcmq+hpeeotmt48OBBVKlSBb6+vhg9ejRWr16Nli1bGi2r1utnzTmq7foBwPLly7F//37ExMRYVN4V19HqhRkrOo1GY3CfiEptK6u8se3uwprza9asmcE6VJGRkUhOTsYHH3yAu+++26H1dBa1XT9rqfUajh07FgcOHMD27dvLLKvWa2jpOartGjZr1gyJiYm4du0aVq5cieHDh2PLli0mP9zVeP2sOUe1Xb/k5GSMHz8eGzduhJ+fn8X7Ofs6SsvLTcHBwfD09CzVCnHx4sVSEaWiVq1aRst7eXm53WrYtpyfMZ07d8axY8fsXT2XUNP1syd3v4YvvPAC1q5di02bNqFu3bpmy6r1Glpzjsa48zX08fFB06ZN0aFDB8TExKBt27aYO3eu0bJqvX7WnKMx7nz94uPjcfHiRURERMDLywteXl7YsmULPvnkE3h5eaGwsLDUPq64jhK83OTj44OIiAjExsYabI+NjUWXLl2M7hMZGVmq/MaNG9GhQwd4e3s7rK62sOX8jElISEBYWJi9q+cSarp+9uSu15CIMHbsWKxatQp///03GjVqVOY+aruGtpyjMe56DY0hIuTm5hp9TG3XzxRz52iMO1+/nj174uDBg0hMTCy6dejQAY8//jgSExPh6elZah+XXEeHDQVWoeXLl5O3tzctWbKEkpKSaMKECeTv70+nT58mIqIpU6ZQdHR0UfmTJ09S5cqVaeLEiZSUlERLliwhb29v+vnnn111CmZZe34ff/wxrV69mv777z/6999/acqUKQSAVq5c6apTMCszM5MSEhIoISGBANBHH31ECQkJdObMGSJS//Ujsv4c1XQNn3vuOQoMDKTNmzdTampq0S07O7uojNqvoS3nqKZrOHXqVNq6dSudOnWKDhw4QK+++ip5eHjQxo0biUj914/I+nNU0/UzpeRsI3e4jhK8lPD5559TgwYNyMfHh8LDww2mMA4fPpy6d+9uUH7z5s3Uvn178vHxoYYNG9L8+fOdXGPrWHN+7777LjVp0oT8/PyoevXq1LVrV1q/fr0Lam0ZZUpiydvw4cOJqGJcP2vPUU3X0Nh5AaCvvvqqqIzar6Et56ima/jUU08Vvb/UrFmTevbsWfShTqT+60dk/Tmq6fqZUjJ4cYfrqCG6OapGCCGEEEIFZMyLEEIIIVRFghchhBBCqIoEL0IIIYRQFQlehBBCCKEqErwIIYQQQlUkeBFCCCGEqkjwIoQQQghVkeBFCCGEEKrilOBl3rx5aNSoEfz8/BAREYFt27aZLLtq1Srcf//9qFmzJgICAhAZGYk//vjDGdUUQgghhAo4PHhZsWIFJkyYgGnTpiEhIQHdunVD7969cfbsWaPlt27divvvvx+//fYb4uPj0aNHD/Tv3x8JCQmOrqoQQgghVMDhywN06tQJ4eHhmD9/ftG2Fi1aYODAgYiJibHoGHfccQcGDRqE119/vdRjubm5Bqt56nQ6XLlyBUFBQdBoNOU/ASGEEEI4HBEhMzMTtWvXhoeH+bYVL0dWJC8vD/Hx8ZgyZYrB9qioKOzcudOiY+h0OmRmZqJGjRpGH4+JicGsWbPKXVchhBBCuF5ycjLq1q1rtoxDg5f09HQUFhYiNDTUYHtoaCjS0tIsOsaHH36IrKwsPPbYY0Yfnzp1KiZNmlR0X6vVon79+khOTkZAQIDtlXem8HDg7Flg926gSRPL9vn+e2DMGKBHD+CXXxxaPSGEEMLRMjIyUK9ePVStWrXMsg4NXhQlu2+IyKIunWXLlmHmzJlYs2YNQkJCjJbx9fWFr69vqe0BAQHqCV6OHweU3jtLu7patuSfycmAWs5TCCGEKIMl8YFDB+wGBwfD09OzVCvLxYsXS7XGlLRixQo8/fTT+PHHH3Hfffc5spruQaOxPHABgEaN+OeZM0BhoWPqJIQQQrghhwYvPj4+iIiIQGxsrMH22NhYdOnSxeR+y5Ytw4gRI/DDDz+gb9++jqyietWtC3h5Afn5QEqKq2sjhBBCOI3Dp0pPmjQJixcvxpdffonDhw9j4sSJOHv2LEaPHg2Ax6wMGzasqPyyZcswbNgwfPjhh+jcuTPS0tKQlpYGrVbr6Kq6xr59QLduPH7FGp6eQP36/PupU/avlxBCCOGmHB68DBo0CHPmzMEbb7yBdu3aYevWrfjtt9/QoEEDAEBqaqpBzpcFCxagoKAAY8aMQVhYWNFt/Pjxjq6qa5w9C2zfDtiSx0bpOpLgRQghxC3E4XlenC0jIwOBgYHQarXqGLC7aBHw7LNAv37AunXW7bt5M5CTw7OVTAxoFkIIIdTAms9vp8w2EmZcvsw/g4Ks3/eee+xaFSGEEEINZGFGVytP8CKEEELcgiR4cbXyBC+ZmZys7uOP7VsnIYQQwo1Jt5GrlSd4yc4GoqM5P8zzzwNGkvUJIYQQFY20vLiaRsNBhy3BS0gIULkyZ+c1sUq3EEIIUdFIy4ur/fILBx+2TPrSaICGDYGkJJ4ufdtt9q6dEEII4Xak5cUdaDRAGct/m6Tkejl92m7VEUIIIdyZBC9qJ4nqhBBC3GIkeHGljAyga1dgwACgoMC2Y0jwIoQQ4hYjY15c6dIlYMcOHnTrZeOlkOBFCCHELUaCF1eyR4K6u+4C1q+XwbpCCCFuGRK8uJI9gpeQEKBPH/vURwghhFABGfPiSrI0gHFEto8BEkIIUeFJ8OJK9gpeNmwA3noLSEwsd5XcwrBhQGiojOMRQghhlAQvrmSv4GXRImD6dGDr1vLXydUKCni9pitXgNdec3VthBBCuCEJXlypoMD2pQGKq0gzjg4f1v++aRNQWOi6ugghhHBLEry40ttvAzduADNnlu84FSl4qVwZGD2af09NBWJjXVsfIYQQbkeCF1fTaABPz/IdoyIFL02aAPPnAy+8wPe//tql1RFCCOF+ZKp0RVA8eCHigEjtRowA4uNlGrgQQohSJHhxpUceAfLygDlzgMaNbT9Ow4b8MzMTuHoVqFHDHrVzvsJCYNcuoF07IDycsw8LIYQQJUi3kSv98Qewbh2g05XvOJUqAbVq8e9q7jo6cgTo1g2oXbv8fxMhhBAVlgQvrpKXB1y/zr/bI0ndypVAUhLQunX5j+Uq8fH8s00bwOPmv2Z6OvDpp8B//7muXkIIIdyKdBu5ys0cL+ThgYQTgaXCyLAwvlmsSxf71c1V9u/nnxER+m3PPgusXg28/DLw3nuuqZcQQgi3IsGLq9wMXrL9aiDiztINYDNmlH8GtTtITeVbSUaDM6XlpXjwMmwYBy/ffcdTy21dfVsIIUSFId1GrnIzePENC0J8PH9ud+uGot9HjbLyeCdOALNn8+BfN7JgAcciJW8LFpQoWFgIJCTw7+Hh+u19+wI1awJpaTxGSAghxC1PghdXuRm8eIUG4bbbOBebnx9/boeHW9llBACnT3M6/S++sHtVy2PUKGD5cv395ctNBGfHjgFZWTz4uHlz/XZvb+Dxx/l3yfkihBACEry4zo0bHK0EBWHWLGDKFMPM+FZTcr2cPu12M3WU3iDASItLyULt2pXuGhoxgn+uXatfD0oIIcQtS4IXV3n8ceDGDRT+uBLLlvGm9HTOMWeTevV4hk5uLnexuIkFC4D339ff37TJRLdR587A3LnAc8+VPkjbtkD79jxDS/ljCSGEuGVJ8OJi2+K8kZLCv+fkAGfO2Hggb28OYAC3yvXy7LP6nHlKHr5u3Yx0GzVpAowbB0RHGz/QiBHcInPunKOqKoQQQiUkeHGxJUsM73/7Lc8YNjZDp0xuuMZRZiZw5Qovnr1mDS/jtG0bkJxs5YGefBJISQHeecch9RRCCKEeEry4ytSpyOv3MFb/mG+wecYME90qlnDD4GXLFv7ZuTPQqpW+YeX114sVSk3lwbiHDpk+UNWqPOtICCHELc8pwcu8efPQqFEj+Pn5ISIiAtu2bTNZNjU1FUOHDkWzZs3g4eGBCRMmOKOKzrdtGzauz0NWnjeCgoCPPuLN9evbOFUacMvgZetW/nn33fxz+nTu/fnjj2JLF23Zwi0rTz1l2UFPnADy88suJ4QQokJyePCyYsUKTJgwAdOmTUNCQgK6deuG3r174+zZs0bL5+bmombNmpg2bRratm3r6Oq5zuXLWIYhAHjs7pNP8mLQZ8/akF1X8dRTwL//cjp9N0Ckb3lRgpfGjflcgWKtL8Yy65ry2GNA06bAb79ZX6EDB4DISOCTT6zfVwghhNtwePDy0Ucf4emnn8YzzzyDFi1aYM6cOahXrx7mz59vtHzDhg0xd+5cDBs2DIGBgWUePzc3FxkZGQY3NchOz8YaDAAADBkCVKvGs4QBfWuF1erUAe64A/D3t0cVy+3MGR5f6+XFMYNi2jQeX/z338DmzTCeWdeUBg34p7U5X/bs4VlLcXHAxImcFE8IIYQqOTR4ycvLQ3x8PKKiogy2R0VFYefOnXZ5jpiYGAQGBhbd6ikzbtwZEdZd7oIsVEGj+gXo1Ik3K60TNgcvbkY5jw4dDOOpBg2AkSP599dfJ1D8zZaX4pl1TVFyvvz6K3DxomUV2bIF6NlTf79vX0AlQa4QQojSHBq8pKeno7CwEKGhoQbbQ0NDkWanXCRTp06FVqstuiVbPY3FBbRaLKNBAIAhg7m7CLBT8PLhhxwZnD9fvjraQcnxLsW9+irPQNq2TYO/tBGAjw+3GpXljjuAO+8ECgqQ/N4P2L8fBrdSs7R+/x144AFewfvee3n609q1QPXq5T4/IYSTfPYZsHSpq2sh3IhTBuxqlE/nm4io1DZb+fr6IiAgwODm7q6duorf0RsAMCRan022Wzf++e+/5Ugku2gRsHgxcORIOWtZfuaClzp1gNGj+ffpeBPUqjUHMJa42fpy5cOvEBFBptdMOnYMGDCAE+j06wesXw9UqWLz+QghjCDicWTffWe/Y+bmcnbLnBzgzz+BF17gRVpXrLDfcwhVc2jwEhwcDE9Pz1KtLBcvXizVGnMrWfWLB/Lgi1beR9CqlX57zZpAixb8+/btNh7cTWYcpaZy7KDRAHfdZbzMlClAJa88xCESG0KGWX7wIUNAPj5ojYN47qE0eHnxepSlZmnddhswdSoP8l21ipdjAPjNNjERuHrVxrMTQhTZswcYP56Di++/t88xX3sNeOUV7uK9917g6ad52ZPHHwdWrrTPcwhVc2jw4uPjg4iICMTGxhpsj42NRZcuXRz51G7thx086HTIa01LPVburiM3CV6U+rdty4ORjalVC3h+ZAEA4PWzT1u+NEL16sjuNwiP4UfMXx2GggJg9epiC1oWFOjLzpzJSwp4e+u39e3Lyw2sWWPlWQkhSvn2W/3v7duX/3h//gl88AH/Pm4cL3uycCEwfDgPtB88WF67wvHdRpMmTcLixYvx5Zdf4vDhw5g4cSLOnj2L0Tf7DKZOnYphwwy/dScmJiIxMRHXr1/HpUuXkJiYiKSkJEdX1SnS0nh9HwAY/IRXqce7d+efyhRjq7lZ8GKsy6i4ybMqw98f2Jfkj3XrLDv2+fNAl3/mYyUegZcXRzxbtnCvUMZr7/E3tawsLqzR8JtfcXfeyT9tmW4thNDLy9MvG79smdlxa6mpKDVGrdQ4tcuXuQUH4GbUATwjEx4enI788cf5y8mjj/ILXty6yAk+//xzatCgAfn4+FB4eDht2bKl6LHhw4dT9+7dDcoDKHVr0KCBRc+l1WoJAGm1Wjuegf3MnUsEEHXqZPzx5GR+3MODyKZT+OknPkDnzuWqZ3m1asXV+PnnsstOmcJl27YlKiw0X3bvXqKwMC5f8nYfNurvfP216YPExXGZwECivDxrTksIUdwvv/BrqVYtovx8/fadO0u9gc2YYfx1O2PGzQI6HdHAgbyxeXOirKzSz5efTzRoEJfx8SE6ftxRZyZcwJrPb6cEL87k7sFL5878upt7xwKib74xWqZxYy7z++82PMG+fbxzaGj5KloO6en6N6aLF80U3L6daNo0Sv9lG1WtWnaw8+OPRJUqcblmzYjWriWKjyd6fbqO3++QRDqA6J13zFewoIAoOJgPVCyQFkJY6X//49fRpEn6bT/8QOTtTdSnD7/WbkpJIfrzT6K6dXmXQYOIdu3i7UREtGABP+DtTbR/v+nnzMvj55092zHnJFxGghc3DV5OnrzZqqIppFSEEr32mtFyI0ZwualTbXiSy5f1kUN2dvkqbCPly1iLFmUUfPVVLvjMMzR9Ov96xx2lW190OqI33tCfVp8+N7/UFRYSvf02aVGV/JBNANHeV360rJJPPMEHmzzZllMUQmRlEVWuzK+jxET99j17iPz8ePvEiUWbT50iatfOsNWlWTOibduI6MYNfZPqBx+U/dxlNdEKVbLm81sWZnQipWu4R8gh1MIFICjIaLlyDdqtXp3nWl+/DlSqZFtFy8nS8S5FmXXDwzFpEhAYyGsz/vijvsiNG9zNrSwlMGkSp2kJCAD3g//0EwKQiQHgAXzf5TxqWSX79OGfMu5FCAMWjU0BgMqVgePHOTVD8aVc7rwT+OYb/v3jj4FFi0DEw1cSEw0PcfQoD1H7/mc/YOdO4OWXOQN2WYqPY8vMBHr35uXqxa3DCcGUU7lzy0vr1vzFYnGLD/iXb781Wu74cX3rqYsaT8qlQweu/9KlZgrpdEQ1a3LB3buJSN+60qwZtzanpvLYIIDIy4to0SIjx/njD6K2benXV3cQwIe0aBhLejoPLAKIzp615TSFqJDKHJtiqVmzil68s58+QQCRpyfR559zd+/vvxNFROiPP3w4UWamDRV+5RU+QJUqRDt22HAA4S6k28gNg5eDB/UByZXWd/Od9euNltXpiOrU4SJ//+3kipaTVmthTHD2LBW9m924QURER4/yGFqA6MknedgOQFS9OtGmTeafNy9PHwv9+quFlZ0zh+ivv4hycy3cQYiKLyWFqG9ffVAxaBD3BBWNTSHiN6my6HREQ4bQj3ik6Fjz5xsWKYj9m2Y98V/Re0azZkT//GNlhbOziXr25ANUrVr0ZUioj3QbuaFly/hn795Ade1pvmOi20ijKWfX0d9/8zTDRYts2Ll8du7kXFKNGgFml5lSuozuuKMoedwPPwBaLW/+6ivgwgX+/YkngHvuMf+83t68wCVgRaLP8eO5zdrSzL5C3ALS0oA//tDfX7ECmDyZu3CLzJ4N3HcfUCKHlwGNBnuf+xLDNPyCHB+xvSirNgAgPR2ewx7H69/fjr9f34zatbkbqWNHYP58WJ73qVIl7kvu3p27kB57rERlRUUkwYsTEOnHuwwZAn3ufxPBC1DO4OXQIU7qtGGDDTuXj8XjXfbfXIyx2ErSo0Zxt7Wy7FDnzrzq9NSplj13dDT/XLNGHwQJIawzbpxhnkeAc1N17w6kp4Pf0L75BvjrLyAlxeRxkpOBBx/zQw75oU+Lk/gwrliqbSLgmWd4IE2LFuj+ckf88w/nj8zNBZ5/nlO5XLtmYaUrV+bFWuvV4+XsP/zQ2tNWj3feAd56i3Ps3MIkeHGCPXuAkyf59dW/Vx43TQAWBS+7dtnwP+rCRHUWBy+HDvHPYsELwH+jBQuAZs2AuXOBqlUtf+6ICKB5c14OxeIM4tu28QBBO61yLoSaZWXxeH+Ak9zGx/N43IAA4Nw5oEsX4MTKRB6oW6kS8PDDRo9z/TrQvz+34rRuDSzf3RieXjfXsyPiL1dr1nCr5w8/AJUrIzgYWLeO4w5vb34Nt2zJcZLJQcPFVakCvPce/x4TwxV2YxYPjC7uzBlgxgxg+nSgXbtbe5CyE7qxnModx7yMH8/dsUOHFtuYnW2231in06ci2bnTyif891/esVo1W6prs+xsHtMDWJA7qrCQ6L//DBLB2GOg4OzZvM8991i4w1NPlZrSKYTbW7uWKDra8D3kpZd4EkBOjs2H/fRTfjk0aWKQooWSkogaNODHalbKoN24s8Qbml5BAVH//lw2JITo9OliD16/TtSypf7F/eGHRo/xzDM2vhfodETduhHdfjvnvHJjNr3f6XQ8EyIkRL/D009ziowKQAbsulHwUlDAyScBonXrrNv34Yd5v7JyrpVy/br+H/vqVSt3tt2mTfyUtWtbNp6vpJQUnoVQ8mYwULAMp0/rT/3MGQt2+Pln/UhBIdzd2bP6LLQAJ4Qj4i8CyrbQUP4ETE216tAFBRy0AESffVb68dRUovD2hQQQVUIWrZlhPDh48UU+hq8vJ6Ez8OWX+npGRZnM15KSwvkj77uPi/r7c/4oi94LUlJUMQg/JYXorbf0fw4fH85htWaNkffPGzf4D6Cc1+XLRCNH6neuWZMDV1veeN2IBC9uFLz89Zd+xoy1r6c5c/RJ2aymRObmMlXamTIzcvBgpz2lUd27cz3eftuCwteu8Txsi5qLhHCR/HxupfD31+cOmDyZv6gQ8YfZ7Nn6aYrK1MbHH+epQhZYtUr/XqUctqTMZeuoN9YTQOThoaPPPzd8fOFC/dMvX27kADod0euvE91/v9lIRPkis3s3UUCAPunlqVMWnYoqnD/PqyAol6p460vr1jylvOhjTFn2pX17w4Ns386ZPQFODJic7PTzsCcJXtwoeFGaP0eOvLlh0yaifv0sSm29fz/vGxBg2IRrESVBysqV1lbZZvfey085b14ZBT//nOdf/vabQ+qxeLH+zc6iLyL33MM7fPqpQ+ojRLnExfHCX8on2113ce4FY/LyOGro0sXw09CCRcbuuouLvvqqmUL/+x/lw5OeabWr6NCvvMINKH/9pf8eMGuWTWdaxFSXiqk14YzKzSX66COi778vX2UcZPhw4+eo/A2VFqeRI4l2dhpPBFDqiCmlW6Vzc/mb2kcfGT6B1R8arifBi5sELzk5POzEIF+Lsn5H//5l7l9QoP/WER9v5ZMri5d98onV9bZFbq5+3aF//y2jsNIhPneuQ+py7Zo+O7lF3d7vvceFe/d2SH2EsITRbtN9Ospr2Zb/P2vU4Mjc0tT4e/fyuJhatYwvcliMslaptze3CJj0/fdEPXqQbn8Cvfmm/kP2nnuoaH2yBx4o4xgWKPm3+Phj/XOtWWPhQRYt0g+8uXatfBVyAKVL7JFHDM81KYnfGlu0MAxq7sRuCsM5y8bHbNvGB9i82ZmnVG4SvLgweCn+ovvoI/4HCw4u1pr39tu8ccQIi47Xpw8X//hjKyty4QJRRoaVO9lu506uZ1CQBe+ttWtz4e3bHVafxx7jpxg/3oLCygBnP78y3+SFcBR9a4OONCgo+oBa/OR2/ppudpVTM24mgSQi/v+eMIHfH4pRXi/Dh1t36OLDb6waWGuDiRP1MZxFSbHz8vT9MsUXjnQDSUlcLY2GhysZo9PxuJ8hEUfIG7lFf1tfX27lMjseUGkGB2xc4dc1JHhxYfBS5ghyZTTbiy9adLx33uHiDz3kqBrbh1LPgQPLKJiaqn/VmupYt4N16/RfuvLzyyis0xHVr8/L3ZpqjhfCwX7+mah1s5yi94ygIKIBA3iMq91eKkqUcs89RS+MU6f0WbGtzW6bksLdxFWrcstrbKz1g+wtlZurX3rkrrsseF0TEW3YoO+LOXzY/pWykTLWtsz3SyKiDh3oAmrSO/23F03+MLGmr97Vq/yhARD16mWHGjuHBC9u0PKyfbv+DeHbb4u9mJUloy0aTcqj9ZU3MnceSG5xC9H69foBKQ5UfLkAE6swGEpNde8/sLBOyf5+ixa8co2EBO5qUYKWyrhOVZFh8OXHz49fY/Pnl3NMZlISrwFU7AvUhAl89/77zex3+jQ3JZeYwaS83+3cyQGFLTMErXHihL4r3ezYnOL69XOrbuG0NG49AW6uqG3OzSYanZcX/RN7oaiHu0oV7hEy+3c+dkz/RVElA3kleHGDMS8//MD/N02alPhMVMZ7LFhg0XFyc/Wrzh86ZEUFrl0jevZZXqTEwR/KVo3NUVZffOIJh9aJiOiFF/ipXD37STiJ0s4eFcU5TxRLl/L0jXPnXFc3I06c4FQpRQM1kUdj8Cn9hl7UBMeKtivj5orfWrXib+/ffcdDW6wKGlauLDrQ1cU/F8UyGzaY2UcZ4FIiwrHbIo5WWLFC/5m8caMFO/z3n346j8ULnznO66/rBx+X+dZ8c7DPkdv72/Z3vvvmOnoWTBBxBxK8uEHworTYlWreU2YBWDD6X6GsOVbmLJ7icnL41Q1wqO9AyqyoqlUtGOA+YICNg3ist2eP/lurxf8OBQXqXMr7VqbT8YdS8Rk21arxdbxxg6hhQ97WsCF/G3WxCxeIxo41nB47GD/Qf2hKKU9Po/i4PIMBnOfPc2/m228TRUbqX9blChqmTiUC6H3vqQTwbFuTH6Q6HSd9A4i+/trgIXvkZrLFqFH6bmGL0tm8/LK+xdfSAc8OkJWlTz76448W7nTgAF2MTSz62ypxZGCgBdkdvvqKCzdtqoqWZQle3CB4adqU/2dKrQrdrh0/UNYyycXYnD+lbl3esVSmKPtS8tFY1Crbpw/3P2/Z4tA6EfFrtVkzrtuXX1qww7vv8mhAE1k/hZspKOBpwcWnEfv6Ej33HNHJk/pyp07pX5ChoUSJiU6rYvEP961buTFUaUkFiKI8Yike7Ynq1bP4NXHhAsf+996rP1atWjwLx+KgoaCA8no+QHVxlgCiJZ+YGVSzezc/SaVKTp0EYE52NjemATxrp8x4RKvlN9Ayp0I61vz5+jjaojE7RuTnEzVuzMcpOTu6lMxMnm+t0RAdOGDbEzqRBC8uDl7y8vRz9Y12NWZnW9UHb3Pm2m7deEclC6eDKK1MMTEW7nDjhu2vXCspGSx79LCgsBKF9ezp8HoJ6xj7hp/y7Ax9FFClCn+7NvXpnZamD3ICAx060604U90qtWvzjBH64AMeRHvlilXHVf4eGzboUxTUqmXd95SlX/C4mlDNBcrZaaa/d+xYfgITywG4SlKSPnizplfE5taicrZcFBbqG7AsyhJh5jNCmQUeFmY4mcyodetkzIsauEPwcvw4/2N5eNinhTI7m1NHA1YmgR02zOH9nTodDyYGiHbscNjT2OzUKX3/eJnTK5UU697ebvMNUzBjQUBdnKWMKrV4HJUlH/5Xr+ozsVWq5JQppD/8oF8TCCBqGJRBH71wQp8HRaez6UPRVFAUGMhjacqi0xGFh/M+b750zXTB3Fz9C9wNp9wqvSIeHhYMfiU+nWHDiPxLDIg22eV2+jRPpWzThvvFLWrCNW7NGn2PZmZmGYWzsriVcMgQozlqcnP1DetWDSdwcxK8uDB4SUnRL25WubL9+oC7drWi+0OhvMM980z5ntyMQ4f040rKXP7ARX2uypg1i9aIUroXVq92dLWEFTZvJnr0UaLKfgVFQxdWrSJKOWtlC15WFvdvAjzuw0FOnNAP7wKIqlcrpDFVv6Y8eNGNBrdT6vHyzX0u2XqwYYN+aE/9+mUHMJs362O4S5eKPVDyRax84oaGOq211Bo6HefhA/jDPD29dJnTp7m7ZsAAoipVdKWCFo2GW4+LepR0OqIvvtC/6Za8TZ5sU12VhnCLdl+6lAs3amTyfVP5nGnQwIqGfDeedUckwYtLgxezo++Tk3n2z+jRVh/31Vf5OBbmtmNff+3wbhClD9eibpkhQ4giIiycImA/ShOr2UGJinHjuHDReg7CVfLzeVx7jx6GryWPmwnc/Px4eJLVMXFuLmeqdUAwnZVFNH06ka8vf0h6aAqpK7bQfrQlAqgAHvQGXqNZr9n/QyQlRZ+TrV498wGMMunR4K1o9Wre8ehR/bYPP+RmXzdedT0zUz8GpFs3bgH+7DNe1kn5LlL8VtP3Gg3D17S43gwKCdEHM9Wr83tFYSHpAxeNhnPiLFrEgw+9va0ar6hQhg15e1s46a1XL97h9ddNFsnO5pgS4BYosw4d4mPec4811XY6CV5c3PKiTH984okSLS9KDu769a0+rpJrqXFjK3basoV36tDBfDmdzub+rSFD+ClmzrSgsNJ+bsOLvzyuXtXnVShznUrlD12njipG51dEaWk8o0JpFlc+Q1rjH/oT99JiPGnwYfTII1YPGTF04wbPObblehcWEh07RrqVq+jH+elUr56+Xj0RS4egz/GeU6s+HVm81aGzcSwJYI4c0f9Ni+KUwkJ900DLlob9GleuOHzGYnkps4+M3Tw9ORaZPZvfi9+beJ4ywQtcvo0pNB0zqSX+LSrfqRNR/Pt/Eb3/fumxIiX7ni1cuVvJDThsmAWFU1L0ScLKmB2n5H257bYyZnomJ1t8TFeS4MXFY16UnEjz55d4QEnQVnJlUAtkZOj/9ywee5Wbqx+7ceAAz6Z58UVuZ+3Vi+tRpw5/HYiN1e/3zz88Y+PPP802Fet0+kVsS82qKik9Xf9u4oJ1Rh59lJ+6zC+QN26Qzo9HQCYtS3Tq9M9bTfGuj337uEv0gQcMpxDXrMmtjnu23aD8gOpEAB2bu5727uVGMmVgvBWTdQwVFur7d1580TCAycvjlAOKkyf5E/CFF/gf6s47iSpXpn/Rknrgr6I6169P9PObSaSrXoOXOB87lr+5O+n/vqwARvmgf/BBIzuGhekjQhUF7ykp3B1T/P9mwABe5frq1dJlz4152yDCyYcnvT1wd9H6TBoN0fPPlxEUHz7M42Beesns+2TxDMYWTXT74AMu3KVLmUUzMniCJEC0bFkZhZVMiNOmWVAJ15DgxcXBi/LGUTweICJOtQvw3D4bKKmxly61YWdlqWVTt+IHVfqoAB6s9/TTvAJ0if7wEyf0TaFlLgm0cSMXbtrUhsqXn9KDFhTETbglA5KCAv4WunIl0dN1f6c7cJC8iq0nUtT1J+zGVBcrQNS5MzeGFMUOymunQQODr5h79+q7BjQazqtkdbf+hx/qn7hdO+5fVAapFh8NqQwUuXm7ikAaj4/JE/ncjeWdTzNm3Hwt2DgQ115MBTCXLukXLTUa7O3YoY8eX3jBqXUuDyUQXrSIByLv21fGF44bN7iJpVYtPs+4OCKdjs6fN0wcGBTErcrK8QzeN5TF65T+KhNPZlEG4+LatDHx7dc4Je9nq1ZlNKAr2f3q1HHbFacleHFh8FJQoJ8ZdOpUiQeVpVEHDbLp2JMm8e6jRpVdtuSAvsNfx1F632GUOepFboH56ituCdq7l+jMGcPAZOtWoqee0r+BK7fAQO4LuznC78svLf6CwPOoy3Hu5fXaa8Y/JNu04QYopVvJ2O2jjyxoeZHkdlZLSeF4QMnw6uvLrQFGM70qs4TeeqvUQ5mZ/O9avNnfkhk3xetxasaXpFO+HhuJWHNziY5uTqE5jeZQH6yj1viHfHGjqFiLFkZe7y5mLIBRPugiIszEVvPm6c+/WjULRuK7nr0z/f79d+lVnY0ed+VK/XLaoaGlmqCvXiXLMhgrEhO5sI8P0eXLFtX16lV9hvNffjFT8MYNHthjcWWcT4IXFwYvp0/rWyNKBbfKJ+jzz9t0bGXgvyXLAlnzYjaZ9+BsPncdPfccFa0IFhRU9NVWWaZpyoAkbs64cMH0O+Ijj3Dh996z6dzLKyWF4yZzjU+VKnHr1mOP8bclZVJKx44mgpfCQm7SGTSI3xR8fCxYH0EoUlJ4IhzAM/NMrpR78CAX8vIyG0GuWMHxNcAfGG++aVkuD+W10hIHqR/WUFvsp1o4T13aZ1OvKB01bszjJoz9zzRsSPT55+7bpZiSYpifT/nsmj3bTJ11Ov6SYlVzgWs5ItNvbi6/ZSt5dAB+b/jrrxLH/e8/fWuJhwenQr7ZBPLuu/pWEYsa4i5e5GmRL79sVV2VxvIOHcp4HiVnj4u+RJZFghcXBi9//sn/G82aGXnwuef4wenTbTq2Mi0Z0K/eWvIFqtPxN6wPPuAP35AQbk6vX5+b4p94gl9by5ZxQqvUVP1aG2YDnYICTqSwbFnRG4Uy3uU3PGAYATRvzv2rxUfKK9MB/vzTpnO3h3//5eppNPxmMngwf5H/5RfOn6MEmzNmEPkjk3piIwH62QizpueXXplWyTql3Bw4Lb2iUTK2lxlgKymmH364zGOeOaMfd2rsdvfd/N1h0CDuvVWGfRX/gDJ1q1yZx7L27En05JP8Go+Lc//xUEqLrVWtErm53FXnZutBOVtKCjdQK0u0ANzK8eabJbons7L03+YAosWLKTdX/x5Z5mygcrp4UZ+wz2yjSny81S07ziTBiwuDly++4P+Nvn2NPKh8m5kzx6Zjm2pNmTCB6Jtv+LVTv37Zb8Ilb35+/A2ySxduYBk6lI/14ov8JWDOHD6vr7/mbOzFWzA0KKTfEUXJqEM6lFh0RZk/nZ/PLS+NGpVzWojtlIBrx46yV79NSSFKi36JCKB+wbsIIBpRdyMVVqvOqbaLD+L8+GMeADd3Lp9ztWqGjwuTlC+BzZqVsbigTsddmQkJFh23oIDolVdMt5aYu3l4cNr5hx/mYyxaxF1b589zNezdPeEMKSmGeWBefNH9Ay53YW5cVsuWJbI+6HQ8tvD++4ny8oqGadWq5Zy3hIkT+fm6djVTSKfjN/fFiy0YqOh81nx+a4iIUIFkZGQgMDAQWq0WAQEBTn/+l18GPvgAGD8emDPHSIGcHP7fr1TJ6mOnpgITJgA//ghUqwbccw+wbx9w7pxhOS8voFMnoEcPvs2eDUyfDpw6BZw+zTfl93PnuDq2qlIF2LKFfw8LykNYQTIf+MwZoHp14KGHbD+4Hc2cCcyaVXr7jBn8WCmbNgH33ovN6I4e2IzKyMJ51EG16h7A1q1Aq1aG5QsLgQYNgPPngVWr3Oa83dXFi0CjRkB2NrB2LdC/v32Pn5oK/PEH8OmnwNGjfLkCA4GwMKB+fSAoCKhRg38SAQUF/Pj06cDcuXyMsDC+lTxuamrp5zNW1l0odc7MBJ5/HvjmG8DDw73r7C5KXu/CQuCXX4D584GrV3lb//7Ahx8Ct912sxARCBq0bw/88w/wdt8dmLrqTsDHx/yTvf8+UKcOMHAgULmy1XVNSeHXVF4evyfffbfVh3A5qz6/HR5KOZmrW14GDuTo99NPHXN8ZcB4yW+LHTvyVMENG3gAY/E+4G7dTH+rzc3lLpM//+RgvFkzoilTuDVn9GgO0gcP5vN64AHOcdS5M0/KaNOGB/6pgdV94rm5RI0bkw6gVn7HCCD68IWT5rOMvsStNRQd7ZBzqEhefJH/VHfeWUYffZkLtxhnTQuJGltTrFHRz88Vrlzh90hlqr63N3eDKh87yvCByt65dBnV+Y3yyBHTB8zI0Pdd7tljc72UkQkqGapUitt1G33++efUsGFD8vX1pfDwcNq6davZ8ps3b6bw8HDy9fWlRo0a0XwLp4wRuT54adWK/3kctQxIRgZ37wQEcF/2unXG00fYOmDXXKBja3nVun6dKD2dFizgv13jxmXMMDx2jEdVq2B2hiulpOin65p9nSQk8AjcSZOsnnZsTbDqiMGe7qSin58rHT6sH9gPEAUH85DGyMib32PuOk75gTX0g6YWLjT+v6ws0tS8ebmm2J86pQ+odu82UzA9nbu6bRzC4ChuFbwsX76cvL29adGiRZSUlETjx48nf39/OnPmjNHyJ0+epMqVK9P48eMpKSmJFi1aRN7e3vTzzz9b9HyuDF4KC/XBs9Ekhg8/zCkWjS3AYaX+/c0/bs0blrXfzG61b3LXr+tnaaxd67zntelD58IFHpnsxgnGlBUYunQpo5qjR3NBN50ZIYRi/frSY/eVW22coxONio34feih0p8ByhoYdlhEVxk3XCoJYXHr1umjLTf6suVWwUvHjh1pdIm1fJo3b05TpkwxWv6VV16h5s2bG2wbNWoUde7c2Wj5nJwc0mq1Rbfk5GSXBS/nzvH/g6enkURZOTn6f14bB606qsXD2g/JW/GbnNIjZHFzrB2CB6uDxKws/RIMe/eW+/kdITlZnwfJ7MSzzEx9/owy0zcL4Xq5uZzQTsnrEhxc7L3xXCEvN6AkAKxdm2jnTt7xzBn9i9vEl3prHD3KMyoBMxl98/P16S9WrSr3c9qL2wQvubm55OnpSatK/HHGjRtHd999t9F9unXrRuPGjTPYtmrVKvLy8qI8I6kzZ8yYQQBK3ewevOh0nAmrbdvS61vcpCTgbNLEyIPnz+sHqNi4jtCt1uLhToqn+E5KMlOwsJCTQzRpUu5ppufP65ufi9/at+eksKX+DZW04oBh8LRgATcZucGqwEpjSvfuZcR3Cxdywdtvd+tWJCGKS0nhoHzGDH7tlvpiFx/PAwurVtVnUnz75lIFFq1ua1kdlHUd77/fzJfLV17hQmU14zuR2wQv58+fJwC0Y8cOg+2zZ8+m22+/3eg+t912G80u0XS2Y8cOAkApRr7aO7XlRRnQ8uuvRh9WMvD36mXkwQMH9OG4jW7FFg93oiyBU2aOQWVF2vffL9fzKYuu+fjwNNcOHfQBlHK76y6iTz4hStyeSXnVgokAOj19sf5/43iWPv1mrVr8hlUyV42TnDql/+JZ5jpEERFc8IMPnFE1IezCoi+YWVn6VhedTp8G+csvnVcHIn4fULoKLFxg0tGsCV48HDHdqSSNRlNyhlOpbWWVN7YdAHx9fREQEGBwc5g2bfjngQNGHz5+nH82bWrkwcuX+WdQkM1PHxYGhIeXvsl0R+cYN45/fvMNoNWaKfj44/xz6VKbn+vkSWDaNP49L49nn+/bB+h0QO/eQLdugEYD7NjB9Wrf1R9R137ETLyOxm8OR0QEEBEBfLswBxg5EqhZE0hLA957D2jRAujSBVi8GMjIsLmO1nrrLSA/H7jvvjKmce7bB8TH89TS4cOdVj8hymvUKP7XLXkbNapYocqVgchI/j0/H5g4EfD3B/73P7vWoUcPvt+4MU+dNqgDADRvDnTuzPO/v/vOLs/tTF6OPHhwcDA8PT2RlpZmsP3ixYsIDQ01uk+tWrWMlvfy8kJQOT747aJ1a/558KDRhx0dvAjX6tEDuOMO4NAh4KuvOOeOUY8+CrzwApCYyIXvuMOq59HpgCef5Pe18HBgwQLOy6FQ8nOcPw/89BOwYmkB4vZ5YTN6YDP4HcvPD6hXD9h2qAautvoATd98B7dd2oWmW79Erb++h+euXcCuXUiNO4PU598EANTLTELN7auBhg31t7Awwye30fHjwNdf8+9vvFFG4QUL+OcjjwDBweV+biGcxercOT4+nDBm8WLAzl+8J0/mLzcnT3L+oh9+MFLoqaeAuDh+Q3vpJf5GpBIODV58fHwQERGB2NhYPFQsaVdsbCwGDBhgdJ/IyEisW7fOYNvGjRvRoUMHeHt7O7K6ZXNxy4twLY2GY5LRo4HPPuMWD6Of60FB3Dyybh23vrz9tlXP88knnAfP35+Dk8aNjZerU4cDqAmXZ+HMvm/xQ9AL+D7kRSQd1iAnBzh2jG/r1wP8Uu8GoBu8vb5EoC4d7ZCIvUs6QLuEjze2bTq6/fMPqmI7qiKTb145qBQWCM/QIFwZNQ3Z4V0BAJUPxqHu7lWoEuABeHqWvv3vf8DttxfV9c03+Qte7976L50mTZnCf0MT7xFCVCiTJ9v1cAsWlE7IuXUrMGQI/zQwaBBnVm3WjLMYuiCxq80c3YelTJVesmQJJSUl0YQJE8jf359Onz5NRERTpkyh6GJJvZSp0hMnTqSkpCRasmSJ+0yVTk7W9xGWyPes0+lHmRsdUqAMYBgxwv71Ek5z/TqvAGBm6BNbvpwLNWhg1QDtw4f1OVC++MLCnd57j3J8qtJArCrVz92rF6fhf+ABXqDPlpT5ys0DBUW/j8Y884XXrDE4J2WsjptOghKiwig5NlJZSRzgt6VS3GiZALdbHmDevHl47733kJqailatWuHjjz/G3Tc7vUeMGIHTp09j8+bNReW3bNmCiRMn4tChQ6hduzYmT56M0aNHW/RcDl0egIhzil+7BiQkAO3aFT104QJQqxZ/O79xA/D1NbJ/Tg73BVStat96Cad66SVOBx4VxSnojcrOBkJDgevXgW3bgK5dyzxuQQFw113Anj187A0bLG/FTTt8FSnZ1UrtULIZOz8f2LsX2L8fSE7mhqFOnYCsLG4ZycvjL2B8I2RmELJvGDYv1awJvDn0MB7PWYIqlQp5x5K38eOBtm0BAEN7pGLZ5jA8+CCwZo1l5yOEsB9l2ZpKlYDt27k72h3J8gCOzPPSowdPddu82WDz9u36L9qiYjtxQp9HwezEnfHjiUaOtHh2z+zZfMzAQG7kcxRrp9wnJ/O/+xdfGK6+/OijZacsOrjqP9KgkHNOfBlvvnBcHK9DERtry2kJIUwoKODWV4Cobl2itDQjhY4ds3jxU0dxm6nSruDw4MVEF8DXX/M/Rs+ejnla4V4efJCv95gx9jleYqJ+GvG331q405w5vKytA1PnE5lfWTcszKCHqJT/RWUQQPQIfuQ+q/feM11fJTXo8OFWnY8QomxXr/L3boCzWxuMfFiyhB9o2dI5S2CbIMGLI4MXE157ja/9qFEmCrz0Ei/Y988/Tq2XcIzYWL7eVaoYX1vKGrm5vMglwA0PFsUip0/ro51yLORmCWPBzldfcR4+JYh5/PHSGc8TEvgxjUZH//Z9RV94wAB+Jy3u6lV9s46SA0MIYVdHj3LLLsA5V4veay5dIgoJ4QdMZL93BglenBG8lPiEGTy4jLxkSsi7aZNj6yWcQqcjatGCL6nZtc10Ov4wXrLEZJFp0/T5C4025xrzzDO80733WlVve8rO5px3ymDc0FDDTONK69SQIcR/hy++0K8N0KgRR0GKTz7h7a1bS0ZdIRxowwb9a3bu3GIPrFp1c2S+B9GuXS6pmwQvjgxeCgo4g2rVqrwI3k0dOvB1X73axH7BnP2UDhxwTL2E0827OeGmaVMzE4qU5gc/PyIj/5Nxcfo3kp9+svCJjx3TTxsqkb3aFeLiuLW5+AynTz/Vvw+uXFmsS2rfPk4XDHBzJREHK3fcwds++8xl5yHErUJZScTTs8QQs8cf5weaNeNvJ04mwYujW16aNuULfHNlOZ1O3xR38KCR8oWF+k+o8+cdVy/hVJmZ+uu+fr2JQjqdvtXt668NHsrO1j80dKgVT/zEE7xTnz62Vt3ubtwgmjpVP5DZ7GDgK1eIXn9dv9aSMtq9cuXy98EJIcqk0xENG8Yvu+rV+fsQERFdvswD2QCiSZOcXi+3Wx6gwimRrO7KFX26eKMJxbRaTpsKSJK6CqRKFU5QCQCffmqikEYDPPEE/15iuYBXXwWOHuWpzCb3L+nQIf1xykxV6zx+fpyL77ffgCZN9Nt/+cVIevTq1TmLltfNHJm7d/PPIUOAwEBnVVmIW5ZGw8nsOnXiBL8PPnhzpZAaNYCFC7nQxx9zShA3JcGLLUosE6Bk1q1Th5etKEXJruvvbyIBjFCrMWP4jWDDBg5EjBo6lH/+9Rcu/JOG/fv5/WHOHN48ZQqQm2vhE86YwY0ZDz/Mixe5mbZtge+/B6ZO5VUR6tWzYKdLl/h1MXasw+snhGB+fsCqVZyf7PBhoF+/mzmgavfDxUFjoX3786JcTe5IghdblGh5MbssACBLA1RglSvr88/NmMHJ3/bvB1JT9WXy6jZGWkRfHNI1xzcjtyEiwrAlYvx4/XI+ZXr0UV5Y0Y1aXYpbsIDT/8fEcCORskCk2fN77TVex6BY0kchhOPVrs1BC8C5NDt25Ndr6IpP8XHOc3ZZ18xRHLq2UYWlBC+HDgEFBTh+nP+MJoOXq1f5pwQvFc6CBfyiB4AVK/gGcFeQry/HrZmZAPArP7BXv6+vL7BxI3c/WbyY26BBwGOPue0CaqNGcRN0SWbPz9+fb0IIp3vjDaBBA168EeA1Gtu0KfaavX4dOHPG6gVmHU2CF1s0bsxfubOzgePHcfx4cwBmgpcHHuClAbKynFdH4RSjRgH9+3ODyKlT+u3FW14AQKMhVKcrCPLUolKz+qgZ5oWrVzlwsZqbBi6ADavqCiFcrk8fYOdO4PffgddfB5Ytu/nAoUP6ppkDB9xqaRv3bRNyZx4eQK9efFELCsruNgL4a3aNGk6pnnAujQZ4912gfn1g4kRg5kzgm2/4zeDoUSA9HcjP1+DyP+fx+LSGOJDkhb/+4u4li7pVAB70++mnVgyOEUKIsi1YwO9Bv//O95OTuSt8wQLwmxoRcPo08MorrqxmKU5ZmNGZHLowowk1a/IHVIm1GsUtYObM0svPAzz+ZebM0ttTU0u3ygBltFhs2gTcey/g7Q389x/QsKHtFRZCiGKKvyfFxfEkBAD44QeeAFj0/gNwP/f99zusLtZ8fkvwUk7XrvHMT35uE61q8+bxf8WQIUDv3g6vk3Aem4KRggLuR65WrewnOHOGx7ns3s3vKp99Vp7qCiGEWS+8wG8ztWvzhNoaNYptrFePNzoopYE1n9/SbVQeRDix9woAIDTUTHfgli3Ad9/xt2ZRoYSF8fLyJW8mA5fvv+c59a+9ZvqgSmKUpk25lWX3bp7X+OqrjjgFIYQo8u67wO23AykpxbIXvPMOJ3BKTgYmTXJp/RQSvNjq+nUgNBTHo54DUMZ4F5kqLRTBwcDFizwtKT+f/49++w04cUJf5tQpTgRz4gTg6clzj3/8kb8KCSGEA1WuzN+1PT154O6KFeDZgF99xQP8vvyS37NcTIIXW1WpAvj44Dg4apHgRVjkvvuAkBAeJBURwX2OfftyB7OiRw9O/rJuHadv3rmTpzQJIYQTdOyob+h97jluhUG3bsCECcDAgW6RIFOCl/Jo00aCF2EdLy9g8GD+/eBBHv/SsKFhauagIE6/268f4KRB50IIUdz06dwFfvUq8PTTPOkI773HaXlDQ11dPcnzUi5t2uD47xK8CCvNmMHBSuPGQM+eJhbEEkII1/H25u6j8HBe/mThQmDUKPcJGaTlpTwsaXnJyeFkdoAEL4LVqMH580eOlMBFCOG2WrbktyqAx+kqOc3cgQQv5XC9SVukgaeVNGlsYsb5FZ6NBE9PWTFXCCGEqowfD9xzD38HHz4cKCx0dY2YBC/lcMLzdgBAENJRPfOs8UK1a3Pry7lzbp3WXQghhCjJwwP4+mtOBbJzJ/D++66uEZPgpRxOnPUGADStdd386pu+vrzuuBBCCKEyDRoAn3zCv7/+OvDPP66tDyDBS7kUrWnUsyFnHhRCCCEqoOHDeUm//HzgkUc4afz+/XwzlmXc0SR4KQeLFmT8/XcgOhpYssQpdRJCCCHsTaMBWrfm348f59yZFi8s6wASvJRDUfBSO5tTuhuTkMAp4XfscF7FhBBCCDubNAn48EP9/ZUr9auZOJv7TNpWoaLgZdS9gMdeICuL16ApTnK8CCGEqCDuuQd4/HHuLnLlAvfS8mKjGzd4jSoAaFr9CqDTAUlJpQtK8CKEEKICWLCAu4mWLgUOH3Ztt5G0vNjo1Cn+GRgIBLWtC2w+Bhw4wOkIi5PgRQghRAUwahTw4IOlt4eFOb8uErzYqPhgXU2b1sDmTbxWTUkSvAghhKgAwsJcE6gYI91GNjKYadSmDd85cKB0QQlehBBCCLuS4MVGFgcvyvIAErwIIYQQduHQ4OXq1auIjo5GYGAgAgMDER0djWvXrpndZ9WqVejVqxeCg4Oh0WiQmJjoyCrazCB4ueMOngR/8SJw4YJhwfPnOYNPs2ZOr6MQQghRETk0eBk6dCgSExOxYcMGbNiwAYmJiYiOjja7T1ZWFu666y688847jqxauRkEL5UrA1OnAp99Bvj4GBb08eGlAby9nV5HIYQQoiLSEJGJ5ZDL5/Dhw2jZsiXi4uLQqVMnAEBcXBwiIyNx5MgRNCujJeL06dNo1KgREhIS0K5dO5PlcnNzkZubW3Q/IyMD9erVg1arRUBAgF3OpaS8PKBSJZ4dnZoqyxYJIYQQ5ZWRkYHAwECLPr8d1vKya9cuBAYGFgUuANC5c2cEBgZi586ddnuemJiYom6pwMBA1HPCGkOnT3Pg4u8PhIaaKXjkCPDEE8Cbbzq8TkIIIcStwmHBS1paGkJCQkptDwkJQVpamt2eZ+rUqdBqtUW3ZCVznAMZTJPW3NyYn88pB3/5RV/wxAnO5lN8mxBCCCHKxergZebMmdBoNGZv+/btAwBoij7Z9YjI6HZb+fr6IiAgwODmaErw0qRJsY3JyZxqcNAgoKCAt8k0aSGEEMLurE5SN3bsWAwePNhsmYYNG+LAgQO4UHLmDYBLly4h1Gxfi/szupp0w4ZAlSrA9evAf/8BLVtK8CKEEEI4gNXBS3BwMIKDg8ssFxkZCa1Wiz179qBjx44AgN27d0Or1aJLly7W19SNGA1ePDx4vfBduzjfiwQvQgghhEM4bMxLixYt8MADD2DkyJGIi4tDXFwcRo4ciX79+hnMNGrevDlWr15ddP/KlStITExE0s1FDo8ePYrExES7jpMpL6PBC8DBC6BfJkCCFyGEEMLuHJrnZenSpWjdujWioqIQFRWFNm3a4LvvvjMoc/ToUWi12qL7a9euRfv27dG3b18AwODBg9G+fXt88cUXjqyqxQoK9IsylgpeSmbaleBFCCGEsDuHLsxYo0YNfP/992bLlEwzM2LECIwYMcKBtSqfs2c5gPH1BerUKfFgyeBFlgYQQggh7E5WlbZS8ZlGHiXbrVq14p9nzwJaLfDbbxzAVKni1DoKIYQQFZkEL1YyOd4FAKpX5yUCmjThphllaQAhhBBC2I0EL1YyG7wAwJgxTquLEEIIcSty6IDdiqjM4EWRk8NLA4wfz4shCSGEEMIupOXFSmUGL9euARs3Av/+y0sDeHoCc+Y4qXZCCCFExSfBixUKC3m5IsBM8HL+PC8RoKhRo9gCSEIIIYQoL+k2ssL589wD5O0NmFy8+vbbeaCuQqZJCyGEEHYlwYsVlC6jRo0AL1NtVt7evDSAQoIXIYQQwq4keLFAaiqwfz/w9998v2ZNvp+aamIHJVkdIMGLEEIIYWcSvFhgwQIgIgKYPZvv79jB9xcsMLGDBC9CCCGEw0jwYoFRo4D4eKBHD77/8st8f9QoEztI8CKEEEI4jMw2skBYGN/S0/l+jx5AeLiZHZTVpQHO8yKEEEIIu5GWFwsRWZGgLjQUWLOGd6hd2+F1E0IIIW4l0vJioQsXgBs3OGVLgwZlFNZogAcfdEq9hBBCiFuNtLxYqFYtICsL6N7dMI2LEEIIIZxLghcrVK4MVK3q6loIIYQQtzbpNrJAaqo+p8u1a5zjBdAP5BVCCCGE80jLiwWUPC8REcC2bfrfTeZ5EUIIIYTDSMuLBUaNMj7+VlpdhBBCCOeT4MUC0j0khBBCuA/pNhJCCCGEqkjwIoQQQghVkeBFCCGEEKoiwYsQQgghVEWCFyGEEEKoigQvQgghhFAVCV6EEEIIoSoSvAghhBBCVSR4EUIIIYSqSPAihBBCCFWR4EUIIYQQquLQ4OXq1auIjo5GYGAgAgMDER0djWvXrpksn5+fj8mTJ6N169bw9/dH7dq1MWzYMKSkpDiymkIIIYRQEYcGL0OHDkViYiI2bNiADRs2IDExEdHR0SbLZ2dnY//+/Zg+fTr279+PVatW4b///sODxpZ0FkIIIcQtSUNE5IgDHz58GC1btkRcXBw6deoEAIiLi0NkZCSOHDmCZs2aWXScvXv3omPHjjhz5gzq169f6vHc3Fzk5uYW3ddqtahfvz6Sk5MREBBgn5MRQgghhENlZGSgXr16uHbtGgIDA82W9XJUJXbt2oXAwMCiwAUAOnfujMDAQOzcudPi4EWr1UKj0aBatWpGH4+JicGsWbNKba9Xr55N9RZCCCGE62RmZroueElLS0NISEip7SEhIUhLS7PoGDk5OZgyZQqGDh1qshVl6tSpmDRpUtF9nU6HK1euICgoCBqNxrbKm6BEhRW1Vaeinx9Q8c9Rzk/9Kvo5VvTzAyr+OTrq/IgImZmZqF27dpllrQ5eZs6cabSlo7i9e/cCgNHggYgsCiry8/MxePBg6HQ6zJs3z2Q5X19f+Pr6Gmwz1UpjLwEBARXyH1JR0c8PqPjnKOenfhX9HCv6+QEV/xwdcX5ltbgorA5exo4di8GDB5st07BhQxw4cAAXLlwo9dilS5cQGhpqdv/8/Hw89thjOHXqFP7+++8KffGFEEIIYR2rg5fg4GAEBweXWS4yMhJarRZ79uxBx44dAQC7d++GVqtFly5dTO6nBC7Hjh3Dpk2bEBQUZG0VhRBCCFGBOWyqdIsWLfDAAw9g5MiRiIuLQ1xcHEaOHIl+/foZDNZt3rw5Vq9eDQAoKCjAI488gn379mHp0qUoLCxEWloa0tLSkJeX56iqWszX1xczZswo1U1VUVT08wMq/jnK+alfRT/Hin5+QMU/R3c4P4dNlQaAK1euYNy4cVi7di0A4MEHH8Rnn31mMCZFo9Hgq6++wogRI3D69Gk0atTI6LE2bdqEe+65x1FVFUIIIYRKODR4EUIIIYSwN1nbSAghhBCqIsGLEEIIIVRFghchhBBCqIoEL0IIIYRQFQleSpg3bx4aNWoEPz8/REREYNu2bWbLb9myBREREfDz80Pjxo3xxRdfOKmmtrHm/DZv3gyNRlPqduTIESfW2HJbt25F//79Ubt2bWg0Gvzyyy9l7qO262ftOarpGsbExODOO+9E1apVERISgoEDB+Lo0aNl7qema2jLOarpGs6fPx9t2rQpyrwaGRmJ33//3ew+arp+gPXnqKbrZ0xMTAw0Gg0mTJhgtpyzr6MEL8WsWLECEyZMwLRp05CQkIBu3bqhd+/eOHv2rNHyp06dQp8+fdCtWzckJCTg1Vdfxbhx47By5Uon19wy1p6f4ujRo0hNTS263XbbbU6qsXWysrLQtm1bfPbZZxaVV9v1A6w/R4UaruGWLVswZswYxMXFITY2FgUFBYiKikJWVpbJfdR2DW05R4UarmHdunXxzjvvYN++fdi3bx/uvfdeDBgwAIcOHTJaXm3XD7D+HBVquH4l7d27FwsXLkSbNm3MlnPJdSRRpGPHjjR69GiDbc2bN6cpU6YYLf/KK69Q8+bNDbaNGjWKOnfu7LA6loe157dp0yYCQFevXnVC7ewLAK1evdpsGbVdv5IsOUc1X8OLFy8SANqyZYvJMmq/hpaco5qvIRFR9erVafHixUYfU/v1U5g7R7Vev8zMTLrtttsoNjaWunfvTuPHjzdZ1hXXUVpebsrLy0N8fDyioqIMtkdFRWHnzp1G99m1a1ep8r169cK+ffuQn5/vsLrawpbzU7Rv3x5hYWHo2bMnNm3a5MhqOpWarl95qfEaarVaAECNGjVMllH7NbTkHBVqu4aFhYVYvnw5srKyEBkZabSM2q+fJeeoUNv1GzNmDPr27Yv77ruvzLKuuI4SvNyUnp6OwsLCUotGhoaGIi0tzeg+aWlpRssXFBQgPT3dYXW1hS3nFxYWhoULF2LlypVYtWoVmjVrhp49e2Lr1q3OqLLDqen62Uqt15CIMGnSJHTt2hWtWrUyWU7N19DSc1TbNTx48CCqVKkCX19fjB49GqtXr0bLli2NllXr9bPmHNV2/QBg+fLl2L9/P2JiYiwq74rraPXCjBWdRqMxuE9EpbaVVd7Ydndhzfk1a9bMYB2qyMhIJCcn44MPPsDdd9/t0Ho6i9qun7XUeg3Hjh2LAwcOYPv27WWWVes1tPQc1XYNmzVrhsTERFy7dg0rV67E8OHDsWXLFpMf7mq8ftaco9quX3JyMsaPH4+NGzfCz8/P4v2cfR2l5eWm4OBgeHp6lmqFuHjxYqmIUlGrVi2j5b28vNxuNWxbzs+Yzp0749ixY/aunkuo6frZk7tfwxdeeAFr167Fpk2bULduXbNl1XoNrTlHY9z5Gvr4+KBp06bo0KEDYmJi0LZtW8ydO9doWbVeP2vO0Rh3vn7x8fG4ePEiIiIi4OXlBS8vL2zZsgWffPIJvLy8UFhYWGofV1xHCV5u8vHxQUREBGJjYw22x8bGokuXLkb3iYyMLFV+48aN6NChA7y9vR1WV1vYcn7GJCQkICwszN7Vcwk1XT97ctdrSEQYO3YsVq1ahb///tvkIq3Fqe0a2nKOxrjrNTSGiJCbm2v0MbVdP1PMnaMx7nz9evbsiYMHDyIxMbHo1qFDBzz++ONITEyEp6dnqX1cch0dNhRYhZYvX07e3t60ZMkSSkpKogkTJpC/vz+dPn2aiIimTJlC0dHRReVPnjxJlStXpokTJ1JSUhItWbKEvL296eeff3bVKZhl7fl9/PHHtHr1avrvv//o33//pSlTphAAWrlypatOwazMzExKSEighIQEAkAfffQRJSQk0JkzZ4hI/dePyPpzVNM1fO655ygwMJA2b95MqampRbfs7OyiMmq/hraco5qu4dSpU2nr1q106tQpOnDgAL366qvk4eFBGzduJCL1Xz8i689RTdfPlJKzjdzhOkrwUsLnn39ODRo0IB8fHwoPDzeYwjh8+HDq3r27QfnNmzdT+/btycfHhxo2bEjz5893co2tY835vfvuu9SkSRPy8/Oj6tWrU9euXWn9+vUuqLVllCmJJW/Dhw8noopx/aw9RzVdQ2PnBYC++uqrojJqv4a2nKOaruFTTz1V9P5Ss2ZN6tmzZ9GHOpH6rx+R9eeoputnSsngxR2uo4bo5qgaIYQQQggVkDEvQgghhFAVCV6EEEIIoSoSvAghhBBCVSR4EUIIIYSqSPAihBBCCFWR4EUIIYQQqiLBixBCCCFURYIXIYQQQqiKBC9CCCGEUBUJXoQQQgihKhK8CCGEEEJV/g9qpvaM4j9JJwAAAABJRU5ErkJggg==\n", + "image/png": 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", "text/plain": [ "
" ] @@ -527,14 +541,18 @@ "source": [ "# Plot the estimated errors (and compare to Kalman form)\n", "plt.subplot(2, 1, 1)\n", - "plt.errorbar(T, xhat[0] - xd[0], P[0, 0], fmt='b-', **ebarstyle)\n", - "plt.plot(estim_resp.time, estim_resp.outputs[0] - xd[0], 'r--')\n", + "plt.errorbar(timepts, xhat[0] - xd[0], P[0, 0], fmt='b-', **ebarstyle, label=\"predictor-corrector\")\n", + "plt.plot(estim_resp.time, estim_resp.outputs[0] - xd[0], 'r--', label=\"Kalman filter\")\n", "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", + "plt.ylabel(\"$x$ error [m]\")\n", + "plt.legend()\n", "\n", "plt.subplot(2, 1, 2)\n", - "plt.errorbar(T, xhat[1] - xd[1], P[1, 1], fmt='b-', **ebarstyle)\n", + "plt.errorbar(timepts, xhat[1] - xd[1], P[1, 1], fmt='b-', **ebarstyle)\n", "plt.plot(estim_resp.time, estim_resp.outputs[1] - xd[1], 'r--')\n", - "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2]);" + "lims = plt.axis(); plt.axis([lims[0], lims[1], -0.2, 0.2])\n", + "plt.ylabel(\"$y$ error [m]\")\n", + "plt.xlabel(\"Time $t$ [sec]\");" ] }, { @@ -544,14 +562,6 @@ "source": [ "Note that the estimates are not the same! It turns out that to get the correspondence of the two formulations, we need to define $\\hat{x}_\\text{KF}(k) = \\hat{x}_\\text{PC}(k|k-1)$ (see commented out code above)." ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0796fc56", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -570,7 +580,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.13.1" } }, "nbformat": 4, diff --git a/examples/kincar.py b/examples/kincar.py new file mode 100644 index 000000000..ab026cba6 --- /dev/null +++ b/examples/kincar.py @@ -0,0 +1,112 @@ +# kincar.py - planar vehicle model (with flatness) +# RMM, 16 Jan 2022 + +import numpy as np +import matplotlib.pyplot as plt +import control.flatsys as fs + +# +# Vehicle dynamics (bicycle model) +# + +# Function to take states, inputs and return the flat flag +def _kincar_flat_forward(x, u, params={}): + # Get the parameter values + b = params.get('wheelbase', 3.) + #! TODO: add dir processing + + # Create a list of arrays to store the flat output and its derivatives + zflag = [np.zeros(3), np.zeros(3)] + + # Flat output is the x, y position of the rear wheels + zflag[0][0] = x[0] + zflag[1][0] = x[1] + + # First derivatives of the flat output + zflag[0][1] = u[0] * np.cos(x[2]) # dx/dt + zflag[1][1] = u[0] * np.sin(x[2]) # dy/dt + + # First derivative of the angle + thdot = (u[0]/b) * np.tan(u[1]) + + # Second derivatives of the flat output (setting vdot = 0) + zflag[0][2] = -u[0] * thdot * np.sin(x[2]) + zflag[1][2] = u[0] * thdot * np.cos(x[2]) + + return zflag + +# Function to take the flat flag and return states, inputs +def _kincar_flat_reverse(zflag, params={}): + # Get the parameter values + b = params.get('wheelbase', 3.) + dir = params.get('dir', 'f') + + # Create a vector to store the state and inputs + x = np.zeros(3) + u = np.zeros(2) + + # Given the flat variables, solve for the state + x[0] = zflag[0][0] # x position + x[1] = zflag[1][0] # y position + if dir == 'f': + x[2] = np.arctan2(zflag[1][1], zflag[0][1]) # tan(theta) = ydot/xdot + elif dir == 'r': + # Angle is flipped by 180 degrees (since v < 0) + x[2] = np.arctan2(-zflag[1][1], -zflag[0][1]) + else: + raise ValueError("unknown direction:", dir) + + # And next solve for the inputs + u[0] = zflag[0][1] * np.cos(x[2]) + zflag[1][1] * np.sin(x[2]) + thdot_v = zflag[1][2] * np.cos(x[2]) - zflag[0][2] * np.sin(x[2]) + u[1] = np.arctan2(thdot_v, u[0]**2 / b) + + return x, u + +# Function to compute the RHS of the system dynamics +def _kincar_update(t, x, u, params): + b = params.get('wheelbase', 3.) # get parameter values + #! TODO: add dir processing + dx = np.array([ + np.cos(x[2]) * u[0], + np.sin(x[2]) * u[0], + (u[0]/b) * np.tan(u[1]) + ]) + return dx + +def _kincar_output(t, x, u, params): + return x # return x, y, theta (full state) + +# Create differentially flat input/output system +kincar = fs.FlatSystem( + _kincar_flat_forward, _kincar_flat_reverse, name="kincar", + updfcn=_kincar_update, outfcn=_kincar_output, + inputs=('v', 'delta'), outputs=('x', 'y', 'theta'), + states=('x', 'y', 'theta')) + +# +# Utility function to plot lane change maneuver +# + +def plot_lanechange(t, y, u, figure=None, yf=None): + # Plot the xy trajectory + plt.subplot(3, 1, 1, label='xy') + plt.plot(y[0], y[1]) + plt.xlabel("x [m]") + plt.ylabel("y [m]") + if yf is not None: + plt.plot(yf[0], yf[1], 'ro') + + # Plot the inputs as a function of time + plt.subplot(3, 1, 2, label='v') + plt.plot(t, u[0]) + plt.xlabel("Time $t$ [sec]") + plt.ylabel("$v$ [m/s]") + + plt.subplot(3, 1, 3, label='delta') + plt.plot(t, u[1]) + plt.xlabel("Time $t$ [sec]") + plt.ylabel("$\\delta$ [rad]") + + plt.suptitle("Lane change maneuver") + plt.tight_layout() diff --git a/examples/markov.py b/examples/markov.py new file mode 100644 index 000000000..5444e4cff --- /dev/null +++ b/examples/markov.py @@ -0,0 +1,108 @@ +# markov.py +# Johannes Kaisinger, 4 July 2024 +# +# Demonstrate estimation of markov parameters. +# SISO, SIMO, MISO, MIMO case + +import numpy as np +import matplotlib.pyplot as plt +import os + +import control as ct + +def create_impulse_response(H, time, transpose, dt): + """Helper function to use TimeResponseData type for plotting""" + + H = np.array(H, ndmin=3) + + if transpose: + H = np.transpose(H) + + q, p, m = H.shape + inputs = np.zeros((p,p,m)) + + issiso = True if (q == 1 and p == 1) else False + + input_labels = [] + trace_labels, trace_types = [], [] + for i in range(p): + inputs[i,i,0] = 1/dt # unit area impulse + input_labels.append(f"u{[i]}") + trace_labels.append(f"From u{[i]}") + trace_types.append('impulse') + + output_labels = [] + for i in range(q): + output_labels.append(f"y{[i]}") + + return ct.TimeResponseData(time=time[:m], + outputs=H, + output_labels=output_labels, + inputs=inputs, + input_labels=input_labels, + trace_labels=trace_labels, + trace_types=trace_types, + sysname="H_est", + transpose=transpose, + plot_inputs=False, + issiso=issiso) + +# set up a mass spring damper system (2dof, MIMO case) +# Mechanical Vibrations: Theory and Application, SI Edition, 1st ed. +# Figure 6.5 / Example 6.7 +# m q_dd + c q_d + k q = f +m1, k1, c1 = 1., 4., 1. +m2, k2, c2 = 2., 2., 1. +k3, c3 = 6., 2. + +A = np.array([ + [0., 0., 1., 0.], + [0., 0., 0., 1.], + [-(k1+k2)/m1, (k2)/m1, -(c1+c2)/m1, c2/m1], + [(k2)/m2, -(k2+k3)/m2, c2/m2, -(c2+c3)/m2] +]) +B = np.array([[0.,0.],[0.,0.],[1/m1,0.],[0.,1/m2]]) +C = np.array([[1.0, 0.0, 0.0, 0.0],[0.0, 1.0, 0.0, 0.0]]) +D = np.zeros((2,2)) + + +xixo_list = ["SISO","SIMO","MISO","MIMO"] +xixo = xixo_list[3] # choose a system for estimation +match xixo: + case "SISO": + sys = ct.StateSpace(A, B[:,0], C[0,:], D[0,0]) + case "SIMO": + sys = ct.StateSpace(A, B[:,:1], C, D[:,:1]) + case "MISO": + sys = ct.StateSpace(A, B, C[:1,:], D[:1,:]) + case "MIMO": + sys = ct.StateSpace(A, B, C, D) + +dt = 0.25 +sysd = sys.sample(dt, method='zoh') +sysd.name = "H_true" + + # random forcing input +t = np.arange(0,100,dt) +u = np.random.randn(sysd.B.shape[-1], len(t)) + +response = ct.forced_response(sysd, U=u) +response.plot() +plt.show() + +m = 50 +ir_true = ct.impulse_response(sysd, T=dt*m) + +H_est = ct.markov(response, m, dt=dt) +# Helper function for plotting only +ir_est = create_impulse_response(H_est, + ir_true.time, + ir_true.transpose, + dt) + +ir_true.plot(title=xixo) +ir_est.plot(color='orange',linestyle='dashed') +plt.show() + +if 'PYCONTROL_TEST_EXAMPLES' not in os.environ: + plt.show() diff --git a/examples/mhe-pvtol.ipynb b/examples/mhe-pvtol.ipynb index 0886f7172..734cd062b 100644 --- a/examples/mhe-pvtol.ipynb +++ b/examples/mhe-pvtol.ipynb @@ -9,7 +9,7 @@ "\n", "Richard M. Murray, 24 Feb 2023\n", "\n", - "In this notebook we illustrate the implementation of moving horizon estimation (MHE)" + "In this notebook we illustrate the implementation of moving horizon estimation (MHE)." ] }, { @@ -35,8 +35,11 @@ "source": [ "## System Description\n", "\n", - "The dynamics of the system\n", - "with disturbances on the $x$ and $y$ variables is given by\n", + "We consider a planar vertical takeoff and landing (PVTOL) aircraft model:\n", + "\n", + "![PVTOL diagram](https://murray.cds.caltech.edu/images/murray.cds/7/7d/Pvtol-diagram.png)\n", + "\n", + "The dynamics of the system with disturbances on the $x$ and $y$ variables is given by\n", "\n", "$$\n", " \\begin{aligned}\n", @@ -69,20 +72,21 @@ "Inputs (2): ['F1', 'F2']\n", "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "Parameters: ['m', 'J', 'r', 'g', 'c']\n", "\n", - "Update: \n", - "Output: \n", + "Update: \n", + "Output: \n", "\n", - "Forward: \n", - "Reverse: \n", + "Forward: \n", + "Reverse: \n", "\n", ": pvtol_noisy\n", "Inputs (7): ['F1', 'F2', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", "\n", - "Update: \n", - "Output: \n" + "Update: \n", + "Output: \n" ] } ], @@ -90,7 +94,6 @@ "# pvtol = nominal system (no disturbances or noise)\n", "# noisy_pvtol = pvtol w/ process disturbances and sensor noise\n", "from pvtol import pvtol, pvtol_noisy, plot_results\n", - "import pvtol as pvt\n", "\n", "# Find the equiblirum point corresponding to the origin\n", "xe, ue = ct.find_eqpt(\n", @@ -117,7 +120,9 @@ "id": "5771ab93", "metadata": {}, "source": [ - "### Control Design" + "### Control Design\n", + "\n", + "We first synthesize an LQR controller that we will use for trajectory tracking, using a physically motivated weighting:" ] }, { @@ -130,20 +135,56 @@ "name": "stdout", "output_type": "stream", "text": [ - ": sys[4]\n", + ": sys[2]\n", "Inputs (13): ['xd[0]', 'xd[1]', 'xd[2]', 'xd[3]', 'xd[4]', 'xd[5]', 'ud[0]', 'ud[1]', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", "Outputs (8): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'F1', 'F2']\n", "States (6): ['pvtol_noisy_x0', 'pvtol_noisy_x1', 'pvtol_noisy_x2', 'pvtol_noisy_x3', 'pvtol_noisy_x4', 'pvtol_noisy_x5']\n", "\n", - "Update: .updfcn at 0x167b58dc0>\n", - "Output: .outfcn at 0x167b58e50>\n" + "Subsystems (2):\n", + " * ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']>\n", + " * ['F1', 'F2']>\n", + "\n", + "Connections:\n", + " * pvtol_noisy.F1 <- sys[1].F1\n", + " * pvtol_noisy.F2 <- sys[1].F2\n", + " * pvtol_noisy.Dx <- Dx\n", + " * pvtol_noisy.Dy <- Dy\n", + " * pvtol_noisy.Nx <- Nx\n", + " * pvtol_noisy.Ny <- Ny\n", + " * pvtol_noisy.Nth <- Nth\n", + " * sys[1].xd[0] <- xd[0]\n", + " * sys[1].xd[1] <- xd[1]\n", + " * sys[1].xd[2] <- xd[2]\n", + " * sys[1].xd[3] <- xd[3]\n", + " * sys[1].xd[4] <- xd[4]\n", + " * sys[1].xd[5] <- xd[5]\n", + " * sys[1].ud[0] <- ud[0]\n", + " * sys[1].ud[1] <- ud[1]\n", + " * sys[1].x0 <- pvtol_noisy.x0\n", + " * sys[1].x1 <- pvtol_noisy.x1\n", + " * sys[1].x2 <- pvtol_noisy.x2\n", + " * sys[1].x3 <- pvtol_noisy.x3\n", + " * sys[1].x4 <- pvtol_noisy.x4\n", + " * sys[1].x5 <- pvtol_noisy.x5\n", + "\n", + "Outputs:\n", + " * x0 <- pvtol_noisy.x0\n", + " * x1 <- pvtol_noisy.x1\n", + " * x2 <- pvtol_noisy.x2\n", + " * x3 <- pvtol_noisy.x3\n", + " * x4 <- pvtol_noisy.x4\n", + " * x5 <- pvtol_noisy.x5\n", + " * F1 <- sys[1].F1\n", + " * F2 <- sys[1].F2\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "/Users/murray/src/python-control/murrayrm/control/statefbk.py:783: UserWarning: cannot verify system output is system state\n", + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/python-control/murrayrm/control/statefbk.py:788: UserWarning: cannot verify system output is system state\n", " warnings.warn(\"cannot verify system output is system state\")\n" ] } @@ -200,7 +241,7 @@ "outputs": [ { "data": { - 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -222,6 +263,8 @@ "# plt.plot(timepts, V0[0], 'b--', label=\"V[0]\")\n", "plt.plot(timepts, V[0], label=\"V[0]\")\n", "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.xlabel(\"Time $t$ [s]\")\n", + "plt.ylabel(\"Disturbance, sensor noise\")\n", "plt.legend();" ] }, @@ -233,7 +276,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", "text/plain": [ "
" ] @@ -291,7 +334,7 @@ "plt.xlabel('$x$ [m]')\n", "plt.ylabel('$y$ [m]')\n", "plt.axis('equal')\n", - "plt.legend(frameon=False)\n", + "plt.legend()\n", "\n", "plt.figure()\n", "plot_results(timepts, lqr_resp.states, lqr_resp.outputs[6:8]);" @@ -315,7 +358,12 @@ " label=actual_label.format(i=i))\n", " plt.plot(timepts[start:], est_states[i, start:], 'b', \n", " label=estimated_label.format(i=i))\n", + " if i % 3 == 0:\n", + " plt.ylabel(\"State, estimate\")\n", + " if i > 2:\n", + " plt.xlabel(\"Time $t$ [s]\")\n", " plt.legend()\n", + " plt.gcf().align_labels()\n", " plt.tight_layout()\n", " \n", "# Define a function to plot out all of the relevant signals\n", @@ -356,6 +404,7 @@ " plt.ylabel(f'W[{i}]')\n", " plt.xlabel('Time [s]')\n", "\n", + " plt.gcf().align_labels()\n", " plt.tight_layout()" ] }, @@ -364,7 +413,9 @@ "id": "73dd9be3", "metadata": {}, "source": [ - "## State Estimation" + "## State Estimation\n", + "\n", + "We first construct a standard linear estimator (Kalman filter). To do so, we create a new nonlinear system that has limited outputs (the original system had full state output):" ] }, { @@ -375,9 +426,8 @@ "outputs": [], "source": [ "# Create a new system with only x, y, theta as outputs\n", - "# TODO: add this to pvtol.py?\n", - "sys = ct.NonlinearIOSystem(\n", - " pvt._noisy_update, lambda t, x, u, params: x[0:3], name=\"pvtol_noisy\",\n", + "sys = ct.nlsys(\n", + " pvtol_noisy.updfcn, lambda t, x, u, params: x[0:3], name=\"pvtol_noisy\",\n", " states = [f'x{i}' for i in range(6)],\n", " inputs = ['F1', 'F2'] + ['Dx', 'Dy'],\n", " outputs = ['x', 'y', 'theta']\n", @@ -394,15 +444,14 @@ "name": "stdout", "output_type": "stream", "text": [ - ": sys[7]\n", + ": sys[5]\n", "Inputs (5): ['y[0]', 'y[1]', 'y[2]', 'F1', 'F2']\n", "Outputs (6): ['xhat[0]', 'xhat[1]', 'xhat[2]', 'xhat[3]', 'xhat[4]', 'xhat[5]']\n", "States (42): ['xhat[0]', 'xhat[1]', 'xhat[2]', 'xhat[3]', 'xhat[4]', 'xhat[5]', 'P[0,0]', 'P[0,1]', 'P[0,2]', 'P[0,3]', 'P[0,4]', 'P[0,5]', 'P[1,0]', 'P[1,1]', 'P[1,2]', 'P[1,3]', 'P[1,4]', 'P[1,5]', 'P[2,0]', 'P[2,1]', 'P[2,2]', 'P[2,3]', 'P[2,4]', 'P[2,5]', 'P[3,0]', 'P[3,1]', 'P[3,2]', 'P[3,3]', 'P[3,4]', 'P[3,5]', 'P[4,0]', 'P[4,1]', 'P[4,2]', 'P[4,3]', 'P[4,4]', 'P[4,5]', 'P[5,0]', 'P[5,1]', 'P[5,2]', 'P[5,3]', 'P[5,4]', 'P[5,5]']\n", "\n", - "Update: ._estim_update at 0x1685997e0>\n", - "Output: ._estim_output at 0x16859a4d0>\n", - "xe=array([ 0.000000e+00, 0.000000e+00, 0.000000e+00, 0.000000e+00,\n", - " -1.766654e-27, 0.000000e+00]), P0=array([[1., 0., 0., 0., 0., 0.],\n", + "Update: ._estim_update at 0x1533a9bc0>\n", + "Output: ._estim_output at 0x1533a9620>\n", + "xe=array([0., 0., 0., 0., 0., 0.]), P0=array([[1., 0., 0., 0., 0., 0.],\n", " [0., 1., 0., 0., 0., 0.],\n", " [0., 0., 1., 0., 0., 0.],\n", " [0., 0., 0., 1., 0., 0.],\n", @@ -412,7 +461,7 @@ }, { "data": { - "image/png": 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\n", 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aNUybNs2aYQoHo9fDww9DfDw0bw5Lltx4jVchKuP6xK5pUzWW8/x5iI0trG0nbK8inzeapvHoo4/St29fRo8ebbXYJCupgKItdp07q+t7lx5SI6iFQ3Fzc+O9996jT58+hIeH89JLL1HH8C4qRCneegvWrlXDdL7/HqQUorAUY2KXfASWLUOnU92xUI3H2Tmw33//nRUrVvDjjz/SqVMnOnXqxEEr5A3SYlcBxbti1fW/tDCu7FiGV8eOtgtMVMrQoUMZOnSorcMQDuDsWfj3v9X1hQvVLEUhLMWY2E16GHxPwPDhdO/uw48/yjg7R9SjRw/0NlhbXlrsKqBoV2zDhhBYI5N83Dm4vhqvQCFENfDOO2pcXa9eag1YISxF04okdk1qqUKJy5YZW+x27FD7CHEjkthVQNEWO50OIlqqTG9vrPUzcSGEdZw7Bx9/rK6/8YZtYxHOLzu7cIJEnYf6qSv/+x8RnTXc3SEpyezl14STksSuAoomdgCdu6v6ObHxQZCba6OohBCW9O676vTu0QN697Z1NMLZGVrr3Mmj5tvXZlX++Sc19u8gPFzdlO5YURGS2FVA0a5YgIi+/gDs1TrBoUM2iUkIYTlJSfDRR+r6tGkyC1ZYnrEbllR0AH37qjv+9z8pVCxMIoldBZRosbtWy+4g7cnbKQXthHA2s2erbrHISLjjDltHI6oDY3Firq0rNnOmuvz2WyLD0gFpsRMVI4ldBVyf2DVtCrVrXCEPT/7St7FZXEII80tOhvnz1XVprRPWUrTFDj8/VbTu5pshJITudf8G4MAByMqyYZDCIUhiVwHXd8XqdEXG2dXoYaOohBCW8N57kJMDXbtC//62jkZUF8USO8NC8itXwsmTNBzehYYNoaBALW0nRHkksbsBTSvZYgdFChVLT6wQTiMlRa3NCWomrLTWCWspltjVq6duhISAqytAsbInQpRHErsbyMuD/Hx1vWhiZyhUvHfHFfVpIIRweHPmqK6uzp1h4EBbRyOqk1Jb7Azy8ozdsTKBQtyIJHY3YOiGhdJb7A7s18j/6hvrBiUq7OLFi8yYMYPExERbhyLsXFoa/Pe/6rqMrRPWZkzsbmkOd95Z+IPMTAgNJXL+w4AUKrZn9vJ5I4ndDRi6Yb28wK3IAmw33QS+nle4Qg2ObJCkwV4999xz7N69m6eeesrWodjUggUL6NChA76+vvj6+hIZGcmvv/5q67Dsyty56nzv2BFkxTlhbYaOnzpP3A8TJhT+oGZN6NSJcPbh6XqV1FT4+2/bxCjKZy+fN1VK7K4YymQ7MUNiZ5g4YeDiAuEt1A9j98jXJ3u0evVqMjMz+fnnn/H392fZsmW2DslmGjZsyNtvv82ePXvYs2cPffv2ZdiwYfz111+2Ds0uXLwI8+ap69Ja57zmz59PaGgoXl5eREREsHXr1go97vfff8fNzY1OnTpZLDZji12dUn44cSIeXKULsYCMs7NH9vR5Y3Jip9frefPNN2nQoAE1a9bkn3/+AeD1119n8eLFZg/Q1gxdsUW7YQ0irs2M3ZsQLHPQ7dDQoUNZuXIlAEuWLGHUqFE2jsh2hgwZwsCBA2nZsiUtW7bkrbfeombNmuzcudPWodmFDz5QS3O2awfDh9s6GmEJK1asYNKkSUydOpV9+/bRs2dPBgwYQHx8fLmPS09P55FHHuH222+3aHzGOnY1skr2td51FzRtSmSBSkRlZqz9safPG5MTu3//+98sWbKEd999Fw8PD+P97du355NPPjFrcPagtBmxBp1vU3fG0hn277deUEJUQUFBAV9//TVZWVlEGqbaXSc3N5eMjIxim7PKySlsrXv9ddUaL5zPnDlzGDduHOPHj6dNmzbMnTuXRo0asWDBgnIf98QTTzBy5MgyzxVzMbbY9esMsbHFf+jqCk89RShxgFrHWIiymPwWtnTpUhYtWsSoUaNwvTYNG6BDhw4cPXrUrMHZg7K6YqFwAsV+OlGwK7bkDkLYkYMHD1KzZk08PT158sknWblyJWFhYaXuGx0djZ+fn3Fr1KiRlaO1nm++UV2xTZvCvffaOhphCXl5ecTGxtKvX79i9/fr14/t5Uwz/eyzzzh58iRvvPFGhY5T2S9E+fmQrhaXKH1WLMBjjxHkdhGA5JOXS/5ciGtMTuwSEhJo3rx5ifv1ej1Xr1416bm2bNnCkCFDCAkJQafT8eOPP5a7/6ZNm9DpdCU2SyaU5XXFtmwJPh55ZOPDsQ0JFotBmGb58uV4eXmRkFD4Nxk/fjwdOnQg3fDuWQ21atWK/fv3s3PnTp566inGjBnD4cOHS913ypQppKenG7czZ85YOVrrWbhQXT7xhLFkmHAyKSkpFBQUEHxdwhQcHExSUlKpj/n777+ZPHkyy5Ytw63ozLlyVPYLUVqautShpzYXISio5E6BgdTt0w6AC/E5FXpeYXn2+HljcmLXtm3bUgecfvvtt4SHh5v0XFlZWXTs2JEPP/zQpMcdO3aMxMRE49aiRQuTHm+K8rpiXV2hU+tcAPa2ce7xW5qmhhHaYjN1av+DDz5Iq1atiI6OBmDGjBnExMTw66+/4ufnZ4HfjmPw8PCgefPmdOnShejoaDp27Mg8Qx/kdTw9PY0zaA2bM9q/H3buBHd3GDvW1tEIS9NdNytG07QS94EarjBy5EhmzJhBy5YtK/z8lf1CZOiG9ecSrrX9wNOz1P2C3lCzLZOpW+GYHJF83lRNxb6GFPHGG28wevRoEhIS0Ov1/PDDDxw7doylS5fy888/m/RcAwYMYMCAAaaGQFBQEP7+/iY/rjLK64oFiOhdi9//hL35HXjYKhHZRnZ26cmtNWRmgo9PxffX6XS89dZb3HfffYSEhDBv3jy2bt1KgwYNjPv8/PPPvPjii+j1el555RXGjx9vgcjtm6Zp5Obm2joMmzK01t1zT+m9X8I5BAYG4urqWqJ1Ljk5uUQrHsDly5fZs2cP+/bt45lnngFUr5Smabi5ubFu3Tr69u1b4nGenp54lpGUlafc4sRF1G0VAMClSzry8qDIMHen4kyfN2fOnGH06NEkJyfj5ubG66+/zv3332+h6BWTE7shQ4awYsUKZs2ahU6nY9q0aXTu3JmffvqJO4sWVbSg8PBwrly5QlhYGK+99hp9+vQpc9/c3NxiH16mDgIvrysWCsfZXT/WVdjW4MGDCQsLY8aMGaxbt462bdsaf5afn09UVBQbN27E19eXzp07c8899xAQEGDDiC3r1VdfZcCAATRq1IjLly/z9ddfs2nTJtauXWvr0Gzm8mUwVCR48knbxiIsy8PDg4iICNavX8/dd99tvH/9+vUMGzasxP6+vr4cPHiw2H3z589nw4YNfPfdd4SGhpo1PmMNu6LLiZUiIEBN7tHr1WNCQswahqik8j5v3NzcmDt3Lp06dSI5OZnOnTszcOBAfEzJHk1kcmIH0L9/f/rbYHXs+vXrs2jRIiIiIsjNzeWLL77g9ttvZ9OmTdx2222lPiY6OpoZM2ZU+pjldcVC4dJi+/bkoz95BpebzHvC2wtv78LfhS2ObaqYmBiOHj1a6riaXbt20bZtW+M3qoEDBxITE8NDDz1kjnDt0vnz5xk9ejSJiYn4+fnRoUMH1q5da7UvY/Zo2TL1P92qFfTqZetohKVFRUUxevRounTpQmRkJIsWLSI+Pp4nr2X1U6ZMISEhgaVLl+Li4kK7du2KPT4oKAgvL68S95tDRVvsXFIvEOjlRXJ2LS5ccN7Ezpk+b+rXr0/9+vUB9T8UEBBAWlqafSV2zZo1Y/fu3dS5roripUuX6Ny5s7GunSW0atWKVq1aGW9HRkZy5swZZs+eXWZiN2XKFKKiooy3MzIyTJrhd6Ou2Natwcs1j8vZHpxY+H+0/M+E0nd0cDqdac3TtrR3717uv/9+PvroI77++mtef/11vv32W+PPz507V6xbtmHDhsUGvjojZ6wxWRWaVtgN++STUpC4OhgxYgSpqanMnDmTxMRE2rVrx5o1a2jSpAkAiYmJN6xpZynGGnYtA4ovJ3a9vDyCspNIpj3JSXro6Jy1eZzp86aoPXv2oNfrLV5lwOTE7tSpUxQUFJS4Pzc31yYfjt26dePLL78s8+eVHfNgcKOuWDc3aF0nhf3JIfx9tICKD7MVlnDq1CkGDRrE5MmTGT16NGFhYXTt2pXY2FgirjWvaqWMji1tALVwXn/8AQcOqKUCH3nE1tEIa5k4cSITJ04s9WdLliwp97HTp09n+vTp5g+KIi12gyJhXDn18gIDqcsxAC7EZwM2GogmgIp93hikpqbyyCOPWKXeb4UTu9WrVxuvx8TEFJvtUVBQwG+//UbTpk3NGlxF7Nu3z9jMaQk36ooFaFLvCvuT4XS8JAe2lJaWxoABAxg6dCivvvoqABEREQwZMoSpU6cax5M1aNCg2JeQs2fPcsstt9gkZmEbhta6ESPUuCUhbKnc5cSK8vRUtezyIflUDpLY2U5FP29ANXzdfffdTJkyhe7du1s8tgondsOvrbOj0+kYM2ZMsZ+5u7vTtGlT3nvvPZMOnpmZyYkTJ4y34+Li2L9/PwEBATRu3LjYmAeAuXPn0rRpU9q2bUteXh5ffvkl33//Pd9//71JxzUtRnVZVlcsQJMmwJ9w+nzlWwZF1QUEBHDkyJES969atarY7ZtvvplDhw6RkJCAr68va9asYdq0adYKU9hYWhqsWKGuy6QJYQ+MiZ13Dmhe5Y4NqOudCRlw4Wz1ntFuaxX9vNE0jUcffZS+ffsyevRoq8RW4cROr9cDEBoayu7duwkMDKzywffs2VNsRqthLNyYMWNYsmRJiTEPeXl5/Otf/yIhIYEaNWrQtm1bfvnlFwYOHFjlWMpyo65YgCYtVEJ3+lL1rZHmSNzc3Hjvvffo06cPer2el19+ucSYUeG8li6FK1egUyeQhlphD4yJXdQj0GtKYbmFUgTVyoEMSE4qOSRK2J/ff/+dFStW0KFDB+MiDF988QXt27e32DFNHmMXFxdntoP37t271PFOBtePeXj55Zd5+eWXzXb8iqhQV2x7Vbw1PjdYfWJ4eVkhMlEVQ4cOZejQobYOQ1iZTJoQ9ig1VQN0N5wVC1DXPx8S4EKydWITVdOjRw9jw5i1VKrcSVZWFps3byY+Pp68vLxiP3vuuefMEpi9qEhXbOMwlfWdpgmcPQulLLkmhLC9zZvh2DH1RW3kSFtHI4SSeqFIYlfacmJFBAWqJCE5rVIf36IaMPk/Y9++fQwcOJDs7GyysrIICAggJSUFb29vgoKCnC6xq1BXbFP1tT9RF0JezYs4aTFwIRyeobXu4YfL/7ImhLVoGqReVJ8hdfz1an27ctR97iHYDBfcLTdpUDg2k4vgvPDCCwwZMoS0tDRq1KjBzp07OX36NBEREcyePdsSMdqMplWsK7ZuXahRAzRNx5ksmWInhD06fx5++EFdl0kTwl5kZEB+/rXErl75SR1AUFu1TmzyBeesYSeqzuT/jP379/Piiy/i6uqKq6srubm5NGrUiHfffdc45ddZ5OQULghc3rd7nQ4aN1bXT5+2fFxCCNN99hlcvQrdukHHjraORgjFMHHCmyxqhNS+4f51VV5HRgZU86WeRRlMTuzc3d2NxVyDg4ONs1b9/PxsVrXbUgzdsHDjZUaa1FdjDU9vOGnBiKyvvMkt1YX8DhyfXg+LFqnr0lon7ElFlxMz8L/wN24uakbshQuWjMz6qvt7rblev8mJXXh4OHv27AGgT58+TJs2jWXLljFp0iSLTt+1BUM3rI+PWni5PE0KVEIX/80OC0dlHe7XxnlkZ2fbOBLbM/wO3G8w9kXYr40bIS4O/PzggQdsHY0QhQqLE+vKX07sGpfkJAL1akqssyR2rq6uACUmY1Y35vqsMXnyxKxZs7h8rSnrzTffZMyYMTz11FM0b96czz77rErB2JuKzIg1aBLqClvhdIqDLHB3A66urvj7+5OcrN5AvL29q92yW5qmkZ2dTXJyMv7+/sY3H+F4DG9NI0eq8bBC2AtjYtepEYwde+MHBAYSRDJJ1CfZSUqeuLm54e3tzYULF3B3d8flRi0pTsbcnzUmJ3ZdunQxXq9bty5r1qypUgD2rCIzYg0at1Z9tacvO8/kiXr16gEYk7vqyt/f3/i7EI7n0iUwLE5Tkc9NIaypwsuJGQQGUpcDAFxIzKeSVcvsik6no379+sTFxXG6Gg9UN9dnjeP/R1hQRWbEGjTppAa9ns5voB5YkQfZOcPJFhQUxNWrV20djk24u7tLS52D+/prVTe8XTso8r1UCLtgTOxq5oLmceOq2QEBBKG+bCefzgGco26Ph4cHLVq0qLbdseb8rDE5sUtNTWXatGls3LiR5OTkEhWV09LSzBKYPTCpKzZMdcGeoRH60//g0raNBSOzLsMMaCEckaEbduxY6640UVBQUG2/EHl4eFS77rTKMiZ2n74LTw8qdzkxAFxdqet1Ga7AhTNXcJbEDsDFxQUvWbmpykxO7B5++GFOnjzJuHHjCA4OdupxV6Z0xTZoAK7kk4cnSX8mE+JEiZ0Qjuqvv2DXLnBzU0WJrUHTNJKSkrh06ZJ1DmiHXFxcCA0NxcNDyrXfSMoFPeBCIClQwW64oJrZcAWSE/MtG5xwSCYndtu2bWPbtm10rAaFoEzpinVzgwZeacRfCeL0wQxCHrJsbEKIGzO01g0efMOVmszGkNQFBQVVy0lHer2ec+fOkZiYSOPGjavd6zdVatJVwJM6pBUWqbuBun5XIQUunK/e5UFE6UxO7Fq3bk1OTo4lYrE7pnTFAjRp5kr8YYgP7kqk5cISQlTA1avwxRfq+mOPWeeYBQUFxqSuToVHwzufunXrcu7cOfLz86VM0A2kJqvhTHV8r95wOTGDoH89Ak9BsqtM6hIlmTwIYv78+UydOpXNmzeTmppKRkZGsc2ZmNIVC9Cks3ojP50rJ5sQtrZmDSQnq96tAQOsc0zDmDrvG1U0d3KGLtiCggIbR2L/Uq8NS68TWPGWzbrt1WfMhRQZxyhKMrnFzt/fn/T0dPr27Vvsfk3T0Ol0TnUim9IVC7KsmBD25NNP1eXo0WqohDVV9+7H6v76TZGarv456wRX/J/UMKygmleiEmUw+e1u1KhReHh48NVXXzn95AmTu2Lr5QKenP4jEahvqbCEEDeQlAS//KKuS+06Ya9ycyErV3W/1mlQ8dmgdc/sBTqTmanWNJei26Iok9txDx06xGeffcaIESPo3bs3vXr1KrY5E5O7Yn1SADi9Nw2q+Zp3QtjSl19CQQF06wZtZIK6uGb+/PmEhobi5eVFREQEW7duLXPfH374gTvvvJO6devi6+tLZGQkMTExZo3HUOrEVVeA38BbK/w4v/iDuKPqvTnLsmLCfExO7Lp06cKZM2csEYvdMbUr1jjGTmuEdvGSZYISQpRL0wq7Ya01aULYvxUrVjBp0iSmTp3Kvn376NmzJwMGDCA+Pr7U/bds2cKdd97JmjVriI2NpU+fPgwZMoR9+/aZLSZDYhcQ6Ipu7KMVfpyubiB1URmdJHbieiZ3xT777LM8//zzvPTSS7Rv377EjKcOHTqYLThbM7UrtnFL1ZR+GV/SDx/Cv0dtC0UmhCjLrl1w5IjqnhoxwtbRCHsxZ84cxo0bx/jx4wGYO3cuMTExLFiwgOjo6BL7z507t9jtWbNmsWrVKn766SfCw8NLPUZubi65ubnG2zeaUJiiOnkqvpyYwbX1Ys/RQMbZiRJMbrEbMWIER44c4bHHHqNr16506tSJ8PBw46UzMbUr1tsb6rqpKU6n9znPChxCOBJDa91994Gvr21jcSTLly/Hy8uLhIQE433jx4+nQ4cOpKen2zCyqsvLyyM2NpZ+/foVu79fv35s3769Qs+h1+u5fPkyAQFlrwceHR2Nn5+fcWvUqFG5z2losQv0v2ra8J1AabETZTO5xS4uLs4ScdglU7tiARr7pHEhPYDTh7Nw/hLOQtiX7Gy1NizYWTdsVlbZP3N1haLLKJW3r4tL8ZHyZe3r42NafMCDDz7I22+/TXR0NB9++CEzZswgJiaGnTt34ufnZ/Lz2ZOUlBQKCgoIDg4udn9wcDBJSUkVeo733nuPrKwsHnjggTL3mTJlClFRUcbbGRkZ5SZ3xuXEdv4CB5pCp04VioW6dQliBwDJZ/MAWeFDFDI5sWvSpIkl4rBLpnbFAjSpk0lsOpw+KUu9CGFtP/wAGRkQGgq33WbraIoo79vhwIGFU3hB1bLIzi593169YNOmwttNmxb25xVViclbOp2Ot956i/vuu4+QkBDmzZvH1q1badCgAQB33303mzZt4vbbb+e7774z+fntwfVVHAxlum5k+fLlTJ8+nVWrVhFUzhImnp6eeHp6Vjie1GvLidUhFep1q/DjqFWLui6poDesFyuJnShUocRu9erVDBgwAHd3d1avXl3uvkOHDjVLYPbA1K5YgCb18+AfOJ3gapmghDBRdHQ0P/zwA0ePHqVGjRp0796dd955h1atWtk6NLMzLCE2dqxq3BKmGTx4MGFhYcyYMYN169bRtm1b48+ee+45HnvsMT7//HMbRlg5gYGBuLq6lmidS05OLtGKd70VK1Ywbtw4vv32W+644w6zxpV6NgfwMWk5MQB0OoJ8suAyJCdcNWtMwvFVKLEbPnw4SUlJBAUFMXz48DL3c6YCxQUFhV+aTUrsbm0Av8NpU759CWFBmzdv5umnn6Zr167k5+czdepU+vXrx+HDh/GpRJedvTp5EjZsAJ0OxoyxdTTXMTT/l8b1ui+B5Y2Gvz5bPXWq0iGVJiYmhqNHj5babdmnTx82FW0tdCAeHh5ERESwfv167r77buP969evZ9iwYWU+bvny5Tz22GMsX76cQYMGmT2u1HO5gA91fK6U/D+4gbovjYVpcKFAJumJ4iqU2On1+lKvO7OiPSEmdcVGqm6L05fLHmArhDWtXbu22O3PPvuMoKAgYmNjuc2u+iurZtEidXnXXYWrwNgNUxJoS+17A3v37uX+++/no48+4uuvv+b111/n22+/Ndvz21pUVBSjR4+mS5cuREZGsmjRIuLj43nyyScBNT4uISGBpUuXAiqpe+SRR5g3bx7dunUztvbVqFHDbGMOU5PVkJ06/qY3iAR1VEXwky9I07QozuT/iKVLlxabzm2Ql5dnPCGcgaEb1sWl+LjmGzEMQSyjNJIQNmeY4Vje7L7c3FyHWgc6N7ewG/aJJ2wbiyM6deoUgwYNYvLkyYwePZqZM2fy/fffExsba+vQzGbEiBHMnTuXmTNn0qlTJ7Zs2cKaNWuM48YTExOL1bT76KOPyM/P5+mnn6Z+/frG7fnnnzdbTMbJEyasE2tg6LmVWbHieiYndmPHji116vvly5cZ60Rr9xSdEWvKqmmGxO78ebiSbN8fhqL60TSNqKgoevToQbt27crcz9SyDba2cqX6gGvQACzQY+bU0tLSGDBgAEOHDuXVV18FICIigiFDhjB16lQbR2deEydO5NSpU+Tm5pZosV6yZEmxruZNmzahaVqJbcmSJWaLJ/WS6evEGgQdU6tmJCfKRD1RnMmJXVmziM6ePWty8/SWLVsYMmQIISEh6HQ6fvzxxxs+ZvPmzURERODl5UWzZs1YuHChScesqMrMiAWoXRt8dKoEQfzW02aOSoiqeeaZZ/jzzz9Zvnx5uftNmTKF9PR042bvq8189JG6HD8e3Ez/jKzWAgICOHLkCB8ZfonXrFq1qkQ3vjCvlFw1gDuwd9lfsspS96hK7LJz3cqtkCOqnwq/BYaHh6PT6dDpdNx+++24FXn3LCgoIC4ujrvuusukg2dlZdGxY0fGjh3Lvffee8P94+LiGDhwIBMmTODLL7/k999/Z+LEidStW7dCjzdFZWbEgmrda+J1nsM5zTj9ZzotzRuWEJX27LPPsnr1arZs2ULDhg3L3dfUsg22dPSoqgDi4qISO2EZ/fv3Z+/evWRlZdGwYUNWrlxJ165dbR2WwyoogIuZqkxJncfKnsBRllohtfDkCrl4ceGCWYdbCgdX4cTOMBt2//799O/fn5pFMh4PDw+aNm1qcnI1YMAABgwYUOH9Fy5cSOPGjY1LvbRp04Y9e/Ywe/bsMo9t6hIvBpUpTmzQxPcSh3Pg9PGSYxGFsDZN03j22WdZuXIlmzZtIjQ01NYhmZVh0sTgwXCDfFVUQUxMjK1DcCqXLhWWGyxnuGuZDOvFnqURFy6okoZCgAmJ3RtvvAFA06ZNefDBB23ybX7Hjh0lloTp378/ixcv5urVqyXWrQU1VmjGjBkmH6uyXbEATYJy4DycPmV6kVAhzO3pp5/mq6++YtWqVdSqVcs4u8/Pz48aRVcxcEA5OWAY8iSTJoQjMUyc8K2lx91NB5g4geLaerFnaSTrxYpiTB5j17dvXy4UmYaza9cuJk2axCLD12YLSkpKKnVJmPz8fFJKq75O5ccKVbYrFqBJI1USJj6pZKIphLUtWLCA9PR0evfuXWx234oVK2wdWpV99x1cvKgmLfXvb+tohKg444zYy6fg0CHTn0DWixVlMHmY8ciRI3n88ccZPXo0SUlJ3HHHHbRr144vv/ySpKQkpk2bZok4jUpbEqa0+w0qO1aoSl2xzVVCdzqtEs19QpiZVonlpRyFYe7UhAkm13cVwqZSz+cDbmo5seBKLNVZty5BHAQg+byGyS1+wmmZ3GJ36NAhbr75ZgC++eYb2rdvz/bt2/nqq6/MOg28NPXq1St1SRg3Nzfq1Klj1mNVqSu2rcoGT2cFmjEiIURRBw/C9u1qFuxjj9k6GiFMk3pKdQvVIQ0q8/lVpw5BqD7Y5LN55gxNODiTE7urV68aW8D+7//+z7g2bOvWrUlMTDRvdNeJjIxk/fr1xe5bt24dXbp0KXV8XVVUpSu28a2q5tdZGuIkK6wJYXcM1TmGDYP69W0bixCmSj2japTU8cqqXHNzjRrUfW4kABfSPcwZWsWkpcHtt8PUqVBNVqRyFCYndm3btmXhwoVs3bqV9evXG0ucnDt3zuRWs8zMTPbv38/+/fsBVc5k//79xurfU6ZM4ZFHHjHu/+STT3L69GmioqI4cuQIn376KYsXL+Zf//qXqS+jArGpy8okdvVb++HmBvkFLpw7Z964hBCQlQVffKGuX1sRSgiHknJWVU0IrHml0s8R1CkEgOQLNuiG/d//OLAhhaRZi2HUKMiTVkN7YXJi98477/DRRx/Ru3dvHnroITp27AjA6tWrjV20FbVnzx7Cw8MJDw8H1Fp+4eHhxnF61y/xEhoaypo1a9i0aROdOnXizTff5IMPPjB7DTuoWlesqysYCvWflhrFQpjd119DRgbcdBP07WvraIQwnRpjB3X8Kr9yhM2WFcvPZ9O8A4Szj+ac4L9fB1IweFjhB6ewKZMnT/Tu3ZuUlBQyMjKoXbu28f7HH38cb29vk5+rvIHdpY3Z69WrF3v37jXpOJVRla5YgCYBl4mLq8Xp7Wfp0UOKawlhToZu2McfV4WJhXA0qanqs68y68QaBB38P+AOks9cAUxY1Lyq3Nz4T6uP0ba7kEVNnuO/LF+/nU8ixxG28X8QKOPLbalSb4maphEbG8tHH33E5WsZkIeHh8mJnT2rSlcsQJNUlXzGb4ozU0RCCIC9e2H3bnB3BydanlpUM6kuQQDUiWha6eeou2sNABfSXLHm5Pdjx2DN9trodDB9OtTyLmAH3Qk/tJQ33/GQXlkbMzmxO336NO3bt2fYsGE8/fTTxpp27777rkXGutlKVbpiAZrUV//Zp89Ic4IQ5mRorbv33sKuKCEcTaqmlpuoM7xnpZ8jqKGaNHEl392qvaAffKAuBw+GN96Aw8dcGdw7kzw8mTbbly5d1JcvYRsmZx3PP/88Xbp04eLFi8Wq1t9999389ttvZg3OlqrcFRuqfrWnLzh2ZX8h7ElKCixbpq7LpAnLuXjxIjNmzLB4pYPqzFiguAqVunzq+1KDbMB64+wuPv4KSxapiR+TJqn7GjaE1Rtqsny56oU9eBC63aIx68n4sp9IWIzJid22bdt47bXX8PAoPr26SZMmJCQkmC0wW6tqV2zjViqhO53ub56AhBBMm6ZmxHbqBLfdZutonNdzzz3H7t27eeqpp2wdilPSNEhNUSVC6gRUoQ/12rJigHWWFUtJ4ZNPXcjO96T9TVn06VP4I50OHnwQjhyBh/sno9d0TP2oMT9+dtEKgYmiTE7s9Ho9BaUUZzt79iy1KttvaYeq3BXbwQ+A01fqWXXsgxDO6tChwm7Y999XHyTC/FavXk1mZiY///wz/v7+LDM0kQqzyc6G3Dz18VvnwtHKP5GVlxXL/2gx/y1Qyf6kKd6lnoOBgfDF9z5E1VX1iMY+7s6pOPkQtCaTE7s777yTuXPnGm/rdDoyMzN54403GDhwoDljs6mqdsU2ilADY7PxJvVcrpmiEqJ60jR44QVVB/Wee6B3b1tH5LyGDh3KypUrAVWZYNSoUTaOyPmkJqox2J5cwSc0qPJPZM0Wu/x8Vr5/ijM0pm6tHEaOKueblY8P0esiuFm3i0v5NXnw9mSZUGFFJid277//Pps3byYsLIwrV64wcuRImjZtSkJCAu+8844lYrS6q1ch91ouVtnEzqthIPVQ41PiY2WFZiGq4qef4P/+Dzw84D//sXU0QlRNyt+qe7IOqegCat9g73LUrWu9FrvVq5mb+jAATz3rjtcNqqt4dApjxdSD+HORP+KCeXWidMlai8mJXUhICPv37+ell17iiSeeIDw8nLfffpt9+/YRFFSFbx52JCur8Hqle5d1Opo0V+MQZc1YISovNxdefFFdj4qCZs1sG48QVZX6TzoAddzSq1aIsVkzgsaonjJLt9jtems927kVd5d8nnq2YiVwm84Yy2ft5gDw3uLa/LxK1ti0BpMLFAPUqFGDsWPHMtZJi0gZumHd3VULQWU16VyHP07A6fNWLBwphJP58EM4cQKCg+HVV20djfNavnw5Y8eO5eTJkzRo0ACA8ePHs2vXLrZu3Yqfn5+NI6y6+fPn85///IfExETatm3L3Llz6dmz7HIjmzdvJioqir/++ouQkBBefvllnjTDdOzU02oQdx2v7Ko9kacndduqBhWLttgdPMi8vT0AeOjuXOrVq2Dq4OLC8F8m8FyLBXyQ9xRjHs5n/2FX48pMzig5WdXaPHVK9f6Vtrm5qS+ozZurLTjYvGOGK5XYObuqzog1aNJEXcqyYkJUTnIyzJyprs+aVYUWdBvRNDVQ3ha8vU37sHjwwQd5++23iY6O5sMPP2TGjBnExMSwc+dOp0jqVqxYwaRJk5g/fz633norH330EQMGDODw4cM0bty4xP5xcXEMHDiQCRMm8OWXX/L7778zceJE6tatW+VlLA3jruv4VH6dWANDR5klW+wSCurxjUsY6OH5V31Me3Djxrz7SR1+f/0Csafr8uCDsGmTajgxp7Q0OHtWtfDnZuSSm5jGlaRL5DZuTm6BO7VqQbvQLJrc5IbOy7PKx9M0OHNGJXH79sHevRp7dxdw7rzpaZWPVz7NQwto3saD5i10PPAAdO5c+dgksStFVWfEGjSucQGoy+k/koB6VQ1LiGrn9dfVmrCdO8Ojj9o6GtNlZ1f9C2JlZWaCjwmfwTqdjrfeeov77ruPkJAQ5s2bx9atW42td6DqlW7atInbb7+d7777zgJRW86cOXMYN24c48ePB2Du3LnExMSwYMECoqOjS+y/cOFCGjdubJws2KZNG/bs2cPs2bOrntglXQWqtk6sQd3YtcBdXLDgsmLzv61Lvl6VGKpMwuE5+gG+uRXCw2H7dnVev/121WLKz4c//oCYn/JY+10me076oxlHl3kC9a9tRflQiwza+RylfeN02nV0o/3tQYT2boKPvzve3uDlVbx3XNMgMRH++ktthw8XXl66VPS5dYAbOvS05DitOIYXV3CvH4h7/9txd1fJrPsn87mS58I/NOMEzTlNE7KuuHHgiBsHjqhnatdOEjuzq+qMWIMmKbHAXZw+LAsjC2GqAwfgk0/U9blzZU1Yaxg8eDBhYWHMmDGDdevW0bZt22I/f+6553jsscf4/PPPbRRh5eTl5REbG8vkyZOL3d+vXz+2b99e6mN27NhBv379it3Xv39/Fi9ezNWrV3EvpckpNzeX3NzCKggZGRmlPndqTdWdU6d11ZdOCdr8LXAXyectU1IkO7uwzJChIHFlNGsGixfD/ffDO+9AZCQMG2bacyQkwJo1EBOjJlOlpwN4AGoVjyDO48UVPMnFU5eHp7uGV1gongE1uXABjv6Vz2W9LzuyOrLjCHAE+LrkcWrUAO8aerx9dFy+DJculd707eYGbdtC+KmVdE7fQGfXP+l4ixc174yErl3BK1DVf+lY5EGP36q+rZ44Acc3knf0H04dzuZEnCsnOt7LicjRRESY9nspEVfVHu6czNYVG6a+Lp/OrEJpcSGqIU1THyJ6vfogKGcYlF3z9saqSz1df2xTxcTEcPToUQoKCggODi7x8z59+rBp06aqB2dlKSkppb6m4OBgkpKSSn1MUlJSqfvn5+eTkpJC/frXtwZBdHQ0M2bMuGE83UY0IaM23Dys7Q33vZG6weobz4UMDzTNzPUdCwpYdudSUlPH0rSpxtChVXvy++5MZ2LNVczPfIThw+Hmm2HCBFXYuKzP2+xsWLkSlnyq57eNOjStMIbataFf+AX6H/wP/R6oTYNhXaBBA6hfH/z9S/wyrua5cnzjWQ7+epZDu7I4+HcNDqaGkKDVJ4/C7tmcHMjJcSE1Td12JZ/mnCCMw7TlL9q6/03YriW0auOCpyewygVqDIFb375xM3nHa1netTc1D6Al0FKvv9bEX+FfZ5kksSuFubpim4SrbxGpBbXJyjKtW0SI6uzHH9U4HE9PePddW0dTeTqd45z3e/fu5f777+ejjz7i66+/5vXXX+fbb7+1dVhmpbvug17TtBL33Wj/0u43mDJlClFRUcbbGRkZNCplpsBDD6nNHOqGqJbDvHxXMjLAnMMhtU2bmbv9ZgCeezIPV9cqjk3z8+O9EbvIXKzxFSPZtcudXbvghUl6HhrpwoQJ0KWL2nXbNvh8icY3K/RcznLFUMSjGzu4q2sad/13EF26gKtLIPBOhTJadw8dbfs3pG3/hkVepAbx8RSciCPnlt5kZ6v8Kvve0WTvPYInubTUncCzWQNo0wZat4Y2vSEsv3B2palNj6VxcTHbuA2zJnahoaH07duXmTNnFhuX4WjM1RXrF9YAPy6Rjj/xx3Jo01nWjRXiRs6dKyxv8q9/QdOmNg2nWjh16hSDBg1i8uTJjB49mrCwMLp27UpsbCwRVe0XsgOBgYG4urqWaJ1LTk4utWUSoF69eqXu7+bmRp0yFnj19PTE07PqA/NN4V3PFx8yyUJ1N5ozsdv2/XkO05eabjk89qR5Pr+85r3D58kP8e5Pr/A5j/AJ4/k7qyUffwwffwwdOkBWpp6T/7igxq25Eso/PMJSHqm7lmaP9IBRoyDc8IxVbKLU6aBJE1ybNKEmRT73d3+uprZmZ6upqzcq3GdHzDpqZcyYMej1em5z8EUczdUVi58fTV3UIshxe1Kr+GRCOL/du9XQlLg4tbD4dUOihAWkpaUxYMAAhg4dyqvX6slEREQwZMgQpk6dauPozMPDw4OIiAjWr19f7P7169fTvXv3Uh8TGRlZYv9169bRpUuXUsfX2YwFV5/Y8YdKEe5qFWe+hNHHB1avJvjMHl6eHcyx8IfYSG9G8SWeHhp//gkn/3HBR5fFWD5lk8ednBjxGtN/7Uazc9tg9mw1C8PSXFzUwMB27RwqqQMzt9hNnz7dnE9nM+bqikWno4VPIgcud+Dv/TKBQojyLFsG48apcgVhYbB6te1mlFYnAQEBHDlypMT9q1atskE0lhMVFcXo0aPp0qULkZGRLFq0iPj4eGNduilTppCQkMDSpUsBePLJJ/nwww+JiopiwoQJ7Nixg8WLF7N8+XJbvoySrq0XG0czs9ey2xvnD0BEuAUmZjRsCC++iO7FF+l99Ci9t2zhg/t0rFypJi8MS/8eH7cCeOA78zZDVgOVTuzy8vKIi4vjpptuws3NuYbqmasrFqBlcDpchuN/VX1auxDOqKAApk5VM+UABg9WSZ6vr23jEiX179+fvXv3kpWVRcOGDVm5ciVdu3a1dVgVMmLECFJTU5k5cyaJiYm0a9eONWvW0ORawdHExETi4+ON+4eGhrJmzRpeeOEF/ve//xESEsIHH3xQ5VInZmepFruCAvZdCgUgvG8Vlj2riNatoXVrAlBf7pRHLHtMJ2ZyRpadnc2zzz5rnO5+/PhxmjVrxnPPPUdISEiJ6eSOyGxdsUDLEZ3gLThOy6o/mRBOJiMDRo6EX35Rt6dMgTffBFdX28YlShcTE2PrEKpk4sSJTJw4sdSfLVmypMR9vXr1Yu/evRaOqop69qTusAJYZd7VJy7/GcdxTX1uhd9V+jhEYZ9MHmM3ZcoUDhw4wKZNm/Aq0u98xx13sGLFCrMGZytm64oFWg5SJ8bxU1VYm0wIJ3TiBHTrppI6Ly/46iu1uoQkdUKYwM+PoNaqAoM5W+wOxKiJIw3czxNUX05KR2Jyi92PP/7IihUr6NatW7Ep32FhYZw8edKswdmKWbtirzXUxceryTWVqS0lhLMoKFBlTJYuhe++U+dEgwaqvImhzIEQwjR1r9U5NmeL3V5vtTZs5x7yoeVoTE7sLly4QJBhcboisrKyyq0H5EjM2WJXx/cqAT560rI8OXE0nw6dnWs8ohAVcfiwSuaWLVPrORp0764SvFJqvQohKiho9y/AIJIT8lAlb6tu3z51Gd7TwRZoFqZ3xXbt2pVfDANiKCzU+PHHHxMZGWm+yGzInGPscHWlZfZ+AI7/buYpS0JU0JYtWxgyZAghISHodDp+/PFHix9T09SSYF26qGV33nlHJXX+/vDkk2rNyG3bJKkToqrqrlsGwIXEArM9p2FoYVXWLBW2YXLzUXR0NHfddReHDx8mPz+fefPm8ddff7Fjxw42b95siRitzpxdsbi40NIviZ2X4HhsBiUXJRbC8rKysujYsSNjx4612qy+vXvVckGg1lQcOBAeeUTNerVyDVer0+v1tg7BpgwrNAjrCArIh4uQnGKe0rRX0nM5fNAVcKNz21zAyU9YJ2NyYte9e3d+//13Zs+ezU033cS6devo3LkzO3bsoH379paI0erM2RUL0DIkCy7B8SPm+zYlhCkGDBjAgAEDLPPkmqZmQjRvXmxZn9hYdXnzzfDzz4XjgJyZh4cHLi4unDt3jrp16+Lh4eE0Q1QqStM0Lly4gE6ns69Cvk6sbpAOTsKFdHezrBd76JfT5GstqaNLpWGzAPMEKaymUgO+2rdvbyx34ozM2mIHtGyhwWE4Hu9Y1atF9ZWbm0tubq7xdkZGRtk7Hz2qKgoHB0OPHmrr2ZODB8IBF3r2rB5JHYCLiwuhoaEkJiZy7tw5W4djMzqdjoYNG+IqU5ytom599VGeX+DCpUtQu4pl5/ZtSAOgs98/6FxKXz5N2C+TEztXV1cSExNLTKBITU0lKCiIggLTWqXmz5/Pf/7zHxITE2nbti1z586lZ8+epe67adMm+vTpU+L+I0eO0Lp1a5OOWxZNM/MYO6BlJ29YBcdT5QQRjiE6OpoZM2ZUbOfjx1Xf6vnz8P33agMOuW4GbqP91b1A9Rmo4+HhQePGjcnPzzf5/dBZuLu7S1JnRV7BftQig8v4cuFC1RM7w/i68KYXqx6csDqTE7uyxk7k5ubi4WHabJwVK1YwadIk5s+fz6233spHH33EgAEDOHz4MI0bNy7zcceOHcO3SFn6umZsDsjLg/xri0SYqyu2eaSKL/WqH6mpUMb60ULYjSlTphAVFWW8nZGRQaNGjUrfedgwuHQJ9uyBrVth61a0bb9z8HIYAO3dj1KdEjvA2A0pXZHCKq6tPnEZX5KTC8tsVda+a0uJycQJx1ThxO6DDz4A1BvWJ598Qs0izVkFBQVs2bLF5FazOXPmMG7cOMaPHw/A3LlziYmJYcGCBURHR5f5uKCgIPz9/U06VkUZumFBrVVsDj4dbqIhZzhLI/4+fJU6PeXNXtg3T09PPE2Z4eDlVdgNO2UKSWcLSG3kiouLRps3R1ouUCGEcb3YkzSvci27/Hw4kN4UgPDbZXydI6pwYvf+++8DqsVu4cKFxZrZPTw8aNq0KQsXLqzwgfPy8oiNjS2xBFm/fv3Yvn17uY8NDw/nypUrhIWF8dprr5XaPWtg0lghCrthvbzUTD6zqFePlh3TOHsAjp90pVvpPc1COI2Dh9X7Q4sWOmrUuHbnpUtqPF63bjaLSwinNGIEQSu9YFPVV584FpvJFa0mNblM837NzBKesK4Kpy5xcXEA9OnThx9++IHaVezET0lJoaCggODg4mvQBQcHk5SUVOpj6tevz6JFi4iIiCA3N5cvvviC22+/nU2bNnHbbbeV+hiTxgph/hmxAOh0tOpehw0H4PgJ80xHF8IUmZmZnDhxwng7Li6O/fv3ExAQUO6wh8o6eFBdtmt37Y7UVLjjDvj7b1i/Hpyk5qUQdiE4mLo3AZuqvvrE3g2XgJp08jiMS+AtVY9NWJ3JbVIbN240awDXlwLQNK3M8gCtWrWiVatWxtuRkZGcOXOG2bNnl5nYmTRWCPPPiDUwjHk4fty8zytERezZs6dYy7bhnBgzZkypi59XlSGxM1ZAqllTTY3dv18VtNu4ETp1MvtxhaiuDPMZq9pit+9CQwA6j+lQxYiErVSqs/Hs2bOsXr2a+Ph48vLyiv1szpw5FXqOwMBAXF1dS7TOJScnl2jFK0+3bt348ssvy/y5qWOFzD0j1qCl2z9AM47tSANk3IKwrt69e1u1aOyhQ+rSmNh5esLKldC/P/z+O/TrB1u2gJlmswtRrWVlEXzgd6AfCQkaUPlCdsYZsd1rlL+jsFsmJ3a//fYbQ4cOJTQ0lGPHjtGuXTtOnTqFpml0NmEKjYeHBxEREaxfv567777beP/69esZNmxYhZ9n37591DfjmkQW6YqFa8uKNePvc97o9eAiPbLCSRUUwF9/qevFapb7+MAvv0DfvurT44471Cza0FCbxCmE09DpaLNmNtCPvw7qgcqVmtHrC9eIlRmxjsvk9GLKlCm8+OKLHDp0CC8vL77//nvOnDlDr169uP/++016rqioKD755BM+/fRTjhw5wgsvvEB8fDxPPvmk8ViPPPKIcf+5c+fy448/8vfff/PXX38xZcoUvv/+e5555hlTX0aZLNUV27RbPdy4So7ei4QE8z63EPbk5Em4cgVq1IBm14+99vODmBhV0DghAe66q/DblBCicry9aed1EoC/T7qQk1O5p4mLg4wM8HTJo41/ohkDFNZkcovdkSNHWL58uXqwmxs5OTnUrFmTmTNnMmzYMJ566qkKP9eIESNITU1l5syZJCYm0q5dO9asWUOTJk0ASExMJD4+3rh/Xl4e//rXv0hISKBGjRq0bduWX375hYEDB5r6Mspkqa5YtzYtuImTHKM1xw/m0qiRrL0nnJNhfF3btlBqjdrAQDWB4pZb1DepuLjrmvaEEKaqXzefgDOppOnrcORI5Vrc9m1KB/xorz+Ae50ws8corMPkFjsfHx9j+ZCQkBBOnjxp/FlKSorJAUycOJFTp06Rm5tLbGxssUkQS5YsYdOmTcbbL7/8MidOnCAnJ4e0tDS2bt1q1qQOLNcVS2AgLd3UzOLjO1LN/ORC2I8SEydKExICP/0EBw5IUieEGeiC6tIedfIZzkFT7f1NrTQRXuuk+Qq5CqszucWuW7du/P7774SFhTFo0CBefPFFDh48yA8//EA3J6hPZamuWHQ6WtZJhfNwfH+WmZ9cCPtRotRJWa6fFWuO1cuFqK4CA2nPQTbT2zh5yVT79qvLzs0umSsqYQMmt9jNmTOHW25RtW2mT5/OnXfeyYoVK2jSpAmLFy82e4DWZqmuWICWTa4AcPxvmTkhnFeJGbEV8cUXalLFdbPshTCHixcvMnr0aPz8/PDz82P06NFcunSpzP2vXr3KK6+8Qvv27fHx8SEkJIRHHnmEc+fOWS9oU11L7KByLXaaBnvjVMWG8M7yBcuRmdxi16zIaGhvb2/mz59v1oBszWJdsUDLNm6wC44nWiBrFMIO5OSAoQ5yhRO7tDSYNEldTpkC771nqfBENTVy5EjOnj3L2rVrAXj88ccZPXo0P/30U6n7Z2dns3fvXl5//XU6duzIxYsXmTRpEkOHDmXPnj3WDL3iAgNpxy6gcoldYiIkX/HFlXw69JEFzR2ZyU1HzZo1IzW15BixS5cuFUv6HJXFumKBls/dBUBcVpA0TAindPiwKpkQGAgVLkcZEACffaauz5kDv/5qsfhE9XPkyBHWrl3LJ598QmRkJJGRkXz88cf8/PPPHDt2rNTH+Pn5sX79eh544AFatWpFt27d+O9//0tsbGyxCX12ZdIk2m3/GIBz59T3JFPsjVV1LltzlBoRMnHCkZmc2J06dYqCgoIS9+fm5pLgBHU8LNkVWz+8Hj4+UFCg49oKbUI4laITJ0waLjd0KDz7rLo+ZoxqPhDCDHbs2IGfn59xCBGoseJ+fn43XJe8qPT0dHQ6Hf7+/mXuk5ubS0ZGRrHNapo2xTeyLdeKSpjcardvhxoq1Nn9ELRoYebghDVVuCt29erVxusxMTH4+fkZbxcUFPDbb7/RtGlTswZnC5bsitXp1NJi+/appcWKrI4mhFOo0IzYsrz7ripYvH8/PPwwrFtXRr0UISouKSmJIMN6W0UEBQWVuS759a5cucLkyZMZOXIkvr6+Ze5n6trkltC+PZw+rca69upV8cftPaJWmgh/+wFwl3HgjqzCid3w4cMBtbbrmDFjiv3M3d2dpk2b8p4TjI2xZFcsQEuOs4+WHN+WDENKvtkI4cgqPCO2NF5e8PXXEBEBGzbAO+/Aq6+aNT7hPKZPn37DJGr37t1AyTXJofx1yYu6evUqDz74IHq9/oZjyk1dm9zs/vtf2seH8jODTW+xM6w4ESFJnaOrcGKn1+sBCA0NZffu3QQGBlosKFuyZFcsQKsLW4GWHIu9DEhiJ5xLpWbEFtWqFXz4IYwdq5avEKIMzzzzDA8++GC5+zRt2pQ///yT8+fPl/jZhQsXbrgu+dWrV3nggQeIi4tjw4YN5bbWgelrk5vd/Pm0OxoOJiZ2qamqlQ9KViESjsfkWbFxTj44zJJdsQAtQ/PhLBw/afKvXgi7lppaODSubdsqPNGYMdChgyxWKcoVGBhYoQaGyMhI0tPT2bVrFzfffDMAf/zxB+np6XTv3r3MxxmSur///puNGzdSp44DzBTt0IH2R1VGd+hQxUtD7t+vLm/yiMfvZIqcew6uwm2uf/zxB79eN1tt6dKlhIaGEhQUxOOPP25ckcKRWbwrtq07AMfPl//NTwhHY2ghCA2t4hcjna74B0tuLly9WqXYRPXVpk0b7rrrLiZMmMDOnTvZuXMnEyZMYPDgwbQqMtC5devWrFy5EoD8/Hzuu+8+9uzZw7JlyygoKCApKYmkpCTy7LmkQYcOtOIYbrp8MjLgzJmKPWzvHjUhMjzvD6hd24IBCmuocGI3ffp0/vzzT+PtgwcPMm7cOO644w4mT57MTz/9RHR0tEWCtBZNs3xXbIub1UmTmFPbmEQK4QyqNHGiLKdPQ48e8NJLZnxSUd0sW7aM9u3b069fP/r160eHDh344osviu1z7Ngx0tPTATh79iyrV6/m7NmzdOrUifr16xs3U2bSWl2HDnhwldaeqmetot2x+7aqD77O7ocwTqsVDqvC/YH79+/nzTffNN7++uuvueWWW/j4Y1U3p1GjRrzxxhtMnz7d7EFaS06OSu7Acl2x/p2aEsR5kgnm77+lxVs4D4skdn/+CXv2qK1bN7jBmCohShMQEMCXX35Z7j6a4c0fNTav6G2H0aEDAO1zYzlECw4ehEGDbvywvftUf214s3RwkckTjq7Cf8GLFy8WG2i6efNm7rrrLuPtrl27cqai7b52qmgLmre3hQ7SogUtOQ7A8b2ZFjqIENZnkcRuyBC1GgXA+PGqArIQonSNG4OvL+001btWkRa7zEw4fk51UYXLjFinUOG/YnBwsHHiRF5eHnv37iUyMtL488uXL+Pu7m7+CK3I0A3r42PBLy01a9KyxlkAjsdKX6xwDppWOCO2UqVOyvPmm3D77ZCVBffcA9Ys+iqEI9Hp1AQK1MloOCfLc+AAaLgQQgLBXRtbOEBhDRVOX+666y4mT57M1q1bmTJlCt7e3vTs2dP48z///JObbrrJIkFai6VnxBoYlhY7frm+ZQ8khJWcPq3OH3d3VYTbrFxdYflyaNgQjh2Dxx4rHDMhhCjuyy9pf2g5AEeO3Hje0W+/qcub2WWBb2XCFiqc2P373//G1dWVXr168fHHH/Pxxx/j4eFh/Pmnn35Kv379LBKktVh6RqxBy1vUBIrjxy17HCGsxdDl06aNSu7Mrm5d+O479eTffw8ffGCBgwjhBJo0oUmYDzVrqqTuRp8z332nviQND9ktiZ2TqPDkibp167J161bS09OpWbMmrtct9fPtt99S09IZkYVZekasgaFF4/jxitcZEsKeWWR83fVuuQXmzoWPP1Zj74QQpdLpVI62c6c6N8uqK3nsGBw8qMPNDYYemgVS6cQpmDySzM/Pr0RSB2rWUdEWPEdkra7Ym/R/o0NPejpcuGDZYwlhDVZJ7ACeekp9WjVrZuEDCeGg9Hp49lnan1Lru5c3geL779Xl7bdL+TpnIlNgirBWV6yXLpcmqPVbjh2VsULC8VktsdPpoOiSTceOWfiAQjgYFxf4+WfaJ60Hyp9A8d136vK+e/RWCExYiyR2RVirK5abbpKSJ8Jp5OUV5ldWHaLz+utqUJ/h00kIoXToQHvUt62yWuxOnoR9+8CVfIa/1EIGfTsRSeyKsFZXLDVq0LKmWlTz+B4p3SAc27FjkJ8Pfn7QqJEVD1xQoAapPv44nD1rxQMLYec6dKDdtZIncXGUusqRoRu2N5sI1KXK8AYnIoldEdbqigVoFaIOdvxIgeUPJoQFGVoE2rWz8kSg6dOhSxe4eBEeeUSNLRJCQPv2BJJKPfcUAP76q+Quxm5YvoO+fcGtwnMphZ2TxK4Iq3XFAi1bqLF1x+M9b7CnEPbNauPrrufhAV99pZaJ2bgR3nvPygEIYacMS4sVHABKjrM7fRp27wYdeu5mJdx5p7UjFBYkiV0RVuuKBVp2UmuWnUgLoEAa7YQVzJ8/n9DQULy8vIiIiGDr1q1meV6bJXYALVoU1rSbOhX27rVBEELYmebNwcuL9vr9QMlxdj/8oC576rYRTDI4eA1aUZwkdkVYsyu2Udd6eHKFPL078fGWP56o3lasWMGkSZOYOnUq+/bto2fPngwYMIB4M/zz2TSxA7USxT33qGqsI0fClSs2CkQIO+HmBmFhtPdXY0+vT+yM3bDatxAaCg6+apQoThK7IqzZFet61500b6Pq/v35p+WPZ/TLL5CSYsUDCnswZ84cxo0bx/jx42nTpg1z586lUaNGLFiwoErPm5GB8YuJzYrW63SqaHGrVvDyy8XLoQhRXW3bRvv/ex9QiZ1hFb6EBNi+XV2/hx+kG9YJSWJXhDW7YvH0pO8d6tf/xRdmfF5NU2ftwoXwzDNw773Ffz5vnpq6OGFCxVaIFg4vLy+P2NjYEkv+9evXj+2Gd/jr5ObmkpGRUWwrjeFfqEEDGxc4DQhQwTz2mCzlIgRAjRq0aaNOh5QUSE5Wdxu6Ybt3zKTBGxPgoYdsF6OwCJsndqaO+9m8eTMRERF4eXnRrFkzFi5caLZYrNkVCyq3Ali1SiMpyQxPqNfDgw/CrbeqCv3/+586i9PSCvfp0EF1VX3yieo7u/NO1YonMwqdVkpKCgUFBQQHBxe7Pzg4mKQy/vGio6Px8/Mzbo3KqGNy9Sp07Qrdupk9bNMVndV37hysX2+7WISwA97eargdFHbHGrthx9RUM8t797ZFaMKCbJrYmTruJy4ujoEDB9KzZ0/27dvHq6++ynPPPcf3hoI8VWTNrliA9pe3E+lzgPx8HZ99ZoYnfP11+OYbCtw8ybjzXhInvsmJ/6zkwDEvtm9Xn3Pr+/2HnN+2w333qQrl//d/MHgwtG4Ny5aZIQgHkJ6uqnPu3QsbNsDKlfDZZ2pbvrzwHwHgzJny1+RxILrrWrI0TStxn8GUKVNIT083bmfOnCl1v169YNcuO6sRfOYMREbC0KGwY4eto6kecnPh3/9W31b791fvJ1On2jqq6u3KFbjrLtqfWQOot7GkJDC0nVzfmSOch00L1xQd9wMwd+5cYmJiWLBgAdHR0SX2X7hwIY0bN2bu3LkAtGnThj179jB79mzuLeO/NDc3l9zcXOPtsrqUwMpdsQAeHjyRNYcdfM7HC67yyivuuFQ21f7iC5g1i3d5ideJJm+9K5TaYKEjICCSceO+5clNCTRbPVeNT/r7b4o1G168qIq+mrs42c6dcOCA6he4cAG8vMDfv3AbMqTwD5CTo1ph3N0r/vyapl7HyZNw4oTa4uJg6VIwrHE8aRIsWWJ8SB7uXKQ2uXiioUO/rRf6ejXR60Gb9SX6735A27WbNm3M8yuwtsDAQFxdXUu0ziUnJ5doxTPw9PTE01HHqtWvr1qmf/5Z/T9t3w4tW9o6Ksd14YJaleD6rWtX+PRTtY+7O8ycqZpwDWQlA9vy8oL9+2l/ZRc/MJCDB6FGDfUWeXPoBRrHboPad1jxA09Yi80SO8O4n8mTJxe7v7xxPzt27CgxTqh///4sXryYq1ev4l5KAhAdHc2MGTMqFJO1u2Lp0oX7+6Ty/MZLxJ3x57ffqjCOde1aljKaV3gX8tVdOh34+BTfUlNVL9V//gOzZzfgrrv+w8SPZzLg/BJcHy4y1uK771RF/yZNVIvegAEQGKiSIxcXlfB5qMkfnDunKmDGx6vWkqLb2bOQmFj4S/3kE1i8uFjoBbiQSh2SCSL5q9s5Ty1SU6HJhq+5ZeUrBPlfhbp1C7fAQPUB8uGHhW9Kr70Gixap1ri8vGLPf54gtvZIY9uxupw5Axd3TSFN9wJpLnW4qPmTqfcp/rvsUfTGFGAKnuGOO9nSw8ODiIgI1q9fz9133228f/369QwbNsyGkVmImxt8/bXqYtqzR/3v7tgBQUG2jsy+6fVw6ZIarwjqHGvWrOxVPYom/i4u6guTj496z2jSRJWiEbbVoQPt1xcuLWboDLs3eQHc8wZs3gy33WbDAIVFaDaSkJCgAdrvv/9e7P633npLa9myZamPadGihfbWW28Vu+/333/XAO3cuXOlPubKlStaenq6cTtz5owGaOnp6SX2/d//NO3ttzUtI6OSL6oy/u//tGf4QANNu2/IlUo/zeaNBZq7a74GmvbKK5qWna1pen3J/fLzNW31ak276y5NU9/d1Na0qabNmqVpv/+uaenpmqa9+aameXkV36nodvZs4ZM+/3zZ+4GmnThh3DX7g4+1td3e0F5ot04LDz6rBXlnaC66gnIf3owT2ki+1ObxrPYHXbVc3NUPTp0qjOFf/zI+4Kyuobas7vPaEw1+0lr7nyv3uYtuHh7qJXt7a1rNmppWq5am+flpWu3amlavXum/9/T09DL/n+zJ119/rbm7u2uLFy/WDh8+rE2aNEnz8fHRThX9HZbDUV5nMUlJmhYaqv64XbtqWmamrSOyL+npmvbrr5o2ebKm9eyp/uEjI4vv07q1pul0mtakiabdeaemPf20ps2bp2lr12paXFwlD2v9/6W0tDTt4Ycf1nx9fTVfX1/t4Ycf1i5evFjhxz/++OMaoL3//vsmHdfm582LL2pHaamBpnl6apqr67W3ZJppmo+PpuXm2iYuYTJT/pdsvoaIKeN+ytq/tPsNTOlSmjixQruZV9++PB72MR8efpYff3Hj/Hkoo3espJwc8PLi7xM67r7XhasFaujcrFmU2aXr6qp6p4YMUb2UCxeq3pRTp+DVVwv3a9LkNdr1nkL7mnG0T9tEu39+ooX+GDXIUd/siw5U9/aGtm3VbNvrNq1hIw5fbkzMHIiJgS1bxpfa8qXTQZ06qlElOFj1yh47pnH4sI5/uIl/uImvGAWAh2s+/jVyoWsN0F3rKda/jS5wFgWaCxdSXeFC8efv0EGNB2vdWs3eDAgofunvX9hT64xGjBhBamoqM2fOJDExkXbt2rFmzRqaNGli69AsJzgY1q6F7t1Vmf0HH1TjKav70kkzZ8Lq1WoF+OsnTR0/rr7nGN5PV69WXdtW68awjJEjR3L27FnWrl0LwOOPP87o0aP56aefbvjYH3/8kT/++IOQkBBLh2l+7dvTnPfxcsnlSq76HAwPOc9N5/6BPoMLe12EU7HZO1xlxv3Uq1ev1P3d3NyoU6eOxWK1KJ2O9jPvp9t9O9ipj2TJR7m8Mq0CiWh+PtxzD6nejRj050LS0ly4+WY1lKyi4/SaN4fZs+HNN2HFCvjmG1VTLyFBLTlz+rQrv9AcaA6MR6dT+VqLVtByhhq21KIFNB01i0uDZpGQoHptzp6FhEPq8uRJOH+++HEbNFDjq/v1gzZtVDIXGFja562OS5fUZ/LOnYVbWpobyZlukFl0X9drm3r94eGqh6FXL+jZs7B3qTqbOHEiE23y7cWGWrZUycntt8M//6gZ4tWlSzYvT40v3LVL1fcz2LMHYmPV9WbNCk+Srl1VLcCiX5KdoDv1yJEjrF27lp07d3LLLbcA8PHHHxMZGcmxY8do1apVmY9NSEjgmWeeISYmhkGDBlkrZPPp0AFX9ITpjrCXTgDcV+Nn9TOpX+e0bJbYVWbcT2RkZIlvWOvWraNLly6ljq9zGMOH83jwa+w8H8nHH17hpdc8b5ycvfgieWt/4x6X3/hb70KTJurzq0YN0w9fowY8+qjaQH32HTqkxmQUvUxPV2M04uPht98q/vxeXuqzo39/tRlqK1WEv796/zG8B2mamguRlVW4j6HwpkGTJuDnV/H4hJPr3h3WrIFOnWxcbM/CNE21uK1bp5rHN20qPFHuv1+tMADw3HOq9fK226BhQ5uFay07duzAz8/PmNQBdOvWDT8/P7Zv315mYqfX6xk9ejQvvfQSbdu2rdCxTJmsZxVt2oCrK+0L9hcmdvGqaLEsI+a8bNonERUVxejRo+nSpQuRkZEsWrSI+Ph4nnzySUCVXEhISGDp0qUAPPnkk3z44YdERUUxYcIEduzYweLFi1m+fLktX0bVubrywIe3MWnMVU5e8GPjRtXAUKYPPkD74AMe5zO26HtSq5aaAFjhLtwbCAhQ7/lFx9Rqmipyefy4mkD799+F10+dUo9p0EB9Thg2w+2OHSuXcJZGp1ONDEKYpE+f4renToWwMBg1yjbxmNtXX6nXdOpU8fuDgtQHeNHZqnfcYdXQbC0pKYmgUlppg4KCyqzjCPDOO+/g5ubGc889V+FjmTJZzyq8vKBbN9onZsM/0L7pZVqe+ku9MZfTUikcm00TuxuN+0lMTCxW0y40NJQ1a9bwwgsv8L///Y+QkBA++OCDMkudOBKf+wbw8EaYP19N7iwzsZs7F154gVm8yuc8iqsrfPut5Zdz0ukKJ6XeeqtljyWERW3ZogaiAvz6qyrk7ShNvJoGx46puPv1U2NbQX2Anzqlxkz17Kl+1r+/KkJe6RpK9m369Ok3TKJ2794NlD4Gu7zx3LGxscybN4+9e/eWO+b7elOmTCEqKsp4OyMjo8zi3lazbRuPpsLup+EJ7x/hM9T/h6zQ4rwsPZPD3th8llI59u9XM5bc3fXa+cSCkju8+66mgbaI8caZnPPnWz9OUcie/5/Myale59WrmjZ9uqa5uBROCb9udr5dyc7WtDVr1IxUwyxf0LQ33ijcJyND0376ySFm/prrf+nChQvakSNHyt1ycnK0xYsXa35+fiUe7+fnp3366aelPvf777+v6XQ6zdXV1bgBmouLi9akSZMKx2iX580//2ja33/bOgphIoeaFSsKdewINzdOYld8PZa88hcvf16kGe7sWS6/MZuJLOVLRgOqbNRTT9kmViEclpsbvPGGGrg5apRq6erZE155BZ5/3nxjGqrq/Hm19u2GDcWLKHp4qEGrRStm16ql6k1WI4GBgQQGBt5wv8jISNLT09m1axc333wzAH/88Qfp6el079691MeMHj2aO67rsu7fvz+jR49m7NixVQ/eFnJy1JgYw1hL4bScs43egT3eThVn/niFH/qCwlkBsecb0jkgji8ZjYuLmsn63nu2ilIIJ9C9O+zfr5I7vR6io1UdIFtIT4cff1Rj5Qzq1FGzWq9cUWOinngCVq1Ss5vWrYMRI2wTq4Np06YNd911FxMmTGDnzp3s3LmTCRMmMHjw4GITJ1q3bs3KlSsBqFOnDu3atSu2ubu7U69evXJn0dql1FS46SY1E+264u3COUliZ2dG/LcHtcjgRG4jNs3dj3YukfffV0tfnkjwplEjVSz8tdecduiMENbj5wdffqkGqt5yi2ohM7h8Gd5/X30wmtuVK2rRzmnTVIJZpw7cfbcqJmmY5u3mptYv/vNPNRV94UK1/q2PT/nPLUpYtmwZ7du3p1+/fvTr148OHTrwxRdfFNvn2LFjpKen2yhCCwoIUP/DeXmqkKesn+z0dJp2fbEI55aRkYGfnx/p6en4+vraOpxSPdVuKwv/6slA93XogF+uqmnpd9+tVuSSmmz2wxH+n8yhurxO9PrCb0wLF6qxDp6ealmyDh1U92ebNqo+XkWneqelFT9pH35YLXlWUFB8v5YtVffw7NlqMoSTqjb/S9jRa+3RA37/XV3/7Tfo29d2sYhKMeV/ScbY2aHH327GwiGw5lpC5+mWz5x5bjz1lExkEsKiijaD16unKl3v26e6SX/8sfBnOp0q8hserm5/8ola5eLKFTWWybAlJUFysmr98/ZW+/r6qqQuKEh9wN55pypB0rixtV6lqG6KLqsjZQ2cniR2dih8cANuDopjV3Iobepf5Ou1tenQwdZRCVHNDB8Ow4apBG7zZjhyBI4eVZdpacULKu7fD99/X/rz6HSq6GOnTur2lCmq5lxIiHxTE9Zxzz2qzE+zZqoFWjg1Sezs1A97mrD+5xzuf6S2DKkRwlZ0OujSRW0GhmrdRWvf3XefGr9Uo0bh5uWlumDbti2+1qqt65qJ6ueZZ9T/q6w2US1IYmenGjRy4dGnzLRcgxDCfAzVuovq3VttQtgjV9fCNSOF05N5lUIIIYQQTkISOyGEEEIIJyGJnRBCCCGEk5DETgghhBDCSUhiJ4QQQgjhJKrdrFjDQhsZGRk2jkQ4A8P/kbMv4CLnjTCX6nLOgJw3wnxMOW+qXWJ3+fJlABpJLSlhRpcvX8avaF0zJyPnjTA3Zz9nQM4bYX4VOW+q3Vqxer2ec+fOUatWLXTXVX3PyMigUaNGnDlzxinXMJTXZ36apnH58mVCQkJwcXHekQ1y3jjn65NzxrLKOm+c+X8K5PVZginnTbVrsXNxcaFhw4bl7uPr6+uU/4wG8vrMy9lbHUDOG3Du1yfnjGXc6Lxx5v8pkNdnbhU9b5z765IQQgghRDUiiZ0QQgghhJOQxK4IT09P3njjDTw9PW0dikXI6xOW4Oy/d2d+fc782uyZs//e5fXZVrWbPCGEEEII4aykxU4IIYQQwklIYieEEEII4SQksRNCCCGEcBKS2AkhhBBCOIlql9jNnz+f0NBQvLy8iIiIYOvWreXuv3nzZiIiIvDy8qJZs2YsXLjQSpGaJjo6mq5du1KrVi2CgoIYPnw4x44dK/cxmzZtQqfTldiOHj1qpagrbvr06SXirFevXrmPcZS/nSNwxvNGzpmSHOHv5iic8ZwBOW9KY3d/O60a+frrrzV3d3ft448/1g4fPqw9//zzmo+Pj3b69OlS9//nn380b29v7fnnn9cOHz6sffzxx5q7u7v23XffWTnyG+vfv7/22WefaYcOHdL279+vDRo0SGvcuLGWmZlZ5mM2btyoAdqxY8e0xMRE45afn2/FyCvmjTfe0Nq2bVsszuTk5DL3d6S/nb1z1vNGzpniHOXv5gic9ZzRNDlvrmePf7tqldjdfPPN2pNPPlnsvtatW2uTJ08udf+XX35Za926dbH7nnjiCa1bt24Wi9FckpOTNUDbvHlzmfsYTraLFy9aL7BKeuONN7SOHTtWeH9H/tvZm+py3sg545h/N3tUXc4ZTZPzxh7/dtWmKzYvL4/Y2Fj69etX7P5+/fqxffv2Uh+zY8eOEvv379+fPXv2cPXqVYvFag7p6ekABAQE3HDf8PBw6tevz+23387GjRstHVql/f3334SEhBAaGsqDDz7IP//8U+a+jvy3syfV6byRc8Yx/272pjqdMyDnjT3+7apNYpeSkkJBQQHBwcHF7g8ODiYpKanUxyQlJZW6f35+PikpKRaLtao0TSMqKooePXrQrl27MverX78+ixYt4vvvv+eHH36gVatW3H777WzZssWK0VbMLbfcwtKlS4mJieHjjz8mKSmJ7t27k5qaWur+jvq3szfV5byRc8Yx/272qLqcMyDnDdjn387NJke1IZ1OV+y2pmkl7rvR/qXdb0+eeeYZ/vzzT7Zt21bufq1ataJVq1bG25GRkZw5c4bZs2dz2223WTpMkwwYMMB4vX379kRGRnLTTTfx+eefExUVVepjHPFvZ6+c/byRc0ZxtL+bPXP2cwbkvDGwt79dtWmxCwwMxNXVtcQ3puTk5BLZtkG9evVK3d/NzY06depYLNaqePbZZ1m9ejUbN26kYcOGJj++W7du/P333xaIzLx8fHxo3759mbE64t/OHlWH80bOGcXR/m72qjqcMyDnjYE9/u2qTWLn4eFBREQE69evL3b/+vXr6d69e6mPiYyMLLH/unXr6NKlC+7u7haLtTI0TeOZZ57hhx9+YMOGDYSGhlbqefbt20f9+vXNHJ355ebmcuTIkTJjdaS/nT1z5vNGzpniHOXvZu+c+ZwBOW+uZ5d/OxtM2LAZwxT0xYsXa4cPH9YmTZqk+fj4aKdOndI0TdMmT56sjR492ri/YRrzCy+8oB0+fFhbvHixzacxl+Wpp57S/Pz8tE2bNhWbpp2dnW3c5/rX9/7772srV67Ujh8/rh06dEibPHmyBmjff/+9LV5CuV588UVt06ZN2j///KPt3LlTGzx4sFarVi2n+NvZO2c9b+Scccy/myNw1nNG0+S8cYS/XbVK7DRN0/73v/9pTZo00Tw8PLTOnTsXm6I9ZswYrVevXsX237RpkxYeHq55eHhoTZs21RYsWGDliCsGKHX77LPPjPtc//reeecd7aabbtK8vLy02rVraz169NB++eUX6wdfASNGjNDq16+vubu7ayEhIdo999yj/fXXX8afO/LfzhE443kj54xj/t0chTOeM5om540j/O10mnZtlJ8QQgghhHBo1WaMnRBCCCGEs5PETgghhBDCSUhiJ4QQQgjhJCSxE0IIIYRwEpLYCSGEEEI4CUnshBBCCCGchCR2QgghhBBOQhI7IYQQQggnIYmdEEIIIYSTkMROCCGEEMJJSGInhBBCCOEkJLETQgghhHASktgJIYQQQjgJSeyEEEIIIZyEJHZCCCGEEE5CEjshhBBCCCchiZ0QQgghhJOQxE4IIYQQwklIYieEg9uyZQtDhgwhJCQEnU7Hjz/+WOznmqYxffp0QkJCqFGjBr179+avv/6yTbBCCCEsys3WAVibXq/n3Llz1KpVC51OZ+twhIPTNI3Lly8TEhKCi4ttvidlZWXRsWNHxo4dy7333lvi5++++y5z5sxhyZIltGzZkn//+9/ceeedHDt2jFq1alXoGHLeCHOxh3PGWuS8EeZi0nmjVTNnzpzRANlkM+t25swZW/9ra5qmaYC2cuVK4229Xq/Vq1dPe/vtt433XblyRfPz89MWLlxY4eeV80Y2c2/2cs5Ykpw3spl7q8h5U+1a7AwtFGfOnMHX19fG0QhHl5GRQaNGjSrc8mVtcXFxJCUl0a9fP+N9np6e9OrVi+3bt/PEE0+U+rjc3Fxyc3ONtzVNA+S8EVVn7+eMOcnnjTAXU86bapfYGZrDfX195UQTZmOv3SxJSUkABAcHF7s/ODiY06dPl/m46OhoZsyYUeJ+OW+EudjrOWNO8nkjzK0i541zD3AQQgAl3ww0TSv3DWLKlCmkp6cbtzNnzlg6RCGEEGZQ7VrshKhO6tWrB6iWu/r16xvvT05OLtGKV5Snpyeenp4Wj08IIYR5SYudEE4sNDSUevXqsX79euN9eXl5bN68me7du9swMiGEEJYgLXZCOLjMzExOnDhhvB0XF8f+/fsJCAigcePGTJo0iVmzZtGiRQtatGjBrFmz8Pb2ZuTIkTaMWgghitPr9eTl5dk6DJtwd3fH1dXVLM8liZ0QDm7Pnj306dPHeDsqKgqAMWPGsGTJEl5++WVycnKYOHEiFy9e5JZbbmHdunXVYlaiJVy8CJs3w2+/wYYNcP48tGgBYWHQpk3h1qQJmOl9WlQjGzfCY4/BggVw1122jsZ68vLyiIuLQ6/X2zoUm/H396devXpVnlgkiZ0j0uvByQt7iorr3bu3sRxJaXQ6HdOnT2f69OnWC8qJ5OYWT+T27lWnYFGpqbBzZ/H7vLygQwcYMQJGjoRrwx2FKNeqVXDqFPz0U/VJ7DRNIzExEVdXVxo1auT0hauvp2ka2dnZJCcnAxQbD10Zktg5mhMnoHt3GDIEXnoJzpyBO++0dVRCOKX4eOjfH44eLX5/q1Zw++3Qty80awbHj8ORI4Xb8eNw5Qrs2qW2l15Sp+no0TB8OPj42OTlCAeQmqouMzNtG4c15efnk52dTUhICN7e3rYOxyZq1KgBqIltQUFBVeqWlcTO0fz6K1y4AJ9+CkuXQmCg+hSRbjUhzOrvv+GOO1RyFxgIgwerZK5PH2jQoPi+4eHFbxcUQFwcrF8PX3wBO3ZATIzaataEe+6BRx5Rz1XNGifEDRgSu8uXbRuHNRUUFADg4eFh40hsy5DUXr16tUqJnbylOJpff1WXb74JoaGQlASzZtk2JiGczMGD0LOnSupatlTdr599Bg8/XDKpK42rKzRvDk89Bdu3q+9e06ap1r3MTPWd7I471D5vvQUJCZZ/TcIxVMcWO4PqULS6POZ6/ZLYOZKcHDWyFmDYMHjvPXV9zhz45x/bxSWEE9m1C3r1UpMiOnaErVuhUaOqPWeLFjBjhhpJsW0bPPEE+PmpVr3XXoPGjWHoUFi9GvLzzfM6hGOqji12wrwksXMkmzergTsNGkC7dqpv6M47IS9PDeIRQlTJpk2qu/XiRYiMVN+jgoLM9/w6Hdx6KyxcCOfOweefQ48eajLGTz+p72uNG8Orr8KhQ+Y7rnAcktiJqpLEzpEYumEHDFCfEDodvP++6vf54Qf1qSSEqJRfflGnVmamSu7WrYPatS13PG9vNc5u61Y14eLFF9VYvsREiI6G9u2hbVuYOROOHbNcHMJ+5OfDpUvqenXsihXmIYmdIyma2Bm0bQtPPqmuT5qkRm0LIUzy7bdqtuqVK2rC+c8/q0kO1tK6NcyercbaffONisHDAw4fhjfeUD/v1EkNpy1Si1pYUHR0NF27dqVWrVoEBQUxfPhwjlk4w754sfC6tNiJypLEzlFcvQr33gsREWrUdVEzZqhqqCNHliywJYQo16lTquUsPx8eegi+/17VoLMFDw+4/3411u78eViyRH2Pc3ODAwdg6lQ1Xq9ZMxg3DpYtU126wvw2b97M008/zc6dO1m/fj35+fn069ePrKwsix3T0A0L0mLnKJYvX46XlxcJRWZAjR8/ng4dOpCenm6TmHRaeZVNnVBGRgZ+fn6kp6fj6+tr63AqJSVFDcAeOFB9EAAq8XN3t2lc1ZEz/D9VhDO/zgceUC12vXqpIsT2uFpEaiqsXKla8zZsKNkw37KlqqnXq5fqwm3Rosh7g51x1P+lCxcuEBQUxObNm7ntttsq9BhTX+vvv6sxlwa5ufb7dzSnK1euEBcXR2hoKF5Fv1WVl0S7uhb/Blbevi4ucK1OXLn7VqLApKZpdOrUiZ49e/Lhhx8yY8YMPvnkE3bu3EmDikyhL6LM3wOm/S9JHTsH9MwzsGKFKsfw7bcQHEzxpE7T1Pg7IUS5tmxR55CLC8ybZ59JHUCdOjB+vNouX1Zf7DZsUJM79u5V5VSOH1eTMkC9jptuUl24bdqoy9at1cSM4GD7fZ32zND6EhAQUOY+ubm55ObmGm9nZGSYdIyiLXagWu3KOZzzK288xMCBamCsQVAQZGeXvm+vXsXHoDdtqlpIrleJdi6dTsdbb73FfffdR0hICPPmzWPr1q3GpM7NzY127doB0KVLFz755BOTj2EqSewcQU6Oehfv04cCT29iYtTdW7dCly7w44+qhxZQI75feUVlfi1b2ipiIexeQQE8/7y6PmGCKm3iCGrVUt2zhqG2Fy+qBHXjRlUz7+hRlfwZkr3Vq4s/3s0NQkKgYUNVxqVhQzXRvnZtVYLFzw98fYtfd3dXyW91/b6oaRpRUVH06NHD+CFdmujoaGbMmFHp41yf2F2+XM0TOwcxePBgwsLCmDFjBuvWraNt27bGn/n7+7N//36rxiOJnSPYvFmVNmnenP1f/82lS+rNPSREzZbr0QM++QRGjUI1O+zfD2+/rVanEEKU6tNP1ani56fqfTuq2rVVmZRhw9RtTVPj7o4eLVzi7OhRleSdO6fGEsbHq81Urq4qMXR1Lbzu4aE2T8+S1/v2BWdYoviZZ57hzz//ZNu2beXuN2XKFKKiooy3MzIyaGRCEcTSErtqrbyBhtc3O19bZ7VU1y/vcupUpUMqTUxMDEePHqWgoIDg4GCzPndlSGLnCAyzYfv0YcMGdbV3b7VU0ahRqjX64Ydh3z54e8rruK1Zo0ZVz5olK48LUYr0dDURAVTiUbeuTcMxK51OtcA1aKDKthSVn68Wqzl7Vi0zffas2hIS1O/k+u36z9WCAtMm3ps4xMguPfvss6xevZotW7bQsGHDcvf19PTE09Oz0se6vnew2k+gMGXMm6X2vYG9e/dy//3389FHH/H111/z+uuv8+233xp/npGRQUREBDVq1OCtt96iV69eZjt2WSSxcwRFypxsWKSu9u2rWhpWrVLlEN56Sy1E8eef3fi6a38CdsfAhx/Cv/9tu7iFsFNvvqmWXG7VCp5+2tbRWI+bm+p6bdgQunW78f4FBarVKD9fXTdcGrb8fFUf3bDl5ha/7siJnaZpPPvss6xcuZJNmzYRGhpq8WNKi51jOXXqFIMGDWLy5MmMHj2asLAwunbtSmxsLBHXxkedOnWKkJAQDh06xKBBgzh48KDFJw1JYmfvTpxQq5G7uZHX83a2jlZ39+mjLl1dVe7WsSM8+qhadLxr8HdsoC1NFiyAKVPM+u1ECEd3/Dh88IG6/v77Mpm8PK6u4O9v6yhs4+mnn+arr75i1apV1KpVi6SkJAD8/PyoUXSGpRmVNnlC2Ke0tDQGDBjA0KFDefXVVwGIiIhgyJAhTJ06lbVr1wIQEhICQLt27QgLC+P48eN06dLForFJYmfvDK11PXqw+5gvWVlqhlz79sV3u/9+NVdi+HD451RNJtf8kOVpQ1UhrOrUJCHEDbz4oqoONHBg8VrfQhS1YMECAHr37l3s/s8++4xHH33UIseUFjvHERAQwJEjR0rcv2rVKuP1ixcv4u3tjaenJ2fPnuXw4cM0a9bM4rFJYmfvinTDbtyorvbpU3IsKKhWu5UrITwcVmQN5nXaEPb++2plCqlvIAQxMWpVCTc3mDPH1tEIe2aLEq+GxM7fXy0tJi12ju3IkSM88cQTuLi4oNPpmDdvXrnlcsxFEjt7lpODMZsbMIAN10oz9O1b9kM6dYK774aVK3XMrDefr98upVaPENXQ1avwwgvq+rPPqvF1QtgTQ2LXpIlK7KTFzrF1796dgwcPWv24sqSYPfPyUvUY5s8n56Z2bN+u7i4vsYPC8gLfnO/NX23uk9Y6IYAFC1Tpj8BAmDbN1tEIUZymFSZ2TZuqS2mxE5XhsC128+fP5z//+Q+JiYm0bduWuXPn0rNnT1uHZV46nWpWaNWKHRvULLOQkBvXHe7QQS0r+/33MHOmqlVsNZmZqnaCYUtOhrAwtUyGpVdVT01VXdfp6WpE/PVb9+5S/qWayslRSyqDmkFeXScECPuVmalalUG12IG02InKccjEbsWKFUyaNIn58+dz66238tFHHzFgwAAOHz5M48aNbR2eRRjq1/XtW7Hq79OmqcTu2281Xq/3Ce30f8J//2vZIKOi1DTD0ri5qXWQbrnFcsd/9121lWXNmsLR8gcPqoVBe/ZUgxPdHPJUEBX0/feQlqZaQsaNs3U0QpRkaK3z8rq2TCSS2InKcciu2Dlz5jBu3DjGjx9PmzZtmDt3Lo0aNTLOYqqM3Fz15j9tWqWWizO/f/5RU12XLgUKEztDmZMb6dAB7rsPNE3HzA/8YP58iIszT2wFBaoZsHNnOH268P7AQHVZq5ZamPL221UQTZuq2R7XpvJmZsJfj89jXaeX+W3cV+z8/CiH/tQTF6dqi+Xk3OBvkJYGn32mkjTD+mqgVnPv2FE1Vw4dqqY93nmnquZ8662F8YFah+2FF9SabAEB6rnefFMlf+VVMHdA+fn5vPbaa4SGhlKjRg2aNWvGzJkz0ev1tg7NagzLMz72mIxMEPbJkNjVqaPeQkG6YkXlOFwzRV5eHrGxsUyePLnY/f369WO7YRBaEaYsyjzyIT15V114tH8izW6tb76gK+OXX+C77yAlhct3P8KuXeruG42vK+qNN9RTfMsDHNS/Sft582Du3MrHlJ8Py5ervqxjx9R9c+aoZcwAnnlGbb6+XLqkFic/cADi6kP88SucvtWL06fV2pZwbSbIAaCUlc9cXVUvdOTN+UQGHCeSHbQ++3+47ItVdf0M6tWD/v3V9YgINSaxiCtX1PHS0tRg5Pr/QGgo6Fq3hkGDVCtiejqsXas2g7/+Ul3IoEr0BwerNZIc0DvvvMPChQv5/PPPadu2LXv27GHs2LH4+fnxvGGxVCf2999qVT4XF1XrUQh7VDSxM4xakRY7URkOl9ilpKSUuh5bcHCwsYBkURVdlNnTEzp6HGX31TB2f33S9ondb7+pywED2LZNNZKFhhYOqq2Idu1Uo9+338IM3uC7xWPVzApTBxjl56uWw1mz4ORJdV/t2qrF69lnuXBBJXF79/peu1QNjsV5Fbvl71tAo5oX0S5nkpkJWZo3mdQkB29Avd7Dh+HwYTcWEwaE4c893MIfdGMnPvV8udy6K5dpx+Xx6g0wI0NdXrpUmMxduVLy5dSrB7feej/d+97Pra8WEO5+CI8dm2H3btizR62xVHQg48yZahUPB7Vjxw6GDRvGoEGDAGjatCnLly9nz549ZT7GlC9E9s6wZPJdd6lF74WwR9JiJ8zF4RI7A911A800TStxH5i2KHPX5mnsPgC7tuYywrzhms6wSHGHDmy4luOZ0lpnoFrtNL7X7uPPzJl0WLQIXn654k+Qn68OvHWruh0YiBb1Iod6P8Oq32qy6k6VC5UmNFTV1GvRQg0Gbty48NLX1xUIVFteHmzfDjExFKxdT3ZYFy5GL2TfPti5E3Ys2M/uzNZcKqhNDHcRw12QhNoqwMVF5aF+fqrxLSlJdbt//z2AK15eHenatSMdOkDjsdC4Xh6Nd7nRuDHUrw+ugYEq83dQPXr0YOHChRw/fpyWLVty4MABtm3bxtxyWm8r+oXI3uXnqxrdIGPrhH2TFjthLg6X2AUGBuLq6lqidS45OblEKx6Ytijzzbd6MP8A7D5R2yyxVsm5c+qyfv1iEydM1bYtPPCAjhUrVKvd9x88D5MmVbxb0c0NbruN/P2H2DZqAatc72HVx+7EvVp8t5Yt1ZC7zp1Vj2inTmroWoV4eKhxcL174xodTa2CAmq5qgRw2DAguhP5+fDnn7Bjh0okCwrUt1pfX3VZdPP3V8euXVtd1qpVOOHkyhX1+N9/V9v27eoNdevWwtwVCn83rq7QoEE0jbep5dq8ijc8OoRXXnmF9PR0WrdujaurKwUFBbz11ls89NBDZT7GlC9E9mzNGpXIBwXB4MG2jkaIskmLnTAXh0vsPDw8iIiIYP369dx9993G+9evX8+wYcOq9Nxd720M8yE2qxX5lzJx87dweY6yXL2qZhEAaV4h7Nun7q7oxInrTZsG33yj8YN2L/sT3qTT//2fmlhQlsuXVV9m48ZoGiwKmcFr7jNIWVg46tzTU81LGDYMhgwpnMVlFqWMbndzK0wcq8LLC3r0UBuoSRrHj6sE78QJiI8v3M6eVS0+8fHq1+GISR2oWeRffvklX331FW3btmX//v1MmjSJkJAQxowZU+pjTPlCZM8MkybGjHHYIZKimki5Vku+aGInLXaiMhwusQOIiopi9OjRdOnShcjISBYtWkR8fDxPPvlklZ63Va961NRlkqnV5Mj3u2g/7mYzRWyi8+fVpZsbmw/VQdOgTRvVLVgZYWEwYoSOr7+GmTXe5of+dxb+8LffVF+poUzMrl0wciT4+XH+xx2Me8qDX35RiVZAgGr1GDYM+vWzfFk6ayhSKrCEggLV2hMfr8btOaqXXnqJyZMn8+CDDwLQvn17Tp8+TXR0dJmJnTM4d07NQQI1G1YIeyZdsY7v4sWLfPDBBzz++OPUr+wHthk4ZGI3YsQIUlNTmTlzJomJibRr1441a9bQxFDVsZJcXSEi8DSbL7Rl9y/JtLfVmBxDYhcczIZNqiJNZbphi5o2TVUoWZlzF598pnI3b/er8PDDqrzHgAEqe5w7F/LzWRU4jvEdXUi5qFrnoqPVMkzVqdyb6oZVmyPLzs7G5brFhV1dXZ2+3Mnnn4Ner1pnW7e2dTRClK+0rtjsbPUFU0r0OIbnnnuOixcvsm/fPn788UebxVGlOnZXSptyaCUTJ07k1KlT5ObmEhsby2233WaW5725XQ4Au/fZ8EyKiFBn9K5dxqViK9sNa9CmDYwapa5PmKBWsHhqXC57GgxD0+tV08bs2WTmezK+2W8MT/mElItudOigxqS98EL1SuqcyZAhQ3jrrbf45ZdfOHXqFCtXrmTOnDnFhjI4G70eFi9W18ePt20sQlREaS12AFlZtolHmGb16tVkZmby888/4+/vz7Jly2wXjGaigoICbebMmVpISIjm6uqqnTx5UtM0TXvttde0Tz75xNSns7r09HQN0NLT00v9+TcfpWmgaRGdC6wcWUlJSZqmRoFpWkpK1Z8vK0vTZs7UtKZNC58XNK1DqyvavL4/ajEtntZuCkrXQNN0Ok176SVNu3Kl6sd1Zjf6f7IHGRkZ2vPPP681btxY8/Ly0po1a6ZNnTpVy83NrfBzOMLrLGrDBvW/XauWpmVm2joaUZSj/S9VhSmv1fC+vH27pun1mubqqm4nJFghUBvLycnRDh8+rOXk5Ng6FJsq7/dgyv+SyS12//73v1myZAnvvvsuHkVGI7dv355PDCOVHVjXfmpG7IE/XUqtgWZNhta6Tp3Ut7iq8vaG119Xpej+7//goYdUN+ufxzx5fsMw+v/9ISeTfWncWB373XcdusqHuKZWrVrMnTuX06dPk5OTw8mTJ/n3v/9d7Px1NobWupEjwcfHtrEIURFFW+x0OhlnJyrP5MRu6dKlLFq0iFGjRuFapOO/Q4cOHD161KzB2UKTJmrlqfx8tWqCTcybB48+yoYvEoCqj6+7nouLWu3rq6/UAPP//lclj6C6aw8cgF69zHtMIazl4kW14gpI7TrhGPLyChO4oiszgpQ8EaYzObFLSEigefPmJe7X6/VcvXrVLEHZkk4HN7e6BMDuN3+1TRDr18Pnn7Nht/rKZu7ErqiAALUK2L59aubnl1+avjCFEPZk2TK19nOHDmopYCHsXVqaunRxKXz/lZInjmH58uV4eXmRkJBgvG/8+PF06NCB9PR0m8RkcmLXtm1bthZWcjX69ttvCQ8PN0tQtta1QSIAu363UaKamMhpGnPygh+urtCzp3UO6+dnneMIYSmaVli7bvz4wsLUQtgzQzds7doquYPCrtjq2GKnaWrSiC02TTMt1gcffJBWrVoRHR0NwIwZM4iJieHXX3/Fz0YfqibPc3zjjTcYPXo0CQkJ6PV6fvjhB44dO8bSpUv5+eefLRGj1XUdUg++gd2XWqgF4q39x0lMZCP9VCxd1eoKQogb27tXDSXw9CycBS6EvSs6vs6gOrfYZWfbrk5qZqZp43J1Oh1vvfUW9913HyEhIcybN4+tW7fSoEidLDc3N9q1awdAly5dLD4fweTEbsiQIaxYsYJZs2ah0+mYNm0anTt35qeffuLOO++88RM4AMMEimO0ImPjJnyHW7Av9HoFBXD+PBtR9U0s2Q0rhLMxvF/ec48JS9oJYWOlJXYyecJxDB48mLCwMGbMmMG6deto27ZtsZ/7+/uzf/9+q8VTqcpk/fv3p3///uaOxW4EBUETnwuczqpL7I9n6DPcigdPTga9nsOEAarFTghxY1euqAlBILXrhGMpr8WuOnbFenvb7nV7e5v+mJiYGI4ePUpBQUGpa9Zbm8lj7Jo1a0aq4b+wiEuXLtGsWTOzBGUPurZUgx53bc+37oET1fi+My5qFQ3DSl9CiPJt2AAZGWqlkN69bR2NEBUnLXbF6XSqO9QWm6njcvfu3cv999/PRx99RP/+/Xn99ddL7JORkUFERAQ9evRg8+bNZvotlc3kFrtTp05RUFBQ4v7c3Nxis0IcXdeeNfhuH+yOC1Rl7F2qtEhHxSUnk4c75/VBADRqZJ3DCuHoDCv4DB1qvdNVCHOQFjvHdOrUKQYNGsTkyZMZPXo0YWFhdO3aldjYWCIiIortFxISwqFDhxg0aBAHDx7E14KD5yuc2K1evdp4PSYmpthsj4KCAn777TeaNm1q1uBs6eah9eAD2E0XtXartRb0vesuEo5chjZqALihppEQomx6Pfz0k7o+fLhNQxHCZCkp6lJa7BxHWloaAwYMYOjQobz66qsAREREMGTIEKZOncratWuN+4aEhADQrl07wsLCOH78OF0sWIupwond8GvvljqdjjFjxhT7mbu7O02bNuW9994za3C2FHGzKzqdRnx+A867gDV7zc8kq+UeGjaUcg1CVMSuXZCUpGaQSzescDTSYud4AgICOHLkSIn7V61aVez2xYsX8fb2xtPTk7Nnz3L48GGLD1urcGKn1+sBCA0NZffu3QQ6eVNSrVrQurWOI0dg924YPNh6xz57Vl1KN6wQFWPohh04EJx4pTThpIoldiNGQHw8tXp8DrSUFjsHd+TIEZ544glcXFzQ6XTMmzePAAtP2Td5jF1cXJwl4rBLXbuiErs/9AwebKVBO9Onc+anNsAIGja0ziGFcHSGL8nDhtk2DiEqo1hi9803ANQ8tQB4X1rsHFz37t05ePCgVY9ZqXInWVlZbN68mfj4ePLy8or97LnnnjNLYPbg5s75LF3qxq7o3+CFCOsUxlqzhjN7VWuotNgJcWPHjsHRo+DuDgMG2DoaIUxnTOx8C1c7qqWppjppsROmMjmx27dvHwMHDiQ7O5usrCwCAgJISUnB29uboKAgp0rsukaqX8/ugnC0nX+gG2iFT43ERM6imuoksRPixgytdX36yLJ4wvFoWuFasYF554z316yhqk9IYidMZXL/4gsvvMCQIUNIS0ujRo0a7Ny5k9OnTxMREcHs2bMtEaPNdOwI7i75pBLIqTWHLX9AvR6SkjiDyuikK1aIG5NuWOHI0tPVgkMAddL+Nt5f60oyIJMnhOlMTuz279/Piy++iKurK66uruTm5tKoUSPeffdd45RfZ+HpCR0aXgRg9+Zsyx8wJQXy842JnbTYCVG+8+dhxw51fehQ28YiRGUYumF9fMDz7Enj/TUzzwPSYidMZ3Ji5+7uju5aDY7g4GDi4+MB8PPzM153Jjd3U6911zG/wq9VlpKYyBU8uYAUJxaiIn76SXVldekiLdzC/LZs2cKQIUMICQlBp9Pxo2H6tRkVmzjxzz/G+2tlqlWIMjPV/3h1oFWXF1oGc71+kxO78PBw9uzZA0CfPn2YNm0ay5YtY9KkSbRv394sQdmTrv3UhIndVzvCoUOWPdi5cyTQAAAvL1nEXIgbkW5YYUlZWVl07NiRDz/80GLHKJbY+fhAvXoA1CIDUO0JV65Y7PB2wdXVFaDEZMzqJjtb9Qy6u7tX6XlMnjwxa9YsLl9rG37zzTcZM2YMTz31FM2bN+ezzz6rUjD2qOstKveNJYKCbV/i2rGj5Q6WmlqsG1aKEwtRtsxMWL9eXZfVJoQlDBgwgAEWnmpdLLGbNg1efx0aNcKnhg+cUD/LzIQaNSwahk25ubnh7e3NhQsXcHd3x6WarQmoaRrZ2dkkJyfj7+9vTHQry+TErugyGHXr1mXNmjVVCsDetWkDPh55ZOXV5EhBS9pZ8mAPP8zZqyPgMemGFeJG1q2D3Fxo1gzatrV1NEKoNdNzc3ONtzMyMm74mBKrTuh0cPYsLoBPTcjKUuPs6tY1f7z2QqfTUb9+feLi4jh9+rStw7EZf39/6l1rsa2KStWxq05cXSGimwdbtsDuWn0tm9gBZ5JUE6yMFxLmlJCQwCuvvMKvv/5KTk4OLVu2ZPHixcUWqnY0hm7Y4cOldVvYh+joaGbMmGHSY0pbTsygZpHEztl5eHjQokWLatsd6+7uXuWWOgOTE7vU1FSmTZvGxo0bSU5ONi41ZpBmKMjjRLp2RSV2u2HsWMse68wZdSktdsJcLl68yK233kqfPn349ddfCQoK4uTJk/j7+9s6tErLz4eff1bX7XF8nV6vr7YfUKA+pKtbdxrAlClTiIqKMt7OyMig0Q3ezI2JXe45qNcZIiNh5UpALW15/nz1KXni4uKCl5eXrcNweCYndg8//DAnT55k3LhxBAcHG2fIOrOuXdXl7p35cCVfzWywhOef5+xPY4DOktgJs3nnnXdo1KhRsTGwTZs2tV1AZrBtmyrqWqcOdO9u62iKy8vLIy4ursSX3urExcWF0NBQPKrZwr2enp54enqa9JiUFHVZ52qSyuJSUmDSJNi0iZr6zYBftWixE+ZjcmK3bds2tm3bRkdLTiKwMzffrC4P7NOTu3YjnsMtNJj2xx85c/ZRQLpihfmsXr2a/v37c//997N582YaNGjAxIkTmTBhQpmPqcxYIWsyVJ0YMgTc7GhAiaZpJCYm4urqSqNGjaplq5Ver+fcuXMkJibSuHHjavHlvyqMLXY5Z9WVZs0gLg4OHKBW82zAz7ItdufPwwMPwOjRMH68BQ8krMXkt8TWrVuTk5NjiVjsVtOmUNvtMhfza3Hs0FU6DLfAQTSt2KoT0mInzOWff/5hwYIFREVF8eqrr7Jr1y6ee+45PD09eeSRR0p9TGXGClmLptlvmZP8/Hyys7MJCQnB29vb1uHYTN26dTl37hz5+flVLt1gS5mZmZw4ccJ4Oy4ujv379xMQEEDjxo3NcgxjYpcRp640awYnVaHimq6q/IVFW+z27lVjjbZsgdatoUcPCx5MWIPJXyfnz5/P1KlT2bx5M6mpqWRkZBTbnJFOByE+lwBIPmuhcTNpaeTkuZBKICCJnTAfvV5P586dmTVrFuHh4TzxxBNMmDCBBQsWlPmYKVOmkJ6ebtzOGAZ/2oGDB+HUKVX+oV8/W0dTXMG1IubVrQvyeobXX2Dpou4WtmfPHsLDwwkPDwcgKiqK8PBwpk2bZrZjGBK7wJSj6kqzZsZFj2u5ZAEWHmM3YAA8/LC6PnJk4cK1wmGZ3GLn7+9Peno6ffv2LXa/pmnodDqHP5HLUrfmFUiHC4n5ljnAuXOcRfW/enuDA49rF3amfv36hIWFFbuvTZs2fP/992U+pjJjhazF0A17553qXLFH1b370Vlef+/evS2+GoKxxS7xWgH8m26CY8cAqKVTGZ3Fx9jNnw87d8KJEzBuHPzwg0w1d2AmJ3ajRo3Cw8ODr776qtpMngCo638VEiDlgoVO8sREKU4sLOLWW2/l2LUPCoPjx4/TpEkTG0VUNfbaDSuEqa5cgWuLDRQmdkVa7GrqVUZnsRY7vZ5LqQU880Ithoxbx4hprdQ3pwULYOJECx1UWJrJid2hQ4fYt28frVq1skQ8diswQM1wu5BmnjozJRRpsZNuWGFOL7zwAt27d2fWrFk88MAD7Nq1i0WLFrFo0SJbh2ays2fVkCCdDgYPtnU0QlSNobXOzU3Dt2dnSDwHwcGFXbH6S4AFW+wOH+brTgtZVvAhywjl3LD1vLCqN0RFwa23QjWaJOlMTB5j16VLF7sab2MtdYNUE9qFdAuNnUlLM7bYyYxYYU5du3Zl5cqVLF++nHbt2vHmm28yd+5cRo0aZevQTPbLL+oyMhKCgmwbixBVZUjsAgJ06Db8BkeOqG8tdepA3brU9FYNChZL7A4cYF9B4RrvUat68UaLr9A0VCzCIZncYvfss8/y/PPP89JLL9G+ffsSM546dOhgtuDsSd2bfAFICWhpmQNERXHmSAF8Ii12wvwGDx7MYCdo4jIUJR40yLZxCGEOZa46ce+9cO+91JoPPG3Brtj9+9nH/QDcfjv89hvM/PshLj50J3MfCDS95UfYBZMTuxEjRgDw2GOPGe/T6XROP3kiMFxlWxfqht1gz8o7m6i6eSWxE6KknBz1wQPSDWsJy5cvZ+zYsZw8eZIGDRoAMH78eHbt2sXWrVvxu9Y9KMzHmNgFaEDJgdU1a6pLS7XY5e87yEFmAmpY3fr18PTT8N/lgaS7w+LF4OaqyaBvB2NyYhcXF2eJOOyeYQHmCxcsdwxDD7d0xQpR0saNKrlr1Ajat7/x/nYlK6vsn7m6Fl/Nprx9XVxUnZcb7evjY1p8wIMPPsjbb79NdHQ0H374ITNmzCAmJoadO3dKUmchxsTun91Qfxh88AHcf7/x57VqqUuLtNhpGkf3ZnOFGtTyKeCmm1xp0QJ8feHRR2HpUsg4ncbytLvwWrlczdYVDsHkxM5RZ9JVlSGxS7mghwJNvRmb0+jRnDm6AKgpLXZClKJoN6zDNSAYml5KM3Bg4eBBUIMHDVMlr9erF2zaVHi7adPCNamKqkSJDp1Ox1tvvcV9991HSEgI8+bNY+vWrcbWO4Ds7GzatGnD/fffz+zZs00+hiiucJ3YBEhLKkzI09LgnnuodS4MmG+ZFrukJPZdVJ/nHTuq7wygStr5+sIDD2j8uDmAwczix4kvUzOm7PJI4v/bu/PwJsvs4ePfdEnpmtIVCrRllX2ryCIKgrLIIjLyG9ThFQQdFxBkmBFlFIoylYFRHB0UGAd0ZhRUREDUggybIqJAQWRf2rIUukB36Jbn/eMmLaVb0iZNmpzPdeVKmzx5ctL0bk/u5dyOxazEbsOGDQwfPhxPT082bNhQ7bGjR4+2SmCOJkTVDSYjzYjxdCJu7dpY7+SaRt4nm7haqP74S2InRHmaVpb7yDCs7YwcOZKOHTsSGxvL5s2b6dSpU7n7FyxYQO/eve0UnfMpTexyktQXrVqpa09P2LEDP64DNuqxS0ggge4A9Igp31ExejR8/bWO0SNL2Jp/L098m8HH+fmOWzhSlGNWYjdmzBguXbpEWFgYY8aMqfI4p55jdyOxK8GDq4lZBFtzDUVmJucLVZegn59GQEBD644QwrYOH4bkZDViec899o6mFqr7z3xr739qatXH3rr3bGJirUOqTHx8PMeOHaOkpITw8PBy9508eZJjx44xatQoDh8+bNXndVWliV1RiuqGjo5WN/j5gZsb/jfq2Nmkx85g4EDoUEiDGxtrlHPPPbDxSzfuGQTrjA+Qt+U7fB+41waBCGsza9GL0Wgk7EZtAaPRWOXFWZM6AL0eDO6qdaWftXIrK1ecWNfwhpmEsDFTb93gwQ2008DXt+rLzfPrajr25vl11R1bC/v372fcuHEsW7aMoUOH8vLLL5e7f9asWcTFxdXq3KJyplH0YDKgWbOy3wWdDgIC8MN2O09offtxoKgzUHliBzBgoI6WfmkU0Igt/3K9MmcNlcWrmT/88EMKCgoq3F5YWMiHH35olaAcVYheta605GvWPbEUJxaiWqb5dTIMaxuJiYmMGDGC2bNnM2HCBObPn8/atWvZt28fAOvXr6ddu3a0a2ejck8uqrTHjoyyYVgTgwF/1P+cggIoKrLucyclQWamGvXtWEWxB50ORt91FYCNu2QBTUNhcWI3adIksrKyKtyek5PDpEmTrBKUowr1USvQ0i4UWvfEN/XYyYpYIcrLyIAfflBf33+/fWNxRleuXGH48OGMHj2al156CYCYmBhGjRrFnDlzANizZw+rV68mOjqaWbNmsWLFCubPn2/PsJ2CKbELIb3SxM7UYwdWnmdXWMiBry8B0KmTGpGqyqgpakj+y6t3YryQYsUghK1YvCrWVK/uVufPn3f6JfGhAdchA9IvW3nI+ZZ9YoUQZb75BoxG6NoVIiPtHY3zCQoK4mgluwysN23KC8TFxZUOw65atYrDhw/zyiuv1FuMzqq0x+6ONnD77eXvNBjQU4Teo4TCYndyc6FxYys98YEDHHgmHnilymFYk7tHGTC455BaEs7enWfp87CVYhA2Y3Zi16NHD3Q6HTqdjsGDB+PhUfbQkpISzp49y7Bhw2wSpKMICVQJXVq6lSfBZWZyHjXXQRI7IcqT3SaEMyopgatqlJPgDSsh/JYDQkMhJAS/68VcyXW37jy7gwfLVsTWkNh5esKwh/xYswY2/NJSErsGwOzEzrQaNiEhgaFDh+J3U10mvV5PdHQ0v/nNb6we4K0WLFjApk2bSEhIQK/Xk5mZafPnNAlt6QsHIN1g5UKNf/kL5zYY4VcZihXiZsXFqscOZH6do5g4caK9Q3AKmZll5QaDgio5YK2qG+cfDVdyrTwUm5DAAV4Eak7sAEaP1rFmDWzcCH/5ixXjEDZhdmI3d+5cAKKjoxk/fjxeXl42C6o6hYWFjBs3jr59+/L+++/X63OH9m0Ln0NaU+vvh3vuvJruKD12QpTZvVv9AwwOBimfJpyJaRg2IEDD07PqUSDT7hPW7LFL/+ks529M/zFne/fhw1VVnsOH4ez+q7Tsaa0xYWELFi+eGDRoEGk37au1d+9eZsyYwfLly60aWFViY2N5/vnn6WLmnkIFBQVkZ2eXu9SWqZadtbcVy8kB03oUSeyEKGMahjX9YxHCWZTOr8s+C+3bV3mcaXDMaj12RiMHDqs+nTaRBQQE1PyQxo3hrvATAGycv99KgQhbsTixe+SRR9i2bRsAly5d4t5772Xv3r289NJLDrlKKi4uDoPBUHppUYfMqXRbscvFVooO0DTOj3wKgAB/Y+mnMyGElDkRzqtcqZPKRsDWroV77sE/9TRgxR6706c5cL0DAD3u8DT7YaN6qRWxG3ZVNm4sHInFid3hw4e54447APjkk0/o0qULu3fv5qOPPmLVqlXWjq/OXnzxRbKyskov587VvshiqJtqiWkJF2q1F2OlcnI4t/MMAC1kfp0Qpc6cgaNHVU/d0KH2jkYI66q2hh2oHUi2b8fvmhoislpil5DAAdTEuh49zU8BRj0ZAcCOK53JunzdSsEIW7A4sSsqKiqdX/ftt9+W7g3bvn17UlJqV+Nm3rx5pStuq7r8/PPPtTq3l5cXAQEB5S61FRKt+sTTCC0bO62rlJSy4sRRFr8dQjgt024T/ftDYKBdQxHC6mpM7G6UD/PX1PQhqw3Ftm9PQvBgwLyFEyZth7ehvcdJivHkm7dPWikYYQsW17Hr1KkT7733HiNGjGDLli28+uqrAFy8eJHg4OBaBTF16lTGjx9f7THRpj307Ci0uUpor+FD/rkUfKzx3+biRSlOLEQlTImdDMMKZ1QusWtdSaWFG4mdX0kmYL0eu7xWXTh+RX1tSWKHTsfo9ic4drgtGz8v4revWSceYX0WJ3YLFy7kwQcfZNGiRTz22GN069YNgA0bNpQO0VoqJCSEENPKBAfm5wd6CijEi7RTWUSZt36jelKcWIgKcnPhxlReqV8nnFL5Hru+FQ8w9dgVqWJ31uqxO3RIzSRq0gTCb62dV4NRY/X89TB8daI1xcXgYXEGIeqDxW/LwIEDSU9PJzs7m8Y3lcF+8skn8amH3bmTk5O5cuUKycnJlJSUkJCQAECbNm3K1dazBZ0OQj0zuVAUTlpiHlHWOGlKCufpBEhiJ4TJt99CYaEaoapmwaAQDVZGhgboah6KLVQZoFV67HJyOPDReaCDZb11N/R9qhvB89PJKAnh+41XGPCgLKRwRLWa1KVpGvv27WPZsmXk3Pht0+v19ZLYvfLKK/To0YO5c+eSm5tLjx496NGjR63n4FkqtJF6vennrTR5VIZihajANAw7YoT6QCWEs0lPNQIQ0iEMoirpJrgxH9yvQCV2Vumx27uXA+98B1g4DHuDe9MwRnRRCxA3bpcSDo7K4sQuKSmJLl268MADD/Dss8+W1rT761//yqxZs6we4K1WrVqFpmkVLgMHDrT5cwOE+F4DIC2lyDonzM2VoVhRr+Li4tDpdMyYMcPeoVTKaJT5dcL5ZVxVhRmD33ql8nInBgN4euLvrcprWaXH7uDBshWxtUjsAEbPVQ/c8LX5pVJE/bI4sZs+fTq33347V69exdvbu/T2Bx98kK1bt1o1OEcU2kw1wDQv62Rh2YuWkYP6ZCY9dsLWfvrpJ5YvX05Xc8rN28n+/ZCSAr6+MGCAvaNxTVevXiU2NrbWlQ5EzUrn2FW15tBggIIC/N5/C7BOYle0/xcO39iXvLaJ3ZAhoNfDyZNw/HjdYxLWZ3Fi99133/HnP/8ZvV5f7vaoqCguXLhgtcAcVeid7QBIb2qNlRNgKqsXGFhWYVwIW8jNzeXRRx9lxYoV5ebHOpp169T18OGVd2QI23vuuef46aefePrpp+0dilPSNNMcu2oSO50OdLrSovXWGIo9tjebAhoR4FNEy5a1O4e/PwyMUSVYNvwrve5BCauzOLEzGo2UlJRUuP38+fP4u8C2CdbeVsyU2MkwrLC1Z599lhEjRnDvvffWeKw1t+Kz1BdfqOsxY+rtKcVNNmzYQG5uLl9++SWBgYH897//tXdITic/HwoK1OTR4NX/qPZY0wf+OvfYFRRw4LQaHerWuQS3OpRNHZ35bwA2fiaFih2RxW/tfffdx5IlS0q/1+l05ObmMnfuXO6//35rxuaQTNuKWWWOXV4e52csBqBFM2PdzydEFVavXs3+/fuJi4sz63hrbsVniRMn4MgRVUbBBf6cOKTRo0ez7ka36apVq3j00UftHJHzMQ3D6inAN7Ka+q/Tp+P//BTACj12R49ywKimYPToXbeu8JEPqvl1359pWvpahOOwOLF788032bFjBx07duT69es88sgjREdHc+HCBRYuXGiLGB1KaNoRANL/90vdT5aSwrnjeQA0byFL/4RtnDt3junTp/Of//yHRo0amfUYa27FZ4n169X1wIFq43EhnNHNNex0rSspdWLy00/47dsOWKHHrtxWYnX7fxM1vi/dSMCIO1+tt9JCQmE1Ftexi4iIICEhgdWrV7Nv3z6MRiOTJ0/m0UcfLbeYwlmFmBZPFNZ+a7JSN5U6aREpiZ2wjX379pGamkpMTEzpbSUlJezcuZN33nmHgoIC3N3dyz3Gy8urdOvA+mQahn3wwXp/aiHqTcbFAsCr6hp2JgYD/pwGIC9PrRiv7RCqds8gErzD4VrtF06U6tyZUb5LOJjXnXUrM5nweGgdT2imwkJ1fcscf1FerepGe3t7M2nSJCZNmmTteBxeaCs1jzCtJKhurQzK7xMrc+yEjQwePJhffinfwzxp0iTat2/PCy+8UCGps5dLl+CHH9TXN7agbvA0Tc2nsgcfH8tqAH788cdMmjSJ06dP06xZMwCmTJnC3r172bVrF4YbBXNdzdKlS1m0aBEpKSl06tSJJUuWcNddd9XpnBnH04DmBLtnQnDnqg80GPBHddWZfpdqu8gu0RhJ1jWVE3XsWLtzlNLpeGhgOq9tgi++C+bAASski5UpKUFzcychAb5a/CvxH1/hmuZNG0MabVtcp20nPW36hNB2SEuCO4TVb83L69fR0jPQhYeBp2OVfpENQSwU2kb9cbtKEMUZV/EIrcN4UUoK51BzHqTUibAVf39/Oncu/8/D19eX4ODgCrfb04YN6p9Xr17O0x7q8o+4rnJzVckYc40fP57XX3+duLg43nnnHWJjY4mPj2fPnj0um9StWbOGGTNmsHTpUu68806WLVvG8OHDOXLkCJGRkbU+b8bJq0Bzgv0Kq8++DQa8uYabzohRcyM3t/a/TwcOqOvOna2Th3Qb34GHN33ExzzCtKlGdn3nVvfEStNgzRqyPonn2516vmo3na8TO6Kq7nQqPeznLCALOAysUbcFel+na69GDBoEg+4qondv0PtaL+G6llvCvpWH2LMxjT0HvNiT3ppLNCGUNMLcMwgL1Qgb1IWwMAgLg2bJP9BjSCgdRrTCQ1+HDqBakMTOQkFNvdBhRMONjNOZhNchsdMuyj6xQpjIalj70ul0LFiwgIceeoiIiAjeeustdu3aVdp7Z5Kfn0+HDh0YN24cixcvtlO09eONN95g8uTJTJmiFjAsWbKE+Ph43n33XbMXIlUmI1mthAhuXMOiOYMBHeDnWUB2oTc5OWqPV4ulpnJgRTJwO9271+LxlRk9mr8G3836K2P4frcPH38MjzxSt1Nm/PF1pvytPV+ynGI84UYPvo8P3DtY4/470gkLg1M/pHHy8HVOJek5eTWY88VNybzWiJ07YedOmIcnPuRxV3ACg2KyGTwuiO6PdMTdx7zpJSUlcCIhn5+/zWTv+Qj27IGEBDeKiyt2S16iKZdKmsIl4KOb7+kL70EjrtE18BwxHfLpeY+Bng80p3N3T5uOJktiZyF3dwhyyyTDGETa6WzC+9T+XFlJmeShPn45Sw+FaBi2b99u7xDKyc4GU31zZ5pf5+Njvc3ba/Pclho5ciQdO3YkNjaWzZs306lTpwrHLFiwgN69e1shQsdWWFjIvn37mD17drnbhwwZwu7duyt9TEFBAQUFBaXfV1UmKEMfAUDwbdWsiIXS/WL9PK6XJna1smcPB75RvUZWGzINCKB5/PvM2ejBnFj405/UFIpaDxU//xbDljzIcdTm0Le1yOP+0Z4Mf0DP3XeDl5cOuDGX78nyc/qupedxKsmTPfv1bN0K/9uYS1q+H/EZvYjfDGyGRk9co7XXKdqEZdFmSGva3B5ImzbQpg0UF8PPm6/w89dp/HzAnX0pTck1+gI3NyIdTbwz6Rt8gj53aPQZG0HL/s3IOJ1J6olMLmd5kerRjNRUSE0p5vSGIyRkRZNDAHsz27H3B1Si+hfQ6TSCg3WEh0N4SDFh6UcJDyoiPMxI+B3RDPhNCK1b1+7nCJLY1UqoPouM60GkJ9dt8sy5q6oFBPkV4GPmJwkhnNE336h50e3aQfv29o7GenQ6y4ZD7S0+Pp5jx45RUlJCeHh4hftPnjzJsWPHGDVqFIcPH7ZDhPUnPT290p9DeHg4ly5dqvQxcXFxxMbG1njubqOjeMgDeozrWf2BBgN4eODvoerF1fpDwqlTHOC3gJXnwsXEMLMTvP9vOHMG4uJgwQLLT5Mw9Z8M/8f/cYmmtAjM5outAfTsaX7D8Q7xpUsIdImBJ54ArcSHw+tP8r+PLrF1tzc7LrUjWwvg14I2/HoOeP/GpZygGxfFhzx66H/l9snd6TtAT58+EBkZiE53R7lHtYgKgkFBt5zLA+iKsaCIUxsOsn99Mvv3FLE/KYj9xV25qgWRng7p6fArHsBNGx6shQ+aIIldfQsJAc5DmrGGT1s1ODfjb7AFWrRyrImXQtQ3024TY8ZYNuFfWM/+/fsZN24cy5YtY/Xq1bz88st8+umn5Y6ZNWsWixYtqrLHyhnpbvmF1DStwm0mL774IjNnziz9Pjs7u9IakJMmqUuNnnkGnn0Wv1462Ff7kiepJ7O4SDN0GOnWzbrzvRo1gjcWGxkz1o3Fi4w8/ribRUnJ//5zkTH/+D9yCKBLk1S+/jmMW0b/LaZzd6PL2LZ0GduW6UBxkUbyjxc59b9kTu3L4lSrIZw6rePUKTh9vAidsYTuJHB7SCK3dy3i9qHBtH+oMx6t7qjxuarj5uVJu3HdaDeuG+MBjEa0X4+Qlp3B5YC2XL4Ml49eIXX1/7icqedyljeXo+6gbdu6zWm1amLXsmVLBg0axPz58yvMy3Amob1aqsSucbs6nef8eXXdIrJ+J1YK4UgKCmDTJvW1zK+zj8TEREaMGMHs2bOZMGECHTt2pFevXuzbt6+0TM769etp164d7dq1c4nELiQkBHd39wq9c6mpqZX2ZoINygTdqLpQ123FEo6qmNqGZuLnd2vvUt2NPvI6Q4hhc9FQ/jDTyBfrzfuf9vHH8NjjERQBA1om88X+SAIDrR4eHp46WvWPoFX/CIbccp/xxwS07Bzc+/QC/zrMrTKHmxu6Lp0JA8KALl2Ae4Ng2kPWfRprnuyxxx7DaDRy9913W/O0Dse0+0R6HbfJM9V8lfl1wpVt307ppHAXmLrlcK5cucLw4cMZPXo0L730EgAxMTGMGjWKOXPmlB63Z88eVq9eTXR0NLNmzWLFihXMnz/fXmHbnF6vJyYmhi1btpS7fcuWLfTr169eYzEldrXtsTt6Tk376dIqz0oRlad76vcsMczDgyLWb3AjPr7mx/xtwXUeeQSKimDcOPjmiG2Supq49e6F+32Dyn7ITsCqPXbz5s2z5ukcVul+sSlFQC2HUc+e5dyK48AwWRErXJppNewDD9StLKSonaCgII4ePVrh9vWmbUBuiIuLK10JumrVKg4fPswrr7xSLzHay8yZM5kwYQK33347ffv2Zfny5SQnJ/PUU0/VTwDJyTB9On4JfwD617rHLjFdJXatWttonkNwMB0WTmTaU2/zJjOZMa2YQ796VFpW5VKKxmv/d5B/fNcdgOeegzfflLZvTbX+URYWFnL8+HGKi4utGU+DEHrmRwDSNu6p/UnOnuX8ZZVXS2InXJXRWLaNmAzDCkfz29/+liVLljB//ny6d+/Ozp07+eqrr4iKiqqfAIqL4Ysv8E85DtSyx07TSMxV88Gj2pm3pWCtTJnC3G7rCeMyx0568M47ZXcVFal2/sCAqzSPMJYmdQuHb2fJEknqrM3iHrv8/HymTZvGBx98AMCJEydo1aoVzz33HBERERWWhjuj0qHY3DpsoXbhAsmocSdJ7ISr2rsXUlLUKMg999g7GmGuiRMn2juEevPMM8/wzDPP2OfJTeVOijOBWs6x0zQSWw6C0xDdzYaFpt3dMbz7On/p9xJTeJ95LxcTE+PBxo3w4coSUjPcAVX3tY/uR1549DxjPhwLsljK6izOk1988UUOHjzI9u3by20ofu+997JmzRqrBueoQpqr1512vfYl5Y3nL5KMqlxeXx/+hHA0pmHY++8HO2xNK4RjC1B7kpu2FatVj52bG4kZav5YdFsbV2Do25dJE+F2fiI7z4MBA2DxYkjNcCecS8xiMb+OfpEfkiIY8+/fyBJ4G7G4x+6LL75gzZo19OnTp9yS744dO3L69GmrBueoQqNU0cK0osBanyPtdDYFNEKHkWbNpB9auCZTYudMRYmFsBpPT/D2xu+a6qqrTWKXlQWZmerr+uhEcFsYx9u/vsbdB2Iwam6MbHucx4/9keGDCvH82+tYb+sLURWLE7u0tDTCwsIq3J6Xl1dlbR9nE9pKffpJNwahlRjRuVuemCWdVnMTIwLz0evttJmkEHZ07BgcP67+dw0fbu9ohHBQBgP+11RGV5uh2KRvjgIdCAksws+vHmqmhoXRZ+/fOZGodj8J820O3z0LQ4ZID109sTgj6dWrF5tMRacoK964YsUK+vbta73IHFhIWzVPoAg92eezanWO5PPqRx8VXlDDkUI4J1NR4sGDS0ecnIamafYOwa5c/fVblcGAH7XvsUtcfxCAKI8L1oyqRtHREBaG2npl6FBJ6uqRxT12cXFxDBs2jCNHjlBcXMxbb73Fr7/+yg8//MCOHTtsEaPD8Q70wpdc8vAj7WQWhqjGFp8j6brq9YyMtHZ0QjQMpmFYZ1oN6+7uDqiqAd7edVhc1cAVFhYCZT8PUQcGA/5u+WCsXY9dYpK6jg6t2xaYouGwOLHr168f33//PYsXL6Z169Zs3ryZnj178sMPP9ClS5eaT+AkQn3zycvzIz1bT5taPD7pwefhbYjqbnlSKERDd/68WhGr06mNw52Fh4cHPj4+pKWl4enpiZsL1nEwGo2kpaXh4+ODh4fsWllnO3fi/70eBteuxy4pRa1Kim7ueqXJXFWtWl2XLl1Ky524qpAOYST+DGmeEbV6fHKyuo6Mdr0//EKsXq2u77wTmja1byzWpNPpaNq0KWfPniUpKcne4diNm5sbkZGRLjPv2qa8vPCrw5ZiiVfUPIfo1tJ76iosTuzc3d1JSUmpsIAiIyODsLAwSkpKrBacIzPVsktLq93jTX/zpdSJcEX//re6/t3v7BuHLej1etq2bVs6HOmK9Hq9S/ZW2kpdthRLzFNbJUW1d92pAa7G4sSuqkmxBQUF6PX6OgfUUJi2FUuvzbZi27aRfLgnYGgQc+xKSkooKiqydxh24enpKfOErOzQIXXR69Uekc7Izc2tXJ1PIWpt9Wr8Vn0L/JOcHNA0C9YhFBSQWKw2I4/uHmirCIWDMTux+/vf/w6ooYZ//vOf+PmVlegoKSlh586dtG/f3voROqjQ47uAu0jbsBvmDLDosbkHT3OlWJXZd+QeO03TuHTpEpmmIkguKjAwkCZNmsiwkpX897/qesQICAqybyxCOLwTJ/CP/xT4J8XFUFhofjHvnFOXuWIqhC/zuV2G2Yndm2++Cah/9u+99165Xgy9Xk90dDTvvfee9SN0UKEG1YOVdtXyaYpJx64BEKjPJyDAx6pxWZMpqQsLC8PHx8flEhtN08jPzyc1NRWAps40GcxOjMayxM4Zh2GFsLqAAHzJK/02J8f8xC4pX80ZCvIrIMAgW7u4CrOzkrNnzwJwzz338Pnnn9O4sWtn/yFhav5IerbljSX5jFqdFBmUAzhmYldSUlKa1AUHB9s7HLsxlaxITU0lLCxMhmXraMcOuHABAgPVNmJCiBoYDHhQgrfbda4ZG5GbWzYVqCaJl9Xfr6i2ktS5Eou7m7Zt22aLOBqc0GZqXl1anuWJWdJ5lRxENXHc4sSmOXU+Po6ZeNYn08+gqKhIErs6Mi2aGDcOZAqaEGYwGADwd8/nmrGRRQsoEhPVdXS01aMSDqxW5U7Onz/Phg0bSE5OrrDy64033rBKYI4utIX6JJRW4G/xY5PT1GMjW1g1JJtwteHXyjT0n0FcXByff/45x44dw9vbm379+rFw4UJuu+22eo3j2jX47DP19YQJ9frUQjRcNxI7P/JIJciikieJOxKBaKL9MwDXHXlxNRYndlu3bmX06NG0bNmS48eP07lzZxITE9E0jZ49e9oiRocUEuULQHpxoMWPTcpWj4lq6zqriIX97Nixg2effZZevXpRXFzMnDlzGDJkCEeOHMHX17fe4ti4Uc0PiopS9euEEGYw9diRDVhW8iTpx8tANNFZCcBgq4cmHJPFid2LL77IH/7wB+bPn4+/vz9r164lLCyMRx99lGHDhtkiRocU2kY1thzNn4JrRry8zazZVFhIsqZWKUV2sry3TwhLffPNN+W+X7lyJWFhYezbt4+777673uL4z3/U9aOPgpQ4E8JMph47o8roLOqxy1SPjWptYUku0aBZ/Of16NGjPPbYY4DaPufatWv4+fkxf/58Fi5caPUAHVVgqyDcdaoYc9p5C+bK6fUkNekNQFQHmb8m6l9WVhYAQdXUGikoKCA7O7vcpS7S0uDrr9XXshpWCAu0agW5ufjfp/5vWDTHLk9tJBDdqf565oX9WZzY+fr6UlCgEpmIiAhOnz5del96err1InNwOi89IWFqIn16nvkVvYuK1KpAgKjohj13SzQ8mqYxc+ZM+vfvT+fOnas8Li4uDoPBUHpp0aJuE0I/+QSKiyEmBjp0qNOphHAt7u7g64ufn/p/YW5il5ujkW5UH96iekjBSFdicWLXp08fvv/+ewBGjBjBH/7wBxYsWMDjjz9Onz59rB6gI6vNtmIXL6paXno9hIfbJi5X9/HHH9OoUSMumDJoYMqUKXTt2rW0t8pVTZ06lUOHDvHxxx9Xe9yLL75IVlZW6eXcuXN1el7TMKz01glRO/4W7heb9IvqZQ/kKoHtm9goKuGILE7s3njjDXr3Vl3C8+bN47777mPNmjVERUXx/vvvWz1AR1aa2KUUm/2YpHe/AqBFQFbDnWeUl1f15fp184+9dq3mY2th/Pjx3HbbbcTFxQEQGxtLfHw8X3/9NYYb81Vc0bRp09iwYQPbtm2jefPm1R7r5eVFQEBAuUttnToFe/aoeXXjx9f6NEK4rhkz8NuxCTC/xy7pYCYAUe7nwVv2iXUlFi+eaNWqVenXPj4+LF261KoBNSQhJ38A+pL+zc/w/8zrrUxOuAJApHca0ECTjJu2k6vg/vth06ay78PCID+/8mMHDIDt28u+j46GW4fzq9ibuDo6nY4FCxbw0EMPERERwVtvvcWuXbto1qwZOTk5DBo0iKKiIkpKSnjuued44oknLH6OhkTTNKZNm8a6devYvn07LVu2rNfnN+00cd990EQ6DoSw3KZN+J8JBUaY3WOXeET93Y32sWBISTiFWiV2P/30U4XdCDIzM+nZsydnzpyxWnCOLtRH9SilpZqffCRduFGcuKnjFid2BiNHjqRjx47ExsayefNmOnXqBKgPIzt27MDHx4f8/Hw6d+7M2LFjnXp3jWeffZaPPvqI9evX4+/vz6VLlwAwGAylO2vYiqaVFSWWYVghaslgwB/VVWduj12iTn2Ai76vja2iEg7K4sQuMTGRkpKSCrcXFBSUm9PkCkIbqyHYtAzzF0EkpanVSVGRNgmpflT3kfHWnRlu7LNaqVvHok1l0q0gPj6eY8eOUVJSQvhNkxnd3d1Ld5K4fv06JSUlaLXoFWxI3n33XQAGDhxY7vaVK1cyceJEmz73jz/C6dPg6wsPPmjTpxLCeRkM+KH+7prdY3dJbe0SfVdD/mcjasPsxG7Dhg2lX8fHx5ebq1RSUsLWrVuJdrF9S0JCVUKXnml+fpycpX5ukQ157z5Litra6thq7N+/n3HjxrFs2TJWr17Nyy+/zKefflp6f2ZmJgMGDODkyZMsWrSIEHM3Xmyg7Jm4mhZNPPig1d5eIVxPLXrskpLUdVSUjWISDsvsjGTMmDGAmr9kqmNn4unpSXR0NH/729+sGpyjCw1XPU5pOWYOZ2kaSddV71FUZylObAuJiYmMGDGC2bNnM2HCBDp27EivXr3Yt28fMTExAAQGBnLw4EEuX77M2LFjeeihh8r16gnrKCqC1avV1zIMK0QdGAz4WbjzROKx60AjovUXgQibhSYcj9nrMo1GI0ajkcjISFJTU0u/NxqNFBQUcPz4cUaOHGnLWElMTGTy5Mm0bNkSb29vWrduzdy5cyvsV1tfQpupLcHS8s3ritDSM0hG1QOL7NbYZnG5qitXrjB8+HBGjx7NSy+9BEBMTAyjRo1izpw5FY4PDw+na9eu7Ny5s75DdQlbt0JGhirrM1h2MxKi9gICSnvszBmKzc+H1OwbQ7E5v9gyMuGALJ5jd/bsWVvEYZZjx45hNBpZtmwZbdq04fDhwzzxxBPk5eWxePHieo8nJFLN1UovNK/3LeNMFvmoYb8WrWWfWGsLCgri6NGjFW5fv3596deXL1/G29ubgIAAsrOz2blzJ08//XR9hukyTLM3xowBD4v/0gghSt00x86cHjvTMGwAWQS2DbVhYMIRmd1j9+OPP/K1aU+gGz788ENatmxJWFgYTz75ZOmOFLYybNgwVq5cyZAhQ2jVqhWjR49m1qxZfP755zZ93qqEdlBJWoaxMUZjzccne7YGIDxco1EjW0YmqnL+/HnuvvtuunXrRv/+/Zk6dSpdu3a1d1hOR9PKErvRo+0bixAN3gsv4L/nW8C8HrvEU2phXxRJ6Jo3s2VkwgGZ/Tl63rx5DBw4kOHDhwPwyy+/MHnyZCZOnEiHDh1YtGgRERERzJs3z1axViorK6vGPS9vTjjruuflzUJ6qeXkRs2Nq1ehpooZZZNZZSsxe4mJiSEhIcHeYTi9/fvV1nm+vjBokL2jEaKB8/XF/8Y0YLN67H7JBoKI1iVDaNVbBwrnZHaPXUJCAoNvmiizevVqevfuzYoVK5g5cyZ///vf+eSTT2wSZFVOnz7N22+/zVNPPVXlMdbe8/Jmnp5gWhxszrZiskpJuArT6PfQoUjvtBBWYKoLf+0aVFJxrJzEY2pXn2jftIplpYTTM/sdv3r1armVgzt27GDYsGGl3/fq1avW+0nOmzcPnU5X7eXnn38u95iLFy8ybNgwxo0bx5QpU6o8t7X3vLxV6bZil2poaUDyarXHbmTBCavGIISjMQ3DPvCAfeMQwikcP47/zLIdcmoajk08reYGRQebuYRWOBWzh2LDw8M5e/YsLVq0oLCwkP379xMbG1t6f05ODp6enrUKYurUqYyvYRPJm2vkXbx4kXvuuYe+ffuyfPnyah/n5eWFl5ftasaFphzkFN1I/+EkDGxf7bFJieo6yjfDZvEIYW+JiXDwoOooGDHC3tEIUTsLFixg06ZNJCQkoNfryczMtF8wmZno//1PPFhKMZ7k5paNFlUm8YL6XxzV1D4VI4R9mZ3YDRs2jNmzZ7Nw4UK++OILfHx8uOuuu0rvP3ToEK1bt65VECEhIWYXib1w4QL33HMPMTExrFy5Ejc7dzOHeGYBkHa+5oUjydmBAES2kRWxwnlt3Kiu+/eved6pEI6qsLCQcePG0bdvX95//337BmMwoAP8dblc1RrXOM8u6ZoaSop+5n7bxyYcjtmJ3WuvvcbYsWMZMGAAfn5+fPDBB+j1ZQnKv/71L4YMGWKTIE0uXrzIwIEDiYyMZPHixaTdNLGtiZ12Fw/1uwaZkJZSXOOxSdfDAClOLJybaX6dDMOKhsw0IrVq1Sr7BgKl3XN+Wg5XqT6xu3YNLqWqrR2j7+9YH9EJB2N2YhcaGsquXbvIysrCz88P91v2BP3000/xM83utJHNmzdz6tQpTp06RfPmzcvdZ69tk0INRXAe0tOqf/78qwWkaepTVGRP597CSriuzEzYsUN9LWVOhKuxWRWGgAAAs4oUJyeraz8/qKZghHBiFo9jGgyGCkkdqOKwN/fg2cLEiRPRNK3Si72EBKlFE2lXqv9Rntuvehf9yKFxK9l1Qjinr7+G4mLo2BHatLF3NELUL5tVYfDxAXd3s4oUJyaq6+iAK+iuyHxuVyTroOsoNFTVpEvLqj6pTTqk5uJFeV5E5yZ17IRzkmFY4chqU4HBEjarwqDTgcFgVo9d4lnV0RF1cTdcuWKd5xcNimz0U0chTdXqo/Rc72qPSz6nkrlI/6s2j0kIeygsVD12IMOwwjFZWoHBUjatwmAw4H9FJXbV9dglnSwAGhFNIjST6uCuSBK7OjJtK5amVT9vLslXTWKNGtfb5jEJYQ87dkB2NoSHwx132DsaISqypAKDw/nxR/yeawyra+ixO34jsWt0WQ3hCpcjQ7F1FHp/LwDSCg1UN9XPNKE1UrYTq1dXr14lNjaWlJQUe4fi9ExFiUeNkmL3ouFLTk4mISGB5ORkSkpKSEhIICEhgVxzNmu1hdBQ/Burvphq59jdGIqNDrFTnMLupMeujkwf/q5fh/x8tTdmZWQ7Mft47rnnuHr1KgcOHOCLL76wdzhOS9Nkfp1wLq+88goffPBB6fc9evQAYNu2bQwcONAuMZkKT1TbY3dRzfeOalZzCS7hnORzdR35+YFpSkVaatVddkk/pwIQlXO4PsISwIYNG8jNzeXLL78kMDCQ//73v/YOyWklJMC5c2rk56YtpYVosFatWlVpBQZ7JXX8978Ebv0MUDu7VDZCVFAAKZlq+DW6pYwOuSpJ7OpIV1JMaOEFAM4eqrx/vKQEzuepEieRwXn1FpurGz16NOvWrQPUH+lHH33UzhE5L9Mw7JAh4F39OiIhRG3s2cNv9s/B062Yb7+FG3/ayjFN+fEhj5BWAfUbn3AYktjVlYcHgzxURdZ/vFv5J6SUixrFeOJOMRHdQuszOiHqhQzDCmFjAQHcxgn+1HMrAM89V3GuXWkNuyjQTXysfuMTDkPm2FnBHyM+4sOkR/h8sx8nTkC7duXvT/41BwigOedxbxFhlxiFsJVz5+DAAbVgYsQIe0fTQF27prbtyMyErCz1H9toVONtgYHQp0/ZsVu2qNoy7u7q4uZW9rWfH9yYCwaoNwfUfBEvL3V/JQXmRQNwY1uxOe0+ZfXVoZw+Da+8Am++WXaIKbGL6ugLbdvWf4zCIUhiZwWdH+7CyNc38qU2isWLYfny8vcnHcwEAojyuACNou0QoWv5+OOPmTRpEqdPn6ZZs2YATJkyhb1797Jr1y4MN/5AupqlS5eyaNEiUlJS6NSpE0uWLOGuu+6q83lNw7D9+kGodEhXpGlw8SIcO1Z2iYyEP/6x7JjgYJXcVaZvX9i9u+z7iRPV+SrTpQscOlT2/X33wfHj5Y8JCFBJQrt28O23Zbe/+aYqaNu4cflLYKA6vg713YQV3Pi75Z2XztKlMHQo/P3vMGEC9OypDjEt0pO3yrVJYmcNTz3FCwsn8KU2ig9WGYmNdaNp07K7k4+rP9iRfg27CrimqZW/9c3HRxVeN9f48eN5/fXXiYuL45133iE2Npb4+Hj27NnjskndmjVrmDFjBkuXLuXOO+9k2bJlDB8+nCNHjhAZGVmnc5sSOylKfJPiYnj2Wdi/XyVyty5jvOOO8oldYKCa+W4wqK/9/FRPnJtbxSGAnj0hIkJN3jUa1bXp61atyh/r6Ql6verhM8nOVhd///LHvv8+/Ppr5a+nefOy3j+Ae+9Vx/r6QqNG6jlMl9BQWLvWnJ+SsITpb1dWFkOGwPjxsHo1/P73sGeP6ohNPGME3Ii+tAcKe6r3Q7gcSeysISqK/qODuHP9d3xf1J8lS2DhwrK7k84a1WFBDbuuUH5+2XL7+pSbW3UZmcrodDoWLFjAQw89REREBG+99Ra7du0q7b3Lyclh0KBBFBUVUVJSwnPPPccTTzxho+gdwxtvvMHkyZOZMmUKAEuWLCE+Pp53332XuLi4Csebu5l5VhZs26a+dsn5ddevw08/qR61a9dg3jx1u4cHbN0Kp0+r793doXVraN9eXbp3L3+ekyfN/wSzcaP58f3yi7rWNJU4ZmerNy0zs+Kxjz2mxvKuXlX3X71a9nVYWPljL1+GS5cqf86bP9UK67kpsQN44w346iv4+Wd47z31OSLxVBHgRfQXS8BdqgC4KknsrOXZZ3lh/UJG05/33tN46SVdaTtMylDZkNQVqj8jR46kY8eOxMbGsnnzZjp16lR6n4+PDzt27MDHx4f8/Hw6d+7M2LFjCQ4OtmPEtlNYWMi+ffuYPXt2uduHDBnC7puH+G4SFxdHbGxsjeeOj4eiImjPUdrF/RXeeceyLLwhOnUKvvlG7Z+2bVvZEGpAALz8ctkcttdeUwlep04qqauu98TWPzOdTvWsNWpUMUkzubkHsSZffqkSvtxc1Rt488VD/q3YxC2JXdOmEBenErqXXoIHH4TERPXBICokT+ZSujBpgdYyeDAjZn1Lx7XXOHLWm3ffBdP/0WStBQCRL/3OjgHWnY9P9YUxbfm8loqPj+fYsWOUlJQQHh5e7j53d3d8bpz0+vXrlJSUoFW3bUgDl56eXunPITw8nEtV9Lq8+OKLzJw5s/T77OxsWrRoUeG4gQPhvYe+xWvtR7BqFezdC598opIZZ/Tww2r862ZNmsCdd6pJhoWFZfVeatiTtEGLipJq6/UtJgbOny9L8FDDsB98oJrds8/CxXS1d3l0ixJ7RSkcgCR21uLmhtuihbzQRY1oLFkCM2aohWilu05EN+yCkTpdw+iM2b9/P+PGjWPZsmWsXr2al19+mU8//bTcMZmZmQwYMICTJ0+yaNGihrt/pAV0twzzaZpW4TYTczczDwuD3396L2z3gEe+gSNHoFcv1XM3aZJlkyMdSXEx/O9/sGaNasym+WidO6seqf79YdgwGD5cLVhoqK9TNBxeXnBjOomJuzssW6ZyPrWxjo5GXCMsSopJujKpY2dlDz8MLVqoKSgfflhWuQDU7cK2EhMTGTFiBLNnz2bChAnMnz+ftWvXsm/fvnLHBQYGcvDgQc6ePctHH33E5cuX7RSx7YWEhODu7l6hdy41NbVCL16tDRyotp8YMkQNTU6eDP/v/9mni7e2NA1+/FEVCGvWTC07/Ne/yor0ATzzDGRkqCHYF16Arl0lqRN21b07TJ9e9n00ieiaSVktVyaJnZV5nj/LzGZrAFi0CM6cUbeHeGTim55kx8ic35UrVxg+fDijR4/mpZdeAiAmJoZRo0YxZ86cSh8THh5O165d2blzZ32GWq/0ej0xMTFs2bKl3O1btmyhX79+1nuisDA172zBArWa8z//KV9Ow1FlZqqY27ZV9eLefhtSU9VG0M88A926lR3buLGaSyeEPcyaBY8/rsrS3GT+fLVwGSCKpAo9e8K1yFCsteXlMWXPZF7lXk6dCmbJG2r5eWTxafCWLjtbCgoK4ujRoxVuX39zjwtw+fJlvL29CQgIIDs7m507d/L000/XV5h2MXPmTCZMmMDtt99O3759Wb58OcnJyTz11FPWfSI3NzWT+667YPNmGDPGuue3hZQUtehB09RcgzFj4JFHVA04T097RydEmX/+Uw0DvfACBAWV3uznp+56ZGQ2vyleC83utmOQwt4ksbO2zp3xG3A7U3e8w3zm8u//qk7RKN05COlRw4NFfTh//jyTJ08u3dR76tSpdO3a1d5h2dRvf/tbMjIymD9/PikpKXTu3JmvvvqKKFtNgL/rLnUxyc1VpTMcYT5CUhLs2KGGigE6dIA//Qk6doTf/KZhTCQVrslgUIndjZWxNxs6FNJPXEGX/Du47TY7BCcchSR2tvDss0zb8TSL+CPXUKsvo/zSVW+GsLuYmBgSEhLsHUa9e+aZZ3jmmWfq/4kzMtReY+np8P33YK15fZY6cULVh/jPf1TvXP/+ZQV9X3/dPjEJYYlbSp7cStcyGlpG11s4wjFJpmELY8YQEuHFFP5ZelNkUJ4dAxLCjgoK1Jy106dVt0JlxXFtKT0dpk5VPXKrVqkVrwMHVr2FlxCOqobETgiQxM42PD3h979nJm/gjipKHBVRWMODhHBSERFqvl14OBw8CKNG1c/edAUFsHgxtGkD//iH2nZrxAi1/9K33zpvrT3hvKpL7C5eVHNFP/igfmMSDkcSO1t58kmiPS/yKi9zJ98xuGu6vSMSwn7atFHbVBgM8N138H//p7asqIrRqIZL6yI7G159Vf0T7N5d1aX78kvo3btu5xXCXkwrsitL7I4dU7udyLQClydz7GylSRN45hlefOt1XvT8G7STxiZcXLduKrEaMgQ2bVIFjD/8UM09PXsWNmxQPXqHDqkN5t3c1FZcbdqosiP33qvOU1KiFmPk5ZVd8vPVdUEBjBypjgsNVTWH9HqYMEG2WBIN3809dnl5cPy42mf41CnYvl3dJ6VOXJ4kdra0ZIm6NGBGo9HeIdid/AysqH9/+OwzeOAB2LJFbZEUGamSuRkzKh7/yy/qMm5c2W3x8WpItSpff612hQB48kmrhi+EXcXGwp//rEqdfPIJTJxY8ZjOnes9LOFYJLETldLr9bi5uXHx4kVCQ0PR6/VVbj/lrDRNo7CwkLS0NNzc3NBXt4m7MN/996uturp0UUkdQI8eqn5c167qYtqmy9Qb0bdv2eNPnVLXbm6qNMmtl0OHyhI7IZxJWFjZ123aqF7ptm3VpU0bVeZk1Cj7xSccgk5z5t3PK5GdnY3BYCArK4sAqSBfrcLCQlJSUsivj4nuDszHx4emTZtWmti5yu+TQ73O4mJ18fKS7bwaIIf6XbIxm75WTZPffxdiye+S9NiJKun1eiIjIykuLqakpMTe4diFu7s7Hh4eLtdb6dA8PNRFCFcmf5NEFeSvo6iWTqfD09MTT9laSQghhHB4Uu5ECCGEEMJJSGInhBBCCOEkJLETQgghhHASLjfHzrQIODs7286RCGdg+j1y9sXl0m6EtbhKmwFpN8J6LGk3LpfY5eTkANCiRQs7RyKcSU5ODgZTVXgnJO1GWJuztxmQdiOsz5x243J17IxGIxcvXsTf379CCYvs7GxatGjBuXPnnLK+krw+69M0jZycHCIiInBzc96ZDdJunPP1SZuxrarajTP/ToG8PluwpN24XI+dm5sbzZs3r/aYgIAAp/xlNJHXZ13O3usA0m7AuV+ftBnbqKndOPPvFMjrszZz241zf1wSQgghhHAhktgJIYQQQjgJSexu4uXlxdy5c/Hy8rJ3KDYhr0/YgrP/3J359Tnza3Nkzv5zl9dnXy63eEIIIYQQwllJj50QQgghhJOQxE4IIYQQwklIYieEEEII4SQksRNCCCGEcBKS2AkhhBBCOAmXS+yWLl1Ky5YtadSoETExMezatava43fs2EFMTAyNGjWiVatWvPfee/UUqWXi4uLo1asX/v7+hIWFMWbMGI4fP17tY7Zv345Op6twOXbsWD1Fbb558+ZViLNJkybVPqahvHcNgTO2G2kzFTWE962hcMY2A9JuKuNw753mQlavXq15enpqK1as0I4cOaJNnz5d8/X11ZKSkio9/syZM5qPj482ffp07ciRI9qKFSs0T09P7bPPPqvnyGs2dOhQbeXKldrhw4e1hIQEbcSIEVpkZKSWm5tb5WO2bdumAdrx48e1lJSU0ktxcXE9Rm6euXPnap06dSoXZ2pqapXHN6T3ztE5a7uRNlNeQ3nfGgJnbTOaJu3mVo743rlUYnfHHXdoTz31VLnb2rdvr82ePbvS4//0pz9p7du3L3fb73//e61Pnz42i9FaUlNTNUDbsWNHlceYGtvVq1frL7Bamjt3rtatWzezj2/I752jcZV2I22mYb5vjshV2oymSbtxxPfOZYZiCwsL2bdvH0OGDCl3+5AhQ9i9e3elj/nhhx8qHD906FB+/vlnioqKbBarNWRlZQEQFBRU47E9evSgadOmDB48mG3bttk6tFo7efIkERERtGzZkvHjx3PmzJkqj23I750jcaV2I22mYb5vjsaV2gxIu3HE985lErv09HRKSkoIDw8vd3t4eDiXLl2q9DGXLl2q9Pji4mLS09NtFmtdaZrGzJkz6d+/P507d67yuKZNm7J8+XLWrl3L559/zm233cbgwYPZuXNnPUZrnt69e/Phhx8SHx/PihUruHTpEv369SMjI6PS4xvqe+doXKXdSJtpmO+bI3KVNgPSbsAx3zsPuzyrHel0unLfa5pW4baajq/sdkcydepUDh06xHfffVftcbfddhu33XZb6fd9+/bl3LlzLF68mLvvvtvWYVpk+PDhpV936dKFvn370rp1az744ANmzpxZ6WMa4nvnqJy93UibURra++bInL3NgLQbE0d771ymxy4kJAR3d/cKn5hSU1MrZNsmTZo0qfR4Dw8PgoODbRZrXUybNo0NGzawbds2mjdvbvHj+/Tpw8mTJ20QmXX5+vrSpUuXKmNtiO+dI3KFdiNtRmlo75ujcoU2A9JuTBzxvXOZxE6v1xMTE8OWLVvK3b5lyxb69etX6WP69u1b4fjNmzdz++234+npabNYa0PTNKZOncrnn3/O//73P1q2bFmr8xw4cICmTZtaOTrrKygo4OjRo1XG2pDeO0fmzO1G2kx5DeV9c3TO3GZA2s2tHPK9s8OCDbsxLUF///33tSNHjmgzZszQfH19tcTERE3TNG327NnahAkTSo83LWN+/vnntSNHjmjvv/++3ZcxV+Xpp5/WDAaDtn379nLLtPPz80uPufX1vfnmm9q6deu0EydOaIcPH9Zmz56tAdratWvt8RKq9Yc//EHbvn27dubMGW3Pnj3ayJEjNX9/f6d47xyds7YbaTMN831rCJy1zWiatJuG8N65VGKnaZr2j3/8Q4uKitL0er3Ws2fPcku0H3vsMW3AgAHljt++fbvWo0cPTa/Xa9HR0dq7775bzxGbB6j0snLlytJjbn19Cxcu1Fq3bq01atRIa9y4sda/f39t06ZN9R+8GX77299qTZs21Tw9PbWIiAht7Nix2q+//lp6f0N+7xoCZ2w30mYa5vvWUDhjm9E0aTcN4b3TadqNWX5CCCGEEKJBc5k5dkIIIYQQzk4SOyGEEEIIJyGJnRBCCCGEk5DETgghhBDCSUhiJ4QQQgjhJCSxE0IIIYRwEpLYCSGEEEI4CUnshBBCCCGchCR2QgghhBBOQhK7BmzgwIHMmDHD3mFUaeDAgeh0OnQ6HQkJCWY9ZuLEiaWP+eKLL2wan3BN0m6EsJy0m4ZDEjsHZfplq+oyceJEPv/8c1599VW7xDdjxgzGjBlT43FPPPEEKSkpdO7c2azzvvXWW6SkpNQxOuGqpN0IYTlpN87Fw94BiMrd/Mu2Zs0aXnnlFY4fP156m7e3NwaDwR6hAfDTTz8xYsSIGo/z8fGhSZMmZp/XYDDY9XWJhk3ajRCWk3bjXKTHzkE1adKk9GIwGNDpdBVuu7VrfODAgUybNo0ZM2bQuHFjwsPDWb58OXl5eUyaNAl/f39at27N119/XfoYTdP461//SqtWrfD29qZbt2589tlnVcZVVFSEXq9n9+7dzJkzB51OR+/evS16bZ999hldunTB29ub4OBg7r33XvLy8iz+GQlxK2k3QlhO2o1zkcTOyXzwwQeEhISwd+9epk2bxtNPP824cePo168f+/fvZ+jQoUyYMIH8/HwA/vznP7Ny5Ureffddfv31V55//nl+97vfsWPHjkrP7+7uznfffQdAQkICKSkpxMfHmx1fSkoKDz/8MI8//jhHjx5l+/btjB07Fk3T6v7ihaglaTdCWE7ajYPShMNbuXKlZjAYKtw+YMAAbfr06eW+79+/f+n3xcXFmq+vrzZhwoTS21JSUjRA++GHH7Tc3FytUaNG2u7du8udd/LkydrDDz9cZTzr1q3TgoODa4z71vg0TdP27dunAVpiYmK1jwW0devW1fgcQlRF2o0QlpN20/DJHDsn07Vr19Kv3d3dCQ4OpkuXLqW3hYeHA5CamsqRI0e4fv069913X7lzFBYW0qNHjyqf48CBA3Tr1q1W8XXr1o3BgwfTpUsXhg4dypAhQ3jooYdo3Lhxrc4nhDVIuxHCctJuHJMkdk7G09Oz3Pc6na7cbTqdDgCj0YjRaARg06ZNNGvWrNzjvLy8qnyOhISEWjc0d3d3tmzZwu7du9m8eTNvv/02c+bM4ccff6Rly5a1OqcQdSXtRgjLSbtxTDLHzoV17NgRLy8vkpOTadOmTblLixYtqnzcL7/8Uu6TmqV0Oh133nknsbGxHDhwAL1ez7p162p9PiHqk7QbISwn7ab+SI+dC/P392fWrFk8//zzGI1G+vfvT3Z2Nrt378bPz4/HHnus0scZjUYOHTrExYsX8fX1tWi5+I8//sjWrVsZMmQIYWFh/Pjjj6SlpdGhQwdrvSwhbErajRCWk3ZTf6THzsW9+uqrvPLKK8TFxdGhQweGDh3Kxo0bq+2mfu2111izZg3NmjVj/vz5Fj1fQEAAO3fu5P7776ddu3b8+c9/5m9/+xvDhw+v60sRot5IuxHCctJu6odO05x93a+wl4EDB9K9e3eWLFli8WN1Oh3r1q0zq9q4EM5E2o0QlpN2U0Z67IRNLV26FD8/P3755Rezjn/qqafw8/OzcVRCODZpN0JYTtqNIj12wmYuXLjAtWvXAIiMjESv19f4mNTUVLKzswFo2rQpvr6+No1RCEcj7UYIy0m7KSOJnRBCCCGEk5ChWCGEEEIIJyGJnRBCCCGEk5DETgghhBDCSUhiJ4QQQgjhJCSxE0IIIYRwEpLYCSGEEEI4CUnshBBCCCGchCR2QgghhBBOQhI7IYQQQggn8f8BujEDgd0BZawAAAAASUVORK5CYII=", "text/plain": [ "
" ] @@ -441,12 +490,22 @@ "plot_state_comparison(timepts, kf_resp.outputs, lqr_resp.states)" ] }, + { + "cell_type": "markdown", + "id": "6417b46d-b527-47c3-a13c-0a8c0d9006d9", + "metadata": {}, + "source": [ + "We see that the Kalman filter does a good job of estimating most states, but the estimates for $x_2$ ($y$) and $x_4$ ($\\dot y$) are not very close." + ] + }, { "cell_type": "markdown", "id": "654dde1b", "metadata": {}, "source": [ - "### Extended Kalman filter" + "### Extended Kalman filter\n", + "\n", + "To try to improve the performance of the estimator, we construct an extended Kalman filter, which uses the linearization of the dynamics at the current state rather than a fixed linearization." ] }, { @@ -459,13 +518,13 @@ "name": "stdout", "output_type": "stream", "text": [ - ": sys[8]\n", + ": sys[6]\n", "Inputs (8): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'F1', 'F2']\n", "Outputs (6): ['xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", "States (42): ['x[0]', 'x[1]', 'x[2]', 'x[3]', 'x[4]', 'x[5]', 'x[6]', 'x[7]', 'x[8]', 'x[9]', 'x[10]', 'x[11]', 'x[12]', 'x[13]', 'x[14]', 'x[15]', 'x[16]', 'x[17]', 'x[18]', 'x[19]', 'x[20]', 'x[21]', 'x[22]', 'x[23]', 'x[24]', 'x[25]', 'x[26]', 'x[27]', 'x[28]', 'x[29]', 'x[30]', 'x[31]', 'x[32]', 'x[33]', 'x[34]', 'x[35]', 'x[36]', 'x[37]', 'x[38]', 'x[39]', 'x[40]', 'x[41]']\n", "\n", - "Update: \n", - "Output: \n" + "Update: \n", + "Output: \n" ] } ], @@ -524,7 +583,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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1TZvKZeeee+Sff/nFaCEJDTBkyBBGjx7Nhg0baNmyJf/995+xQ7I46j51qn9JL7xAv8u/AY24X50Z27NnD6tXr+a3336jW7dudOvWjePHj+v1nqKmTgNVaurC5f5Xh47ZwOHD0K+fESMT6mPkyJGMFNWseqVUgmoy9WbN5K8P3q/k99+t+GXBJWbP9Ko+Ik0waZs2bTJ2CBYvJ7MMsMVz/VKIegKefJL+b+7kE14R/erMUN++fVEaeOFr8V9VA6o+dU2aQNeuoEBJOi25tL161asgCHKZUf0va9pU/joiuhxbSonP8SF+lVhqTxAqkyTIvSK/JXvu/k3e+dhj3KGQR4yfOlX3rB2CACKp00jlmromTSDYU56s9vC2q8YLShBMmKrp1d7+5mwCbl52RPmdBGDtN+LdSRAqKyyEkjJ57jVPvxsT3Ldogcew3oQiN9nt3m2s6ARzIZI6DVTuUwcQFloKwKHjtkaKSBBMW+WRr5U9OKocgF/+80cMgxWEm1T96Rwoxsm30pqhTz1Ff+S2153bKowQmWBORFKngco1dQBhA+Xs7lBOK8jPN1JUgmC6Kg+SqGzkax2xoYxjpR05s+GMweMSBFNVZY4670pTZg0dSj83uaZu1wbxfiPUTSR1GqiW1PW/kdQRJg+WEAShitpq6tz9mzDI6xgAa+enGjYoQTBhldd9rZLU2drSb1wgAEeSm4p6BKFOIqnTQOWBEgDdu8tfk2jN5V0njROUIJiwynPU3erBocUA/LJHTPosGNaCBQsICgrCwcGB8PBwdmk4T8iePXuwsbGhW7dueout1po6oMUnMbRuLaFUKti7V28hCBZAJHUauLWmrmlTaO13HYDDXcYbJyhBMGG1Nb8C3PtaB6yo4GBxJ5KOimoHwTBWr17N1KlTeeONNzh8+DD9+vUjOjqalJSUOs/Ly8tj/PjxDBo0SK/xVVn39ZakDjs7+veXp9MSU5sIdRFJnQZuHSgBENZHXkz4UGITI0QkCKattuZXAK+OngzsJdfWrY11NVxQQqP26aef8uSTT/LUU0/RsWNH5s+fT6tWrfjmm2/qPO/ZZ5/lkUceITIyUq/xqWvqJgyHvn2r/V41JequtZegQgyYEGomkrrbkKTqNXUAqrV6Dx0yfEyCYOrqqqmDm2vBitUlBEMoLS3l4MGDREVFVdkfFRXF3jraM5ctW8a5c+eYOXOmRvcpKSkhPz+/yqYpdZ+6wCY35wGqpH9feeLH/aebcn3DVo2vKzQuIqm7jeLimzMvVE7qwsLkr4c2ZYnBEoJwi7r61AGMGiV/3b9fIjdHTG0i6FdOTg4VFRX4+PhU2e/j40NmZmaN55w5c4bp06fzww8/YGOj2eJL8+bNw83NTb21atVKixjlr56eNf++TTsrfJ3yKMWe/d8d0/i6QuMikrrbUA2SAHByuvm9arDE6Sve5P8lFuUzVVeuXGH27NlkZGQYO5RGpa7mVwBfXwj1yECSFGz94oTB4hI0Y6nlRqFQVPlZkqRq+wAqKip45JFHmD17Nu3bt9f4+jNmzCAvL0+9paZqPsI7J03ukuCxrebqa4UC+nXIBmDnGTHIyBSZQrkRSd1tqJpenZ2rLlXp5QWt3OTFLY9uu2yEyARNTJkyhf/++4/nnnvO2KE0KrdrfgUY7CUvFbb5r+v6D0jQiqWVG09PT6ytravVymVlZVWrvQMoKCjgwIEDPP/889jY2GBjY8OcOXM4evQoNjY2bN1ac/Onvb09rq6uVTZN5VyS+8l5/vd3rceEdygE4FRuLdV5glGZQrlpUFJ3/brl/zOuaZCESlinEgAOHdOsal4wrPXr13Pt2jX+/PNPmjZtyg8//GDskBqN29XUAQweLH/dHO+n93gEzVliubGzsyM8PJzY2Ngq+2NjY+nTp0+1411dXTl+/DhHjhxRbxMnTiQ4OJgjR47Qq1cvnceYc+XGEmFe1WsOVXxayO812YXOOr+/0DCmUm60zkaUSiXvvvsuCxcu5NKlS5w+fZrWrVvz1ltvERgYyJNPPqmPOI2mpkESKmH9m/B73I2VJa5erbtaQjC4kSNHMnLkSACWL19u3GAamdv1qQPo/1hrbL4q4/z1FpxPKKF1R3vDBCfUyVLLTUxMDOPGjSMiIoLIyEgWL15MSkoKEydOBOSm0/T0dFasWIGVlRWhoaFVzvf29sbBwaHafl2QJMjNl9d79fStfflJr1byrAvZJWLUuKkxlXKjdU3dO++8w/Lly/nwww+xs7NT7+/cuTPfffedToMzBbdOPFxZWF+5k90hwsQwWEGoRJPmV5ewdkTaHgBgy7K65woThIYaM2YM8+fPZ86cOXTr1o2dO3eyYcMGAgICAMjIyLjtnHX6cu0alFbINXUeLRxqPc47SK6hyypvJtZOFmqkdVK3YsUKFi9ezKOPPoq1tbV6f5cuXTh16pROgzMFddbU3RgBG08nivYeMVhMgmDKrl+HErlnQp01dSgUDG4vv4lu/rtM/4EJjd6kSZNITk6mpKSEgwcP0r9/f/Xvli9fzvbt22s9d9asWRw5ckQvcalGvjpShFOL2guNV7umAGTb+CJVKPUSi2DetE7q0tPTadu2bbX9SqWSsjLt/jHv3LmTESNG4Ofnh0Kh4Lfffqvz+O3bt6NQKKpt+kwm6+pT5+sLPk2uocSaY3GFeotB0M6qVatwcHAgPT1dve+pp56iS5cu5OXlGTGyxkFVS6dQ1FxuKhscJX8w3JLYAqV4jzIqUW6Mp8oSYTUM3FBRNb+WlltTUGRd63GC4ZhaudE6qQsJCalxvbw1a9bQXTXPh4YKCwvp2rUrX331lVbnJSYmkpGRod7atWun1fnaqKumTqGAsF5y/4dDQ9/UWwymQJKgsNA4m7atDGPHjiU4OJh58+YBMHv2bDZt2sTff/+Nm5ubHv46QmWVm16tbvMfpsf/gnGxukZumRtHj+o9NIMT5UbQhHri4RrWfa3Myenm1FrZ2QYIzEhEuak/rQdKzJw5k3HjxpGeno5SqWTdunUkJiayYsUK/vzzT62uFR0dTXR0tLYh4O3tTVMDDUqoK6kDCI+05+8tcOhw7SOWLEFRUe1/A327dk2eUkZTCoWCd999lwcffBA/Pz8+//xzdu3aRYsWLdTH/Pnnn7z88ssolUpee+01nnrqKT1E3jhpMvJVxTasMwOHwR9/wObNN+d/tBSWVm7uu+8+tm/fzqBBg/hFLAeiM+p1X/uFQFRwncd6Ny0luciOrJPZtGnjZYDoDM+Syk1qairjxo0jKysLGxsb3nrrLUaPHq2n6OtRUzdixAhWr17Nhg0bUCgUvP322yQkJPDHH39w99136yPGarp3746vry+DBg1i27ZtdR7bkGVboO6BElBpZQkxTsKkDB8+nE6dOjF79mx+/fVXQkJC1L8rLy8nJiaGrVu3cujQIT744AMuXxZzDeqKJoMkKlNNbXLLbBOCEdRVbkCeh2vFihVGis5yqZtfW9jfNqPwKjgHQPbOBH2HJWiornJjY2PD/PnziY+PZ/Pmzbz00ksUFuqvu1a9JlgbMmQIQ4YM0XUst+Xr68vixYsJDw+npKSE77//nkGDBrF9+/YqHV4rmzdvHrNnz673PW9XU6dK6k4cKafkz23YDzdMYmtoTk43/xbGuLe2Nm3axKlTp2pcGmj//v2EhISoP0kNHTqUTZs28fDDD+si3EZPm5o6uJnU7dqp5Pp1KxxqH/xndiyp3ADceeeddQ4mEOrndkuEVeblXAQFkJ1huYOLLKnc+Pr64usrrwDi7e2Nu7s7ly9fxlmb6kAtaJ3UtW7dmv/++w8PD48q+69evUpYWBjnz5/XWXC3Cg4OJjj4ZtV0ZGQkqampfPzxx7UmdTNmzCAmJkb9c35+vlbr8dU1UALA3x/c7a5xubQJ8f+k0X24xpc2KwqFdlXSxnTo0CFGjx7NokWL+Omnn3jrrbdYs2aN+vcXL16s0qTUsmXLKp1chYbRZI66yjqSgC9uZJT4sXdXBXfdbTkdwC2p3Aj6k5taBDjheTgWqLtiwMu1BDIh65JBQjMKSy03Bw4cQKlUapWDaEvr5tfk5GQqKiqq7S8pKTHKG2Pv3r05c+ZMrb9vyLItcPuaOoUC2nvKTXdJZ8q1urage8nJyQwbNozp06czbtw45syZw9q1azl48KD6GKmGnrA1rf8o1I+2za+K9u0YbLMdgM0/5eglJqFumpQbQX9y0uTVmTxO7rjtsd7N5PeZ7FyxyqexaVNucnNzGT9+PIsXL9ZrTBrX1K1fv179/aZNm6qM6qioqGDLli0EBgbqNDhNHD58WF21qQ+361MH4N+8lH0X4UKqKGTGdPnyZaKjoxk5ciSvv/46AOHh4YwYMYI33niDjRs3AtCiRYsqH0DS0tL0suxPY6Vt8ys2NgzukM73J+TBEu/pKzChRpqWG0F/crLlD5qeTatXmNzKy1M+Nvtq7StPCPqnTbkpKSnhvvvuY8aMGTUuS6dLGid1o0aNAuQajQkTJlT5na2tLYGBgXzyySda3fzatWucPXtW/XNSUhJHjhzB3d0df3//Ksu2AMyfP5/AwEBCQkIoLS1l5cqVrF27lrVr12p1X+1ilL/WldQFBCrgEKRkWVBnIDPk7u5OQkL1zsO///57lZ979uzJiRMnSE9Px9XVlQ0bNvD2228bKkyLp21NHcDg4Q5wAg6keHHlihYJodBgmpYbQX9yLssVAhr1qWsud0/ILhDvN8akabmRJInHHnuMu+66i3Hjxuk9Lo2TOuWNmUGDgoL477//8NTk1XcbBw4c4M4771T/rOr7NmHCBJYvX15t2ZbS0lJeeeUV0tPTcXR0JCQkhL/++ouhQ4c2OJba3K5PHYB/e3nNygt5Yi4nc2BjY8Mnn3zCnXfeiVKpZNq0adX6iAr1p22fOgC/4WF0ev8k8YSwbavE/Q+I5nBTNGTIEA4dOkRhYSEtW7bk119/pUePHsYOy+zl5stvxZ7Nb/+W7N1CrqHLKjbSnB+CVvbs2cPq1avp0qWLeoGF77//ns6dO+vlfloPlEhKStLZzQcOHFhj/yaVWxfFnTZtGtOmTdPZ/TWhUU1dZzmZSyltLq+RZEnD9yxU5cWXBd3SuvkVICKCwdbfEV8RwuZfrnL/A6KqzhRt2rTJ2CFYHEmCnEJHADz87G97vFe4PwDZjv56jUvQjb59+6orxQyhXlOaFBYWsmPHDlJSUigtLa3yuylTpugkMFOhSVLn30n+5QUCIDMTjNC3UBBMRX2aX7G3Z3Cni3xxHDZvFbV0QuNRUABlSvmt2CPg9rVvXl3kPuTZ15yQJHmwniCoaJ3UHT58mKFDh1JUVERhYSHu7u7k5OTg5OSEt7e3xSV1mgyUCAiUS1UOXhR5e1GPaW4EwWLUq6YOGBATjvWTSs5kNeXCBQgI0HlogmByVHPUOVGIU0v32x7vdWMRiZISudLhdusrC42L1sM1X3rpJUaMGMHly5dxdHRk3759XLhwgfDwcD7++GN9xGg0SqW8FhzUndS5ud0sWJW6AApCo1SvmjrA9bH76dVb/pe0ebNuYxIEU6Ve97WFPdx3322Pd3YGJwd5lGzWhWJ9hiaYIa2TuiNHjvDyyy9jbW2NtbU1JSUltGrVig8//FA9rNdSFBffXNy3rk9DCsXNWgWR1AmNWUUFqFbiq88IVtXqEiKpExoL9bqv3jYaV7t5lcpTMmUfz9RXWIKZ0jqps7W1VU/U6uPjox6d6ubmVmWkqiVQ9adTKMDRse5j/R2zALiw2LI6Etc1kKWxEH8DzeXl3fxe25o6gMGd5Wnyt2wsxYB9i3Wusb9mGvvj14Y2S4SpeNleBSD7QpHuAzKixv660cXj1zqp6969OwcOHADkdQDffvttfvjhB6ZOnaq3IbrGoupP5+wMVrf5SwU4ZQOQcvSKnqMyDFtbedh8UZFl/dOoD9XfQPU3EWqnanp1cgI7O+3P7/XvFzhzjeyrdhw/rtvYDEGUG5lqAJ21teUs+aYvOalyE6pnykE0/STj5SDXOGSlld7mSPOgep3cOvCysdHFe43WAyXee+89Cm5kO3PnzmXChAk899xztG3blmXLltU7EFOkychXFf8gG9gBKbmWMUzC2tqapk2bkpUl10A6OTk1uqW0JEmiqKiIrKwsmjZtKt6gNFCfOeoqs7urLwM+3sEGhrF5M3TtqrvYDEGUG3lO0+zsbJycnLCxqdcEC41KTkoh4Ihn0gGwCtfoHO8mRZAH2RmWsTSljY0NTk5OZGdnY2tri9XtalEsjC7fa7QucREREervvby82LBhQ71vbuq0SeoCOsrJ3IWC249eMhfNmzcHUL9BNVZNmzZV/y2EutV35Ktanz7crZjFBmkYm/+8zssvm9+cj6LcgJWVFf7+/o0uoa2P3Ay5dsqjSYnG53i5lUI6ZGfrKyrDUigU+Pr6kpSUxIULF4wdjtHo4r1GfIyqgyarSaj4d5XfxVLK/eQhs87OeozMMFQFzdvbm7KyMmOHYxS2trYmWUO3YMECPvroIzIyMggJCWH+/Pn069evxmO3b99eZeUWlYSEBDp06KDTuOo78lXNzY3BHdIhAXbstaGkBOxvPx+rSRHlBuzs7BpdbUt95VySm1w9m2pe6+blLo9+zb5sOX9jOzs72rVr12ibYHX1XqN1Upebm8vbb7/Ntm3byMrKqjZT8uXLlxsclKnQqqYuRD4olVZUXDiPdadgPUZmWKqRzoJpWL16NVOnTmXBggXccccdLFq0iOjoaOLj4/H3r32W+cTERFxdXdU/e6kmvNKhBtfUASFDWuKTkMml0ubs2wcDBugkNIMT5UbQRM5luTbT00PzTvKqopt1tR4dV02YlZUVDmJFpgbROs3/3//+R2xsLBMmTODjjz/ms88+q7JZEk0mHlbx9QUbyijHlsxjjbfZRdC/Tz/9lCeffJKnnnqKjh07Mn/+fFq1asU333xT53ne3t40b95cvekj4WhwTR2gGNCfwchzmsTGNjwmQVBZsGABQUFBODg4EB4ezq5du2o9dt26ddx99914eXnh6upKZGSkXpZJy7kqd4r39Nb87di7b3sAsp0CdR6PYN60rqnbvXs3u3fvpqu59WCuB21q6qytoaVjLsnFzblwppQW+g1NaKRKS0s5ePAg06dPr7I/KiqKvXv31nlu9+7duX79Op06deLNN9+ssUlWpaSkhJKSm3188lWTz91GQwdKANCvH4N5hR/4H5s3lvHOO2LUsdBw2tZw79y5k7vvvpv33nuPpk2bsmzZMkaMGMG///5L9+7ddRZXbqHcv8CjheY1VF595Zag7OtiOQmhKq1r6jp06EBxceOYxVqbPnUA/mFynXhKu0F6ikho7HJycqioqMDHx6fKfh8fHzIza56I1NfXl8WLF7N27VrWrVtHcHAwgwYNYufOnbXeZ968ebi5uam3Vq1aaRSfLppf8fBg0MrHAfjvsK36moLQENrWcM+fP59p06bRo0cP2rVrx3vvvUe7du34448/dBaTJEFOsdz/2rPVbSZDrUTV/JqdfXOCfEGAeiR1CxYs4I033mDHjh3k5uaSn59fZbMk2tTUAQS0lpuzGvHgHcFAbh1VKElSrSMNg4ODefrppwkLCyMyMpIFCxYwbNiwOpf1mzFjBnl5eeotNTVVo7h00fwK0OrR/gQHy9N2bd/esGsJgqqGOyoqqsp+TWq4VZRKJQUFBbi71z7DQUlJiVbvifn5UC7JDWYeT91+iTAVbze5Fv369ZvvU4IA9UjqmjZtSl5eHnfddRfe3t40a9aMZs2a0bRpU5o16OO56dE2qVPV4FvYwhqCCfH09MTa2rparVxWVla12ru69O7dmzNnztT6e3t7e1xdXatsmtBJ8+sNd98tfxVLhgkNVZ8a7lt98sknFBYW8tBDD9V6jLY13KrVJJydwbG5m0ZxADgXZeOIPFFtdpaoqhNu0jqpe/TRR7Gzs+PHH39ky5YtbN26la1bt7Jt2za2bt2qjxiNRpuBEgABDvISRxd+O6SniITGzs7OjvDwcGJvGUEQGxtLnz59NL7O4cOH8fX11XV4uml+vWHwlTUAbP67cU4LIuieNjXcla1atYpZs2axevVqvL29az1O2xru3Fz5q4fH7WOvwsMDL+RJ6rKTC7U8WbBkWg+UOHHiBIcPHyY42HKm7KiN1jV1PnKVeEqmZQ0zF0xLTEwM48aNIyIigsjISBYvXkxKSgoTJ04E5DeW9PR0VqxYAch9gwIDAwkJCaG0tJSVK1eydu1a1q5dq/PYdNX8CjDw9GKsuJ/E87akpoKG3foEoZqG1HCvXr2aJ598kjVr1jB48OA6j7W3t8dei4kVczLKAFs8r6dCibfmkzI6OuKlyCVFCiDr/DUYpOGblGDxtK6pi4iI0Lh/jbnTdqBEQJj8ceuCspXcWUIQ9GDMmDHMnz+fOXPm0K1bN3bu3MmGDRsICAgAICMjg5RKfQBKS0t55ZVX6NKlC/369WP37t389ddf3H///TqPTZc1dW5RvejJfkA0wQoNU98a7lWrVvHYY4/x448/MmzYMJ3HlZMsNwd5ZieAlut9ettfBSA7pXEMXBQ0o3VN3QsvvMCLL77Iq6++SufOnastPNulSxedBWds2tbUteogj2LKx428hFO49dKsH5IgaGvSpElMmjSpxt8tX768ys/Tpk1j2rRpeo9JknRbU8dddzH43c3sI5LNmyUef1wsOSXUn7Y13KtWrWL8+PF8/vnn9O7dW13L5+joiJub5v3f6pKTUgS442l/DbRcgcPLsRCuQ3Z641yBQaiZ1kndmDFjAHjiiSfU+xQKhbpvQkVFhe6iMzJt+9Q5O4On9WVyKty5cCiXLr30F5sgmJrCQii/sdKRTsZMRUYy2HYe75TB5k0VSJINYilRob7GjBlDbm4uc+bMISMjg9DQ0DpruBctWkR5eTmTJ09m8uTJ6v0TJkyo9sGpvnIvyl12PJpc1/pcrybFcAWyL1nOe67QcFondUlJSfqIwyRpW1MH4O+cS06+OykJhVhOnaUg3J6q6dXGRkdLHzs60vsOa5y2F5KV68yJE9C5sw6uKzRa2tRwbzfAXDo5NxIyT1ftBwN5NS2DVMjKFp90hJu0TupUn2oaA2371AH4u1/jUD5cOCtG7AmNS+WmV13VqNkP7kf/7TvZSDSbN4ukTrAsqilNtFn3VcV7YCc4Dtm2Yv0i4SaNkrr169cTHR2Nra0t69evr/PYkSNH6iQwU1CfmroA31JIhpRcXVRVCIL50OUcdWp33cXd9n+wsSSa2Fh46SUdXlsQjCznivwW7Oml/acgr6ju8CVkl4i+28JNGiV1o0aNIjMzE29vb0aNGlXrcZbUp06plPsIgZbNr6PCIQ4uBA3UR1iCYLJ0OfJVrVcvBu/tAeGwYweUloKdmDFIsBC51+QXs4ev9i/qykuFCYKKRsNtlEqlesJFpVJZ62YpCR1AUdHNNfW0qqlrI+fJYlUJobHR6chXFSsrQrvZ4O0tl8l9+3R4bUEwshx7uenUc4L206V4O8jTZmVdUor1XwU1reepW7FiBSUlJdX2l5aWqoeCWwJV06tCAU5Omp+nWipMrP8qNDZ6qalDnulh0CD5ezFfnWApJAlycuRmV8/W2jehep2R16y9XmKlblUSBK2Tuscff5y8vLxq+wsKCnj88cd1EpQpqNyfTptO3wEt5drKjItKSrOu6j4wQTBReqmpA0hPZ3DsawBsjlXq+OKCYBx5eaBq3NJ6mTDAuUVTHJAnHhZNsIKK1kldbWvlpaWlaT0h486dOxkxYgR+fn4oFAp+++23256zY8cOwsPDcXBwoHXr1ixcuFCre2qqPoMkALyaW+NAMRJWpB3UbKFoQbAEehkoAeDry+CyvwHY/5+CGj5TCoLZyc2R20ydbUtwKNV+BSKF5831X7OydBqaYMY0Tuq6d+9OWFgYCoWCQYMGERYWpt66du1Kv379brsu3q0KCwvp2rUrX331lUbHJyUlMXToUPr168fhw4d5/fXXmTJlil7WsNR24mEVhQL87S8BkHL0qm6DEgQTpq/mV6ys8B/cnnacpqJCgQGmDxMEvZNXkwDPsgx5ckdteXjgjZzNZV8UU2gJMo1fSapRr0eOHGHIkCE0qZTt2NnZERgYyAMPPKDVzaOjo4mOjtb4+IULF+Lv78/8+fMB6NixIwcOHODjjz/W+t63U9+aOgB/lyucLgkkJbFIpzEJginTW/MrwKBB3L02ljO0Z/NmuPdePdxDEAwo51we4Iyn1WVwCtT+Ak2b4oU80V32hSJAN0uXCeZN46Ru5syZAAQGBjJ27Fjs7e31FlRt4uLiiIqKqrJvyJAhLFmyhLKysmrr0AKUlJRUGdiRn69ZNXdDkroAzyLIgQvnRf8fofHQW/MryOvAMp0FTL7Rr07rniOCYFJyLsijGzztr9XvAlZWeNnlQSlkpxYjkjoB6vGf8a677iK7Uq/M/fv3M3XqVBYvXqzTwGqSmZmJj49PlX0+Pj6Ul5eTo5qa+xbz5s3Dzc1NvbVq1Uqje9VnNQkV/xZy79eUjOpJpiBYKr01vwK0b8/A5olYUcGpRCvS0vRwD0EwoJz0G+u+OhXX+xpeznJimJVu+ObX8nJ49134eXkR0oOjITnZ4DEI1Wmd1D3yyCNs27YNkJOswYMHs3//fl5//XXmzJmj8wBvdesgDenGBD01Dd4AmDFjBnl5eeotNTVVo/s0qKbuxlx1F3LEqhJC46HX5leFgmaDw4ngAABbtujhHoJgQLmZciLm6Vpa72t4DwwBILtcH5+k6nD1Kv8XuZA334QxjzsxZu1orvQdAWfPGjYOoRqtk7oTJ07Qs2dPAH7++Wc6d+7M3r17+fHHH6stiKxrzZs3JzOz6ojSrKwsbGxs8KhlTLi9vT2urq5VNk3Ud6AEgH8HeWK7lBJv7U8WBDOl15o6gNGjGRwu30TMVyeYu5xsuULC073+3XS8hvcCILuoHm9UDbFyJYsOhKl/XMNDdE3/i529XoWEBMPGIlShdVJXVlam7k+3efNm9VqvHTp0ICMjQ7fR3SIyMpLY2Ngq+/755x8iIiJq7E/XEA2qqRvRBYAUyV/M9C00CmVlN5fV00tNHcDIkQz+aAggJ3WibAnmLOeKNVC/dV9VjLJUmCRxeP4O/qMnttYV/PUXtA0qJxV/7rz8C29G/E3ZwWMGDEioTOukLiQkhIULF7Jr1y5iY2O55557ALh48WKttWW1uXbtGkeOHOHIkSOAPGXJkSNHSLmxxtaMGTMYP368+viJEydy4cIFYmJiSEhIYOnSpSxZsoRXXnlF24ehQWzy1/r0qWvpb4VCAcXFUEtXP0GwKKqmV9BjUgf06QMODpCZCYmJ+ruPIOhbjp/84d9j9F31voaX9WXAwH3q9u1j0Tl5iZcH7q1g6FA4fMyGxx++jhJr3i2KoV+vUs79ftxwMQlqWid1H3zwAYsWLWLgwIE8/PDDdO3aFYD169erm2U1deDAAbp370737t0BiImJoXv37rz99tsAZGRkqBM8gKCgIDZs2MD27dvp1q0bc+fO5YsvvtD5dCbQsJo6Ozvw9ZW/F8uFCY2BqunV1RWsrfV3H3tFKb07yjfbsUN/9xEEfcu9fGOJsMD6N516b/0JgOwsw820UPDV//EDjwLw7At2gPw+ufRHB35eeo2m1vn8WxFBt/tbs2a15awHby60nvFw4MCB5OTkkJ+fT7NKnWeeeeYZnLRZJPXGtaQ62lBq6qM3YMAADh06pNV96qMhSR2APxe4SAApfxwlIqKr7gITBBOk10ESlf36KwMOJ7CdWezYAc8+q+f7CYKeqFpxPD3rfw2vVg4AFFfYU1gIzvoem3flCqt+tuYaLrT3L2bAAMcqvx79eBN69Sxg3MCT7MwJ4X/joW17uFFvIxhAvSZ7kiSJgwcPsmjRIgpujCiws7PTOqkzZQ0ZKAEQoJBrGC+cLNBRRIJguvQ6R11ld97JAOQquu1blaJfnWCWlErIzZZrsTwVufW+ThM/V+y5DhhoqbDvv2dR+RMAPPOCQ43rovuHuLA1M4R774XSUhg79mYliaB/Wid1Fy5coHPnztx7771MnjxZPWfdhx9+qJe+bcbS4Jo6H3kOohTR/Co0AqrmV73X1Hl707vdZewoIeOSlZhBQTBLebnlVEhyPwWPpvVvolR4VloqzACDJQ4UduQQ4djZVDDhsdoHeFhbw5Il0KIFnD4NL46+qP/gBKAeSd2LL75IREQEV65cwdHxZtXrfffdxxYLmjyqIQMlAAJayVUIFy4ZfuUNQTA0g9XUAY4DetKT/YDoVyeYp9xzVwFoQgH2ftoNMKzCwwMv5GzOEEndovN3A/Dgg4rbNht7eMAPz+5EgZKlG/34aalYNtMQtE7qdu/ezZtvvomdnV2V/QEBAaSnp+ssMGNrcE1dW/nvk3JFs3nxBMGcGTKpo29fdROsSOoEc5Rz9iqAvO5rQ0YWVU7qsvTbFyE/H1atkr9/9jnNUocBL0fwZrMF8jkTFSQl6Ss6QUXrpE6pVFJRUb26OC0tDZf6VmuZoAb3qQuV/xYXihrQC1YQzITBml8B+vW7mdRtF/3qBM0tWLCAoKAgHBwcCA8PZ9euXXUev2PHDsLDw3FwcKB169YsXLhQJ3HkXJBrDTztGtjnulJSl5VyvaFh1e7yZX6YtIfCQujYEfr10/A8JyfeXtOZPuwhv8yRR4bnUWb4Fc0aFa1Hv959993Mnz9fvdarQqHg2rVrzJw5k6FDh+o8QGNpcE1dmJzM5Sg9KLqmxKmJWIBcsFwGrakLCqKPz3lsLpWRmmZLcjIEBRngvoJZW716NVOnTmXBggXccccdLFq0iOjoaOLj4/H39692fFJSEkOHDuXpp59m5cqV7Nmzh0mTJuHl5dXgabRyUuUEzMOpgU2SDg54R7aFOMi+rL+5hKQV37PohwEAPPMMNQ6QqI3NoAH8+OgbdP0hhH3xTZn1einvfmR3+xN1QJLkCprMi0rsHRT4Byi0ir0+97twrpzd3ydx9sg1KsoleasAZYVEhVdzKnxbYm8P7dpKdHBJJ3hAc7x8bXQXl6Sl9PR0qX379lLHjh0lGxsbqXfv3pKHh4cUHBwsXbp0SdvLGVxeXp4ESHl5ebUeU1EhSfLTI0n1fkilpZIrVyWQpIR/a7+XYN40eT1Zgts9zgcflMvLl18aKKA//pAiOxdIIEnLlhnonoJOGKvM9OzZU5o4cWKVfR06dJCmT59e4/HTpk2TOnToUGXfs88+K/Xu3Vvje9b2WD8euUMCSXo0YKfG16rNvHly2ZswocGXqplSKe0LHCOBJNnblEm5ufW4Rl6e9LPHRAkkSUGFtGWLbkPMyJCkb76RpMn3pkgPdD4l9fE9J7V2zpCcrIrU7+UgSe5Ny6XBgyVp2jRJ+mllmXT6tPx+X1/l5ZJ06JD8f2/MGElq4V1S5X6abs2sr0qR7qekx7oclN6felE6caL6vTQtN1rX1Pn5+XHkyBF++uknDh48iFKp5Mknn+TRRx+tMnDCnBVV+vBU35o6bG3xD3XlxAlIuepKB51EJgimyWDz1KkMH86APRB3XO5X99hjBrqvYJZKS0s5ePAg06dPr7I/KiqKvXv31nhOXFwcUVFRVfYNGTKEJUuWUFZWVuPSlCUlJZSUlKh/zs/Pr/HaOdnyZMGezRo+Oa/elwrbvZtFyfLf4aEHlLi71+Marq6M/n4kTw9dzLc8w//GlnEs3rZBc/RlZsLaZfms2eTKzp2qZQNb1XisC/lcx4HLV+3YvFm1drSc/rjYFNGl5WXat4P2Ea60D3ehXXsFbduCKqUpK4Pz5+VVbBJPlpP4Xz6J8eUcPe9CQVnlvMcOG8oItz5KV98s7GwlrK3B2kr+atU2COvOIRQWQuL+qyTuu0KyFMCVCjfiLrsRdxk4Bs27QUhI/f4uWid1AI6Ojjz++OM8/vjj9buriVM1vSoUN5/U+ggIUHDihFhVQrB8Bm1+vWHAAHj/fTFYQri9nJwcKioq8PHxqbLfx8eHzMzMGs/JzMys8fjy8nJycnLwVS0bVMm8efOYPXv27ePp1B/iwPOeHlo8iprJfeq8yEovBXTfrHn1q5X8xGfAzRUk6iU6mvmTp7H71zwSLroxZAjMmAH33guaLt1+8SKs+76QNUvy2HWmORI3ByL26gV3NTtMi5yjNPezonmgA77tXfDp6I5zm+aUFpVzorgNhw4hb8uPcrS4PQXlTuxJdmJPMlB1aXn8/cHBQeL8WSXlSlXztg1wM7N1tbpGn6gm3HEH9O0LPV3O4tStuwYDYJqC0pXi06mc2ZZK4r95nDpZzinfuwgLq//4hHoldZau8iCJhrRzq7ppVFrpTBAskkEHStxwR/ZvWCtGkJRkTUrKzfImCLVR3PIPXZKkavtud3xN+1VmzJhBTEyM+uf8/HxatapeezT/cyveeBOcdbAEhPeqz4F3yE65js6TusuXWbnOkWKcCGldRJ8+DVtgwOmrD/npGXkN50OHYPRoeUnNp5+Wt5Ytqx4vSRAfD7//quT37/PYf7oZ4Hxjg97EMXqMNQ9+2PNG+e9+Y6vODggDwsJu7PigNWX7/iXhz3PE77/G6bNWnMlpxmnakWjVkTyl6433bgVgjROFtOc0wSTSwT6ZYP9iQrrbETImFOv77610p46a/0GsrHDsEECXDgF0eU7z0+oikroaNHSQhEpA9gEgggt/HYe5nRsclyCYKmPU1LmsWkyY5Md/9GTHDhg3znD3FsyLp6cn1tbW1WrlsrKyqtXGqTRv3rzG421sbPDwqHluOXt7e+ztbz83qbOz7pb08vKRB+FlX3PQzQUrkf6JZVH5kwA8+6KjTjrzd+kCp07Botcv8O339mRkNGfOHHj3XYl771Xw3HNgbw+//w6//QbnzoE8UYf8z6U3cTzkt4cHJjTB/7lhUEPSrBEXF2zvHkiXuwfSRbWvuBgSEpDyDpETOpDTp+VdwTu/pYVrAVZdQiGkH/iNaViNjx6JpK4GDZ14WMXf/hIAKel6XOFcEIxMqbxZU2fIpI5+/Rjw9w6R1Am3ZWdnR3h4OLGxsdx3333q/bGxsdx77701nhMZGckff/xRZd8///xDREREjf3pjMWrhVw7V1Rmp/P1Xw/sKuYEnXG0LmHceN1NpN+yJczt8ANvMYtfuY8FTGJnxQDWrYN166oea28vMajiH+6138SIR1zwnTIaQvW0epWjI4SFoQC8uNlfkcFP6+d+eiDm2aiBzmrq2suF4EJe04ZdSBBMWEGBqpOyYZtfq05CLCarE+oWExPDd999x9KlS0lISOCll14iJSWFiRMnAnLT6fjx49XHT5w4kQsXLhATE0NCQgJLly5lyZIlJrccpotvE/X6r7oeLLG3/WMA3D1I0n3Zfv117M4mMObtDuwImMBxQpnMV7iQTzOrq/zvfxK//AI5OQr+ivPgmdx5+C6eDaGhOg7Esoiauho0dOJhFf8uTQFIK/GioqJhE4cLgqlSNb3a2zdsYJHWevSgr+1+FGVKzp614uJF8PMz4P0FszJmzBhyc3OZM2cOGRkZhIaGsmHDBgICAgDIyMggpVIH6KCgIDZs2MBLL73E119/jZ+fH1988UWD56jTNYWnPAFxGq3IzobAQN1d+9gx+Wu33rpv2gWgTRuYPRtmziR0506++r//44ufZ0CTJli9t/9m02pEhH7ub4F0mtQFBQVx1113MWfOHFq0aKHLSxuUrmrqfLv5YEMZ5diSka6kpb+oGBUsj1GaXgEcHGjaK5huu49wmDB27ICHHzZwDIJZmTRpEpMmTarxd8uXL6+2b8CAARw6dEjPUTWQR9WkTpeOHpW/dulS93ENZmUFAwfCwIFYLV0q7zPRPmumTqdZxoQJE1AqlfTv31+XlzU4XSV11i19aUkaABcOX25gVIJgmgw+R11lYh1YobGrvFRYlu4uW37qLCcPy3Pude2qu+velkIhEroG0GlSN2vWLJYtW8Y5ebiK2dLVQAlsbGhtnw7AucN5DbyYIJgmY4x8Vau8DqxI6oTGKCgI787yCF5d1tSd+SeJ60p7nK2KaN1ad9cV9KveSV1paSmJiYmUl5frMh6ToKuaOoBgj1wAEs+J7ouCZTLGHHVqd91Fv3h5HepTp+DSJSPEIAjG5O2N1+BugG6TumN75c7lnd0vYiV6DpkNrZ+qoqIinnzySZycnAgJCVF3LJ0yZQrvv/++zgM0Bl0NlABo/8pIAE5fD2j4xQTBBBm1ps7BAY+O3uo+Pzt3GiEGQTAyfSwVdvS4PLKvS+truruooHdaJ3UzZszg6NGjbN++HQeHmyNiBg8ezOrVq3UanLHosqaufbDcN+D06YZfSxBMkVGTuhsGDJC/iiZYoTHykuTOdFkXy3R2zWNpcoHu2l1U05kTrZ+t3377ja+++oq+fftWWSqlU6dOZt+XTkVnfeqA9u3lr2fOyJO0CoKlMWrzK8DJkwzYLq+3uX27kWIQBCPyXjALgOyUYt1csKKCo/lyR7ouA91vc7BgSrRO6rKzs/H29q62v7CwsM419MyJLmvqAq1SsFWUUVwMaWkNv54gmBqj19S5udH/+FcAnDwJOTlGikMQjMTLvQKA7Mu6mQz18tFU0pAXYu0c5auTawqGoXVS16NHD/766y/1z6pE7ttvvyUyMlJ3kRmRLpM6G69mtJHOAnD6kOibIFgeo81Tp9KyJV6BTejESUD0qxMaH1Wfuqyrdjq53rE9csfyQNt03NzFrPnmROshmfPmzeOee+4hPj6e8vJyPv/8c06ePElcXBw7LKRDiy4HSuDiQnv7C5wq6cjpuBwGj9LFRQXBdBh1njqVfv0YkLyDeELYsQPuv9+IsQiCgXn5ym/lRaW2FBWBk1PDrndM6gxA1yifhoYmGJjWNXV9+vRhz549FBUV0aZNG/755x98fHyIi4sjPDxcHzEanC5r6gCCveSJhxOPlujmgoJgQoze/ApiEmKhUXNt7oQd8vuLLkbAqleSCBNTcZmbej1jnTt35v/+7/90HYvJ0OVACYD2AaWQBqfPiVFEguUx+kAJuDEJ8dsAHDsmkZOjwNPTiPEIggGp1n9NpyXZ2RDQwBm0VGu+GnQlCUEntM4yrK2tyaphLZLc3FysLWTFel3X1LUPsQXgdKarbi4oCCbEJGrqOnSguUc5XTiKJCnYuNGIsQiCoelwqbDyMokTB68D0KWlWN7S3Gid1EmSVOP+kpIS7Oy076S5YMECgoKCcHBwIDw8nF27dtV67Pbt21EoFNW2U6dOaX3f2iiVUFgof6+zpC5CTuaSr3lSIlpgBR3QptwA7Nixg/DwcBwcHGjdujULFy7USRzFxahf00ZN6hQKGDqUYUEJAFQayyUIlq9HD7xby01LDW1+Pbs7k+uSA04U0qar6ANubjRufv3iiy8AebTrd999R5NKGU9FRQU7d+6kQ4cOWt189erVTJ06lQULFnDHHXewaNEioqOjiY+Px9/fv9bzEhMTcXW9WevlpRr6owOqhA50l9T59ArEVVFAvuTCuXPQqZNuris0TtqWm6SkJIYOHcrTTz/NypUr2bNnD5MmTcLLy4sHHnigQbGoml4VCt11V6i3FSsYtgfm9YWNG6G8HGxElyChMejWDa9I4HzDk7qjsVmAL50dzmLlINpfzY3G//I+++wzQK6pW7hwYZWmVjs7OwIDA7X+9P/pp5/y5JNP8tRTTwEwf/58Nm3axDfffMO8efNqPc/b25umGnbgKSkpoaRS9Vh+fn6dx6uaXq2swNFRo1vclqJLZ9qHw4ED8soSIqkTGkLbcrNw4UL8/f2ZP38+AB07duTAgQN8/PHHDU7qKo98NYX1IXv3Bnd3uHwZ4uKgXz9jRyQIhqGrpcKO/Se/X3ZtLhZSNkca/xtOSkoiKSmJAQMGcPToUfXPSUlJJCYmsmnTJnr16qXxjUtLSzl48CBRUVFV9kdFRbF37946z+3evTu+vr4MGjSIbdu21XnsvHnzcHNzU2+tWrWq8/jK/el0OZeyamWJxETdXVNofOpTbuLi4qodP2TIEA4cOEBZWc3LCpWUlJCfn19lq4nR56i7hbU13NNfrm4XTbBCo6FUqvvUXcqsuYuUpo4myst/dukg+gqZI60/W2/bto1mOvgPnpOTQ0VFBT4+VefB8fHxITMzs8ZzfH19Wbx4MWvXrmXdunUEBwczaNAgdtYx2+iMGTPIy8tTb6mpqXXGpetBEiqqpO50YsMKnNC41afcZGZm1nh8eXk5ObUsv6DphyGTmKOuspgYhv32NCCSOqERKS8naP4UAM6cqmjQpY5dkv9XdO3pcJsjBVNUrx4naWlprF+/npSUFEpLS6v87tNPP9XqWrcuLSZJUq3LjQUHBxMcHKz+OTIyktTUVD7++GP69+9f4zn29vbY29trHI9OJx6uJPjU78C9nN6cAjRwvLnQ6GlTbmo7vqb9KjNmzCAmJkb9c35+fo2J3ZAh8mg7kxkAFBrKEP4PKyo4ccKalBSoo3uuIFgGOztCHc9DMZyIVyBJ9WtpupwrkVoqJ3Wd726u4yAFQ9A6qduyZQsjR44kKCiIxMREQkNDSU5ORpIkwsLCNL6Op6cn1tbW1WoXsrKyqtUq1KV3796sXLlS4+NvR281dd5XAUjMaqrbCwuNSn3KTfPmzWs83sbGBg8PjxrP0fTDkI3Nzb48JmHoUDx4kt7sYy938Ndf8Nxzxg5KEPQv2OsyNill5F+zJS0NbtPTqEbH/ysGnAi0uoBbeFudxyjon9bNrzNmzODll1/mxIkTODg4sHbtWlJTUxkwYACjR4/W+Dp2dnaEh4cTGxtbZX9sbCx9+vTR+DqHDx/G11d3Cw7reuJhlXa93AHILnFTN1kJgrbqU24iIyOrHf/PP/8QERGBra2t3mI1iubNISKCYchtr6IJVmgs7Dxdac9pAE6cqN81jp6W1xfrMtxfdyMFBYPSOqlLSEhgwoQJANjY2FBcXEyTJk2YM2cOH3zwgVbXiomJ4bvvvmPp0qUkJCTw0ksvkZKSwsSJEwE5gRw/frz6+Pnz5/Pbb79x5swZTp48yYwZM1i7di3PP/+8tg+jVvqqqXPpEoQf6QCcOS361Qn1p225mThxIhcuXCAmJoaEhASWLl3KkiVLeOWVV4z1EPRr2DB1Urd1qzyXnmBYhYWwYgXs32/sSBoRDw9CkbO5kyfrd4mbK0nocJSgYFBaJ3XOzs7qKUL8/Pw4d+6c+ne1dbquzZgxY5g/fz5z5syhW7du7Ny5kw0bNhBwY42TjIwMUlJS1MeXlpbyyiuv0KVLF/r168fu3bv566+/uF+Hq3frq08drVurP0WdPnRNxxcXGhNty01QUBAbNmxg+/btdOvWjblz5/LFF180eDoTkzVsGF04RktFGsXFcJsB8oKOSBLs2QNPPQW+vjBhAtyYCcvorly5wrhx49QDf8aNG8dV1dDtGpSVlfHaa6/RuXNnnJ2d8fPzY/z48Vy8eNFwQWurUlJX75q6o3KFQ5cuugpKMDhJS/fee6+0ePFiSZIk6dVXX5Xatm0rvfPOO1JYWJg0aNAgbS9ncHl5eRIg5eXl1fj7d96RJJCkJ5/U/b2fcf5eAkl687FU3V9cMIrbvZ4shVk9zooKSfLxkZ5hoQSSNHmysQOybGlpkvTee5LUvr38v1O1tfEvkT76qPrxxngt3XPPPVJoaKi0d+9eae/evVJoaKg0fPjwWo+/evWqNHjwYGn16tXSqVOnpLi4OKlXr15SeHi4Vvc16GOdPFlaxygJJEnLMCVJkqTycklysLougSSdXhGn+/iEBtH0taT1QIlPP/2UazfaKGfNmsW1a9dYvXo1bdu2VU9QbM701acOoH3zAjgHp+PLdX9xQRBkVlbwzjsMSwxm8cdyv7ovv9TtvJONnSTB9u3w0UewaZOEUin/cZ2dlIx+yIrHH4d+PskogtsbN1DkLkMbN25k37596rlUv/32WyIjI0lMTKwyo4KKm5tbtX6oX375JT179iQlJaXOFY+MJjqaUM7C1xAfDxUV8ryNmjpzBq4r7XGikNbtLGMd98ZI66SudevW6u+dnJxYsGCBTgMyNn31qQMIDm8iJ3XZJjJTqyBYqqeeYlAh2H8JycmQkCBWctEFSZL7Kc6eDTeXG1bQj508zjJGv9yRJnOm3dhv/IQO5Mm33dzcqkyO37t3b9zc3Ni7d2+NSV1N8vLyUCgUda5mpO0KRjo1bBit7wGHJXI/0qQkaKvFANZjewoAFzpzHOuQEL2FKeiX1n3qWrduTW5ubrX9V69erZLwmSt9JnXt544D4PQlN5RK3V9fEISbnJ1h4ED5ezEKtmEkCWJjoV8/icGD5YTOnutM5itO046d/uN4fIYvTR6919ihVpOZmYm3t3e1/d7e3rVO2H2r69evM336dB555JEq647fStsVjHTN2ho6dpS/17Zf3dEdVwHo4nzeBBZyFupL66QuOTmZiorqM1aXlJSQnp6uk6CMSW8DJYCgIHler6IiMOX+toJgEU6fZpjNJkAkdfUlSfDPP3DHHRAVBXv2KLDnOi/wBees2vPVw3tpt/07uVrovfdAw1ovXZg1axYKhaLO7cCBA0DNk2xLt5mwW6WsrIyxY8eiVCpv2zKl7QpGOiVJcOECoc3k92Ftk7pjR+Wahq7+Ys4tc6Zx8+v69evV32/atAk3Nzf1zxUVFWzZsoXAwECdBmcM+qyps7WF1q3h9Gl5ubCWLUUnH0HQmz/+YNhfXzOF8+zeLa9TazLLmZmBpCSY9HQZG7fIcxk6OMCzz8K0is/wc7gMU/bUb4ZbHXn++ecZO3ZsnccEBgZy7NgxLl2qvjh9dnb2bSe6Lysr46GHHiIpKYmtW7fWWUsH2q9gpHPh4YTmPgF8qH1NXZL82LqEimYkc6ZxUjdq1ChA/sSjmqdOxdbWlsDAQD755BOdBmcM+hwoQXk57VO2cZq7STxQwF2D6v4HIQhCAwwbRutXXqEDpzhV0YF//oGHHjJ2UKavrAw+/biC2TOVFJfZYkcJk8ZcZtpnvsjzvM8wdoiAvLqKp6fnbY+LjIwkLy+P/fv307NnTwD+/fdf8vLy6pzoXpXQnTlzhm3bttW6+orJUCggJITQndpPa3LlCqQWyH29u/QV70vmTOPmV6VSiVKpxN/fn6ysLPXPSqWSkpISEhMTGT58uD5jNQh91tRhY0N722RAzFUnCHoXHAxt2jCMPwHRBKuJvXshrH0B01+3prjMljvZyrE29/PZC+fR4cI9BtWxY0fuuecenn76afbt28e+fft4+umnGT58eJVBEh06dODXX38FoLy8nAcffJADBw7www8/UFFRQWZmJpmZmdXWOzcpISHqueoSE0HTUFWTDgfYXsStVwc9BScYgtZ96pKSkjT6dGSu9JrUAe1bFAJw+lT1fomCIOiQQlFldYm//0YMUKrFlSvw7Jgr3HEHnEh2wZNs/q/JJLZ8dYrgU7/LnerM2A8//EDnzp2JiooiKiqKLl268P3331c5JjExkby8PADS0tJYv349aWlpdOvWDV9fX/W2d+9eYzwEzYSE0IpUXGyKKC+XpynRhHoliWg/qDRKWDA/Gje//vvvv1y+fJno6Gj1vhUrVjBz5kwKCwsZNWoUX375pXH7E+iAPgdKAAS3l+AUJKY66ecGgiDcNGwYfb9YgKsin+xsV/77T7xn3er33+GZZySysuTmtycUy/jw2XN4vPcuNLOM6Zfc3d1ZuXJlncdI0s3lGwMDA6v8bDZCQ1EAodYJxJWHc+IEaDI7ydGj8lexkoT507imbtasWRxTpfPA8ePHefLJJxk8eDDTp0/njz/+YN68eXoJ0pD0XlPX3RmApCvNNK4aFwShngYMwNbZnihJjIK91fXrMGUKjBoFWVkKOvrlsSNyOktO3YHHN+9YTELXqNzI4EJLDgKa96s7dkRuOeraVS9RCQakcVJ35MgRBg0apP75p59+olevXnz77bfExMTwxRdf8PPPP+slSEOpqJCnGwH9TdPjG+5HEwpQYsX58/q5hyAIN9jbw+DBDHPYAoikTiUxQUlkm0t8+aX888svw+FzrvTf+z60N41Jg4V68PQEb2+t1oCtqIATR28kdXu/0Wd0ggFonNRduXKlyvDvHTt2cM8996h/7tGjh2Hn5NEDVUIH+qupU7RvR3tOA5B4ygyr9wXB3CxcSPTpLwA4dAjOnjVyPEa24ss8wjuXcOSiD56KHDb8cIWPPwZ7BzHFkkWYO5fQ6SMAzZK6s2ehuNxOXh7M/NcPaPQ0Tup8fHxISkoCoLS0lEOHDhEZGan+fUFBAba2trqP0IBU/emsrOQ5mfQiKIj23nJnXLEGrCAYQPPm+LSyQ9UdeP58o0ZjNNeuwfi7M5gwxY3CCkfutNrO0Q//IfrhpsYOTdClZ54h9KW7ATh3rmplRU3++0/+GsoJrEM76jk4Qd80Turuuecepk+fzq5du5gxYwZOTk7069dP/ftjx47Rpk0bvQRpKJX70+lt8W87O9pPvAuA00nmnQQLgjl5+WX567JlcPmycWMxtGMHywgLyOH7zb5YUcFc7y+IPeyF3yuP6PGfnWAs3t5yS6wkyese12XdGrnpdTCbxQLJFkDjpO6dd97B2tqaAQMG8O233/Ltt99iZ2en/v3SpUuJiorSS5CGoteJhytRdVk5fVq/9xEE4YYNG7jr2XZ0bZpMUREsXGjsgAxnzQ+lRPYs58xlT1qSyvaRn/Fm0lNYdxGLtlukkhLYupVQD3ktyrqaYK9dg783yUn9aNd/wMvLEBEKeqRxUufl5cWuXbu4cuUKV65c4b777qvy+zVr1jBz5kydB2hI+h75qqKa7zIxQcxVJwgG0awZinNneblEHqH/5Zfye58lUyrhzTfhof/ZUaR0JMp6M0eWHKLf76+Ak5hSyWIVFcGgQYQmrgXqTur++guul1jRljN07awUtbYWQOvJh93c3LC2tq62393dvUrNnTkyVFLXbs8yAC5lW3NjrktBEPSpZ0/w82NM8TL8mhaSmQmrVhk7KP3Jz4dR90q8+6788ysxFfx1PACPJ+41bmCC/jVrBr6+6hGwJ0/WfuiaNfLX0axB0de8J5gWZFondZZM3xMPq7i19caHTEDzGb8FQWgAa2t48UXsKGOKrdz2+umncp8jS3PmtETvdjn88acCe3uJ77+Hjz6xxqZjO2OHJhhKpeXCaqupKyyEDRvk7x/snw2VZrMQzJdI6ioxVE0d7cS0JoJgcM8+C66uPJP9Ds4O5Rw/DrGxxg5KtzatL6FnaBEJWZ60II1db2/mf/8zdlSCwYWEEIJcRZeaSo0tQhs2QHExtG4N3bd/BgMHGjZGQS9EUleJoQZKEBREsEJO6k4fLNDzzQRBAMDNDSZNohlXebLpOgA++cTIMemIJMGnM/MYeq8NV8uciSSOA/M20+P1u40dmmAMISE0JY+W9tlAzU2w6qbX0aIrnSURSV0lBqups7WlfbMcAE4fu67nmwmCoPbii2Bvz9TM17CykvjnHzh+3NhBNUxZGUwcncvLc9xQYs2T9t+zbWMJzac/ZuzQBGNRLRdWSxNsUdHN1VVG33HRkJEJeiaSukoM1acOoH2APPQu8ax4CgTBYJo3h88/J2jvj9x/v1w98emnRo6pAa5cgeheuSxe64ECJZ96v8+3J/pgP2SgsUMTjEm9BuwBoHpSt2GDnNgFWqcQNrIF7Npl6AgFPREZRSUGq6kDgjvJI4hPZ7hYZGdtQTBZzz4LkZHqyYh/+AEyMowbUn2cOwd9+sCWwx44c43fO7/FS6eeRdHWvCeBF3TAzQ1WrCD0jVFA9aTul1/kr6MrfkJhbw89ehg2PkFvRFJXicH61AGtR4ZipVBSWGZvlm8ogmDueveGPr0rKCuDr74ydjTa2b0bevWCU6egZUvY/VM6Iw7MlKezEASAceMIvU8e8Vw5qSsuhj//lL8fzRr5k4He1sUUDE0kdZUYsqbO7qFRBLWW//x1zSMkCIIelJfDiy/y8rHHAPjmG3mKB3Owcsl1Bg0oIzcXIiJg/37oNiYYzHyeUEH3OnaUB0FkZ0NWlrzv77/l13qgUxYRHIA77zRukIJOiaSuEkMmdQB33Jjrcfly/d5HqYTTe7L5+YWdfHbvNk79naTfGwqCqbOxgZMnubfoR9q4ZXPliv7LYUMplfDm1GuMe8qBUqUt9ztsYMem6/j6GjsywSRlZuK0cjGtPeT5TFS1dapRrw9Ka1CASOosjEjqKjHkQAmAKc/KgyV+/lkiLU0315RKyzj48Ta+HfkHk++/yB13gKsrBPf1YsxX/YlZfycdhwYx0OM4P75+gpLrokOf0Ei99hrWKJlaJC8d9tln8khSU5SfD/cNzufdz+V/Tq85fM6af9xwchfNZkItLlyAZ58ltCAOkFuEKje9Pli8Ql4urmdPIwYp6JrRk7oFCxYQFBSEg4MD4eHh7LrNKJwdO3YQHh6Og4MDrVu3ZqEOV+Y2dE1d+BNd6c8OyssVOunTU3G9jEf9dxHx6p0888cIFvzqx969clW7g4NET5d4BrsfxIoKdlzuzKPzQmnR5Cqv3HOCxGMWvhCmINxq8GDo3p3Hyxbh4VjEuXPwyivGDqq6c+cgsksh67e5Ys91Vvi8yvvHh2HVTyzrJNShUyeg6gjYTZvk9zn/Zvn0ZD/07Sua7S2MUZO61atXM3XqVN544w0OHz5Mv379iI6OJiUlpcbjk5KSGDp0KP369ePw4cO8/vrrTJkyhbVr1+okHkMOlADgjjuIQZ5PYdGim/evD0mCKT32surSXdhSSpTfCabdf4Yff4T4eCgoUPBvfidic8O5sOMCs3r8RUtFGrkVzfhkUygdutpz552wePHNvhcWp6JC7lxy9iwcOACbN8PatbBkiTwL7ZYtN48tLoZ16+QOKDt3Gi9mQX8UCpg+HWeKWGr9NABffAErVhg5rko2b4YeXUuIv+CMH+ns7PIC405Oh7ZtjR2aYOpcXMDfv8pcdeqm14dtUfzyi2l+ihEaRCFJxptQo1evXoSFhfHNN9+o93Xs2JFRo0Yxb968ase/9tprrF+/noSEBPW+iRMncvToUeLi4mq8R0lJCSUlN2uh8vPzadWqFXl5ebi6ulY51sdHTmiOHYPOnRv66DRw8iQVoV3owCnO0o4vv4Tnn6/fpWYN3c/sv3uiQMlPrx7ioQ8jbntOefYVNk7byqItbdiQ3g2lUt5vpVDSt0UyD9wvcd/UAFoF2dQvqBuUSricco2sTYfJSiokK7WErIwKcnIVKK1tsXa0w6ZNANadgrGxAWsqsEm/gJOnE65+zrj4OOPiZoWrq/x/ysUFrKzkvu4VFfJX9ffXrpN/OpPc+EtcPnuZyx7tyG3WlsuX4fK5yxT8tZNiHCnCqdpXnJyw93DBzg7sFKXYnz2JHaXYW1fgOKgPmzZVf2z5+fm4ubnV+HqyJBb7OCsqIDgYzp1j5pB9zNnUCwcH2LMHwsKMF5YkwZdfQkyMREWFgl7sY92I5fj9PN/sRypa7GupBkZ/rEOHcuLvFDpzQl1ZUVAAcXHy6G/BfGj6WjJaUldaWoqTkxNr1qzhvvvuU+9/8cUXOXLkCDt27Kh2Tv/+/enevTuff/65et+vv/7KQw89RFFREba2ttXOmTVrFrNnz662v6Y/jLOzPCHj+fMQFNSQR6eFESP4+k9/nudr2raVpyiwttbuEl+/kMDzX3UEYMGoTTz36xCtw0hNhZUrYd2acg4crprE9fA4x6hB12jhb41DCw8cW/vi4AAOVqU4piRiY6Uk61wB6WeLSL9QQfolay5ediTdJZiLZd5kZaFOGM2Vg4NceXcro//TNhCLfpwLF8Jzz6HsP5CRLtv46y/w94eDB8HT0/DhlJTApEmwdKn884T/VbAw4jscXnha/jRj5iz6tXQLoz/WadMo/Wg+zlbFlCvlN5ZWreTudmJpMPOi6WupYVUwDZCTk0NFRQU+Pj5V9vv4+JCZmVnjOZmZmTUeX15eTk5ODr41DAObMWMGMTEx6p9VNXU1+e47+VOMt7e2j6YBXnuNx/4cwlvM5exZd/78E+69V/PTV6+GF74KBmBW1195bt2oeoXRqhXMmAEzppZxYelGfl12lXVHW7O7vDf/5bbhv59rOssOqKNK82rVH5tZ5+HtkI+3cxHeTUvwbKbEhjLKi8upaN6Cihb+co3b1QLKt+6ksMSagnJHCnAhH1cKcKEAF4pxUl/TykrCRlkq1+5RjjUVuCiu4eFQiHuTMjyCXHDvHoiHBzRrKuHqIuHobIWTEzg6ov7q6Chfr7RU3kpKqn5v7kmpUIfHHoNdu7D64gtWWsvzsJ49C2PGyH2QbAz4X/LECRj/aAWHj1ljZSXx8ccKpk61RqF41nBBWKArV64wZcoU1q9fD8DIkSP58ssvadq0qUbnP/vssyxevJjPPvuMqVOn6i9QXQsJwY4ygh1TOFko11Q80Pk0irk/wf33Q2iokQMUdM1oSZ2K4paPC5IkVdt3u+Nr2q9ib2+Pvb29RrE8/LBGh+nWHXfgHNmVZ+MW8T4z+PRTzZO62FgYNw4krJjc5xBvb45u+McvR0cCJg9n6mSYWl5O5h//8vs3F9l6wJWCcgeKvQO57tWK4mK4fq2M4uQsSrHFyz6fFq4FtPAspUUL8Au0o0WYDy16+NG8uVzjYWvrBrhpEIQLMEz+tqQEcnLkvnDZSZCdTXnr9hARgbU1KMrL4d//5BnUVZtry1r+DoobmyBU4uAgLysBNAV++02e2HfrVpg+HT7+WP8hVFTIy5W9+YZEaZk17uSyauyfRL00Qf83bwQeeeQR0tLS2LhxIwDPPPMM48aN448//rjtub/99hv//vsvfn5++g5T91TLhZUf5SRyUjf6ymKY+Yk8IlAkdZZHMpKSkhLJ2tpaWrduXZX9U6ZMkfr371/jOf369ZOmTJlSZd+6deskGxsbqbS0VKP75uXlSYCUl5dXv8D14bffpDT8JBtFmQSS9N9/tz9l/74KydlZkkCSxoyRpIoK/YcpVGeSryc9aCyPU5IkSVq0SFrz6r+S3LNNklat0u/tzpyRpDvukNT3G856KcO7iyQdOKDfGxuJoV9L8fHxEiDt27dPvS8uLk4CpFOnTtV5blpamtSiRQvpxIkTUkBAgPTZZ5/Vefz169elvLw89ZaammrcclNcLElbt0pzXyuQQJJatFBKFc4u8gvt8GHjxCTUi6blxmgdNOzs7AgPDyc2NrbK/tjYWPr06VPjOZGRkdWO/+eff4iIiKixP53ZGDGCFnFrGfOIXHH62Wd1Hx4fD9EDiygshLsHK1mxwiK62giC8f3yCzz7LA8uuIvpT8jDwJ94Ao4e1f2tJAkWLICuXSX27AEX8lnCE6zvPY/mRzZCeLjub9oIxcXF4ebmRq9evdT7evfujZubG3v37q31PKVSybhx43j11VcJuVHjdTvz5s3Dzc1NvdXW1cdgHBzgzjsZ/XgTAgLgzUeSsCosAHd36NLFuLEJemHUVCAmJobvvvuOpUuXkpCQwEsvvURKSgoTJ04E5P5w48ePVx8/ceJELly4QExMDAkJCSxdupQlS5bwirkPy7aygt69eekl+ceff6bWyYh/+gl6h5WQe70JPdjPulfixDRDgqAro0bBoEFQWMg7m3sTNbCU4mIYOVKetFVXw8rOnYMhQ2DyZCgqUjCQbRyjC088bYNi+zbEMhG6k5mZiXcNHaW9vb1r7b8N8MEHH2BjY8OUKVM0vteMGTPIy8tTb6mpqfWKWdeCgyE5GSY2Wy3vGDhQ1ARYKKM+q2PGjGH+/PnMmTOHbt26sXPnTjZs2EBAQAAAGRkZVeasCwoKYsOGDWzfvp1u3boxd+5cvvjiCx544AFjPQSdCg+HAXeUUV5efYHx4mJ49lm5319BiT392MmGt/bRZIiYgFQQdMbGRv5U1bYt1ilJrLp+H21aS6SkwIgR8tJ+27bV79JKJWzcKF+nXTu5T6yDvZLPrWPYYj2EwG+myxNWatgHuLGbNWsWCoWizu3AAXni3Zr6XEt19N8+ePAgn3/+OcuXL6+zj/et7O3tcXV1rbIZ3aFD8Npr8gLHW7fK+8TSYJbLMK3BpsOk+wZt3Sr9bj9aAklq2lQpFRTIuxMSJKlzZ7kbhIIK6U3mSGXT35QkpdK48Qqm/XrSocbyONXi4yXJRe57lDvuRWnaq0rJ0fFmv7fBgyXp3381u9SVK5L06aeS1LbtzfNBkoYMkaRTpyRJ+vFHSdq5U5+PxqTo6rWUnZ0tJSQk1LkVFxdLS5Yskdzc3Kqd7+bmJi1durTGa3/22WeSQqGQrK2t1RsgWVlZSQEBARrHaBLlZsUK+QXXu7ekfhGfPGm8eIR60fS1ZNTJh43B6PMG1SU/H2WrAILz96snI3Zzg+eek5f68uYSK/kfd7/dB2bNEhMNmQCTfj3pUGN5nFVs2ADDh8s52BdfkPHgC7z7rrzqimqN2JEjYcoUsLWVB2pX3kpL5UleV66U578EcGtSwWPNfmfSLG/aP9HXeI/NiAz9WkpISKBTp078+++/9Lyxzum///5L7969OXXqFMHBwdXOyc3NJSMjo8q+IUOGMG7cOB5//PEaz6mJSZSbQ4du9s90dZXnb8rIEO8fZkbj15JBUkwTYhKfnOoyfbr0Nc9JIEmOjkr1p/o7FVulizSXpNmzjR2hUInJv550pLE8zmo+/FCSFApJev999a6kJEl67DFJsrKqWvNW19a5Y6m0sM//SQU0kXdERDTamnZjvJbuueceqUuXLlJcXJwUFxcnde7cWRo+fHiVY4KDg6vNxlCZJqNfb2US5aaoSH4NgySlpUnS2bPGi0WoN01fS0afp064xYsvMuGTjrxZ9g5Xit2xsoKZM+GNTlexPjNFniFYEATDeOUVuOuuKiNRAzd/x7LRfrw2LZo5cxXExclrotvbV9+ae5bxuM1K+v70PIqEG9V1Dz0E8+aJmhID+uGHH5gyZQpRUVGAPPnwV7d0XE5MTCQvL88Y4emXoyO0aSPPqH3qlDwQSLBYIqkzNc2b4/zYaBZ8O4lFrtN4+7ewG31a77vdmYIg6JpCUXVqkatXISYGCgro0LUrP86YAd8/KI9kungRvLygWTP52D175ATu4kX559694ZNPoJYpmwT9cXd3Z+XKlXUeI92mJ1JycrIOIzKwkBA5qTt5UiR1Fk6MaTZFr7zCWFazLT+cO5sdMXY0giCoSJI8DN3ZWZ68buxY+XsXF3neiL/+unns1atyQhcYKM9FtHevSOgE48jPl7+++KJx4xD0TiR1pqh9exg/Xq4lOHjQ2NEIgqDSrBl89BGkpMiDlZo1k0dFgJzcFRbePLZrV1i6FBIS5IVkRXOrYCxz58qvv+nTjR2JoGdi9KupKi+H3Fzw8TF2JEIdzOb11ECN5XFqrbhYnim8eXO5tk64rcb0WjKpx3rlijz61drauHEI9aLpa0n0qTNVNjYioRMEU+foKM8kLAimTtXXU7BoovlVEMzIlStXGDdunHptyXHjxnH16tU6z3nssceqzbTfu3dvwwQsCIIgGIyoqRMEM/LII4+QlpbGxo0bAXjmmWcYN24cf/zxR53n3XPPPSxbtkz9s51YMFgQBMHiiKROEMxEQkICGzduZN++ffTq1QuAb7/9lsjISBITE+uc5d7e3p7mzZsbKlRBEATBCETzqyCYibi4ONzc3NQJHUDv3r1xc3Nj7969dZ67fft2vL29ad++PU8//TRZWVl1Hl9SUkJ+fn6VTRAEQTBtja6mTjXYV7xJCbqgeh0ZYhB5ZmYm3t7e1fZ7e3uTmZlZ63nR0dGMHj2agIAAkpKSeOutt7jrrrs4ePAg9vb2NZ4zb948Zs+eXW2/KDdCQxmyzBibeL8RdEXTctPokrqCggIAWrVqZeRIBEtSUFCAm5tbvc6dNWtWjQlUZf/99x8AihrmOpMkqcb9KmPGjFF/HxoaSkREBAEBAfz111/cf//9NZ4zY8YMYmJi1D+np6fTqVMnUW4EnWlImTEX4v1G0LXblZtGl9T5+fmRmpqKi4tLtTfC/Px8WrVqRWpqqvHnFNID8fh0T5IkCgoK8PPzq/c1nn/+ecaOHVvnMYGBgRw7doxLly5V+112djY+Wkx/4+vrS0BAAGfOnKn1GHt7+yq1eE2aNBHlxgIfn7mWGXNR2/uNJb+mQDw+fdC03DS6pM7KyoqWLVvWeYyrq6tFvhBVxOPTrYbWNnh6euLp6Xnb4yIjI8nLy2P//v307NkTgH///Ze8vDz6aLH8VG5uLqmpqfj6+mp8jig3lv34zK3MmIvblRtLfk2BeHy6pkm5EQMlBMFMdOzYkXvuuYenn36affv2sW/fPp5++mmGDx9eZeRrhw4d+PXXXwG4du0ar7zyCnFxcSQnJ7N9+3ZGjBiBp6cn9913n7EeiiAIgqAHIqkTBDPyww8/0LlzZ6KiooiKiqJLly58//33VY5JTEwkLy8PAGtra44fP869995L+/btmTBhAu3btycuLg4XsayVIAiCRWl0za91sbe3Z+bMmbWOCDR34vGZP3d3d1auXFnnMZVHRzk6OrJp0ya9xmTpf3dLfnyW/NhMmaX/3cXjMx6F1BjGlQuCIAiCIFg40fwqCIIgCIJgAURSJwiCIAiCYAFEUicIgiAIgmABRFInCIIgCIJgAURSJwiCIAiCYAEaXVK3YMECgoKCcHBwIDw8nF27dtV5/I4dOwgPD8fBwYHWrVuzcOFCA0WqnXnz5tGjRw9cXFzw9vZm1KhRJCYm1nnO9u3bUSgU1bZTp04ZKGrNzZo1q1qczZs3r/Mcc3nuzIEllhtRZqozh+fNXFhimQFRbmpiUs+d1Ij89NNPkq2trfTtt99K8fHx0osvvig5OztLFy5cqPH48+fPS05OTtKLL74oxcfHS99++61ka2sr/fLLLwaO/PaGDBkiLVu2TDpx4oR05MgRadiwYZK/v7907dq1Ws/Ztm2bBEiJiYlSRkaGeisvLzdg5JqZOXOmFBISUiXOrKysWo83p+fO1FlquRFlpipzed7MgaWWGUkS5eZWpvbcNaqkrmfPntLEiROr7OvQoYM0ffr0Go+fNm2a1KFDhyr7nn32Wal37956i1FXsrKyJEDasWNHrceoCtqVK1cMF1g9zZw5U+ratavGx5vzc2dqGku5EWXGPJ83U9RYyowkiXJjas9do2l+LS0t5eDBg0RFRVXZHxUVxd69e2s8Jy4urtrxQ4YM4cCBA5SVlektVl1QLRPl7u5+22O7d++Or68vgwYNYtu2bfoOrd7OnDmDn58fQUFBjB07lvPnz9d6rDk/d6akMZUbUWbM83kzNY2pzIAoN6b23DWapC4nJ4eKigp8fHyq7Pfx8SEzM7PGczIzM2s8vry8nJycHL3F2lCSJBETE0Pfvn0JDQ2t9ThfX18WL17M2rVrWbduHcHBwQwaNIidO3caMFrN9OrVixUrVrBp0ya+/fZbMjMz6dOnD7m5uTUeb67PnalpLOVGlBnzfN5MUWMpMyDKDZjec9fo1n5VKBRVfpYkqdq+2x1f035T8vzzz3Ps2DF2795d53HBwcEEBwerf46MjCQ1NZWPP/6Y/v376ztMrURHR6u/79y5M5GRkbRp04b/+7//IyYmpsZzzPG5M1WWXm5EmZGZ2/Nmyiy9zIAoNyqm9Nw1mpo6T09PrK2tq31SysrKqpZlqzRv3rzG421sbPDw8NBbrA3xwgsvsH79erZt20bLli21Pr93796cOXNGD5HplrOzM507d641VnN87kxRYyg3oszIzO15M1WNocyAKDcqpvbcNZqkzs7OjvDwcGJjY6vsj42NpU+fPjWeExkZWe34f/75h4iICGxtbfUWa31IksTzzz/PunXr2Lp1K0FBQfW6zuHDh/H19dVxdLpXUlJCQkJCrbGa03Nnyiy53IgyU5W5PG+mzpLLDIhycyuTe+6MMDjDaFTDzJcsWSLFx8dLU6dOlZydnaXk5GRJkiRp+vTp0rhx49THq4Yqv/TSS1J8fLy0ZMkSkx1m/txzz0lubm7S9u3bqwzFLioqUh9z6+P77LPPpF9//VU6ffq0dOLECWn69OkSIK1du9YYD6FOL7/8srR9+3bp/Pnz0r59+6Thw4dLLi4uFvHcmTpLLTeizJjn82YOLLXMSJIoN6b+3DWqpE6SJOnrr7+WAgICJDs7OyksLKzKMOwJEyZIAwYMqHL89u3bpe7du0t2dnZSYGCg9M033xg4Ys0ANW7Lli1TH3Pr4/vggw+kNm3aSA4ODlKzZs2kvn37Sn/99Zfhg9fAmDFjJF9fX8nW1lby8/OT7r//funkyZPq35vzc2cOLLHciDJjns+bubDEMiNJotyY+nOnkKQbPfoEQRAEQRAEs9Vo+tQJgiAIgiBYMpHUCYIgCIIgWACR1AmCIAiCIFgAkdQJgiAIgiBYAJHUCYIgCIIgWACR1AmCIAiCIFgAkdQJgiAIgiBYAJHUCYIgCIIgWACR1AmCIAiCIFgAkdQJgiAIgiBYAJHUCYIgCIIgWACR1AmCIAiCIFgAkdQJgiAIgiBYAJHUCYIgCIIgWACR1AmCIAiCIFgAkdQJgiAIgiBYAJHUCYIgCIIgWAAbYwdgaEqlkosXL+Li4oJCoTB2OIKZkySJgoIC/Pz8sLKy3M9IotwIutJYygyIciPojsblRmpkUlNTJUBsYtPplpqaatDX8ddffy0FBgZK9vb2UlhYmLRz585aj127dq00ePBgydPTU3JxcZF69+4tbdy4Uav7iXIjNl1vhi4zxiDKjdh0vd2u3DS6mjoXFxcAUlNTcXV1NXI0grnLz8+nVatW6teVIaxevZqpU6eyYMEC7rjjDhYtWkR0dDTx8fH4+/tXO37nzp3cfffdvPfeezRt2pRly5YxYsQI/v33X7p3767RPUW5EXTFGGXGWES5EXRF03KjkCRJMlBMJiE/Px83Nzfy8vJEIRMazBivp169ehEWFsY333yj3texY0dGjRrFvHnzNLpGSEgIY8aM4e2339boeFFuBF1pTK+lxvRYBf3S9LVk2R0aBMHClJaWcvDgQaKioqrsj4qKYu/evRpdQ6lUUlBQgLu7e63HlJSUkJ+fX2UTBEEQTJtI6gTBjOTk5FBRUYGPj0+V/T4+PmRmZmp0jU8++YTCwkIeeuihWo+ZN28ebm5u6q1Vq1YNilsQBEHQP5HUCYIZunUknSRJGo2uW7VqFbNmzWL16tV4e3vXetyMGTPIy8tTb6mpqQ2OWRAEQdCvRjdQQhDMmaenJ9bW1tVq5bKysqrV3t1q9erVPPnkk6xZs4bBgwfXeay9vT329vYNjlfQrdJSyMgANzd5E7Nk6M+8efNYt24dp06dwtHRkT59+vDBBx8QHBys93sXFEAjGEdSTUVFBWVlZcYOwyisra2xsbFp8NQ3IqkzVxcuQHo69Olj7EgEA7KzsyM8PJzY2Fjuu+8+9f7Y2FjuvffeWs9btWoVTzzxBKtWrWLYsGGGCFVooJISOH4cDu69zqH9FRw8YsXx0/aUlskNLNbWEs2aKXB3Bw8PcHeH5s1h0CAYMkT+Wai/HTt2MHnyZHr06EF5eTlvvPEGUVFRxMfH4+zsrLf7/vQTPPIILFkCjz+ut9uYnGvXrpGWlkYjG7tZhZOTE76+vtjZ2dX7GiKpM0dbt8LQoeDrCwkJ4OBg7IgEA4qJiWHcuHFEREQQGRnJ4sWLSUlJYeLEiYDcdJqens6KFSsAOaEbP348n3/+Ob1791bX8jk6OuLm5ma0xyFUdz23kK/fyuSHfW04fhzKywGqlm9bSinDjooKBTk5kJNT9RpLloCVlcQddygYPhyGD4eOHUWtnrY2btxY5edly5bh7e3NwYMH6d+/v97uu2cPSBLs3994krqKigrS0tJwcnLCy8ur0U3ULEkSpaWlZGdnk5SURLt27eo9MbdI6sxQkk9v/nJ4hSeSP8Vp/nyYPt3YIQkGNGbMGHJzc5kzZw4ZGRmEhoayYcMGAgICAMjIyCAlJUV9/KJFiygvL2fy5MlMnjxZvX/ChAksX77c0OELNVAq4afXj/HGJ81ILm+j3u/hAeGKg4TlbibcKYFwt7ME2mdQcjGXKyWO5Lq3J3fdDi5fhtxcODt3FRtSQjiu7MKuXbBrF7z2GgQFKhk+worx4yE8XCR49ZGXlwdw21HjJSUl6p/rM2pclaRfu6b1qWarrKwMSZLw8vLC0dHR2OEYhaOjI7a2tly4cIHS0lIc6ltZo/cptU1MXl6eBEh5eXnGDqV+UlOlYdEVEkjSWH6UlM5NJCkjw9hRNVpm/3rSUGN5nMaw7berUoTHOUmun5EkP+sMadFr56TkZElSKiVJKi298U0lSqUkZWdLUkJC1f2TJkmSh4eUjL/0Nc9J0fwl2VOsvjZIUvfukvTNN5JkrKfSHF9LSqVSGjFihNS3b986j5s5c2aNqwBo81gHD5afp3vvbWDQZqS4uFiKj4+XiouLjR2KUdX1d9C03IjRr2am5JHH2fZ3MQA/8TCLCh+FN980clSCIGgr/qTEiLB07hzlxoHc1jShgHci/+LMxSY8835rAgJu1KjZ2lavWlMowNMTOnSouv/rryE7m4CTfzNpYVc2PPIDub6d+Z2RPNJyB/b2cPgwPPec3HvjqafkZr5G3I1JI88//zzHjh1j1apVdR6ni1Hjubny18ZUUyfojkjqzEl+Pvv2VlCEMwqF/F94KvM5vOSQ/J9aEASzMHeOROdQJX8eboE15UxqtopzfyXyxt5hOHk3adjFFQro1AmefRZ++AHn9NOMPP85PxzsSHo6fPYZdGhXTlGR3P+uVy/o3h0WLpRHXQpVvfDCC6xfv55t27bRsmXLOo+1t7fH1dW1yqYtVfOreC6E+hBJnTnZsoUtFQMBGDNGwYgRUIIDo/mZvBfeFB+3BcEMfPopvD1TgRJrRil+5+QLi/g68wG8h0bo54YKBQQFgbc3Hh4wdSrE93iMnfTjf75bsLeTOHr0Zu3ds8+Kz4ggd15//vnnWbduHVu3biUoKMgg922MfeoE3RFJnTn5+2+2MAiAwYNh+XIIaFnOOdry1OUPkMrKjRufIAh1+v57ePll+ft5c8r49WR7gr+YDA2YwkBrkoSiU0f62f/H9xmDuWjdis9G7aBDB4nCQli8GMLC5Bq8ZcugqMhwoZmSyZMns3LlSn788UdcXFzIzMwkMzOT4uJivd2zqAhUlxc1dUJ9iKTOXEgS+X/t4l96AXJS5+4Oq3+xwdZW4peEUL5ebGvkIAVBqM1ff8Hjj8u16TEx8NqbtvJcI4amUMAbb8DRo9C/P+7F6Uz9bSDx7v3Y/kM6Y8fK3fj274cnngA/P3jxRUhMNHyoxvTNN9+Ql5fHwIED8fX1VW+rV6/W2z0rT08jaurMw6pVq3BwcCA9PV2976mnnqJLly7qEdOGJJI6cxEfz86LbajAhjatldyYvYJeveDDD+VO1DExcOCAEWMUBKFGe/bA6NFQUaFgXLM/+OiRw8afViQ4GLZtg0WLwMUFxd49DHgmmFVPbyUtDT74AFq3hrw8+OILeUzG3XfDb7+p5s+zbJIk1bg99thjerunapAEyDV1jb5HTWFh7dv165ofe2vtam3H1cPYsWMJDg5m3rx5AMyePZtNmzbx999/G2UeUJHUmYu//2Yz8tJOgwZXfdpefBHuuw/KyuChfhe5ejrLGBEKglCDEyfkCYCLi2Eof7Hk6oNYWRs7o7vBygqeeQaOHYMBA6BpUwgLw9sbpk2DM2dg40YYMUKu4Nu8Wf5f07o1vPsuXLpk7AdgWSrX1JWXy8vCNWpNmtS+PfBA1WO9vWs/Njq66rGBgTUfVw8KhYJ3332X7777jvfee4/PP/+cjRs30qJFCwBsbGzo1q0b3bp146mnnqrXPbQhkjpzMXw4W3weAeSm18oUCli6RCLIPp2k6348MSRNfMITBBOQnCwv2XX1KvRxOMgaRmP7wkTo1s3Ikd0iMFBeqWb3bjmxA5AkrPbsYsgQWL8ezp+X5zn39ITUVHkmpVatYMIEOScUGu7W1UFEvzrzMHz4cDp16sTs2bP59ddfCQkJUf+uadOmHDlyhCNHjvDdd9/pPRaR1JmJzKYdOHHJG4A776z++6bNFPz8dQ52lPBrchgr3kmpfpAgCAaTlQVRUXDxIoR4Z/HH9btxau4Gc+YYO7SaWVnJyZ3KsmXQvz+MGwfZ2QQGwrx5ckK3YoXc9aOsTP6+a1f5sW7aJJoMG+LWpK7R96u7dq32be3aqsdmZdV+7N9/Vz02Obnm4+pp06ZNnDp1ioqKCnx8fOp9HV0QSZ2Z2LpV/tq9u/xJuSYRT3Zldmf5hf7uh7ZUVBgoOEEQqpAkGDtWbr709ytnU14k7lyBTz4Bc1lvNzNTTvRWrpSTvZdfhowMHBzkPG/fPnkwxZgx8mGxsXDPPXKCt3w5VFotS9CQqKm7hbNz7duty2jVdeytS4/Vdlw9HDp0iNGjR7No0SKGDBnCW2+9VeX3+fn5hIeH07dvX3bs2FGve2hDJHXmYMUKtnx7HoBBg+o+9PlFnXEnlzPXfFmzINsAwQmCcKuNG+UxCPb2sKn9C7QoOS9XsT/8sLFD09zrr8uLx4aHy3NtfPqpPN/d5MlwY23hHj3gp5/g3Dl5/rsmTeD4cXkh+sBA+PBDoz4Cs1N5oASImjpTl5yczLBhw5g+fTrjxo1jzpw5rF27loMHD1Y55uDBgyxcuJDx48fXaz1gbYikztRJEtLbM9m83Rqo3p/uVk0iOzO19XoA3ptThlKp7wAFQahMqZTzIYDnnymlQ9lxeY6Qr7+uvtyXqevTB/77T26+6tNHrn5bsECeobiSwEB5pYrUVHnUbIsWckXfoUPGCdtciZo683H58mWio6MZOXIkr98o8OHh4YwYMYI33nhDfZyfnx8AoaGhdOrUidOnT+s1Lhu9Xl1ouMREzl2wJoUAbG0l+va9/ZvC8++14KOx+RzP8ePPn4sYOdbJAIEKggDwyy9w5Ai4uMD0t+3Afae8RIMx5qTTBYVCblcdMgR27IC5c+UREyr798vznAwcSNM+fZg2rQlTp8LPP0PnzsYK2jyJPnXmw93dnYSEhGr7f//9d/X3V65cwcnJCXt7e9LS0oiPj6d169Z6jUvU1Jm6SlOZREYqNGr2bzZ6MJODtwDw7qcOouOyIBhIeTmoutS8/PKN/q9WVnITprlTKGDgQNiyRZ7+ROX33+URFEOGQLNm0KcPdrNe538tt9O1q9GiNUuqpM5abpgRNXVmLiEhgYiICLp27crw4cP5/PPPcXd31+s9RU2dqdu4kS3Ic9vcrulVzcqKl3bex+eBsP8/K7Zs0eJcQRDqbflyOH0aPD2UxFh/Cbn/Aw8PY4elX337yvOabN8OFy5AXJy8HT8uJ4GCxlRJXatWNwdoCuarT58+HD9+3KD3FDV1pqyoCOX2nWzlLuD2gyQq8/aGp5+Wv3/3XT3EJghCFdevw+zZ8vevD9iLy1tT5SUYLF10tJzNJidDUpL8/WOPwf33GzcuMyNJNwdKBAXJX0VNnaAtkdSZsm3bOFLakct44OIi0aOHdqe/MqkIW+sKtm+HvXtEG6wg6NM330BaGrRsKfHcyeflnY8/btygDC0wUK61W7as8T32BioqurnylWq6QFFTJ2jLbJO6BQsWEBQUhIODA+Hh4ezatcvYIene4cNsQa6eGzBAga2tdqe3alrAeGkFAO++ekXX0VUnSXD5stzssnEjLFki97Ux1MfNS5dg6VJ56oUvv4SFC+UYKk/Yl5+PmMBP0LWCAnjvPfn7mQ+dwiHxqDy/x4QJxg1MMBuqpld7e1DNXytq6gRtmWWfutWrVzN16lQWLFjAHXfcwaJFi4iOjiY+Ph5/f39jh6c7b77J5tjrsLOefeJ8fJj+wBmWralgQ5w7hw/LkxfrVGEhmcOeJPlcBWlZtqSXepFGS9JpQRrtuGTVH58NTWjXHtq2hXY5e2nbTkHb+7vSxFs3o3KLlv/MxYXrubg/jQzJBweu48Ml9eZU+Y11+nRYtUruB9S/v7yFhaF1xiwIlXz6qfym3L49PHbuxkiJ8ePB1dW4gQlmQ5XUeXrKI6dB1NQJ2jPLpO7TTz/lySefVC+OO3/+fDZt2sQ333zDvHnz6nXNkhJ5fcPDh+U+aKYwnVRJCez6T541W5v+dJW1nfkoY9asZhWP8N7r11jzd/0WLa5CkkCh4PhxeOstZ37f8VPtxyrh9G7YtVu1o4/85TlobpdLgHcxrYKdaBnajFb+Clq1gpYt5Xmurl+H7OwatrTrZF6x5+JFBRcvQl7eQ8BDtYbg3FTCx0f+9Nv29HDCrtrR/c/DdPvzHdzIBycneQ6uvn3h7bdN48kXzEZOjrxQBMDcF3OweeFX+YdJk4wXlGB2akrqRE2doC2zS+pKS0s5ePAg0yvPkwRERUWxd+/easeXlJRQUmm9mrpmc/7fo0pKy6x4IjqDtv18dRd0fUgScXEKiovlZKTS+sDaCQnh9Tu+ZNWeR1i70YmEhAZMl1VeDj/+yLl3VjEz9Bd+/M35Rn4n4e9dQsuWEi0CbWkZYEOLFnJy5u0NGRnycklnTpVzdnMyZ3LdyVW6k1nqQWYa/JsGbNEmEIdqe5xsS2nRQkHzVraUlMgtsZcuyYlhYaGC8+flBcnjGMr3DFWf19bqHN2LDhK2+RDtT2TR/G4FzZtD8+bg9P7bYGcHERFw113y94Jwi/ffl998u3eHB9Pmy7MP33lnAwqt0BipBkl4esot9yBq6gTtmV1Sl5OTU+OiuT4+PmRmZlY7ft68ecxWDUmrg709hNmfZF9ZZ+J+OG/8pO7RR9myfzjwCIMGNazyKHTmA9wb9Ru/M4oP5pay/Ectk5Pycvj+e9Jnf8fcC+NYwnrKz8jNlaNHw5w5Cjp0qJ5oVWcDtAVJ4srhJM6uOUzq1jOkHr1MWoknqe0HkeYVRmoqXExX4qS8hpeUhSc5eJFdZWv+YD/8nrsXPz/w8wMXF7tqfyNJkt9sVQleZiacPCnXxh46JM9+f1bZhrO0YQ0PQSZwx83zXXgFXzJorrhE8wes+f4HkdcJVaWlwVdfyd+/+y5YbSyQm/InTzZuYILZETV1gi6YXVKnorjlHVySpGr7AGbMmEFMTIz65/z8fFq1alXjNSPb57LvEMTtrmCcbsPVXnw8m8+9ANS/6VVt8GDeaP0wv58fxcrVNkx/Gzp00PDcCxfIfeg53t9/J1+xmevICyNH313OO+/bEBZWj3gUCpqFBdEjLIgeAGVl8rxW3k6giuvocejWTf6+XTu531tYGISFy1UiGsz9pVDIXZpcXeVLADz44M3f5+TICZ5qu3BBTvwyMuQavgJcKcCV01IwThtMq9vdggUL+Oijj8jIyCAkJIT58+fTr1+/Wo/fsWMHMTExnDx5Ej8/P6ZNm8bEiRMNGLFlmjtX7ibRr5+86ALRn8trhFn63HSCzqmSOg8PUVMn1J/ZJXWenp5YW1tXq5XLysqqVnsHYG9vj729vUbXjhxgz2eHIO68t05ibYj89AL+k1Oehk8crFDQY+ZQ7n71CLFZ3ejeXZ7t/rXXbn4irMn1H9by5VNHePf6j+TRFIC+kRW894E1/frp8KVjaysPWKisUyd5MtNu3cDNTXf3qsTTU55G7NapxFQ1fJmZN5O8wkLT6Wqn7UChpKQkhg4dytNPP83KlSvZs2cPkyZNwsvLiwceeMAIj8AyFBTACnlwOe+8U+n1UcP/IUG4HVFTJ+iEZIZ69uwpPffcc1X2dezYUZo+ffptz83Ly5MAKS8vr9rvUvdflECSrCiXrl26prN4tVZaKq1nhASS1DaoTGeXTU6WpAEDJElOWyTJx0cpLV4sSeXlVY+rqJCkFSskyd8lV31s147XpQ0bJEmp1Fk4FqGu15O+9OzZU5o4cWKVfR06dKj19T9t2jSpQ4cOVfY9++yzUu/evTW+Z22PMy9PknbulKRt2zS+lMX4v/+Ty0b79pKkPHNWkk6cMHZIZsEYZcZYtHmso0fLr6cvvpCkI0dU/6MNEKQJKC4uluLj46Xi4mJjh2JUdf0dNH0tmeU8dTExMXz33XcsXbqUhIQEXnrpJVJSUhrcnNSyhy8trS+ixJr/fjyjo2jr4dIlNt+Yn25wlLXOLhsQANu2wa+/VNDO5jyXLil45hnoFlrGP//Ix8RuUhIeLs/GkFLgTqumBfzfknIOHrcnOtp0aqsaK9VAoaioqCr7axsoBBAXF1ft+CFDhnDgwAHKyspqPKekpIT8/PwqW022/VNG//7w8jO1D0CyVD/+KH999FFQvPcuhIaK5VuEeqs8UELU1JmnK1euMHv2bDIyMowWg1kmdWPGjGH+/PnMmTOHbt26sXPnTjZs2EBAQECDrx3pmwxA3N9XG3ytesvI4BByZ7W+/XSbRSkUMKpjIiecezOfF2nGZU6csmXIEGjnnkvUPVYcOSK3eH7wASRedGH8EzbqBaYF49J2oBBAZmZmjceXl5eTo2rzucW8efNwc3NTb7X1Q22jOA/AubMSUiNatOTSJdi8Wf7+4XuuyHMfgjzqVRDqoaY+dUVFYq50czJlyhT+++8/nnvuOaPF0KCk7rpqTRMjmDRpEsnJyZSUlHDw4EH639onq54iw0oBiDvmrJPr1UtGBhfxA+TaNZ3r1Am7i8m8uDyMsxEP8xKfYkspZ694YEspL0Wf4tw5mDYNHB31cH+hwTQdKFTX8TXtV5kxYwZ5eXnqLTU1tcbjWveVX6d5khuXLzSeaoU1a+Q32x49oN2O7+SRNd27Q2SksUMTzFRNfepA7s8rmL7169dz7do1/vzzT5o2bcoPP/xglDi0TuqUSiVz586lRYsWNGnShPPn5U/qb731FkuWLNF5gIbW+6lQAOLKI4xW8yBdL1EndX5+erqJkxNMmID7f5v49Ohg4h99j4+8P+LUnDV8+lewGLxnorQdKATQvHnzGo+3sbHBo5Yn2t7eHldX1ypbTZx8XPBTyE0N5/bUXFNoiVRNr4+MVcKCBfIPkyeL/glCvUhS1aTOwQGsbrw7ixGw5mHkyJH8+qs88fjy5ct59NFHjRKH1kndO++8w/Lly/nwww+xqzRpV+fOnfnuu+90GpwxhEV5YmcHOTkKzp0zTgxXBo9WTx2it6Susi5daLtyFq9cepXWbz0q3phMmJ2dHeHh4cTGxlbZHxsbS58+fWo8JzIystrx//zzDxEREdjqYJ6WNs5yUnf2YF6Dr2UOzp+XZ+BRKGCM5xZIToZmzeDhh40dmmCmCguhVG4kwtNTfm2JfnVCfWid1K1YsYLFixfz6KOPYl2po1WXLl04deqUToMzBnt71HOvxcUZJ4aLF+Wv7u7yJzZBqOx2A4VmzJjB+PHj1cdPnDiRCxcuEBMTQ0JCAkuXLmXJkiW88sorOomnjYeczJ07VaqT65m6n26sinfXXeC76lP5hyeekGu/BaEeVLV0jo43XkbXr4u56oR60TqpS09Pp23bttX2K5XKWkfSmZvIQLnmIe6rg0a5vyqpM0gtnWB2bjdQKCMjg5SUFPXxQUFBbNiwge3bt9OtWzfmzp3LF198obM56tq2kpfhO5dsdtNeak2SQNVV5tEHS2D/fvmHZ581XlCC2as8SIKff4YmTXBRyh+WRE2daVu1ahUODg6kp6er9z311FN06dKFvDzDt15o/V84JCSEXbt2VRtpumbNGrp3766zwIwp0i+Zz/Al7rhxBkukv/kN8BwtXPKBmvsyCY3bpEmTmFTLgvHLly+vtm/AgAEcOnRIL7G0aW8Nu+HspSZ6ub4pOXYM4uPlGv37H7aHxy/C3r03lywRhHqo3J+OMWMAaJJxGujRKGvqJEke+WsMTk7a9UAaO3Ys77//PvPmzeOrr75i9uzZbNq0iX379uGmp4nz66J1Ujdz5kzGjRtHeno6SqWSdevWkZiYyIoVK/jzzz/1EaPBRY4NhE/hWHE7rl0qpImPYZO7i6fkOb/8PBtHc5Zg3trc3RqWwjnaGDsUvVMNkBg2TLXQib2YxkRosCpJ3auvwkcf4eKigILGWVNXVHRzWhdDu3YNnLV4y1coFLz77rs8+OCD+Pn58fnnn7Nr1y5atGihPsbGxobQUHkQZkREhF7HH2jd/DpixAhWr17Nhg0bUCgUvP322yQkJPDHH39w963rLZkpo05CXFHBxWtyD1m/ILF6vGD62g6Rk7nMy/YWPf2CUnlzOrpHxlTQqCbmE/Sq8sTDtG8PQBMneYK6xlhTZ26GDx9Op06dmD17Nr/++ishISFVft+0aVOOHDnCkSNH9D6gtF6dYIYMGcKQIUN0HYtJifS9wJo0P+I2XuXOlwx445wcLkq+APi1NeJceYKgoWbN5O3KFTh3Drp0MXZE+rFnD6SmgqsrDM1cCu0/lGtVnnnG2KEJZq5KTd2N6YNclHKLTWOsqXNyMl4yW5/xTps2beLUqVM1TgxvaFrX1LVu3Zpc1ceKSq5evUrr1q11EpQpiAyXO3/HHTXwiLaLF0lHrrZt4S+WcRDMQxu/YgDOHbhi5Ej0R9X0ev/94PjbKjh7FmpZPk0QtKEeKOFQKC/lAzS5MVCiMdbUKRRyE6gxNm1n9Dp06BCjR49m0aJFDBkyhLfeeqvaMfn5+YSHh9O3b1927Niho79SzbSuqUtOTqaihnVLSkpKqoz+MHeRwz3hd9iX1RpJKaGwMtDcbRkZXESu6hCjXwVz0SZzNwe4m3PbU+GJZsYOR+dKS+VBiQCPRl+Gh2/8Yx492nhBCRZDXVNXngk3BjS5lF8FGmdNnblITk5m2LBhTJ8+nXHjxtGpUyd69OjBwYMHCQ8Pr3Kcn58fJ06cYNiwYRw/frzWCd0bSuOkbv369ervN23aVGVUR0VFBVu2bCEwMFCnwRlT94faYfd0CTmSJ2f/u0y7Xu4GuW9FeiaZyIuvi6ROMBdtm1+DXDh3RmnsUPTin3/g8mXw8YE7s1bLHex69dLTOn5CY6NO6kovqvc1uXcQrGicNXXm4PLly0RHRzNy5Ehef/11AMLDwxkxYgRvvPEGGzduVB/rd+PNPDQ0lE6dOnH69GkiIiL0EpfGSd2oUaMAeaTHhAkTqvzO1taWwMBAPvnkE50GZ0z2rvaE96wgbj/EJbrTrpdh7pt91ZYKbLBSKPHxadDSvIJgMG0CK+AknE2zN3YoeqFqeh07FqzX3qiye+gh4wUkWBT1QImiG/NLPvEELp2CAFFTZ6rc3d1JSEiotv/333+v8vOVK1dwcnLC3t6etLQ04uPj9dpVTeOsQalUolQq8ff3JysrS/2zUqmkpKSExMREhg8frrdAjSGyr9ynbd8+w90z/a5xAPg0V2Bj+XO5ChaiTUc5mTuXa3lNr9euger/9CNDckHVJ+bBB40XlGAQO3fuZMSIEfj5+aFQKPjtt9/0ch91n7orZ+VvAgPVy4SJmjrzlpCQQEREBF27dmX48OF8/vnnuLvrr+VP67QhKSlJH3GYpMhI+WtcnAQYpk/dzdUkxPqrgvloGy53x7hQ7EVpKdhZ0Gw869fL82a1aQM9zv0kT2XSuzf4+xs7NEHPCgsL6dq1K48//rjOVmC5lSRVan7NubHU5qFDNPHeAQwQNXVmrk+fPhw/ftxg96tXXVBhYSE7duwgJSWF0tKqE+ROmTJFJ4GZgshuxYAjx44ouZZZTJPm+p8NUZXUVZq3UBBMnm+4H44UUYwTF5Il2rW3nA8lqqbXRx8FRc8e8NRTcn86weJFR0cTHR2t8fElJSWUlJSof87XYHR0QQGoVtj0SD8mf/Pbb7hQDgwQNXWCVrRO6g4fPszQoUP5//bOOzyKOn3gn03vG0gljVAEAoRiaEEUsAAKFjw5OTWHysGpP0TBBnLSlMvZzrOciliw4OGpoNgop0gRQjXUEDrpJJR0SNmd3x+TXRJS2E12s5vd9/M882R29jsz72TyZt55v28pLy+nrKyM9u3bc+bMGXx8fAgNDXUooy6yqzfRrtlk6iLZ8fkRRs60fhu07Bc/A+4jwrcIaP0WI4LQHDQdY+jMYQ7Qm2O7Crmqm2NMw5aVwbp16vrddwM9B8GgQTaVSbBfkpOTWbBggVn7GLx0Pj4KPlmHjdv9UK058dQJ5mB2JP6MGTO49dZbOXfuHN7e3qSkpHDq1CkSEhJ45ZVXrCGjTUmMOAXA1tWt0JhXUcg5pXo+I8IdM4tQcFA8POjaR63peCy3lWs7WpFff1XLmcTGQlycraUR7J3Zs2dTVFRkXDIzM6+4T51uEkeOwPr10LUr/qjWnHjqBHMw26hLTU3liSeewNXVFVdXVyoqKoiOjuall14ypvU6EkOuVv3irVKE+Px5cvThAER0c/zm6IJj0eVGNaPraKbjZMCuWaP+HD0aNG++oWZNSXswoRE8PT0JCAios1wJY5JEkEZ9exgxAtq1E0+d0CzMNurc3d3R1JRcDgsLIyNDTcHWarXGdUci8dZgAFIK1CLEViUnhxzUejaRse7WPZcgWJguagtYjh2zrRyWxGjUDTgLjz2mZk85UJF1wfbUaRFmICCgjqfOWd4jFGe50EawxPWbbdT179+fnTt3AjBy5Ejmzp3LsmXLePzxx4mPj2+xQPZG/z9ehQc1RYh/PmXdk+XmGluESeFhoa3RpZ06j3Rsr2PMF508CYcPg6srXH+mpjbdNddAVJRN5RIcC6NRdzET5syBjRshIMDoqauuhlq5Fw6Jq6taPuzyxEtno7y8HFCdZ83F7ESJv//975TU+IOff/55Jk2axMMPP0zXrl356KOPmi2IveLp70GC/z62lsSz9cssrrop1mrnqsg4zRlCADHqhLZH18xfgT9wPNMdvR5c2njtbIOXLjERtN8vUz9IWzCnorS0lKNHjxo/nzhxgtTUVNq3b0+MhUraGI26s+nw97+Dt3cdo06VA7y8LHK6+ixfDj/9pDY1vv12K52kadzc3PDx8aGgoAB3d3dc2vo/DzNRFIXy8nLy8/MJDAw0GrnNwWyjrnZri5CQEH788cdmn7ytkBhfxtYtsDU7hj9b8Tx5R1Rj2cOliqAgmX4V2hYxCSG4Us1FvSc5OW3foWWceh1SBK/8pn6QgsNOxc6dOxk5cqTx88yZMwGYNGkSS5cutcg5DIkSQRey1JXYWBg5EtcxY/B+QM+Fiy6UlFw2PWtJ1q2DTz5Rz3v77epcr7ld7VuIRqOhQ4cOnDhxglOnrDwjZscEBgYSHh7eomNIzwITSJwxBGqMOmuSXayWEI/wL0GjaZ1es4JgKdyviqUjpzhOF44d1hEV1fy3TVtTVQU//6yuj9bVvLgOGyYFJJ2MESNGWD3Oy+ipK64p7B8bq07zA/6PwYWLVs6ANRTG7dED5s2D3Fx47z0rnrBhPDw8uOqqq5x2Ctbd3b1FHjoDZht1Z8+eZe7cuaxfv97YLqw2586da7FQ9oahs8S+fWomkqF9i6XJuf4+eAciejlGjS/ByYiMpKvmZ44rXTi2u4jh17fdF5OUFCguhqAguHrrv9WN0utVsAJGo+78EXUlNtb4nZ8f5OdbMQNWryd3/1n+yEb+8L0Xj//nXtVTN24c3HablU7aOC4uLnhZbZ7ZOTDbqLvvvvs4duwYkydPJiwszJgJ68hERkJ4uEJenob0fZUMGGqdHkjSIkxo07i60sU/H4rh6J5SoO0adYap15uur8Z1fbr6Yfx42wkkOCxGo05/GtzdoUMHyMqC7dvx19wE+FvPU3fiBF9eGMtmrmXz51Ax/Hue2TAWpkxRvRkhIVY6sWAtzDbqNm/ezObNm+nbt6815LFbwovSyaMHBb/sg6EJVjmHtAgT2jpdQkuhGI4dbtvFs43xdLe4wX/y4MCBth8kKNglRqOOM9Cxo5puvXEj3Hsvftp9QG/reer27WMPl57lszbcgkf4i8zIewb++lf4+utWj68TWobZKSY9evTgwoUL1pDFrgnxUl+VCrKtNN+vKGS/9wOgxtQJQluka2w1AMeyrOPNbg3OnIFdu9T1UaNQH7J9+thUJsExUZRaiRKcvTT1WlO02B+1d6zVPHX79xuNumuvVTfNzHuat12mwcqV8NlnVjqxYC3MNurefvtt5syZw4YNGzh79izFxcV1FkclxP8iAAW51dY5QUkJOefVWIIIKTwsNML58+dJSkpCq9Wi1WpJSkqisLCw0fFVVVU888wzxMfH4+vrS0REBH/+85/JMbiFLUyXScMAOFoS1mYLpq5bpz5s4+MVIjq00YsQ2gTFxWodOoCgtN/grbfUDzVGnZ9OfaZay1NXffos++kNwIcfwjPPqNv/T/8m7zMZpk0DE1qdCfaD2UZdYGAgRUVFXH/99YSGhtKuXTvatWtHYGAg7do5boB/iFb10BUUWOkEubmXukl0lkBRoWHuueceUlNTWb16NatXryY1NZWkpKRGx5eXl7N7926ee+45du/ezYoVKzh8+DC3WSkIuvOd/QAoKnGlreZMGadeBxWq02EPPeQ8Jf2FVsUw9errC949OkL37uoGg6dOVwhYz1N3+OHXqMALPz+Fzp0hORlmzFC/m8p7fHJhAvz+u3VOLlgFs2Pq7r33Xjw8PPj888+dJlECICRIjREqOGelMg25ueTQH5DCw0LDpKWlsXr1alJSUhg8eDAAS5YsITExkfT0dLobHgi10Gq1rFu3rs62N998k0GDBpGRkWGxAqoGfHzUOO/cXLVdWFCQRQ9vdRQF1q5V10f7bla9FGlpElckWIUGW4TBJU9d1XnAep66PXvUn/HxGmOx8FdfhcpK+Pe/XXhAtwTPCxruts7pBStgtlG3f/9+fv/99wYfII5MSJj6T72g2DqxQqUnCihGC4hRJzTM1q1b0Wq1RoMOYMiQIWi1WrZs2WKyThYVFaHRaAgMDGx0TEVFBRW1ehOZHFqh09G1XSG5uUEcS69m0KC2VQpz3z7VIPX2hmGnarpI3HSTbYUSHBajUXchA55bAo8+CqGhlzx11aq721qeOoNRVzvvUaOBN95QW5O9/76Ge+9VX9ZuvdU6MgiWxezp1wEDBpDphHPsoR3Uh1NBqY9Vjp9zWH0V83O7YLU6eELbJi8vj9DQ0HrbQ0NDycvLM+kYFy9eZNasWdxzzz0E1Dw4GiI5OdkYt6fVaomOjjZNSI2GLofUhJ+jv7e9hB/D1OuI4QpeGwx1TcSoE6yDMUki/xC88MKlALuah4ChVZhVPHUrVpC6OAWAfv3qfuXiAosXw5//DDodPD39Ipw/bwUhBEtjtlH36KOP8thjj7F06VJ27drF3r176yyOSkgv9WGa79bBKsfPOa56RSL9HTfZRGiY+fPno9Fomlx27twJ0GC4g6IoJoVBVFVVMXHiRPR6PW+//XaTY2fPnk1RUZFxMflFzsWFLu1V78KxAxdN28eOMMbTxZ2CwkIIDIRarREFwZJcKmdSAB4eYGgR5ekJ772H//1qWzqreOp27GBPYUegrqfOgIuL6rFz1eg4dNKLkx+tt4IQgqUxe27k7rvV2fUHH3zQuE2j0RgfLDqdznLS2REh18UBUFBlnWSQ7AtqodaIoIorjBQcjWnTpjFx4sQmx8TGxrJ3715Onz5d77uCggLCwsKa3L+qqoo//vGPnDhxgl9++aVJLx2Ap6cnnp6eVxa+AbpGXIAzcOxE24pDKyuDTZvU9dG6n9SV669XS5oIghWoV6OudiP7KVPwcweWWsdTl78zgzw6oNEoxMc3rKtaLQyNPMWmrM6s/qqUh2ZaXg7Bspht1J04ccIactg9hsLaJSVqrEEzn3eNknPt3fAdRAy2bn9Zwf4IDg4m2IRu3YmJiRQVFbF9+3YGDRoEwLZt2ygqKmLo0KGN7mcw6I4cOcL69esJsnL2QpeuGtgLR3N9rXoeS7NhgxogHhMD3X9frm6UqVfBitQx6mq1BzNgCMWxhqfOEE/XNfICvr6NhxWNubGaTUthdWo4D1leDMHCmD392rFjxyYXRyUwENzc1LIGBXmW90ZKNwnhSsTFxTFmzBimTJlCSkoKKSkpTJkyhXHjxtVJkujRowcrV64EoLq6mrvuuoudO3eybNkydDodeXl55OXlWa1xdpfe3gDklfpTVmaVU1gF49TraNAMGQw9e4pRJ1gVg1FXp/CwgW3b8EvdDFjBU1dczJ4CNZSo79VN+3Zu/ovaSeXnC4lUpjunU6ctYZKnbtWqVdx88824u7uzatWqJsdaq/6VrdGgEFx9mjzCKdh/mqiOlk1RvdT31aKHFRyMZcuWMX36dEaNGgWo+vaWoWBpDenp6RQVFQGQlZVl1Nl+l0VDr1+/nhEjRlhcxva9OtCOc5ynPcePQ3y8xU9hFWobdfzhJXjpJZvKIzg+hkQJ1VPXr+6XTz+N/8YqYIvlPXW1Okn0Hdh0RYe+iT6EuZ/ldFUQv32wiZEvdbKwMIIlMcmou+OOO4yZd3fccUej4xw5pg6NhhC3c+RVh1Nw0sLuh/JyslelAQlEBFcCbbfFkmBd2rdvz2dXaN2j1CqUGxsbW+dzqxAbSxeOsZP2HD3aNoy6U6cgPV0Nn7vhBltLIzgLTU6/BgTgxynACp66ffvYQyLQcJJEbVxcYHSPDD7ZF8TqH/SMlHcdu8ak6Ve9Xm8spaDX6xtdHNagqyHEU9WsgkwL977NzSXnopqAEdFRWoQJbZy4OLokqokbx47ZWBYTMXjpBg+GwINb4GLby9wV2h5Go+6HT2Ds2LpfBgTgj/rMsbSnrqIC0lCT/65k1AGMuVV9Lq0+3Fm6q9g5ZsfUffLJJ3WKkhqorKzkk08+sYhQ9kqor+qhy8+2bP9XJadWi7CotpUxKAj18Peny0g14aetGXVjhhbDNdeomVFtKSBQaHPo9bWmX/tFqammtQkIMNapKy9X68VZirTr/ko17gQGKphSgvKmh7qgQc/e6p7kZItRZ8+YbdQ98MADxnid2pSUlPDAAw9YRCh7JSRAfXsvOK236HHPHTlLBWq/1w7WKYMnCK1K167qz7Zg1On18Msv6voo75qaJnFxakNOQbASRUWXDLUGE9JreerAsu8YlzpJaEzqgBcc7c2gwaq5sHqt2WaD0IqYfXcaK3SalZWF9vI3DQcjpJ3qoSs4Y1lvWs5h9W0syKPY4qVSBMEWdKlMA+BoGyhAnJ6u1hn29oaEo1+oG2+80aYyCY6PwUvn53YBz/ferD8gIABPKnDVqJafxeLqFKXB9mBXYswY9efq1RaSQ7AKJtep69+/v7G6/Q033ICb26VddTodJ06cYIzhrjsoIUE1JU0KLdvPMudETTeJgBKg6aKwgtAW6LLtc+B5MnLdqaoCdzsOFd2+Xf2ZkKDg9sta9YOUMhGsjDGerjoPvv9e7ftam4AANIC/2wUKq/wsF1e3bh173vIGrjXPqLuhigUL3Fm36gLVVV64uUuokD1isnViyHpNTU1l9OjR+Pn5Gb/z8PAgNjaWP/zhDxYX8HIWLVrEDz/8QGpqKh4eHhQWFlr9nAZCeofBaihwDbfocXOy1OnciPb279UQBFPo0LMd3pRzQfHh1KlL07H2iMGoG9TpDGw+rXYvb6KYsyBYgjqZr50aKBMyciS8+y5+z7lRWGA5T52ydx97qiYB5nnqBiboac9ZzlUEsf2/Jxh6r5Q2sUdMNurmzZsHqCUSJk6c2OwWQi2lsrKSCRMmkJiYyAcffNCq5w65dQi8AgWaplsymUt2tXq8iHAJQBUcA5cunejMcQ7Qm6NH24hRp2xTV667zvItYwThMq7UTYLevaF3b/xfBwoslwGbvT2bswTjqtHTq5fpEViuPp6MCvuN5aev56fPzohRZ6eYHVN3/fXXU1BQYPy8fft2Hn/8cd577z2LCtYYCxYsYMaMGcTboPiVoVVYrcu3CDkJtwIQcZ0dP/kEwRxqatWBfSdLXLx4KWh8cMaX6opMvQqtgCGmrsFuErUwTIpZylO353c1Rq97ZCleXubtO2aYmq2xept1eqALLcdso+6ee+5h/fr1AOTl5XHjjTeyfft2nn32WRYuXGhxAVtKRUUFxcXFdZbmUlOqj8JCtUekpZAWYYLD0akTXTkKwLFDVTYWpnFSU6GqSn1h6/jGE2oXCQftiiPYF1f01JWVwS+/4F+pWn8W8dTpdOw5GQhA337m7z5qklqeYef5ruTnWbYKhGAZzDbq9u/fb2wm/t///pf4+Hi2bNnC559/ztKlSy0tX4tJTk5Gq9Ual2hTivI0Qruik7iiZsAaFNISSIswweEIDKSLl/qHfXS/hYt1WxDj1Osg0PTtA089Zd9zxYLDcCZf9Zg1atRlZcENN+B3QA0LsIhRd/w4e6p7AtB3qN8VBtenw5i+9HNRXdtrP8y0gECCpTHbqKuqqjLG0/3vf/8z9nrt0aMHubm5zRJi/vz5xszaxpadO3c269izZ8+mqKjIuGRmNv8P0SW4veoqBwoyLPSgysoie/dpQIw6wbHoEqHqyLHj9pslV9uoE4TW5EyWWvUg2K0IwhqI0w5QKyH4V58HLDT9Wqvna7+rm1Fvzt2dm2MPAbB6RbkFBBIsjdm1OXr16sW7777L2LFjWbduHc8//zwAOTk5BDVYQfHKTJs2jYkTJzY5JraJmIOm8PT0tFxSh78/IWSQTxgFx0tgiHeLD6nLyCZPNwCQ6VfBsei4cDLcB5nnzfcItBZGoy79U/gUuOMO8Pe3pUiCk3Cm3AeAoDfm0mAF4Bqjzs+CrcLKXfw4wlWAeZmvtRkzSk/yu7BmfxR6vdobVrAfzDbqXnzxRcaPH8/LL7/MpEmT6Fvzl7Fq1SrjtKy5BAcHExwc3Kx9WxWNhhCPIqiEglOWeUvJP3QOPa64oCM01NUixxQEeyDytgQASko0FBcbn1F2w7lzcOSIuj7oPzPg87OQmSlGndAqGFuE9QhpeICPD7i44K9XjTpLeOr2d7gJPWp8eHgzK3Mlzrke/0/1nCnzZ/duGDCg5XIJlsNso27EiBGcOXOG4uJi2rW7lAEzdepUfHx8LCpcQ2RkZHDu3DkyMjLQ6XSkpqYC0LVr1zq186xFiFepatRl1e9/2xyy09XXr3CvQlxdm+fpFAR7xN9fNeSKiyE72/6Muh071J9doy7QPuus+pSLirKtUILTYEyUaMyfodGo/V8L1WeEJTx1zekkcTnuUWHcOApWroSffhKjzt5oluNUURR27drF4sWLKal5ffDw8GgVo27u3Ln079+fefPmUVpaSv/+/enfv3+zY+7MJcRP9dAV5FZb5Hg5J9U02gh/S5ULFwQ74fRpIr3PAapRZ28Yp17DM9SVhATbCSM4FXo9nD2jZo8GH01pfGCt/q8t9tRVV7Nnh/q8aYlRB9IyzJ4x26g7deoU8fHx3H777fzf//2fsWbdSy+9xJNPPmlxAS9n6dKlKIpSbxkxYoTVzw0QqlWVoiDfMoWCDd0kIoOkm4TgYOTlEXl6F2DnRp2LKqMYdUJrUVgIekV9/AaVN5G8FxCAHxby1O3bx54laiZti4266AMApGzVc/58C+USLIrZRt1jjz3GgAEDOH/+PN7elxIFxo8fz88//2xR4eyRkJ5q/EO+3jIxgDn56gx4RLjOIscTBLshLIwosgDIzrSvmlaKcsmoG3z2R3VFjDqhlTBMvfpTjEfXmMYHPvMM/g/+EWi5p07Zt5+99AFabtTFRFTTkwPoFRf+t9oys1aCZTDbqNu8eTN/+9vf8PDwqLO9Y8eOZNvj67iFCbn7esBy/V+z9Woxx4hos8MbBcG+CQ4mErVWXdYx+/JEnzoF+fng5qbQ78RKdaMYdUIrcTZPLcjdaI06A/fdh98dNwIt99Sd3JxFMVo8XKro0aNlxyI+njFeGwD4adm5Fh5MsCRmG3V6vR6drr5XKSsrC38nyBqzdKuwnG4jAIgc2c0yBxQEe8HNjUjfQgCyT9rX27zBS9f3qnK89OVqnTApFCk0wNtvv02nTp3w8vIiISGBTZs2tfiYZw6prrpgzblLrYoawfBYbamnbs9O1ZDsGVmEu3vLjoWLC2MHqPVV/7sukLy8Fh7vClRXw6YfS/hb5Ic80WEZ7163jJ9nfE/G93vRl9vXC6OtMds9dNNNN/Gvf/3L2OtVo9FQWlrKvHnzuOWWWywuoL1hNOryFaDlRVWlm4TgyES1L4cyyM6xtSR1McbTjfCFX09DRkbDtcIEp+aLL77g8ccf5+233+aaa65h8eLF3HzzzRw8eJCYmCamTa/AmSPngQ4E+5Q3/Xd38iR+aQXAwBZ76vYc8wWgb2/LhPqMvDuUwZtT2FY5hPnz4d13LXJY1WOybh0Fa3bzU34CP2r/xJo1UFjoDzyojskDNgH/Ai8u0NXrCFfFVtPvT3GMGaM63V2tUSFMr6c0ZT8pSw+x+ZdKcs56EhpYSViInrAod8Km301YuIawMAj0vIDGp+W1bM1GMZPs7GylW7duSlxcnOLm5qYMGTJECQoKUrp3766cPn3a3MO1OkVFRQqgFBUVNWv/01+sV9SIHEWpqmq5PEFB6rH27m35sYTWp6V/T22F5l7nrgFTFVCU8MByK0nWPK69VtW7pUttLYnz0ZZ0ZtCgQcpDDz1UZ1uPHj2UWbNmmbR/Y9f60l3bFFCU+yJ+bvoAM2Yoh+imgKIEBpolel3OnVPuYIUCivLPRRbSxRMnlE2uwxVQFFcXnXLgQMsPqfttq/K651PKIFIUDTrjsxYUpX17Rbn3pjzl8ZsPKWO7HFS6+WQoblTWGWNYgoP1yj1dtymf/nWTkr83t0Uy5ecryooVijJjhqIM9D2guFLV4DkvX/xcy5ThPtuUJ/r9T1k+M0U5tvOcotc3Xw5T9cZsT11ERASpqaksX76cXbt2odfrmTx5Mvfee2+dxAlHJSjKGw16FFw4e7bh7i6mUrFzH2fPxgPiqRMck8hIYCecLvKiqoqWT/tYgOpq2FWT8CrtwYTGqKysZNeuXcyaNavO9lGjRrFly5YG96moqKCi4lIN0+Li4gbHnSmoKWcSfIUqCpdlvypKMx3KBw4Y24P1tUAnJABiYxn2SB/ueHMl3+jH88zTCt9933xvt27PfqaOPMqHlS8Zt/XrVMjYiQGMvdWFQYPA1TUMuPTQra5SOJWSw5H/neJwSQc2ZsSybh2cOaPh8zOD+PwoaBbrSfDax4huucT196L78HC6j44lOMKjngxlZbD7xzx2rsxkxzYdO7PCOVIZW2uE2jc3xjufa3uepWucB2fyqjidB6fLfDntHk1enlqbs1Tnw4byQWxIBVKBf0J710IGRObRL9GLyKGxhIer5THD/UoJ7+yDv9al5RMGzbcb2yYtfks8elQJJl8BRdm3r2WynPjgZwUUxVNzsUUWvGA7WtvrcO7cOeW+++5TAgIClICAAOW+++5Tzp8/b/L+U6dOVQDltddeM+u8zb1O3Z59irub+sZ96pRZu1qN1FT1TdrfX6/oRt+sKLNnK0pFha3FchraiqcuOztbAZTffvutzvZFixYp3bp1a3CfefPmKUC95fJrnTJF/Rt84W8Xmhbi1VeVIvyN3p8LVxjeGEW/7jYe48yZ5h2jQQoKlEO9/6C4uqg6vn598w5TmX5c+ZOX6kl0oVp56YUKJSurmceqVJSNy7OV2UN+Ufp5H2rUkxYUpChDhyrKA/dcVO4fekjpFZipuFDd4NhevRTlr39VlM/eLVFOHbny/4oLFxRl36bzykf/t0N5JH6DMtB7r+LBxSt693woVTqHlSpffVX/mFbz1Dk9wcGEkM0ZQsjPqoTe9a19U8k5rEa+RnidR6OxTDat4Njcc889ZGVlsbqm6ufUqVNJSkriu+++u+K+33zzDdu2bSOiFd3CLn16ExGpZptmZ0MLwpAshiGebmC3YlzW/AS/74JFi2wrlGC3aC5znSiKUm+bgdmzZzNz5kzj5+LiYqKjo+uNW7wYXn0VwKvpkwcE4EuZ8WNpKXhdYZeG2OvaH1A9581s0d4wwcF03/slf52m4e234cknVf0ypx9sxUWFiYNP8s3F8bhRxecfXmTCA81PunR3h2vvjuDauyP4O5CbVsiad46ze8sF0k94cuh8GBlKNGfPwpYtsGWLJ9DduH8E2Qxsd5QBvS4y8KZABkzqRVBHQ7cq07pWeXlB72GB9B42gPtrtlVmnmbfZ3vY8VMBaZVdyes4mLw8yM1RyDtaQgkBlOPL8dMtC+8Vo85cAgIIYQ9pQMGJUqB9sw+VfaKmm0RACSBGndA0aWlprF69mpSUFAYPHgzAkiVLSExMJD09ne7duze6b3Z2NtOmTWPNmjWMHTu2tUQG1AfJqVOQldWqp20UY5JE+6PqSkKCJEkI9QgODsbV1ZW8y1I78/PzCWsk7sbT0xNPT88rHlujMbHFcEAArujxcblAud6bkpIm2oo1wQG1VjB9+pi/7xXRaJg3Dz79VA1rWL4c7rnHtF3Ly+HOOzWsKRyJp6aCr5aWMe7PzX+mNkSHuEDuf+Nqo3GFolBWouPIcVfS0+HQsl3o9+wjYZArA+6MIeLWBPAbblEZADyiw0iYPYqE2Zd/o4EL7pTl5HH6aAm5LpF079/87lxi1JmLRkOIZzFUQEFGOS0x6nKy1HgK6SYhmMLWrVvRarVGgw5gyJAhaLVatmzZ0qhRp9frSUpK4qmnnqJXr14mncvU2KArkp1NZFkZ0M1uukoYiw7rauKipD6d0AAeHh4kJCSwbt06xo8fb9y+bt06br/99tYRoqZhsp9LOeV672ZnwGYcLAX86NzZcqLVJjRIx6zh25nzfSKzn67mzjvdruhRLCmBW2+FDRvAxwdWfevBDTde2SBuMRoNvgGu9OsH/foBdycANv4f4O2NbxdvOncJp6W3qFm9X52dEB/VHV6QXdWi41zqJmGZlmOCY5OXl0doAzWtQkND63kTavPiiy/i5ubG9OnTTT5XcnIyWq3WuDQ0hWQSWVlE7fkesI9WYaWlsH+/uj4o91t1RYw6oRFmzpzJ+++/z4cffkhaWhozZswgIyODhx56qHUEqHHn+dckSzS3Vl3GZxsBiClLs4hY9XBx4fHTs4kki4xsN958s+nh5/OrGNXpCBs2qJe4Zg3ccKN4yy2BGHXNIKRbOwAKLpg2v94YOefVLKSIaGsU1BHaCvPnz0ej0TS57Ny5E6gf3wNNx/js2rWL119/naVLlzY6piFmz55NUVGRccnMbKI/ZVOEhhKJas2pcee2ZfdutZl6ZIRCxOFf1Y1i1AmNcPfdd/Ovf/2LhQsX0q9fPzZu3MiPP/5Ix44dW0eALl3gH//Ar4Nq3DXbU1eqPrOiu1rJE6bR4POvv/MCfwNg0fM6zp6tP6yyEr74+CLXdssj5exVtNec45fvyhg2zDpiOSMWNeo6derE5MmTHb5dWMi9owAoIKRFx8lGDViP6OL4pWCExpk2bRppaWlNLr179yY8PJzTp0/X27+goKDRGJ9NmzaRn59PTEwMbm5uuLm5cerUKZ544glim2hP5OnpSUBAQJ2lWdQy6rJO2b6/sTGe7qrzoNOp1cSjomwrlGDXPPLII5w8eZKKigp27drFdddd13onDw9X+792VMN8muWp0+nIrFT/P8T0smLXp6FDSbrrAn3YQ1GJKy+8cOmrzEx47m8KMaEXmHi/FweKognlNOvfOsiA4b7Wk8kJsWhM3aRJkzh16hTXXXcdx44ds+Sh7QpLtQrLbNcXzkL0cCsFOghtguDgYIJNiH5OTEykqKiI7du3M6imwNq2bdsoKipi6NChDe6TlJTEjTfeWGfb6NGjSUpK4oEHHmi58FfC15dIr3NwEbIzbe+pM8bTxeSq8UqSJCG0AfxqJoWa46nTnT5DFuqLS0zfdhaUqj6uLybz8jePMrr6B/79lp6rr3ZhxQpYtUpBr9cA3oSTy1Ttf3n4nT6E/2mkVeVxRixq1M2fP9+Sh7NbDGFNBfl6muvsVBT17QWgueFKgnMRFxfHmDFjmDJlCosXLwbUkibjxo2rkyTRo0cPkpOTGT9+PEFBQQRdVsPA3d2d8PDwJrNlLUlUSAVkQvZp1+YXT7UQ27apPwc90AuWnlerhAqCPbN7N/4XooDQZnnqTu8voIowXNDRIdrKuZGdOzPqsThGv7qaNdVj+POfDV9oGMkvPOzxAXfM7on7MzPBCZoV2IJmT79WVlaSnp5OdbV9NepuDUL+9x8A8o83vxlfQQFUVKgPuMhIS0kmODrLli0jPj6eUaNGMWrUKPr06cOnn35aZ0x6ejpFRUU2krA+ER1UD11FpQvnztlOjry8Sy1eExJQi2kFBtpOIEEwheuuw2+DmmzUHE9d5gH1xSXSPR+31qh3MWcOLwe8gC+lBPhW8+ijcOCPC/jlj4uZcCQZ9/lzxKCzImbf4vLych599FE+/vhjAA4fPkznzp2ZPn06ERER9VqqOCIhYaotfLbCD52ueY2DM/7zG3ANHbzP4+FhXZe44Di0b9+ezz77rMkxitL0NOfJkyctKNGV8ezQnmAKOEMIWVkWLn5qBjt2qD/j4oyVIgTB/gkIwL9MddE1x1OXcVgtmRXjdw7oYEHBGqFdO+I/fpKMdifwGhiPjw+g+1vzHpSC2ZjtqZs9ezZ79uzh119/xatWIZobb7yRL774wqLC2StBsWqwqULzPQ8ZB1TtjPGsH/guCA7FwoVEdVOLadoyh8qYJNG5ALp2hYcftp0wgmAql/V/NZcMjZqpGx3bimVp77iD9sNrDDoQg64VMduo++abb3jrrbcYNmxYnRIJPXv2dOjkiNq4h7WnHao119xkiYyTaiZgTPuyK4wUhDZOnz5EXqVmuNmDUTfY7yAcOwYnTthOGEEwFX9//GmBp869CwAxN7VODK1gW8w26goKChosgFpWVmZWHaw2TXAwIajWXHONuswc9a0pOrxlBYwFoS1giBu1Vaswvb6Wp+7CBnVF6tMJbYEWeuoMCXn20HdZsD5mG3UDBw7khx9+MH42GHKGHpROQVDQJaMup3lGWcYZNVBUFE1weE6dIvKU2pLLVp66o0ehsFBttB1/8jt1oxh1QlsgIKBlnrqjao9xedY4B2ZPsicnJzNmzBgOHjxIdXU1r7/+OgcOHGDr1q1s2LDBGjLaH1otoQaj7mQZEGj2ITKK1X1iurVCrztBsCUnThC15hNgqM2MOkMpk6v76XHfmap+EKNOaAu0NKbuQDEQTPT5vUAfi4om2B9me+qGDh3Kb7/9Rnl5OV26dGHt2rWEhYWxdetWEpzln6SLCyGd1Bih/LPNqwqTcVGdwo7pJWl4goNTp1WYbUQwTr3G5kN1tZqCK64LoS1w1134PzABMN9Td6FcoUCvFjaP6RNoYcEEe6RZ6TDx8fHGkibOSsg9o2ARFFw03yirKKsmTwkHIKa/jeo7CEJrERZ2qVVYlgK0fuytwVM32GefuiKdJIS2wq234hcEfGS+py7rQBEQiC+ltOtePxZecDzMdjO5urqSn59fb/vZs2dxdaK05Za0Css+rdrSXl4Q1CXQckIJgj3Srh1RLrkAnD+v4cKF1j19RQWkpqrrg+JKoH9/cJb4X8Eh8K9p2Wqupy5jbyEA0S45aLy9mh4sOARmG3WNFTatqKjAw8OjxQK1FYxG3Wm92ftmZKg/Y2LEWSA4AS4uaEM98UEt39PaU7CpqVBVBcHB0OmJO2H3bnCSloaCA1BYiN/J/YD5nrrMQ6rOxXi3sFG50GYwefr1jTfeANRs1/fffx8/Q4dhQKfTsXHjRnr06GF5Ce2UkP/+G/g/CtLOAOa5tWsbdW0BnU5HVZVzll5xd3d3Kg+0tdCEhRKZl80RupGdrdb+bS2M8XSDWu8lSq/XU1lZ2Tons0M8PDxwcWl2F0qhNj/8gP99jwFnKC/HrC5GGcfU/9sxWvtpGyhYF5ONutdeew1QPXXvvvtunQedh4cHsbGxvPvuu5aX0E4Jaaf2vC0oNd+lnfHfFGAIMWd/B/pbVjALoigKeXl5FBYW2loUmxIYGEh4eLjz1GG0BjVxdUfo1uq16ozxdFdXQiVg5RmFyspKTpw4gV5vvhffUXBxcaFTp05ONXtjNWplvwKUlZne5i4jU/2fFR3SyjEPgs0w2ag7UVN9feTIkaxYsYJ27Zy7X2lolPrP6swFX/R6tTe4qWRmqFPY0V727RI3GHShoaH4+Pg4nVGjKArl5eXGGNIOHVqhb6Kj8uKLRLlGwU+tP/1qMOoGFf0P/O6AKVPg3/+2yrkURSE3NxdXV1eio6Od0lul1+vJyckhNzeXmJgYp/u/YXECAvCkAjeqqMadkhLTjbpMfQQAMQPCrCigYE+Ynf26fv16a8jR5giOUZva6RRXCguhfXvT980oqCk83NEKglkInU5nNOiCbNWB3Q7w9lbvVX5+PqGhoTIV21z69SOyD61u1J07pxYeBhhUuFYNrjP1idgMqqurKS8vJyIiAh9j40vnIyQkhJycHKqrq3F3d7e1OG2bgAA0gJ+mjEIl0Ky4uoxyNfg75p5h1pFNsDuaVdIkKyuLVatWkZGRUS9u5J///KdFBLN3PMLbo6WQIgLJzzfTqCvWAhDTxX6nJgwxdM78YDJg+B1UVVWJUdcCbNEqzBBP17UrtD+wSf1gxXqaOp3a09nZpx0N16/T6cSoayk1LyH+lFBIoMkZsIrS9uK3hZZjtlH3888/c9ttt9GpUyfS09Pp3bs3J0+eRFEUrr76amvIaJ/U9H8tIpCCAjA1R0RRIONCzdtTL38rCmgZZOpEfgcW4fhxonbtA25vVU+dwagbPFAPX9XUqGuF/1PO/jfj7NdvUWqMOj9FteZM9dSdOwfl5ep6VJQ1BBPsEbMDPmbPns0TTzzB/v378fLy4uuvvyYzM5Phw4czYcIEa8hon9Tu/2pGaFzheYVSRc0cju7nvNOagpNx+DCRHy8CWnf61RhPF5uvTr36+UGnTq0ngCC0lFqeOjC9Vp0hdjuU03hlHbWKaIL9YbZRl5aWxqRJkwBwc3PjwoUL+Pn5sXDhQl588UWLC2i3hIY2q1Zd5oFiAIIpwLuzBN4LTkKtVmG5uWpZBmujKLU8db5qnS969ZLikELbwtMTnn0Wv85qsoOpnrqMdDXjNYYMkCQvp8Fso87X15eKigoAIiIiOHbsmPG7M2fOWE4yeycwkJDbhgJQYEb/14yj6u8uxqtAVVZBcAbCwgjjNC7o0Ong9Gnrn/LECThzBtzdoW/5VnVjr17WP7EgWJpFi/CPjwVM99RlHFAHRrvlgq+vlQQT7A2zY+qGDBnCb7/9Rs+ePRk7dixPPPEE+/btY8WKFQwZMsQaMtotoTU1h82Zfs24oO4UM6anFSQSBDslJAQ3dHQgl2yiyM6GiAjrntLgpevXD7z6x8Hdd8PIkdY9qSBYCUOrMFM9dZlHLwIQ43/eShIJ9ojZnrp//vOfDB48GID58+dz00038cUXX9CxY0c++OADiwtozzRn+lWykazPf/7zH7y8vMiuFbz1l7/8hT59+lBUJJXVbYKHB7RrZ5yCbY24OmM83SDgrrtg+XK47z7rn7iNInpjx2Rl4XdRnQkz2VN3So2pi2lfZi2pBDvEbE9d586djes+Pj68/fbbFhWoLRGy9CXgafIPFgCmFXd0CKOurIl/Eq6u4OVl2lgXF6ipA9fo2GZMG0ycOJF//OMfJCcn89Zbb7FgwQLWrFlDSkoKWq3W7OMJFiI0lMjzqsHQGmVNjPF0g61/rivSmjoDojeOxj334L9pHPC06TF1uWopmehw52zx6Kw0y6jbsWNHvYK0hYWFXH311Rw/ftxiwtk7Ib5qvnjBOdN/jZkbjgOdiT7yC3C9dQSzNrX6/tbjllvghx8ufQ4NvZRXfznDh8Ovv176HBurBkHVRlHMFk+j0bBo0SLuuusuIiIieP3119m0aRORkZGUlJRw/fXXU1VVhU6nY/r06UyZMsXscwjNICyMqHTVmrO2p66qCnbvVtcHxZXAsXw169VWHR5aU2fA4npjoLy8nLi4OCZMmMArr7xi9jmEZlKrVZjJ2a9n1fqabdqBIJiN2f/hTp48aSywWZuKioo6bntnICRU/fUVFJteaDTjvPrPPSaw2CoyCSrjxo2jZ8+eLFiwgJUrV9KrJkDex8eHDRs2kJqayrZt20hOTubs2bM2ltZ0zp8/T1JSElqtFq1WS1JSkkm9edPS0rjtttvQarX4+/szZMgQMgxu49bitdeInHYnYH2jbt8+uHgRAgPhqvTv1erDN95o3ZM6AI3pjYFFixYZw2+EViQgwFjSxBRPXXU1ZJcFAhBzw1VWFEywN0x2Ma1atcq4vmbNmjrueJ1Ox88//0xsbKxFhbN3Qjqov74zZd4oypUrJVRXQ/YFtfVETFwbzkZq6r/K5R0XavqmNsjlXpOTJ5st0uWsWbOGQ4cOodPpCAu7NDXu6upq7BBx8eJFdDodSjO8GrbinnvuISsri9WrVwMwdepUkpKS+O677xrd59ixYwwbNozJkyezYMECtFotaWlpeNWe8msNrr6ayIPAW9Y36mrH07kcrClncpUNH25tQGegcb0BOHLkCIcOHeLWW29l//79Fj2vcAXM9NTl5IBer8HdHcIeuMXKwgn2hMlG3R133AGoLnpDnToD7u7uxMbG8uqrr1pUOHsnJFp9KFbp3SgqUr0CTZGbCzrccKeS8N7B1hfQWpgTr2OtsU2we/duJkyYwOLFi1m+fDnPPfccX375pfH7wsJChg8fzpEjR3j55ZcJDm4b9yItLY3Vq1eTkpJi9JYsWbKExMRE0tPT6d69e4P7zZkzh1tuuYWXXnrJuK12bGxDVFRUGEsXARQXW8az3FqtwuokSew7oH6wZTkTO9cZuLLePPnkk7z88sts2bLFYucUTCQgAH9Uz7opnrrMTPVnVJTtIg4E22Dy7dbr9ej1emJiYsjPzzd+1uv1VFRUkJ6ezrhx46wpq93h1aEdfjUucVPKmmQcrwYgiixcoqxcz8FJOXnyJGPHjmXWrFkkJSWxcOFCvv76a3bt2mUcExgYyJ49ezhx4gSff/45p1ujaJoF2Lp1K1qtts7015AhQ9BqtY0+aPV6PT/88APdunVj9OjRhIaGMnjwYL755psmz5WcnGyc4tVqtURHR7f8Ag4fJmr1+4DqqbOmg7ROksSBGqOud2/rnbCNcyW9+fbbb+nWrRvdunWzsaROipmeOmNCXmS1FYUS7BGzbfgTJ07YzLNx8uRJJk+eTKdOnfD29qZLly7MmzePyspKm8hDUBChqFMlphh1mfvVsgDRmqxL9VAEi3Hu3DluvvlmbrvtNp599lkAEhISuPXWW5kzZ0698WFhYfTp04eNGze2tqjNIi8vj1BDccRahIaGkpeX1+A++fn5lJaW8o9//IMxY8awdu1axo8fz5133smGDRsaPdfs2bMpKioyLpmGV/+WcOAAkS9NB9SkTQs5/+pRVASHDqnrg3qXg6FAuhQebhBT9CYlJYXly5cTGxvLk08+yZIlS1i4cKEtxXYuzIypyzimZrzGbP5cVQjBaTB5+nXbtm1G5TfwySefMG/ePMrKyrjjjjt488038bRil4RDhw6h1+tZvHgxXbt2Zf/+/UyZMoWysjLbZGJ17EiItpLjRU2HwRjISCsDgojxPiM+cSvQvn170tLS6m3/9ttvjeunT5/G29ubgIAAiouL2bhxIw8//HBrilmP+fPns2DBgibH7NixA2i4UbqiKI02UNfr1RqKt99+OzNmzACgX79+bNmyhXfffZfhw4c3uJ+np6fldTk0FB8uEOhSRKFeS3Y2WKNSxs6dqhcwNhZCzx1SPwQFXaoWLtTBFL1JTk4mOTkZgKVLl7J//37mzp3bajI6PYMG4Xe/Bpaa6KlLLwe0RLvlGXvHCs6ByUbd/PnzGTFihNGo27dvH5MnT+b+++8nLi6Ol19+mYiICObPn28tWRkzZgxjxowxfu7cuTPp6em88847tjHq+vYl5FrgexOnX/PULNmYyFZofCk0SFZWFpMnT0ZRFBRFYdq0afTp08emMk2bNo2JEyc2OSY2Npa9e/c2OFVcUFBQL6jdQHBwMG5ubvTsWbeDSVxcHJs3b26+0M2hRsYoJYtCtGRlQU8rNFapE093oFY8nfR8FdoqQ4bg334ILDUxpu6EOu0aE1gsf/dOhslGXWpqKs8//7zx8/Llyxk8eDBLliwBIDo6mnnz5lnVqGuIoqIi2rdv3+j31gr4NmDsKmGKUVcZDkDMExMsKoNgOgkJCaSmptpajDoEBwebFNKQmJhIUVER27dvZ9CgQYDqQS8qKmLo0KEN7uPh4cHAgQNJT0+vs/3w4cN07Nix5cKbQ42nLFLJZD+9rJYBWyeeLj4e5syRYl0W5P7777e1CE6JodRhSQlXrLaQkaVmVMeEXmwFyQR7wuQ5wPPnz9fxBmzYsKGO12zgwIGWibsxg2PHjvHmm2/y0EMPNTrGKgHftTAadflXjvo2/Hrk+SI0h7i4OMaMGcOUKVNISUkhJSWFKVOmMG7cuDqZrz169GDlypXGz0899RRffPEFS5Ys4ejRo7z11lt89913PPLII617Af7+4OV1xVZhJSUwbJhaZzclxbxTKMplnrp+/eCFF2Dq1GaLLTgnixYtYujQofj4+BB4pdIG1qaqCv8zJwDQ6aCWn6JBMgrUygzRUW2nXJNgGUw26sLCwjhxQv2jqqysZPfu3SQmJhq/Lykpwd3dvVlCzJ8/H41G0+Syc+fOOvvk5OQwZswYJkyYwF/+8pdGj22VgO9ahHygxpkUHL1yMKohI8nCdqXgRCxbtoz4+HhGjRrFqFGj6NOnD59++mmdMenp6XV6dY4fP553332Xl156ifj4eN5//32+/vprhg0b1rrCazRqqzCabhX28svw22+wcSMkJsKkSWo5IFPIyoK8PLX029VXW0huwSmprKxkwoQJNo+5BeDYMXz7djF+bCqurrQUzperRl1MJ9fGBwoOicnTr2PGjGHWrFm8+OKLfPPNN/j4+HDttdcav9+7dy9dunRp4giNY2pMkYGcnBxGjhxJYmIi7733XpP7WSXguxYh7urDsyBf3+S40lI4d05dj0lfB71vsppMguPSvn17PvvssybHNFRM+cEHH+TBBx+0llimExZGVEbjrcJycsBQ7vL66+GXX+CTT2DFCnjuOXjsMWhKnQ1euvh48NFcgLWb1Hi6Wq2uBMEUDMlLS5cuta0gAAEBuKDgSyll+FFa2ngBBYPfQkshAbGNhyYJjonJRt0LL7zAnXfeyfDhw/Hz8+Pjjz/Gw+NSe6wPP/yQUaNGNUsIU2OKALKzsxk5ciQJCQl89NFHuNg4izS0XRXkQcGZpuWoo2jt5O1JcFLefpvI37TweMNG3fz5atvTxET43/9gxw6YPl011p55BpYsgX/9C8aObfjwdeLp9u+H0aMhPNx0V58gtACrxXD7+wPgV2PUNeWpM84ItSuriUEQnAmTjbqQkBA2bdpEUVERfn5+uF7W2ubLL7/Er6mm1RYgJyeHESNGEBMTwyuvvEJBreyE8PBwq567MUJCgDQoKGz6V2ksBkmGeA0E52XAACJrVOVyo+7gQfjgA3X95ZfV2dpBg2DLFvj0U5g1C44ehXHjVOdb585qxfzo6Es/DSUHBw1CNepA6tMJrUZycvIVyxM1C19f0GjwV0o4TXiTGbDG2O3ESLhenjXOhslGnQFtI4WlmspAtRRr167l6NGjHD16lKioqDrf2ap/Z0iY6qHLL/ZqMiMp88hFwItoMiHiutYTUBDsDIPq5uerAd+G6dRZs0Cvh/Hj4ZprLo13cVHj6saPh0WL4LXX1EolhmolDTF4MPCRHbQHE+wKU2tCDhgwoFnHnz17NjNnzjR+Li4utkxynosL+PvjV3zlrhJGB4Ik5DklZht1tuT++++3u3T6kEh1CrpS50ZJSeN1HtXCw17EuOcaXemC4HSkpRH03fd4us+kosqV3Fy1SPCGDfDdd2qCQ02N23oEBMCLL6rTsXv2qB6JzEw1OcLwMysLBg6EHj0QT51QD3Pjt83FqjHcAQH4F1+5q0TGST3gopYzUTylTp2T0aaMOnvEJzwAH8oox5eCgiaMumNqK7MYrZV6IwlCWyA1Fc0zTxPhdS8nqiLIzoaOHeGpp9Svp06FWtVZGiQysvEIhjrecun5KlyGOfHbdoeJ/V8zj1UA3kQvegjmf9Q6sgl2g/Sqaik9ehDurWbAHj7c+LCMTPVJI8UgBafGUIBYoyYuZGfDl1+qCRG+vjBvXssObzToioou1UyxRtsKweHJyMggNTWVjIwMdDodqamppKamUmpKSwdrMGkS/j3Ut5kmPXWG6degMvHSOSFi1LWU229nzAMRADSV+Z5xTk0iieklU6+CE2NoFaY7BcDx4zB7tvrV008bv245Bi9dZCTYunCs0CaZO3cu/fv3Z968eZSWltK/f3/69+9fr2Zqq/H00/gN7Qs07qlTFMg8XdOOMqK6tSQT7Agx6izAlCnqz5UrG24XptdD1nnVqIt+6dFWlEw4f/48CxYsIFdKWtgHBk9d5XFATXo4flytOlIrvrzldOkCH34I0nS+WYjeqPXpDD2iay8jRoywmUyGcOzGPHUFBVBR5YoGPZExUjrLGRGjzgL06wcDByhUVcHHH9f/vqBAzfLTaKSaSWszffp0duzYYR9V4QUICgIXF2NXifx8dfOCBZd6W1qEsDB44AFpD9ZMRG/skJIS/KoLASgsbHiIYeq1A7m4R4a2iliCfSFGXUs5dw4CA5my868AvP++6gKvjUHRIiKgmZ3UhGawatUqSktL+f777wkMDGTZsmW2FklwdYXgYKK41CMsLg7sodmFoCJ6Y6fMnUu3f08H4LPP4NSp+kPq1EPt0KEVhRPsBTHqWkpAABQXM5H/4OujJz0dNm2qO8SoaGd2XSqzIFid2267zdjYfunSpdx77702lkgA6vR/BbVMiZul8/A//FCtRFxZaeEDOz6iN3ZKQAD38DlDw49RXAx//jPodHWHGLtJkKl6EQSnQ4y6luLmBu3a4U8p99yiZsFe3o4245TaFzam4kjjNU8EwVn44AP6b36LAVfruO8+tUOERTl3DiZPhuHD1bgHQXAEAgJwQ8eng/+Nn5/6zvLKK3WHGLtJ9GkHffu2voyCzRGjzhLUVAyf0l3tUfTVV+pzxUDmYbWMSTRZ4hIXhEGD8L7manbscuXTT61QdcGQ+dqxoxT6FhyHGodAZ+UYb76pbnruOdi9+9IQ46zQ5JugmV0xhLaNGHWWoGZ6YsCPC+nbV6GiQo15MJBxVPUWxPifl6C6VuA///kPXl5eZNdqLvqXv/yFPn36UFRUZEPJhFZBOkk0C9EbO8cwy1NczKRJcOedUFWlPn7Ky9WvpEWYIEadJXjwQfD0RPP7bqbedBJQp2ANCRMZGTWFh4PLbSSgZVAUKCtr/cXctr4TJ06ke/fuJNf0m1qwYAFr1qzhp59+arR3sdCK7NsHL70EX3xhneMbPHV2YNTZSmdEbxyQWkadRgOLF6sTP4cOwTPPqF9lZqg3PbpdEy0nBIdG2oRZgqAgmDgRPv6YezOSedL7PQ4cgJQUSEyEDEMxyCi9jQVtGeXlFi47YSKlpWq3AVPRaDQsWrSIu+66i4iICF5//XU2bdpEZE09mZKSEq6//nqqqqrQ6XRMnz6dKYZig4L12bFDfQrdfDPcfXf977OzITUVbrmleXOzdtQezFY6A5bXGwPl5eXExcUxYcIEXrk8qEuwHrWMOoDgYPjoIxgzBt56C268EXLzahwIdw+FvH22klSwIeKpsxTTp8PMmWgXPc0f/6hueu89NU77dLEPADGdxYZuLcaNG0fPnj1ZsGABK1eupFctr42Pjw8bNmwgNTWVbdu2kZyczNmzZ20orZNRU4DYWKTuchYuVLMnHnuseceX6ddm05TeGFi0aBGDBw+2gXROTnQ0PPSQmvZaw+jR6qMH4L771J9eXCA40tMGAgr2gFgZluLqq9UFtd7pxx+rs0uP1jSQ8Ha5SPv+HW0oYMvx8Wm656A1z2sua9as4dChQ+h0OsIu6z3l6uqKT81BL168iE6nQzF3rkpoPob7cfp0/e+OHIEPPlDXJ0ww/9j5+XDmjOrhi4trvowWwlY6Yzi3uTSlNwBHjhzh0KFD3HrrreyX8kytS0wMvPNOvc3/+Af8739w8KD6OZpMNBGSkOesiFFnBRIT1R7iBw+qCgcQc5UXmsem21awFqLRmDedYyt2797NhAkTWLx4McuXL+e5557jyy+/rDOmsLCQ4cOHc+TIEV5++WWCg4NtJK0TUttTpyh1p1jnzlWLb918M1x7rVr0cf58tQefKeWAtFq11sPJk82zaixMW9EZME1vnnzySV5++WW2bNliIymFy/H2hmXLYNAgNXFCCg87NzL9amk2bUJz1x+Yco0a12P4nyjZSK3DyZMnGTt2LLNmzSIpKYmFCxfy9ddfs2vXrjrjAgMD2bNnDydOnODzzz/ndENeI8E6GIy6ykqonVWZmgrLl6vrf/87VFerSUi//AJPPWXasT09VWMwKcmiIjs6pujNt99+S7du3ejWrZsNJXVyzp1TX1iqq+ts7tdPLeINMIQUKTzsxIhRZ2lWrYIVK0g6Oh/PWmENYtRZn3PnznHzzTdz22238eyzzwKQkJDArbfeypw5cxrcJywsjD59+rBx48bWFNW58fa+VD+udlyd4R5NnKg+pdzc1L57oAaorl3bqmI6C6bqTUpKCsuXLyc2NpYnn3ySJUuWsHDhQluJ7ZxER0OnTpeqDNdixgw4dsNU5jNfPHVOjBh1lubhh0GjIWj9V/xh1KW08uivXzNmLQnWoX379qSlpbF48eI627/99ltWr15t/Hz69GmKa+5FcXExGzdupHv37q0qq9NzeVzd5s3w449qb9jahsLw4ZcCUydPruvZa4i33lJbhBUUWF5mB8VUvUlOTiYzM5OTJ0/yyiuvMGXKFObOndva4jo3l2XAXk7not9xQydGnRMjRp2l6dxZjQcCpnh9atwcU5om1e3thKysLK677jr69u3LsGHDmDZtGn369LG1WCZx/vx5kpKS0Gq1aLVakpKSKCwsbHKf0tJSpk2bRlRUFN7e3sTFxfFOAwHXrcqnn8KuXcbkInQ6NRB18mS46qq6Y5OToUsXyMqCJ55o+HiKosbfzZmjHiMnx7ryC4ItMBh1JY3UobvvPvjrX6FHj9aTSbArJFHCGjzyCPz4I8PXzqFnxz9z8JQf8aGnrdAPSWgOCQkJpKam2lqMZnHPPfeQlZVl9KBMnTqVpKQkvvvuu0b3mTFjBuvXr+ezzz4jNjaWtWvX8sgjjxAREcHtt9/eWqLXZciQup+HD4e9ey+Vxq+Nr69akGv4cDUz9g9/ML44UVUFX38Nr74KO3eq27RaEM+rVbn//vttLYJzcgVPXbPLAAkOg3jqrMGYMdCpE5qiQn6KfYQ1jGJAJ6mDJrSMtLQ0Vq9ezfvvv09iYiKJiYksWbKE77//nvT09Eb327p1K5MmTWLEiBHExsYydepU+vbty06DEdQAFRUVFBcX11msjqtr497sa6+99MBauVL9+dZbqgfvT39SDTpPT5gyRS1u7OVlfXkFobW5klEnOD1i1FkDV1c1tg6I2fApo1gHl1VlFwRz2bp1K1qttk7h1yFDhqDVapssMTFs2DBWrVpFdnY2iqKwfv16Dh8+zOjRoxvdJzk52TjFq9VqiY6Otui1kJqqtgp75BHVy3bhwpX3WbRIzY41xH7t368GjIeGwoIF6vp779WfvhUER6Epo66kBNLSrhx3Kjg0Mv1qLR58ED7/XFWyigpJMRdaTF5eHqGGciC1CA0NJS8vr9H93njjDaZMmUJUVBRubm64uLjw/vvvM2zYsEb3mT17NjNnzjR+Li4utqxht2XLpYaVAHr9lcuW+PjUbSv2xBMwcKDa0Vw8c4IzYPBkN2TU/fabGpbQt6/60iQ4JWLUWYugIPj9d/WB8/nn4qkTGmX+/PksWLCgyTE7duwA1P6cl6MoSoPbDbzxxhukpKSwatUqOnbsyMaNG3nkkUfo0KEDN954Y4P7eHp64ulpxVZDtY3TkBC1/ZG5XHWVeOUE5+L669WXm/h4OHVKdRqkp6vL1q3qGMl8dWrEqLM2XbuqQeFttGCnXq+3tQg2x9q/g2nTpjFx4sQmx8TGxrJ3794GiyQXFBQ02NIJ4MKFCzz77LOsXLmSsWPHAtCnTx9SU1N55ZVXGjXqrE5teefMcbjMcGdvO+fs12817r9fXQB694YDB+qPGTiwNSUS7Awx6qzNggXq0sbw8PDAxcWFnJwcQkJC8PDwaNIb5IgoikJlZSUFBQW4uLjg4eFhlfMEBweb1KYsMTGRoqIitm/fzqBBgwDYtm0bRUVFDB06tMF9qqqqqKqqwsWlbvisq6urbQ32Hj1Uj0NUVPO8dHaKu7s7Go2GgoICQkJCnE5nQNWbgoICNBoN7u7uthbHcenbV/3ZvfulJS5OjDonR4w6oUFcXFzo1KkTubm55Dh5zS8fHx9iYmLqGUatTVxcHGPGjGHKlCnGQrFTp05l3LhxdYon9+jRg+TkZMaPH09AQADDhw/nqaeewtvbm44dO7JhwwY++eQT/vnPf9rqUtQp18OH1XIl1pzmbWVcXV2JiooiKyuLkydP2locm6HRaIiKisLV1dXWojguy5bZWgLBDhGjTmgUDw8PYmJiqK6uRqfT2Vocm+Dq6oqbm5vdeFyWLVvG9OnTGTVqFAC33XYbb731Vp0x6enpFNXKgFu+fDmzZ8/m3nvv5dy5c3Ts2JFFixbxkK09ZA4aZ+rn58dVV11FVVWVrUWxGe7u7mLQCYINEKNOaBLDFIpMo9gH7du357PPPmtyzOXxTOHh4Xz00UfWFEu4DFdXVzFqBEFodaROnSAIgiAIggMgRp0gCIIgCIIDIEadIAiCIAiCA+B0MXWGeKNW6WUpODyGvyNHr8sleiNYCmfRGRC9ESyHqXrjdEZdSUkJgOV7WQpOTUlJCVqt1tZiWA3RG8HSOLrOgOiNYHmupDcaxRlel2qh1+vJycnB39+/XpkKQ3/LzMxMAgyNkx0IuT7LoygKJSUlRERE2LyOnTURvXHM6xOdsS6N6Y0j/02BXJ81MFVvnM5T5+LiQlRUVJNjAgICHPIP0YBcn2VxdG8DiN6AY1+f6Ix1uJLeOPLfFMj1WRpT9MaxX5MEQRAEQRCcBDHqBEEQBEEQHAAx6mrh6enJvHnz8HSgXpS1kesTrIGj/94d+foc+drsGUf/vcv12Q6nS5QQBEEQBEFwRMRTJwiCIAiC4ACIUScIgiAIguAAiFEnCIIgCILgAIhRJwiCIAiC4ACIUScIgiAIguAAOJ1R9/bbb9OpUye8vLxISEhg06ZNTY7fsGEDCQkJeHl50blzZ959991WktQ8kpOTGThwIP7+/oSGhnLHHXeQnp7e5D6//vorGo2m3nLo0KFWktp05s+fX0/O8PDwJvdpK/euLeCIeiM6U5+2cN/aCo6oMyB60xB2de8UJ2L58uWKu7u7smTJEuXgwYPKY489pvj6+iqnTp1qcPzx48cVHx8f5bHHHlMOHjyoLFmyRHF3d1e++uqrVpb8yowePVr56KOPlP379yupqanK2LFjlZiYGKW0tLTRfdavX68ASnp6upKbm2tcqqurW1Fy05g3b57Sq1evOnLm5+c3Or4t3Tt7x1H1RnSmLm3lvrUFHFVnFEX05nLs7d45lVE3aNAg5aGHHqqzrUePHsqsWbMaHP/0008rPXr0qLPtr3/9qzJkyBCryWgp8vPzFUDZsGFDo2MMinb+/PnWE6yZzJs3T+nbt6/J49vyvbM3nEVvRGfa5n2zR5xFZxRF9Mbe7p3TTL9WVlaya9cuRo0aVWf7qFGj2LJlS4P7bN26td740aNHs3PnTqqqqqwmqyUoKioCoH379lcc279/fzp06MANN9zA+vXrrS1aszly5AgRERF06tSJiRMncvz48UbHtuV7Z084k96IzrTN+2ZvOJPOgOiNvd07pzHqzpw5g06nIywsrM72sLAw8vLyGtwnLy+vwfHV1dWcOXPGarK2FEVRmDlzJsOGDaN3796NjuvQoQPvvfceX3/9NStWrKB79+7ccMMNbNy4sRWlNY3BgwfzySefsGbNGpYsWUJeXh5Dhw7l7NmzDY5vq/fO3nAWvRGdaZv3zR5xFp0B0Ruwv3vn1upntDEajabOZ0VR6m270viGttsT06ZNY+/evWzevLnJcd27d6d79+7Gz4mJiWRmZvLKK69w3XXXWVtMs7j55puN6/Hx8SQmJtKlSxc+/vhjZs6c2eA+bfHe2SuOrjeiMypt7b7ZM46uMyB6Y8Ce7p3TeOqCg4NxdXWt96aUn59fz8o2EB4e3uB4Nzc3goKCrCZrS3j00UdZtWoV69evJyoqyuz9hwwZwpEjR6wgmWXx9fUlPj6+UVnb4r2zR5xBb0RnVNrafbNXnEFnQPTGgL3dO6cx6jw8PEhISGDdunV1tq9bt46hQ4c2uE9iYmK98WvXrmXAgAG4u7tbTdbmoCgK06ZNY8WKFfzyyy906tSpWcf5/fff6dChg4WlszwVFRWkpaU1Kmtbunf2jCPrjehMXdrKfbN3HFlnQPTmcuzu3tkgOcNmGNLMP/jgA+XgwYPK448/rvj6+ionT55UFEVRZs2apSQlJRnHG1KVZ8yYoRw8eFD54IMP7DbN/OGHH1a0Wq3y66+/1knFLi8vN465/Ppee+01ZeXKlcrhw4eV/fv3K7NmzVIA5euvv7bFJTTJE088ofz666/K8ePHlZSUFGXcuHGKv7+/Q9w7e8dR9UZ0pm3et7aAo+qMooje2Pu9cyqjTlEU5d///rfSsWNHxcPDQ7n66qvrpGFPmjRJGT58eJ3xv/76q9K/f3/Fw8NDiY2NVd55551Wltg0gAaXjz76yDjm8ut78cUXlS5duiheXl5Ku3btlGHDhik//PBD6wtvAnfffbfSoUMHxd3dXYmIiFDuvPNO5cCBA8bv2/K9aws4ot6IzrTN+9ZWcESdURTRG3u/dxpFqYnoEwRBEARBENosThNTJwiCIAiC4MiIUScIgiAIguAAiFEnCIIgCILgAIhRJwiCIAiC4ACIUScIgiAIguAAiFEnCIIgCILgAIhRJwiCIAiC4ACIUScIgiAIguAAiFEnCIIgCILgAIhR14YZMWIEjz/+uK3FaJQRI0ag0WjQaDSkpqaatM/9999v3Oebb76xqnyCcyJ6IwjmI3rTNhCjzk4x/KE1ttx///2sWLGC559/3ibyPf7449xxxx1XHDdlyhRyc3Pp3bu3Scd9/fXXyc3NbaF0grMieiMI5iN64zi42VoAoWFq/6F98cUXzJ07l/T0dOM2b29vtFqtLUQDYMeOHYwdO/aK43x8fAgPDzf5uFqt1qbXJbRtRG8EwXxEbxwH8dTZKeHh4cZFq9Wi0WjqbbvcHT5ixAgeffRRHn/8cdq1a0dYWBjvvfceZWVlPPDAA/j7+9OlSxd++ukn4z6KovDSSy/RuXNnvL296du3L1999VWjclVVVeHh4cGWLVuYM2cOGo2GwYMHm3VtX331FfHx8Xh7exMUFMSNN95IWVmZ2b8jQbgc0RtBMB/RG8dBjDoH4+OPPyY4OJjt27fz6KOP8vDDDzNhwgSGDh3K7t27GT16NElJSZSXlwPwt7/9jY8++oh33nmHAwcOMGPGDO677z42bNjQ4PFdXV3ZvHkzAKmpqeTm5rJmzRqT5cvNzeVPf/oTDz74IGlpafz666/ceeedKIrS8osXhGYieiMI5iN6Y4cogt3z0UcfKVqttt724cOHK4899lidz8OGDTN+rq6uVnx9fZWkpCTjttzcXAVQtm7dqpSWlipeXl7Kli1b6hx38uTJyp/+9KdG5Vm5cqUSFBR0Rbkvl09RFGXXrl0KoJw8ebLJfQFl5cqVVzyHIDSG6I0gmI/oTdtGYuocjD59+hjXXV1dCQoKIj4+3rgtLCwMgPz8fA4ePMjFixe56aab6hyjsrKS/v37N3qO33//nb59+zZLvr59+3LDDTcQHx/P6NGjGTVqFHfddRft2rVr1vEEwRKI3giC+Yje2B9i1DkY7u7udT5rNJo62zQaDQB6vR69Xg/ADz/8QGRkZJ39PD09Gz1Hampqs5XM1dWVdevWsWXLFtauXcubb77JnDlz2LZtG506dWrWMQWhpYjeCIL5iN7YHxJT58T07NkTT09PMjIy6Nq1a50lOjq60f327dtX5w3NXDQaDddccw0LFizg999/x8PDg5UrVzb7eILQmojeCIL5iN60DuKpc2L8/f158sknmTFjBnq9nmHDhlFcXMyWLVvw8/Nj0qRJDe6n1+vZu3cvOTk5+Pr6mpUSvm3bNn7++WdGjRpFaGgo27Zto6CggLi4OEtdliBYFdEbQTAf0ZvWQTx1Ts7zzz/P3LlzSU5OJi4ujtGjR/Pdd9816Zp+4YUX+OKLL4iMjGThwoVmnS8gIICNGzdyyy230K1bN/72t7/x6quvcvPNN7f0UgSh1RC9EQTzEb2xPhpFceTcXsGWjBgxgn79+vGvf/3L7H01Gg0rV640qYq4IDgSojeCYD6iNyriqROsyttvv42fnx/79u0zafxDDz2En5+flaUSBPtG9EYQzEf0Rjx1ghXJzs7mwoULAMTExODh4XHFffLz8ykuLgagQ4cO+Pr6WlVGQbA3RG8EwXxEb1TEqBMEQRAEQXAAZPpVEARBEATBARCjThAEQRAEwQEQo04QBEEQBMEBEKNOEARBEATBARCjThAEQRAEwQEQo04QBEEQBMEBEKNOEARBEATBARCjThAEQRAEwQEQo04QBEEQBMEB+H/DfsZQtEHIsgAAAABJRU5ErkJggg==", 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" ] @@ -540,6 +599,24 @@ "plot_state_comparison(timepts, ekf_resp.outputs, lqr_resp.states)" ] }, + { + "cell_type": "markdown", + "id": "b4ee15d5-fc01-40d1-aaf6-194d4c734c80", + "metadata": {}, + "source": [ + "This estimator does a considerably better job, though still with fairly significant errors (~15%) in the $\\dot y$ estimate." + ] + }, + { + "cell_type": "markdown", + "id": "09c3c9db-f781-4009-897a-da5fe93d286b", + "metadata": {}, + "source": [ + "## Fixed horizon, maximum likelihood estimator (MLE)\n", + "\n", + "We now create an estimator that tries to find the disturbances and noise that maximumize the likelihood of the signals given a Gaussian noise model. This estimator will compute the estimated state over a finite horizon." + ] + }, { "cell_type": "code", "execution_count": 12, @@ -551,13 +628,13 @@ "output_type": "stream", "text": [ "Summary statistics:\n", - "* Cost function calls: 5051\n", - "* Final cost: 354.3319137685172\n" + "* Cost function calls: 5050\n", + "* Final cost: 485.715540533845\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -586,7 +663,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -611,13 +688,13 @@ "output_type": "stream", "text": [ "Summary statistics:\n", - "* Cost function calls: 9464\n", - "* Final cost: 212754409.97292745\n" + "* Cost function calls: 10947\n", + "* Final cost: 212754409.96759257\n" ] }, { "data": { - "image/png": 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\n", 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", 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" ] @@ -645,7 +722,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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sWQNjY2Npq9ubLW0ZGRnlWuTKcnV1VXoddcjIyEBqaioEAkG5i0D0hZWVFVatWoXTp0+jdevWyMvLw9KlS+Ht7Y0vv/wSz58/5ztEQoiOUaqws7CwqPQfyYsXL2BhYVHtoGoaKuxqDhsbG0REROD48eNypw0bNvAWW7du3XDt2jUkJCRIp8DAQAwbNgwJCQmoX78+XF1dER0dLV2nsLAQsbGxCA4OrnC7QUFBMusAwNGjRytdRx0krXUNGzaEtbW1RvelacHBwTh//jz++usvtGrVCjk5Ofj222/h7e2NWbNmISkpie8QCSE6QuEBigFg8ODBCAsLw8qVK9G9e3fpQKtZWVmIjo7G559/jqFDh2okUENGhV3N4e/vDwDo3Lmz3Nft7e15uwjJxsYGfn5+MvOsrKzg6OgonT916lQsXrwYDRs2RMOGDbF48WJYWlrK5P2IESNQp04daT+9KVOmoFOnTli6dCn69u2LP//8E8eOHcPp06c1+vvo24UTVREIBOjduzd69eqFffv2Yd68ebh27RqWLVuGZcuWoXXr1hgxYgSGDBkivdKeEFLzKFXYfffddyguLsawYcNQXFwMU1NTANy3dmNjY4wePRrLly/XSKCGjAq7mmPo0KF49epVha+7urpi3rx5WoxIOTNnzsSrV68wYcIEvHjxAm3btsXRo0dhY2MjXSY5ORlCYenJgODgYGzfvh1ffvklvvrqKzRo0AA7duxA27ZtNRqrpMVOny6cUIRAIED//v3Rt29f7N+/H7/88gsOHTqEixcv4uLFiwgPD0doaCi6d++Orl27okmTJlX2gSSEGA4BU6F5QCQSIT4+XtpvxtXVFQEBASp3cK4JRCIR7OzskJWVVe59OnHiBLp27YpGjRrh5s2bPEVIKlPZ50c0T5X3v3Hjxrh58yYOHz6MHj16aDhCfmVkZOD3339HVFSUtKVSonbt2ujSpQu6du2Kli1bwtfXV6Hb2qkb5RAh2qFSYUeUV9k/tevXr8PPzw8ODg7UGVpH0UGJX8q+/zk5ObC1tQVjTO5wK4bs2rVr2L9/P2JiYnDmzBnk5+eXW8bNzQ2NGzeGr68vvL29Ubt2bTg7O6N27dqoXbs2HB0dYW5uLtPyWpWioiK8evVKOllaWsrcaYVyiBDtULiwW7NmDT755JNKh0Eo66effsKwYcNkTtHUZJX9U3v69Kn0H2BhYaHOj5BfE6njoEQ5pDpl3/+zZ88iODgYbm5uSE1N1UKEuqmgoAAXLlxATEwMTp8+jcTERKXGGjUxMYGZmRnMzc1hZmYGgUCAkpISiMVi6aOkoCsuLpZZd+LEifjxxx+lP1NhR4h2KNzHbtq0aRgyZIjCB6WZM2ciJCSEDkoKqFWrFgQCARhjePbsGVxdXfkOiWgA5ZD26OPAxJpgZmaGjh07omPHjtJ5IpEIN27cQFJSEpKSkvDo0SM8ffoUGRkZ0seioiIAXCtcUVERcnJylNqvhYWFUq19hBD1UbiwY4yhW7duMDZWbJXKOogTWUZGRnB0dERmZiaePn1KhZ2BohzSHn0emFjTbG1t0aZNG7Rp00bu64wx5OTkID8/HwUFBTKPAPf/SigUSh9NTExgYWEhnczNzeliDUJ4pHBhp+yVen379kWtWrWUDqimcnZ2lhZ2xDBRDmkPtdipTiAQwMbGhlqKCdFTGivsiHKcnZ2RlJREhZ0BoxzSjuLiYly7dg0AtdgRQmoepTpBSJrf35wcHBzQrl077NmzR1NxGjxVx7LLycnB+fPnsXHjRkyaNAldunRB48aNcfjwYU2ESaqJckjzbty4gYKCAtjY2KB+/fp8h0MIIVql1ADFe/bskdt34uXLl7hw4QI++ugjREVF4YMPPlBbgDWFsoUdYwwDBw7E3r175b7ev39/HDx4EG+//bbaYiTVRzmkeZLTsC1atKAO/ISQGkepwq5fv34VvhYWFoYmTZpgxYoVdFBSgaSwy8jIUGj5M2fOSIs6V1dXNG/eHM2bN0ezZs2we/du7N+/H++9955W7slJFEc5pHl04QQhpCZT69fZkJAQ3Lp1S52brDGUbbGT3Cx+1KhRSEtLw5EjR7B8+XKMGDECO3fuREhICHJzcxEaGor4+HiNxU3Ui3Ko+ujCCUJITabWwu7Vq1cKj9FFZEkGKFaksHv+/Dl27twJABg3bly5183MzLB371506tQJIpEIISEh0s7kRLdRDlUPY4xa7AghNZpaC7uNGzca9D/TyMhIeHt7w9zcHAEBATh16pTatq1Mi92vv/6KgoICtGzZEq1bt5a7jKWlJQ4cOIC2bdvi+fPn6N69O92HVg8Yeg5pWnJyMl68eAFjY2M0adKE73AIIUTrlOpjFx4eLnd+VlYW/v33X9y9e1etxY4u2bFjB6ZOnYrIyEi0b98e69evR2hoKBITE1G3bt1qb1/Rwo4xhvXr1wMAPvnkk0oHArWxscHff/+Nt99+GwkJCWjfvj1+++039OzZU+U4S0pKcPPmTcTHx0s/cwsLC1hbW8PKygrW1tawtraGs7Mz3N3dUadOHbi7u8PZ2RlGRkYq79dQ6HIOrVu3DuvWrcODBw8AAE2bNsXXX3+N0NBQAKjwb23ZsmWYMWOG3Ne2bNmCkSNHlpuvqZZJSWtd06ZNYWZmpvbtE0KIrlOqsJP0XXmTra0tevbsiQkTJsDLy0stgemalStXYvTo0RgzZgwAYPXq1Thy5AjWrVuHiIiIcssXFBSgoKBA+rNIJKp0+5LC7tmzZygpKamwCDpz5gySkpJgaWmJoUOHVhm3g4MDjh49Ku1rFxoairlz52L+/PkK3wHh3r17+OWXXxAbG4tLly4hNzdXofXKktxdw8rKCpaWltJHCwsLiMViFBcXy0yFhYUoLCxEQUGB9LGoqEh6f8qy96oEuKKj7CQUCuU+SoqTNx/l2bVrl8ytmNRBl3PIw8MDS5YswVtvvQUAiIqKQt++fXH58mU0bdoUaWlpMsv//fffGD16NAYOHFjpdm1tbcu1FmvqdPOxY8cAAIGBgRrZPiGE6DqlCruYmBhNxaHTCgsLER8fj9mzZ8vMDwkJQVxcnNx1IiIisGDBAoX34ejoCIBrkXv+/Lm00HuTpLVuyJAhsLOzU2jbzs7OOH36ND7//HNERkbi22+/xZkzZ7Bt2za4ubnJXaewsBD79u3Dxo0bpQdLCUtLS7Rq1QqBgYHw9fVFUVERcnNzkZOTg9zcXGRnZ+PJkydITU1Famoqnjx5gpKSEoWv+NUVkvtlqpMu51CfPn1kfv7222+xbt06nDt3Dk2bNi13q7s///wTXbt2rXKsOIFAoJXb5InFYuzevRsAN9wPIYTUREoVdjVVZmYmSkpK4OLiIjPfxcUF6enpcteZM2eOzGk3kUgET0/PCvdhYmICBwcHvHjxAk+fPpVb2D1//hx//PEHAO40rDLMzc2xdu1adOzYEWPHjsWJEyfQqlUrrFmzBvb29sjKyoJIJIJIJMKDBw+wbds2ZGZmAuAOzCEhIRg8eDBat26Nxo0bK3Vatbi4GE+ePEFmZiZevXqF3Nxc5OXlIS8vD69evZLeb9LY2BjGxsYwMjKCqakpTE1NYWZmJn00MTGRDugrFAqlE8AVxGUnsVhc7rmkdY8xJrNORa129erVU+o9NiQlJSX4448/kJubi6CgoHKvP3nyBAcPHkRUVFSV28rJyYGXlxdKSkrQsmVLfPPNN1X2I1S2xRsA4uLikJaWBltbW7zzzjtVLk8IIYaICjslvFkAVFYUmJmZKd3Hx9nZWVrYybN169YqL5qoyuDBg9GqVSt88MEHuHbtGgYNGlThsu7u7hg1ahRGjx5drSLH2NgYderUQZ06dVTeBtGOa9euISgoCPn5+bC2tsbevXvlXoQQFRUFGxsbDBgwoNLtNW7cGFu2bEGzZs0gEonw/fffo3379rhy5QoaNmxY4XrKtngD3KlzgLvHLvWvI4TUVFTYKcDJyQlGRkblWucyMjLKteJVh7OzM27duiX3lKUyF01UxcfHB+fOncOsWbPw999/w9raGra2trCzs4OtrS3s7e3Rs2dPhIaGKtwPjxgGHx8fJCQk4OXLl9i9ezfCwsIQGxtbrrj7+eefMWzYsCr7yrVr1w7t2rWT/ty+fXv4+/vjhx9+wJo1aypcT9kW77KnYd9///1KYyKEEENGR20FmJqaIiAgANHR0TJ9d6Kjo9G3b1+17aeysexOnz6NGzduwNLSEsOGDav2viwtLfHDDz9UezvEsJiamkovnggMDMTFixfx/fffS79UAMCpU6dw8+ZN7NixQ+ntC4VCtG7dGrdv3650OWVbvM+fP49Hjx7BxsYGISEhSsdFCCGGggo7BYWHh2P48OEIDAxEUFAQNmzYgOTkZIwfP15t+6hsyJOyF03Y2tqqbZ+EVIYxJtPXDQA2b96MgIAAtGjRQqXtJSQkoFmzZuoKEUDpadg+ffrQAM+EkBqNCjsFDRo0CM+ePcPChQuRlpYGPz8/HDp0SK1DU1RU2D179kx64JJ3pwlC1OGLL75AaGgoPD09kZ2dje3bt+PEiRM4fPiwdBmRSIQ//vgD3333ndxtjBgxAnXq1JEOAbRgwQK0a9cODRs2hEgkwpo1a5CQkIC1a9eqLW7GmDQ/6DQsIaSmo8JOCRMmTMCECRM0tv2KCrtFixZJL5qg8bmIpjx58gTDhw9HWloa7Ozs0Lx5cxw+fBjdu3eXLrN9+3YwxjBkyBC520hOTpZeqQwAL1++xCeffIL09HTY2dmhVatWOHnyJNq0aaO2uC9evIjk5GRYWVlVa/BtQggxBFTY6RB5hd3Jkyfx/fffAwAWL15crYsmCKnM5s2bq1zmk08+qXSonRMnTsj8vGrVKqxataq6oVVKMgRQ7969YWFhodF9EUKIrlPrvWJJ9bxZ2OXm5mLkyJFgjGH06NHSWzsRQjhlT8N+8MEHPEdDCCH8o8JOh7xZ2M2ZMwf37t2Dp6dnhX2aCKnJ4uPj8eDBA1haWtIXH0IIARV2OkVS2GVmZuL48ePS4Ug2b96s8O3DCKlJJK11vXr1gqWlJc/REEII/6iw0yFOTk4AuNs5ScaqGzdunEzndUIIhzEm7V9HV8MSQgiHCjsdYmZmJm2ZS09Ph5eXF5YvX85zVITopoSEBNy7dw8WFhZ49913+Q6HEEJ0AhV2OkZyOhbgbttkY2PDYzSE6C5Ja11oaCisra15joYQQnQDFXY6xsPDAwAwceJEvP322zxHQ4huKnsalq6GJYSQUjSOnY5ZsWIFjh49iilTpvAdCiE6q6SkBFOmTMG+ffvQq1cvvsMhhBCdIWCMMb6DqAlEIhHs7OyQlZVF93rVQ/T58Yvef/1HnyEh2kEtdloiqZ9FIhHPkRBVSD43+h7ED8of/Uc5RIh2UGGnJdnZ2QAAT09PniMh1ZGdnU1jCvKA8sdwUA4Roll0KlZLxGIxUlNTYWNjI73fq0gkgqenJ1JSUujUhJpo6j1ljCE7Oxvu7u4yN7kn2kH5ox2afE8phwjRDmqx0xKhUCi94vVNtra2dGBSM028p9TKwB/KH+3S1HtKOUSI5tHXJkIIIYQQA0GFHSGEEEKIgaDCjkdmZmaYN28ezMzM+A7FYNB7WnPQZ61+9J4Sov/o4glCCCGEEANBLXaEEEIIIQaCCjtCCCGEEANBhR0hhBBCiIGgwo4QQgghxEBQYUcIIYQQYiCosONJZGQkvL29YW5ujoCAAJw6dYrvkPTa/PnzIRAIZCZXV1e+wyIaRDmkPpQ/hBgOKux4sGPHDkydOhVz587F5cuX0bFjR4SGhiI5OZnv0PRa06ZNkZaWJp2uXbvGd0hEQyiH1I/yhxDDQIUdD1auXInRo0djzJgx8PX1xerVq+Hp6Yl169bxHZpeMzY2hqurq3RydnbmOySiIZRD6kf5Q4hhoMJOywoLCxEfH4+QkBCZ+SEhIYiLi+MpKsNw+/ZtuLu7w9vbG4MHD8a9e/f4DoloAOWQZlD+EGIYqLDTsszMTJSUlMDFxUVmvouLC9LT03mKSv+1bdsWW7duxZEjR7Bx40akp6cjODgYz5494zs0omaUQ+pH+UOI4TDmO4CaSiAQyPzMGCs3jyguNDRU+rxZs2YICgpCgwYNEBUVhfDwcB4jI5pCOaQ+lD+EGA5qsdMyJycnGBkZlWtZyMjIKNcCQVRnZWWFZs2a4fbt23yHQtSMckjzKH8I0V9U2GmZqakpAgICEB0dLTM/OjoawcHBPEVleAoKCpCUlAQ3Nze+QyFqRjmkeZQ/hOgvOhXLg/DwcAwfPhyBgYEICgrChg0bkJycjPHjx/Mdmt6aPn06+vTpg7p16yIjIwOLFi2CSCRCWFgY36ERDaAcUi/KH0IMBxV2PBg0aBCePXuGhQsXIi0tDX5+fjh06BC8vLz4Dk1vPXr0CEOGDEFmZiacnZ3Rrl07nDt3jt5TA0U5pF6UP4QYDgFjjPEdBCGEEEIIqT7qY0cIIYQQYiCosCOEEEIIMRBU2BFCCCGEGAgq7AghhBBCDAQVdoQQQgghBoIKO0IIIYQQA0GFHSGEEEKIgaDCjhBCCCHEQFBhRwghhBBiIKiwI4QQQggxEFTYEUIIIYQYCCrsCCGEEEIMBBV2hBBCCCEGggo7QgghhBADQYUdIYQQQoiBoMKOEEIIIcRAGPMdQE0hFouRmpoKGxsbCAQCvsMhSmKMITs7G+7u7hAK6fuQtlH+6D/KIUK0gwo7LUlNTYWnpyffYZBqSklJgYeHB99h1DiUP4aDcogQzaLCTktsbGwAcP/UbG1tUVwMpKUBOTmAry/PwZEqiUQieHp6Sj9HUurkyZNYvnw54uPjkZaWhr1796Jfv37S1xljWLBgATZs2IAXL16gbdu2WLt2LZo2barwPt7MHwB4+hTIyADq1QOsrNT5GxFNoBwiRDuosNMSyekjW1tb2NraIiEBaNUKcHXlCjyiH+g0YHm5ublo0aIFRo4ciYEDB5Z7fdmyZVi5ciW2bNmCRo0aYdGiRejevTtu3ryp8EH+zfwBgIAA4M4d4ORJoGNH9f0+RLMohwjRLCrseOLszD0+fQqIxQB1OSH6KjQ0FKGhoXJfY4xh9erVmDt3LgYMGAAAiIqKgouLC7Zt24Zx48bJXa+goAAFBQXSn0UiUbllatfmCruMDDX8EoQQYiConOCJpLArKQFevuQ1FEI05v79+0hPT0dISIh0npmZGTp37oy4uLgK14uIiICdnZ10kte/TpJDVNgRQkgpKux4YmoK2Nlxz+nARAxVeno6AMDFxUVmvouLi/Q1eebMmYOsrCzplJKSUm6Z2rW5x6dP1RcvIYToOzoVy6PatYGsLO7A1Lgx39EQojlv9qtijFXa18rMzAxmZmaVbpNa7AghpDxqseNR2X52hBgiV1dXACjXOpeRkVGuFU9Z1GJHCCHlUWHHI2pxIIbO29sbrq6uiI6Ols4rLCxEbGwsgoODq7VtSWFH+UMIIaXoVCyPqMWBGIKcnBzcuXNH+vP9+/eRkJCAWrVqoW7dupg6dSoWL16Mhg0bomHDhli8eDEsLS0xdOjQau2XWrwJIaQ8Kux4RAcmYgj+/fdfdO3aVfpzeHg4ACAsLAxbtmzBzJkz8erVK0yYMEE6QPHRo0erPVAttdgRQkh5VNjxiE7FEkPQpUsXMMYqfF0gEGD+/PmYP3++WvcryZ9nz7hhg4yM1Lp5QgjRS9THjkd0KpYQ1Tk5cY9iMfD8Ob+xEEKIrqDCjkfUYkeI6kxMAAcH7jl9OSKkZvn4449l7kmtD7QVMxV2PKI+doRUD/WzI8SwPXjwAAKBAAkJCTLzv//+e2zZskXj+9fHApIKOx5JDkqZmdzpJEKIcujLESE1k52dHezt7fkOQydRYccjSR+hkhLgxQt+YyFEH1GLHanpGANyc7U/VXK9VAVxMixbtgz169eHhYUFWrRogV27dgEAXrx4gWHDhsHZ2RkWFhZo2LAhfvnlFwDcWJgA0KpVKwgEAnTp0gVA+Za0Ll26YNKkSZg6dSocHBzg4uKCDRs2IDc3FyNHjoSNjQ0aNGiAv//+W7pOSUkJRo8eDW9vb1hYWMDHxwfff/+99PX58+cjKioKf/75JwQCAQQCAU6cOAEAePz4MQYNGgQHBwc4Ojqib9++ePDggcy2w8PDYW9vD0dHR8ycObPSi8zUiQo7ACdPnkSfPn3g7u4OgUCAffv2ybzOGMP8+fPh7u4OCwsLdOnSBdevX6/2fk1NAckXDmpxIER51E+V1HR5eYC1tfanvDzl4vzyyy/xyy+/YN26dbh+/TqmTZuGjz76CLGxsfjqq6+QmJiIv//+G0lJSVi3bh2cXrd8XLhwAQBw7NgxpKWlYc+ePRXuIyoqCk5OTrhw4QImTZqETz/9FB988AGCg4Nx6dIl9OjRA8OHD0fe6+DFYjE8PDywc+dOJCYm4uuvv8YXX3yBnTt3AgCmT5+ODz/8ED179kRaWhrS0tIQHByMvLw8dO3aFdbW1jh58iROnz4Na2tr9OzZE4WFhQCA7777Dj///DM2b96M06dP4/nz59i7d6+yH69qGGGHDh1ic+fOZbt372YA2N69e2VeX7JkCbOxsWG7d+9m165dY4MGDWJubm5MJBIpvI+srCwGgGVlZcnMb9iQMYCxkyfV8ZsQTano8yPaUdH7/9VXXP58+ilPgRGFUQ5pRk4OlwPannJylIkxh5mbm7O4uDiZ+aNHj2ZDhgxhffr0YSNHjpS77v379xkAdvnyZZn5YWFhrG/fvtKfO3fuzDp06CD9ubi4mFlZWbHhw4dL56WlpTEA7OzZsxXGOmHCBDZw4MAK98MYY5s3b2Y+Pj5MLBZL5xUUFDALCwt25MgRxhhjbm5ubMmSJdLXi4qKmIeHR7ltaQKNYwcgNDQUoaGhcl9jjGH16tWYO3cuBgwYAID7VuDi4oJt27Zh3LhxctcrKChAQUGB9GeRSCR3OWdn4PZtanEgRBV0KpbUdJaWQE4OP/tVVGJiIvLz89G9e3eZ+YWFhWjVqhXmz5+PgQMH4tKlSwgJCUG/fv1UuuVg8+bNpc+NjIzg6OiIZs2aSedJ7k+dUeYfxk8//YRNmzbh4cOHePXqFQoLC9GyZctK9xMfH487d+6UG2Q9Pz8fd+/eRVZWFtLS0hAUFCR9zdjYGIGBgVo5HUuFXRXu37+P9PR0hISESOeZmZmhc+fOiIuLq7Cwi4iIwIIFC6rcPo1lR4jq6OIJUtMJBICVFd9RVE78+urAgwcPok6dOjKvmZmZwdPTEw8fPsTBgwdx7NgxdOvWDRMnTsSKFSuU2o+JiYnMzwKBQGaeQCCQiWfnzp2YNm0avvvuOwQFBcHGxgbLly/H+fPnq/x9AgIC8L///a/ca86Sf0o8osKuCunp6QBKK30JFxcXPHz4sML15syZI721EsC12Hl6epZbrjp9hMRioKgIMDNTfl1CDAG12BGi+5o0aQIzMzMkJyejc+fOcpdxdnbGxx9/jI8//hgdO3bEjBkzsGLFCpiamgLgLkZQt1OnTiE4OBgTJkyQzrt7967MMqampuX27e/vjx07dqB27dqwtbWVu203NzecO3cOnTp1AgAUFxcjPj4e/v7+av4tyqPCTkGSSl+CMVZuXllmZmYwU6DiUrXFgTGgXTvudkqXLgF2dsqtT4ghoBY7QnSfjY0Npk+fjmnTpkEsFqNDhw4QiUSIi4uDtbU17t69i4CAADRt2hQFBQU4cOAAfH19AQC1a9eGhYUFDh8+DA8PD5ibm8NOTQe8t956C1u3bsWRI0fg7e2NX3/9FRcvXpReiQsA9erVw5EjR3Dz5k04OjrCzs4Ow4YNw/Lly9G3b18sXLgQHh4eSE5Oxp49ezBjxgx4eHhgypQpWLJkCRo2bAhfX1+sXLkSL1++VEvcVaGrYqvg6uoKoLTlTiIjI6NcK54qVD0V++IFcPEicO8e8PPP1Q6DEL0kyZ9nz4DiYs3t5/Fj4MoVzW2fEEP3zTff4Ouvv0ZERAR8fX3Ro0cP/PXXX/D29oapqSnmzJmD5s2bo1OnTjAyMsL27dsBcH3T1qxZg/Xr18Pd3R19+/ZVW0zjx4/HgAEDMGjQILRt2xbPnj2Tab0DgLFjx8LHxweBgYFwdnbGmTNnYGlpiZMnT6Ju3boYMGAAfH19MWrUKLx69Uragvf5559jxIgR+Pjjj6Wnefv376+22CsjYAr05Lt69arSG27SpAmMjfWvQVAgEGDv3r3S8XEYY3B3d8e0adMwc+ZMAFyHz9q1a2Pp0qUV9rF7k0gkgp2dHbKysmSabrdtA4YNA7p2BY4fVzzOy5cBSYuutzd3AQbdBF1zKvr8FFWTckgTKnr/S0q4W4sxBqSnA2r4riVX69ZAQgLw33+Aj49m9mHoqptDhBDFKHTUaNmyJQQCgcJXcwiFQty6dQv169evVnDakpOTgzt37kh/vn//PhISElCrVi3UrVsXU6dOxeLFi9GwYUM0bNgQixcvhqWlJYYOHVrtfavaYpecXPr8/n3gwAFAjV9kiJoZeg7xxcgIcHTk7t6SkaGZwq64mPsiVVICHDlChR0hRLcp3Bxw/vx5ha72YIzBz8+vWkFp27///ouuXbtKf5Zc9BAWFoYtW7Zg5syZePXqFSZMmIAXL16gbdu2OHr0aLlLnVWh6sUTb1638f33VNjpOkPOIT45O3OFnab62T1+zBV1AHDyJDB5smb2Qwgh6qBQYde5c2e89dZbCt+XrVOnTrCwsKhOXFrVpUuXSltSBAIB5s+fj/nz56t935Lj/LNn3FWuQgV7PUpa7N5/H9i7F4iJAa5eBcoM40N0iKHnEJ9q1waSkjR3ZWyZuwTh5EnutG8l100RQgivFCojYmJilLrZ7qFDh+Dm5qZqTDWKqveLlbTYdegASPpjrlmj3tiI+lAOaY6mx4IsW9g9fQrcuKGZ/RBCiDrQVbE8K3u/WGVaHCQtdnXrAlOmcM//9z/ulBQhNYmm7xdbtrADgNhYzeyHEELUQanC7tGjR5g7dy66du0KX19fNGnSBF27dsXcuXORkpKiqRgNnipjcUkKOy8voH177grZ/Hxgwwb1x0e0IyUlBaNGjeI7DL2j6iDFBw4APXvKXogkj6R1XNKllgo7QoguU7iwO336NHx9fbF37160aNECI0aMwEcffYQWLVpg3759aNq0Kc6cOaPJWA2WsqeS8vO5oR0ArsVOIChttYuM5O5GQfTP8+fPERUVxXcYekeVL0YPHgBDhnBXuf72W9XLAsCHH3KPkn52hBCiixS+KnbatGkYM2YMVq1aVeHrU6dOxcWLF9UWXE2h7KmkR4+4R0tLbqgHABg0CJg5k7uCb/duYPBg9cdJqmf//v2Vvn7v3j0tRWJYlG2xE4uBkSNLb5x++3bly0sKu0GDgF9/BVJTgbt3gbfeUilcQgjRKIULu//++w+/VfLVdty4cfjpp5/UElRNo2yLg+TUkKS1DuDuFzt+PLBgATf0CRV2uqdfv35VjmVX2W3qiHzK5s8PPwAnTpT+XFlhV1ICSHqZNG4MtGkDnD7NnY6lwo4QoosUPhXr5uaGuLi4Cl8/e/YsXcWnImVbHMr2rytr/HhuFP5z54ChQ4Hp04GlS7lbjh04AGjpNnWkAm5ubti9ezfEYrHc6dKlS3yHqJeUyZ8bN4DZs7nnY8Zwj7duVbx8aio3QLGxMeDuDkjuX37ypOrxEkJ0x8cffyy905ShULjFbvr06Rg/fjzi4+PRvXt3uLi4QCAQID09HdHR0di0aRNWr16twVANV3Va7MpydeVuT7ZlC/D77+XXa9MGOH9e5TBJNQUEBODSpUsV/hNR5s4UpJQkf16+BAoLuSvN5SkuBsLCuD6q3bsD330HbNrE5V1WFiDvvuKS07B163J3uejUCfj2W7qAghBtmj9/Pvbt24eEhAS+Q9ELChd2EyZMgKOjI1atWoX169ej5PVQ7EZGRggICMDWrVvxoaR3MVGKshdPVNRiB3Bj2XXtCqSlcduTTEeOABcucOu+WRAS7ZgxYwZyc3MrfP2tt95CTEyMFiMyDLVqcQN7i8XccD/u7vKXW7qUywE7O64V29aW+zKUns6djg0MLL+O5EuUJNeCg7kC7+FDbnozB/PzgQ8+ABwcgI0buS4ShBCiTUoNdzJo0CCcO3cOeXl5ePz4MR4/foy8vDycO3eOirpqUPbiibJj2L3JxgYYMQKYNQtYsQKIigIOHQLateNe//vv6sdLVNOxY0f07NmzwtetrKzQWXKuzwBFRkbC29sb5ubmCAgIwKlTp9SyXaGw6lbvhASu/ynA9bHz8OCeN2zIPVbUz07SYlevHvdobQ0EBHDP5Z2OXb2a6/bw669c63lxseK/hyrS04G8vKqXy8wEliwBrl+vetmkJK4IVuSLZnIyUEkPHaIFjDHk5uZqfVL27MLhw4fRoUMH2Nvbw9HREb1798bdu3elrz969AiDBw9GrVq1YGVlhcDAQJw/fx5btmzBggULcOXKFQgEAggEAmzZsgUPHjyAQCCQacV7+fIlBAIBTrzuRFtSUoLRo0fD29sbFhYW8PHxwffff6+Ot12nqTRAsYmJCdzc3ODm5gYTExN1x1TjqOtUbGXefZd7pMKO8GHHjh2YOnUq5s6di8uXL6Njx44IDQ1FclWDyCmosi9HjAEff8wNA9S/P/DRR6WvSQq7ivrZvVnYAaX97N48HfvkCbB4MfdcIOCuTh8zhmtJVDeRCJg2jStQGzUCzp6teNnERKBtW2DOHO7x0KGKlz1yhFtm9myu68a1axUv+8cfQJMm3Diao0YBlTRGEw3Ky8uDtbW11qc8Rb5RlJGbm4vw8HBcvHgR//zzD4RCIfr37w+xWIycnBx07twZqamp2L9/P65cuYKZM2dCLBZj0KBB+Pzzz9G0aVOkpaUhLS0NgwYNUmifYrEYHh4e2LlzJxITE/H111/jiy++wM6dO1V5q/WGQqdiBwwYgC1btsDW1lahjQ4bNgyrVq1Cbck5RlIpyduUmVn1/WLF4tKr9OSdiq1IaCjw5ZfAsWNAQQGdItK2mp5DK1euxOjRozHm9RULq1evxpEjR7Bu3TpEREQovT3GmMyBRTLsT0pK+QLj3j3gyhWu791338m2cElyKClJfmEiGYHG1bX09bZtuccTJ2TXmTMHyM7mBgufPp0rIKOiAHNzrvX8zQues7KAgwe5IYpevACeP+ceX7zg7kbTvz/Qqxd3yrj09+YKqjlzuEIS4Nbv2JErKidMkN1PdDTXgp+dzV1YlZsL9O7NvQ+ffCIbz+bNXLEoFnPLPngABAVxp6179SpdrrgYmDePu/pe4pdfuOJy61au2JOwtLSkK70JAGDgwIEyP2/evBm1a9dGYmIi4uLi8PTpU1y8eBG1atUCwHVNkbC2toaxsTFcXV2V2qeJiQkWSJrqAXh7eyMuLg47d+406LOMAqZAe6qRkRFu3boFZ8nX4kowxuDp6YmEhATUr19fLUEaApFIBDs7O2RlZZU7uBcVlXb4fvq09P6x8qSnA25uXPGXn8/9A1aEWAzUqcOtf+wY0K2bir9IDVXZ56eImpxDhYWFsLS0xB9//IH+khsbA5gyZQoSEhIQK+dKhIKCAhQUFEh/FolE8PT0lL7/ubm5sLa21kr8RHU5OTmwsrICUP0cIvK9+SVHW5Qt2u/evYuvvvoK586dQ2ZmJsRiMXJzc3Hw4EEcOHAA169fl/u/AJB/8cSDBw/g7e2Ny5cvo2XLlgC4U7EODg6IiYlBly5dAAA//fQTNm3ahIcPH+LVq1coLCxEy5YtceHCBQDcVbEvX77Evn37VHkbdJJCLXaMMTRq1EjTsei8yMhILF++HGlpaWjatClWr16Njh07Vnu7JibcN/SXL6su7CRnrtzdFS/qAK4Q7NmTu2L20KHqF3Y5OcDXXwO7dnH9jpyducnJiWuBDAkBOnSo3j7KKiriTrPVqaPYsoxVfHUkH2pyDmVmZqKkpAQuLi4y811cXJAuuYXKGyIiImS+aRNC5BMIBNLiWZf16dMHnp6e2LhxI9zd3SEWi+Hn54fCwkJYWFgovT3h61NbZdumit647dLOnTsxbdo0fPfddwgKCoKNjQ2WL1+O8wY+PIRChV1MTAwuXboEf39/hTdcR5EjsB6R9BGKjIxE+/btsX79eoSGhiIxMRF1VbjM9M1vWU5OXGH38GHlfedu3uQe69RRvk/L229zhd3Bg8DChUqHLPXPP8CkSbL32ExKkl3mm2+4CzZmzOCKvDe/2BUVcePtXb3KDSHRrJn8fYnFwM6dXLzJydwwFbNmlV4MUlZmJndLtZ9+4oa9GDkSmDpVfjH46hW33e3budN4I0dyVxOXPQ2uztNIlEPlB19mjFX4/s6ZMwfh4eHSnyUtdhKWlpbIkdw6AtyFAYsWccOZrF0ru6333gOOH+dOHY4eLftaXl5pV4gHD2S/VKWmcv3XhELuNKlxmf+Ws2Zx+xkzBujRg7sS1sQEuHxZtj8ewF1Q8eWXpT8bGQH9+gFjx3L90yr6E2MM+O8/7tTrrl3c33/Pntxp3Tf3AXBDGX30EXdFvMS33wKTJ5ffx86dwLhxpbcfNDYG1q3jbrP2pg0buFPLkr6C9etzwyk1bSq7nFjMneJduJCLvUMHwMLCUv4vR2qUZ8+eISkpCevXr5c2hpw+fVr6evPmzbFp0yY8f/5ceiq2LFNTU+lIHBKSsx9paWlo1aoVAJQbDuXUqVMIDg7GhAkTpPPKXrBhsJiCBAIB8/f3Z5GRkezly5eKrmYw2rRpw8aPHy8zr3Hjxmz27Nlyl8/Pz2dZWVnSKSUlhQFgWVlZjDHGcnJyGACadHjKycmRfp5ZWVkyn58qamoOFRQUMCMjI7Znzx6Z+ZMnT2adOnVSaBtVvf8//cQYwNh778nOF4sZc3bmXrtwQf62PT251+PiZOefOcPN9/Iqv86ePdxrPj7cBDA2c2bF8S9dyljz5ox98w1jaWkVL1cRsZix7Oyql8vIYKxXL+533r+/8mVPnWLMyYmxWrUYO3Gi8mWPHePeh/ffZ+zFi8qXjY3l3tPoaNn56sghop9KSkqYo6Mj++ijj9jt27fZP//8w1q3bs0AsL1797KCggLWqFEj1rFjR3b69Gl29+5dtmvXLhb3Oin/97//MSsrK3b58mX29OlTlp+fzxhjrF27dqxjx47s+vXrLDY2lrVp04YBYDExMYwxxlavXs1sbW3Z4cOH2c2bN9mXX37JbG1tWYsWLaSxhYWFsb59+2r5HdEshQu7uLg4NmbMGGZra8ssLCzYsGHD2PHjxzUZm85Q5cA0b948ucUCFXb6M509q97CribnUJs2bdinn34qM8/X17fCL0Zvqur9372bK66CgmTnp6Rw842MGHv1Sv62336bWyYqSnb+//7Hze/cufw6T59yr0kmZ2fGdKlWF4sVWy43l7Ey31/URt57TYVdzRYdHc18fX2ZmZkZa968OTtx4oS0sGOMsQcPHrCBAwcyW1tbZmlpyQIDA9n58+cZY1xDycCBA5m9vT0DwH755RfGGGOJiYmsXbt2zMLCgrVs2ZIdPXpUprDLz89nH3/8MbOzs2P29vbs008/ZbNnz6bC7k15eXlsy5YtrHPnzkwoFLL69euzRYsWsZSUFE3EpxMeP37MALAzZ87IzP/2229Zo0aN5K5TVYudWCxmOTk50mnkyBwG5LAvvsiRmf/m1KsXt9yqVZUvV9E0fz63fs+elS+XlpbDfvklh/Xrl8MsLLh1gBzWsGEOi45WfH/JyTls7twc5uDArd+iRQ6bNSuHxcTksKwsbpnbt7nf29W1dD9ADjM2zmETJ+awBw9kt3njRg4bPz6HmZmVLtuhQw77888clp0tu2x0dA7r3l12u5065bDffy/df05ODsvIyGHr1uWwgIDS5Q4eLD06qvOgVBNzaPv27czExIRt3ryZJSYmsqlTpzIrKyv24MEDhdav6v0/dYorsBo0kJ3/55/c/GbNKt72uHHcMnPnys7/9ltu/ogR8tdr2rS0sFu3TqFfo0ajwo4Q7VC6sCvrzp07bO7cuczT05MZGxuz0NBQdcWlUySFXdwb52oWLVrEfHx8FNpGVf/U5s7lDhATJ1a+nVatuOUOHFBot+UkJHDrW1rK/1b96BFjAwYwZmYm2yJRrx5jCxZU3OpRlfx8xp4/r3yZwkLGdu1irF8/xsaOZezevcqXT09nbONG7pRZVeLjGVu1irGrV6te9tIl7rRacXHpPE0dlGpKDjHG2Nq1a5mXlxczNTVl/v7+LDY2VuF1q3r/b9zg/k5tbWXnz5/PzQ8Lq3jb333HLfPhh7LzP/mEm//11/LXmzCBe71pU8aKihT+VWosKuwI0Q6FbykmT4MGDTB79mx4enriiy++wJEjR6qzOZ3l5OQEIyOjclfwZWRklLvST1WK3lZMlcGJy2renLuiNjWVGzk/JKT0tcJCYMAA7rZLADd46/vvc1OrVhV38laEmVnVY+eZmAADB3KTIlxcSm/kXhV/f25SRKtW3KQNNSWHAO62hGU7MauTJH9EItlxGi9d4h4r+zwruvuEvMGJy5oxgxsfbvp02QsrCCGETyrdeQIAYmNjERYWBldXV8ycORMDBgzAmTNn1BmbzjA1NUVAQACio6Nl5kdHRyM4OFgt+1DktmI5OdzVeYBygxOXJRBwgxUD5e9CMWsWV9TZ23OPN29yg576+1evqCPy1aQc0jR7+9LiquyXI0lhV1lRX7awKzuqp6SwqyjX6tXjBuRt3lyFgAkhREOUKuxSUlLwzTffoEGDBujatSvu3r2LH374Aampqdi4cSPayRuDwkCEh4dj06ZN+Pnnn5GUlIRp06YhOTkZ48ePV8v2FbmtmGR4ETs72dHolSW5vVjZWwvt3csNywBwo+W3bk3FnCbU5BzSJIGg/Jejp0+BR4+456/HL5Wrfn1uSJOcHG4Ab4Ar8CT5VlGLHSGE6CKFTyB0794dMTExcHZ2xogRIzBq1Cj4+PhoMjadMmjQIDx79gwLFy5EWloa/Pz8cOjQIXip2nT2BsmppMpa7CQHmuru8p13uNaNW7eAu3e5g+LIkdxrn3/OjftF1K+m55Cm1a7NjeEm+XJ0+TL32LAhYGNT8Xqmplzxdu8e12rn5sbdris/nyv4PDw0HjohhKiNwoWdhYUFdu/ejd69e8PIyEiTMeksTfYRkrQ2PHtW8f1iq9u/TsLWlhs89MQJYN8+brDRrCzuvpAq3LaTKIhySLPebLFT5DSsRMOGpYVdp06lp2Hd3XXrDiaEEFIVhQu7/fv3azKOGk8y4r1YzPWjk3dbMXW12AFcP7sTJ4AvvuAumqhVC9ixQ7nblBHlUA5p1psXIClb2B05wrViA6Vfoug0LCFE36h88QRRLxMTwMGBe17R6VhJYVfdFjugtJ9dYSH3+OuvQJk7NhGid95ssZOcilXkCmfJbXwlV8ZWdUUsIYToKirsdEhVF1BIWhHU0WLXtGnpdmbNKi30CNFXZVvssrKAO3e4nxUp7N4c8qSqK2IJIURXUWGnQ6oay06dLXYCAbB9O7BmDXfzdEL0XdkWO8m9wOvWld+t4U2Swu7OHa47BLXYEULU6cSJExAIBHj58qXG90XDauqQysayKy4uHbpBHYUdALRrx02EGIKyX4wUGZi4LC8v7krx/Hwuz6iPHSFEX1GLnQ6p7FRsWhpQUsL1xXNz025chOiDskMGSfrXKXq3EWNjoEED7vmtW3QqlhBSHmMMxcXFfIdRJSrsdEhlY9lJWhA8POQPhUJITVe2xVuZK2IlJKdj4+KAV6+45+pqHSdEYxgDcnO1P5W9TYsCunTpgkmTJmHq1KlwcHCAi4sLNmzYgNzcXIwcORI2NjZo0KAB/i5zS6TExES8++67sLa2houLC4YPH47MzEzp64cPH0aHDh1gb28PR0dH9O7dG3fv3pW+XlhYiM8++wxubm4wNzdHvXr1EPF6TK8HDx5AIBAgQdJvA8DLly8hEAhw4sQJAKWnT48cOYLAwECYmZnh1KlTYIxh2bJlqF+/PiwsLNCiRQvs2rVL5vc9dOgQGjVqBAsLC3Tt2hUPJN8WtYBKBB1SWYudOoc6IcQQSb4Y5eYCSUncc2Xu+Ssp7CR3DnR3r/r+xoTwLi8PsLbW/pSXp3SoUVFRcHJywoULFzBp0iR8+umn+OCDDxAcHIxLly6hR48eGD58OPLy8pCWlobOnTujZcuW+Pfff3H48GE8efIEH374oXR7ubm5CA8Px8WLF/HPP/9AKBSif//+EIvFAIA1a9Zg//792LlzJ27evInffvsN9VToXzFz5kxEREQgKSkJzZs3x5dffolffvkF69atw/Xr1zFt2jR89NFHiI2NBcDdYWjAgAF49913kZCQgDFjxmD27NlK71dljGhFVlYWA8CysrIqXGbbNsYAxrp0Kf9aRAT32ogRGgySVEiRz49ojiLvv1jMmKkplycAY7Vrc/MUtW4dt56xMfcYHKyGwIkU5ZCG5OSU/tFrc8rJUSrMzp07sw4dOkh/Li4uZlZWVmz48OHSeWlpaQwAO3v2LPvqq69YSEiIzDZSUlIYAHbz5k25+8jIyGAA2LVr1xhjjE2aNIm9/fbbTCznH8H9+/cZAHb58mXpvBcvXjAALCYmhjHGWExMDAPA9u3bV+btzmHm5uYsLi5OZnujR49mQ4YMYYwxNmfOHObr6yuz31mzZjEA7MWLF5W8S+pBF0/oEEVOxVKLHSHyCQRcDkkuMvL3V+5+x5IWO0kXGso1ohcsLbkbHfOxXyU1b95c+tzIyAiOjo5o1qyZdJ6LiwsAICMjA/Hx8YiJiYG1tXW57dy9exeNGjXC3bt38dVXX+HcuXPIzMyUttQlJyfDz88PH3/8Mbp37w4fHx/07NkTvXv3RkhIiNJxBwYGSp8nJiYiPz8f3bt3l1mmsLAQrV6fIkhKSkK7du0gKPMPKCgoSOn9qooKOx2iyKlY6vNDSMWcnWULO2VICjsJuiKW6AWBALCy4jsKhZi8cWsjgUAgM09SCInFYojFYvTp0wdLly4ttx2311cQ9unTB56enti4cSPc3d0hFovh5+eHwtcj7/v7++P+/fv4+++/cezYMXz44Yd45513sGvXLghfd1ZnZfoKFhUVyY3bqsz7KykeDx48iDp16sgsZ/a670bZbfKBCjsdImmxy8zkroAteztRarEjpGqSHAKU618HcBcmmZtzQ54AVNgRwid/f3/s3r0b9erVg7Fx+VLl2bNnSEpKwvr169GxY0cAwOnTp8stZ2tri0GDBmHQoEF4//330bNnTzx//hzOr1tS0tLSpC1tZS+kqEiTJk1gZmaG5ORkdO7cucJl9u3bJzPv3LlzVW5bXejiCR3i6Mg9MsbdL1aCsdLCjlrsCKmYpNUbUL7FTigE3nqr9Gf6EkUIfyZOnIjnz59jyJAhuHDhAu7du4ejR49i1KhRKCkpgYODAxwdHbFhwwbcuXMHx48fR3h4uMw2Vq1ahe3bt+PGjRu4desW/vjjD7i6usLe3h4WFhZo164dlixZgsTERJw8eRJffvlllXHZ2Nhg+vTpmDZtGqKionD37l1cvnwZa9euRVRUFABg/PjxuHv3LsLDw3Hz5k1s27YNW7Zs0cTbJFeNL+y+/fZbBAcHw9LSEvb29nKXSU5ORp8+fWBlZQUnJydMnjxZ2tSrTmXvF1v2dOzLl6VdKOh+roRUTNJiZ2cHeHsrv37Z07HUYkcIf9zd3XHmzBmUlJSgR48e8PPzw5QpU2BnZwehUAihUIjt27cjPj4efn5+mDZtGpYvXy6zDWtrayxduhSBgYFo3bo1Hjx4gEOHDklPw/78888oKipCYGAgpkyZgkUK3obpm2++wddff42IiAj4+vqiR48e+Ouvv+D9+p9O3bp1sXv3bvz1119o0aIFfvrpJyxevFi9b1AlBIzvk8E8mzdvHuzt7fHo0SNs3ry53O0+SkpK0LJlSzg7O+O7777Ds2fPEBYWhgEDBuCHH35QeD8ikQh2dnbIysqCra1thcs1bgzcvAlMmgR07codaHJygKAgrjVC3oUVRPMU/fyIZij6/i9dCsyeDXTpAsTEKL+fWbOAZcu453l5gIWFavGS8iiHCNGOGt/HbsGCBQBQYTPp0aNHkZiYiJSUFLi7uwMAvvvuO3z88cf49ttvK/wHVVBQgIKCAunPIpFIoXjq1eMKux9+4Kay6DQsIZXr04e7B/KECaqt36gR9+jiQkUdIUQ/1fhTsVU5e/Ys/Pz8pEUdAPTo0QMFBQWIj4+vcL2IiAjY2dlJJ08Fz6FGRgJffw0MHgwEBABl60YtXi1NiF5q0oS7ndgHH6i2flAQd5Fh27bqjYsQQrSlxrfYVSU9PV06to6Eg4MDTE1NkZ6eXuF6c+bMkenIKRKJFCru6tcHXjciAuAunMjM5O4V6+urfPyEEMU1aQLcv8+12BFCiD4yyMJu/vz50lOsFbl48aLMoIOVEcgZ5ZQxJne+hJmZmXRMG8nygOKnZGW3xZ2iffWq9B6WRLskn1sN75LKm+rkj7IcHIDCQm4i6kM5RIh2GGRh99lnn2Hw4MGVLqPo/eJcXV1x/vx5mXkvXrxAUVFRuZa8ymRnZwOAwqdkiW7Kzs6GnZ0d32HUOJQ/hoNyiBDNMsjCzsnJCU5OTmrZVlBQEL799lukpaVJR7s+evQozMzMEBAQoPB23N3dkZKSAhsbG2lLn+T0bEpKCl0lpiaaek8ZY8jOzpbpa0m0h/JHOzT5nlIOEaIdBlnYKSM5ORnPnz9HcnIySkpKpCNPv/XWW7C2tkZISAiaNGmC4cOHY/ny5Xj+/DmmT5+OsWPHKvWPTygUwsPDQ+5rtra2dGBSM028p9TKwB/KH+3S1HtKOUSI5tX4wu7rr7+WjhYNQHprkZiYGHTp0gVGRkY4ePAgJkyYgPbt28PCwgJDhw7FihUr+AqZEEIIIUSuGj9AMZ9owE71o/e05qDPWv3oPSVE/9E4djwyMzPDvHnzZK6eJdVD72nNQZ+1+tF7Soj+oxY7QgghhBADQS12hBBCCCEGggo7QgghhBADQYUdIYQQQoiBoMKOEEIIIcRAUGFHCCGEEGIgqLDjSWRkJLy9vWFubo6AgACcOnWK75D02vz58yEQCGQmV1dXvsMiGkQ5pD6UP4QYDirseLBjxw5MnToVc+fOxeXLl9GxY0eEhoYiOTmZ79D0WtOmTZGWliadrl27xndIREMoh9SP8ocQw0CFHQ9WrlyJ0aNHY8yYMfD19cXq1avh6emJdevW8R2aXjM2Noarq6t0cnZ25jskoiGUQ+pH+UOIYaDCTssKCwsRHx+PkJAQmfkhISGIi4vjKSrDcPv2bbi7u8Pb2xuDBw/GvXv3+A6JaADlkGZQ/hBiGKiw07LMzEyUlJTAxcVFZr6LiwvS09N5ikr/tW3bFlu3bsWRI0ewceNGpKenIzg4GM+ePeM7NKJmlEPqR/lDiOEw5juAmkogEMj8zBgrN48oLjQ0VPq8WbNmCAoKQoMGDRAVFYXw8HAeIyOaQjmkPpQ/hBgOarHTMicnJxgZGZVrWcjIyCjXAkFUZ2VlhWbNmuH27dt8h0LUjHJI8yh/CNFfVNhpmampKQICAhAdHS0zPzo6GsHBwTxFZXgKCgqQlJQENzc3vkMhakY5pHmUP4ToLzoVy4Pw8HAMHz4cgYGBCAoKwoYNG5CcnIzx48fzHZremj59Ovr06YO6desiIyMDixYtgkgkQlhYGN+hEQ2gHFIvyh9CDAcVdjwYNGgQnj17hoULFyItLQ1+fn44dOgQvLy8+A5Nbz169AhDhgxBZmYmnJ2d0a5dO5w7d47eUwNFOaRelD+EGA4BY4zxHQQhhBBCCKk+6mNHCCGEEGIgqLAjhBBCCDEQVNgRQgghhBgIKuwIIYQQQgwEFXaEEEIIIQaCCjtCCCGEEANBhR0hhBBCiIGgwo4QQgghxEBQYUcIIYQQYiCosCOEEEIIMRBU2BFCCCGEGAgq7AghhBBCDAQVdoQQQgghBoIKO0IIIYQQA0GFHSGEEEKIgaDCjhBCCCHEQBjzHUBNIRaLkZqaChsbGwgEAr7DIUpijCE7Oxvu7u4QCun7kLZR/ug/yiH+UP7oP2Xyhwo7LUlNTYWnpyffYZBqSklJgYeHB99h1DiUP4aDckj7KH8MhyL5Q4WdAiIiIrBnzx7cuHEDFhYWCA4OxtKlS+Hj46PwNmxsbABwH4qtra2mQiUaIhKJ4OnpKf0ciXJOnjyJ5cuXIz4+Hmlpadi7dy/69eun8PqUP/qPcqhUZGQkli9fjrS0NDRt2hSrV69Gx44d5S574sQJdO3atdz8pKQkNG7cWKH9Uf7oP2Xyhwo7BcTGxmLixIlo3bo1iouLMXfuXISEhCAxMRFWVlYKbUPS/G1rawtbW1swBowZA7z9NjBwIGBursnfgKgLncZQTW5uLlq0aIGRI0di4MCBSq//Zv4AwI4dwIULwKJFgIWFWsMlGlTTc2jHjh2YOnUqIiMj0b59e6xfvx6hoaFITExE3bp1K1zv5s2bMkWZs7OzwvuUlz8V+eurr2Blb4+3P/9c4e0T7VEkfwSMMaaFWAzK06dPUbt2bcTGxqJTp05ylykoKEBBQYH0Z0m1nZWVBVtbWxw/mIluvZsCaA5zo6bo0tQdn45thnfHdIUxVXk6RyQSwc7OTvr5EdUJBAKlW+zefP9FIsDdeQNyC/PxVr0B+G27B9q21VzMpPoohzht27aFv78/1q1bJ53n6+uLfv36ISIiotzykha7Fy9ewN7eXqF9VHX8qUj61auo06IFBABOrF2LDhMmKPx7Ec1SJn+oB6sKsrKyAAC1atWqcJmIiAjY2dlJpzf7N+TfPAAgA8Ax5Jd8j8NXZ6HvpHdhamGHJlZeePLffxr8DQjRbQUFBRCJRDJTWTbWYtiVzAAwBXceeKFduxD0brkIT+894idgQhRQWFiI+Ph4hISEyMwPCQlBXFxcpeu2atUKbm5u6NatG2JiYipdtqrjT0WuHzkCMYASAEMnT8bzu3cVWo/oFirslMQYQ3h4ODp06AA/P78Kl5szZw6ysrKkU0pKiszr3cYMwL+//ooNIz5Gn3qd4WjcDIA1GAqRlJeMSR9+reHfhBDdVdWBqTg/H7P6voMAKzsAYgDROHjlK7g0aIB3XBrjzj//8BI3IZXJzMxESUkJXFxcZOa7uLggPT1d7jpubm7YsGEDdu/ejT179sDHxwfdunXDyZMnK9xPVcefity4eFH6PKWkBGM6dgQTixVal+gOKuyU9Nlnn+Hq1av4/fffK13OzMxM2p9BXr8GM1tbBHz0EcZG/YL9908gs+gqUh6+QBtvril+f9JpFOfna+z3IESXVXVgMrG0xOTdu/Fvzkvc+ecfDGk2CEZoCIZC/JNxE517DkZhTg5P0RNSuTf7STHGKuw75ePjg7Fjx8Lf3x9BQUGIjIxEr169sGLFigq3X9XxpyI3b94EAPRwdIQJgL1paVg3dKhivxTRGVTYKWHSpEnYv38/YmJiNHK5vkddY/y6fwIARxTgKX6e/p3a90GIPlDmwNTg7bex7ep2pKbfQLc2JwA4I7U4E1++SwckolucnJxgZGRUrnUuIyOjXCteZdq1a4fbt2+rOzzceMR1ZfiwTx8se90HNnzHDlzdtUvt+yKaQ4WdAhhj+Oyzz7Bnzx4cP34c3t7eGttXIz9b1LPtDQBYvmWfxvZDiKGp7SLE0bOd4eM6DwDw3akjuPPPcZ6jIqSUqakpAgICEB0dLTM/OjoawcHBCm/n8uXLcHNzU3d4uPG6/3jjdu0wZfdu9K5dGwUABg0bhtyMDLXvj2gGFXYKmDhxIn777Tds27YNNjY2SE9PR3p6Ol69eqWR/X0+aQQA4E7uZTw4f0kj+yBEm3JycpCQkICEhAQAwP3795GQkIDk5GS17kcoBPYd/xQCdIUYhRjQ71PqI0R0Snh4ODZt2oSff/4ZSUlJmDZtGpKTkzF+/HgAXDeEESNGSJdfvXo19u3bh9u3b+P69euYM2cOdu/ejc8++0ytceVmZCClpAQA4PP22xAIhfjl5Em4C4W4UViIKRWMs0d0ECNVAiB3+uWXXxTeRlZWFgPAsrKyqly2qIgxU0FbBoANajakGpETdVHm8yPlxcTEyM2hsLAwhdZX9v2f9vE5BpgyAGz9mM+rETlRF8qhUmvXrmVeXl7M1NSU+fv7s9jYWOlrYWFhrHPnztKfly5dyho0aMDMzc2Zg4MD69ChAzt48KBS+1Pkvb+0bRsDwJwFApn5MatWMcHrfD2xerVS+yXqo0z+0Dh2WqLsGE59/Rdh/+WvYC5wR27hQwiNaSxpPtEYXPxS9v0vKgI8HWbgSe4KmMMeaQ8SYO/lpYVISUUoh/ijyHv/+6RJGPrjj+hga4tTr0/JSgyqWxc7U1KwpGdPzPr7b22ETN5A49gZgGU/fwrADvksFVvn/Mh3OIToFRMTYM+BrwE0Qj5eYlinEVWuQ0hNduPqVQCAu6M33hy+rvHrfuX3HjzQclREFVTY6Siflo7wsgkFACxZ/wfP0RCif4K72GBQlwUAgEPJp3B87RZ+AyJEh924fx8AsOfhULz1Fnery0uvu3g3eH1f9LtPnvAVHlECFXY6bNr44QCAm9kXkXL5Os/REKJ/fj44GNbGHwBg+HDKfIiLi/kOiRCddOPpUwBAsbgpAGDPHiAgAAgNBXKsuSt272Vn8xYfURwVdjps4uJ3YSJoCaAIc0Yt5zscQvSOpSWwdeNiAHZ4VvIQaz75hu+QCNE54uJiJOUXAQDMzRri8GFg2DDuKvPDh4GJq3oAAJKLi1GUl8dnqEQBVNjpMGNjoIdfLwDAnisxNGwDISro//FbaOw4GACwcOvvlEeEvOHy3+dRhBIAppg/zws9egC//QbcugWMHQsYG7sAsEAJgORz53iOllSFCjsdt3TzRABWeMWS8b+vNvAdDiF6acMv0wFY4kXJbayf+j3f4RCiU2Z/zfWvMxfURfh0M+n8Bg2ADRuAGTOEAOoDAO6eP89HiEQJVNjpuCat3eBpxTWDL4mk27oQooqOfd5CQ/u+AICvftrKczSE6I6YGOBYQiYAIMjJCCYm5Zdp3hyQFHb3rl3TXnBEJVTY6YGwvt0BALde3uA5EkL017rIcAAmyCxKwJa5W/gOhxDeFRQA3A0vbgIAgho6yl2ubGF35/YdrcRGVEeFnR4YHt4LgABFeIzbcfRtiRBVdBsSiPo2PQEAs1ds4jkaQvi3dCnXj85UwB1XGjdrJne5Ro0AIwE3lt1/yZlai4+ohgo7PdAowBMmaAgA+P3HQzxHQ4j+WrtqGgAhnhSewY5lf/EdDiG8uX0bWLyYe26JywCAxkFBcpc1NgbqurgBAG6+oCFPdB0VdnrC04YbIPLYqcs8R0KI/uo5uivqWnYGAHw+fy3P0RDCn6+/5k7Fvt05Ey8ZN4SJT7duFS7fvAnXYpdWlE1Xlus4Kuz0RLsm3KCR19Nv8RwJIfptzaLPAACPX0Vjz9pTPEdDCD/WrQM++wyYGHoYAOAuFMLWw6PC5dt1aQxAgAIUIPPmTS1FSVSh0J3lRSKR0hummzyrV98POmHb+SV4XnwThXn5MLU05zskoiDKH93Sd9oA1JnTBo8LLmDqrO8xYGJHvkMiakB5phx7e+CHH4Ctn5wEAPhU8V74t7UBUAfAI9yLi4Ozr6/GYySqUaiws7e3h0AgUHijAoEAt27dQv369VUOjMjqO6E7MN0WgAgH1h3CgM8H8B0SURDlj+5ZPmsshi68gJTc/fhryxX0+bgF3yGRaqI8U82N//4DADSuU6fS5UqvjH2EG/9eRdvRGg+NqEihwg4Adu3ahVq1alW5HGMM7777brWCIuWZWRjD3rgJXhafw74/TlFhp2cof3TLkAVjMC1iNZ4UXcdnk5ajz8e/8R0SUQPKM+XdePAAANDYx6fS5VxcADNhXRSIgXPxDxGmhdiIahQq7Ly8vNCpUyc4Osof4+ZN9evXh4m8UQ5JtTRx9UHco3M4l5jEdyhECZQ/uml5+FiMWDoVyTl/YM/G6RgwtiXfIZFqoDxTzc3nzwEAjQMDK11OIADc7Wrj/gvgv4c05IkuU+jiifv37yucLADw33//wdPTU+WgiHzdOrQCADzMpgso9Anlj276aPEkuJo0BVCISVOX8R0OqSbKM+UV5+fjdkEBAMCnU6cql/ep6w4AuP/yuUbjItVDV8XqkaGTewEACnEf9+Jp9G9CqkMgFOL7LyYAAFLzduH3NRd5jogQ7bp/6hSKAFgA8GzbtsrlA1pxQ55kFlKLnS5TqrDLzc3Fxo0bMXLkSISGhuLdd9/FyJEjsWnTJuTm5moqRvJa46C3YAIusbZ9f5DnaIg6PXnyBAsXLuQ7jBrnw/kTUMesGYAiTJu9AozxHRFRh0ePHiEnJ6fc/KKiIpw8eZKHiHTTjdfvhY+FBYTGVffM6vwud2eKAmTi1YuXGonpaVISji1bhuL8fI1svyZQuLBLTExEo0aNMHPmTLx48QJ169aFh4cHXrx4gRkzZsDHxweJiYmajJUA8LBuBACIjr3EcyREndLT07FgwQK+w6iR1s6fDAB48moPtq44x3M0pDrS0tLQpk0beHl5wd7eHmFhYTIF3vPnz9G1a1ceI9QtNy5xx5HGzs4KLR8c2gCADQCGfw+qP1dyMzLQoUULdJ81C762ttj6ySdU4KlA4cJu4sSJ6NSpE548eYJ9+/Zh/fr12LBhA/bt24cnT56gU6dOmDhxoiZjJQDa+DQBAFxPo352+uTq1auVTjdpwE/e9J09Bl7mLQEUY/pX1Gqnz2bPng0jIyOcP38ehw8fRmJiIrp06YIXL15Il2H0AUvdvMN16fHx9lZoeStrIcwE3CDGsUfU35AzrXNn3CoqAgDcKSpC2MaNVOCpginIwsKCXb9+vcLXr127xiwsLBTdXI2TlZXFALCsrKxqbef3xbsZAAbYsqKCIjVFR6pS3c9PIBAwoVDIBAJBuUkyXygUqjlqw6Gu/KnIoe+2vM4rIVu/8KRG9lHTafozZIwxd3d3dv78eenP+fn5rG/fvqxly5bs2bNnLD09vUbmWUXvfXsbGwaA/T5pksLbqmPemQFg/ZpNUWuMe2fPfp2DAgbsZ8BSBji9ngfmIKzNNk37Tq371CfK5I/CLXYODg64fft2ha/fuXMHDg4OqtaXREH9Jr0LrqurCAc3HOM7HKIgR0dHbNy4Effv3y833bt3DwcOHOA7xBotNDwM9S39AYgxZ9FKarXTU1lZWTLHITMzM+zatQv16tVD165dkZGRwWN0uufG69PUjdu3V3iderWcAAA3H6WrLY60hASMWbr09U/T4efXB926zYSNzT0ASwE44YU4A2NWzUBfv89QXET3qq2MwoXd2LFjERYWhhUrVuDKlStIT0/HkydPcOXKFaxYsQKjRo3CuHHjNBkrAWBubQ47I+5WLnu2xfIcDVFUQEAAUlNT4eXlJXeqU6cOnSLi2YYVnwMAnhfux49fxvAcDVFF/fr1cfXqVZl5xsbG+OOPP1C/fn307t2bp8hkRUZGwtvbG+bm5ggICMCpU5Xfszg2NhYBAQEwNzdH/fr18dNPP1U7hsybN/Hs9f+cRt26KbyeXwM3AMDj7KfVjgEAxMXF+Pidd17H0hJWVguxfz9w7Bjw8qUNrl+fiXWrkuBuOQCAGPuvr0Ud2w+QmKD8LeRqDGWaApcsWcLc3Nykp40kp5Dc3NzY0qVLVW1hrBHUeRoiyH0wA8Aa2fVRQ2REEdX9/Pbs2cN+/fXXCl9//vw527Jli6rhGTxtnMZjjLFGVoEMALM36cPEYo3uqsbRxmc4c+ZMFhISIve1oqIi9t577/F+Knb79u3MxMSEbdy4kSUmJrIpU6YwKysr9vDhQ7nL37t3j1laWrIpU6awxMREtnHjRmZiYsJ27dql8D7lvfenIyMZAFbXyEip+LdMX8EAMAF8WZEaegOt7t//9elWcwYkss2b5S9XUlzCBrX6lAFCBoAJEcSWfHmXlZSovu+SoiL25L//WNLhY+xY5P/Y1tk/smVhi9jsPnPYwsHfsDUTvme/ztvKDq07yM7/eZ49uPKA5ecWqL7DalAmf5Qq7CTu3bvH4uLiWFxcHLt3754qm6hx1PlP7YuBEQwAMxX4qCEyoghtFRZEPm29/yc27pT2tYtadkaj+6pptPEZFhUVVbr94uJi9uDBA43tXxFt2rRh48ePl5nXuHFjNnv2bLnLz5w5kzVu3Fhm3rhx41i7du0q3Ed+fj7LysqSTikpKeXe+01hYQwAC3F0VCr+W8f+kRZi/12rRlXFGLu6axcze92HDljL+vZlVX6h2vL5MmYE69fr1GVN6seypUsZu3at6nXv/HuXLRm5moXUH8rczQKYEDbSPnzKTdZMCFdmgreYhaAZsxK2YNZCf2ZrFMjsjdswR5N2rJZxW+Zg3JrZG/kzO6OWzNaoObMR+jEroS+zFDRiFoIGzFzgzcwEdZkpPJkpPJgpPJgJ6jATuDMroezxXeOFHVGeOv+pXTueIO1k+uC/x2qIjlSFCjt+afP99zJvxQAwT6tBGt9XTUI5xFhBQQEzMjJie/bskZk/efJk1qlTJ7nrdOzYkU2ePFlm3p49e5ixsTErLCyUu868efPkFiRl3/vpgVzr9OQWLZT6HQpzcxlgxACwyMX/KbXum9tpZm7+OrZezNm5hD15oti6iQcPMWcjxzK/mxcDPmYOVhvYkPeS2NatjH37xX02sONW1sw1nDmavsuMUL/SQg2ow4TwYaaCAGYlDGYWwkBmAl8mRF0mQC0GmKhYBKo2CeAg8zsrkz8K3Ss2PDwc33zzDaysrBRZHHPmzMGMGTMUuhkzUZ5f1xYwRh0U4zH+t/IvfLGZ+jbqMsof/RIxfQyGLpqIlNw/EbPnBroOaMx3SEQB+pBnmZmZKCkpgYuLi8x8FxcXpKfLvxghPT1d7vLFxcXIzMyEm5tbuXXmzJmD8PBw6c8ikajc7dNm/fYbeh8+DKd69ZT6HUwsLWEjqIVs9hRnY2/g0zlNlVpf4viqVbiWnw+gFoDN2LRJiNq1FVvX991Q3Lx9EcOC3saRJ8kQ4yGALXiRuwW/7wd+3+8A4IX8+OENV0tfNPduirdD2iNkeHe4elrC3h6oaozm7EwRUm+nIv1+Bp6kPMXTtBd48UyEgoIiFBUVo6ioBIWFJSgsKoFAIISRsRGMjCSTMYyMjWBiYgJjEyMYGxvDxNQEQiMhjIyEEAiEEAgFEAq5Sx/MLc0UfCflUKQ6FgqFLCMjQ7FSmjFmY2PD7t69q/DyNYG6v63Ws+zKALDO9caqZXukctX5/Ch/qk+brT3ikhJWy8iHAWDNnEdrfH81haY/Q33Is8ePHzMALC4uTmb+okWLmI+P/K41DRs2ZIsXL5aZd/r0aQaApaWlKbRfdb/3PpaNGADWwlP14UfGN235unVqLBs1SvVYstPS2OFFi9jnrYOYj7kLE0AgbfWyF9ZmLexas2Eth7Hvx69k9/69qfqOeKb2FjvGGBo1agSBQKBQsUi3F9O81o188SAhBv89poFtdR3lj34RCIWYNXgQZv1vIa493Y0b8d+gcUD5VhGiW/Qhz5ycnGBkZFSudS4jI6Ncq5yEq6ur3OWNjY3h6OiosVgr09DJHjeTgQcZqg15wsRi7E66BwCoZdMdq1apHou1qyt6zJ2LHnPnYgUA0aNHuH3iBBp26QJbDw/VN6zHFCrsfvnlFyQnJ6Nu3boKb7iiP1KiHu/1DcIfCZF4VnQdRYVimJgqddtfokWUP/rn85/nYv62X/CKpWDC4GU4frsaRx6iFfqQZ6ampggICEB0dDT69+8vnR8dHY2+ffvKXScoKAh//fWXzLyjR48iMDAQJiYmGo23IgENa+NAMpBVkIqsLMDOTrn1kw4cwFOxCIAZxox+B7a26ovN1sMDAR99pL4N6iNFmwHt7OzY1q1bVW9HrOHU3RSe+zyLAWYMAPt9xVG1bJNUrLqfH+VP9fDR8f6zrlNed2Kuw54+ztbafg2VNj5DfcgzyXAnmzdvZomJiWzq1KnMyspKerXu7Nmz2fDhw6XLS4Y7mTZtGktMTGSbN29Wy3An1bFz2rTXpzuD2KlTyq//Vbf3Xq8fypKS1BKSwdPInScWL16MiRMnYuDAgXj27Jn6K0yiFEsHW9Q2aQYA2LLxIM/RkKpQ/uifpbvmwwjOYHiMSQOW8x0OUYA+5NmgQYOwevVqLFy4EC1btsTJkydx6NAheHl5AQDS0tKQnJwsXd7b2xuHDh3CiRMn0LJlS3zzzTdYs2YNBg4cyNevgAYBAa+f3cW1a8qvv+3MfwAAT/tOaEzXJqmfMhXjvXv3WNeuXZmLiwv7888/Va48ayJNfFv9oMUnDACzM26vtm0S+dTx+VH+qI6voTIG+o1kAJgxGrH8PLo3c3Vo6zOkPCtP3e/9iwcPpBcojB7xXKl1n925wySDDM+deFkt8dQEar94QsLb2xvHjx/Hjz/+iIEDB8LX1xfGb1wffOnSpepXm0Qhn3zWB3+M3YCs4nik3n0O9wY0PIYuo/zRPz/umoc9jf9AMW5h7tC1WLF3Ct8hkSpQnmmevZcXrGGGHBTg9Mk7AForvO7W+WsBiAH4YcIXLTUUYc2mVGEHAA8fPsTu3btRq1Yt9O3bt1zCEO3pNupdGI91QTGeYO2X2/Ht7xP4DolUoSbnT2RkJJYvX460tDQ0bdoUq1evRseOHfkOq1KuPl5oX6cnTj/ehZ/2/4rlbAoUvOiS8Kgm55m2vGVhhoRXBbj54DEePmyN12eSq/TLgQsAAC/b9nB312CANZkyTYEbNmxgNjY2rH///kqNF0Q0dxrCz74nA8CaOg5W63aJLHV8fjU5f5S9P+ab+LxrwbV/LjHAlAFg34zcoPX9GwptfYY1Oc8qoon3fpCn5+vTsd+x1asVW6cw7xUTwo4BYLOH/U9tsdQEGrmlWI8ePZiDgwOLioqqVnA1lab+qc0dMP91H6CGdNNyDaru51fT80fZ+2Mqcq9LbfJ35q7iMxM04e0m4PpOG4VdTc+zimjivZ8TFPS6sJvAOndWbJ1NMze9XqcWy0x/pbZYagKNXBVbUlKCq1evYsSIEepqLCRqMOGbEQCEKMZt/PN7PN/hkArU5PwpLCxEfHw8QkJCZOaHhIQgLi5O7joRERGws7OTTm/eDknb/ji0HALYooAl4pPu83mNRdel3U7F6s824uBPR7S+75qcZ9rWoFGj189u49Qp4OnTqtdZv/UfAEBdy9ZwdDHXXHA1nMKFXXR0NDxq6CjOusy9iTfsjZoAANav3M1zNKQiNTl/VLk/5pw5c5CVlSWdUlJStBFqheoHNsJ7TYcAAH6L+xnpd1Ubcd/Q5GXl4dcFOzE4YBoa2obCXNgQ7o08MG3tJ5g3/2etx1OT80zbAt99FwAgwCmIxa/wxhjK5ZSUAJfTucaH97u003R4NRr1KDUA7bxb4vCd/3Dyv4t8h0JIhd681RNjrMLbP5mZmcHMrBo3wdaAX08sQy3nv1GMZAzuNgsnHkTxHZLWFRcWY+/3B/Fb1BGcvZ2Ap4VXAZS/NZcR6sDSjFpkDFnz99+Hp5ERUkryAcRg7953MWpUxctvX3MexbgFwAgzvgvTVpg1Et2HygB8HNYDAJBRcBFZz/J5joYQWarcH1MX2TjZYnLv8QCA2Id/4NLf//IckXak3krFtF7foJ5Vd5iaueHDmf2w//o6PC08CyAXAjjAySQIHTyH46tB3+FqzH8oZo9w8mHNK3xrEoFQiN7S0YX/QnQ0kJ1d8fI/rtkPAHA1aQzXxt6aD7AGo8LOAAyc8SGEsAeQhY1f7+I7HEJklL0/ZlnR0dEIDg7mKSrVLN83C9ZCfwCvMGTwF3yHozE3z93CqI5z4GLWEXV86mP1oa/xMO8YGDIBWMHZNBh9mkzA9qX7UFiQgaeFcTiVvBULt4ejWZemfIdPtKT3Bx8AAIzwJwoKGA4flr9cfj5w8SE3zEk//+baCq/GosLOABibmaKeVUsAwM59x/kNhhA5wsPDsWnTJvz8889ISkrCtGnTkJycjPHjx/MdmlKERkJ8N2sWAOCWKBq7lu7kOSL1eZn+ElNCF6K2WXs0DmqCX04vQUbhaQAFMEE9tHUbgh+m/IzsZ+nIKDiD/dfXYtDMvjA2pR49NdXbU6bAEkAJ0gAkYO9e+cv98dtTlLCTAIDPvhiqtfhqKirsDMS77biRv6+m0YjqRPdUdX9MffLJ4g/hYRkKABj/ZQSYWMxzRKoTl4ixYdZvaOLQFw5uXlhzeB6eFsYBKIG5wBddvUdi7/eHkF98F+dSt+Gz1SNhXcua77CJjjC3t0d3V9fXP/2FgweBwkLZZV6+BD6f/g+AQtgLndCk97tajrLmocLOQEz8mrtir4Bdxb/HbvMcDSHlTZgwAQ8ePEBBQQHi4+PRqVMnvkNS2dafFwIwx7PiBMz/cAHf4Sjt4dWHGNBiMiyMG2HcsuFIerkfgAhGqIP2HsNx5OfjeCVOxPF7P6Pf5FAIjehQQeTr04Pr420i2AeRCDhe5qQRY8CoUcDTLK5le1ATLwiE9LekafQOG4jGnVrBStgQAEPkoh18h0OIQes6KBD+rtyXqYW7l2Pf8m08R6SYHUv3wde+D+q1aIK9V39AIe4CsER9655YMe4n5Bc8wOmUrQgZ2ZXvUIme6BUeDgAoYpcBpMmcjv3xR2Dv3kQA3MzJ336t/QBrICrsDIi/O9cp9djF8zxHQojhOxK/BtZG7QG8wvszJ+HCnhN8hyRXXlYepoQugK1RSwye3R83sg4AyIOZoCEGB0zD45u3cTf7b3z+0zjqL0eU5tq8OdpYWb3+6SD+/JMbsy4+Hpg+HQCWAgD6u7mhyXvv8RVmjUKFnQEZOvBtAMCjvIsoyNfffj+E6AMnd2vEX/gDpgI/lOA5Or0/Ag/ik/gOS+pu/D2E+oyFrX0DrDk8H9niKwCMUdfyHfww5WfkFd3A7/+uhHsjuhM7qZ7ebdsCAIwFe/HkCXD0KPDhh0Bh4QMI8BsAYE5EBJ8h1ihU2CkhMjIS3t7eMDc3R0BAAE6dOsV3SDLC5n8EwBIMT/BrxEGVt/PvvhNYO+lHTO4+B70bj0Ir537wsOiCt2x64kP/Kdjy9Q6IMnPUFzgheqqRvxuid2+DEJ4oYClo1XYgslIzeI3p4E9H0LRWX7wV6IfDtzahBOkQwAld6n2MK/9cxsPcaHy2eiT1myNq0+eTTwAAjP0D4BU++AC4dw+wMfkGDGK8U6sWWofRoMTaImCMMb6D0Ac7duzA8OHDERkZifbt22P9+vXYtGkTEhMTUbdu3SrXF4lEsLOzQ1ZWFmxtbTUWp4d5OzwuOA8Hk3fx8OmfsLFT/NTKteiLeK9fOB7knVZgaVNYChvD274Rmr/VAB06NUevj7vAq6nq3/4LcvPx9GEGnj56jtysPDAxg1jMUFIiBhNzf6ZmFqYwNTeFqbkJTKXPjUufW3CTsalxhQcuJhZDXFI6AdzVgYxBZp6lnaX01JS2Pj8in66//7+v+AdDZ3wI4Dk8zdvi7vMTMLHQ3p0XiguLsejjNfhx1048KyrtimEu8MXwTgOw4o+ZsHXm933T9c/QkGn6vWdiMbxMTZFSUgLgIIB3YWSUDuOSOiiAGP+sWIG3P/9c7futSZT5DKmwU1Dbtm3h7++PdevWSef5+vqiX79+iJDTxFxQUICCggLpzyKRCJ6enhr/p7Z93noMWfgZgGI0dBiFG5mbUdVFSEWv8jGq4xT8L/5/YMgFYARjNIClkSMczGuhtq0DPJwd8ezlSySm3cKzohtgeCF3W0bwgIOJF6xNbWAsNIKR0AjGQiGMjYxRIi5BbkEu8ory8KokF4XiPBSzXJQgDwx5AHTrrhkrxv2Ez38aB4AOSnzTh/d/6YRtmL1uNIB8+Np2wemr/0MtL82e5ky5nowpQ5biwLWDKMLD13OFcDXriFljPsLk70fpTMucPnyGhkob7/0EPz+su34dRoKxKGEb8LbXBzj+cBfaWlnhrEhEV8NWk1KfISNVKigoYEZGRmzPnj0y8ydPnsw6deokd5158+YxAOWmrKwsjcc7p9dU6f7eaTyn0mV3LfkfsxE2ki5vIWzFfln4V6XrlBSXsBO/xbDJIV+y1i4DmbNJW2YEF7m/r2qTkAHmDLBggBUDrBlg83qyZIDp62XUtT/504pxP0l/56ysLK19fqQ8fXn/x/dYKf3bNIYLm//hAiYuKVHrPooKitiqiRtYA+ser/NB8jdrxwJdBrGTO+LUuj910ZfP0BBp470/tGABA8CchJZs6fzHzPb13+W+OZUfg4hilPkMqcVOAampqahTpw7OnDkjcwukxYsXIyoqCjdv3iy3Dl8tdhLvNx2M3Yk7AAgxIXQl1h6aIvP6+T2n8MmYRbj6Ihpc/tmjb4vx2B73DcwtVbsy7kHCXfz9ayzOnPoPotw8FBUVo6hEjKLiEhSXFEMgEMDe2ga17G3hWMsWzi4OcHZxQO06DnB0tYezhxNqeznBrratQq0M4hIx8nPyUVxYjMJXhSjML0RRfhEK84tQXFQsXUaCiRkEQgGERkIIhEIIhUIIjYTSfQmNuBvSCwTcMs51nWBuzZ1Oo9YGfunT+z978EYs3xEBMe4DANzN2mLP7yvQtn+Ham335I6z+HrWBpx5+A+KkSKdbypoiAEB72H1jplwqV+7WvvQJH36DA2NNt77/Jcv4ejggDwAH3h44I9Hj9DUzAxXc3IgNKarrauLWuzU7PHjxwwAi4uT/Sa8aNEi5uPjo9A2tP1tVVxSwlo5vvP6m7wF+2H6H4wxxg6sO8zesunOACPpN303i17s3MHrWolLX1FrA7/07f1/fOcZC3QbxQATaQ52bxDG9v94kBUVFCm2jVtpbN7Q75l/7UHMXND4jRZlG+Zj14f9NPNXVlKs3hZBTdG3z9CQaOu97+vqKvN3+uv48RrdX02izGdIZbQCnJycYGRkhPT0dJn5GRkZcHFx4SmqygmEQpx7+CfqOXZGWsG/mLziUyz+8Sek5R8Hl3OAvXEHzB77KWauHQqBgN94CTEk7g1q4WLqZuxeMwQjw79AdslFRN+NQvRnUcBntnAwboLGLj7o2K4VTE1NkJaWiaeZL/BcJMKLHBGSs+4hu+QagKIyWxWglnFrDOrcEwt/ngSnuk58/XqEyNWnRw/8GRUFAKhnbIzBq1bxHFHNRIWdAkxNTREQEIDo6Gj0799fOj86Ohp9+/blMbLKmVpZ4uqNP1GvQTfkim8gLf8fAICrWWcsmvEZRn/zPs8REmLYBk5+B/0mvI1x7yzAH6f/hqjkOgARXhSfw9nH53B2d1Sl6xujDt6ya4EeQW0wbu4H8O3QRDuBE6KCXuHhwOvCbub778PYXHtXhpNSVNgpKDw8HMOHD0dgYCCCgoKwYcMGJCcnY/z48XyHVimneu44d2w7uvUYA1szW6xYNAV9p9Do34Roi5GxEJtOLMAmLEB+Tj72R/6Nv3afwoUb/yEl+x6EMIK5kQ2sTa1hY2YDB2sbNPB0x6hJ76HDhx3oakKiN1ybN8cXwcG4nZyMkWVGkCDaRYWdggYNGoRnz55h4cKFSEtLg5+fHw4dOgQvLy++Q6uSX9cWeFJ4ke8wCKnxzK3N8eHM/vhwZv+qFyZED3175gzfIdR4VNgpYcKECZgwYYJK67LXFx+LRCJ1hkS0RPK5MbqInBeUP/qPcog/lD/6T5n8ocJOS7KzswEAnp6ePEdCqiM7Oxt2dnZ8h1HjUP4YDsoh7aP8MRyK5A+NY6clYrEYqampsLGxgeD1JaiSse1SUlJoXCc10dR7yhhDdnY23N3dIaQ+T1pH+aMdmnxPKYf4Iy9/AMohTdCFYxC12GmJUCiEh4eH3NdsbW0pqdRME+8ptTLwh/JHuzT1nlIO8aOy/AEohzSBz2MQfW0ihBBCCDEQVNgRQgghhBgIKux4ZGZmhnnz5sHMzIzvUAwGvac1B33W6kfvac1Cn7f66cJ7ShdPEEIIIYQYCGqxI4QQQggxEFTYEUIIIYQYCCrsCCGEEEIMBBV2hBBCCCEGggo7QgghhBADQYUdTyIjI+Ht7Q1zc3MEBATg1KlTfIek1+bPnw+BQCAzubq68h0W0SDKIfWh/Kl5KH/UR9fyhwo7HuzYsQNTp07F3LlzcfnyZXTs2BGhoaFITk7mOzS91rRpU6SlpUmna9eu8R0S0RDKIfWj/Kk5KH/UT5fyhwo7HqxcuRKjR4/GmDFj4Ovri9WrV8PT0xPr1q3jOzS9ZmxsDFdXV+nk7OzMd0hEQyiH1I/yp+ag/FE/XcofKuy0rLCwEPHx8QgJCZGZHxISgri4OJ6iMgy3b9+Gu7s7vL29MXjwYNy7d4/vkIgGUA5pBuVPzUD5oxm6lD9U2GlZZmYmSkpK4OLiIjPfxcUF6enpPEWl/9q2bYutW7fiyJEj2LhxI9LT0xEcHIxnz57xHRpRM8oh9aP8qTkof9RP1/LHmJe9EggEApmfGWPl5hHFhYaGSp83a9YMQUFBaNCgAaKiohAeHs5jZERTKIfUh/Kn5qH8UR9dyx9qsdMyJycnGBkZlftmlJGRUe4bFFGdlZUVmjVrhtu3b/MdClEzyiHNo/wxXJQ/msd3/lBhp2WmpqYICAhAdHS0zPzo6GgEBwfzFJXhKSgoQFJSEtzc3PgOhagZ5ZDmUf4YLsofzeM7f+hULA/Cw8MxfPhwBAYGIigoCBs2bEBycjLGjx/Pd2h6a/r06ejTpw/q1q2LjIwMLFq0CCKRCGFhYXyHRjSAcki9KH9qFsof9dK1/KHCjgeDBg3Cs2fPsHDhQqSlpcHPzw+HDh2Cl5cX36HprUePHmHIkCHIzMyEs7Mz2rVrh3PnztF7aqAoh9SL8qdmofxRL13LHwFjjPGyZ0IIIYQQolbUx44QQgghxEBQYUcIIYQQYiCosCOEEEIIMRBU2BFCCCGEGAgq7AghhBBCDAQVdoQQQgghBoIKO0IIIYQQA0GFHSGEEEKIgaDCjhBCCCHEQFBhRwghhBBiIKiwI4QQQggxEFTYEUIIIYQYCCrsCCGEEEIMBBV2hBBCCCEGggo7QgghhBADQYUdIYQQQoiBMOY7gJpCLBYjNTUVNjY2EAgEfIdDlMQYQ3Z2Ntzd3SEU0vchbaP80X+UQ/yh/NF/yuQPFXZakpqaCk9PT77DINWUkpICDw8PvsOocSh/DAflkPZR/hgORfKHCjstsbGxAcB9KLa2tjxHQ5QlEong6ekp/RyJdlH+6D/KIf5Q/ug/ZfKHCjstkTR/29raVppYUVHc9OuvQJ062oqOKIpOY/BD0fy5dw8YNgyYPRvo21db0RFlUA5pn6L5wxgwYgRgawv8+CNAH5XuUSR/qKODjvnxRyAmBvj2W74jIUT/7NoFnDsHTJgAFBTwHQ0h+uXBA+C334DISODUKb6jIaqiwk7HZGRwj7/8UvqcEKIYSc6kpgLbtvEbCyH6puwxZ8UK/uIg1UOFnY55+pR7zM8H1qzhNxZC9I0kfwBg+XJALOYvFkL0Tdn8+esv4OZN/mIhqqPCTofk5gKvXpX+vHYtkJ3NXzyE6JuyB6akJODgQf5iIUTflM0fAFi5kp84SPVQYadDJEllbg40agS8fAls2MBrSIToFUkO+flxj8uX8xcLIfpGkj8NG3KPW7dSlyB9RIWdDpEkkLMzMGMG93zVKqCwkL+YCNEnkhxauBAwMeE6gJ89y29MhOgLSf706QO0bs11CYqM5Dcmojwq7HSI5NuSszMwfDjg5gY8fgz873/8xkWIvpDkUIsWXA4B1GpHiKLKHoOmT+eer10r20WI6D4q7HRI2aQyMwOmTeN+XraMOoETUpWyfVTLHpj27aNO4IQoQnIMql0bGDAA8PICMjO5U7JEf1Bhp0PKJhUAjBsH2NkBN25wVygRQiomyR8zM8DaGvD1Bd57jxt09bvv+I2NEH1QtnHB2BiYOpX7eeVKalzQJ1TY6ZCySQVwo39/+in3fOlS7gBFCJGvbP5IBmefOZN7jIoC0tP5iYsQffHmMWj0aK5x4dYt4MAB/uIiyqHCToe8mVQAMGUK1wJx9ixw+jQ/cRGiD+TlT/v2QHAwdwESjQtJSOXezCEbG+7MEUCt3vqECjsdIu/A5OoKhIVxz9eu1X5MhOgLefkDlLbaRUbSFeaEVCQvj5sA2RyaPJk7LXvyJBAfz09sRDlU2OmQig5Mw4Zxj9RiR0jFKsqfPn2AWrWArCzg6lXtx0WIPpDkj6kp11InUacO0Lcv9/zvv7UfF1EeFXY6pOw4dmUFBABGRtzQJ48faz8uQvTBmxcfSQiFQJs23PPz57UbEyH6Ql4fVYlOnbjHc+e0GxNRDRV2OqSiFgcrq9KR9C9c0G5MhOiLivIHKC3sKH8Ika+y/GnXjns8d44u4tMHVNjpiFevuHG4gMoPTNTiQIh8FbV4A0Dbttwj5Q8h8kny580WbwBo2ZK7iO/ZM+DuXa2GRVRAhZ2OkHxbMjHhhjl5k+TARC0OhMinSIvdzZvAixfai4kQfVFZ/piaAv7+3HM6Hav7qLDTEZX1bwBKD0wXLwIlJdqLixB9UdmByckJaNCAe37xovZiIkRfVJY/gOzpWKLbqLDTERV1/JZo0oTra5eTw92JghAiq6oDE52OJaRiVNgZDirsdERVSWVkBAQGcs/VdWC6dg1Ytw6IiwPy89WzTUL4kJ/PfekBKs4hdfdTzcoCVq0C/vyTu58mIfpM0cLuypXS8e6qizG6GEMTjBVdcI0Kw7aPHDkSNmUHxCEVqiqpAK7FITaW62c3alT19peZCbz9dukBycQEaNGC20e7dty4RfTRqQ/lj2aV7aNqZyd/mbL9VBmT3+VBGaNHA7t3l/7cuDHQoQPQsSPQowfg4lK97RPlUI5VT1XHIE9PwM0NSEsDLl3i/tar49EjLlcePwYcHbnuEpLHhg2BGTO48SeJ8hQu7KZOnQoPDw8YGRkptHxKSgp69+5NSaOgyq7ok1Bni8Pnn3NFneTUb0YG8O+/3LR2LZfA330HDB5c/QMgofzRNMlBycmp4r/Xli25wu/pU+DBA8DbW/X97d/PFXVGRoCPD5CYyHWRuHED2LQJsLQEZs8Gpk8HLCxU3w9RHOVY9VRV2AkE3Jf+vXu507HVKezEYuDjj7k8BLj7OL95L+fffuOmzp1V30+NxRQkEAjYkydPFF2cWVtbs7t37yq8vKLWrl3L6tWrx8zMzJi/vz87efJkpcufOHGC+fv7MzMzM+bt7c3WrVtXbpldu3YxX19fZmpqynx9fdmePXuqvd83ZWVlMQAsKytL7uujRnGN0osWVbyNlBRuGSMjxnJzldq9jCNHuO0IBIydPcuYWMzY/fuMbd/O2LRpjHl7SxrIGevcmbFr11Tfl6Go6vOrCuWPZvPn8GHu77VFi8q307o1t9zvvyu1exkiEWMeHtx2Zs/m5mVmMrZ/P2MzZzLWsmVp/tSty9jOnVyO1XTVzaGq6EqOMcZfnlVEkffexob7m715s+LtLF3KLTNggMK7lmvFCm47lpaMnTnD2KVLjB09yuXlmjWMNWpUeoz68kvGCgurtz9DoEz+KFzYzZ8/n+UqUU0sXryYvXjxQuHlFbF9+3ZmYmLCNm7cyBITE9mUKVOYlZUVe/jwodzl7927xywtLdmUKVNYYmIi27hxIzMxMWG7du2SLhMXF8eMjIzY4sWLWVJSElu8eDEzNjZm586dU3m/8lT1ofTpw/0hr19f8TbEYsbc3LjlTp1SeNcycnIYq1eP28bkyfKXefWKsW++YczCorSQnDaNMQ39P9YL1T0oUf5oNn9+/ZX7W33nncq389ln3HJTpyq863ImT+a2Ub8+Y3l55V8Xi7kvSZ6epQVex47cwasm03Rhpws5xhh/eVaZqt77/PzSv9XnzyveTmwst4ybm+pfVhISGDM1rfx4l51d2tgBMNauHWMaqsH1hjL5I2BMf7outm3bFv7+/li3bp10nq+vL/r164eIiIhyy8+aNQv79+9HUlKSdN748eNx5coVnD17FgAwaNAgiEQi/F3mJng9e/aEg4MDfv/9d5X2K49IJIKdnR2ysrJga2sLxhjyyvRA7dqVG4Zh2zbgvfcq3s7gwcCBA8C33wJTpii0axlffAGsWcPd/+/ffyvvR5ecDMyaBfz1F/ezoyMwaRLwySfyx9qrTEkJkJrKDW55717p46NH3NW+9vaAgwM32dtzp6+EQu5Ul2QyNgbMzbnJwqL0eUkJd3P3oiLuUfJcLOZeY6z0ubzpzQyQ/DxsmCXq1ePO6735+ekjQ86fH34A5swBPvgA+OWXirfz++/A2LFct4bjxxXatYx//wW6dOGe//kn0K1bxcvm5QHff891aZBcnPT++9wp2saNld/3q1dc3ty5A9y+Ddy6VTpYbK1aXH46OnLPbW25/BEKuVNokudmZlzuWFpyj5I8KyjgpsLC0kexmJsk+SOZSkqA4uLS/BGLuRgkeSN5bNoU6NXLEgKB4eSQIvjKs8rIe+/L5tDjx1yXAiMj4OXLirsz5OUBrq7cZ37jBuDhUeWuZbx6xfWru3ED6NUL2L698q4+u3dzxxyRCLC2BsLDgaFDld+vWMyd9k1N5boglZ1ycrhx+t6cLC25ycqq9NHUtPRYVPbYJO+4IsmR4uLSqewxp2w+AbIXkjDGHdvGjlUxf1StHouLi1l6ejp78uQJKy4uVnUzCisoKGBGRkblmp8nT57MOnXqJHedjh07sslvNEvt2bOHGRsbs8LXbbuenp5s5cqVMsusXLmS1a1bV+X9MsZYfn4+y8rKkk4pKSky1XZOTg4DQJMOTwcO5Eg/T023Nmga5Q9NfEw5OYaTQ4rgK8/eVFX+MEY5pA+Tqvmj9HAne/fuRfv27WFpaQl3d3e4ubnB0tIS7du3x759+5TdnMIyMzNRUlIClzcuNXNxcUH6m70uX0tPT5e7fHFxMTJfXw5a0TKSbaqyXwCIiIiAnZ2ddPL09FTsFyU6Q9tXNSYlJaF+/foa2TblD6kprly5gkWLFiEyMlL6dyohEokwqrpDClSCrzx7E+VPzabwVbEAsH79ekyePBmjRo3CjBkz4OLiAsYYMjIycOTIEQwePBg//PADxo4dq6l4pc2SEoyxcvOqWv7N+YpsU9n9zpkzB+Hh4dKfRSKRTHJZWloi5/XAWwUF3CkUgDv9Wdkl3iIR4O7OPb97V/HiY+VK4OuvuVOd8fEVD4RclZIS7hTUsmXAf/+Vf712bcDXlxvlv25drsm8bl1ucnPjmq31haWlpVb3V1hYiIcPH2p0H4aYPwDw4YfAoUPcqc/RoyvcLABuKJ9//uFy4pNPKl9W4v59oHVr7pTq+vXAsGGKrSfPlSvA4sXAwYPlXzMz405hNm7M5Y67O9dtok4d7rmjo35dpa7tHDp69Cj69OmDhg0bIjs7G/PmzcPOnTvRtWtXAMCrV68QFRWFn3/+WaNx8JVnElXlDyCbQ5IuCl26cF19KiNZtm1bLo8U8fQpt3xGBjB+PLBihWLryXPvHtdl6X//A1JS5C9ja8vlUcuW3NSiBXeq2cRE9f3yQdX8UaqwW758OSIjIzFazn/Ofv36oXXr1vj22281Utg5OTnByMio3DeUjIyMct9kJFxdXeUub2xsDMfXlVRFy0i2qcp+AcDMzAxmZmYVvi4QCGBlZQWA69MAcOft69Th+rxUxMqKuwtFYiJw/TqgSAPP/fvcgQTgBlStzjAPADB8OHdgO3AAOHGC256fHzdVNlxLTVf2H608TyXjDWiAIecPUHr/Vw8PLkcq0749d0BKSKh6WQBgjBtTKz+fG/tx7NjqFVfBwVzuXLrEDY1iY1N6AGrYkPs/QFQzf/58TJ8+Hd9++y0YY1ixYgXee+89/PHHH+jZs6fG989Xnr2pqvwBZHMoO1sSS9U5IRl+JCGBK5RMTStfHuD6yWVkcMXWypXVGwKoWTMgIoLrZ37qFNdP29m5dHJy4vqn1WRKnYp9/PgxOlQyeE1wcDBSU1OrHZQ8pqamCAgIQHR0tMz86OhoBAcHy10nKCio3PJHjx5FYGAgTF6X7hUtI9mmKvtVVtkxuCor6iQk49lduKDY9mfP5g5KXbsCI0aoFuObhELuIo+VK7mk7dqVirqqfP/994iNjcXly5flTjc0eK84Q84fQLEBviWUHQ/y0CHg8GHuAPbTT+prMfP3ByIjgaVLgSFDuJZuKuqq5/r169JTrQKBADNmzMCGDRvw/vvv4y/JVWAaxFeeVZcy+fPWW9xZpYICrvW5KjdvAlFR3PNNm9Q3rqNQyBWZw4YBISFAq1bcF7uaXtQBgFIXTwQEBLDw8PAKXw8PD2cBAQHKbFIpksvIN2/ezBITE9nUqVOZlZUVe/DgAWOMsdmzZ7Phw4dLl5dcRj5t2jSWmJjINm/eXO4y8jNnzjAjIyO2ZMkSlpSUxJYsWVLhcA0V7VcRlXV8lIwr16yZYttat45bvnv3qpc9c6Z0PKArVxQOl7xBHR2/fXx82K+//lrh65cvX2ZCoVDl7VfFUPOHMcbs7Li/86SkqreVkaHY0A6MceNn+fhwy86YoXC4RA5tXDzh7OzM/v3333Lzt2/fziwtLdm6des0mmOSffGRZ5Wp6r0fM4b7G1+4ULHf8d13ueXXrKl62UGDuGX79FFs20Q+jYxjxxg3iKKVlRVr0qQJmzp1KouIiGBLlixhU6dOZU2bNmXW1tZKDzyqrLVr1zIvLy9mamrK/P39WWxsrPS1sLAw1rlz53Ixt2rVipmamrJ69erJHfjxjz/+YD4+PszExIQ1btyY7d69W6n9KqKyD+W337g//LffVmxb8fHc8vb2jJWUVLycWMxY27bcsqNHKxUueYM6DkpDhw5lUysZQC0hIYEJBAKVt68IQ8yfgoLSQu3ZM8W2V78+t/yRI5Uvt2YNt5yzM2MvXyoVMnmDNgq77t27s+XLl8t9bdu2bczExETjhR1j/OVZRap67/v25f7O5exWroULueWHDq18uYSE0txMSFA4XCKHxgo7xhi7f/8+mzlzJuvUqRNr1KgRa9SoEevUqRObNWsWu3//virx1giVfSirVnF/+IMGKbatwkLGzM25dW7cqHi533/nlrGyYiw1VbW4CUcdB6W0tDSlWqlIqcre/8ePSwfSruyLTllDhlTdQvH8OWO1ail3wCMV00Zht2fPnkq/PG3bto116dJFY/vXVVW998HB3N95mUbCSh09WjpId2UkA+8remwjFVMmf5Tu0VGvXj0sXbq0+ueAiZQy/RsArsOqvz8QF8f1E/LxKb9Mfj7Xtw7gBhl2c1NPrER1rq6ufIdgkJTtowpwV+j9/nvl/VQXLgSeP+c6fI8ZU/04ieb1798f/fv3r/D1IUOGYMiQIVqMSD9IckjR0RLatOH6mt67x10UIW+9c+e4we2FQmDBAvXFSqqm9Dh2RP2ULewA7sAEVHxg+v574OFD7irbzz+vXnyE6LKMDO5RmfwpewGFvHvv3LoF/Pgj93zlSrqogRg2ZXPIzo672Aeo+CKkL7/kHsPC5Dc+EM1RuLCrVatWucEeK1O3bl2Nj8llKFQp7Cq7su/p09LhTRYv5m6HQvhF+aM5quRPq1Zcy/fTp9ytht40YwZ3C6B33+WuuCO6j3JMNYWFQFYW91yZHGrXjnuMiyv/WkwMN6SQiQk3firRLoW/h758+RJ///037OzsFFr+2bNnKJHcBI1UqjotdpcvAx99xN13r0cP7jL0+fO5gYz9/bnXCP8ofzRHlfwxN+cGLf33X+7+sgMHcjnUrBl3UNq/nxtM+7vvNBMzUT/KMdVIamEjI24Ae0W1awf8/DM3WP3Nm8DkyaVj3M2dyz1+8glQr55awyUKUOoEQ1hYmKbiqNFUOTDVq8eNhH/xIjcC9//+x/VlCAri+jYA3CkkRfscEc2j/NEMVfIH4Ma/+vdf7k4s8fHAF19w42BJTs1OmMDdAYLoD8ox5Unyx9FRuePF++8Du3cDR44Ae/dyU7NmQLduwNmz3Hh1kgKPaJfChZ1YLMbt27fRsGFDTcZTI0n6Nyhzmy+BgGsCP3uWuzXRwYPcLb7OnOFe79ev9NsT4R/lj+aoWthNnQr0788NQHzoEHfq6NEj7jUHB2DePLWGSTSMckw1quaPgwM3cPf161x/1K1bgWvXuAkAPvuMLtrji1Itdj4+PqhTpw7efvttdO3aFV27doWXl5emYqsRiopKbymmbGIZGwMdO3LTkiXcxRKHDgE3bgBz5qg9VFJNlD+aoeqBCQC8vIBPP+WmV6+4W+TFxgI9e5bev5noD8ox5VUnfwDuqvF167j+3D//zN1NxciIG42B8EOpwi42NhaxsbE4ceIEJk6ciPz8/7d37zFN3W8YwJ8OBZFLKaDcRYLKvFQnuiEuAo0bSubU+HNTJEyzzckfXpjRqDEboMaNXdBkmS66TbfMLC5Gky1b5sgE0YGIyJw6R2BWUYGhgC3BgWC/vz8IlQ7QXj3t6fNJGulpz+nbU5+ct6ffc04HRo0aZRKiiIgIR9UqS73jG556qmd8nC16N1LknJgfx7B1w9TL2xtIS+u5kWtixixnr/yoVD1nYOBZGKSnEGKgg/0fr6urC2VlZSguLkZxcTHOnDmDzs5OjBkzBtXV1fau0+Xp9XoolUrodDr4+/sbp//xR88g7hEjHv4kS85nsM/PWsyPZR61/p9+umfwdnExhx84M3tn6HGYsYcete7feQfYsaNnTOmnn0pUID2WJfmx+uxMQ4cORVJSEp599lkkJibi+PHj2L9/P2pra61dpFuy17clci3Mj/0wQzQQZsw8lp6cmJyfxY1dR0cHSktLUVRUhOLiYlRUVCAmJgbJycnYu3cvkvmV2SLcKLkX5se+urt7rg4BMEPUgxmzDLdB8mNRY5ecnIyKigrExsYiKSkJa9asQXJyMkJCQhxVn+wxVO6D+bG/3jGqCoXtY1TJ9TFjlrPmyi3k3Cxq7EpLSxEWFgaNRoOUlBQkJSUhODjYUbW5BTZ27oP5sb++5+Dy8JC2FpIeM2Y5boPkx6LT1969exf79u3D8OHDkZ+fj4iICKjVaqxevRpHjhzB7d7/IWQ2hsp9MD/2x/xQX8yY5Zgh+bH6qFgAaGtrw+nTp41jGS5cuICxY8fi0qVL9qxRFgY7ouV//wOOHgU++aTnhI7knBxxRB/zY77B1v/hw8DSpUBSUs/558h5PemjYgFmrNdg6767u+d6rgDwzz88gMKZWZIfmy445ePjg8DAQAQGBkKlUmHIkCG4cuWKLYt0OzwiyX0xP7bj3gZ6FGbs0Zqbe/5VKHhCbjmxaIydwWDAuXPnUFxcjKKiIvz2229ob29HREQENBoNPv30U2g0GkfVKkvcMLkP5sf+mB/qixmzTG9+AgM5RlVOLGrsAgIC0N7ejrCwMKSkpKCgoAAajQaxsbGOqk/2uGFyH8yP/XGPN/XFjFmG2x95sqix+/DDD6HRaDBu3DhH1eNWHjzgObjcCfNjf9wwUV/MmGX4xUieLGrsVq1a5ag63FJzM9B76ArHN8gf82N/bOyoL2bMMsyPPNl08ATZpu/4hiFWX9yNyH1xw0RkPeZHntjYSYihIrINz5pPZD3mR57Y2EmIoSKy3oMHD0/XwAwRWY47F+SJjZ2EGCoi67W0cIwqkS24DZInNnYSYqiIrNebH5Xq4dnzich83AbJExs7CfFQcyLrcaNEZBtmSJ7Y2EmIoSKyHr8YEVnPYHg4RpUZkhc2dhJiY0dkPeaHyHotLT3NHcAxqnLDxk5C3DARWY/5IbIex6jKFxs7CXHDRGQ95ofIesyPfLGxk4jBANy50/M3g0VkOZ4Hksh6zI98sbGTSN/xDcHB0tZC5Iq4x4HIesyPfLlMY9fa2orMzEwolUoolUpkZmbi7t27j5xHCIHc3FyEh4fD29sbKSkpuHz5svHxlpYWrFmzBnFxcRg+fDhGjRqFtWvXQqfTmSxn9OjRUCgUJrfNmzfb9H56Q6VUAp6eNi2K6LHklh+AGyZyPpbmrKurC5s2bYJarYaPjw/Cw8Px2muvob6+3uR5KSkp/TK0dOlSm2plfuTLZS49v2zZMty8eRM///wzAOCtt95CZmYmfvjhh0Hn+eCDD1BQUICDBw9i3Lhx2LFjB1588UVUV1fDz88P9fX1qK+vx0cffYQJEybg+vXryMrKQn19PY4cOWKyrG3btmHlypXG+76+vja9H5UKeO+9nssiETma3PIDAGvXAlotEBdn86KI7MLSnN27dw/nz5/HO++8gylTpqC1tRXZ2dmYP38+zp07Z/LclStXYtu2bcb73t7eNtWq0fRcuSU+3qbFkDMSLuDPP/8UAMSZM2eM08rKygQA8ddffw04j8FgEKGhoeL99983Tuvo6BBKpVJ89tlng77Wd999Jzw9PUVXV5dxWnR0tNi1a5dN70Gn0wkAQqfT2bQckoYrf37MDzkDuX+G1uRsIGfPnhUAxPXr143TkpOTxbp166yuTe7r3h1Y8hm6xB67srIyKJVKJCQkGKfNmDEDSqUSpaWliBvgK7tWq0VjYyNSU1ON07y8vJCcnIzS0lKsWrVqwNfS6XTw9/fHkCGmqyY/Px/bt29HVFQUXnnlFWzcuBGej/gNtbOzE52dnSbLBQC9Xm/emyan0vu5id6Lk7oQ5oecgStnyBzW5GwgOp0OCoUCAQEBJtMPHTqEb775BiEhIUhLS0NOTg78/PwGXAbzIz+W5MclGrvGxkaMHODU2CNHjkRjY+Og8wBASEiIyfSQkBBcv359wHmam5uxffv2fhutdevWIT4+HiqVCmfPnsWWLVug1Wrx+eefD1rze++9h7y8vH7To6KiBp2HnF9bWxuUSqXUZViE+SFn4ooZMoc1Ofuvjo4ObN68GcuWLYO/v79xekZGBmJiYhAaGopLly5hy5YtuHDhAgoLCwdcDvMjX+bkR9LGLjc3d8D/fH1VVFQAABQKRb/HhBADTu/rv48PNo9er8dLL72ECRMmICcnx+Sxt99+2/j35MmToVKpsHjxYuTn5yNokFN2b9myBevXrzfeNxgMaGlpQVBQkPH19Xo9oqKicOPGDZMQk/UctU6FEGhra0N4eLjdlmkr5of5sTdHrlNnzJA5nkTOgJ4DKZYuXQqDwYA9e/aYPNZ3fOqkSZMwduxYTJ8+HefPn0f8AIPkzMkPwAw5gjNsgyRt7FavXv3YI3tGjx6NP/74A//880+/x27fvt1vj0Kv0NBQAD3fosLCwozTm5qa+s3T1taGuXPnwtfXF8eOHcPQx5yGe8aMGQCA2traQTdMXl5e8PLyMpn2313rvfz9/RkqO3PEOnW2vQzMTw/mx/4ctU6dLUPmcGTOenV1deHVV1+FVqvFiRMnHrvu4+PjMXToUNTU1AzY2FmSH4AZcgQpt0GSNnbBwcEINuMkbomJidDpdDh79iyee+45AEB5eTl0Oh1mzpw54Dy9u60LCwsxdepUAMD9+/dx8uRJ5OfnG5+n1+sxZ84ceHl54fvvv8ewYcMeW09VVRUAmGzwiJ405ofI8RyZM+BhU1dTU4OioqJBv+z0dfnyZXR1dTFDNDBHHcFhb3PnzhWTJ08WZWVloqysTKjVajFv3jyT58TFxYmjR48a77///vtCqVSKo0ePiosXL4r09HQRFhYm9Hq9EEIIvV4vEhIShFqtFrW1taKhocF46+7uFkIIUVpaKgoKCkRVVZW4evWqOHz4sAgPDxfz58+3+T3xSCX74zodGPND5uA6tY2lOevq6hLz588XkZGR4vfffzfJUGdnpxBCiNraWpGXlycqKiqEVqsVP/74o3j66afF1KlTjTmzFj9v+3OGdeoyjV1zc7PIyMgQfn5+ws/PT2RkZIjW1laT5wAQBw4cMN43GAwiJydHhIaGCi8vL5GUlCQuXrxofLyoqEgAGPCm1WqFEEJUVlaKhIQEoVQqxbBhw0RcXJzIyckR7e3tNr+njo4OkZOTIzo6OmxeFvXgOh0Y80Pm4Dq1jaU502q1g2aoqKhICCFEXV2dSEpKEoGBgcLT01PExsaKtWvXiubmZpvr5edtf86wThVCyPTYcyIiIiI34zKXFCMiIiKiR2NjR0RERCQTbOyIiIiIZIKNHREREZFMsLGTyJ49exATE4Nhw4Zh2rRpOHXqlNQlubTc3FwoFAqTW+9JdkmemCH7YX7cD/NjP86WHzZ2Ejh8+DCys7OxdetWVFVVYdasWUhLS0NdXZ3Upbm0iRMnoqGhwXi7ePGi1CWRgzBD9sf8uA/mx/6cKT9s7CRQUFCAN954A2+++SbGjx+P3bt3IyoqCnv37pW6NJc2ZMgQhIaGGm8jRoyQuiRyEGbI/pgf98H82J8z5YeN3RN2//59VFZWIjU11WR6amoqSktLJapKHmpqahAeHo6YmBgsXboUV69elbokcgBmyDGYH/fA/DiGM+WHjd0TdufOHTx48KDfRaFDQkLQ2NgoUVWuLyEhAV9//TWOHz+O/fv3o7GxETNnzkRzc7PUpZGdMUP2x/y4D+bH/pwtP0MkeVWCQqEwuS+E6DeNzJeWlmb8W61WIzExEbGxsfjqq6+wfv16CSsjR2GG7If5cT/Mj/04W364x+4JCw4OhoeHR79vRk1NTf2+QZH1fHx8oFarUVNTI3UpZGfMkOMxP/LF/Die1PlhY/eEeXp6Ytq0aSgsLDSZXlhYiJkzZ0pUlfx0dnbiypUrCAsLk7oUsjNmyPGYH/lifhxP6vzwp1gJrF+/HpmZmZg+fToSExOxb98+1NXVISsrS+rSXNaGDRvw8ssvY9SoUWhqasKOHTug1+uxfPlyqUsjB2CG7Iv5cS/Mj305W37Y2ElgyZIlaG5uxrZt29DQ0IBJkybhp59+QnR0tNSluaybN28iPT0dd+7cwYgRIzBjxgycOXOG61SmmCH7Yn7cC/NjX86WH4UQQkjyykRERERkVxxjR0RERCQTbOyIiIiIZIKNHREREZFMsLEjIiIikgk2dkREREQywcaOiIiISCbY2BERERHJBBs7IiIiIplgY+fmcnNz8cwzzzzx1y0uLoZCoYBCocDChQvNmic3N9c4z+7dux1aH5E5mB8i6zE/jsHGTsZ6/xMOdluxYgU2bNiAX3/9VbIaq6urcfDgQbOeu2HDBjQ0NCAyMtKxRRGB+SGyBfMjHV4rVsYaGhqMfx8+fBjvvvsuqqurjdO8vb3h6+sLX19fKcoDAIwcORIBAQFmPbe3Vg8PD8cWRQTmh8gWzI90uMdOxkJDQ403pVIJhULRb9p/d4WvWLECCxcuxM6dOxESEoKAgADk5eWhu7sbGzduRGBgICIjI/Hll1+avNatW7ewZMkSqFQqBAUFYcGCBbh27ZrFNR85cgRqtRre3t4ICgrCCy+8gPb2dhvXBJHlmB8i6zE/0mFjR/2cOHEC9fX1KCkpQUFBAXJzczFv3jyoVCqUl5cjKysLWVlZuHHjBgDg3r170Gg08PX1RUlJCU6fPg1fX1/MnTsX9+/fN/t1GxoakJ6ejtdffx1XrlxBcXExFi1aBCGEo94qkd0xP0TWY37sQJBbOHDggFAqlf2m5+TkiClTphjvL1++XERHR4sHDx4Yp8XFxYlZs2YZ73d3dwsfHx/x7bffCiGE+OKLL0RcXJwwGAzG53R2dgpvb29x/PjxAespKioSAERra6txWmVlpQAgrl279sj3Eh0dLXbt2vXI5xDZE/NDZD3m58niGDvqZ+LEiXjqqYc7c0NCQjBp0iTjfQ8PDwQFBaGpqQkAUFlZidraWvj5+Zksp6OjA3///bfZrztlyhTMnj0barUac+bMQWpqKhYvXgyVSmXjOyJ6cpgfIusxP7ZjY0f9DB061OS+QqEYcJrBYAAAGAwGTJs2DYcOHeq3rBEjRpj9uh4eHigsLERpaSl++eUXfPLJJ9i6dSvKy8sRExNjxTshevKYHyLrMT+24xg7sll8fDxqamowcuRIjBkzxuSmVCotWpZCocDzzz+PvLw8VFVVwdPTE8eOHXNQ5UTSY36IrMf89MfGjmyWkZGB4OBgLFiwAKdOnYJWq8XJkyexbt063Lx50+zllJeXY+fOnTh37hzq6upw9OhR3L59G+PHj3dg9UTSYn6IrMf89MefYslmw4cPR0lJCTZt2oRFixahra0NERERmD17Nvz9/c1ejr+/P0pKSrB7927o9XpER0fj448/RlpamgOrJ5IW80NkPeanP4UQrngsL7m64uJiaDQatLa2mn2CyF6jR49GdnY2srOzHVIbkbNjfoisJ/f88KdYklRkZCTS09PNeu7OnTvh6+uLuro6B1dF5BqYHyLryTU/3GNHkvj3339x69YtAD2XagkNDX3sPC0tLWhpaQHQc7STpQNjieSC+SGyntzzw8aOiIiISCb4UywRERGRTLCxIyIiIpIJNnZEREREMsHGjoiIiEgm2NgRERERyQQbOyIiIiKZYGNHREREJBNs7IiIiIhk4v94KFzQZhTRtwAAAABJRU5ErkJggg==", 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" ] @@ -665,7 +742,7 @@ "source": [ "### Bounded disturbances\n", "\n", - "Another thing that the MHE can handled is input distributions that are bounded. We implement that here by carrying out the optimal estimation problem with constraints." + "Another thing that the maximum likelihood estimator can handle is input distributions that are bounded. We implement that here by carrying out the optimal estimation problem with constraints." ] }, { @@ -676,7 +753,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -691,6 +768,8 @@ "plt.plot(timepts, V[0], label=\"V[0]\")\n", "plt.plot(timepts, V_clipped[0], label=\"V[0] clipped\")\n", "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.xlabel(\"Time $t$ [s]\")\n", + "plt.ylabel(\"Disturbance, sensor noise\")\n", "plt.legend();" ] }, @@ -705,14 +784,14 @@ "output_type": "stream", "text": [ "Summary statistics:\n", - "* Cost function calls: 3572\n", - "* Constraint calls: 3756\n", - "* Final cost: 531.7451775567271\n" + "* Cost function calls: 3896\n", + "* Constraint calls: 4082\n", + "* Final cost: 715.5190193022809\n" ] }, { "data": { - "image/png": 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4OKKjowEyfX+J+6tQASIi4Pvv4b//hTJljI7IeFK2pGwJ21BKERkZiaOjI1WrVi12E1dbs2zlOal76aWXOHToEFu3br3vsXf/81NKZbo/1cSJExk/frzl59T5We42Zgwsm3OJ7Scr8qz6ijUbemHasAFGj4YePXSC9/jj4Oqam6dWZKWkpFg+dMoX06QWoESJEgBER0fj5eUlzUV50KEDNGwIR47AwoUwdqzRERlLypYmZcvGoqNh3TqIjYXSpWHoUKMjKhDJycnExcXh6+uLu7u70eEYwlplK0/p8Msvv8xvv/3Gxo0b7ztruLe3N1FRURn2RUdH4+TklOU/R1dXV8vcPtnN8ePoCAt+r4ibG6yjJwseXQVNm0JyMqxaBcOGwfTpeXmKRVJqP5/iWijSS30Nimvfp/wymeDFF/X92bOhGLeIAFK20pOyZUP//KMTuRdfhGefhatXjY6oQKSkpADg4uJicCTGskbZylVSp5TipZdeYsWKFWzYsAE/P7/7ntO2bVvWr1+fYd+6deto0aJFpv3pcqtePXjvPX1//PrenP/9AISE6J0RETB5cr6vUdQUt2ahzMhrkH9PPgkeHnDyJPz1l9HRFA7yvpLXwKYuXUq7n5AAixcbF4sBivt7yxrPP1dJ3Ysvvsh3333HDz/8QOnSpYmKiiIqKor4+HjLMRMnTmTYsGGWn0eNGsXZs2cZP348ISEhLFiwgPnz5/Paa6/lO/hU48ZB69a6xvq550DV84d//xukz4cQeVaqFDz1lL7/xRfGxiJEsZA+qQP46iu4011JiJzIVVI3Z84cYmJi6Ny5Mz4+PpZt6dKllmMiIyM5d+6c5Wc/Pz9WrVpFUFAQTZs25b333uOzzz6zynQmqRwd4ZtvdNe51ath0SKrPbQQxdro0fr2jz/g7FljYxHC7qUmdUOHgru7bnXats3YmESRkuvm18y24cOHW45ZuHAhQUFBGc7r1KkT+/fvJyEhgdDQUEaNGmWN2DMICEhraR07Vre88sMP0KkT3JkjTwiRO/7+0LWr7lMnC8EIYWN3Rj9S685MDiBzColcsatxw6+9Bi1awPXr8PzzoCKjYPNm2LTJ6NDEfSxZsgQ3NzcuXLhg2Tdy5EgaN25smQ9RGCN1wMS8eXD7trGxiLyR8lVEpNbUVayo+xIB/PRTsRkwURQVtrJlV0mdk5NuhnV21s1F31/vo3+xe7exgYn7Gjx4MPXq1WP6ndHKU6ZMYe3ataxevRpPT0+Doyve+veHqlXh8mVYtszoaEReSPkqItInda1aQePGerRSSIixcYksFbayla8VJQqjhg3h3Xf1OIkxX9Sjm8kH74gI3R7r62t0eMbJZn1eHB3BzS1nxzo4wJ35dLI8tmTJXIdnMpmYNm0ajz32GL6+vsyaNYstW7ZQuXJlAP744w9effVVzGYzb775JiNHjsz1NUTeODnpmu9//1sPmHjySaMjKmQKsmyBTcrXww8/TFBQEF27dmWZZO7Gef99CA3VI/9MJr20S9WqUFyn+ijiZSs8PJyhQ4dapnGbNGkSAwcOzPU1ckUVATExMQpQMTExOTo+MVGpZs2UAqWeL7dU31m50sZRGi8+Pl4FBwer+Pj4e3+px1BlvvXpk/FYd/esj+3UKeOxFSrce0w+NGvWTLm4uKigoCDLvqSkJFWnTh11/vx5FRsbq2rXrq2uXLmS7eNk91rk9v1kz3L6WkRFKeXsrP+8+/YVUHCFSKEpWzYoX0optWHDBvXbb7+pRx999L6PIWUrZ+S1yLks31NFvGxFRESoAwcOKKWUunjxoqpcubK6efNmlo9hjbJlV82vqZyd0+au25jcUd+RJthCb+3atRw7doyUlJQMS8vt3r2bBg0aULlyZUqXLk2fPn1Yu3atgZEWP5UqQeoXTJnepGjKqnwBdOnShdKlSxsUmbgvs1kv7yIKpazKlo+PD02bNgXAy8uLcuXKcdXG/SPtrvk1VZs2+vZErDfXKEPZPXuMDchoN29m/bu7lyNJHYGVmbvX5AsLy3NI6e3fv5+BAwfy1Vdf8eOPPzJp0iR+/vlnACIiIizNRABVqlTJ0ClVFIzRo/WA8h9+gI8+gnLljI6okCjkZQuyL1+ikLh5E375RX+D6tEjbX9UFLRtq28jIqBsWeNiLGh2VLb27t2L2WzOdMlTa7LbpK58eT0q/PRp2OvZje5exWP91yzlpq+ArY7NQlhYGH379mXChAkMHTqU+vXr07JlS/bt20dgYKBlreD0ivvM40Zo1w6aNNErGX3zDbz6qtERFRKFuGzB/cuXKCTCwvTSluXL61FJqSpV0jOB374N338PL71kWIgFzk7K1pUrVxg2bBjz5s2zynWzY5fNr6latdK3u1/7Cb77zthgRKauXr1K7969GTBgAG+99RYAgYGB9O/fn7fffhuAypUrZ6iZO3/+PD6yWkiBS78e7Jw5sh5sUZCT8lVczJ49Gz8/P9zc3AgMDGTLli05Om/btm04OTlZmtFsJv3I1/RMprTpTb7+WlaYKCRyWrYSEhJ4+OGHmThxIu3atbN5XHZbUwc6qVuyBHbvkVqdwqpcuXKEZDJc/9dff7Xcb9WqFUeOHOHChQt4eHiwatUq3nnnnYIMU9wxZAi8/rquAV+7Fnr3NjoikZ2clK/iYOnSpYwdO5bZs2fTvn17vvrqK3r37k1wcDDVqlXL8ryYmBiGDRtG165duXjxom2DzCqpAz3k/I034PBh2LUrrX+RMExOypa6szjDgw8+yNChQwskrmJRU7dr150vN9kNeRaFlpOTEx9//DFdunShWbNmvP7665QvX97osIqlkiXh6af1/dmzjY1FWE/Pnj0ZOHAgq1atokqVKuyxsz7In3zyCSNGjGDkyJEEBAQwc+ZMqlatypw5c7I97/nnn2fIkCG0bdvW9kFml9SVLQuPP67vz51r+1iEVWzbto2lS5eycuVKmjZtStOmTTl8+LBNr2nXSV2zZrov5cWLcN6nJfTsaXRIIo8GDBjAiRMnOHXqFM+lNkXYmc2bN9O/f398fX0xmUysXLnyvuds2rSJwMBA3NzcqFmzJl8WwFpezz6rb9ety74fsyg61q5dy6VLl4iLi+P8+fO0bNnS6JCsJjExkX379tEj/eADoEePHmzfvj3L87755htOnz7Nu+++a+sQteySOkhrgv3xR4iPL5iYRL488MADmM1mDh48aNkaNWpk02vadVJXooSekBtg98VqsH8/JCcbG5QQWbh16xZNmjTh888/z9HxoaGh9OnThw4dOnDgwAHeeustxowZw/Lly20aZ0AA1KgBiYlw1zLPQhQ6ly9fznQal0qVKhEVFZXpOSdPnmTChAl8//33ODnlrJdSQkICsbGxGbZcSR29mVVS166d/lCLi4PIyNw9tig27Dqpg3SDJVw66G83R48aG5AQWejduzfvv/8+jzzySI6O//LLL6lWrRozZ84kICCAkSNH8swzzzBjxgybxmkypfWlW7PGppcSwmruHjGvlMp0FH1KSgpDhgxhypQp1K1bN8ePP336dDw9PS1brqeuuF9NncmkO4mvW6dHxAqRieKT1Ll31nfsrK+IKL527NhxT5NSz5492bt3L0lJSZmek6vahOvX9YzDmXwR6tVL365eLYPxROFWoUIFHB0d76mVi46Ovqf2DuDGjRvs3buXl156CScnJ5ycnJg6dSr//PMPTk5ObNiwIdPrTJw4kZiYGMsWHh6eu0BffVXPFdS9e9bH/Otf+vdWmpJD2J9ik9TtjQsgBQdJ6oTdiIqKyrRJKTk5mcvp57lKJ1e1CVOnwrJl0LSpHvJ644blV1266JVbzpyBU6es8WyEsA0XFxcCAwNZv359hv3r16/PdIoJDw8PDh8+nKEf1KhRo6hXrx4HDx6kdevWmV7H1dUVDw+PDFuutGkDw4fr/g1C5JHdJ3UBAfpLzc1EV47hL8uFCbuSWZNSZvtT5ao24ZVX4OGHdT/UGTPA3x+WLgWlKF0aOnTQh0kTrCjsxo8fz7x581iwYAEhISGMGzeOc+fOMWrUKECXi2HDhgHg4OBAw4YNM2xeXl64ubnRsGFDShpZS7ZrF8ybp/uHC5EJu0/qHB2hRQt9fzet9Dw/MnJI2AFvb+9Mm5ScnJyynPIlV7UJ1avDihWwapVeniUiAgYP1s0/x45laIIVojAbNGgQM2fOZOrUqTRt2pTNmzezatUqqlevDkBkZCTnzp0zLkCzGRYt0oUpi64TAMyfr4ef//lnwcUmihS7T+ogXb+6agP1lPhxccYGJIQVtG3b9p4mpXXr1tGiRQucnZ2td6HevfVi4lOngpsb/P03/Pe/lqQuKEi+J4nCb/To0YSFhZGQkMC+ffvo2LGj5XcLFy4kKJuh3JMnT+bgwYO2C+7aNXjqKejTh/g4xe3bWRyXuuCyjReFF0VX8UrqKvSBWbP02npCFDI3b9609OEBPWXJwYMHLTUI6ZuIAEaNGsXZs2cZP348ISEhLFiwgPnz5/Paa69ZPzg3N5g0SQ+aGDwY/vMfGjaEypV1QpfDFZeEEJm5M/L1locPdRu6ULMmZDqFniR14j6KVVJ36JDUKIjCa+/evTRr1oxmzZoBuh9Qs2bNLEui3d1E5Ofnx6pVqwgKCqJp06a89957fPbZZzz66KO2C7JmTT2tgpcXJlPaKFjpVydEPtxJ6la4DeH8eT0NXefOuvtcBqkVEleuFGh4ouiw67VfU1Wtqqf1uXgRDmyLo53HkbRMT4hConPnzpaBDplZuHDhPfs6derEfqM6TcfE0KvsMebTmtWr4ZNPjAlDiCLvTlK38PZgQNeAX7igu88dPAiffqpHm0tNnbifYlFTZzKla4Lt8Ta0bq3n4BKF0rVr15gyZQqRMmt64RUbC1Wq0G1GTxwdFceOQViY0UGJnJDyVQhdusQ5qrIxtjkAW7fCe+/pX33xhR6bdOkSktQVcoWhbBWLpA7ST0LcRd/Zu9e4YES2xowZw549e3jhhReMDkVkxcMDOnSgDDG09TkLwNq1BsckckTKVyF06RKLGYrCgS5d9DJ8//43/PorlC4NmzZBy5ZwMNpXHy9JXaFUGMpWsUnqUueL3G26k93JJMSF0m+//cbNmzf5448/KFOmDN9//73RIYmsPP00AL1ifwJkapOiQMpX4aSiL/EtTwF6EGyqAQNg506oXRvOnoV2T9Vm2ditul+rKFQKS9kyqew68RQSsbGxeHp6EhMTk/tZuu+4di2t5voy5Sn/UEf45RcrRmm827dvExoaip+fH25ubkaHY6jsXgtrvJ/sRb5ei9u3wdeX/ddqEMh+SpXS/bddXGwTq5GkbKWRspUzuXktti88Qfun61KyRApR0Y6UKpXx99eu6UHn69ZBiRIQFaUry+2FlC/NGmWr2NTUlS0LqWsz76Gl1NQJkV9ubjBkCE05iJdrDDdvZjENgxAiWwu36w+nxx6/N6ED/fm1apW+jY8HI+dJFoVbsUnqIF2/OlNrPbQoIsLYgIQo6p5+GgcUPZP+AKQJVojcio/Xq+9BxqbXuzk6gu+dLnVRC1bp6joh7pLrpG7z5s30798fX19fTCYTK1euzPb4oKAgTCbTPduxY8fyGnOeWZK6Ug/qO1JbV2gsWbIENzc3Lly4YNk3cuRIGjduTExMjIGRiWw1bw4NG9JL6WxO5qsrnKR8FV6//qoHk1f3iqNTy+xXO/L21rdRn/6gl7wUhitsZSvX89TdunWLJk2a8PTTT+dqktPjx49naAeuWLFibi+db5akjlaombMwNWlS4DEUNKWMWRXN3V1PJZNTgwcP5j//+Q/Tp0/n888/Z8qUKaxdu5adO3fi6elpu0BF/phMsGABPUpUxdRYT/AdEZFWo2DPjCpbIOXLnixcqAATw6Jn4HB1OJSqluWxPj76Ngpvux4BK2Ur73Kd1PXu3ZvevXvn+kJeXl6UKVMm1+dZU5MmegLHSzdKcPZfY6hRw9BwCkRcHJn20bC1mzehZMmcH28ymZg2bRqPPfYYvr6+zJo1iy1btlC5cmXLMX/88QevvvoqZrOZN998k5EjR9ogcpFrLVtSQd+we7eurXvmGaODsj2jyhbYpnw9/PDDBAUF0bVrV5YtW2aDqMXdIiIgdfnmYSyCiq9ne7ylps7Okzp7Klvh4eEMHTqU6OhonJycmDRpEgMHDrRR9AXYp65Zs2b4+PjQtWtXNm7cmO2xCQkJxMbGZtiswc1NJ3agP3xE4dKvXz/q16/PlClT+OWXX2jQoIHld8nJyYwfP54NGzawf/9+PvzwQ67a8T+1okiWDCvcsitfoOfYWrRokUHRFU/ffQdms4kH2ELtklF6aGs2iktSV9RkV7acnJyYOXMmwcHB/PXXX4wbN45bt27ZLBabLxPm4+PD119/TWBgIAkJCSxevJiuXbsSFBREx44dMz1n+vTpTJkyxSbxtGql5x3evTKCx03b4LHHclfXWsS4u+tvHkZcN7fWrl3LsWPHSElJoVKlShl+t3v3bho0aGD59tOnTx/Wrl3LE088YY1wRX5duEDv1R8ylc9Yv16RnGzCyc4XITSqbKVeO7eyK18AXbp0ISgoKP/BiRxRClJX/nuKbyEHXZIyJHVXDFoesADYU9ny8fHB5067uZeXF+XKlePq1auUzE11YC7Y/N9uvXr1qFevnuXntm3bEh4ezowZM7JM6iZOnMj48eMtP8fGxlK1alWrxNOqFcyeDbuXnIIlj+u1VypUsMpjF0YmU+6qko2yf/9+Bg4cyFdffcWPP/7IpEmT+Pnnny2/j4iIyNBUVKVKlQwdU4XBvLxoGfoTZZnMtevl2L0b2rUzOijbKiplC+5fvkTB27sXQkLAzSWFgYk/Q8V69z0nNamLxMeua+rstWzt3bsXs9lstXwmM4Z8l27Tpg3fffddlr93dXXF1dXVJtdOHSyxjxYk44hTeLhdJ3VFQVhYGH379mXChAkMHTqU+vXr07JlS/bt20dgYCBApgvdm+y4hrXIcXbGcegQeny6jqUMZvVq+0/qioqclC9R8L79Vt8+0iwUz12xua+ps+OkrqjITdm6cuUKw4YNY968eTaNyZB56g4cOGCpjixo9erptfTicCeY+hAebkgcQrt69Sq9e/dmwIABvPXWWwAEBgbSv39/3n77bctxlStXzlAzd/78ecPeQyILw4fTGz21yerfkwwORkDOy5coWAkJ8MMP+v5T9e+sQ56LpO4q5Ul48x0bRSdyIjdlKyEhgYcffpiJEyfSzsbfdnNdU3fz5k1OnTpl+Tk0NJSDBw9Srlw5qlWrxsSJE7lw4YKlw+3MmTOpUaMGDRo0IDExke+++47ly5ezfPly6z2LXHBw0KP0NmzQU5s0lqm5DVWuXDlCQkLu2f/rr79m+LlVq1YcOXKECxcu4OHhwapVq3jnHfmnVqg0bkyPRlFwGPYfcuLq1bSl+YQxclq+RMH64w+99FflytB1fBPo/C1Ur37f88qV0zM4JCVBdLUW2K4RT9xPTsuWUorhw4fz4IMPMnToUJvHleuaur1799KsWTOaNWsGwPjx42nWrJnlAzYyMpJz6RKlxMREXnvtNRo3bkyHDh3YunUrf/75J4888oiVnkLupTbB7qK11NQVEU5OTnz88cd06dKFZs2a8frrr1O+fHmjwxJ38XmuP/6EoJSJzZuNjkbkRs+ePRk4cCCrVq2iSpUq7JHJ2W0mtel16FBwbBgAw4ZBp073Pc9kStcEKwtKFAnbtm1j6dKlrFy5kqZNm9K0aVMO23Di6FzX1HXu3DnT/k2pFqYO57njjTfe4I033sh1YLbUooW+PUhTCM9+ehVReAwYMIABAwYYHYbIzuDBdHn5Z44RwMbfbvDQQ6WNjkjk0Nq1a40OoVi4eFGv4wrZLwuWFe9KZsLDHYhatA5adLfr2RvswQMPPIDZbC6w6xWrtV9T1dVrJ3OaWlJTJ4Q1VahAl4f0yjEbd+VhbgAh7FxYGNSooVuM/P2B33/XWd716zk639tLJwhRn/8MNpzvTBRNxTKpq1lT316jHFdfsc18eEIUV52//j8ADgc7cumSwcEIUci0bg0nT+p+dQC89BL07QvHj+fofG9fRyB1rrorNopSFFXFMqkrWTJtDb3T1R80Nhgh7EzFitCwob6/aZOxsQhRGJlM6Qa7pn7zyeF66N4+urnV3ueqE3lTLJM6gNq19W26gbxCCCvpEhgDwIYfLxociRDa7Nmz8fPzw83NjcDAQLZs2ZLlsStWrKB79+5UrFgRDw8P2rZta5s+h7duQXy8vp/TpE7mqhPZkKRu5RGws5UJshvIUlzIa2CsLhF6Eq6NG+zr7yDvq6L5GixdupSxY8fy9ttvc+DAATp06EDv3r0zzNSQ3ubNm+nevTurVq1i3759dOnShf79+3PgwAHrBpZaS+fqmuMV7O05qSuK7y1rssbzl6Tup32wdauxwViJs7MzAHFxcQZHYrzU1yD1NREFq9NTNTBh5tg1byIjiv4/ailbaYpi2frkk08YMWIEI0eOJCAggJkzZ1K1alXmzJmT6fEzZ87kjTfeoGXLltSpU4cPPviAOnXq8Pvvv1s3sPRNrzkcxWqPSZ2jo+4nmJiYaHAkxrJG2bLzJbezVquWvj1FbQjfYWwwVuLo6EiZMmWIjo4GwN3dvdgtpaWUIi4ujujoaMqUKWP5ZyEKVrmHOtLU9A8HVDOCvr/AE69XMTqkfJGyVXTLVmJiIvv27WPChAkZ9vfo0YPt27fn6DHMZjM3btygXDazaSckJJCQkGD5OTY29v4PnMv+dJAxqVOXr2AP70InJyfc3d25dOkSzs7OODgUr/oma5atYpvUpdbU6WlNfjI2GCvyvlPiUz98iqsyZcpYXgthgJIl6VLtDAfONmPjsitFPqkDKVupilrZunz5MikpKVSqVCnD/kqVKhGVwxl8P/74Y27dusXjjz+e5THTp09nypRczqaQj6QuHndu9H4cj9xdsVAymUz4+PgQGhrK2bNnjQ7HMNYoW8U2qUutqbuINzdOR2MvU6SmFg4vLy+Skorn+pvOzs5FphbBnnXp6cInX8PGw/ax8oeUraJdtu6uWVVK5ai2dcmSJUyePJlff/0VLy+vLI+bOHEi48ePt/wcGxtL1ar3WcirQwe9vESFCveNI5W7O3h4QGwsRJWqbRdJHYCLiwt16tQptk2w1ipbxTapK1MGKngkcDnWldNnTDQ1OiArc3R0LLL/fIV96DC6IQ5fp3Aqvgrnj8ZQpYGn0SFZhZStoqVChQo4OjreUysXHR19T+3d3ZYuXcqIESP4+eef6datW7bHurq64urqmrvgatZMmzg1F7y97yR1UWmT6dsDBwcH3NzcjA6jSCteDdd3qV09GYBTF+RNJIS1eTbxI9DtKAAbv48wOBpRXLm4uBAYGMj69esz7F+/fj3t2rXL8rwlS5YwfPhwfvjhB/r27WvrMHPFu7yuzYr8Y5/BkYjCplgndbXq6YrKU7FecPu2wdEIYX+6DPEFYENkgMGRiOJs/PjxzJs3jwULFhASEsK4ceM4d+4co0aNAnTT6bBhwyzHL1myhGHDhvHxxx/Tpk0boqKiiIqKIiYmxrqB/f03/Pkn5LKfprfLNQCiFq+/z5GiuCnWSV3t+i4AnO42ShZFFsIGugzUfYU2bjQ4EFGsDRo0iJkzZzJ16lSaNm3K5s2bWbVqFdWrVwcgMjIyw5x1X331FcnJybz44ov4+PhYtldeecW6gb39NvTrBzkchZvK21d/XkXdKGndeESRV2z71AHUrqMLxqnkGpDLrhBCiPt74AFwcoKzZyH0tBm/WsX6e6Qw0OjRoxk9enSmv1u4cGGGn4OCgmwfEORp9CuAd1VdIREV7wFKSaWEsCjW/2FlqTAhbKtUKWhZ8zIAG8f9ZnA0QhQyeU3qauh+4FFmL5AJsUU6ktQB589DfNAuY4MRwk51qauX4du4reisQCCEzSUkwI0b+n5uk7rqumnJnlaVENZRrJO68uXBw1UPkDjz+SqDoxHCPj34VDUANl5tgoqINDgaIQqJ1Fo6Jyc9x1Yu+KT2qZOkTtylWCd1JhPUrnQTgNOh0idBCFto17csLqZELlCFUwvtY51lIfItNamrUCHXfeJSFx2IxouUS5LUiTTFOqkDqO2XOledu8GRCGGfSpSANtX0PHUbf75scDRCFBJ57E+XeoqDg8KMI5cqNbRyYKIok6TO/85cdVezXqhZCJE/XXro0XobjlTUfYmEKO4aNoRFi2DSpFyf6ugIFSveaYJNyX1SKOyXJHVN9aqvp5KqgbUnlhRCANDlCd1eFJT8AGqLNMEKga8vDB0KAwfm6fTUJti7Vj8TxVyxT+pq1dejiE5RG9JNPimEEWbPno2fnx9ubm4EBgayZcuWLI8NCgrCZDLdsx07dqwAI86ZNu0ccHNK4iLehFzzNjocIYo8b49bAERtO21wJKIwKfZJXeq0JmepTuKZ88YGI4q1pUuXMnbsWN5++20OHDhAhw4d6N27d4aZ7jNz/PhxIiMjLVudOnUKKOKcc3WFdh31lCYboxsYHI0QhcD27fDHH3pOrTzwvqmTuajVB6wZlSjiin1S5+MDJVxSMOPI2UqtjA5HFGOffPIJI0aMYOTIkQQEBDBz5kyqVq3KnDlzsj3Py8sLb29vy+bo6FhAEedOly76VpYMEwKYMQP694fff8/T6d4VUwCIuibLIYk0xT6pM5mgdj39IXjqWnmDoxHFVWJiIvv27aNHjx4Z9vfo0YPt91kXslmzZvj4+NC1a1c23idjSkhIIDY2NsNWUFKTuqC/kzFv2VZg1xWiUMrH6FdI16fuhkEzN0REwOzZcP26MdcXmSr2SR3IcmHCeJcvXyYlJYVKlSpl2F+pUiWisugJ7ePjw9dff83y5ctZsWIF9erVo2vXrmzevDnL60yfPh1PT0/LVrVqVas+j+y0agWl3JK4ct2Jf178usCuK0ShlM+kzqeqnrkh6paHtSLKnRdf1Fv79hAWZkwM4h65Tuo2b95M//798fX1xWQysXLlyvues2nTJgIDA3Fzc6NmzZp8+eWXeYnVZmr56LXzTq06YXAkorgz3TUJqVLqnn2p6tWrx7PPPkvz5s1p27Yts2fPpm/fvsyYMSPLx584cSIxMTGWLTw83KrxZ8fZGTq1101Gfx32gkhZXUIUY/mtqbuzVFhkggHTcV26pPsDAgQHQ5s2sGdPwcch7pHrpO7WrVs0adKEzz//PEfHh4aG0qdPHzp06MCBAwd46623GDNmDMuXL891sLZSu4Rem/L0ZhkoIYxRoUIFHB0d76mVi46Ovqf2Ljtt2rTh5MmTWf7e1dUVDw+PDFtB6tZPL0T+F93y3JdIiCIvOTltea+8JnU1dbNrVEoFa0WVcz/8oJ9DgwbQpAlcvAgjRoDZXPCxiAxyndT17t2b999/n0ceeSRHx3/55ZdUq1aNmTNnEhAQwMiRI3nmmWeyrU0oaLWb3ZmrLq6yvCmFIVxcXAgMDGT9+vUZ9q9fv5527drl+HEOHDiAj4+PtcOzmu7d9e1mOnJ7+Z/GBiOEUa5c0bcmE5TLW02bd139hSwWT+KuxFsrspzZto1NdKSX+U8WvbAD8xP/Bz//DA7So8toNv8L7Nix457O3z179mTv3r0kJSVlek5Bd+au3VoPkDiDHylRl2x6LSGyMn78eObNm8eCBQsICQlh3LhxnDt3jlGjRgG66XTYsGGW42fOnMnKlSs5efIkR48eZeLEiSxfvpyXXnrJqKdwX/Xrg3eFJG5Tgu0bbsONG0aHJETBS216LV9eLw+RBx6VS+PmrJe5vHipgJOppUsZV28Va0Oq89SoEgSGfMdf4fXSfr9tm67JEwXO5u+EqKioTDt/Jycnc/ly5utAFnRn7ip+zriQQBIuhO+R6bmFMQYNGsTMmTOZOnUqTZs2ZfPmzaxatYrq1asDEBkZmWHOusTERF577TUaN25Mhw4d2Lp1K3/++WeOa9GNYDJBt166g/dfyZ1gzRqDIxLCAL6+8N138NFHeX4Ik4MJ78p3BksU8LQmR46aOHC8JE5O4OEBBw/qWvjeveHw3B3QuTP8619w82aBxiUKaPRrZp2/M9ufqqA7czs6Qk033Wn71P6Cm+JBiLuNHj2asLAwEhIS2LdvHx07drT8buHChQQFBVl+fuONNzh16hTx8fFcvXqVLVu20KdPHwOizp3u3XW5/4tusGqVwdEIYYBy5eD//g+GD8/XwxT4UmHJyRAXx+LF+sd+/eD0aRgzBpyc9He0pqPaMFLNJWLVASjErQb2yuZJnbe3d6adv52cnChfPvN54YzozF2rrO7jcDok0ebXEqI469pV3+41teTq9K+MDUaIIszb7ToAUUcyb/WyurVrSfGuzPez9TrpQ4dChQowaxaEhMBjj4HZbGJ+ynDqcJI/vr0Mf/9dMLEJoACSurZt297T+XvdunW0aNECZ2dnW18+x2p763X0Tp2Rjp5C2FLlyhAQAEqZ2LjNxehwhCh4Bw7o0d9nzuTrYXxC9cTkUbsLaN3yb79l441ALtz0pGxZ6Ns37Ve1a+uxEtu2Qdu2EEdJnuQ7zjzzPsQX8ECOYizXGczNmzc5ePAgBw8eBPSUJQcPHrT09bm7M/eoUaM4e/Ys48ePJyQkhAULFjB//nxee+016zwDK6ndoyYAp8q1NDgSIexft2769q+/jI1DCEPMmwcDBsDChfl6GO+ytwGIjC6AyoirV+HXX1nMUAAGDdJrOt+tXTvYtAnatU4mhjIMPDeD25Om2T4+AeQhqdu7dy/NmjWjWbNmgB6x16xZM9555x3g3s7cfn5+rFq1iqCgIJo2bcp7773HZ599xqOPPmqlp2AdtTtXAeBUZCmDIxHC/qVObfLXj5d0p+o7/WyFKBbyOfFwKu/yeoRp1JUCqPFeupRbiU4sdxgI6KbXrDg7w48/O1G+dAL7CeTVT3z1JMXC5pxye0Lnzp0tAx0yszCTbx6dOnVi//79ub1UgUpdKuz0aT1VnUy3I4TtdOoEjo6KU9crErYpjBr790NgoNFhCVEwrJXUVdKfxVGxJfIb0f19+y2/8DC3zO7UqqWbWLNTtSosXupKnz4wW42m4z9mBtW3fZhGUwpOnoSYGKhXT48OLkiSutxRvXIyjg5m4uMh8lzm8+cJIazDwwNat043CvbXXw2OSNi72bNn4+fnh5ubG4GBgWzZsiXb4226vKW1krrKeo67qJs2bmE6fhx27WIxumvV0KF6eqL76d0b3npL3x/5nAMnDFqJMyXFdo0BsbG6G8l77+k+hhUq6GSuVSvw9IRq1fTr8OqrsGAB7Npl25leJKm7w9nVgermMABO775ibDBCFAOWJli6QQ7WkBYir5YuXcrYsWN5++23OXDgAB06dKB3794ZugqlZ/PlLa2V1FXTza5Rt8vYtgfDt98SgQ9/oYeuZ9f0ercpU3TN/M2bMPBRM/Fno20UZEZXr8LCL24xoEUEJd2SqVb+Fu8+F8nZEwn5fuzz5+HNN6FRIyhTRv8ve+cdPUPT1au6r2HqdDPh4Xqql08+0SuptWkDZctCr14wd27aW8FqVBEQExOjABUTE2PT6/QosVmBUvPfPG7T6whjFdT7qSgw8rXYskUpUKoC0SoFk1KnTxd4DMK6CmvZatWqlRo1alSGff7+/mrChAmZHv/GG28of3//DPuef/551aZNmxxfM8vXIiVFKUdH/eaPiMjx42Xm9u/rlK6DUurKlXw9VPZOnVIfdVujQKn27XN/+oULSlUsm6hAqWerrlbKbLZufCdPKvXSSyqq/SPqyye3qO7dlXJyUpbXJv1mIkX1KLVV/fTALHV7xZ+5uszRo0oNH66Us3PGx6xeXanBg5WaNUup3buVSkjQx1+9qtTWrUp9/bVSr7yiVLduSvn4ZDzXwUGpzp2V+vxzpS7suaBUFmUnp2Ur133q7FntcldYdwFOhUjzqxC21ro1lCoFl29W5B+a0OzXX2HcOKPDEnYmMTGRffv2MWHChAz7e/Towfbt2zM9J6vlLefPn09SUlKm03ElJCSQkJBWC5Tl8pbXrun2QNBtdfng2rwBZd1vcy3OjaioPC8je3+1arHoYi0gd7V0qXx94YePIugxsipzw3vR8eWdPPl5m/zHdf48vPceQfNOMdX8NkF0Rm1La4BsVD+FR298w7/8T3D8QinmnujI38mdWXezPeu2tqfC/jiGjYL+/cGvRBSVv5yE0wNt9BDeevUsneu3boX//lfPQpOqY0d48UXo0AGyWm67bFlo7xtK+6j9UDUMUsLALYwTZ5xYHtmO5bHd2JfSjKAgCAqCl00+tK0fwzsfQ8+eeXtJJKlLp7ZPHFyAU6HSKi2ErTk762aZP//UTbDNVq6UpE5Y3eXLl0lJScl0ucq7J8ZPdb/lLX0y+RSfPn06U6ZMuX9AJUroJcKuX9eFID98ffGuDtdCIDJSr61sC//8A4cPg4sLPP543h6j24jqvLM4iCmbOvP8F42p0TeWB3rncRRBdDRMn86hL7YwIWkqq0lbSadlwzgefdKdRx6BOnUcgZEANAUGKcWZbREs+N8tvlntTcSN0nzyiW4aBW8cmUPVheHUIIwaLgeoVh3+imvH9gs1AN2P8OGH4fVxybRp56BX2Dh3Dtae1qMsz5zRt59+CjX0OSxeDO++myH8usBEVjIRCF2ykxUXWrN8OezYYWL70TIk5aNeSZK6dGr5mWEvnIosaXQoQhQL3bvfSepc+vJ6wzjdIpGTHthC5FJmy1VmtVRlVsdntj/VxIkTGT9+vOXn2NjYzNctd3fXS4RZiY+PXs3BJkuFxcbCyJEsTpwG1KF/f137lFeTVrVla8Ud/B3Xlg59oHeLaN793IvWrXP3OGf7juadvf1ZzMcoHHByNPPc8w688QZUr+6e9YkmEzUf8OX9B2Bysu7rtnAhHDoEZ8PMJCY5EYYfYfhBInBSn+ZCAk/1vMirs6pRrx6weAl0flr/vzKb773OqFFpSV2DBrojXY0aaVuVKrpatUwZ/GrU4NUSeiDFhQu6e3Fqf+O8kKQundr1dafT09fLy2eLEAUgdRLizQ6duP1xZ9ykzAkrq1ChAo6OjpkuV3l3bVyqvC5v6ZrZbLw25u1wEahE1NkEwMrXX7aM5J9X8L3jF0Deml7Tc3R35affS/Bqvx9ZHP8Yq/d6sboN9Oxh5t3JDllPkxIcDBUrcsWhIh98AJ8fXEoieuTv448r3n/fgTp1cheLk5Neu7ZfP/2z2exAZCSEhUHYqWTCdl0k7MA1KpvDed77V3w+HAv17pwcHZ3WhF6iBNSsqbdatfRWr17ahR59VG85ULmybtLND0nq0qnZzBMTZmKTS3L5cr4HJgkh7qN+fV3TEBlpYvt2ePBBoyMS9sbFxYXAwEDWr1/Pww8/bNm/fv16/vWvf2V6Ttu2bfk9fQcqCufylgDeW5cDo4k6GQtY+UPrl1/4m65EpVSkfHk9NUd+lXuwKd9E1eTfr85g2nxvFjGUtescWbtO11D9+99QvjycOZbImZWHCN0QypkIV854VeBUDOhui4506aL48EMTLVta55ugg4NOqipXhvbtneCpykBloCFw1xMfMwYGD9YneXsXqhog6TyWjlv7QKpU0MuunDplcDBCFAMmU7olw9aZ9SROsk6ksLLx48czb948FixYQEhICOPGjePcuXOMGjUKKLrLWwJ4l9aTnkVdyKQZML+OHmXRnbnpBg/WfeqswsODWnMnsCC4DSd2XWfECF1ztn697mfbsCEMeMyFsd+1YFbEQH5nAEejvUhIgCZNdLPp33+baGnUqp7Ozjr78/EpVAkdSFKXUYUK1G6k2+MlqROiYFiSuv8F674nQUGGxiPsz6BBg5g5cyZTp06ladOmbN68mVWrVlG9enWg6C5vCeDtqSsioi5aObmIj+dG6GV+Qddupst5rcffn5otyzNvHpw4Ac9WXYMLCXhynebs49ESf/J6++3MmX6NtWv1Sg0HDuiRoYUslyo0pPn1LrVqwcaNktQJUVC66vlM2RtXn6uUpdyaNdZp5xEindGjRzN69OhMf1dUl7cE8C6XCEDUFSt/nJ88yQoeJh536tZVVmvmzIpfdTNfP7+POQvG4OhfRw826N1bV+GJHJOaurvUNut1TE4fiDE4EiGKh8qVISAAFA5spItuWxFC5Ii3l252jbruZt0HPnaM5eiayaFDTbavGXNwgLffxvH0CT0kvn9/SejyQJK6u9Q+8DMAJ4/kfykRIUTOWJYMM/XQ7TBnzhgbkBBFhLev/hi/fMudxEQrPvC5cxygGQCdO1vxcYVNSVJ3l7q1kgE4FuFh27X0hBAWln51rn31nbVrjQtGiCKkvK8rTujZaqOtuKzqtRGvcR49z16jRtZ7XGFbktTdpW4DFxxIITbBjchIo6MRonjo1Em3tJy6XYUz+EkTrBA55NCvD5XK6JYla05AfPiwvq1WDTw9rfe4wrYkqbuLq58vtTgN6Fm6hRC25+EB7dvr+6vpDX//nTohlRAiO4GBeNcqBdgmqWvc2HqPKWxPkrq7VatGADqbCw42OBYhipE+d5ZvXFV3rG5+lU7SQuSIt7e+tVpSd/48hz74A5Cm16JGkrq71alDfXQ2F3I0xeBghCg+Umcx2XCuDvHN24Ojo7EBCVEUxMXhbY4ArJjUHT3K4YhygNTUFTWS1N3N15cAZ938GnxAmn+EKCgNG+p1rm/fhk2bjI5GiCIiMhLv1QsA6yV15uBjHEZX0UlNXdEiSd3dHByo//EIAELCShgcjBDFh8mUrgn2i1B44QW4cMHYoIQo7MqVwxudzUVFWKd16ezeS9ykNC6OydSta5WHFAVEkrpM+D/dFoDoaBNXrhgcjBDFSGoT7Kr1TvDllzK1iRD34+mJt0nPZRIZbp2k7tAhfVu/SizOzlZ5SFFAJKnLRKlSehg3yAhYIQpS1656rezTCVU5SW2Z2kSI+3FwwLv0LcB6za+HQ/Vo2kYNzNZ5QFFgJKnLzLlzBJQIBSSpE6IglS4NHTvq+6voA+vXQ3KysUEJUch5l70zT91lx/xPmn/tGodu1QSgcbtS+XwwUdAkqcvMuXPUP/4LINOaCFHQLE2wTv+C69dh1y5D4xGisPOuqJtd4247cvNmPh8sMpJDjnp5sEYtrLyerLA5SeoyU6eOZa66kKNS/SxEQUodLLHJ/AC3cJcmWCHuo5SXO6W4AUBERP4eK96vPidVHUCmMymKJKnLjJcXASXOAhB8WOaqE6Ig+ftDjRqQYHZhI10kqRPifkaNolYV3QR78mT+Hio4GMxmKF8+bVJjUXRIUpcZk4mAOrofT3iUMzduGByPEMWIyZSuCZY+EBMD8fHGBiVEYda/P/7tKwD57weefnkwkymfcYkCl6ekbvbs2fj5+eHm5kZgYCBbtmzJ8tigoCBMJtM927Fjx/IcdEEoH+CFFxcBKOShCmF3UptgV1d5DnX8BJSQOSOFyI6/v77N7+fVoXeWAdCoyrV8RiSMkOukbunSpYwdO5a3336bAwcO0KFDB3r37s25c+eyPe/48eNERkZatjp16uQ56AKRfrkwGQErRIHq0gVcXSHsvJN8qRLifqKjCUg5AuQzqUtK4vD5MgA0bpzfYbTCCLlO6j755BNGjBjByJEjCQgIYObMmVStWpU5c+Zke56Xlxfe3t6WzbGwr+uYfrCEJHVCFKiSJaFTJ31/1SogKUl39BFC3Gv9evzf/z9Af17leVqTU6c4pO4sD9ahrJWCEwUpV0ldYmIi+/bto0ePHhn29+jRg+3bt2d7brNmzfDx8aFr165s3Lgx22MTEhKIjY3NsBW4Pn2o/0Z/QKY1EcIIlibYj4/qXtv79xsbkBCFVbly1OUEJsxcuwaXLuXtYS7uDCWaSpgw06ChdKgrinKV1F2+fJmUlBQqVaqUYX+lSpWIymIqax8fH77++muWL1/OihUrqFevHl27dmXz5s1ZXmf69Ol4enpatqpVq+YmTOuoUIGAHvq6UlMnRMFLTeo2R9Xlxg0lo2CFyEq5cpTgNjWczgN5b4I9vDUGgFqloylZ0lrBiYKUp4ESpruGxCil7tmXql69ejz77LM0b96ctm3bMnv2bPr27cuMGTOyfPyJEycSExNj2cLDw/MSZr4FBOjb06fh9m1DQhDFTG4GIQFs2rSJwMBA3NzcqFmzJl9++WUBRWp7depArVqQpJz5m66S1AmRlfLlAfBXOpvLa0XE4UO63bZx1evWiEoYIFdJXYUKFXB0dLynVi46Ovqe2rvstGnThpPZTKbj6uqKh4dHhs0IPlt+wtMlDrM5/3P/CHE/uR2EFBoaSp8+fejQoQMHDhzgrbfeYsyYMSxfvryAI7cdSxMsvWHHDrh61diAhCiMypUDwD+fgyUOhenPWlnztejKVVLn4uJCYGAg69evz7B//fr1tGvXLsePc+DAAXx8fHJzaUOYfv+NgMR/AOlXJ2wvt4OQvvzyS6pVq8bMmTMJCAhg5MiRPPPMM9nWghc1qUndKud/ocxmWLnS0HhE0XPt2jWGDh1q6c4zdOhQrl+/nuXxSUlJvPnmmzRq1IiSJUvi6+vLsGHDiMjvUg225Omp51e9M7gvz82viXUBWfO1KMt18+v48eOZN28eCxYsICQkhHHjxnHu3DlGjRoF6KbTYcOGWY6fOXMmK1eu5OTJkxw9epSJEyeyfPlyXnrpJes9C1upW1emNREFIi+DkHbs2HHP8T179mTv3r0kJSVlek6hGISUC506gZsbnE/y5igNYOlSo0MSRcyQIUM4ePAga9asYc2aNRw8eJChQ4dmeXxcXBz79+9n0qRJ7N+/nxUrVnDixAkGDBhQgFHnkqMjlCuHP3lvfk1JgaN3krpGfQzoxy6swim3JwwaNIgrV64wdepUIiMjadiwIatWraJ69eoAREZGZmguSkxM5LXXXuPChQuUKFGCBg0a8Oeff9In9St4YVanDgHsAySpE7aVl0FIUVFRmR6fnJzM5cuXM60Nnz59OlOmTLFe4DZWogQ8+KCe1mQVfWj49yd6aF/FikaHJoqAkJAQ1qxZw86dO2ndujUAc+fOpW3bthw/fpx69erdc46np+c9rVH/+9//aNWqFefOnaNatWoFEnuuTZ6Mv7k8vAJnz0JcHLi75/z0U6d033F3d6hZS0a+FlW5TuoARo8ezejRozP93cKFCzP8/MYbb/DGG2/k5TLGq1OH+iwGpPlVFIzcDELK6vjM9qeaOHEi48ePt/wcGxtrzOjyXOjT505S5/U0b4wrp2slhMiBHTt24OnpaUnoQPfp9vT0ZPv27ZkmdZmJiYnBZDJRpkyZLI9JSEggISHB8nOB14K/9BIVgfJT4coVOHECmjbN+emH/lGAiQYNpIgVZbL2a3bSTUB84oQiOdngeITdyssgJG9v70yPd3Jyovyd0XB3KyyDkHIjdR3YbVcDuD5qgqVTuChgcXHwww/w1FOYU4rGagNRUVF4eXnds9/LyyvLGvC73b59mwkTJjBkyJBsy0uhmIqLtOXCctu6dPiTdQA0djth5YhEQZKkLjuenlSvGE8J4khMNHHmjNEBCXuVl0FIbdu2vef4devW0aJFC5ydnW0Wa0GrWRMaNIDkZPjlF6OjKYa2bYMRI4ivVINf/28pQxd1o2aVBNJVShW4yZMnZ7qmePpt7969QOa11verAU+VlJTE4MGDMZvNzJ49O9tjDZ+KKy4OgoLwd9UfVLkdLHHoTGkAGlWWEeZFWZ6aX4sTh7q18b90jAM0JyQE6tY1OiJhr8aPH8/QoUNp0aIFbdu25euvv75nENKFCxdYtGgRAKNGjeLzzz9n/PjxPPvss+zYsYP58+ezZMkSI5+GTTzxBPz73/DD4mSedlkKZcpA375Gh2X3bt2C1a/vYtmO7vzJTG6iP/iJgo0boVcvY+J66aWXGDx4cLbH1KhRg0OHDnHx4sV7fnfp0qX7TsOVlJTE448/TmhoKBs2bLhvrbarqyuurq73D95WwsOhSxcCnCcA03Of1F2tAsjI1yJPFQExMTEKUDExMQV/8TNn1JDHbitQ6oMPCv7ywvoMfT/dxxdffKGqV6+uXFxcVPPmzdWmTZssv3vqqadUp06dMhwfFBSkmjVrplxcXFSNGjXUnDlzcnW9wvxapHfmjFKglIMpRUXgrVT79kaHZJ/27FFJA59Qy2ZfVI88olSJEvp1T92qVjWrsWOV2rpVqZSUe08vbO+n4OBgBahdu3ZZ9u3cuVMB6tixY1mel5iYqB566CHVoEEDFR0dnadrF/hrkZiolJOT+oM+CpRq1Cjnp8ZG3LD8jS8dv2K7GEWe5fT9JEldDrz/vn6zDx1qyOWFlRn9fipMitJr0batLoefMlbfOXfO6JDsg9ms1OrV6soDA9R/eENV5WyGRK5mTaXeeEOpXbv0odkpjO+nXr16qcaNG6sdO3aoHTt2qEaNGql+/fplOKZevXpqxYoVSimlkpKS1IABA1SVKlXUwYMHVWRkpGVLSEjI8XUNeS38/dUpaipQytVVqeTknJ22/ZtjCpTycYi0bXwiz3L6fpI+dTmQulyYTGsihHH+7//07felntN3fv7ZuGDsQVISLF5McL2HGdU7jCpblzCBDwmnGhXLJjNhAhw4oKe6+PBDaNUKctANrdD5/vvvadSoET169KBHjx40btyYxYsXZzjm+PHjxMTodU/Pnz/Pb7/9xvnz52natCk+Pj6WLas5IwsNf39qEIarUzIJCXpqk5w4vPU6AI09c3iCKLSkT939XL1K/RWzgX8TEgJmMzhIKixEgRs4EF55BfbeDOAEdaj744+QbnoWkXMqMYl1tUfzcfhA1rPSsr9pg0Reec2FwYOdcHMzLj5rKleuHN999122xyiVNpq3Ro0aGX4uUvz9cWQldT0ucvhqZY4d0wON7ueQXjiJRrLma5En6cn9uLpS6/spOJHErVtw/rzRAQlRPHl5QeoCGj/wf7BnDzIkPff274euvZzpFT6X9fTAwWTm4X6JbNoE+w+7MHw4dpPQFTt35jPxd9KLlee0denw9TuDJJpJPU9RJ0nd/ZQsibOvF3XRc/fIJMRCGGfIEH37Q4kRKICffjIynKIjNpZzz73P0N6XCQzUI1ddXRVjX0zi9BkHVvzuQseORbN5VaRzJ6kLiNsP5GxaE6Xg0OXKADQa29VmoYmCIUldTtSta5mEWPrVCWGchx7SS4edjK/CXlroDl8ia2YzMXN+YILPt9Sd+xrfrakA6P6Jx4+b+PRzZ2rUMDZEYUX168PXX+M/Xi/DmZOk7sIFuH5dryKR2n9cFF2S1OVEupUlpKZOCOOUKgX/+pe+/8OIv2HePGMDKsRSDhzii9qfUGt0Dz6Me5kE3Ojc+Ap79sB338Gd5bqFPSldGp59Fv+HdI1dTiohDu3XSyXVqwdGTrMnrEOSupyoU4f66GxOauqEMFZqE+yPf3qQkmJsLIXSrVscH/kRHZvf5KXQ17hCBQK8LvP7iiQ2HCxPixZGByhsLXVJ2ytX4PLl7I89/Ivuf9c4ZouNoxIFQZK6nLirpq6oDowSwh707KmXf426s6oB168bHVKhkZICH48Ipun8l9hOO0o7xfHFtOsculCBfg87S5+54uDkSdx/mEd1rzjg/k2wlpGvZWUUoD2QpC4n6talHscxYebaNYiONjogIYovFxc9vQncaYKtWBGOHjU2KKOZzRw7Bg88AK8tbcltStCjWTRHTrsz+q0yOMmgxuJj1SrdBMtxIPvWpeRkCDruDUCzRskFEZ2wMUnqcqJuXUpcCsevpv6aK02wQhgrdSLi5RFtuJ3sCEuXGhuQUcxmUr74ko+qzKJpU8XOneDhobsartnnRbVqRgcoClzqCNjkw0D2NXVr1kBEXFkqcImuvZwLIjphY5LU5YSTE1SoQP36OqmTwRJCGKt9e6haFWKTS/InfXVSV9z6RRw7xumWg2n/UlPeiBxHQoKJXr3gyBEYMUKmJym27gxh9b++E8g+qZv/la6dG8piXB5oZfPQhO1JUpcLslyYEIWDg0PagInvHYbBiROwebOxQRWUxER4/31+ajiV5vvnsos2eJZIYME8M6tW6WRXFGNVqoC7O/5m3SUhq8+rqCj4fZVOAUZ4rwI/v4KKUNiQJHU5tWwZAWtnAlJTJ0RhkJrU/UkfruOplwwzm40NytZ27ya+eXtGTarAoJQfiMWT9i1uc+iYK0+PcJDaOaG/8dSrZxncFxYG8fH3HrZoEaSYHWjDDhr0qCxVu3ZCkrqcCg+nwaEfAL3IdVKSwfEIUcw1bgwNG0Ki2Znlbk/q9a++/dbosGzq2L+/o/XR+XzFKEwmxVsTFUE73KTvnMjI35+KXKJsiXiUgpMnM/5aKZg/X98f2eIf6N+/4GMUNiFJXU7VqUNz9uPldIVr12DtWqMDEkJYlg2r8rq+8/XX9te37s5kfN9+C4FbZ3KYxnhVNLN2rYlpH5hkZKu4l78/JsC/1AXg3ibYrVt1j4WSJeHxDaPgsccKPkZhE5LU5VSdOjiRwhP8COgZ2YUQxnriCX278XQ1zr67ADZssJ9mpGvXYMQIbj35PE89BcOHQ1y8A127wj+HHOje3egARaE1ZAj8/TcB3fSarncPlkitpRs0SC9CIeyHJHU55ecHjo4MTV4AwK+/QkyMwTEJUczVqAFdu4JSJiaffVovDGsPfvkF6tfn1IJNtP1xDIsW6a5S77+vWwm8vY0OUBRqtWvDgw/i30yXh/RJXUwM/Pyzvj+yW5j91WwXc5LU5ZSLC9SoQXP2E1D9Frdvw/LlRgclhJg2Td9++y0cPoxuriyq/SMuXoTHH4dHHuH3qBa0cNjPYRpTqZJePePtt/XC60LkxJ0p6zI0v/74I8TFQYDbGdoM8YMlS4wJTtiEJHW5UacOJuDJZnr4qzTBCmG81q31ChNKwZuvm6FlS+jVCzZtMjq0nFNKD0cMCCDl5+VMMr3PAH4nxuxB+/Z6DEjHjkYHKYqUX37B/48ZABw/njYw3DJAIuELTAAdOhgSnrANSepyo04d8PDg/xodAiAoCMLDjQ1JCAEffKDnCF+91oGNVYbqnePGWQYZFHq3bsHEiVy5ZqJv6S28r94G4OWXdTdBX1+D4xNFz9y5+H09ARenFG7fhnPn4NAh2LMHnJ3MDFWLoFYtmdjQzsi4qdz46COYNYvqJhMdN+m5Tn/4Ad5808rXSUzUnb2d7yzbcuUKrF8PDz8Mrq5WvpjxkpP1U7x8OeN25Yre3N3Bx0f3I/LxSbvv5mZ05KKwqF0bRo2Czz+HN8JfYpfHVBwOHICFC/XyCoVRQoLu1mEyQalS7B//HY9Oa0bYtTKUKKEH8j75pNFBiiLL3x+n1aup4xnN0Ss+HDsGq1frXw3wO0LFk5ehy0OGhihsQOXBF198oWrUqKFcXV1V8+bN1ebNm7M9PigoSDVv3ly5uroqPz8/NWfOnFxdLyYmRgEqJiYmL+HaxNy5SoFSDRooZTbn88Fu3VJq1Sql3npLqY4dlXJzU2rNmrTff/yxvljFikpNmKDUmTP5vKAxEhOVOnJEqaVLlXrnHaUefVQpf3+lHB3108vtVqaMUm3aKDVunFI//6zU+fM5i6Mwvp+MYk+vxcWLSpUqpd8bS578Q9+pVEmpwvjcNm5Uql49pb75Riml1IIFSrm66pBr1VLqn38MjS7P7On9lF+GvxZff60UqEcrbVGg1PTpSpUrp99jq2u9qO98/70xsYlcy+n7KddJ3Y8//qicnZ3V3LlzVXBwsHrllVdUyZIl1dmzZzM9/syZM8rd3V298sorKjg4WM2dO1c5OzurZcuW5fiahheOu92+ra6diLb8E96/Px+PFRWl/4vfnbFMnZp2zJw5Svn6pv3OZFKqVy+lVq5UKikp30/Hmm7cUOrQIaV+/VWpTz9VaswYpfr1UyogQCknp6wTNJNJqfLl9edcu3ZKDRig1DPPKPXaa0qNHq3Uww/rBK569bQPv8y2atWUGjRIqZkzldq9O/OXp9C9nwxkb6/F1Kn6feBXw6xu16qvf5gwweiw0ly6pNRTT1nesLcDmqrnnjVb3r99+yp17ZrRQeadvb2f8sPw12LzZqVA/dvzMwVK+fjo91jVyikqmTvfpC9cMCY2kWs2S+patWqlRo0alWGfv7+/mpDFP8433nhD+fv7Z9j3/PPPqzZt2uT4moYXjvR+/10pb2+lHn9cPfaYLhfjx+fxseLilGrdWj+Il5dSTz+t1Lx5Sh07dm/1X1KSUr/8olTPnhmzmFq1CjSxuxoRrw7sSlArV6SoWbP0c3/0UaVatNAViferXStVSj/lZ57RFZBr1ih17pxSyck5j8FsVurqVV3rt3ixTvqaNlXKweHe612+fO/5her9ZDB7ey1u3tTFE5SaOfKwvuPiotTBg8YGlpKiy3b58pZvMeeenKhaBSZZvtRMnaoPK8rs7f2UH4a/FtHRSoH6jv/L8D9x0uPH9J26dY2JS+RJTt9PuepTl5iYyL59+5gwYUKG/T169GD79u2ZnrNjxw569OiRYV/Pnj2ZP38+SUlJOKf2G0snISGBhIQEy8+xsbG5CdO2qlXTKyEvW8bQLy+ybFklfvgBPvyQ3M/s/uqrsGsXlC2rp/iuU8fyqwsXYNUq+PNPfbkGDZxo0uQhGk94iMbTQin305ewYAE0b57xwkuXQrduUL587p9bUhKcPq2nGm/fnkvm8uzbB/sWHGTf6kvsu1WPc+r+6xGVdbpBTc/L+FW4Sc0qCfj5mfDzdyWgeQmqtvLBVNI997GlSknBdPMmZW/doKzDDRrUieXJitdhUhNulvJmzx7Y/vMFdqy/wTUv/zy9DKLoKlkSpkyB55+H935pwPDuj+F5/SzUrGlcUIcPw3PPwc6d+ufGjdnw7BIGTanP5cu6+P/wgx6wK4TVVKgA5crhfzVtPhOTCZ55pwo89WfmC8KKoi83meKFCxcUoLZt25Zh/7Rp01TdLLL+OnXqqGnTpmXYt23bNgWoiIiITM959913FXDPVmi+/fXooRSohBfHWb54r12bh8cJDVWqeXOlNm5UKSlK7dyp1KRJSjVrdv8arypVlOrTK1lNfPmGWr5cqbAwpczBIfqXzs5KPfKI7i/x559KrVunVFCQ7nSU6uxZpRYtUmriRKUeflhdrN1OrXboo97nLfUwy1U1r7gsr+1FlGrBbvUoP6vxzFCznj+qVq7UzdDXPp6ffeA//ZQWw7p1SrVvr9taH3pItz11765Up066DXbTprRjv/9eKXf3rB936dK0Y5ct01WCWTD8G3QhYo+vRVKS7qsJSk18NUE3eRppxw5LNbV5xsfqww+SLbXKTZsW2S6ymSqM76erV6+qJ598Unl4eCgPDw/15JNPqmu5aON+7rnnFKA+/fTTXF23ULwW7dqpG5S0/Jvs1s24UET+2KSmLpXprmV4lFL37Lvf8ZntTzVx4kTGjx9v+Tk2NpaqhWnY9Wuvwbp1uCz8mkGDPmD2AjcWL4a7KiTvr0YNErft4Z3JDnwzCKKj035lMun5t/r10xV4R47o4eiHDkFoKJw/D+fPO7JqTSn4nz6nQplatCi9lRY3NhC4Yh/1V0xGYSIZJ5JwJvn9D0l6sCfJyXD1pxMc/PwU+2jDfkZzgSoZY7sTS926EFg/jhbeFwhs50qzzp54eLhBkh8kVYGkVvobYWrlW+mOUH4hREbeu12+rI9NdeYMbNuW9euT/gVxdtYzZqZycoJSpcDTE8qUybiSQMOGejoLpexnySiRY05O8J//wEMPwczZLrw4rgKVU3/5n//odZFefNF2ASilC2qTJvrnNm1g7lxi2vfhmX/7smKF3v3UUzBnjv0sglFYDRkyhPPnz7NmzRoAnnvuOYYOHcrvv/9+33NXrlzJrl278C2qc8p8/TWlSpXCr4siNNRUaAeCCyvKTaaYkJCgHB0d1YoVKzLsHzNmjOrYsWOm53To0EGNGTMmw74VK1YoJycnlZiYmKPrFopvPOmZzUo1bqwUqO2jvlWgK5Fu3MjBudu26QEOSvdRbds2rbLJw0Opxx9X6ttvdXeIrFy/rtTWrUrNnq3UyJG6Zi+7QQg52Uwms/KvnaiGDDGrGTP04Lzr163yamUtNFQPW/3yS70tWKDUd9/pWrdffsnYiTc2VqnTp/ULEx+fryHHhe79ZCB7fS3MZqUeeEC/t0eMuLNz69a0N/y771ph2Homtm3TF3Z1VerUKcvuTZv0IJ7UivQ5c2xzeaMVtvdTcHCwAtTOnTst+3bs2KEAdezYsWzPPX/+vKpcubI6cuSIql69etGsqbvj77+V+u9/lUrZEKQHDt3V2iYKP5sOlHjhhRcy7AsICMh2oERAQECGfaNGjSq6AyVSLVqkFChzJW9Vq6YevbZ48X3OOXNGjyYwmdSWDzZbOnR7eupWyRzmuJmKj9ejPWfP1oMQmjRRqnRppcqW1Zf08dEfKjVr6v6xzZopNWyYUrNmKbVli86ZiotC+X4yiD2/Ftu36/Ll4HBnhiCzWanJk9MSuxdftM7IBLNZqfXrlercOe2x3d2VWrZMJSToz1CT6c6oXD/dzcJeFbb30/z585Wnp+c9+z09PdWCBQuyPC8lJUV16dJFzZw5UymlcpTU3b59W8XExFi28PDwQvVaKKWUeumltPe+KFJsPqXJ/PnzVXBwsBo7dqwqWbKkCgsLU0opNWHCBDV06FDL8alTmowbN04FBwer+fPnF/0pTZRSKiFBqcqVlQL17pATCnRXuyxdv65U/frKDOp/Vf6jnJx0ItiwoVInTxZY1EIV0veTQez9tXjyybQBsJYGhi++SMuyBg7MewE0m5X67TelWrVKS+acnXXV4NmzKjg4Y//Yp5+2/y9Phe39NG3aNFWnTp179tepU0d98MEHWZ73wQcfqO7duyvznerUnCR1hbIv+M2belLQJ57QX2AaNtRvxlx8/orCwWZJnVJ68uHq1asrFxcX1bx5c7UpXYf2p556SnXq1CnD8UFBQapZs2bKxcVF1ahRwy4mH1ZK6QmD//lHnTyZViOQ6diPW7eU6tZN3aKEGlriZ8s/+cGDdZkTBavQvp8MYO+vRUKCskw95OioK9iVUkr9+KNOwFIL46RJOXvA9G2m16/rPhOgJwx/+WWlzp1TZrNSn3+ud4Ge8HX5cqs/tUKpoN5PWSVQ6bc9e/ZkOYivdu3aavr06Zk+9t69e1WlSpXUhXTdP4psTV1Skv5GA7opJ/X9bvTgIZFrNh0oMXr0aEaPHp3p7xYuXHjPvk6dOrF///68XKpw690bgNrovtA7d8KSJTB+vF48+fZtiI+4Rvzgp7m47yojTTs4GN8ER0e94tjYsdKPXwhbcnHRZbJUKb1i2LBhcOMGjB49SK819/77ehHnNm3STjpwAL7/Hrp00YN1Tp1K206f1vMNlSypB+lMmAAxMXpgTqVKhIXBC33gTp98evbUMw8V1X72hdVLL73E4MGDsz2mRo0aHDp0iIsXL97zu0uXLlGpUqVMz9uyZQvR0dFUq5Y2fVNKSgqvvvoqM2fOJCwsLNPzXF1dcS1syzg6OemRdkePwpdf6n2NGmUcsCbsSwElmflSFGoTvvjvDQV6wELqN/TMtooVldqwwehoi7ei8H4qKMXltUhJ0aubpJbDDJU0V67oKr1Ub7yR/aiiQ4fuefwLF5R64YW0yj83N6U++8w+B0Nkp7C9n1IHSuzatcuyb+fOndkOlLh8+bI6fPhwhs3X11e9+eab9x1ckV6heS0efTTj+/eugYuiaLBpTZ1IRyl4/nkGLfydSZ7hXI1xIjk54yHOJFKilCOt2zoyfz4UptlZhCgOHBxg5kw9m8m0aTBxIsTG6vumcuUyHtytm66N278fqlSB2rUzbukmCb98Wc+S8sUXumYeoHt3fa369Qvs6YksBAQE0KtXL5599lm++uorQE9p0q9fP+rVq2c5zt/fn+nTp/Pwww9Tvnx5yt81a7mzszPe3t4Zziky/P0z/tylizFxiAIhSV1+mUxw6xblk6I41X00kVO+xr2EooS7CXd3KOGmcLp2Hby8jI5UiGLNZNKtrR4e8OabMH26bjmdMuWu1qju3fWWjZgY+Phj+PRTuHlT72vfXieJnTrZ7jmI3Pv+++8ZM2aMZWWjAQMG8Pnnn2c45vjx48TExBgRnu2lT+pMJujY0bhYhM2ZlLozE3AhFhsbi6enJzExMXh4eBgdzr3274fAQHB01F/Zf/0VVqwANzejIxOZKPTvpwJUXF+LOXP0/MOp81MHBuplunr21N3r7l7yLzZWF/M9e2DvXli/Hq5d079r3lwni716SR/Z4vp+ykyheS327oWWLaFiRT3Ze7qaZlF05PT9JDV11tC8OTz4IGzYAKNG6X0zZ+pO1EKIQueFF/Rn3Hvv6cUf9u7VW2pNXteuOtELCdH7jx+/9zHq14epU+GRRySZE4VYapNxTIweHCTsmiR11vLaazqpAz3E7tVXjY1HCJGtxx7TW0QErFsHa9fqGrgrV+CXX/SWXvXq0KKFrvRo2VI3szo6GhO7EDlWurQetV2t2r1V0MLuyF/YWnr1gnff1QVo/Hj56i5y5dq1a4wZM4bffvsN0P1+/ve//1GmTJkszxk+fDjffvtthn2tW7dm586dtgzV7vj6wvDhektJ0c2sa9boWrr69XUiFxioa/aEKJJq1jQ6AlFAJKmzFpMJJk82OgpRROV10fFevXrxzTffWH52cXGxaZz2ztExrSZOCCGKGknqhDBYSEgIa9asYefOnbRu3RqAuXPn0rZtW44fP57tNAqurq54Sz8ZIYQQgIPRAQhR3O3YsQNPT09LQgfQpk0bPD092b59e7bnBgUF4eXlRd26dXn22WeJjo7O9viEhARiY2MzbEIIIeyDJHVCGCwqKgqvTOYx9PLyIioqKsvzevfuzffff8+GDRv4+OOP2bNnDw8++CAJCQlZnjN9+nQ8PT0tW1WZCVsIIeyGJHVC2MjkyZMxmUzZbnv37gXAlMnAGqVUpvtTDRo0iL59+9KwYUP69+/P6tWrOXHiBH/++WeW50ycOJGYmBjLFh4env8nKoQQolCQPnVC2IgtFx3PjI+PD9WrV+fkyZNZHlMoFx0XQghhFUUiqUtd9EL6/whrSH0f2XoxlQoVKlAhw/pTmWvbti0xMTHs3r2bVq1aAbBr1y5iYmJo165djq935coVwsPD8fHxyfE5UraENRVU2SoKpGwJa8px2VJFQHh4uAJkk82qW3h4uNFvbYtevXqpxo0bqx07dqgdO3aoRo0aqX79+mU4pl69emrFihVKKaVu3LihXn31VbV9+3YVGhqqNm7cqNq2basqV66sYmNjc3xdKVuy2WIrTGXLKFK2ZLPFdr+yVSRq6nx9fQkPD6d06dL39DGKjY2latWqhIeH2+Vag/L8rE8pxY0bN/D19S2Q6+VEbhcdd3R05PDhwyxatIjr16/j4+NDly5dWLp0KaVLl87xdaVsyfOzpsJYtowiZUuenzXltGyZlCra9eSFZtFkG5HnJ4xi738beX7CKPb+t5HnZxwZ/SqEEEIIYQckqRNCCCGEsANFPqlzdXXl3XfftdtpGuT5CaPY+99Gnp8wir3/beT5GafI96kTQgghhBB2UFMnhBBCCCEkqRNCCCGEsAuS1AkhhBBC2AFJ6oQQQggh7EChT+pmz56Nn58fbm5uBAYGsmXLlmyP37RpE4GBgbi5uVGzZk2+/PLLAoo0d6ZPn07Lli0pXbo0Xl5ePPTQQxw/fjzbc4KCgjCZTPdsx44dK6Coc2fy5Mn3xOrt7Z3tOUXl72cvpHylKUrlS8pW4SdlK42UrQJk6/Xv8uPHH39Uzs7Oau7cuSo4OFi98sorqmTJkurs2bOZHn/mzBnl7u6uXnnlFRUcHKzmzp2rnJ2d1bJlywo48vvr2bOn+uabb9SRI0fUwYMHVd++fVW1atXUzZs3szxn48aNClDHjx9XkZGRli05ObkAI8+5d999VzVo0CBDrNHR0VkeX5T+fvZAyldGRal8Sdkq3KRsZSRlq+AU6qSuVatWatSoURn2+fv7qwkTJmR6/BtvvKH8/f0z7Hv++edVmzZtbBajtURHRytAbdq0KctjUgvGtWvXCi6wfHj33XdVkyZNcnx8Uf77FUVSvjIqSuVLylbhJmUrIylbBafQNr8mJiayb98+ywLnqXr06MH27dszPWfHjh33HN+zZ0/27t1LUlKSzWK1htSF2suVK3ffY5s1a4aPjw9du3Zl48aNtg4tX06ePImvry9+fn4MHjyYM2fOZHlsUf77FTVSvrJWVMqXlK3CScpW1qRs2V6hTeouX75MSkoKlSpVyrC/UqVKREVFZXpOVFRUpscnJydz+fJlm8WaX0opxo8fzwMPPEDDhg2zPM7Hx4evv/6a5cuXs2LFCurVq0fXrl3ZvHlzAUabc61bt2bRokWsXbuWuXPnEhUVRbt27bhy5UqmxxfVv19RJOXrXkWpfEnZKrykbN1LylbBcSrwK+aSyWTK8LNS6p599zs+s/2FyUsvvcShQ4fYunVrtsfVq1ePevXqWX5u27Yt4eHhzJgxg44dO9o6zFzr3bu35X6jRo1o27YttWrV4ttvv2X8+PGZnlMU/35FmZSvNEWpfEnZKvykbKWRslVwCm1NXYUKFXB0dLznm010dPQ9WXEqb2/vTI93cnKifPnyNos1P15++WV+++03Nm7cSJUqVXJ9fps2bTh58qQNIrO+kiVL0qhRoyzjLYp/v6JKylfOFJXyJWWr8JCylTNStmyj0CZ1Li4uBAYGsn79+gz7169fT7t27TI9p23btvccv27dOlq0aIGzs7PNYs0LpRQvvfQSK1asYMOGDfj5+eXpcQ4cOICPj4+Vo7ONhIQEQkJCsoy3KP39ijopXzlTVMqXlK3CQ8pWzkjZshEDBmfkWOqw8Pnz56vg4GA1duxYVbJkSRUWFqaUUmrChAlq6NChluNThxaPGzdOBQcHq/nz5xfaYeEvvPCC8vT0VEFBQRmGTsfFxVmOufv5ffrpp+qXX35RJ06cUEeOHFETJkxQgFq+fLkRT+G+Xn31VRUUFKTOnDmjdu7cqfr166dKly5tF38/eyDlq+iWLylbhZuULSlbMqVJFr744gtVvXp15eLiopo3b55h2PRTTz2lOnXqlOH4oKAg1axZM+Xi4qJq1Kih5syZU8AR5wyQ6fbNN99Yjrn7+X344YeqVq1ays3NTZUtW1Y98MAD6s8//yz44HNo0KBBysfHRzk7OytfX1/1yCOPqKNHj1p+X5T/fvZCylcny89FqXxJ2Sr8pGx1svwsZavgmJS606NPCCGEEEIUWYW2T50QQgghhMg5SeqEEEIIIeyAJHVCCCGEEHZAkjohhBBCCDsgSZ0QQgghhB2QpE4IIYQQwg5IUieEEEIIYQckqRNCCCGEsAOS1AkhhBBC2AFJ6oQQQggh7IAkdUIIIYQQdkCSOiGEEEIIOyBJnRBCCCGEHZCkTgghhBDCDkhSJ4QQQghhBySpE0IIIYSwA05GB5ATZrOZiIgISpcujclkMjocUcQppbhx4wa+vr44OBTv7zVStoQ1SdlKI2VLWFNOy1aRSOoiIiKoWrWq0WEIOxMeHk6VKlWMDsNQUraELUjZkrIlbON+ZatIJHWlS5cG9JPx8PAwOBpR1MXGxlK1alXL+6o4k7IlrEnKVhopW8Kaclq2ikRSl1p17eHhIYVDWI00iUjZErYhZUvKlrCN+5Wt4t3pQQghhBDCTkhSJ4QQQghhB4pE86sQQggh7JvZbCYxMdHoMAzh7OyMo6Njvh9HkjohhDDQpUsQEgKnTkGFCuDvD35+4OxsdGTCELt36z9+s2ZGR1KgEhMTCQ0NxWw2Gx2KYcqUKYO3t3e++qRKUlcQ4uLg6lUo5kP8hSjOzGbYvBn++UcnccHB+vby5XuPdXKCWrV0glevnt5atYIGDUDGINixa9egdWt9PzkZrFBzUxQopYiMjMTR0ZGqVasWuzkOlVLExcURHR0NgI+PT54fS5K6grB6NTz2GPzrX7BypdHRCCEK2KFD8MILsH175r+vUQPq1NEJ3vHj+nvg8eN6S8/LCx58ELp21Zufn81DFwUpNDTtflwcFJOpYZKTk4mLi8PX1xd3d3ejwzFEiRIlAIiOjsbLyyvPTbGS1BWEtWv1rZ8fJCbC/v3Qpo2xMQkhbO7GDZg8GWbNgpQUKFUKuneHgACoX1/f1qsHJUumnWM2w4ULOqE7dkzfBgfDjh0QHQ0//qg30P9SunaFgQP140otXhF39Wra/Vu3ik1Sl5KSAoCLi4vBkRgrNaFNSkqSpK7QUgrWrNH3mzWDwEA4fRqOHIGaNY2NTQhhE0rBihXwyis6QQNdWf/pp/fvheHgAFWr6q1bt7T9CQmwcyf8/bfedu3SFTvz5umteXOYOBEefrjYtNrZn8hIfdu1K3h7GxuLAYr7/IbWeP7Fq+HaCMeOQXg4uLnp/+oVK0J8PDz/vP7PL4SwK2fOQN++urhfuKC/u61eDT//nMNutQkJ+sP9yBHdCe/YMQBcXaFTR8XUJ0LY9vtVrl1V/PknjB4N7u66AWDgQF0DuGCBbhQQRUxqUpePPlWieJOkztZSa+k6dtT/eb/+Wid4f/0FCxcaGpoQwrqWL9eDGVavBhcXmDRJ52a9egG3b8O+ffDrr3DgQNpJZ8/qzvHVq+v2WTc38PWFRo2gUyeYMyft2OvXddZWvjylK7jS5/mqfLGrBWdbPsakgGWUcU/gxAkYMQJq1VLMGrKLW7uOFPTLIPIqJQXKlNF/fyHyQJI6W0vtT9ezp76tXRumTNH3X30VoqKMiUsIYVVbtsCQITp3e7CLmUM/HWNqtXmUGPu8bhstXRpatICHHsr4ha5ECT2Nxblzuh8V6DbY8uWhbl0oWzbt2GvXwNNT309KgvPnYd8+KmxaztSQgZx7YgIffaQres6fNzF2SWvqtCnHDw2mof74U3fYE4XXxIm66vX4cV31KkQuSZ86W4qPh02bAIgM7IeKuPMFbPx4WLpUF9oxY+Cnn4yNUwiRLyEhenB7YqLO2ZbNisSxesC9B1aooEc3VK6ccd/KlVCpku6eUa6cTtwym9ahZk1dW3f7th41cfGi3q5dg5gYSjdowGtd4KWXYNHnsfxn0k1Cb/vyf8Fv81X/TXxe4yEavdkHhg3TLQei8Nm4UY+KGT5cfxkQIhekps6WHBzg22+Jf/E1Ap+oQ7VqMG4cxNxygvnzdW/mn3/WzTFCiCIpKgp699Z5VZs28P334Fitsh7a+uCD8OabsGyZbmaNjta1cm+8kfYADg46I2zTRk9OV7Zs5gldem5uUK0atGwJ/frB0KE6k+vSxfLr517zIPiaL9Nev04Jp0Q204lmYSsY+8JtrlduAFu32vBVEXmWOhT65k1j4xA5smTJEtzc3LiQOiIKGDlyJI0bNyYmJqbA45GkzpZcXeHxx9nxyEdERppISYGZM/WEot8fbYp67XXo3Fn3kRFCFDk3b+pBEWfPQm3Xc/z27r60CrCjR/Uw1f/8Bx59VCdhBTy6z80N3vpvGY6dduGxh5JIwYlZjKVezC4WHmourbGFiVJ6hum//tI/pzbFF2e3bmW93b6d82Pj43N2bB4MHjyYevXqMX36dACmTJnC2rVrWb16NZ6pXSUKkCR1BSAoSN+2bq0nGI2KgiefhM7bp3Fk1t96pxCiSElK0qNN9++Hik5XWZPQhYpvPpPWb60QTc9QrRr8/Isz69aBv78iWnnx9IvutGsHmzcp/Q9p0SLpc2ek2FjYs4cknEjCSZI60AOHstoefTTjsV5eWR/bu3fGY2vUyPy4PDCZTEybNo158+bxwQcfMGvWLNasWUPlO10snJycaNq0KU2bNmXkyJF5ukZuSFJnKxcuwPvvw759lqRu5Eg4fBimTdN9ozdvcaBpcwfGj9flWQhRNCilV4hYswZKONzmj+Re1PK6qSenK8RLHHXvDv/8Y+Kjj/Rn2K5d0KmziX7fD+bwUx/pb55bthgdZvEUEUESTjTgKK3YjbopSV1R0a9fP+rXr8+UKVP45ZdfaNCggeV3ZcqU4eDBgxw8eJB58+bZPJbC+9+nqFu9GiZNIn70q+zapXd17qxbZN96S3esfvhhPYL900+hSZXLXH1qnKEhCyFy5v33dbdYB5OZH82P06pkMKxapfvEFXIuLvDaa3DypE5MHR0Vf9KPJvzD8L0vcq7j/+kqyNOnjQ7VUJs3b6Z///74+vpiMplYaeslHiMjOU8VTlKXgzQj7rpMNMjNm1lvy5dnPDY6OutjV6/OeGxYWObH5dHatWs5duwYKSkpVKpUKc+PYw2S1NnKnfnpdtZ/hsREPeo1/f/76tX1l/rVq6FqpQTCblTg7cX1dOccIUSh9fPP8M47+v7n6kUGOK3WhTkw0NjAcsnbG2bPhuBgEwMHgsKBbxlOXU7w2rLWXKnTBvr3L7bNCLdu3aJJkyZ8/vnnBXPBiAgukpYQxF6XpnBKlsx6c3PL+bF31lW977F5sH//fgYOHMhXX31Fz549mTRpUobfx8bGEhgYyAMPPMCmO7Nh2JQqAmJiYhSgYmJijA4lZ5KSlPL0VArUOyPCFSg1ZEjWh2/apBQoZSJF7Xp8RoGFWVwVufeTDclrkTvx8UpVqaLL62v8V99ZvNjosKxi1y6lOnfWTwmUKk2MmljhaxV90Zx20M2b2T6Gvb6fAPXLL7/k6pxcvxb//a/6lf6W1/9YiPn+59iJ+Ph4FRwcrOLj440OJVdCQ0OVt7e3mjZtmlJKqb179yqTyaT27t1rOebChQtKKaUOHz6sqlWrlu37IbvXIafvJ6mps4XduyEmBsqVI+iE7izZuXPWh3fsCMO6RaBw4IWfHyQl+krBxCmEyJXZs/V8v1Uqm3mv32746CM9yMAOtGoFGzbo1oMmTeAGHky//CzVa5gYPx4iTt7SIy4efVR3DhYZJCQkEBsbm2HLlcjIDDV1N24WnoE24l5Xr16ld+/eDBgwgLfeeguAwMBA+vfvz9tvv205zvfO6iANGzakfv36nDhxwqZxSVJnC3eaXm8/2Iddu3XBzC6pA/jvYh/KOMayXzVjztO7bRygECK3YmPhgw/0/clTHHD7danunGZHTCa9pNn+/Xo+5BYt9GwQn34KfvXdGH31Pc6u2AvJyUaHWuhMnz4dT09Py1a1atXcPYCDAxfdalh+LKat3kVGuXLlCAkJ4auvvsqw/9dff2XNnRzg2rVrJCQkAHD+/HmCg4OpWbOmTeOSpM4W7iwNtrPmEBIS9JI9tWtnf0olbxMfPKUz+LdXtyfqTJytoxRC5MLHH8OVK1CvnuKppyjUo1zzK3U+5N279XfU9u0hMdmROYymtsMZnvlfM8LCjI6ycJk4cSIxMTGWLTw8PHcPMGMG0c+m1fDEfvmDlSMUBS0kJIQWLVrQpEkT+vXrx6xZsyhXrpxNr2m//5WMEhen1+0DgpIfAHQtXU6mrHpudlNauB4iVnnw2qBc/kOwgqtX9eIW//0v/Pmn/lkIoQfWffyxAmCawzs4XSgeA5pMJr1s9ZYter7Nbt0g2ezIN9/ApUtGR1e4uLq64uHhkWHLrYsX0+7H7jluxeiEEdq1a8fhw4f5559/OHjwIA899JDNr1lga7/Onj2bjz76iMjISBo0aMDMmTPp0KFDQV2+4Li760+AAwcIerM0cP+m11SOrk7MmXCOVlMa8v3eeozYaFn1xyaiomDz5rQts24y/v7Qti20a6dvAwJyX0ERG6uncElKAmdncHLSt6n3XV31UpiOjtZ5XkJY27RpcOuWiRbs4ZFr88HrLaNDKlAmE3TqpLedO3XtXcuWRkdlf6Kj0+7fiJel2UXuFci7ZunSpYwdO5bZs2fTvn17vvrqK3r37k1wcDDVqlUriBAKlosLt5u0ZudO/WNOkzqAFpP78cIl3SF79Gj45x89r5S1JCTA55/D119DZv01A0whNFBHOOzaguMJfhw7BseOwTff6N97ekLdunp6ltq19W3q5u2tZ2Q5eFDHnbqFht4/ripV4Jln9Fa9uvWerxD5FRYGc+YowMR/mIDp7bfunSKhGGnTRm/27ubNm5w6dcryc2hoKAcPHqRcuXLW/9y6cQPateNi6B+A/gcYe9uK//hFsVEgSd0nn3zCiBEjLEtkzJw5k7Vr1zJnzhzLeml2QSlLO+uuXTqB8vbO/Spg77+v1/8+dgw++QQmTLBOaL/+Cq+NvsXpSD0fj8mkR7l1DFtEx+u/0oEteKk7bSoJcJny7KzxBNuf+B87dqQN6t2zR293c3DIepWhypV1JWZSku5jnZSUdj8+Xo8onDoV3ntPz3r/7LMw4IGruJwOgQYNoEwZ/UAnT8KBA3rW5pQUSEzUL3TqNniwZIXCqiZPhqQkE91YT9cqJ/TSMMLu7d27ly7pmkrGjx8PwFNPPcXChQute7GICDhyhIuUtuyKTSigpO70adi7V084bcf9RIsLmyd1iYmJ7Nu3jwl3ZSY9evRg+/bttr58wTp0CB57DB55hCD3D4Gc96dLr2xZmDEihGHTA5g6OYUnnnDMV57yzz8w7pUUNm5yBEriTSTvjbvGY+/U17nSSg9QT4LP6zoLdXaGzZupsH49/epVod+b+nGSYuIIqdCBUy71OV2jK6fLBnI6xY/TF0ty9qwJs1nXKjZooJPF9Ft2fUNvX41j5ZxI5n1fgr9DfFm3Dtatgwqk8BQ7GPl9CfyHNNcH//knjMtm5Y1mzSSpE1Zz5AgsWqRr6T7gLXj77XsnPRV2qXPnziilCuZikZEk4cRV0v5RxiYWUG3wyJG6w2RwMEyZUjDXFDZj86Tu8uXLmS6dUalSJaKiojI9JyEhwTIMGMh6vp9z5/RM7s7O8OKLVos5z9auhVOn4MgRgu4MXs1N02t6T8bPZR7/YnNCJ156CX75Rfc/y42LF+Hf/4b58xVKOeJGPK/yMRNGxVDqg/cg9bMps86bTzyht3Scw07SuORpGsfsh+Dv0n7h7U3S492J6jcS78c74uyMroJLSdEd5lKZzbp99p9/dCc9Ly8A3OZ9zuB/v8lg4DQ1WcAzfMPTROLLx7zGx/8HHb+C556DRytVx61jR/1iODrqv72ra9rm7Z27F0mIbPz733pa8EdZRstq0bp/gBDWFhnJJSpm2HXD7K7/j+b2H38uqaAgbuNGCXvs414MFVhdq+mu6iql1D37UuV4vp+jR3WtzcyZVo42j+5MZXK7a19Lf7pOnfL2UKbx45jtOAYnkvjjD2jYEJYuzbp5M73z5/WHUZ06innz9IfSYJZwrFx73v+zOaXmfJS32oYmTeDyZd0OO326Hgrn5gZRUTj/uJiq0ft0Qgewdav+XenS4Oenzy1TBmrW1IveBgWlPW7jxlCpEnTtSq1X+jNtbiXObTnHr0vi6NdPtwhs3qznePV98WHGNd9E8P/+1tV5f/6pE/slS2DhQv1YhdTs2bPx8/PDzc2NwMBAttxn4fRNmzYRGBiIm5sbNWvW5Msvv7znmOXLl1O/fn1cXV2pX78+v/zyi63CL3Z27NBdFhxI4X3+DZMmWbeDqxCp7loiDCAWD7h1y7bXvX6dl/kfXkSzJaWdba8lCkbuF8bInYSEBOXo6KhWrFiRYf+YMWNUx44dMz3n9u3bKiYmxrKFh4dnujzGzwtvqn/xi/qS55SKirLZc8iRW7eUcnZWCtSm784pUKpSJaXM+Vnp5amn1GL+T5V3ibEsHdOkiVK//Xbv45rNermxxx5TytExbamfluxSW2mnVK9eSkVG5ucZZu72baU2blRq0iSljh9P2//zz2lBpN9cXJRq2lT/Pn3w2QgPV2rKFKWqVs34UO3bK/Xxx0r99ZdSFy/mPGQjljL68ccflbOzs5o7d64KDg5Wr7zyiipZsqQ6e/ZspsefOXNGubu7q1deeUUFBweruXPnKmdnZ7Vs2TLLMdu3b1eOjo7qgw8+UCEhIeqDDz5QTk5OaufOnTmOy16Xdcovs1mpTp30+2zE0ylKLVmiVGKi0WEVevJ+SpOr1+LVV9UaemT4/9alY1I+P0ByYO9eVZNTCpSqUUOp2BizUtev2/aamSiqy4RZmzWWCSuQtV9btWqlXnjhhQz7AgIC1IQJE3J0flZP5oMP9Jt/CN8ptXy51eLNkyNHdDBly6opk80KlBo0KJ+PefSoUqBi8FBT2q1RHh5mS4Fv3VonM7duKTV3rk720v9D6NRJqWWvblcpLm5KzZypVEqKFZ5kLpjNSl27ptTJk0rt3KnUqlX6NcrHB2NyslJ//qnUQw9lTFxTt0qVlOreXanx45VauFCpffv0Mrx3M+KDp1WrVmrUqFEZ9vn7+2dZBt544w3l7++fYd/zzz+v2rRpY/n58ccfV7169cpwTM+ePdXgwYNzHJd8CGduzRr9nnJ1VercOaOjKTrk/ZQmV6/FE0+obxmq1wA36fdeixa2jzF5yU/KiUTL/9Bnq65WKjBQ/7MtQJLUaUVm7dfx48czb948FixYQEhICOPGjePcuXOMGjUqX4/boIG+Daa+bu4zUkSEvq1cmaBNOVsa7L7q14exY/Eglne29yK0UlsmvJ6Cu7seXdutG1SooEeK/vMPlHAz82zfCP75R7duPjqjLQ6nT8IrrxT8qCaTSTe31q4NrVtD7976D2Zpn809R0fo00f3Lzx3Dv7zH92SW7u2vtzFi7B+vR4xPHw4BAbavvUiJ1IHC/Xo0SPD/uwGC+3YseOe43v27MnevXtJSkrK9pjsBiDleH3KGzf0i1lMm3M/+kjfvjgqmdyu9iRErrm5cbGEH6CX14WCWSbswoFoknHGwaT79cwN78WqfV6wYIHtLy5sokA+6QcNGsTMmTOZOnUqTZs2ZfPmzaxatYrq+RylWL++vj2GPylbd1gh0nyIjAQgoVI1dtwJJa/96TL49FP44w+oWpVyQ3ox/b+OnD4NY8bo7j3x8eDnp5jx6A4uONXg611NaOyTbqr3KlWsEETh4+sLb76pu9OdPKlzkF27YO5c/dp07gzNm+t59YyWl8FCUVFRmR6fnJzM5cuXsz0mq8eEXPRXPXYMevTQkyUWM+HhsGGDHvX48k8d9RtMCFtasIDoF/XI09QlJWPDr+tyaEOhh28CULPsNcaO1ftGMo8rb/5XlhTKg2vXrjFlyhQi7+QDRiiw6pvRo0cTFhZGQkIC+/bto2PHjvl+TD8/cHM1c5sShO6/ppfoMoqrKzRqxG7P7ty+rQd2+vtb6bH79tXDzSdOBPQAz1nD9hH64U/s+vkcJ6t149Xl7Sh7M1zPAnzjhpUuXHSULAmtWunR+bNmwcaNsG+f0VFllJvBQlkdf/f+3D5mjtenTJ1cMSqq2L2fvv9eDy7qRBA1PK7qfzRC2FjqEmGWpC7eWc8WYENh/V8CoEZ9dz74APzrKSLx5cVr78E779j02vZozJgx7NmzhxdeeMGwGIr0TIOOjuAfoD/AgqmvEx+jDBoEhw4R1HQskLf56bJVqlTa9CBJSTByJL7jBtHq8Ro4btqgZ/b99FPYtk2PMBWFRoUKFXB0dLynBi06OvqemrZU3t7emR7v5ORE+fLlsz0mq8eEXKxPWaaMbtsHPU1PMaFU6rx0MIxFel46G08pIQSkLRGW+n0qjpKkxNq2/0joRT0RvV9ACUqUgMXfmXB0MLOUwSydfUX36xE58ttvv3Hz5k3++OMPypQpw/fff29IHEU6qQOoX19nTkffWQotWhgcTdpMHVZpes2KyaRXTnBz059C3brpWVLHjpUFVAshFxcXAgMDWb9+fYb969evp127zKcRaNu27T3Hr1u3jhYtWuB8p19iVsdk9Zi5lvrpUoyaH/ftg5AQE27E81i5jfD440aHJOzdmTPQqBEXd54BdGNLqhtXEm166bAwfZtaGd2iBbz9b50WjFafE/n8ZP0ZI+5rwIABlimlFi5cyP/93/8ZEocdJHX6Nvhk3jvgW0tCApb+dPkeJJEdJyfdoez4cfjrLz1fmzQRFWr3Gyw0ceJEhg0bZjl+1KhRnD17lvHjxxMSEsKCBQuYP38+r732muWYV155hXXr1vHhhx9y7NgxPvzwQ/766y/GpnaOya+6dfVtZosE26lFi/Ttw/yCxzOPZZw8WwhbOH9eLxF2U9eaVa0Krg46mYu9mmy76549S+h6XQtfo0ba7n//G5o3TOQq5Rl5eAwqwrj+YSL3inxSZxkBa2DLKwDNm7PHfyjx8VCxIgQEFMA1q1WDrl2t3M4rbOF+g4UiIyM5d+6c5Xg/Pz9WrVpFUFAQTZs25b333uOzzz7j0UcftRzTrl07fvzxR7755hsaN27MwoULWbp0Ka1bt7ZO0MWspi4xEZZ8nwLcaXp99lmDIxLFQmQkZkxcMutuFZUqgYfzbcDGSd2hQ4RF6MqQ9HUCzs6w6EcXXF3MrIrrwrxVvraLQVhdkU/qUmvqQg4lYm7eAg4fLvgglILgYILC9Ae01fvTCbuQ3WChhQsXEpR+lQ2gU6dO7N+/n4SEBEJDQzOdAuixxx7j2LFjJCYmEhISwiOPPGK9gItZUrdmDVy+6kgloujWKTmtplIIW4qI4BplSVa672bFilDaVS+TeeN6is0um3QilPPo2RHS19SBrix5f5pOD8aP11NIicwtWbIENzc3Lly4YNk3cuRIGjduTExMTIHHU+STupo170ztkexC2IGrxsxXd/06JCSwCd2Rzqb96YQoKO3bwzffFJ5l+Gxs8WJ9+3/DHHGa8R9jgxHFR2SkZYmwMmV0i7+H653m1xjb9Wc79881zDji5pREZmOrxo3TU4zevAlLv75usziKusGDB1OvXj2mT58OwJQpU1i7di2rV6/G04A5tYr8sC4nJ6hXT1fQBVOfmtu2QUEPJ46IIBFnttEesHF/OiEKSuXKehbnYuDaNfjtN31/2PiK0KRi9icIYS0REUTjBWBJrjzqeMEliP3XUJtdNuyYbuKtUeEWJlOZe37v6AgPNznNrl212PX5Hni/u81iuZtSxs1Q5u6eu5Y2k8nEtGnTeOyxx/D19WXWrFls2bKFypUrW45xcnKiYcOGALRo0YJ58+ZZO+y0a9nskQtQgwZpSV2/rT8VfACRkZymFvG4U7p0WpOwEKJo+Okn3aeucWNo0sToaESxkq6mzkvndniU033dYhNsN1An9KxuqPOrlnUTb5vuHvA17IwJ0MvzlCxps3jSi4vTs3gZ4ebN3D/Nfv36Ub9+faZMmcK6detokNrZ/44yZcpw8OBB6wWZjSLf/AppSdRRGurJGtO1bReIiAjOodd2qV5d+tMJO7J/P3z1lb61Y4vm6lqLYc4/QLINO6cLcbfSpbnorucWTa2pK11a39ps3u/kZMIu66zJz98ly8Na9K6IAylcoArn1x61UTBF39q1azl27FimKwcVNLtK6oLdA/WdbdsKNoDISMLRyy2lrtsnhF344gsYNQp+/93oSGzm1CnYvs8NB1IY4vaLTDYsCtbKlUSP0/2xLM2vCXqpx9i1Nlr+8vx5Qs36w6pGg6yrpUqWhMaeelWLXb9dtE0smXB31zVmRmzu7rmLdf/+/QwcOJCvvvqKnj17MmnSpHuOiY2NJTAwkAceeIBNmzZZ6VXKnF3890qt6QxJqo0ZEw5btxbspKGenpyr1BIuIot/C/tSDEbALv7WDDjQg3X4jBlodDiiGEpdIszS/Jp8BahI7JGzQFvrX7B6dcJaVYbd4Fcz+7qd1nWvc3AP7NwFj2Z7pPWYTAXW0psvYWFh9O3blwkTJjB06FDq169Py5Yt2bdvH4GBgRmO8/X15ciRI/Tt25fDhw9nvZpPPtlFTV2tWnpunVtJrpzzbl3wq7iPGkV47+cBqakTdsbOkzqzGRZ9HQ/AsNIr4aGHDI1HFE+pSZ2l+dVDfzTfuG2jSfVNJkLD9WPfPZ3J3dp00s2zO8O8bRNLEXX16lV69+7NgAEDeOuttwAIDAykf//+vP322xmO9fXVc/01bNiQ+vXrc8KGE7rbRU2ds7OeUuroUQiev4MafQo+htR10aWmTtgVO0/qtm2DsOiSlCaWf42sqOdHEqKg7N4NTz9NdPRvQK205tcyOqmLTXCzyWXj4yHyzkIR91uMqM1jVWEG7L3dkKSoKzh7l7dJTEVNuXLlCAkJuWf/r7/+muHna9eu4e7ujqurK+fPnyc4OJiaNlyf3S5q6iBdvzqDVpZInZxRkjphV2rX1rfXrsGVK8bGYgOLvtA90R9jGe6jhxsbjCh+zp6F4GAu3igBpB/9qutbYpNsk9Sde/srAEq5p1CuXPbH1m3pSRmXOG5TgkNHZW3x3AoJCaFFixY0adKEfv36MWvWLMrd70XPB7tJ6jIsF2Y26w+hgqAUyseX8FN69Jw0vwq74u6u56sDu6uti4+Hn1bqJqhhzY+mJbBCFJSICACik/WHvKWmrvydKU2Sc9lrP4dCfzkIgJ/XrfvO1uDgAK066zh2HS9jk3jsWbt27Th8+DD//PMPBw8e5CEbd/Gwm6TOMq3Jtmt6nZWCGigRE8PlqCRuK/2NKt18g0LYBzttgv3tN928Va1MDB3f72F0OKI4iozkJiWJS9GfH5Y+dXeSuhspNkjqlCLswp3+dH45SwHatNG3O3daPxxhXXbRpw7SNb+e90DFXcW0c6eeb8rW0xNERFimM/H21ku8CGFX3ntP1343bmx0JFb1ww/6duiLnjj07mlsMKJ4ioiwTDxcokTaiE+PijrJi1WlISlJdxy3lqgoQpN07YNf/RI5OqVNawWY2PnXTVAlZTLWQsxuaurq1NH52804R86XCtATzhw6ZPsLR0ZaJh6W/nTCLj3wAHTsqBemtBOJifD33/r+owU1T4MQd4uMzLBEWGqu5OGrJwaOLVvd+hUTp08TRg0A/GrnrI9cq8a6e9HJyFJcOVTAk/uLXLGbpM7FJa2V6Gi9R/SdgpiEOF1NnfSnE6Jo2LEhjlu3wKt0PE0aZr1MkhA2lW6JsPQLEZT2vDP69aaj9WvFTp8mFD3k9X7TmaQqX6UEdV3DANj9U5h14xFWZTdJHaRrgvXqpO9s3Wr7i6ZbIkxq6oRdio+HefPg7bf1Stt2YN1cPVy9u1qHg5Nd/RsURUn58lwsWQtIG/kKkDovbVISJCRY+ZqnTlmSuvtNZ5Je62p6DpSdQbetHJCwJrv6b2YZAWtqqO9s22b7DyFZIkzYOwcHeP55+OCDtFlSi7h1m/R8dD3a35L+QcI4mzYR/ebHQMaauvSL2cceDbfqJW+eieYyFYGc19QBtGlpBmBniO0m91d28qUxr6zx/O0qqbOMgL3spfshXLiQNoGcrVSqxDn3AEBq6oSdcnVN+8ZiByNgL0cls+9KDQC6P5eLqgohbODu1SQAHB2hlOkmALEnoqx6vbAJXwJQtow5V4svtRmgqxJ3XauDOcm6XRYcHXXfvsTERKs+blETFxcHgHM+BsbYzehXSNf8eswRNWIkpvLlbD/6deJEwmcDcVJTJ+xYnToQFqaTug4djI4mX/7+8iSKABo5HsVnQEujwxHF3N3rvqYq7RjPzeRS3Lhi3UQn7Kyumc7pdCapGg3wowRxxFCGE2tP4d/PevM6Ojk54e7uzqVLl3B2dsbBwa7qm+5LKUVcXBzR0dGUKVPGkuTmhV0ldXXr6m84sbEQMWlOgcwZl5xsmT9SauqE/apTB9avt4uaunXLYgHoUe8sODUwOBpRbP3+O0yYQHTMr0DtDDV1AB7OcUQmQ+zVZKteNjRU3+amPx2AcwknWngcYUtsU3aujLJqUmcymfDx8SE0NJSzZ89a7XGLmjJlyuDtnb81du0qqXN11ZPCHz+uV5YoiKQuIkJP4eXszD2FUgi7YScTECuzYt0xXaXe45HSBkcjirXQUL1EWGk9V9y9Sd1tiLdyUhccTNjME8BDuU7qAFo/WIotK2FnUnOGWy8qAFxcXKhTp06xbYJ1dnbOVw1dKrtK6kA3wR4/DkcPpdC9TrgeuRcQYJuLxcRwLuBxYC1VKiscHKTDtbBTdpLUHdt7k/MpPrhymw4vNzU6HFGcRerRpBcTywL3Nr96uOphr7HX/7+9Mw+Pqrwa+G8SspOFELJBEAgmIJsQtiACLg2guFFQCkXtp3FpkbrQFrWoUClt1Vql1Q+pYiso7uKCbMrmB0G2IEsIa0ggC1lgspFtcr8/3kz2ZTKZLTPn9zz3mcyd9973zGTO3HPPe5Zqy8155Ahnz6jLfnuSJIyM/WV/+AKSDlmnfZmbmxve3tbpd+sqON3CdW1c3c585V+eOtV6k2VmklGqevb1vkoMOsGJMRp1p0516rImm3Yr79yEG7vgEyqeOsGOZGVRgQeXy5WB1NhT5++tPFZFhRbUt3o16szx1BnbhR0+rOr7C46H0xl1tWVNztf8YOflWW8y6SYhuAp9+6oWDMeP21uSDrFpk3pMmOp0ixRCZyMzs7abRJcu0K1bw5cDfNSya2GhBec8daq2m4Q5nrqePaFXaDnV1bD/C8uWWhEsg9MZdbVlTU55owGUlKglWGsg3SQEV8HDA268Ud29dNK6buV5RWzbprweCQl2FkYQ6rUI69FDlYOsT8A4VW+1cJjlss0vp+ZwGWU9mmPUAYzR7QUg6ZPzFpJKsCROZ9TFxCjluKzXke3eS+3Mz7fOZOKpE4ROw/8t20FpqY4wHz1DhthbGsHlycxstkWYEf+Imv6vlT4WmzLtZCUAPbpV4udn3jnGDikBIOmAp6XEEiyI0xl1Pj7Qr5/6+1hATQCAtZZgxVMnmMClS5eYO3cugYGBBAYGMnfuXC5fvtzi+MrKSv7whz8wZMgQ/Pz8iIyM5N577yXTWDunhkmTJqHT6Rpss2bNst4b2bMHnnkGVq+23hxWZNM65bFPiD3XWZ2NgrNgMEBUFDn+qixI4yQJqGsVVlRkoTmvXOHsRRW/Z048nZGxNytjMymrd2cOr3VanM6og3pxdd4j1B/iqRPsyOzZs0lOTmbDhg1s2LCB5ORk5s6d2+L40tJSDhw4wKJFizhw4ACfffYZJ06c4Pbbb28yNjExkaysrNptxYoV1nsjP/4Iy5bBp59abw5rUVLCpjOqx2bC3d3aGCwIVsbdHZKTufjH5UDznroAvYpZKzycZpk5MzNJ84wBoO/V5seUjri7P12oJLuqBxkpki3haDhltPA118C6dXCUGuvOSp660sj+FNAdEKNOaJ6UlBQ2bNhAUlISY8aMAWDlypXEx8eTmppKbGxsk2MCAwPZvHlzg33Lly9n9OjRpKen07ueW9jX17fDxSpNphOXNbn48XYOarcAcPP9vewsjSAommsRZiSgIA2IovBsPtQkN3SI6GjOPvQX+Cf06WO+q9q3bxjDPH9if8VQkj48R+/FUsDbkXBKT11tWRO/kfCHP6hAOyuQ8fCLAPj7064eeoLrsHv3bgIDA2sNOoCxY8cSGBjIrl27TD6PXq9Hp9MRFBTUYP+aNWsICQlh0KBBLFiwgCKLrdU0Q/2yJtUWrJ1lA7asUl6PYSEXCI+QtVfBMWipRRiAf5C6PBdVeFlsvrNp6rvfkeVXgDFRKhQk6fvSjookWBin9NQZl1+PFkSiLfuL1eJn0tPVYydOCBSsTHZ2NqHN/GKHhoaSnW1ao+6ysjIWLlzI7NmzCTAG2gBz5syhb9++hIeHc+TIEZ5++mkOHTrUxMtXn/LycsrLy2ufF7anXsJVV6ks2PJyyMhQzzsDBgOb9qi7roSbLNuIXBDM4p134OWXuVj+FRDdvKeum7o8WzRRIk09mpv5amTsyCreOA1JKQFtD7YRWVnQvTt4unj+hlN66mJjlZFVUAAXL1pvnoyaMj2SJOF6vPDCC02SFBpv+/btA1Rfw8Zomtbs/sZUVlYya9YsqqureeONNxq8lpiYyM0338zgwYOZNWsWn3zyCVu2bOHAgQMtnm/ZsmW1CRuBgYFEtSduoEuXuiykTrQEq+3azabyCQAk3B9pZ2kEATh9GlJSyNErL1yzRl13DwAKqyzTvUH79W84m6KShTrqqRv7m5EAHCiOwd5dvc6fh3vvhchIGD3aeiH0nQWnNOp8feu+tMe+z4YzZyw/SWoq6Y8uAySezhWZN28eKSkprW6DBw8mPDycHOMaSz1yc3MJa6NZcGVlJXfffTdnz55l8+bNDbx0zTFixAg8PDw42YrB9fTTT6PX62u3jIx2FhDthHF1R92GkEUkPh6VjJ/klIsTQmfD2CKsPAhoYfk1RBl8hQYza480In/DXkoMyuvXUUdE//HhBAdDebmOQ4csIJwpHDkCc+bA/PmwYQMl+WU8/7yKrnrvPTXk0CH42c/g0iXrilJYCDt2OGYtdqf9hRs0SNlyx2b/iRtuSoUtWyw7QWYmGRVKE8VT53qEhIQQEhLS5rj4+Hj0ej0//vgjo0ePBmDPnj3o9XrGjRvX4nFGg+7kyZNs3bqV7t27tznX0aNHqaysJCIiosUxXl5eeHl1IEanflxdJ8G49DrxJg+kraTgEGRmUo2O3JLmW4QBBPRQelqkdUXTOhjiU1XF2XTVLD4yrApv745d+nU61TJs/Xr4/juNUaNsEH/k4wOffUZ1WTnvLdfzDEPJRHnex48q47EF3jz2GBw8qIqLb94MjUKQzaKwUJ1z//667cSJum6JTz4JS5fiML8tTumpg3qdJRhknezXzEwpZyK0ycCBA5kyZQqJiYkkJSWRlJREYmIi06ZNa5D5OmDAAD7//HMAqqqqmDFjBvv27WPNmjUYDAays7PJzs6momat4/Tp0yxZsoR9+/aRlpbG+vXrmTlzJsOHD+e6666z3ht68kk4exb+9jfrzWFhaluDSRcJoZ288cYb9O3bF29vb+Li4ti5c6dlTpyVRT7dqdbUJbhHj6ZDAsKUV03DjZKSDs6Xnk6aQWV994l27+DJFHddp2KbVvw5D4O1QlXruQG1ftFs++3njO5+hvv5D5lE0pczfMwMduz14e5Tf+a77yAkBPbtgylTOtZi7fJlmDxZGYaTJsFTT8H770NqqjLojEUH/v53tex75EhH3qjlcFqjzni9PMnV1jHqsrKk8LBgEmvWrGHIkCEkJCSQkJDA0KFDec+4XlBDamoqer0egPPnz/Pll19y/vx5rr32WiIiImo3Y8asp6cn3333HZMnTyY2Npb58+eTkJDAli1bcHe3zI92s/TqpaKsrTmHBSlb/Qnbv1c9NMWoE9rDhx9+yOOPP86zzz7LwYMHuf7665k6dSrpxgy5jlCv72twsMo/aozPVaG4uyt3UKG+g1V+T5/mLComqW9fy3jVZo9PJ5h8zhb14Jv3CixyzlouXIDp0ym6djxf/PkYDz2knCc3/HUK+/P7EBCg8bcnsji25FNmXH8Rnbs7jB7N4MFqUS44yMCePXDLLVBsRim9oiKYOlXdEGqamvvOO+FPf1LeyZwctYL+5ZfKID98GEaOhNdeqykMcPgwvPgivPuuEuj4cfMEMQetE6DX6zVA0+v1Jh/z3XeaBpoWS4qmeXtrWnW1RWWqfvwJzYcSDTTt5EmLnlqwMuZ8n5wVZ/8sNg9+XANNiwwssvRPgNAMzvR9Gj16tPbII4802DdgwABt4cKFJh3f4mdRXq5poH3HDRpo2sCBLZ8jKEhdx1JS2it9I958U3uENzTQtD/+sYPnqscfer6ngabd1OeUZU5oMGgnFv1Xe8VroXYTmzUPyjVlVqnNx0fTHn1U03JyGh1XUKBpFRW1T/fPXKYFUaCBpk0YlKcVFxpMFqGkRNMmTFDzBQdr2oEDzQzKytK0zz7TtBde0LLvfFi7JWBHrYyTJ2ta5if/pzUQ3LgFBmraoEGa9vHHdec6ckTTbrlF06ZN07Q77tC0/fublctU3XLamDqj9yyd3mhlZehKSzG72V0zFJwr4goqHqKX1DIVXImlS9Wd6N/+5thu6qoqNh1X3vSEG6qk7JBgMhUVFezfv5+FCxc22J+QkNBifUmTywUVFsKwYeScGwCXm4+nMxIQoJYBO1x+8vRp0rgR6Hg5k/r8elF3XnrEwHdp0RzdW8qgUR3L1H118gae2jIHrd4iYnQ03Hqr8rpNnNhC7Fq3hl1iRvilskk3hZu1Tew42p3bw3bx1bxN+MYNhGuvrVvKa0RZmfLI7dihPvtNm2D48JoXT52CRYsgKamuNgwQBnzNCt782Wc8tfMuNm6EIXvH8rfr3mZ49X56FfxESNZhdIV60NdsNYkygFpJXL++7vmjj7bnI2uC0xp1RkPrCr4UEEz3vDyLGnXp55Q7PDSgDG9HiZAUBFvwwQdw9Cjcf79jG3XHj/Nd1UQAfvZzx6mnJTg+eXl5GAyGJhnqYWFhLdaXXLZsGYsXL2775CEhqkXYa8DjzWe+GgmozAe6U3juEozqQHu7igrO6vqB1vFyJvXp/WACdz21iU9LprL8ybP8707zu0v86+GfeHKL6vpyQ3Q6t/+6F7dMczOvd8CqVYx64RwbnnmPhPfv4/sr4xj/kjcv8ALTxr+B287tdWPffBOqq6nI1TPjvTvZfOYa/Nyv8G3074jbEAFxz6px3t6wdq36W6eDwYPVmuvAgegGDODXw4dzQ5FK0D140I0H/u9/gP8BVO28nn2q6dW9jF4BhfRN8WTMlxAfDz1iY2HVKtUPuLq6LiHAXEz2SZrJiy++qMXHx2s+Pj5aYGCgWecw16UfGqo8nge4VtP27TNr7pZYl/BPDTRt5MAii55XsD7OtETUUcz6LO68UynW669bTzALcOWt/2pdqNBA09LS7C2Na+AsunXhwgUN0Hbt2tVg/4svvqjFxsY2e0xZWZmm1+trt4yMjFY/i6efVmr02GMtyxHvuVcDTfvspY4tb1ZXa5q3d7UGmnb6dIdO1YTt8z9RS6O6Uq0gz/Rlzvr8+2/5tSuUT4/cZNFQiZ0bSzR/77pl3CEhmdr772taVZWmaUVFmgZaJe7az/lYA03zplTbykQ1eObMhid7+WVN27xZ01r5fpeXa9rzz2taXJymhYdrmk7X/EqscYuO1rRf/lLT/vUvTTt4UNMqK5s/r6m6ZfVEiYqKCmbOnMmjHXQpmkPtEuz0J1SpaQuSfttvAIga0NWi5xUEh6eT1Ko7tCmHKjzo4VPk0A5FwfEICQnB3d29iVfu4sWLLdaX9PLyIiAgoMHWGq31fTUS0EUVCy4sqDJd+GbIzoayMh1ubpav1nD9i5MZ5naYK5oPb/8upd3Hr14NiX9QXsgnen7E0h8mWjRUYnyCLyfTPPnDH1RLz8N5EcyeDQMGwL9XGLgy817ui9rKp8zA072Kdfd/waQ37lErEr/7XcOTPfUU3HyzWpttAU9PeOEFlYGblaUa8Jw7B//3f/Dhh/DKK/DAA3UOudOn1Wfwm9+opd6gIFizxvz3a/XlV6M7+t1337X2VE3o3Vt9sOmT7rVIP+T6SDcJwWUxroc4uFG3d5+6MoyKKUSn87ezNEJnwtPTk7i4ODZv3sxdd91Vu3/z5s3ccccdFpnD2O2o1eVXzytQCkWXO2bUGUPAevVqPtO2I+j8uzJ/+nke+GQI/9wygCcMpifHf/wx3HcfaJqOX/8aXvnHdHQeljdLwsLgL39RreD/+U/4xz9UiFzigkCe6PofiotVw5xPPutCwm2/sOjcHh7KTmjOVrh8GfbsgV27YPdu9XdhIfTsaf58DlnSpLy8nMLCwgabOdR66iyQgd4ATWvQ91UQXIr+/dXj6dP2laM1qqvZd14Vkhp5neUaoguuw5NPPsm///1v3nnnHVJSUnjiiSdIT0/nkUcescj5TfLUeam6lIWXO1DS5NIlzv5yEQB9+3awNEoL/OK/U+neHc5luPPVV6Yds24dzP5FNdXVynO1fDlWMejq062bynU4d055zCIiVKURNzdVg+6226w6fROCglQtvMWLVVJGQYHKQRs71vxzOqRR16H+lPWoNepSilXdG0uxbRsZH+9uMIcguAzGLKTMzLqy6o6Gmxt7+88CYNTkYDsLI3RG7rnnHv7xj3+wZMkSrr32Wnbs2MH69eu56qqrLHJ+U4w6f+9KoIN16nJzOXumGrBcjbrG+PjAQw+pv19/ve3x69fDzBnVVBnc+GXQ16xYVoCbDa2Rrl3r6qivXg3ffw8zZ9pu/pZwd1f5Fx3JvTTrY2xPM3Nz6HB/yhpqjbpvagoBWoqsLNINyj8qnjrB5TC2ISsttV1BzXZSVAQpqWoNaORoh7x3FToBv/71r0lLS6O8vJz9+/czYcIEi5xX00xcfvVVRl1RR9QsN5e0mvgjS5Yzacyjj2i4u1WzdSsc3tx8hjCoBNLp0zUqq9yYyUesunkN7iEdyOztAF5eKlt14kS7TG8VzPJ1zps3j1mzZrU6pk8Hvj0d7k9Zg9GoyyDKol0lqjKyanvOiadOcDn8/FRQaViY5QN0LMTBg+rC2atXXTsfQXAUiopUTTRoY/nVV/XfKizuwI1JXl69bhLmn6YtonrrmN59Gx/n3sDyBed461BDxdPrYd485RkDHXfwBWsGvEiXVbs62NhWqI9ZRp2pzcztjdGLlkkklbmXsdTlJ+t0KQa60MXNQFhY52iXJAgWxcErbu/9w8fATEZdUwJYrj6lIFgC49Jr167g20q93oDbJ8F+KOwRbf5keXmcQ6VaWmjluEXmP+XJxwth9U9DWHaumO5XqeoQO3fC3Lka587pcMPAIv7Es11fx+PzXepDECyG1dcl0tPTSU5OJj09HYPBQHJyMsnJyRTbYNkmNBQ8ParRcONCtuWMr4yzyiXeM7C4s7TAFATXoaqKfT+qGKSR15TaWRhBaIopS68A/jEq1KGwvANBVnl5ZKO8ZpGR5p/GFK5bEM9wr6NcwZe35x+iogKefRYmTYJz53T04zQ/MJ4Xuv8Tjw9Xq7oigkWxulH33HPPMXz4cJ5//nmKi4sZPnw4w4cP71DMnam4uUFUuDLA0nN9LHbe9AvKwdk7rLyNkYLgpKxdq4JRPvzQ3pI05dgx9laPAGDUFMvWpxQES2BKkgTUlUPrSJuwkkw9xfibNF9H0bm7Mf9u9eaWf92HccOv8Oc/q0YJv/plBckDZxP/97tV+uktt1hXGBfF6kbdu+++i6ZpTbZJkyZZe2oAekepO/Z0faDFMvUyctVdU1QvB838EwRrc/CgqgGQlGRvSZpQsP0wp1FlV+JGSZKE4HgYPXVtGnUXTwFQmGn+ylZOqTLofDwq8bdBucZZ/xhLiC6P89U92X/Mh27dND7+GN55zxP/o0nwxBMWbdkpNMTpf/F691NetXRDpAW6Iisyug1R5+7vaZHzCYKjceIEvPyyaknYLMZ1nMxMm8lkKvu3XAIgOjCPYKlmIjggRk9dm8uvJw8AUFhQaf5c//M0AGE9u9gkH8E72JffTzkMwM1+uzj8fR4zZtS8KAkRVsf5jbq+NUbdtXdYzFOXfs1UAKKG2CcNWxCszaFDqkPOW2+1MMCBjbq9B1Sgq0qSEATHw+Tl12B1/SqsND98yNjpLDzcdgbVgq8nkf5DOpv0Y+l5bQ+bzSvYoE2YvamtVRcxBgItc05pESY4O7Gx6jE1Vd0LNbnBNtaqczSjrqqKfZnK4Bw5oZW0QkGwI6YmSgR0VzUbrlR7U1WlWlm1F1MNSEuic9MRdZ1cIO2B83vqjLXqzKtf3BRpESa4AFdfrQy5S5daKPFY31PnSF0lLlxgr24UIEkSguNiqqHlH1JXr9Xc6KHsv7wLQHiAZIK7Ak5v1BkNr/S0aosUIL7y349rTyOeOsFZ8fGp+36npjYzwOipKytTXakdhGyvqzhviESn0xge5/Q/b0InxVSjzjPIFy9UlWKzWqBXVJBz7oqay4bLr4L9cPpfPaNRV1jshn7F2g6fLyNFZSH5dSkjKKjDpxMEh8W4BHv8eDMv+vio7tg6Xd0VygEwVkoaOFBnk0w/QTAHU5df8fMjAGXNmeWpy8+vrVEXflXHuzQJjo/TG3Vdu0KwtwqYTj9r6PD5Mk5XANA7QC+JPIJTY6wL2qynDiAlBSoqHKqA6N696nHUKPvKIQgtUVamWmaBCXFu9Yw6szx1ubnkoCYJi3D6y72ACxh1AL2D1C1O+vmOv930DGXJRYVc6fC5BMGRqZ8s0SxhYeZFbluLykr2vfQ9IJ0kBMfF6KXz8KDt1Z6oKPyjlTvPLKOuXjcJ6YHsGriGUReqDLD07I7XlcvIUefo3bOqw+cSBEemTaPOwdCOHmPvlcEAjLq+A22VBMGKFBWpkNSePU0o2+bjQ0BP1VbCHKNOy82r9dSJUecauIZRF6kMsPT8jlexTi9QzYejrpKmr4JzYzTqzpyByuZqn377rWoVtny5TeVqifRNx8kllC66KoYNd4mfNqETMmiQSho/c8a08R1pFVZ8QU8p6rpny5Imgv1wiV++3r3V7VBGYccL1WWUqBL1vWMt10tWEByRnj1VN5+qKjh9upkBp06pVmHbt9tctubY950KVBrSIxtvcdQJDo6pMdkBeUr5CnPaH/KTU6Dq3HX1KJPOXC6CSxh1UdFqyTT9SkjHTlRZSbpPjDrnoICOiiUIDo1O18YSrIN1ldh7WGX3jRpSZmdJBMFy+B/YAZjX/zV7yv0AhPWWzFdXwSWMut5DlIcu3bdjWXpaFw8yNFUjpfcAqVYvOD+dxqirrGRfttLNkTfIDZfgPAR41tSpu9T+6g3GakO2bBEm2BeXMurOF3fD0IGqJpcuQUlNO8levSwgmOASXLp0iblz5xIYGEhgYCBz587lchsFe++//350Ol2DbezYsQ3GlJeX89hjjxESEoKfnx+3334758+ft6jsJhl1WVl27ypRffgo+7QRAIy6RXpNCs5DgHc5AEWF1e0+1tj3VeLpXAeXMOoiIsDdHQwGdf0xF2Odu5AQVXtVEExh9uzZJCcns2HDBjZs2EBycjJz585t87gpU6aQlZVVu61fv77B648//jiff/45a9eu5YcffqC4uJhp06Zh6MidSyNaNeqM6XQVFVBQYLE5zeHUaR16gvB2K2fQYPFKCM5DgLfKUjIn+zX7zc8BCPfIt6RIggPjQEWmrIe7O/TqWc25dDfSj5fSq5d5S6cn//4VcCdXe6YBfSwooeCspKSksGHDBpKSkhgzZgwAK1euJD4+ntTUVGKNVlMzeHl5Ed5CHQK9Xs/bb7/Ne++9x8033wzA6tWriYqKYsuWLUyePNki8rfaVcLLC7p3h/x8tQTb3X69VvdVDgPg2tFeeHjYTQxBsDj+Pqp6Q2FR+29Wcs6qeo1hQeUWlUlwXFzCUwfQu+AQAOkbU8w+x4kzyga+Oti+Xgmh87B7924CAwNrDTqAsWPHEhgYyK5du1o9dtu2bYSGhhITE0NiYiIXjVVLgf3791NZWUlCQkLtvsjISAYPHtzqecvLyyksLGywtUaMygsiP19tTYiMBDc3yM1t9TzWRjpJCM5KQFe17FpU3M7LtaaRXaZCj6RFmOvgOkZdwCUA0s+1Py7ByIkLKic8pk+FRWQSnJ/s7GxCm2nwGBoaSrYx4KUZpk6dypo1a/j+++955ZVX2Lt3LzfeeCPl5eW15/X09KRbt24NjgsLC2v1vMuWLauN7QsMDCTK2By5Bfz86vonN7sEu20blJfDjTe2eh6rYjCwb4/yZowcaT8xBMEaGI26wpJ21kYtKSGnWsWXhvWTeiaugusYdcEqHTzjgvlFg09eUstLMQNc5mMTWuCFF15oksjQeNtX011e10xBKk3Tmt1v5J577uHWW29l8ODB3HbbbXz77becOHGCb775plW52jrv008/jV6vr90yMjLafK+txtUFB9u9VVjVgZ84sFsZu+KpE5wN/1/NAKDQPah9B+bm1rUI6yOFG10Fl4ipA4gKVd619Ivmu6FPlKqU16uvlXImrs68efOYNWtWq2P69OnDTz/9RI6xrkA9cnNzCWtHSlpERARXXXUVJ0+eBCA8PJyKigouXbrUwFt38eJFxo0b1+J5vLy88PJqnw7ExsKWLY7bLizlmzOUMpyu7qXExIhuCs5FwJiBABSWtO9yrVqEDQEk+9WVcBmjrndPlRGYXuBv1vEFF6vIr1bdJPqPtl9AuOAYhISEEBLSdjHr+Ph49Ho9P/74I6NHjwZgz5496PX6Vo2vxuTn55ORkUFERAQAcXFxeHh4sHnzZu6++24AsrKyOHLkCH/729/MeEct02qyxK5d8M9/QnQ0/OlPFp3XVPZtU174uMgs3N2j7SKDIFiL+m3CNM30ThT6c5cpR3noxKhzHVxmHbF3X/VW04uCzDr+5B6VHNGT83Tt1zRGShCaY+DAgUyZMoXExESSkpJISkoiMTGRadOmNch8HTBgAJ9/rsoPFBcXs2DBAnbv3k1aWhrbtm3jtttuIyQkhLvuuguAwMBAHnjgAZ566im+++47Dh48yC9/+UuGDBlSmw1rKQbU1Oxu1lN38SJ88AFs3mzROdvD3mMqXmjUtc01qBWEzk3ABZXcV1mpwldNJSdb1Y4M6FIiJbhcCNcx6vqrJaeCygCK299thROn1Ed1dXC+qpEiCCayZs0ahgwZQkJCAgkJCQwdOpT33nuvwZjU1FT0etW71N3dncOHD3PHHXcQExPDfffdR0xMDLt378bfv87T/Oqrr3LnnXdy9913c9111+Hr68tXX32Fu4W/n0bb8/Rp1Qe2AfbuKlFVxb68qwAYeVOQfWQQBCvSdUddfcr21KrLHqoy48P7SUiCK+Eyy6+BI6IJ8LhCYaUPGRkwcGD7jj9RoJbaYmYMs4J0gjMTHBzM6tWrWx2j1evI4OPjw8aNG9s8r7e3N8uXL2f58uUdlrE1evVSxbavXIGzZ+Hqq+u9WL+rRHW1Km9iQ8p+OkGypnRy1K3iQRecD7eArnSliGL8KSyEZpLpm8UYyhsWJsW4XQmX8dQxaBC9Y5UPOj29/YfXxKfX1u0SBFfBza3ue99kCTYsTAX5VFVBXp7NZUv+KoNKPAnpcom+0a7zcya4EH5+BKBcdEVFph9mrGzUQv1ywUlxqV/B3r3VowlVHJpwIlXVCmrgpRAEF6HFsiYeHnWug4704DOTPQVKIcdenW9yALkgdCr8/PBHWXPtWX7N+eA7AMIqzLjgCZ0Wl1l+BYgKKwe8anq4mh53pGlw4kgF4E1M8kdw+93WEtFiGAwGKitdM3Dcw8PD4nFlro4xWaLZDNjISLXWk5kJw2wbnrAntx8AY2b3t9mcoluiWzalnqeuXTF1Z68AEO6jB1ovMi44Dy5l1PX+4G/AItKPFQFBJh+XlQUlVd64YaBfH/M7UtgCTdPIzs7m8uXL9hbFrgQFBREeHt5qIV7BdFotQBwZCYcOtdBHzLrs2aMe63VhsxqiWwrRLRtjplGXU9wVgLBe0gzZlXAtoy5QD2WQfq59xxnj6fpyFs8+kZYXzIIYLzqhoaH4+vq63A+vpmmUlpbW9kk11nUTOkarRt3776t+Yjb24OSeLebMGXXhskUnCdEt0S274OdHACrroV0xdWVBgHSTcDVcy6gLKYUcSM9q39s+kaoBOq7mJPR03EwJg8FQe9Hp3t11CyT71BRlunjxIqGhobJcZAGMiRIXL8LlyxAUVO9FY3VUG7PnvRPACAZ4niYoyLpFh0W3FKJbdqBvX/wnBMGOdnjqDAZyDOp7GhYtfV9dCddKlAhXrcIy8nyobscq6onDquJjDCfqSjg4IMY4H19fqUtk/AxcNfbJ0vj71331HaVd2J7tZQCMibB+ILjoVh2iWzYmMJCAoX0A0406reASOag2EuExgVYSTHBEXMqo69kTdFRTUeVOzQqCSZw8qozBGL8LdIbS3K62LNQc8hlYnhaTJY4ehV/8Ah5+2Kby7DmmCjGPGVZmsznleyWfgT2o3yrMFC6dyqcSTwBCe0pMnSvhUkadR2g3IlGV79tT1qS2m0RoO6JUBcHJaDGu7soVWLsWvv7aZrJUV8OPF1UniTE3yvKS4NwEpP0EQGFB45YuzZOdrhwR3dwu4+VlNbEEB8SljDpCQuiNqjxsagFigwFOZanlhphJjrv0KgjWpkWjzrgum5OjFMYGnDhcjr46AB9KGXJ7X5vMKQj2wv+jfwNQmGda89ec0CEAhMnSq8vhWkbdyJFE9VMuaVONunPnoLLKDS8viFr5nBWFEwTHpkWjLjRUtZ0wGCA31yay7PlCedzjuhzCo09Pm8wpCPYiwEt53gr1WhsjFXXdJGSp3NVwLaPuppvoPX0kYLpRZyxnEh1t84oNguBQGI26kycbOeS6dFHtwkAVILYBe7bVS5KQGC/ByQnwUUkppsbU1fV9tZJAgsPiWkYdda3CTDXqTpxQjzExpt0hCebzwQcf4O3tzYULF2r3PfjggwwdOhS9Xm9HyQRQuuPtDRUVkJbW6EXjEqyNjLqknD4AjLlDrlqmILrVufH3VXdRhUWm3cBkf7UXgHC9g6SqCzbDtYw6TaN392LADKPu61dh2zbryGULSkpa3srKTB975UrbY81k1qxZxMbGsmzZMgAWL17Mxo0b+fbbbwkMlNgQe+PuXtf7uMW4OhsYdaWl8NMJlYU+5ncTrT5fm4huCVYmwK/GqCs27ZKdc059l8LItppMgmPiUsWHKSig95ybgYOkp6uCwm1hXH6NqToK3X9mVfGsSteuLb92yy3wzTd1z0ND1ZWzOSZObGjc9ukDeXkNx2jmeTV1Oh1Lly5lxowZREZG8tprr7Fz50569uxJUVERN954I5WVlRgMBubPn09iYqJZ8wjmExsLhw8ro+6WW+q9EBmp4ups4PU5cEAt/4aHQ5QjtLTs5LplpLS0lIEDBzJz5kxefvlls+YRrENAV1VYtbDUtEt2tl4l94VHuJbfRnA1oy4oiN6686DBxYs6ysrUclJrnEitBtxquklIQLa1mTZtGtdccw2LFy9m06ZNDBo0CFAFT7dv346vry+lpaUMHjyY6dOnu3R1f3vQYrLEK6/Av/5lk8DTPd+XAH6MGVyCTiflTEylJd0ysnTpUsbYoomu0G6MdeqKy7pQXa3un1ojp7Sm72uUp5UlExwN1zLq3N0JDgbf/BJK8eP8eejfv+Xh5eVwLl1582K80qFbNxsJagWKi1t+rfGFuLXKzI1/TZoEV3WMjRs3cvz4cQwGA2H1onzd3d1rK9mXlZVhMBjQzPRaCObTolHnZzvjas/Gy4AfY4/8G/itzeZtkU6uWwAnT57k+PHj3HbbbRw5csSi83ZGli5dyjfffENycjKenp5cvnzZrvL4P5kI34Gm6SgpUR1eWiO7XF2rwvs6frF8wbK4nG9W1yOEKFTl4bbi6s6cgepqHf4UEtbLo3Nn2fn5tbw1dle2NrZxR43mxpjJgQMHmDlzJitWrGDy5MksWrSoweuXL19m2LBh9OrVi9///veEhISYPZdgHi12lbAhe46q79iYoS0sY9oaJ9CtBQsW1MbbCVBRUcHMmTN59NFH7S0KAD5TJ9XeH7TVKqy6Gi4a+772b8P6E5wOlzPq2lOA2JgkcTUn0fWSpVdrkpaWxq233srChQuZO3cuS5Ys4dNPP2X//v21Y4KCgjh06BBnz57l/fffJ8eYty/YDKOnLju70cUlMxNmzYI777Tq/NnZkK4PQkc1IyfJBcsU2tKtdevWERMTQ0xMjJ0ldRwWL17ME088wZAhQ+wtCqD8Caa2Csu/UIahZhEuNLYTry4JZmFVoy4tLY0HHniAvn374uPjQ3R0NM8//zwVFRXWnLZ1zDDqYjgBvXpZWTDXpaCggKlTp3L77bfzzDPPABAXF8dtt93Gs88+22R8WFgYQ4cOZceOHbYW1eUJCFAJCtBoCdbdHT78EL78EqpMa2VkDnv2qMdBHMV/zDVWm8dZMEW3kpKSWLt2LX369GHBggWsXLmSJUuW2FPsTkl5eTmFhYUNNouRmoq/u8p+buu0OadVOEB38vAIkcxmV8OqMXXHjx+nurqaFStW0L9/f44cOUJiYiIlJSX2y64yx6iL1cHYsVYWzHUJDg4mJSWlyf5169bV/p2Tk4OPjw8BAQEUFhayY8cOh1kacTViY5XHLDUVRo2q2dmjhzLsDAZV+dRKSUVJ28sBL8awB4bdZZU5nAlTdGvZsmW1S6/vvvsuR44c4bnnpHtOe1m2bBmLFy+2zsk/+oiAvDuBIW0addlVKiwl/JrunTtkSDALq3rqpkyZwqpVq0hISKBfv37cfvvtLFiwgM8++8ya07bOpEn0nhQNNBPs3QhjOZOr/zgL5s2zsmBCa5w/f54JEyYwbNgwxo8fz7x58xg6dKi9xXJJmk2WcHOrc+FZsVbdnu2q/taYoBMgmc9CO3jhhRfQ6XStbvv27TP7/E8//TR6vb52y8jIsJzwfn4EoKy5No26mtJ0YdIizCWxefarXq8nODi41THl5eWUl9c1LraoG3vOHCbEg64//PCDMtyMBVUbU9dNwnLTC+YRFxdHcnKyvcUQaCVZIjISLlyArCyrzGswwN6jKgN6zBAHSZJwMu6//357i2A15s2bx6xZs1od06dPH7PP7+XlhZeXl9nHt0o9o66tmDpjqLHxHktwLWxq1J0+fZrly5fzyiuvtDrOqm5soF8/mDoV1q+HN96AV19tOqaoqO7adHV/0woVC4IrYPTUNVnVs3JXiZQUKC73xM+rikHP3GGVOQTnJSQkpPNmzPv54Y+y5tr01G0/DgwgLPMgMNzqogmOhVnLr+a4sTMzM5kyZQozZ87kwQcfbPX8VnVjV1fDpUvMuycXgFWrmu++c+qUeuzBRbr18qu7/REEF2d4zXUiJaXRBcbKRp0xSWJUfBfcp3Ti7i6Cw5Oenk5ycjLp6ekYDAaSk5NJTk6muLWahNakHcuvOedUa7rwK2etLZXggJjlqWuvGzszM5MbbriB+Ph43nrrrTbPb1U39o8/Qnw8k3v3ITr6LKdPw5o18NBDDYfVL2dCebnE7whCDRERqoNVWppSp5tvrnkhMlIlS7TWo/Tjj8HXF269td3zGo06aXogWJvnnnuO//znP7XPh9fcyWzdupVJkybZXqCuXQlAGWlteuoK1LUzLFSKs7siZnnqQkJCGDBgQKubd03RzQsXLjBp0iRGjBjBqlWrcGurv4m1qXG/u+Xn8utfq13//GfTlooNypmEh0MX12q+IViOS5cuMXfuXAIDAwkMDGTu3LltVqhvyQP+0ksv1Y6ZNGlSk9fbutmyFOPGqcddu+rt/N3v1A1QS+EV27bB3XfDbbfBd9+1e849P6hSSGN8fmr3sYLQHt599100TWuy2cWgg/bF1BXV9H3taf2WfYLjYVULKzMzk0mTJhEVFcXLL79Mbm4u2dnZZBvTc+yBMaaipIRfzbqCj49qUP7DDw2HNTDqpOer0AFmz55NcnIyGzZsYMOGDSQnJzN37txWj8nKymqwvfPOO+h0On7+8583GJeYmNhg3IoVK6z5VmoxGnW7d9fb6eXVeu/X0aPVo6bBnDl1aXomUFwMR46rG6sx655pp7SC0Mm5+mr8Z0wBTPDUlaqi3NL31TWxqvtp06ZNnDp1ilOnTtGrUfFeu/XtDAysrafVrTqfX/6yFytXKm/d9dfXDastZ8JJKTwsmE1KSgobNmwgKSmptln6ypUriY+PJzU1lVhj1kEjwhulrq1bt44bbriBfv36Ndjv6+vbZKwtiI9Xj7t3Y1KDcUAtu5aWqvXTw4dh9mzYvLl1Q7CGffugWnOjFxlEjhZ9FFyMHj0ISOgBn7Ru1BkMkFepCg6H9/O1kXCCI2FVT93999/frAvbro3Ydbo6b11+Pr/5jfrzs8/q4rs1ra4Gl3jqhI6we/duAgMDaw06gLFjxxIYGMiuBmuXLZOTk8M333zDAw880OS1NWvWEBISwqBBg1iwYAFFbazNWKrq/dChykbT6+tlwV65Avfco+6O6neNKS5WVxtQ/U0/+kj1Md26Ff70J5Pmq42nYw8MG2aWzILQmTG2CWtNZXNzoRp3dFQTEi3dJFwR1+v9CnVGXV4ew4bB+PGqs5ExhyM/H4whT/05JUadYDbZ2dmEhoY22R8aGmpyGMJ//vMf/P39mT59eoP9c+bM4YMPPmDbtm0sWrSITz/9tMmYxixbtqw2ti8wMJCoqCjT30w9unSpS1ioXYL19obPP1exDPWzxefPV4MPHFDPBwwA4zLxkiUmxdc1MOquvdYsmQWh01Jdjf9JpT9FhS07RXKy1Ws9yKVLeCct3yJ0CJc36qCuWcSKFcrBYFx67RVUhO8dCeIZEJrQnrI+umZa9Wia1uz+5njnnXeYM2dObfKRkcTERG6++WYGDx7MrFmz+OSTT9iyZQsHjMZTM1iyXJBxCbbW4ajTNS1rsmePqhu0f79KojAyZw48+KDy3BUUtDpPSQn8sKMagLHsAQdpsi4INkPTCFg0H4DCy9UtDsvOUb8pYYNDxRnhorhmSue0aTBwoKrLANx1l0pwzc5WjoYyVeaHmBH+8MUXdhPTlbl06RKvv/46Dz30EBEREfYWpwmmlvX56aefyGmmxmFubi5hYWFtzrNz505SU1P58MMP2xw7YsQIPDw8OHnyJCNGjGh2jCXLBTWbARsRAefOKaOuuhoee0ztv/feOivQyOuvw4IFddWMW+CPf4TcfDd6c47R0fnQtatF5HdVHF23hGZwdyfAqwLKW19+re0mEaEDNymY74q4plG3YEGDp56e8PDDsHixSpiYMEHtl/Zg9mP+/PlcunSJgwcP8oUDGtamVqePj49Hr9fz448/Mrom+3PPnj3o9XrGGa2iVnj77beJi4tjmAne4qNHj1JZWWmzC/XYseoxNVWFLHTvTkNP3apVsHcv+PvDX//a9AQ+Pg0NurIytYRbj6QkeO019fcKHsZr+DWWfyMuhqPrltA8Ab5VUA5FJS0ba7V9X9u+XxScFNdcfm2Ghx5ScUI//KC8dQAxvUqaFrATrM6XX35JcXExX3/9NUFBQaxZs8beIpnNwIEDmTJlComJiSQlJZGUlERiYiLTpk1rkPk6YMAAPjd+8WooLCzk448/brYDy+nTp1myZAn79u0jLS2N9evXM3PmTIYPH851111n9fcFyogzvoWkpJqdRqPu2DF4+mn19/PPt92IcscO1YT5m2/UequmUV4ODzygVPDeOQamHPwLLFpklffiKjiTbrka/n5q2fVKmRuVlc2PyTlwAYDwUzttJZbgaGidAL1erwGaXq+3zAkNBk0rKNC0rKwGu+++W9PUJURtX3GrpkVGWmZOG3DlyhXt2LFj2pUrV+wtit1p7bOw+PepDfLz87U5c+Zo/v7+mr+/vzZnzhzt0qVLDcYA2qpVqxrsW7Fihebj46Ndvny5yTnT09O1CRMmaMHBwZqnp6cWHR2tzZ8/X8vPz2+XbB39LH71K6UrzzxTs+PPf26oRAMGaFp5edsnevjhhsfpdNpznss00LTQLvlaXp5Z4lkM0a06HEm3HBlLfxYVA4fWqse5c82PmT32lAaa9lLMWxaZU3AcTP0+uaan7v33ITgYZs5ssNuYMGEkhhNqnCB0gODgYFavXl1bQmT16tUEBQU1GKNpGvfff3+DfQ899BClpaUEBjYtTRAVFcX27dvJz8+nvLycU6dO8dprrxFs4+9rkyLERk+dkddfV/ENbfHqq6p1WE3yyE/aYP5c8RQA/wz6o3TpE1wej65exKMCWP/+9+bH5OSpiKrwkCpbiSU4GK5p1Bkr2+/a1aD5+Pjxqv4WgLtbNX05KxlEgtAKxtyHPXtUWSBmz1Y16QwGVVj4Zz8z7UQ+PvD112AwUHW5mAeG7acKD+684TIz1v+P1eQXhE6Dnx+LeR6AN9+E5hLXsy/X9H2VmDqXxTWNupgYuO46lZ1Xr2mzTkdtMeL+wQV4UCXdJGzIBx98gLe3NxcuXKjd9+CDDzJ06FD0er0dJRNaYuBA1aSltBR++gnw8FDtJdzc4Oab239CnY5/rPRj3yEPAgPhX6uD0I0aaXG5XQ3RLSdg/nxufnMGE0dfoaICli5tOiSn2A+Qvq+ujGsadaAisAHeeadBMsSvfqWyYN8Y+57aIZ46mzFr1ixiY2NZtmwZAIsXL2bjxo18++23zS5BCvbHza1hy7COcupUXS7EK680Xc0VzEN0ywm46y50jzzMn172AeDtt+HMmbqXKyshr0z1fQ2/yjJli4TOh2uWNAEVTzd/vrqK7NgBEycCytHw3HPAbd+rcZ3cqNM05UWxNb6+teFRJqPT6Vi6dCkzZswgMjKS1157jZ07d9Kz5n9QVFTEjTfeSGVlJQaDgfnz55OYmGgF6YX2EB8PGzaoaAajp9scqqtVPeKyMrjpJvgfB191dSbdMlJaWsrAgQOZOXMmL7/8sgWlFizF9dfD5MmwcaNyQBgXm3Jz1aM7VXTv428/AQW74rpGXdeuMGsW/PvfyltXY9TVYlym6OTLr6Wl9qnVWlys2nu2l2nTpnHNNdewePFiNm3axKBBg2pf8/X1Zfv27fj6+lJaWsrgwYOZPn063SWK3q40W4TYDP79b9i+XRktb73VfsPF1jiTbhlZunRpgz7FggNx9iycPg09e/LiiwPZuBFWr4aFC1UYhLFGXSgXcQuVFmGuiusuv0KdK+DLLxu2MAKYOhXuvBP697e5WK7Mxo0bOX78OAaDoUnHBXd3d3x9fQEoKyvDYDCgSR1BuzN6tFqGTUuDrCzzzrF3Lzylkl1ZuhT69bOYeEINrekWwMmTJzl+/Di33HKLHaQT2uStt1Ti0YoVjBypLk/V1fDCC+plYzeJsGERKmZccElc11MHqiT+O+/A7bdD49ZJzUWhdkJ8fdWdvT3mbS8HDhxg5syZrFixgrVr17Jo0SI+/vjjBmMuX77MxIkTOXnyJC+99JJJXR0E6xIQAIMHq0SJ3bth+vT2HX/sGEyZor6nN91U11nM0XE23VqwYAEvvfQSuzrqchWsg9E9W1ICwJIlsG4dfPSRqvNt9NSFR+jAXRIlXBXXNup0OpUZ4cTodOYt1diatLQ0br31VhYuXMjcuXO55pprGDVqFPv37ycuLq52XFBQEIcOHSInJ4fp06czY8YMk3qoCtZl3Dhl1O3a1T6j7uxZ5XwoKFAev88/7zzXI2fSrXXr1hETE0NMTIwYdY5KI6NuyBC45x5Yu1bFgRvDIOTn0LVx7eXXxlTVFGwsLVVXGVnaswkFBQVMnTqV22+/nWeeeQaAuLg4brvtNp599tlmjwkLC2Po0KHs2LHDlqIKLdCkCLEJZGUpgy4zEwYNgvXrVZtYwXKYqltJSUmsXbuWPn36sGDBAlauXMmSJUvsJbbQHMYAzhqjDlSihJsbfPUVfPlRGQDhyRvsIZ3gILi2p87I99/DH/8IY8aoyvZffgm/+IVaC9qyxd7SOT3BwcGkpKQ02b9u3boGz3NycvDx8SEgIIDCwkJ27NjBo48+aisxhVYwljXZt0+FpzaOZmhMQYHK4Dt9Gvr2hU2bkK4RVsBU3Vq2bFltuZN3332XI0eO8Nxzz9lERsFEjJ66emv+MTFw332wahXsPugNQFjmAWCKHQQUHAHx1IGqobB7N7z3nroiGTNfQ0PtK5fQgPPnzzNhwgSGDRvG+PHjmTdvHkONLUAEuxIdDT16QEUFHDjQ+tiSEtUR7PBhCA9XjSekHp0gtEGj5Vcjzz2nSnEZCQ9qlPQnuBTiqQPlMujZUxlzX35ZZ9R18hp1zkZcXBzJycn2FkNoBp1OLcGuW6fuj4yeu8aUl8Ndd0FSEnTrpgy66Gjbyiq0TuMexIKD0IJR16ePqu/45pvqeViPatvKJTgU4qkDFZlt/CF7+204f1793clr1AmCLTEaci3F2RcWqtKQmzer69O336qsWUEQTCA2Fl56SRWma8Qf/wjeXSoBuKpnla0lExwIMeqMGLNgN22qi/YWT50gmEz9IsSNc4y+/15l633xBXh6Ko+e1LgVhHYQFQULFsCcOU1eioyEDXf8L6uZQ3Q/SfBzZcSoMxIdDZMmqauR0VMnRp0gmMzIkdCli8pqTU9X+0pLVTe+m25S+/r2VblHN91kX1kFwdmY6L2HObwPUrvTpRGjrj4PPNDwuRh1gmAyPj4wfLj6e9cutV17LSxfrvY98oiqZXf99XYTURA6L1VV8OOPsHVr8+W2ylRJE3r0sK1cgkMhiRL1mT4d1qyBgwfV2lBEhL0lEoROxbhxquXXokWqsHB1tbo3evttlY8kCIKZlJXVxSysWKEUKzS0bvvkE2X4SX1Vl0aMuvr4+qro7U5OdbVkP8lnYB/GjYPXXlP15wDuvVc9Dwqyq1gWQ75X8hnYDV9f8PZWxt3DDzd8TadT9YS6yCXd1ZFvgBPh6emJm5sbmZmZ9OjRA09PT3Q6nb3FsimaplFRUUFubi5ubm54enraWySXYtIkldnq56f6j99xh70lsgyiW6JbdsfNDVavVu0jLl6s23JyVANmMegExKhzKtzc3Ojbty9ZWVlkZmbaWxy74uvrS+/evXFzk7BRWxIaCmlpyqlgTuN5R0V0qw7RLTvy85+rrT6a1qR2neC6iFHnZHh6etK7d2+qqqowGAz2FscuuLu706VLF5fzpDgKzpp8J7oluuWQ6HR1fWEFl0eMOidEp9Ph4eGBR/3eMYIgdBjRLUEQHBnxnwuCIAiCIDgBYtQJgiAIgiA4AWLUCYIgCIIgOAGdIqZOqymmWFhYaGdJBGfA+D3SpEin6JZgUUS36hDdEiyJqbrVKYy6oqIiAKKiouwsieBMFBUVERgYaG8x7IrolmANRLdEtwTr0JZu6bROcEtVXV1NZmYm/v7+TVLpCwsLiYqKIiMjg4CAADtJaD3k/VkeTdMoKioiMjLS5WttiW7J+7Mkolt1iG7J+7MkpupWp/DUubm50atXr1bHBAQEOOWXx4i8P8vi6l4EI6Jb8v4sjeiWQnRL3p+lMUW3XPtWShAEQRAEwUkQo04QBEEQBMEJ6PRGnZeXF88//zxeXl72FsUqyPsT7IWz/2/k/Qn2wtn/N/L+7EenSJQQBEEQBEEQWqfTe+oEQRAEQRAEMeoEQRAEQRCcAjHqBEEQBEEQnAAx6gRBEARBEJwAhzfq3njjDfr27Yu3tzdxcXHs3Lmz1fHbt28nLi4Ob29v+vXrx//+7//aSNL2sWzZMkaNGoW/vz+hoaHceeedpKamtnrMtm3b0Ol0Tbbjx4/bSOr28cILLzSRNTw8vNVjOsv/z1kQ/aqjM+mX6JbjI7pVh+iWDdEcmLVr12oeHh7aypUrtWPHjmm//e1vNT8/P+3cuXPNjj9z5ozm6+ur/fa3v9WOHTumrVy5UvPw8NA++eQTG0veNpMnT9ZWrVqlHTlyREtOTtZuvfVWrXfv3lpxcXGLx2zdulUDtNTUVC0rK6t2q6qqsqHkpvP8889rgwYNaiDrxYsXWxzfmf5/zoDoV0M6k36Jbjk2olsNEd2yHQ5t1I0ePVp75JFHGuwbMGCAtnDhwmbH//73v9cGDBjQYN/DDz+sjR071moyWoqLFy9qgLZ9+/YWxxgV49KlS7YTrAM8//zz2rBhw0we35n/f50R0a+GdCb9Et1ybES3GiK6ZTscdvm1oqKC/fv3k5CQ0GB/QkICu3btavaY3bt3Nxk/efJk9u3bR2VlpdVktQR6vR6A4ODgNscOHz6ciIgIbrrpJrZu3Wpt0TrEyZMniYyMpG/fvsyaNYszZ860OLYz//86G6JfLdNZ9Et0yzER3WoZ0S3r47BGXV5eHgaDgbCwsAb7w8LCyM7ObvaY7OzsZsdXVVWRl5dnNVk7iqZpPPnkk4wfP57Bgwe3OC4iIoK33nqLTz/9lM8++4zY2FhuuukmduzYYUNpTWfMmDH897//ZePGjaxcuZLs7GzGjRtHfn5+s+M76/+vMyL61ZTOpF+iW46L6FZTRLdsRxebz9hOdDpdg+eapjXZ19b45vY7EvPmzeOnn37ihx9+aHVcbGwssbGxtc/j4+PJyMjg5ZdfZsKECdYWs91MnTq19u8hQ4YQHx9PdHQ0//nPf3jyySebPaYz/v86M6JfdXQm/RLdcnxEt+oQ3bIdDuupCwkJwd3dvcmdzcWLF5tYxUbCw8ObHd+lSxe6d+9uNVk7wmOPPcaXX37J1q1b6dWrV7uPHzt2LCdPnrSCZJbHz8+PIUOGtChvZ/z/dVZEv0yjs+iX6JbjILplGqJb1sFhjTpPT0/i4uLYvHlzg/2bN29m3LhxzR4THx/fZPymTZsYOXIkHh4eVpPVHDRNY968eXz22Wd8//339O3b16zzHDx4kIiICAtLZx3Ky8tJSUlpUd7O9P/r7Ih+mUZn0S/RLcdBdMs0RLeshB2SM0zGmBb+9ttva8eOHdMef/xxzc/PT0tLS9M0TdMWLlyozZ07t3a8MbX4iSee0I4dO6a9/fbbDpsW/uijj2qBgYHatm3bGqROl5aW1o5p/P5effVV7fPPP9dOnDihHTlyRFu4cKEGaJ9++qk93kKbPPXUU9q2bdu0M2fOaElJSdq0adM0f39/p/j/OQOiX51Xv0S3HBvRLdEtKWnSAv/617+0q666SvP09NRGjBjRIG36vvvu0yZOnNhg/LZt27Thw4drnp6eWp8+fbQ333zTxhKbBtDstmrVqtoxjd/fX//6Vy06Olrz9vbWunXrpo0fP1775ptvbC+8idxzzz1aRESE5uHhoUVGRmrTp0/Xjh49Wvt6Z/7/OQuiXxNrn3cm/RLdcnxEtybWPhfdsh06TauJ6BMEQRAEQRA6LQ4bUycIgiAIgiCYjhh1giAIgiAIToAYdYIgCIIgCE6AGHWCIAiCIAhOgBh1giAIgiAIToAYdYIgCIIgCE6AGHWCIAiCIAhOgBh1giAIgiAIToAYdYIgCIIgCE6AGHWCIAiCIAhOgBh1giAIgiAIToAYdYIgCIIgCE7A/wOvydR3HLPshwAAAABJRU5ErkJggg==\n", 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", 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\n", 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", "text/plain": [ "
" ] @@ -746,7 +825,7 @@ "est_clipped = oep_clipped.compute_estimate(\n", " Y_clipped, U_clipped, X0=lqr0_resp.states[:, 0])\n", "plot_state_comparison(timepts, est_clipped.states, lqr_resp.states)\n", - "plt.suptitle(\"MHE with constraints\")\n", + "plt.suptitle(\"MLE with constraints\")\n", "plt.tight_layout()\n", "\n", "plt.figure()\n", @@ -767,7 +846,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -800,7 +879,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "MHE for continuous time systems not implemented\n" + "MHE for continuous-time systems not implemented\n" ] } ], @@ -819,7 +898,7 @@ " )\n", " plot_state_comparison(timepts, est_mhe.states, lqr_resp.states)\n", "except:\n", - " print(\"MHE for continuous time systems not implemented\")" + " print(\"MHE for continuous-time systems not implemented\")" ] }, { @@ -833,18 +912,19 @@ "output_type": "stream", "text": [ "Sample time: Ts=0.1\n", - ": sys[9]\n", + ": sys[7]\n", "Inputs (4): ['F1', 'F2', 'Dx', 'Dy']\n", "Outputs (3): ['x', 'y', 'theta']\n", "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "dt = 0.1\n", "\n", - "Update: at 0x168af1360>\n", - "Output: at 0x168598940>\n" + "Update: at 0x153eb9da0>\n", + "Output: at 0x1533aa5c0>\n" ] } ], "source": [ - "# Create discrete time version of PVTOL\n", + "# Create discrete-time version of PVTOL\n", "Ts = 0.1\n", "print(f\"Sample time: {Ts=}\")\n", "dsys = ct.NonlinearIOSystem(\n", @@ -863,7 +943,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] @@ -884,6 +964,8 @@ "# plt.plot(timepts, V0[0], 'b--', label=\"V[0]\")\n", "plt.plot(timepts, V[0], label=\"V[0]\")\n", "plt.plot(timepts, W[0], label=\"W[0]\")\n", + "plt.xlabel(\"Time $t$ [s]\")\n", + "plt.ylabel(\"Disturbance, sensor noise\")\n", "plt.legend();" ] }, @@ -909,7 +991,7 @@ "outputs": [ { "data": { - "image/png": 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s3N2BPn2A334Ddu+mxHXGenAmuX1jKL5OYsAAWp4548D1IB2Uvn37osIGlhy22OkjOJjMBG7GfTzGCDtfX40ZXKsjFMO4PLVZ7ACgXz9a7tmj/HgYxpGoTdiFhABSIiZbvF0DlxJ2RiVPmIExrliAkigAoEoHKIZxaSRhV1P3nb59ablnj6Z9H8Mw1duJ6aNzZ1qeOKH8eBjb41LCzmiLHQBcvAg88QS5ZWvBGIsdoLk4nTpFqecMwxhnsevRA/D0BNLTgcuXrTMuhnEEJItdSIjhbTp1ouXx48qPh7E9LinsasyKlVCpgG++ocKGtShBY4VdWBhVUxGCMvwYhjFO2Pn6krgD2B3LMNrU5ooFNMKOLXaugUsKO6MsdlFRpNTUaipAVwPGumIBjdVu924jxsAwLoAxwg7QdccyDEOYIuxOnuSqDK4ACztDuLlRAgVQ622OsRY7gIPAGaYqpgo7viliGA3GCLtWrQBvb6CwkEMZXAGXEnYmJ08Yab+uraWYNtLF6dAh6ljGMK5ObeVOJKSi7efPa6zkDOPqGCPsPDyADh3oObtjnR+XEnYmWewAoyNOa2sppk2LFhRrV1JC4o5hXB1jLXb161O3P4BqcjEMY5ywAzjOzpVwSWFnVPIEYLLFzhhhp1KxO5ZhtDGm3ImEZHU4dUq58TCMI2GqsOPMWOfHJYWd0Ra7jh0BLy+gXr0a/aamJE8AHCukFLdv38a8efOQnp5u66EwJmCsxQ7QNINhYacMPIcci4oKTeksttjZHnuZP9xSrCYCA8kc5+lpcJPycqCoiJ4bY7EDNBa7fftoYhrZ4IKphenTp+P27ds4evQofvrpJ1sPhzESU4SdZLE7fVq58bgyPIcci5wcTZZrcHDN20rC7vJlMkYYa4hgjMde5o9LSQqThR1Qo6gDgIICzXNjhV3HjpShlJsLJCebMBbGIJs2bUJ+fj5++eUXBAUF4ZtvvrH1kBgjMUfYscVOfngOOR6SG9bfn5xLNRESAkRE0HNuLSY/9jR/XErYWdRSzEAfI8kN6+FBYs0YPD01F6ijR80YC1ONUaNG4ccffwQArFy5Eo8//riNR8QYgxCmCbt27WiZng5kZys3LlfEGefQrl27MHLkSEREREClUtVqRUlMTIRKpar2OHfunHUGbCJSO7Gauk5oI4Uy2OnbcWjsaf64lLAzy2J36BDQvTtw7716X9ZOnFCpjD9s1660ZGHHuDJqNVBaSs+NEXb+/kDTpvSc3bFMbRQUFKBz58749NNPTdrv/PnzSE9Pr3y0atVKoRFahrGJExLS27h4UZnxMPaBS8bYGZ0VC9DVJikJ8POjHSWz311MqWGnDQs7htHUsFOpjA9laN8euHqV3LFSvCrD6GPo0KEYOnSoyfuFhoYiKChI/gHJjDF9YrVp2ZKWLOycG7bY1UbLloCPj8GS3abUsNOGhZ08rFmzBj4+Prh+/XrlukmTJqFTp07IlVQDY7dIblh/f+OTiDjOTl54DlWna9euCA8Px+DBg7Fjxw5bD8cgkivWWItdixa0vHRJmfG4IvY4fxQVdvYW32CWsHN3B1q3pufnz1d72ZQadtp06kRWiowMejDmMX78eERHRyMhIQEAMG/ePGzduhW//vorAo0pjGbn2NsckhtT4uskODNWXpx9DplCeHg4li5divXr12PDhg2Ijo7G4MGDsWvXLoP7qNVq5OXl6TyshakxdtoWOwNh44yJ2OP8UdQVK8U3PP3003j44YeN3u/8+fMI0Pqlb9CggSzjMTt5onVrKv5z4UK1l8x1xdapA0RHUxDr0aOAGd4CxRCCDJS2wM/PtFhFlUqF+fPnY+zYsYiIiMBHH32E3bt3o1GjRpXb/PLLL3jttddQUVGBf/3rX5g0aZICI1cGe5tDcmOOsNOuZSeEad8Xa+Fsc2j06NFITEzE4MGD8cMPPygwavsgOjoa0dHRlX/HxsYiNTUVCxYsQP/+/fXuk5CQgHnz5llriDqY6opt3py+G3l5JArt9GfBqeZPamoqJkyYgMzMTHh4eGDu3LkYN26cQqMnFBV29hbfYJbFDtBEnOoRdua6YgFyx9qjsCssNO/9yEF+PoleUxgxYgTatWuHefPmYdu2bWgvXfkBlJWVIT4+Hjt27EBAQAC6deuGMWPGILi2ok92gr3NIbkxR9i1bUs/vLduAZmZ1KLP3nCmOQRQfa5nnnkGX331lYwjdQx69+6N1atXG3x99uzZiI+Pr/w7Ly8PkZGR1hiayRY7Hx+gcWMgNZWsdvYq7Jxp/nh4eGDhwoXo0qULMjMz0a1bNwwbNgx1TD2JCdhljJ1S8Q1mJU8ANQo7c12xAMfZycXWrVtx7tw5lJeXI6zKVf6vv/5C+/bt0ahRI/j7+2PYsGHYunWrjUZqPRwlRsgcYefrq3EpcZydPNQ0hwBg0KBB8HfRirZHjx5FuNSkWA/e3t4ICAjQeVgLU2PsAE6gUIKa5k94eDi6dOkCgG64g4ODka1wrSa7yoqV4htiYmKgVquxatUqDB48GImJiQbN4Gq1Gmq1uvLvmuIbzLbYtW0LtGkDNGtW7SVT24lpIwm7Y8dM31dJ/Pw0gtUW5zaFI0eOYNy4cfj888+xdu1azJ07F99//33l62lpaToupcaNG+sEuTobSs8huTFH2AEUxnDhAvD338DgwfKPy1KcaQ45Mvn5+biopWCSk5Nx7NgxBAcHo0mTJpg9ezauX7+Or7/+GgCwcOFCNGvWDO3bt0dJSQlWr16N9evXY/369bZ6CzViqsUOIGG3Y4d9CztnnT+HDx9GRUWF4hZduxJ2Ssc3mC3sevcGzp7V+5IlFru7Ih4XL9IFzoo3ejWiUpluirYFV65cwfDhwzFr1ixMmDAB7dq1Q48ePZCUlISYmBgAgNATIayyx6AsmXC0GCEpaczUGGN7r8flTHPIkTl8+DAGDRpU+bfkMn3qqaewcuVKpKenIyUlpfL1kpISzJgxA9evX4evry/at2+PzZs3Y9iwYVYfuzGYGmMHOIbFzhnnz61bt/Dkk0/iiy++UHxcdumK1aZ37964oMcFKjF79mzk5uZWPlJTUw1ua1HnCQNYIuxCQijeAQCOH5dvTK5AdnY2hg4dilGjRuH1118HAMTExGDkyJGYM2dO5XaNGjXSsdBdu3atRreKMyLnHJIbcy120sWphrfF1IKxc8iRGThwIIQQ1R4rV64EQB0CEhMTK7efOXMmLl68iKKiImRnZ2P37t12K+oqKpxX2DkCpswftVqN0aNHY/bs2ejTp4/iY7Mri50+jIlv8Dayl5fZFjsJIShAz0PzsVniigWo7Mm1a1S6gYutGk9wcDDO6rGibty4Uefvnj174tSpU7h+/ToCAgKwZcsWvPnmm9Yapl0g5xySG3OFnb1b7BwBY+cQY5/k5mrixTnGzvoYO3+EEJg4cSLuvfdeTJgwwSpjU1TY2Vt8g0XC7o03gE8/BV5/HZg5s3K1JRY7gHpfbtkCnDlj3v5MzXh4eOCDDz7AoEGDUFFRgZkzZ6K+Kb+CNsbe5pDcWGqxu3RJb0MYRmaGDBmCI0eOoKCgAI0bN8aPP/6IHj162HpYLo0UX+fvD3h5Gb9f8+a0zM4Gbt8G6tWTf2yMhr1792LdunXo1KlTZR3SVatWoWPHjoqdU1FhZ2/xDWZnxQJ05cjNreb7MbeOnYTU1JyFnXKMGjUKo0aNsvUwzMLe5pDc5OTQ0tTKLE2a0MWspIQs3lL/WEYZXCGT3NEwJ3ECICNEw4ZUGP/SJWqFzihH3759UVFRYdVzKirspPgGQ0hxDhIzZ87ETC1rmNxYZLEzUPLEkjp2AAs7pmbsbQ7Jze3btDRV2Lm7k+Xh3DmakizsGFfDnPg6iZYtSdhdvMjCzhmx++QJOZFF2P39t85qOVyxAJCerrnIMYyrIFnszHEHcawQ48qYa7EDeO44Oy4l7CzKipWEXXq6ToEdS12x/v6AVNKGrXaMq2GuxQ6osW44wzg9LOwYQ7iUsLPIYhccrEk90poNlrpiAXbHMq4LW+wYxjzM6TohERVFyytXZBsOY0e4pLAzK3kC0GsisNQVC7CwY1wTtRooKqLnbLFjGNOwxGInNVG6elW24TB2hN3XsZMTi+vYDRpEpoW7tRmKiykrDzC9cr429iLsagrSdwVc/f1bG8lap1KZ13XF3kqe8PeHPwNrYknyhJRslJpK10MPO1ECrv79kev928m/0zpIX96KCnq4mWqv/L//0/lT+8Ikh8Xu9Gnzj2EJ7neviCUlJfD19bXNIOyAkrsq3d3WCsFFkOZPYKAZcxFU8sTT0/YlTzw9PQEAhYWFLj1/APoMAM1nwiiHJRa78HCaO6WlwPXrts8q52sQIdf8cSlhp329Li8372KijXafS0uOJQm769fpmJZY/8zBw8MDfn5+uHnzJjw9PeFm6QfjgFRUVODmzZvw8/ODh73cvjo5liROAJqSJ+fPU5ydrS5O7u7uCAoKQmZmJgDAz8/PqfsR60MIgcLCQmRmZiIoKIhvjqyAJTF2bm40Xy5epDg7Wws7V78GyT1/XOoKpn29LiujOxazuHMH8Pc3u4F5VYKCgIgIIC0NOHsW6N3bsuOZikqlQnh4OJKTk3HVhYMu3Nzc0KRJE5e7KNsKSxInJFq1ImF34QIweLAswzKLhg0bAkCluHNVgoKCKj8LRlkssdgBFGcnCbsBA+QalXnwNYiQa/64rLAzK4Hi5k0gOpr6IBUXIyeHDiiHha1dOxJ2Z85YX9gBgJeXF1q1alXpjnRFvLy8XO5O0ZZYarED7CeBQrowhYaGorS01LaDsRGenp5sqbMSFRXUEgywTNgB9pNA4erXIDnnj8sKO7MSKOrXBwoLSRVeu4bc3GYA5BN2v/9uuzg7gCxWPj4+thsA41LIYbFr0YKWly9bPBxZcHd3Z3HDKE5ODok7wDxXLKBxv9pTyRO+BsmDS5kntH9vzRJ2UmACAFy+XOmKtcTiIBEdTUtbWx4YxlrIYbGT6nElJ1s8HIZxGCQ3bGCg+SFFksXOnoQdIw8uJezc3DRJDmaXPGnenJbJybLF2AH241JiGGshh8VOEnaXLwMuXimBcSEsSZyQYGHnvLiUsAMsbCsG6JgItMs1WIok7C5ftqCAMsM4EHJY7KSL0507mpgjhnF2LE2cADRzJzWVrznOhssJO4uLFGsJOzktdpGRgJcX1eRKSbH8eAxj70g3RpYIO19fqskFsDuWcR3kEHZSLbuyMkrcY5wHlxV2Zt+hSK5YmWPs3N01geDsjmVcATlcsYDOlGQYl8CSrhMS7u5U5Btgd6yz4bLCzmyLXdu2wAMPAPfeK6vFDuA4O8a1kMMVC3ACBeN6yGGxA+wzM5axHJcqdwLIIOzatQN+/RUAkDOQVrGwYxjTkctix8KOcTXkSJ4AOIHCWXE5i53FyRNasMWOYcxHLosdu2IZV+PGDVqGhlp2HHsrUszIg8sJO4stdhK5ucjNoQqRcsTYASzsGNdBCHmSJwC22DGuhyTswsIsOw5b7JwTlxV2FqV3P/kkEBSEnExqfSK3xS45WR6LIsPYK3fuaCrny+WKvXqVyzYwrgELO6YmXFbYWSScQkMhAOQVUclvuYRdo0aAjw+NjSca48xI1jovL/rOW0KjRlS2obQUuH7d4qExjF0jhHzCTkqeSEnR3Ggxjg8LO3OIikIB6qBcUMCeXMLOzQ1o2ZKeszuWcWa0EydUKsuO5e6uuUCxO5ZxdnJzqd4pYLmwi4ig+VNaCqSnWz42xj5wOWEnS/JEVBRyQWrOwwPw87N8XBIcZ8e4AnIlTkhotxZjGIldu3Zh5MiRiIiIgEqlwk8//VTrPjt37kRMTAx8fHzQvHlzLFmyRPmBmoBkrQsIsNza7eEBNG5Mz7kwvvOgqLCzx0kll8UuB0EAgMBAYbHFQRsWdowrIFepEwmtFs4MU0lBQQE6d+6MTz/91Kjtk5OTMWzYMPTr1w9Hjx7F66+/junTp2P9+vUKj9R45HLDSkjWbs6MdR4UrWMnTaqnn34aDz/8cK3bS5Nq8uTJWL16Nfbu3YupU6eiQYMGRu1vDLIIu6ZNKy12gf4VANwtHpcECzvGFWCLHWMNhg4diqFDhxq9/ZIlS9CkSRMsXLgQANC2bVscPnwYCxYskO0aZClyCzup+wQLO+dBUWFnj5NKlqxYPz/kBjUDcoBAbzUA+XyxLOwYV0CuUicSbLFj5GD//v2Ii4vTWTdkyBAsX74cpaWl8PT0rLaPWq2GWq2u/DsvL0/RMbLFjqkNu4qxMzSpDh8+jNLSUlnOIVcdu9zYBwDId2GSkITdlSuaAFnGdbHHcAY5kCx2crli2WLHyEFGRgbCqiimsLAwlJWVIUtq91CFhIQEBAYGVj4iIyMVHaNcxYklWNg5H3Yl7MyZVGq1Gnl5eTqPmpBL2OWMehIAEBguY+YEgPBwSsaoqOCSJ4xzxggB8lvspHpcGRlAcbE8x2RcE1WVoGkhhN71ErNnz0Zubm7lIzU1VdHxscWOqQ276xVr6qRKSEjAvHnzjD6+XC3F5G4nJqFSUcmTEyfIHdu6tbzHZxwLewxnkAO5kyfq1wfq1AEKCii7j+cNYw4NGzZERkaGzrrMzEx4eHigvoHGrN7e3vD29rbG8O6Oh5ZKCDshLC8/xNgeu7LYmTOpTL1bks0Ve1fYBbnJH08huWMvXpT90IyTo2Q4w/79wNypt7Du6d+An36y6BZf7uQJlYqr6DOWExsbi+3bt+us27ZtG7p37643vs4WKJU8kZ+vueFiHBu7EnbmTCpvb28EBAToPGpCluQJADmnSEAG/vKNZQfSAxcpZsxFyXCGQ4eAfy+ujw0rc4HRo0lJ9e4NfPKJyQGhclvsAG5ozlQnPz8fx44dw7FjxwBQqMKxY8eQcrdo2+zZs/Hkk09Wbj9lyhRcvXoV8fHxOHv2LFasWIHly5djxowZthi+XuQWdr6+QIMG9JznjnOgqLCzx0klm8VO3C13knvXfi0jbLFjLMGccAZjgr8lb5M6PAqIiaFWKQcPAtOnA7GxwNmzRo9RbosdwBY7pjqHDx9G165d0bVrVwBAfHw8unbtijfffBMAkJ6eXnk9AoCoqChs2bIFiYmJ6NKlC9599118/PHHdhXGILewAzjOztlQNMbu8OHDGDRoUOXf8fHxAICnnnoKK1euNDipXn31VXz22WeIiIiQfVLJJuzK6wAAAktuArduASEhFo5MA1vsGHMxN5xBmpsAlWvQJ+4qhV3nnsCvhylT4bvvgHnzgCNHgG7dSOh16lTrOOVOngA0FycWdozEwIEDK29s9LFy5cpq6wYMGIAjR44oOCrzyc8HCgvpudzC7vBhFnbOgqLCzh4nlWzJE3foQEHIoSuJjMKuaskTLy/ZDs04ObGxsfj555911hkTzmBM8Lf0Paws2dWwIVnrxo4Fnn8eeOgho0QdwBY7hjEHyVrn5wfUrSvfcdli51zYVYydNZCt3EkOLQORK3tVVC55wkjYUziDpP2qhdNFRACbNgHPPmvUcYqLKXsVkPV+iIUd4/Qo4YYFNMKO+8U6By4r7CxNnqgsd4Jc2a8kUskTgN2xro49xQhVumLVel7Ujt/LygLmzjUYe3rrFi3d3eUtFyQJu/R0A2NkGAdHaWHHFjvnwO7q2CmN3OVOlBB2ALljT5zgBApXx57CGWoUdhIVFUC/fsC5c5QxO3x4tU0kYVe/vrw1s0JCyNJdWAikpmpujhjGWWBhxxiDy1rsLBF2FRXAnTv0POjJBwGtBBG5YIsdY28YJezc3ICRI+n5rFl6TeNS1RUDuRxmo1JxAgXj3Cgl7KRadpmZQFGRvMdmrI/LCTs5kify8jRepsCl71PwuMxwyRPG3jBK2AHA7NlUoO7UKWDVqmovSxY7OePrJDjOjnFm5O4TK1GvniYZg+PsHB+XE3ZyWOwkN6y3t+ZiJzdssWPsjWpZsYaoVw94/XV6PnduNROAUhY7gIsUM86NUhY7bWs3zx3Hh4WdGej0ic3PB06elN1+XbXkCcPYGoNZsfqYNg2IjASuXQO+/VbnJbbYMYx5KCXsAL4pciZcVthZkhVb2Sc2CED79lS76/hxS4emA5c8YewNo12xAODjA7z8Mj3/6COdDFklLXYcY8c4M9YQdjx3HB+XFXaWWOwqa9gFAmjenP64fNmSYVVDpdJY7dgdy9gDJgk7AHjmGTLL9eqlKZcPttgxjLlITWVY2DE1wcLODLTLNSgl7ACNsPv7b9kPzTAmIwm7sjKyJNdKvXpUd2TZMqBOncrV1oixu36dQxgY5yIvjyJ/AKBRI/mPz9Zu58HlhJ0cWbHZ2bQMDoaiwq51a1qyxY6xB7QThYy22vn4VFulpMUuNJROKQSF9zGMs3D9Oi0DA+VtJybBMXbOg8sJO9ktdlFR9Adb7BgnR7tnscmdHZKSgB9/BKCsxU6l0lygZO70xzA2RbpRadxYmeNL8yYtjTu3ODouK+wsSZ5gix3jimgLO5PcnL//DnTvDjz/PFBSoqjFDtDca7GwY5wJyWKnhBsW0HRuEYIiKBjHxWWFnewxdteuyX6bI1nsUlK4Gjhje9zcAE9Pem7SV33AAKBhQ+DmTZRs+AV5ebRaCYsdoOi9FsPYDMlip5Sw484tzgMLOzPQsdg1aABMnQokJFjegLYKISF3S6oAuHRJ1kMzjFmYnBkLkBp8+mkAQPbn3wMgkSh9t+WGLXaMMyJZ7JRyxQIcZ+csuJywkyN5Qsdip1IBn30G/OtfOpl/cqBd8oTj7Bh7wCxhBwDPPgsAyEo8BYASZqW5KDdssWOcEaVdsQCXPHEWXE7YyW6xUxiOs2PsCbOFXYsWwL334hZo0igVXwcoms/EMDZD6eQJgF2xzgILOzPQsdgBVHz11Cng9GmLxqYPttgx9oTZwg4AJk9GFkjR1Q8WtWxsPpKwy8oC7txR7DQMY1XYYscYi8sKO3OzYtVqoKCAnlda7FasADp2pIbnMsMWO8aekDJjzSr+O3o0btUhk0CIb4F8g6pCYKBmbnKcHeMMqNVAZiY9t4aw4xg7x8ZlhZ25Frvbt2np5na3pRjA3ScYl8Eii523N7ImzQIA1G+qQIVVLTiBgnEm0tNp6eWlbBgDd25xDlxO2FmaPCG5YevVI3EHQFfYCXldTJKwu3EDlWUiGMZWWCTsANxyDwWg7MUJ4AQKxrnQdsOqVMqdR+rcUlHBnVscGZcTdpZa7KTECZ0aXNJtzp07GuUnE4GBNNkAdscytsdSYVfZdaJeOXDxojyD0gNb7BhnwhqJEwDXsnMWWNiZiKTbdDJifXw0gQ/cgYJxYiy22EldJ96bCdx3H5kGFIAtdowzYY3ECQmOs3N8FBd2ixYtQlRUFHx8fBATE4Pdu3cb3DYxMREqlara49y5c7KNx9LkCYOlTqwQZ3f+vOyHZhiTkM1iV5pBV47ffpNnYFVgix3j8GzdCqxZAwiheNcJbSSLHc8dx0VRYbdu3Tq88sormDNnDo4ePYp+/fph6NChSElJqXG/8+fPIz09vfLRSlI2MiCXxa5aO6QWLWipgHupbVtanj0r+6EZxiQsyoqFlsVuRG96smiR5YPSg3SflZwse9gr42DYm3HBKAoKqLfyY48By5dbpeuEBFu7HR9Fhd2HH36IZ599FpMmTULbtm2xcOFCREZGYvHixTXuFxoaioYNG1Y+3GUsUS9XjF01i93YscD//R8wZIjZYzNEu3a0VKBMHsOYhGwWu4kj6cmWLYqYBpo0oXihoiJKPGJcE3s0LtTIc88BEydS3M2wYbTu66+tarFr2ZKW3MbScVFM2JWUlCApKQlxcXE66+Pi4rBv374a9+3atSvCw8MxePBg7Nixo8Zt1Wo18vLydB41IVdWbDWL3fDhwOzZQI8e5h24Btq3p+X580BpqeyHZxijsUTYlZUBOTn0PKR7MyAujsxptdzomYOXFxAZSc/Z8uC62KNxwSB5ecCqVcBXX9Fk+de/aP2+fbieSrGo1rDYKeh8YqyEYsIuKysL5eXlCAsL01kfFhaGjIwMvfuEh4dj6dKlWL9+PTZs2IDo6GgMHjwYu3btMniehIQEBAYGVj4ipV9zAyhmsVOQJk2oDW1pKd9FMbbFEmEnzR2AygXhxRfpj+XLybQmMxxn59pYy7ggGxs2AMXFQHQ0EBNDwW7t2qGivAJpabSJNSx2krDLytLciDGOheLJE6oqRXeEENXWSURHR2Py5Mno1q0bYmNjsWjRIgwfPhwLFiwwePzZs2cjNze38pGamlrjeBSLsQPIpLZxo+wF59zcNHF27I5lbIklwk5yw9ard3ceDh9OF6/sbOD332UbowTHCrk21jIumOo1Msjq1bR84glNsbphw3ATDVBa7gaVCggPN+/QpuDvD0gfGRsSHBPFhF1ISAjc3d2rTaDMzMxqE60mevfujQs11Pnw9vZGQECAzqMmFMuKBSi+7qGHgBMnzDt4DUjuWBZ2roc9BX9bIuyq3RS5uwOffQYcOgSMHCnL+LRhlxIDKG9cMNVrpJcbN4A//6Tnjz2mWT90KK6DzHRhYQKenqYf2hw4zs6xUUzYeXl5ISYmBtu3b9dZv337dvTp08fo4xw9ehThMt6mKFKgWEIqOKdA/y8Wdq6JvQV/W5IVK/W6lApuAyCrXffuFo9LH9Jb5vqProm1jAumeo30kpRE8abt2mlMzQDQty+u+JC7pklD6wVYS8KOb4ocE0VdsfHx8fjiiy+wYsUKnD17Fq+++ipSUlIwZcoUADQhnnzyycrtFy5ciJ9++gkXLlzA6dOnMXv2bKxfvx7Tpk2TbUxyJU/otdgpKOykzNgzZ2Q/NGPH2FvwtyUWO+n62rChgQ3u3DFrTIZgYefaWMu4YKrXSC8nT9KyUyfd9V5euDyT5nqLtl6mH9dM2Nrt2HgoefBHHnkEt27dwjvvvIP09HR06NABW7ZsQdO7FRDT09N1LA8lJSWYMWMGrl+/Dl9fX7Rv3x6bN2/GMCntWwYssdgVFWlivPVa7KKjaamgxU7KjLWWSZ6xHVLw96xZs3TWGxv8XVxcjHbt2uGNN97AoEGDDG6rVquh1lJqNcUIKSLsKiqAqVMpI/DoUc0NkoVIVgcpCDwoSJbDMg5EfHw8JkyYgO7duyM2NhZLly6tZly4fv06vv76awBkXGjWrBnat2+PkpISrF69GuvXr8f69euVHWhICNC7N9CzZ7WXLmWSUNQ25CkNW+wcG0WFHQBMnToVU6dO1fvaypUrdf6eOXMmZs6cqeh4LBF2khvWw4MCTKuhoMVOyowtKKDJJiVTMM6LJcHfMTExUKvVWLVqFQYPHozExET0799f7z4JCQmYN2+eUWNSRNi5uQFpaUBhIfDRRxR3JwP+/nSujAyy2ilQiYixc+zRuKCXZ5+lhx6k5J/mUQIQ0CRWKAgLO8fGZXvFmpM8oZ04oXduScLu4kXzszMMoJ0Zy+5Y18KeMsvlEHZ6w5tefZWWK1fq1kWxEHbHMlOnTsWVK1egVquRlJSkc4OzcuVKJCYmVv49c+ZMXLx4EUVFRcjOzsbu3buVF3W1ICUwtHjtIav9+EvCLj2djAmMY+Gyws4ci12N8XUAmdW8vemqV0twuzlwAoVrYY+Z5ZYIO6kDhN4Yu4EDgS5dyGq3dKnpBzcACzvG7ikupoceysqopTIANM89Ahw/bpUh1at3t9YkuFyQI+Jyws6S5IkaM2Klg3/4IbB2rSIVjFnYuRb2mFkuCTtzsmJrTJ5QqTRWu08+Mb8ZbRUkYadAdATDyMOmTUDdusD48dVeunaNrlVebqVohOvAsWNWGxa7Yx0XxWPs7A1FLXYABYErhCTspAQqxvmxt+BvqdyJqRY7IYzIih0/Hpg1i+Ltvv8eePxxs8cpwRY7xu45eZJCd+rWrfaS5IaNalAAtxvC6sLu0CEWdo6Iywq7igq62JgSh1qrxU5hunWj5blzVBlCbwIH41TYW/C3ua7Y27c1fY4NepG9vIBp04A5c4BPP5Vd2Jk63xnGKkh36h07VntJcoO2aAngBkjYWemLzEWKHReXFXYA3SR5mPAJGGWxu3MH2LOH6is8+qg5QzRIw4bU2Dw1lepZDhwo6+EZO8WeMsvNFXaSta5ePc0x9PL883TwF14wa3xVkS5OOTk0f0NCZDksw8jHqVO0rEHYNe9YF9jvBty8SZPJCr3F2BXruLhcjJ22kDPVHWuUsDt/Hhg2DHj5ZbqzkhmpzNFff8l+aIapFUuFXa05H/XrA/Pm1eCvNQ0/P03jdHbHMnZHQYFGvXXoUO3lyozYaA+gTRv6w0ruWEnYcXyq48HCzgSkrL4aL07t21NtEunOSmYkYXfokOyHZphaMTd5osaM2JqoqDBxh+pwnB1jt5w+TQaA0NAqvfaISotdc5CL5t57NYGuCiOV10pNlb0pDKMwLifstDsrKSLsfH019exOnDDtBEYgFVllix1jCyy12Bkt7PbuBe67j+LtLESajizsGLujBjcsoGWxawEq3P3HH8DgwVYZWr16Go8v1051LFxO2ClusQM0/f4UqDkUE0NxsykpmvEwjLUwNyvWZGGXlUUXsaVLqbadBbDFjrFbmjShJCE9yU23b1NsKABERVl3WBJcYssxcTlh5+amSSgyRdgJYYKw69yZlgoIu4AATagFu2MZa2M1i92IEUCzZpSKvmaNaSerAteyY+yW++4DVq8G4uOrvSS5YRs2pFjRSrRTzBWmXTtasrBzLFxO2AHmtRXLzdXEFRltsVPAFQtwnB1jO6wm7NzdNTUhFy827WRVkGKFzp2TvdMfwyiGjhtWondvyt47csQqY2CLnWPi0sLOFIudZK0LCAB8fGrZWLLYnTtnXu+lWuDMWMZWaAs7U5K+zUqemDiRfL9JScDhwybsqEuLFhT6WlTENbkYO6KsjOIDDFyIdBInJOrUoeXZs8qO7S4s7BwTlxR25rQVM9oNCwCNGwOrVpFJzZRCeUYiJVAcOqRIRRWGMYh2DTpT5o/R5U60adAAGDeOnltgtXN3564tjB1y6RJl9oSG6v0hl7SbFEoAQOMbtbKwu3aNvFaMY+CSws4Si51RFyaVCnjiCWpqrp2GKxOdOpEh49YtLh7JWBdtYWesMbq8nKr/AGaUO5EKFa9ZQ7FFZiIlHbKwY+yG8+dp2ayZ3k4SUoi25AACoBF2VkpTDQoCIiKsekpGBljYGYlJwk5hvL0p1AIAduyw7VgY18IcYXfzJpWjc3MjI5xJ9OkDTJhAFjtfXxN31iAJO4XCXhnGdCRhFx1d7aWSEo2Q0hF2UsCoFVWWZLVjYec4uLSwMyWQ2mRhl54OfPwxsGCBSWMzFqmU0Z9/KnJ4htGLuzsJNMB4YSe5YRs0MMOArVIBX38NPPWUEcGthpHymdhix9gNNQi7c+co8TUoiCqiVCJZ7JKTKWjUCnCcnePh0sJOUYtdWhq1FfvPfxQJhLv3Xlr++SfH2THWxdTMWLO7TsiIZLG7dIm6ODGMzalB2Elu2E6dqnhpGzSgrFghNPsrDJc8cTxcUthZkjyhp+uLftq1Azw9KRAuOdmk8RlDz55U2+jmTU3xcoaxBqYKO5NLnegjOxtYuBB4912zdg8NpZsyIfgCxdgJRgg7HTcsQCrv0UeB556zKDTBFNhi53i4pLCzisXO11eTvrprl/EnMhIvL6BfP3rO7ljGmpjaL9asjNiqnD0LvPoqWcDz8sw6BCdQMHbD7duajCKp550WBoUdAHz6KfD553oFoRJIFrvr1zWdMBj7hoWdkZiVPNG/Py0VEHaArjuWYayFqRa79HRaWmSx69OHAscLC4G1a806BCdQMHZDRQXw5pvA888DdevqvCRELcLOygQFAZGR9PzYMVuOhDEWFnZGYFI7MW2sJOwSE03ve8sw5mKqsEtJoaVOELipqFTApEn0fNkysw7BCRSM3VC/PjBvHrBkSbWXMjLImOfmpnGDVqOoyGq17ACgVy9a7t9vtVMyFuDSws7YrNj8fE0CkknC7p57aHZeukR2bJnp2hUIDKhAXh5wdOjrQNOmVHSoeXPg7bdlO48QdHHeuhX45BPgo4+AgweNd8UxzoWXFy2NFXZXrtCyaVMLTzxhAsWtHj5sVh9mbYsdJxwx9or01W7d2kAYXVoadaDo1MlqPWNjY2nJws4xUFzYLVq0CFFRUfDx8UFMTAx2795d4/Y7d+5ETEwMfHx80Lx5cyzRc0djKaZa7CRrnZ9fNat5zQQEkPpyd5ffTKBWw33hBxhY+CsAYPPvXqS+0tMpWUM7y+PmTeCNNzRXWCPZvp16sYeF0UX5gQeA6dOBV16hOnoBAeQqePRRqkhhpex7xsaYarGTvnbNmll44gYNgIceoudffGHy7u3a0X3WrVsa9zDD2ISjR2liVFRUe6lWN2x4OAm7sjKrVajXFnZCgBI/pk0DxowBhg2j9n8//6xIC03GDISCrF27Vnh6eoply5aJM2fOiJdfflnUqVNHXL16Ve/2ly9fFn5+fuLll18WZ86cEcuWLROenp7ihx9+MPqcubm5AoDIzc01uE3PnkIAQmzaZNwx9+6l7aOijB6GhvPnhbhzx4wda+HqVSG8vcXXeEIAQkTXvykq/vhTiCNHhNizR4ibNzXb/u9/9AZUKiFGjBBiyxYhystrPPSYMbSL9PDwEKJdOyFGj6ZD1K+v+zogRL16QkyeLMTy5UKcPi1ERYX8b9tWGPO9chZqe6+xsfT/3rDBmGNpvh95eTIMbutWOlhQkBCFhSbv3rEj7W7CTwojE64yh2p9n2VlQjRsSF/E33+v9vL48fTS//1fDSfp0cOqX+TiYiG8vOiUF9YfF8LNrfoFQJqXS5daZUyuhinzR/5Gplp8+OGHePbZZzHpbmzMwoULsXXrVixevBgJCQnVtl+yZAmaNGmChQsXAgDatm2Lw4cPY8GCBXj44YdlG5e5Fjuzsvr0ZDzJQpMmwL//jYd8Q+EzQ+D8rRAcDRqEbl31bNu+PXDffcDvvwO//EKPqChg8mTgsccqfWTl5eRqfeMNqvXl7g68+CJt0rmzbn1YIcgweOYM9az96ivg6lUKf5JCoDp2pBu5QQMFor97F36luXRHV15O/wRPTzKBtmpFbjaJzEygXj16/e65jh2j81y8SDEoDRvSR+DnR7t4e9Mufn5kkbl1i3b386PDd+2qt2sPYwamZMVevUrL+vUBf38ZTn7fffQP7dyZMgtNLPnQvz8Zz3ftAmT8SWEY49mzh37E6tXTlDbQ4sgRWtaYONG2Lf0gnj5tlS+ytzfQrRtw4ACw/04HtOzUiX6AH3iALgzHjwM//EAhRxYUEk9JAXbupI/o5k26XAQEUEH+uDgL43RdCMWEXUlJCZKSkjBr1iyd9XFxcdi3b5/effbv34+4uDiddUOGDMHy5ctRWloKz7sXem3UajXUWubfPCNKIVhV2MnJ9u1AixYUQwcAM2bAH8DIncD331M7zW7d9Ox3//30OH+egnVXriRV9vrrwDvvAFlZyCqqg5EjaeICQN++wKJFmrgkAPSB3f3wVKUlaP7VfDS/cgUjrlzBm7iGbe7t8Gd5fxxCDxxw64OTJ73w2msAoIIKbyAa59EH+9AVR+GLQniiFF5Ig1c7AVVdEnBqNZD7wofIyVUhp25j3PRrgj/ye+FqobEFBPXTrBkwonMKIusXIax+GXp0LEbbNgIqXx/czPdFao4/KkJCUVGhcT0z+jHFFSubG1bCzY0uZnp+C4yhf3/gs88Uy2dimNr57jtaPvSQJmD1LleuAH//TTfVkvtTL9bu81VcjNje3jhwQIX9B90wYc8ecgdr8+GH5I4dNUqz7tIl+jH1qFlqqNXAa6/R3NSHlAjfti15fidMsI+MYbtFKbPh9evXBQCxd+9enfXz588XrVu31rtPq1atxPz583XW7d27VwAQaWlpevd56623BIBqj5rMlffeS1bjNWuMey9vv03bP/eccdtXY+FCIXr1EuL77808gBBi924hfHyECA8X4vJlnZc2bKDxNW5co4dVQ0GBECtWCDFwoBBjx4qcHCG6daNjBKpyxOeYLMoDgoQIDRUiIoL8rn5+QowcqTlGRYUQvr76zfENGojsfzwvFi8WYtAg/W5bUx++KBBD8Kt4ufEPIiFBiJdfJnfxCM9fxQhsEoOxXXTDYRGNs6IP9oiR2CiG19sj+venoes7Zjiui0ZIrbb+lVeqf2Su4kYSovb3OmoUfU7GeFw+/pi2HTNG5kGaSVqaJiohJ8fWo3EtbDWHPvvsM9GsWTPh7e0tunXrJnbt2lXj9omJiaJbt27C29tbREVFicWLF5t0vhrfZ1mZEGFh9CX89ddqLy9aRC/17VvLSX75hTbs0MGksZlFRYUQDz4ovhvwqQCE6NLFyP2ysoRo1EiIe+6h+B4DXLokREyM5ve3Vy8h/vlPIRYvprCet94Sok+f6t7fsWOFOHFClnfoENiNKxYAVFX8X0KIautq217feonZs2cjPj6+8u+8vDxESkV3DGCuxc7orhP6DnDwIGUYjB1r+v4nTlAWQ3ExEBMDNG6s8/LQoUBgIHDtGpmwpSorBvHzA55+Gnj6aRTklWP4MDL/h4QI7Mrtj7alJ4A80EMb7YhzlQqYMYNcYVFRZCNv3JgCez09UQ/AFABTptDmmZnAX38Be/dq+iBKD8ny4+ZGhpigQIFAryIEueUhsOwWugRdQVy9Q/DLyyA33GtaY+r7b3LJFRfTwbT/qd27A5s2obAQ+PVXYP/MH3EzxwMp6jAcKOyEdBFRuWm45014hjeAmxt5SBjDmJIVK7vFTpvz58m8MXKk0buEhwMtW5JLf+9euvtnnJd169bhlVdewaJFi3DPPffg888/x9ChQ3HmzBk00ePXS05OxrBhwzB58mSsXr0ae/fuxdSpU9GgQQN5woF276brQb16mobfWvxKuXC1fy8li9358/S7Z6YF2yh++gnYuBGxnicAvIgTJ6hSRK2JhKdOAXfu0ETr0gX48kvgwQd1Njl+nJxJN29SuMaqVXQ9q8rbb9PP/Pbt5PHVfnTqBIwfT2E/4eGyvGOHRzFhFxISAnd3d2RIZefvkpmZiTADPs2GDRvq3d7DwwP169fXu4+3tze8Jd+QkZjaUsxiV+yECUBCAs3azEzTFOKNGyTqcnMpHuO776pNYh8fSk768kuaGLUKu7vcuQOMGOmOvXtJGG7bpkLbLseofdOtWxREVVpKvjcfHyAkRPcA77xj9NsIDaW3MWKEMVurAPjdfTQE0B7AcP2b7tlT69H8/CgM5eGHR1euKy4moenmRiZ9f/8GxgyMgWmuWCnGTnZht2cPzYf69emOxoS4nn79SNjt2sXCztmxuzjv775DHvwRMGZMtd/x4mLgjz/ouT5xo0OTJlTXsXVrZYVdYSF1fAHQeOZjaPwVTbdDh4BBg2rZd8AAyv4dP552eOgh4MkngQ8+AEJCcOAAvc+cHNJ9mzZpCiHro1494B//oMepU3T5+ekngRMnVDhxAnhzbgXGjnPDI4/Qqest+y8VeZViuj086HfCz48Cfv/9bwrgA+gHoagIaNQIqFcPuXkqXL1K7zU1le4fL1ygTVQqsmeEhtIlMSCADle3Lj0CA+lnKSSEEvmt1PlNB8WEnZeXF2JiYrB9+3aMHq25oG7fvh0PVlHtErGxsfj555911m3btg3du3fXG19nLlaPsWvblqxHhw9TsMD06cbtp1YDo0fTN6t1a/rmG/iWTJxIwm7lSjKk1dZtJjeXLmr79tEX87ffKMEAUNG30oCQdhZ8fIwXwIwuNo2xk4iNpatAaiqwfj3w+ONG79q/P82VWiovWYQQdC3buJFuoOrUoR/80FAybA8YoPkcGWWwtzjv06cEnlv+DErwNP4adxtVfVC7d5OOCg83In7Mzc3sQt0m8d//0t1ZZCQwezb6XCDbws6dRgg7gOLB9+wB5swhQff118CWLTj14mLc/8FY5OdTU5nNm6nDRa2kpwN796LDgQP4Lu0AbntdxY+l92MFnsHe8r5Yu5YusSoV0CVoHAbfVqEv9iAEWfDHHXhDDS+UIA8BuNbXHWm5lO+Rti4NGedykI5iJCMKtxBS+1iMpI5HMWKCkzEo6Bg6+/6N+m63Ee5xE80Db8F91UqgYUOUlwPZKzfh1h/HcGvs8+gcF2ZaabWqKOkTlsqdLF++XJw5c0a88sorok6dOuLKlStCCCFmzZolJkyYULm9VO7k1VdfFWfOnBHLly9XpNzJ6NHkozc2dKJlS9p+506jh1EdKdioe3fjtq+oEOKppzQp5OfP17rLiBG0+YgRNW+XlydE796aQx88aNyQXBlbxtjZVYyQEOKFF+i789ZbtR9Liq88ftykIRjHO+8YGZCky6VLtJunJ4WbyklBgRAffCBEs2Y1x4w2aCDErFlCnDrlXGWBasLac8je4rwzM4Xw9q4QgBB7EkurHefVV+m78cwzprxLBUlNpbhuQIh164QQQqxcSX+2a2fG8Q4cEKJjR5GNINEiIFMAQvTvL0R+bpn+4PCCAro4ab82blz1yeTlJUTHjuJI/5fFi88WiTZtLI/pBoQIQabogiNiOH4Wr7yoFosWCbF6tRCr+i4RS/CceBdzxHQsFE9juRiL78RQbBb9sFN0al8qIiI05WFqihvv3E4tmjQRwt1d97W//qr+cdhNjN0jjzyCW7du4Z133kF6ejo6dOiALVu2oOndlMP09HSkSP2GAERFRWHLli149dVX8dlnnyEiIgIff/yxrKVOANM6TwihaRphkf9+/HggPp6sdmfPkhWvJu7codg6Nzdg3TqjyqYsWECWt19+AbZto/TwqhQWkjv0wAEybf/+u4FMWsYusLsYIRhvsbtzhzz6gEJZxs8+S22Z9uyhGiY6KdyGiYqiBi1paRT6apTlwQhWrSJreWYm/V2nDoX/NW9O5YOkvu/Hj9O5//MfejRpQob56dM1Ce+MfNhLnHeDBsDjj6uwYgXw0WceuGeA7utbttDS6PCA0lLyD+blUcV4ufnkE/IP33MPMG4cAE0i75kz5A7t0MGE4/XqhfK/kvBYTDounWmApk3J2F5n5xbgiSdoIjRoQAF8WVlkKayooOtlmzZ0jP79yS/apw+95+7d6dro4YGuAD69e6r0dOqh/scfNN/y8uhRUkK/W3XrkuU8IoK8r+Hh9LxhPTWa+maiuftV+N+6Qhf/9HTgf8NRaWI9mQzk7NO4d729NeFK3t7A8o5AvXoQArjz/W+4tvMS9mS2RmJKFJKzA3Er3xvXsv1QVOKH41WSmoN8ihAc5onSUgulmRm6264xRtU++iip4oULaz/ejRuaLLriYgsHJ6UTvvaacdvfuSPE5s0mnUK662vdWoj0dN3XrlyhRFhAiIAAIQ4dMunQLo2tLHY9e/YUU6ZM0VnXpk0bMWvWLL3bz5w5U7Rp00Zn3fPPPy969+5t9Dlre6//+hd9h159tebjnDxJ29WrZ/SpTUeqpP3CCybtJhWBNfAxmsySJZq77agoIZYtM1w/ubRUiB9/FGLYMI1BBKCsv3HjhFi7Vre+uFyo1UJkZAhx9iwZY0qrG40UxdpzSK1WC3d3d7GhSiXt6dOni/79++vdp1+/fmL69Ok66zZs2CA8PDxESUmJUeet6X0eP07/a3d33UTRAwdovYeHCdnaW7ZYYD6rhbIyymgFhNi4UeelBx+k1XPmmH5YqcKEr68QR4/eXTl1qmGzVmgoFSV3MsrKhDh3ToiffxZi3z4hrl2rfT6aMn9cUtg98QR9ZxYsqP14Bw/Sto0ayTC4LVvIbXTpkuFtanrNCG7f1hQ1b9qUGlEkJQnx5puai0idOvRlYozHFsLOHi9MQtB3CaDf45qQKjJ07WrUac3jjz/oJH5+QmRnG73bmjW0W8uWlrtCly7VXIdefVUIIz9mIQR5m37+WYgHHtC9nqlU5Kb6/HMhUlKMLGMk6L3cukUXzV9+IYE5fboQnTvTMaueIyiIqm80aUL6oFcv+onq35/G9NhjdB+6bRsJQ0uwxRzq2bOneKGK6G/btm2NN0Zt27bVWTdlyhRZb4wGDaLPf+ZM+ru4WIj27WmdVmRS7Vy9qlGDlv5z9JGVRSFEVb585s6dxERNyZJVq7ReUKspJmHbNvJ1btxI3ZMMuL5dFbtxxdorpiRPyBr8PXQoMGQIuVf1sWkTmbz//W/gn/806xRBQRSEO2wYWemrulkHDgQ+/VSTLc/YL1lZWSgvL6+WRR4WFlYte1wiIyND7/ZlZWXIyspCuJ54AlOLfBtb7kTRUicSgwaRCzYtjXxDeir562PECPKcXLxIXU266uvYYgRr1gDPPUfPX32V4sNN6XDi56fJFj9+nGLLt2/XdMeQCil7epI7W6osVF5OHqu8PHJ55+bS4/ZtCrcwhEpFSRz5+fT7l5Nj3Dg/+IAy/1q2pASQ1q3po27Rgj7Dixfp/AUF9J7++1/jPwMliY+Px4QJE9C9e3fExsZi6dKlSElJwZS7dZhmz56N69ev4+uvvwYATJkyBZ9++ini4+MxefJk7N+/H8uXL8eaNWtkG9PLLwM7dlDuw6OPAhs2UM3tBg2oxq/RREbSP+XOHfqxl/tHvX594KWXqq0eMYJy+C5epDJZMTG1HyorizoYVVRQpa0nntB60cuLxs4XJdlgYVcLilTOl/jjD3LwBwQACxcC//sfDergQZoBhgRgLbRsSTF048ZRnEFQEAm8556jVHFureVYKB0jlJCQgHnz5hk9HmNj7KS5o2gXD5WKillFRppUV6BuXbr52bCBuraYI+x++42qNwDUes9UUVeVzp3pGAC1Vlq3joTjiRMUTiUJKGNo0EBTVrJ5cxJh99xDf7u50c/LzZskBEtLqYxDfj5phPJyehQWUuWj06cpbvfGDapeAQBbt1IIlj7Cw+1H2NljnPeIESSIL13S/d599ln1ilI1olIB7drR9eL0afmEUUFB9a4SWtStS7Gj331HGai1CbuKCqrakJZGoXKGvjeMfLCwq4XkZFrKanUQgvqn/O9/9Le7uyaT44knqBaDmaJOIjiYdGN2NiVJsJhzPKxVC9LUIt/G9oq1isUOMLsf87hxGmE3f75pc+TQIaodWVZGVpePP5Z3jjVpQkb7f/6TzpGWRr9FyclU4cXLiy6w/v6aOlqBgXQT16hR7RrXzY3KNxlbwqmigqyIaWnU5vTIEfIMpKXRjWTrlhWo73UHdd2LUL99Q4vfv5xMnToVU6dO1fvaypUrq60bMGAAjkgNWxXA3Z06b735Ji3VaqqzaU7terRvrxF2cvH446T6P/3U4B3P+PEk7L74AvjXv2oWpB9+SOVMvL3pZqUGzcjIhEsLO2OyYqWLU1SUjANQqajc9t9/061vWRll+cyeDQwfLusVIjhYtkMxVsZatSBNLfJtrMVOseLEhqiooMKM99xj1Bwy1x2blUUX4qIiiqxYudLi+7Aa8fAgodekCdW/swVSIe/OnQGUleHpEbfJLAjQ3WN4OCn9jh2Bz0/YZpAORNu2dEORl0eF0vv1M/NnX7LSySXssrJIhZWVVetjq82oUdTx4cQJ4K23DPd4PXCALmsA8NFHtA+jPAr+HNkvNnXFSgwdSv6N9HQSeHv30pWGTWuMFvHx8fjiiy+wYsUKnD17Fq+++mq1GKEnJX8gKEbo6tWriI+Px9mzZ7FixQosX74cM2bMkG1Mxgg7IehrDViphEdZGZU+6NePqs0bgeSOBegiawzl5WTQSE2l7nbr1tV4/XMOhKA+gAsXkg8uOJhKzUgEB5NbwMvLBT4MeQkIAO67z4Ji1VKJn+PH5RnQ2rU0l2JianTturuTUAOAJUvImluV8+fJKl5WRiFAUiwqozwuabEztqWYEFZwJ4WEmBhYwbgS9hgjZIywS06mYHovL00JKkXx8CCr99Gj1GvIyOJ0jzxC7thly8ilFBhY8/Zvvkk1In19qQZXbds7NDt2AD/+SDegUkyKxJkqBbiOH6ffMenHlbEOMTH0pezeXZ7jffUVLbVuFg0xcCBZrtevB6ZNo6+Jvz+9tn8/2Smys6kL0rJlbLOwKorn6FoZY1KCja3DlZGhKQugRDY54zjYsvOEtantva5fT/OiTx/Dx/j+e9omJkahQeojJYXaSQBC1NKdQ6KkRIjoaNpl9uyat12+XFMqRKdcg7NQtXaF1J5Gqu5///1CvP8+1U8qKzP58K4yhxz2fZ4+rSmfcuOGUbtcviyEtzftFh4uxP/9nxAPP6xZ17MnddxgLMeU7xW7YmtAstY1asQeBoaRMMZiJ2VPWrWrSWQk8Mwz9Pzdd43axdNTk8H5v/+Ri1UfW7dqXEmvv16lXIMjIwR1w3n5ZXJL5OZqXnvqKfo8N24k08u2bdRao1s3tsw5I6tW0XLoUKppYwRRUdQxo0ULiip6/XWy4KnV5LX/809NKCZjPVxa2NWWPGG1rD6GcSCMyYqVhJ259eHMZtYsmuDbt1PaphGMHElJCcXFFOh9tzpMJdu2kcupvJwE3b//rcC4rc3Vq0BCAsVR9ehBab0pKeR6lZgyBVi+nCLlOZXRfsnJoRqo331n/jEqKoBvvqHnRrhhtbn3XiohmZBA8YJvvEFZ4xs38tfGVri0sDPWYsfCjmE0GGOxk6pFWF3YNWumCex/9VW6YNWCSkV9lgG6tj38MFV7KC8Hvv2WYoUKCigDdvlyB48VOnGCVGyzZmReOXuWUoMfeYSyIR9/3NYjZEzl0CHgwQfp/2kuFRXUuHjsWPrCm4iPD91Tbd9OxvLu3R18njg4LinsjE2eYGHHMNWpTdilp1MxWzc3G5U3eOcdiuL28KDyDUbQvTtl+Xl6ktGqSRN6n48/TgV8H3mELBAOGZKhbVqtXx/Ys4euuoMGkVLNyKBsyGHD6ANgHAsp3uHSJao4bQ4eHtQa4vvvSaUxDo1LCjvJYmcXLZEYxsGoTdhJ1ro2bai9lNUJDaXiYPv2GR0rBADTp9Nu7duTW7a8nH4r4uPJkmd2SQpbUFFBaYr33w9o1UBEo0b0Zq5epQCoZ55x8tReF6B+fU2hVQULKzOOg0uWO5GEmlRnyxBShr+sxYkZxsGpTdjZJHGiKmbWWOnShcZ//jyVZwsN1dwIOgTFxdRwdsEC6h8KkIsiIwNoeLcjxPjxthsfowwxMXTBSkoCBg82bd99+6gp8fjxbMVwElzSYifF/Ug9GPUhhA0q5zOMAyC5I4uLqycaADZMnNBHQQHF2h06ZPQunp5Ahw5ARIQDibrsbOqL1rQp8PzzJOoCA6l14YULGlHHOCdSw9akJNP3/fxzyhp6/315x8TYDJcUds2bU8VvtZoKquvjxg26cLm5UTNthmGIhg0pDKeoSH/Be5slTuhj7lzqmDBxIk1oZ+W77ygdMTOTAgQXLgSuXSPLHbscnB+pQPHhw6btV1ysyYR+9FF5x8TYDJcUdm5u5HIBDIckSF4MrmHHMLr4+WlacVWtsHD7tiY21S6E3Zw55E89cwZ4+21bj0Y+Dh0C/vhD8/dTT5ELbtUqan778svUM41xDaS4h8uXgVu3jN9v40bgzh26GejTR5mxMVbHJYUdoJkHktuoKgcP0lKycDMMo+Ef/6Dld9/pumN/+omW0dFAUJC1R6WH+vWpmSUAvPceJRQ4KuXl9AH36wf07Am8+KKmnIuvL/D771RojzNbXY/gYGD1asqMrV/f+P1WrqTlU0+RxYNxClz2PylZEwxZ7Pbvp2Xv3tYZD8M4EsOHk5a4dElzcyQE8Omn9FxqAGEXjB4NTJ1KzydMoEE7EoWFwKJFlBAyejSVK/H0BHr1AvLzbT06xl54/HGKMzKW69ep+jZAwo5xGlxe2B07Vr2GqRAaYRcba9VhMYxDULcuiTtA4449eJBulLy97UzYAdQvLDaWqvSPHg3k5dl6RMaxZg25yV58kVys9epRoPuVK9SwPSDA1iNkHJVVq+ji168f9QRjnAaXFXZt21IA+J07FJagTUoKFVn18NDEpDIMo8sjj9BScsd+9hn9/eijQEiI7calFy8vKr4aFkalP65ds/WI9COEbh2ZsDCKmYqKorZfqanA//0fpewyTFVWrQIeesi4enYFBWR2f/ppxYfFWBeXFXYeHkDHjvS86hw4cICWnTvbqMAqwzgAw4bR/EhOBgYO1FjuXnzRpsMyTKNGwK+/Us2udu1sPRpd1GqywMXEUMKHxKBB1OrrwgXgpZe4+SZTMz/+SAkRxsSSvvsulX/guoZOh8sKO8BwAgXH1zFM7fj5AW+9Rd2pdu2izlU9e9q5lbtrV93ixYmJ5J61FdnZwL//TfXnJk6kH6Nvv9X0O1SpSEFLfRAZpiaGDqXlb78Zt72/P1ntGKdCUWF3+/ZtTJgwAYGBgQgMDMSECROQU8uP6MSJE6FSqXQevRVSWIYSKDi+jmGMY+ZMKuT9739T96qPPrL1iEzgwAG6EN5zj6ZGi7VISaHCyU2aUK29GzfIopiQAJw86UCVkRm7QhJ2Bw/STYM+Ll+mbhOM06KosHvsscdw7Ngx/Pbbb/jtt99w7NgxTJgwodb9HnjgAaSnp1c+tmzZosj4JIvdX39RsVWA6jVKFjwWdgxTO5GR5D3cts3BrNw+PlQm4swZustbt856516wgIoIFxRQzMc335BPe9Ys08pVMIw2jRtT25SKCmDDBv3bzJtHNzNz51p3bIzVUEzYnT17Fr/99hu++OILxMbGIjY2FsuWLcMvv/yC8+fP17ivt7c3GjZsWPkIDg5WZIxdu5IHJCdHU87nyBFqMxYaygXbGcap6dKFLBs9e9KPwPjxVDIiLU3e85SVUdyTdsxHfDxw333kMjt6FHjsMa4/x8jDk0/S8t13q3dbSU6mmwgAGDnSuuNirIZiwm7//v0IDAxEr169Ktf17t0bgYGB2FeLGTgxMRGhoaFo3bo1Jk+ejMzMTIPbqtVq5OXl6TyMxcMDmDGDnr//vub3l8ZK4S0MwzgxjRtTXbi5c6lA67ffAq1aUf9MS7l0ibpdNGtGmYrvvqt5rVkzYPt2YMgQ/qFh5GXaNHLrp6RoUtUByrieO5cKXcfF0Q0N45QoJuwyMjIQGhpabX1oaCgyMjIM7jd06FB88803+PPPP/HBBx/g0KFDuPfee6HWLgGgRUJCQmUMX2BgICIjI00a5zPPAA0a0I3MxInAf/9L66VSDgzDODmensA771DMXWwsFQQuL9e8np+vW4KkJk6fph+RPn2Ali3J7XX9OtV/adNGt00HwyiBry99n/v00W0T9u67ZK1TqYA337Td+BjFMTlC9+2338a8efNq3ObQoUMAAJWeO1EhhN71Eo9oKaoOHTqge/fuaNq0KTZv3owxY8ZU23727NmIj4+v/DsvL88kcefnR20V33hDY6GePp08IwzDuBA9egB791K9OykIHSDr3dy59Hp0NMVo+PmRIMzOpoLBUtbq88/TMQCyAN53H90xjhlDlZsZxhpMnEj16aRr7cyZ5JYCqD3MPffYbGiM8pgs7KZNm4bxtdS9adasGU6cOIEbN25Ue+3mzZsICwsz+nzh4eFo2rQpLly4oPd1b29veFv4gzl1KvCf/9CN+ciRwIcfWnQ4hmEcFZVK0whXQsqu2rWLHlV5/nky+wMk4AICSBiOHQuEhys/ZkYvt2/fxvTp07Fp0yYAwKhRo/DJJ58gqIYmxhMnTsRXX32ls65Xr144IBU3dRSq9n2Vwp/mztW012OcFpOFXUhICEKMKCsfGxuL3Nxc/PXXX+h515d/8OBB5Obmoo+2ebgWbt26hdTUVIQr+ANZrx4V7N65k6zVXDKKYZhK1q4lk/7x48D589S1oriYCvfVq6frXo2Ppwdjcx577DFcu3YNv92t6fbcc89hwoQJ+Pnnn2vc74EHHsCXX35Z+beXl5ei41QcIajw9ahRwD//aevRMFZAsWJJbdu2xQMPPIDJkyfj87uByM899xxGjBiB6Ojoyu3atGmDhIQEjB49Gvn5+Xj77bfx8MMPIzw8HFeuXMHrr7+OkJAQjB49WqmhAqDY5oceUvQUDMM4IioVtamRWtUwdo9UleHAgQOVCXzLli1DbGwszp8/r3MNqopUlcFpUKkcrMAkYymK1rH75ptv0LFjR8TFxSEuLg6dOnXCqlWrdLY5f/48cnNzAQDu7u44efIkHnzwQbRu3RpPPfUUWrdujf3798Pf31/JoTIMwzBOgrWqMjCMPaJoefPg4GCsXr26xm2ElhvD19cXW7duVXJIDMMwjJNjSVWGcePGoWnTpkhOTsbcuXNx7733IikpyWAst1qt1qnaYErJLYZRApfuFcswDMM4Dm+//Xa1lpNVH4cPHwZgflWG4cOHo0OHDhg5ciR+/fVX/P3339i8ebPBfSwtucUwcsMNCRmGYRiHwN6qMgCWl9xiGLlhYccwDMM4BPZYlUGOklsMIydOJ+ykmD2Oc2DkRPo+CRfoHMBziFECa84hW1Zl4PnDKIEp88fphN2dO3cAgE3hjCLcuXMHgYGBth6GovAcYpTEWnPom2++wfTp0xEXFweAChR/+umnOtvoq8rw9ddfIycnB+Hh4Rg0aBDWrVtnUlUGnj+Mkhgzf1TCyUwQFRUVSEtLg7+/f7UgWSn2ITU1FQEBATYaofLw+5QfIQTu3LmDiIgIuFWt6u5kuPoccoX3CFj/fbrKHHL1+QO4xvu05/njdBY7Nzc3NG7cuMZtAgICnPbLpg2/T3lxdkudBM8hwhXeI2Dd9+kKc4jnjwZXeJ/2OH+c97aJYRiGYRjGxWBhxzAMwzAM4yS4lLDz9vbGW2+95fSp6fw+GaVwhc/cFd4j4Drv055wlc/cFd6nPb9Hp0ueYBiGYRiGcVVcymLHMAzDMAzjzLCwYxiGYRiGcRJY2DEMwzAMwzgJLOwYhmEYhmGcBKcTdosWLUJUVBR8fHwQExOD3bt317j9zp07ERMTAx8fHzRv3hxLliyx0kjNIyEhAT169IC/vz9CQ0Px0EMP4fz58zXuk5iYCJVKVe1x7tw5K43adN5+++1q423YsGGN+zja/9Ie4flTHZ4/jCnwHKqOo80hh58/wolYu3at8PT0FMuWLRNnzpwRL7/8sqhTp464evWq3u0vX74s/Pz8xMsvvyzOnDkjli1bJjw9PcUPP/xg5ZEbz5AhQ8SXX34pTp06JY4dOyaGDx8umjRpIvLz8w3us2PHDgFAnD9/XqSnp1c+ysrKrDhy03jrrbdE+/btdcabmZlpcHtH/F/aGzx/9MPzhzEWnkP6cbQ55Ojzx6mEXc+ePcWUKVN01rVp00bMmjVL7/YzZ84Ubdq00Vn3/PPPi969eys2RrnJzMwUAMTOnTsNbiNNqtu3b1tvYBby1ltvic6dOxu9vTP8L20Nzx/98PxhjIXnkH4cbQ45+vxxGldsSUkJkpKSEBcXp7M+Li4O+/bt07vP/v37q20/ZMgQHD58GKWlpYqNVU5yc3MBAMHBwbVu27VrV4SHh2Pw4MHYsWOH0kOzmAsXLiAiIgJRUVEYP348Ll++bHBbZ/hf2hKePzx/HP1/aWt4DjnXHHLk+eM0wi4rKwvl5eUICwvTWR8WFoaMjAy9+2RkZOjdvqysDFlZWYqNVS6EEIiPj0ffvn3RoUMHg9uFh4dj6dKlWL9+PTZs2IDo6GgMHjwYu3btsuJoTaNXr174+uuvsXXrVixbtgwZGRno06cPbt26pXd7R/9f2hqePzx/HPl/aQ/wHHKeOeTo88fD6mdUGJVKpfO3EKLautq217feHpk2bRpOnDiBPXv21LhddHQ0oqOjK/+OjY1FamoqFixYgP79+ys9TLMYOnRo5fOOHTsiNjYWLVq0wFdffYX4+Hi9+zjy/9Je4PlTHZ4/9v+/tCd4DlXH0eaQo88fp7HYhYSEwN3dvdqdUWZmZjUlLdGwYUO923t4eKB+/fqKjVUOXnrpJWzatAk7duxA48aNTd6/d+/euHDhggIjU4Y6deqgY8eOBsfsyP9Le4Dnj2nw/GGqwnPINBxpDjna/HEaYefl5YWYmBhs375dZ/327dvRp08fvfvExsZW237btm3o3r07PD09FRurJQghMG3aNGzYsAF//vknoqKizDrO0aNHER4eLvPolEOtVuPs2bMGx+yI/0t7guePafD8YarCc8g0HGkOOdz8sUHChmJIqebLly8XZ86cEa+88oqoU6eOuHLlihBCiFmzZokJEyZUbi+lKL/66qvizJkzYvny5Xafav7CCy+IwMBAkZiYqJOKXVhYWLlN1ff5v//9T/z444/i77//FqdOnRKzZs0SAMT69ett8RaM4rXXXhOJiYni8uXL4sCBA2LEiBHC39/fqf6X9gbPH4Lnj2P8L+0RnkOEo88hR58/TiXshBDis88+E02bNhVeXl6iW7duOinYTz31lBgwYIDO9omJiaJr167Cy8tLNGvWTCxevNjKIzYNAHofX375ZeU2Vd/ne++9J1q0aCF8fHxEvXr1RN++fcXmzZutP3gTeOSRR0R4eLjw9PQUERERYsyYMeL06dOVrzvD/9Ie4fnD88dR/pf2Cs8hx59Djj5/VELcjfBjGIZhGIZhHBqnibFjGIZhGIZxdVjYMQzDMAzDOAks7BiGYRiGYZwEFnYMwzAMwzBOAgs7hmEYhmEYJ4GFHcMwDMMwjJPAwo5hGIZhGMZJYGHHMAzDMAzjJLCwYxiGYRiGcRJY2DEMwzAMwzgJLOwYhmEYhmGcBBZ2DMMwDMMwTgILO4ZhGIZhGCeBhR3DMAzDMIyTwMKOYRiGYRjGSWBhxzAMwzAM4ySwsGMYhmEYhnESPGw9ALmpqKhAWloa/P39oVKpbD0cxkkQQuDOnTuIiIiAm5tz3w/xHGKUwFXmEM8fRglMmT9OJ+zS0tIQGRlp62EwTkpqaioaN25s62EoCs8hRkmcfQ7x/GGUxJj5Y3fCLiEhARs2bMC5c+fg6+uLPn364L333kN0dLRR+/v7+wOgNx8QEKDkUBkXIi8vD5GRkZXfL2eG5xCjBK4yh3j+MEpgyvyxO2G3c+dOvPjii+jRowfKysowZ84cxMXF4cyZM6hTp06t+0um74CAAJ5UjOy4gmuF5xCjJM4+h3j+MEpizPyxO2H322+/6fz95ZdfIjQ0FElJSejfv7+NRsUwDMMwDGP/2J2wq0pubi4AIDg4WO/rarUaarW68u+8vDyrjIthGIZhGMbesOvUJCEE4uPj0bdvX3To0EHvNgkJCQgMDKx8cNAqwzAMwzCuil1b7KZNm4YTJ05gz549BreZPXs24uPjK/+WAgwZ8ygvL0dpaamth2ETvLy8nLoMA6M8rjx/PD094e7ubuthMA5MRUUFSkpKbD0MmyDn/LFbYffSSy9h06ZN2LVrV42pvd7e3vD29rbiyPRz6xawaxcwfDjg5WXr0ZiOEAIZGRnIycmx9VBshpubG6KiouDliP9AB0cIYNs2oGtXIDTU1qMxHZ4/RFBQEBo2bOj0CRKyoVYDW7YA994LBAbaejQ2paSkBMnJyaioqLD1UGyGXPPH7oSdEAIvvfQSfvzxRyQmJiIqKsrWQzKK+Hjg66+B/v2BH38EDIQE2i3SRSk0NBR+fn4u98MsFRVNT09HkyZNXO7925pff6Wbon796AbJ0XD1+SOEQGFhITIzMwEA4eHhNh6Rg/D668CHHwJjxwLff2/r0dgMIQTS09Ph7u6OyMhIl/OcyD1/7E7Yvfjii/j222+xceNG+Pv7IyMjAwAQGBgIX19fG4/OMNLFaNcuoHdvulC1aGHbMRlLeXl55UWpfv36th6OzWjQoAHS0tJQVlYGT09PWw/Hpfj9d1ru3g2cOgUYCKm1S3j+ENLvc2ZmJkJDQx3WLWtpLVWT+PBDWv7wg/zHdiDKyspQWFiIiIgI+Pn52Xo4NkHO+WN3snjx4sXIzc3FwIEDER4eXvlYt26drYdmkKws4MoVeh4ZCVy4AIweDThKqI0UE+SqE0pCcsGWl5fbeCSux/79mufLltluHObA80eD9Bk4cpyhVEv1wIED2L59O8rKyhAXF4eCggJbD81pkX5zXT0MRq75Y3cWOyGErYdgMocP07J1ayAxEejYETh5EvjkE3LROgqu5j6qiqu/f1uhVgNHjmj+/vpr4D//AezYQK8X/v44x2dg1VqqbdoA5845ZmC2AjjD98cS5Hr/dmexc0QOHaJljx5AeDjw3nv091tvAdeu2W5cDOMIHD0KlJQAISFAs2ZATo7Le6YYO6K2WqoWsWYNLevVk//YjMvCwk4GJGHXvTstn34aiI0F8vOBV1+13bgYxhGQ3LCxscCkSfTc0dyxjHNiTC1VtVqNvLw8nYfRtG5NgdmJifIMmGHAwk4WJFdsjx60dHMDliyh5Q8/AH//bbuxMYy9c+AALXv3ppsiNzdKokhLs+24GEaqpbpGsqzpwewi+dnZgIcHpYK3aSPTiBmGhZ3FXL8OpKcD7u5Ug0uiUydgyBB6/vXXthmbK7BmzRr4+Pjg+vXrlesmTZqETp06VbpQGPtG22IXEQF07kx/795tuzG5Cjx/DCPVUt2xY0eNtVRnz56N3NzcykdqaqpxJ3jlFaBOHWDxYnkGzNgEe5xDLOwsRHLDtm8PVE2Ke+opWq5aBThkzcWCAsOP4mLjty0qMm5bMxg/fjyio6ORkJAAAJg3bx62bt2KX3/9FYEuXvDTEbh+HUhNJSudZPHu14+WDi/seP44JEIITJs2DRs2bMCff/5Zay1Vb29vBAQE6DyM4vRpoKwM2L4dmDcPuHpVhtE7GTyHzEM4Gbm5uQKAyM3Ntcr55swRAhDimWeqv1ZUJERgIL3+xx9WGY5ZFBUViTNnzoiioiLdF6ghgP7HsGG62/r5Gd52wADdbUNC9G9nJj///LPw9vYW8+fPF/Xq1ROnTp0SQgiRl5cnunfvLjp37iw6dOggli5dat7nIKz/vbIl1nyv339P//rOnauv69RJ8dPLgrPOH4mCggLRpEkT8dprr9V6LGeYQy+88IIIDAwUiYmJIj09vfJRWFho1P5Gvc/yciF8fen/Jv3vf/1VpnfgeDjzHHJ3dxedO3cWnTt3Fs8++6x5n4Mwbf7YXbkTR0M7I7YqPj7AI48AS5cCX31FXWMY+RkxYgTatWuHefPmYdu2bWjfvj0Aqgm0c+dO+Pn5obCwEB06dMCYMWNcuoisvSHF18XGatZJFruTJylDNijI2qNyLQzNH4n58+ejV69eNhqd9Vl81zU6cOBAnfVffvklJk6cKM9JrlwhK5K3N108du6kvpSMQ1LTHAoKCsKxY8esOh4WdhYgRPXEiapMnEjCbv164LPPgLp1rTY8y8nPN/xa1arYd1uh6KVqexipmrNMbN26FefOnUN5eTnCwsIq17u7u1cWfCwuLkZ5eblD1kl0Zo4fp6WUUQ4AYWFAq1ZU6HvvXmo15pA4+PwBgAsXLuDcuXMYOXIkTp06Jet57RWr/EacPk3L6Gj6wgNU6Z7RxQnmkC3gGDsLuHqVEps8PQ23QOrdmzLaCwqAn3+27vgspk4dww8fH+O3rVpp1tB2ZnDkyBGMGzcOn3/+OYYMGYK5c+fqvJ6Tk4POnTujcePGmDlzJkJCQsw6D6MMZ8/Ssl073fV9+9Jyzx7rjkdWnGD+zJgxozJ2iJERSdi1bw9IHgS22FXHCeZQXl4eYmJi0LdvX+zcudOsc5gKCzsLOHqUlh06kEVdHyoV8NBD9LxKQXPGQq5cuYLhw4dj1qxZmDBhAt555x2sX78eSUlJldsEBQXh+PHjSE5OxrfffosbN27YcMSMNrm5lDwBAG3b6r7mNAkUdkxt82fjxo1o3bo1WrdubeOROiFnztCyXTsWdg6MMdegK1euICkpCUuWLMGTTz5pWp1DM2FhZwFSGyTtMif6kMqebNtG7lvGcrKzszF06FCMGjUKr7/+OgAgJiYGI0eOxJw5c6ptHxYWhk6dOmHXrl3WHipjgHPnaBkeXj2OThJ2hw5VT35jLMeY+XPgwAGsXbsWzZo1w4wZM7Bs2TK88847thy289ClC/DMM8DgwdRyBWBh52AYew2KiIgAAHTo0AHt2rXD31YobMsxdhYgWey6dat5u3vuoVIoGRnAiROaOl2M+QQHB+Os5MfTYuPGjZXPb9y4AV9fXwQEBCAvLw+7du3CCy+8YM1hMjUg/fuqWusAoEULoGFDmjN//QXI3aLT1TFm/iQkJFS6YVeuXIlTp07hzTfftNoYnRrtJuKXLtGSY+wcCmPm0O3bt+Hn5wdvb29cu3YNZ86cQfPmzRUfGws7CzDWYuftDQwaBGzeTO5YFnbW4dq1a3j22WchhKisTdWpUydbD4u5i7Y3qioqFdCnD7BhA5CUxMKOcWLuv5/aijVqZPKuQgDffUe7//03xXN/8kn1XAHGNpw9exbPP/883NzcoFKp8NFHHynTc7gKLOzMJCODOk6oVNRlojaGDCFht3Ur8K9/KT8+hszi1k4zZ4ynJosdQLGrGzYALpKMadfIVuaDIW7doqy7unUpK9bMTMply4Dnn9f8/fvvwNixZEhgbE+fPn1w8uRJq5+Xdb2ZSG7Y6GjjSphIcXZ79tScwc0wrkJtwk4qBSUlEDKM09ChAxAYSMUazaSgAHjrLXr+2GNAXBw9X7FChvExDg0LOzORhF1tbliJVq2AZs2A0lIgMVGpUTHOxq5duzBy5EhERERApVLhp59+qnH7xMREqFSqao9zUqaCnVBUBFy+TM/1uWIBTQmh06c56YhxMqSMIB8faiv26afA228DarXRh1i4kDxHUVHAl18CUl7LDz9QxjnjurCwMxMpvq62xAkJlUpjtdu6VZkxMc5HQUEBOnfujE8//dSk/c6fP4/09PTKR6tWrRQaoXn8/TeJtXr1gNBQ/du0akXeqvx8ICXFuuNjGEXRFnbu7sCrr1K/2Js3jdo9Kwv473/p+fz5gJcX0LMn3SQVFwNr1yo0bsYhYGFnJqZa7ADgvvtoyRU3GGMZOnQo/v3vf2PMmDEm7RcaGoqGDRtWPtyrVmm3MdpuWJVK/zaenhTqALA7lnEihNBY5ry9aQJIAfVGljz54AMgL4+uP488QutUKqqgArA71tVhYWcGOTkaN5Ipwk6qpi/1wGQYpejatSvCw8MxePBg7Nixo8Zt1Wo18vLydB5KU1NGrDYcZ8c4HaWlmtgCqXuCCbXsysuBr7+m53Pn6mbAPvEE4OFBJYJ4zrguLOzMQEq0bNpUc6NlDA0bAi1b0pzev1+RoTEuTnh4OJYuXYr169djw4YNiI6OxuDBg2sszJyQkIDAwMDKR2RkpOLjrC1xQkISdpwZyzgN2hW3JWEndZ8wopbdzp1AWhqFMVTtoxwWBgwbRs83bJBhrIxDwsLODA4domVMjOn7OkUPTMZuiY6OxuTJk9GtWzfExsZi0aJFGD58OBYsWGBwn9mzZyM3N7fykZqaqvg4jRV22gkUDOMUaCdIeHnR0oS2Yt98Q8uxYzW7ayMJu+3bLRgj49CwsDODw4dp2aOH6fuysGOsTe/evXHhwgWDr3t7eyMgIEDnoSRlZZQ8ARhvsTtzBqioUHRYDGMdPDyAp56iGiWSH9VIYVdcTFmvAPD44/q3kcqe7N8P3Lkjw3gZh4OFnRlIFjtLhN1ff5mU2c4wZnP06FGEh4fbehiVJCdTmJGvL9CkSc3btmhB8eVFRbQfwzg89eoBK1dqTG+A0TF2W7ZQ0kRkpKafclWiomjelJVxaS1XhYWdiWRlaS4w5rhiW7emOVxcrCmZwsjD7du3MW/ePKSnp9t6KLKRn5+PY8eOVXbQSE5OxrFjx5Byt/7H7Nmz8eSTT1Zuv3DhQvz000+4cOECTp8+jdmzZ2P9+vWYNm2aLYavl/Pnadm6de2tj9zdNVY9dscqizPOH4dh0iRy48yYUeNmkhZ89NGa545ktdu2TabxMUZhL3OIhZ2JSG7YVq2AoCDT91ep2B2rFNOnT8ehQ4fwwgsv2HoosnH48GF07doVXe+mX8fHx6Nr166VzdjT09MrRR4AlJSUYMaMGejUqRP69euHPXv2YPPmzSaXS1ESqVZymzbGbc8JFNbBGeePXVJWBhQWUnqrRMuWwD331Ngv9s4daksJkBe3Ju6/n5YcZ2dd7GUOsbAzEUvi6yQkYbd3r+XjYYhNmzYhPz8fv/zyC4KCgvCNtpvDgRk4cCCEENUeK1euBACsXLkSiVr+lpkzZ+LixYsoKipCdnY2du/ejWFSNLWdYKqw4wQK5XHW+WOXHDgA1Klj/AS4y5YtFL7TqlXt/ckHDSJr9/nzXNzbWtjTHPKw2ZkdFEvi6yS0LXZCGC7QyhjPqFGjMGrUKACoFD2MfSK5YqXiw7UhbXfxojLjYXj+WBUpuFoqdQJQx4k1ayhD6JVX9O62fj0tH3649mtGUBB1oti/n6x2zz5r8aiZWrCnOWR3FjtTe2NaG0nYde9u/jG6dqWA8Fu3+GLFuB6mWuxatKAlzxXGKZDq2Hl7a9ZlZQEvv6xp+FqFoiKy2AEk7IxBirNjd6zrYXfCztzemNbg+nUgPZ2CVk3pOFEVLy9N4gUXKmZciVu3NDVYW7c2bh9J2GVnA7dvKzMuhrEa2n1iJfz9aXnnjqYrhRZbtwIFBZRFbmzS3oABtNy3z4KxMg6J3Qk7c3tjWgMpvq59ewqRsITYWFqysLOMNWvWwMfHB9evX69cN2nSJHTq1Am5ubk2HBmjD8kNGxlp/ByqU4e6tgDApUvKjMtV4fljA/S5YiVhV1amtw6W5IYdM8b40J0ePcgIkZpKRglGGexxDtmdsLNn5Iivk5CE3YEDlh/LlRk/fjyio6ORkJAAAJg3bx62bt2KX3/9FYGBgTYeHVMVU92wEi1b0pKFnbzw/LEB+lyxdetqnlepKlxSAvz8Mz031g0rHVJKsmADgnLY4xxy+OQJtVoNtdYdjpINzKUs1l69LD9W7960PHECyM/Xnde2RgjKxrcFfn6mJZOoVCrMnz8fY8eORUREBD766CPs3r0bje6WDbhz5w7uvfdelJaWory8HNOnT8fkyZMVGj1TG+YKuxYtKNnIEeLsnGn+SBQWFqJt27YYN25cje3pGCPQZ7Fzd6d/XmEhXRAaNKh8KTERyM2lPrB9+ph2qthY6m2+fz+1IHMUnG0OeXh4oMPd9P7u3bvjiy++kHvYOji8sEtISMC8efMUP09Jica6Zqjityk0akTuqNRUcvEOHGj5MeWisNB2QjM/33Q394gRI9CuXTvMmzcP27ZtQ3up8BkAPz8/7Ny5E35+figsLESHDh0wZswY1Jda+DBWRXLFOrPFzpnmj8T8+fPRS447WgZo1ox8qtLdvUTduvTlqWKx27SJlqNG1V7QuyqxscDixY5nsXO2ORQUFFRZZN4aOLwr1loNzJOSyIIeEmL6RckQ0rx2tElnb2zduhXnzp1DeXk5wsLCdF5zd3eHn58fAKC4uBjl5eUQeoKTGesgWeyMLXUiwZmxylHT/AGACxcu4Ny5c3ZXD9FhGTqUgub++U/d9doJFHcRQlfYmYoU8pOUxC0slaS2OWRtHN5i5+3tDW/tWAWF2L2bln37yld3LjYW+P57+4uz8/OjuxZbndsUjhw5gnHjxuHzzz/H2rVrMXfuXHz//fc62+Tk5GDAgAG4cOEC3n//fYRIfRkZq1JaCly+TM+d2WLnbPNnxowZeP/997GP0yuV5auvSMl17Fi56sQJ8ur4+gKDB5t+yBYtyBiRlQUcPVrdSGivONscysvLQ0xMDHx9fTF//nwMkFKWFcLuhF1+fj4uat2WS70xg4OD0aS2juEKsmsXLeVww0poW+zsqVCxSmV51q81uHLlCoYPH45Zs2ZhwoQJaNeuHXr06IGkpCTEaNUECAoKwvHjx3Hjxg2MGTMGY8eOtYu7Klfj0iVK+qtbF4iIMG1fyWKXlkZuGlN/fK2JM82fjRs3onXr1mjdujULO7moqKAvSdUf/HvuqbapZK2LiyNxZyoqFRkQfv6ZrjOOIuycaQ5J20VERODUqVMYPnw4Tp48iYCAAMXGZXeu2Np6Y9qCigpN4oScwq5bN6ppd/MmkJws33FdgezsbAwdOhSjRo3C66+/DgCIiYnByJEjMWfOHL37hIWFoVOnTtglqXTGqpw9S8voaNNvYoKDgXr16Llk9WPMx9j5c+DAAaxduxbNmjXDjBkzsGzZMrxjoIguYyTx8ZQsYcQ1zRI3rASH/CiDKdegiLt3sh06dEC7du3w999/Kzo2u7PYSb0x7YlTp4CcHLqDsKQwcVW8vUncHThARSSbN5fv2M5OcHAwzkpKQYuNGzfq/H3jxg34+voiICAAeXl52LVrl80bNLsqUq9XPbH5RtGyJZUcunhR0z+WMQ9j509CQkJlGYeVK1fi1KlTNr3JdgrUanLReFS5/CYmku81Nhbo0QNpaZRYp1IBw4ebfzqumaoMxs6h27dvw8/PD97e3rh27RrOnDmD5gpf7O3OYmePSPF1sbHV56KlSOnr7OVQhmvXrqF///7o3Lkz+vbti2nTpqFTbR20GUU4c4aW7dqZt7/kjnWEODuGMYi+OnYAsHo1tRXbtg0A8MsvtLpXLyp1Yi5SoeJr16hzEmNdzp49i+7du6Nz584YMWIEPvroIwQHByt6Truz2NkjkrCT0w0r0acP8OGHGlcvIy8xMTFWTTNnDCOHxQ7gzFhbMXHiRFsPwTnQ11IMqJYV++uv9OeIEZadrm5dSlY6c4ayYy09HmMaffr0wcmTJ616TrbY1YIQyiROSEgWu5MnAQVrKzOMTSkr09SwY4sdY8/s2rULI0eOREREBFQqFX766Sd5TyDVHalqsdMSdmVlwJ9/0p9xcZafsnt3WiYlWX4sxv5hYVcLp06R+drXVxOrICfh4UBUFAnIgwflPz7D2AOXL9P1zNeX6rOaA1vsGGtQUFCAzp0749NPP1XmBLVZ7PLzcegQ3egHB1MctqVICZpSv3PGuWFXbC389hstBw6sPg/l4p57KCt2717g/vuVOQfD2BIpvq5tW9Or50tIFrurV6kmnqenPGNjGG2GDh2KoUOHKncCI1yxd8PsMHgwJdBaiiTs2GLnGrDFrha2bqXlkCHKnYMTKBhnx9L4OgBo2JCuhRUVFAjOMA5Jz550QanSj1db2G3fTk/lcMMCQJcudEOVnk61IBnnhi12NVBQoEmcUFLYSXUpDxwAysvluUNjGHtCEnbmxtcBVPahWTNqS5acTCEMDGNr1Go11Fr9uvJqC5b+z3/0r7/bHDX3dgUOHKNVcnlw6tQha/np02S1M7VAOONYsMWuBnbuBEpKgKZNTe9taQrt2wMBAZQMdeqUcuepDXurH2htXP39K4nkirXEYgdo4vPssaA3f39c8zNISEhAYGBg5SMyMtK8A/XuDWzejMRHP0d5OdCqFV175MIR3LGu+P3RRq73z8KuBqT4uiFDlG335e6uqQ5ui7InnneDlQoLC61/cjuipKQEAODOJlNZKS8nKxtgmcUO0Fjprlyx7DhywvNHg/QZeLpQAOTs2bORm5tb+UhNTTXvQGFhwLBh2HaZsoTkcsNK2HNmrPSbK/0GuypyzR92xdaAFF/3wAPKn+uee6gu5e7dwNSpyp9PG3d3dwQFBSEzMxMA4OfnB5W9NK61EhUVFbh58yb8/PzgIXcVahdHjoxYCXu02PH8IUtDYWEhMjMzERQU5FI3R97e3vCuWrqkJlq0oD6Su3cDnTtXe/n332kpdyKdPWfGenh4wM/PDzdv3oSnpyfczM2wclDknj98BTNAcjLw999kTbv3XuXPN2AALRMTqfSJta8LDRs2BIDKi5Mr4ubmhiZNmtjVRXnXrl14//33kZSUhPT0dPz444946KGHatxn586diI+Px+nTpxEREYGZM2diypQp1hmwHqT4ujZtLI8ftUeLHcDzRyIoKKjys3BU8vPzcVGrpk5ycjKOHTuG4OBgNGnSxPIT5OVR3E3VG8jiYqQt3oi//34Ebm4CAwbI+zskJVBkZFACRUQEqMDk5MlAUBDwv//Jej5TUKlUCA8PR3JyMq5evWqzcdgaueYPCzsDbNhAy759gcBA5c/Xqxdl/GVkUCHXNm2UP6c20sQKDQ1FaWmpdU9uJ3h5edndnaJUU+vpp5/Gww8/XOv2ycnJGDZsGCZPnozVq1dj7969mDp1Kho0aGDU/kogV3wdYJ8WO4DnD0DuI2ew1B0+fBiDBg2q/Ds+Ph4A8NRTT2HlypWWn8BQuRO1GjvjfwLwCLp2EQgKklfY+flRKMSpU2S1GzUKwI8/AitX0gXHhsIOoN/fVq1auaw7Vs75w8LOAN9/T8t//MM65/PxoQLIO3bQw9rCTsLd3d0pfpydBVNrai1ZsgRNmjTBwoULAQBt27bF4cOHsWDBApsJOykhSA5hJ1ns0tLIvWuKB8wa8PxxfAYOHKhsEL+UQVtV2NWti0QMpDH0KgbgJ/upY2JoPiYl3RV2UhFmqQryuXPA3LnAa69pAr+tiJubG3yUKhjrQtiXecJOuHqVukCoVMCYMdY7r3STmJhovXMyzsX+/fsRVyXqesiQITh8+LDNLElSm8SOHS0/Vv36VLoBoHnKMA5FeTlV1waq35W4uyNRRReBgd2U6S+pkxl74gT1y/TwAN5/n154/33ghx+A11+nmCDGIWFhp4cffqBl//5UFNVaaAs7IUCF9C5f5gnGGE1GRgbCwsJ01oWFhaGsrAxZWVl691Gr1cjLy9N5yEVJiSYjVg5hp1JprHb25o5lmFrRqndX1WKXlgb8LVpDhQr0jb6pyOl1hJ1krRszRlPY7q23AC8vchtJWRyMw8HCTg+SG3bcOOuet0cPyhzMzATOnBbAffdRBlWTJsCUKVQ2nGFqoWryh+RWMpQUIlsdLj2cO0fx2YGBgFyHleLs7C2BgmFqpQZht3MnLbviKIJUuYqcXieBYtUftHLaNM0GTZoAzz9Pz5ctU2QMjPKwsKtCSorGDWvtkCRvb00XisSdKmD+fPrj2jXg888p//32besOinEoGjZsiIyMDJ11mZmZ8PDwQP369fXuI1sdLj1IbtgOHeTL9GaLHePQDB5MWXlVsmKlEJyBSATy8xU5tZRAAQBJxe2ATp1oLNo8+SQtf/1Vk+jBOBQs7Krw3Xe0tLYbVmLgALKu7NgBqrNSUABs2UKm8tOngQcfBIqKrD8wxiGIjY3FdqnR5F22bduG7t27Gyx66e3tjYCAAJ2HXMgZXyfBFjvGYalXj1ycUq9KLXSE3Z07ig2hsp5d+4lknat6xxUTQ31s8/OBP/9UbByMcrCw06KiggxjAPDYYzYYQF4eBn05AQDw558CZWWgW6yhQ6kNRkAA/SBMmmSDwTG2ID8/H8eOHcOxY8cAaGpqpaSkACBr25PSHTaAKVOm4OrVq4iPj8fZs2exYsUKLF++HDNmzLDF8BURdmyxY5yN9HSqm6pSCfRb82J1K5qMVMbZNXtYfzV8lYoMCADw00+KjYNRDhZ2Wvz+O3DxIuknmwi7Dz5Az8trUd/tNm7fVum2F+vYEdi4kSq87t5NlcsZp+fw4cPo2rUrunbtCoBqanXt2hVvvvkmACA9Pb1S5AFAVFQUtmzZgsTERHTp0gXvvvsuPv74Y5uVOmGLHcPUzp49tOzcWYWg8Q8A4eGKncuonrEPPUTxE7aqu8VYBNex02LRIlpOnAjUrWvlk2dmAh98AA+UY3i/XHy9sx42bdJ0pAAADBxIlZMHDQL8/a08QMYW1FZTS1/B1AEDBuDIkSMKjso4cnIAKVyvQwf5jitZ7DIzKVJBKn/CMHbPX39Rj8pWrSiY+y6SsFPQUFdJl5BrcHNrhIwMlaYDRVXuu09zV8Y4HGyxu0tKCvDzz/T8hRdsMIDFi+kq1b07Rk1rCoAMdNWu6aNGsahjHAKpMHFkJIUWyUVQkKYbDNeyYxyKwkJKgKtSUqhS2EVeBb76Cti/X7Eh+H29BO0qaHIatNrZUVtFxnRY2N3l888pxm7wYBtZn7dupeWUKRjygApeXsClS8DZswa2r6gAVqzQ/CIwjJ2hhBtWguPsGIdET9eJO3eAuyG0uCfte3IZrV6t3BgOHkQMSNEdPlzLtoWFnEDhgLCwA3DrlsYNqy+WVHFyc8lEDwD334+6dUlgAsCmTQb2ef994Nlnqb6di/amZOwbFnYMUwWpfIhW14mDB+k+vVkzoHGjuy4apbJiKyqAv/5Cd5CiO3SolrGGhdHF6PJlZcbDKAILOwDvvkvxQJ06aZKBrMqOHdRqpnVrKhCJu338UIOwmzQJCAmhEigff2ydcTKMCSgp7KQEChZ2jEOhx2KnE18nhdkoVMcO584BeXno5X0cAIlKgyG8Pj6aHrKSR4lxCFxe2F24AHz2GT1fsICSTq2OVO17woTKVSNH0vLAAQMNJ+rXB957j56//TYVMWYYO0EIttgxTDUki50eYXfPPdAIO6UsdgcOAAA69/SGtzeQnU2VIAzywAO0/O03ZcbDKILLC7vZs6nl0dCh1NjBJnTrBixZArzxRuWqRo2A3r3pArlqlYH9Jk4E+vShu7t//tMqQ2UYY7h6lSIMPD2ViVmVhB2XPGEcCslid9cVW1paqbXIYieVY1BK2N3NxPWKjakseyKdXy+SsPvjD2r8zDgEdivsFi1ahKioKPj4+CAmJga79VTqtpSvvgLWr6feee+/L/vhLWbyZFouXUqhEdVwc6NGzioVsHYtsGuXRee7cQM4f54KZV6/Tt5hhjGH4+TpQbt21FNcbthixzgkwcFAz55AdDQAmicFBZTp3a4dlLfYSWWQevZEr170VKvqSnU6d6Y4u4IC6BZWZewZuxR269atwyuvvII5c+bg6NGj6NevH4YOHapTiNVSdm3KweTJFFwwZw7Qvr1shzaNo0eBffvIbFiFRx6hYsmXLt1tMaaPrl01TZunT68hYKI6paVU4uWpp4DmzamFWps29JvTuDF5C1q2BB59lPTjkSNaeRplZQbUJsMAJ07QslMnZY4vxdjl5NCDYRyChx8mJfXf/wLQdcO6uUF5YTdnDvDOO0CPHujdm1bVaLFzcwOGDKHn7I51HIQd0rNnTzFlyhSddW3atBGzZs2qdd/c3FwBQOTm5urfoKhI7Br1vghGlgCEGDdOiPJyOUZtJuPHCwEI8fbbel+eOpVe/sc/ajhGVpYQ998vxL59Rp0yL0+It94SokEDOrb0cHMTIihIiMBAIdzddV+THr6qIjHQbadIwL9E0ncXRUnJ3YOuWiVEjx5CjBwpxLPPCvHGG0J8+60QJ04IkZ9vyidil9T6vXIi5HivDz9M35cFC2QcWBWk7++RI8qdg5EPV5lDprzPcePoOzx//t0V2dlCfPONEL/9puwghRBXr9K5PTyEKCysYcNvv6UNO3VSfEyMYUz5Xtld54mSkhIkJSVh1qxZOuvj4uKwb98+i46dfDAT/xp6HN/fpr6ZPTyOYuUXHeHmZsOPQXpPOi0mNDz3HJVi+fFHqrQfGqpno/r1gW3baj2VEMCyZcDcuXQsgKzsjz1GoRS9e5OFECA3bNqh6/h7yZ/YtykLe263w0H0Qq4IQqLoj0T0x+x/UAxVu3ZAp4oO6HSyP6JxHuE4hjBsRQPchA/uxpTs3g307YuKCqDgzFXcSs5DdsN2yM51r7S65ORQ7c7bt+l5Xh7duBYVUcyxEGRF9PPTPOrUoaWPD+BdnAvfomwE5KchKPcq6uZnoE7+DQTXUaNRu0CEfjgL3kG+urU31WoK1Dp5EmL/AajvGw4xcBB8fWv9OJkakFyxnTsrd46oKOqsd+UKGa4ZxtGQ6hD36XN3Rb16VutnGRlJXpqMDPLG3HOPgQ3j4oAPP9TE2zH2jxWEpklcv35dABB79+7VWT9//nzRunXratsXFxeL3NzcykdqaqpeVatWC9EwrIIsUygTz3utELdQT4jNmxV9PzWSna0xhd2+bXCznj1pkzffNPK4589XM0OmpQkxZIjmdC1bCrFunRClpTUcZ80azQ7+/qL80cfFmYSfxGdv3RAjh6hFQECFXque9qMO7gh/5Apfn3Lh4VHzttZ6+PgIEeqVLaLdzovWOCcicE34I1e4oUwAQvzzn9U/ClexNghh+Xu9c0cIlYo+68xMmQenxSOP0Dk++EC5czDy4SpzqMb3OW+eEJGRQrz3nkhJoe+vu7uVnBqnT9P1LiWlctVDDylvWWfkwaEtdhKqKi1NhBDV1gFAQkIC5s2bV+vxvLyAf81S4ZfvC/Hhm7no9Otx4KPbwNdfA8OGyTZuk5ACkZo2pehZA8yYAfzjHxSW8fTTmvgivXz0EWXIvvce8OqrAKgE0RNPAFlZZNmaPx+YNk1PUPvmzbQcPlyzHDYMGD8eGDMGbnXqoC2AtgCmgmTS1atknTl5kpZXrtAdYEYGheEV4G6WV7HuqbxRjPq4hXq4jSDkVC6DkIN6rz6NoMZ1ERQE+H+zGH5//gKfuwcogi8K4Yci+KJAVRdF//0UhUUqFBUB6p+3oSj1JnLrNEKOTxjyVQHIr/BFVq4X0nN9UVpOtWyKi4Fi1EMm9Pe5UqqElKtw6hR9N8LDgQYNlDsPJ1AwDsfNm9RAOT+/0lnTpUuVfscbN5K74uGHyR0hF6tXAwkJ1DPzbkX+3r2Bn36qJc6OcTjsTtiFhITA3d0dGRkZOuszMzMRFhZWbfvZs2cjPj6+8u+8vDxERkbqPfb06cDLL/tBpfIDGjxJIuinn8jvV4OwUgwjI8zHjgUGDgQSE4HXXqNMXoP4+FCGw7/+hYqmUfi/Mw/hzTfpQtulC/DNN3ezr7QpLgbi46lfbWQkFbH086NAXkns6UGlIpHZrFn1ws5CULmLW7foubs7uW0lV6qvlydUp25S8sixY1RQ8PJlIC0NWPCiJq1n5wHAYxv52nr1os8qOpp8xj4+QDQASe//X5zBsVZU0G+lWk2u3bzkW7h9vRBu/nVQN7Ie6tRVoW5dqjYg52+pKyK5YZVKnJBgYcc4HFqdJyQ3bGxslW0ee4xaed1zD2W1ycWFC7Rs1apylZQZu38//U4bbBGrVgPffktZfCtWAB52Jx0YLezuv+Pl5YWYmBhs374do0ePrly/fft2PKinLYS3tze8tdqz1ISbdg5w165k0Ro8WFM7yNoYGYikUgGffELCbMMGCqeLM6RhnnsO2LkTmWt+x9NjvbBFaFZ//LFOJxviyBFqTSY1Kxw3zqTM2prGHBRUk152pzfUpYvu6qq/Lv/5D91d6tzSmo6bW5WxNKsPoL5Fx2T0Y434OoC7TzAOiFaBYsliVxlfJ+HvT8JO7sxYSdi1bl25qmdPuuG+fp3uq1u0MLCvhwe5jrKz6WLSt6+8Y2NkxS7LncTHx+OLL77AihUrcPbsWbz66qtISUnBlClT5DuJSkUBocOH2+7uw4SaEB06AC+9RM+feELTWrYaKhV+e3wVOvn8jS1iGLxRjOUxi/D5hD3w9ror2MrKqObd448DMTEk6kJCKJ39gw8sFlEWUfWWMTzctuNhTMZawk67SLEM9yIMozx3CxQXudXB0aO0Sq+wA+QVdkLotdj5+aGy7Mmff9awv7u7xprAZU/sHrsUdo888ggWLlyId955B126dMGuXbuwZcsWNG3a1NZDk5cPPgAWLtQzs/Uzbx41qbh5k1yz332nW0T45EnSqUNHuONGcRDa10vDIfTAM0kvkiqURFNFBXDffWRaB0jgHTumqVfEMGZSUaFpJaa0sGvShL7ShYU0JxjG7rlrsTuc3ghlZXTferc9uAYlhF1aGk0Ud3fNHdFd7r2XljUKO0CTFfvrr/KNi1EEuxR2ADB16lRcuXIFarUaSUlJ6N+/vzIn2ryZAkpt0Wu1Xz/g5Zepf5gRBAQAO3fS/CoqogLGYWHUDq1JEzL8bdlCBshXXgEOXY9AxxPfApMmUeychJcXibh//INcsatXGz0GhqmJK1foeuTlVVlcXzG8vTVfW3bHMg7BXWG3L4W+uH366Ilrk2pOySnsJGtds2bke9VCW9jVaPmWhN2RI9SmiLFb7FbYWY233gJefx04fNjWIzGKunWBTZso3KFePUpO+O03SrRyc6NEqtOngf/9D1SLrWNHKl63caPugX7+GVi3jguAMbIiuZc6dLBOhAMnUDAORdOmQPv22HdVI+yqoYTFTo8bVqJXL7pWZGYCZ87UcIywMHIZAeyOtXNY2Ekdys+ds+55t24lS5kZbdI8Pam3bWYmWfA+/ZSSlXJzgR9+0ImN1WAw3Ylh5EMSdtLvv9JwAgXjUCxbBnHyFA5cpgoPUnybDpKwy8uT77xxcXS9mT692kve3ppciFrdsVJpsE2b5BsbIzss7CRhd/68dc/72WfAhAnVLWkm4OEB9O8PvPgixdzZKrmXYSSkHuPWMgSzxY5xNFJS6Kbcw8PAPJkyhUSYnPVVmzalWOqhQ/W+LLljDfYkl3joITIS5OdzxpIdY3flTqyOFAhkbYudtVIHGcaKWNti17IlLS9dss75GMZSDh6kZefO0N+6UKl48hoYNIiWiYmUkOfubmDDbt0oEaNhQ2sNjTEDtthpu2KtdQeSk6NxwSpdxZVhrER6OnUccXOz3tdaEnZSCBHD2DUDBuCvF74EQDXkrEJFBXmItm6lUld6iIkhD/Dt2xqru15UKhZ1DgALu5Yt6cuak2O9mgmS2zciwjYdLxiHYtGiRYiKioKPjw9iYmKwe/dug9smJiZCpVJVe5yzgkVasta1aWO97h2SsEtNpUxxhrFr/v4bB7PpSyt1fahGaiqF6OzZI88509Koh6TUKlIPHh6aMnUbNhh53Oxsg0KRsS0s7Hx9NRHY1nLH/v03LZWuB8E4POvWrcMrr7yCOXPm4OjRo+jXrx+GDh2KlFqSbs6fP4/09PTKRys92XByY+34OoDqagcG0nOOs2PsnbKiUiQhBkANFrtt2yiW7T//keekqam0bNSoxlT1ceNo+f33Rjivxo8HQkOBGm4yGdvBwg4AvvqKqqrqTVFSAEnYWeFiyzg2H374IZ599llMmjQJbdu2xcKFCxEZGYnFixfXuF9oaCgaNmxY+XA3GDQjH5LFzprCTqXSWO0uXrTeeRnnxhQruSmcKmqBIvghwL/C8H29VMdOrqxYSdgZ6KEuMXw4td++dEnTYdIgfn4UjPfjj2YNqayMwsx/+QX4+mtg+XIqKXvsWGVzDs2GFRWav0+fpmbpBw+yib4GWNgBVCi4QweqqmoNJGGnty4JwxAlJSVISkpCXJXGwHFxcdgnNZo0QNeuXREeHo7BgwdjR62pbvIgWeyslTghwXF2jJyYayWvFSFwsITuenp0KdXtXa6N3HXsjBR2detqvLXff1/LMceMoeW6dUBJiVHDqKggHRgXRzVYu3QBRo4EnnqKauiPGEE3hQEBQGy3Ysy5JxEHw0ahIkWrecA33wBjx5IRpm5d+rF56y26q+Qs3UpY2NmC99+nFhEPPWTrkTB2TFZWFsrLyxEWFqazPiwsDBkZGXr3CQ8Px9KlS7F+/Xps2LAB0dHRGDx4MHbt2mXwPGq1Gnl5eToPU7l9m7pOAPSDbU3YYsfIiblW8lopLcVf6AEA6NWjBhEidx07I4UdYII7dsgQKlicmUnF7mvhl18ooWrMGGD7dqqWEhBASRtxcVTZpVs3IDiYdOKBoz74v30D0Tt7Cxp1b4jnnqNj5Ee0pqrOoaGkFI8eBd55h3Zu00bzI+TicLkTgIJAV6yg5In33lP+fE2a6GkQyDD6UVUpLi2EqLZOIjo6GtFaPp7Y2FikpqZiwYIFBtvyJSQkYN68eRaNUXLdREXR3bg1kSIaWNgxliJZyWfNmqWz3hgrea2o1TgIypjoFVuDTUXulmImCDvJHXvxIs1pg2EVnp7AM89QO85ly6jlkR6ysqgm8po19HdAAOVxjB8PtGtXvayKWLQYl178EHvRB1tCnsSvBf2RccsLy5bRaTw8JqJnz4no/7RAnzbZiMn5A+E710K19TcSwtrv8eOPKUU/NpY8ct7eKC+nQv5ZWfTIzqbd8vLoeVYWUFBA7mC1mtrrqtUUxxscTDG9YUFq1L9zBXVvJqPurasIdM9HoEcBAryKEfBAH3g/PILOf/488OijJH7r1iUhHB0NDB5MhWer3LBDCODqVao5aGFDARZ2AFBaCvzzn/RhzptH32yGsTEhISFwd3evZp3LzMysZsWrid69e2P16tUGX589ezbi4+Mr/87Ly0OkERcBbWyROCHBFjtGLsyxkqvVaqi1AsMMWbzv3C7DGbQDAPTsU8Ol10auWID0x7BhlBm7alUt8/nZZ0nYbdtGljIpCfEuSUnkXpVKIMXHA3Pm1FAI4uOPoXr5ZbQE0PL5wXjqs4FQl7lj505qdLF5M51m3z5g3z4VgPoA/gEfn3+gaWQ5QvwKUe8hd3h4AOXlAiXbOqJI7YYCCNzGNWS7N0BueV0Iix2V3gCi7z6q8AV18ggKAgLrNIfv5RXwRRF8UAyv8yXw2FUGLAOAw1C1aAFV2zYoLQXUt+5Affw8ikrdUdS8EdZt8LSoxC0LO4DMukFBVPLk4kVS90px5gzZubt3rzH9nGG8vLwQExOD7du3Y/To0ZXrt2/fjgcffNDo4xw9ehTh4eEGX/f29oa3t7dFY5VaLcfEWHQYs5CEXUoK3V1b+FYYxiQrubEW77SiemjbjqxADSOMsNgVF5PRwdPT6HHr5ZNP6LpmsL6KLs8+S8Ju6VJqox4SYmDDFi3I+vTHH+Txeuedype2bAH+8Q+yfrVtS/mJPXrUcNKffwZeeYWez5kDvPsuoFLB251ctXFx1DozOZm6Y+zbB+zdS+HqxcXA+QvuOA9/4Lh0QBWAQbrnKNc89XcvQIOmdRAcTB93wNFEBLvnoX5AKer6lMHbrRQ+KIJf2R14du2AvN5xyM4GbqaV4Mbyzcj2CEWBbwjuuAcit8QXOWpfFJZSjL5aDdy4AdyAJ4Auht/zpbsPGhGA7vT0MlkVLYGFHaBJrTt8GLh8WVlht3cv8PbbwAMPsLBjaiU+Ph4TJkxA9+7dERsbi6VLlyIlJQVTpkwBQNa269ev4+uvvwYALFy4EM2aNUP79u1RUlKC1atXY/369Vi/fr2i4zx0iJY1/ngrRGgoWRry8+mHX6o5zjCmYo6V3FiLd3Q0JXXqZH3qIyCACgpLAs9SevUyWtQB1HWsa1cKX/voI9JYBpk1i5IZHnusctWKFcDkyRQCd//91L+81rfSvTsF5/buXSnq9BEVRY9nnqG/S0vphi41lVyp2dl0Xjc30sO+voCfr0Bw6Q3Uu3EOwYXXUO9OCryaNwaefJIOUlwM+N4VgVl6Tlq3O/CtlMDmBczuQtbJKmMsKyMja14e2Yjy8ihxt7CQ4gbVakokBgBRVAQhVKjw8oGXF+ClzoPPjRT4dmoF3yBvixtSsbCTiIwkYSeZrZWCM2IZE3jkkUdw69YtvPPOO0hPT0eHDh2wZcsWNG3aFACQnp6uk61XUlKCGTNm4Pr16/D19UX79u2xefNmDJOz72QVbt/WtPSyhcVOpaI4u6NHyTDBwo4xF3Os5KZavGvd1MMDmDrV6OPJjUoFvPEGhc19/DHw2ms1uE/vu48eoBCx//6XtB4ATJxIVj+jDI7h4cCuXRQGZUJ8macnGQ5btKjxHQFoePehBw8PYP9+MrPdvKnJ8vX0BOrUoeA6baQG1XoOU68ePe7+PNdA1V5yAQDkMyixsJOQ7rBY2DF2xtSpUzHVwA/9ypUrdf6eOXMmZs6caYVRaZDcsC1aVP8NtBYtW2qEHcNYQm1WcociOZlaibVpQwH7RvLQQ0D79mRh/OQTYO7cmrevqABmxJfjfx9RNsSsWcD//V8tGu3aNfJgPfII/V23rtHjkxUPD+vVsLUSLOwkGjempdLCTiq2xcKOcRJs6YaV4Fp2jFzUZiW3CgcPkgUpNhZo0MD84+zfD7zwAok6E4SdmxuFuj32GAm0uDjD3ly1Gpj4QDrWJlIc7wcfUKJEjWRlUTjS6dMUQ/Hss0aPjakdrmMnYQ2LXXm5xqTAwo5xEiSLXffuthsDZ8YycjJ16lRcuXIFarUaSUlJBksFKcaUKcCDD2rSzc3FhIzYqjzyCIWBFxcDo0bpLxF38SLF0a1NDIcnSrDa6xnEj0+r+cDZ2eS+PX2a+qXfdeUy8sHCTiIuDjhxgvKqleLqVYr29PY2a6IxjD3CFjuGkRm5ihRbIOzc3IC1aymnITOTBNyaNWShO3OGKoN17EjtYuvWFdjcZgYeL/mSqhwXF+s/6K1bdKDjx6mO2x9/GBOQxpgIu2IlgoOVDxCS4utatoThfjIM4zhkZFCojJub9VuJaSPVZL5yhbLQ/PxsNxaGsRi5ihRLwk4KNTKRunWp40OvXmSde+wxCkkrK9Nsc//9wOLFKrQonQrErqJaJM8+C6xerRtkd/Ys9RC7dIncy3/8wZlOCsHqwpoMHgycOwdUCXhnGEdFcsO2bWu72GeASp7Ur0+ZeefO2W4cDCMLdmCxk2jUiDzCb79NntOyMkpeHTSItNvWrXezUtu0odomHh7At99SIbsTJ+ggmZkUL3jpEpUK2bGDsjMYRWBhp82iRZRmLlnW5MbTk0wLtgxGYhgZkdywtv5Kq1Sa8pOnT9t2LAxjMXJ1n5BB2AF04/TWW2QRP3OG6rT9+Sfw+ONVMl8HDwaknro//KD5gQgNpd5iffsCf/3Fok5hWNhp8/XX9KXkKwPDGIU9xNdJSNcKnr6MwyO5Yi2x2BUXU/YpIFtMt6cnWedrrMU3aRL9MEyerFO4GG+/Te5XS7J8GaPgGDttIiMpzVypzNg33iAb9rPPUkFGhnFghKDpAgA9e9p2LAALO8aJkMNi5+4O7NxJQbD16skzLmPp3r26Gd/NDfDysu44XBQWdtooWctOCOB//6PIbqkgI8M4MH//TZULfHxgcQscOWBhxzgNQ4dSk9ZOncw/hqcnYO0yLYxdwMJOGyVr2WVmkqhTqTi9m3EK9u2jZY8e9nEjLgm75GRqPl6njm3HwzBm07OnfZjBGYeEY+y0UVLYXb6sOYc9XAUZxkL276dlnz62HYdESAjFaANUWYFhXJrdu4ElSywvcsw4HHYn7ObPn48+ffrAz88PQQY7DyuENYRd8+byH5thbIBksYuNte04tJGsdqdO2XYcDGMRubkUH7drl/nH+OEHaif2/ffyjYtxCOxO2JWUlGDcuHF44YUXrH9ySdilpVH7LzlhYcc4ETk5mlg2exR2HGfHODSnTlFvV0t6qKbdbe0VESHLkBjHwe5i7ObNmwcAWGmLIr7h4VRQMTJS/s4QLOwYJ0LKhm3RQuP+tAe4lh3jFMiRFcvCzmWxO2FnU9zcqPmdErCwY5wIyQ1rL/F1EmyxY5wCFnaMBTi8sFOr1VCr1ZV/51nagkUpfvsNuHqVGh8zjINjb4kTEpKwS0mha6J0fWQYh0IqUFxYSD28PEy8VAvBws6FsUqM3dtvvw2VSlXj47DUdNJEEhISEBgYWPmItLTC9saN1FZs40bLjlMVX1/qpWftQpEMIzPl5cCBA/TcnuLrAJpeUu3vkydtOxYdpA/t7beB++6jfpmbN2tev3ED+Ogj8nEnJ1N5pH37qM3ha6/ROok9e4AXX6TK/uPHA3FxVAy2eXPyi//wg2bb7GwqOFhRYa13ysiB9h1Jfr7p+2dnAyUl9LxhQ3nGxDgMVrHYTZs2DePHj69xm2bNmpl17NmzZyM+Pr7y77y8PMvE3Z491FbM2xt48EHzj8MwTsrJkxprmBTTZk907w78/DO1pLSpRVEI4Pffge++oxvFmzd1X79wQfN8xw7glVcMH2vwYCAqip6fOUOCzxD162ueb9oEPP00FfXr0gV46CFq88RWHPvGy4seJSXUVszUChHp6bQMCaml/xfjjFhF2IWEhCAkJESRY3t7e8Nbzi9uo0a0lMzYcnDwILB0KV1lLMlyYhg7QKrAcM891LXI3oiNJWG3f3/NWklxtm6lDgISQUFkrbv/fvIZt2uneS04mLY9fhy4fRsoKiLx1bUrEB2t2+szJgZ4803qLODvT/vWq0eCzs8PaNlSs21ODnkLCgqAvXvpMXMmHS84mD4kHx/a9qefqMdn7970ISr0m80YSUAA9Xo1J84uKorKpZhj7WMcHruLsUtJSUF2djZSUlJQXl6OY8eOAQBatmyJunXrKj8ASdhdvy7fMQ8fBlasIPM4CzvGwdm5k5b22q2od29aSnGANiMujm7mOnUCxo6lD8zT0/C2cXGav2uKq4qJoYcxvPIK8NJL5I7duRNYvZrEXUoKPW7d0vzmff898O23mn1btKCxd+lCKr5fPy6ubk3efpusvuakndepY78TlFEcuxN2b775Jr766qvKv7t27QoA2LFjBwYOHKj8AJQQdpwRyzgJQmgsdgMG2HYshujRgxLcU1NpGktTWilKS6nPeuqhDJQtXYEuK6YjuEldGsTu3eaVTjI1WL4m3N2Btm3pMWUKuelSU0nUSUH6ALlpfXxIEZ89C1y6RI8ff6T3kJOjEXY3b5KFUO6yUIyGF1+09QgYB8XuhN3KlSttU8NOQtsVKwT1drUUFnaMk3D2LHmHfH0pls0eqVuXqhYdP075Cg8/LP85hKDchi+/pBA68pY1BPA60JS8oWPHAs8844ZWreQ/v0WEh2syTLQZN44eAHkXjh2jup5//UWuXO2A/nHjKKHjiSeA557j/tf2xi+/kHgfOJAEPeNS8O1WVaQfvJISuoLJgZTRJgU/M4wJLFq0CFFRUfDx8UFMTAx2795d4/Y7d+5ETEwMfHx80Lx5cyxZskS2sUjWuthY+/bKSdm6UvaunGRmkljs2xdYvpxEnQ+K0BIX0NyL2hFevAj85z9A69YUUrdjB4lBhyE4GLj3XnLlfvutbpWA3FwSfSkpwP/9H92wPvgg8M031RNEGPO5dIkm3LVrpu+7YgVVd9ixQ/5xMXYPC7uqeHkBDRrQczncsUKwsGPMZt26dXjllVcwZ84cHD16FP369cPQoUORkpKid/vk5GQMGzYM/fr1w9GjR/H6669j+vTpWL9+vSzjsff4Ogml4uy2bKG8hx9/JG/pxLg07PS6DwWogwv9J+HSDX/cukXhasOGkafy999JI8XGAmvWaKpQOCyBgeTOXbeOkkEqKij79oknKCmjsNDWI3QOXn+d4h02bDB9X65h59oIJyM3N1cAELm5ueYf5OJFIW7fFqKiwvIBZWcLQfJOiIICy4/H2ARZvldm0LNnTzFlyhSddW3atBGzZs3Su/3MmTNFmzZtdNY9//zzonfv3kaf09B7ragQIiKCvso7dhh9OJtw/jyN09tbCLVanmNu3iyEpycdt3NnIY5+flAIHx9aMWKEEIWF1fa5ckWIF1+kcUg/A2FhQsybJ0RmpjzjqonychpWebmCJzl7VohZs4To0kWIe+81uJmt5pC1ke19TppEX5h33zV938hI2vfgQcvGwNgNpnyv7C7Gzi5o0UK+Y6WSawahoVSKgGGMpKSkBElJSZg1a5bO+ri4OOyTenpVYf/+/YjTzq4EMGTIECxfvhylpaXwNJSVaQSXLpEhwMsL6NXL7MNYhVatyJuYnU2xdj16WHa8P/8ExoyhRIlHHgG+XqaGV/uxQHExMHw4FQXWU3apaVPg00+BuXOp4tGSJfQZvvUWkJBAu8bFkUWvRQvjQ3orKsholpJCjoXUVHqemkqu4ps3KZIkO1tTm9jbm2rVNmpETongYMp/CA2lv+vV0xR4btyY4iiNok0bejMJCU5gjrQjpMQWU7spSV8OgC12LgoLO6Xp1IkCj2/csPVIGAcjKysL5eXlCKvShi4sLAwZGRl698nIyNC7fVlZGbKyshCuJ2je2LZ8Unxdz8h0+PrqCb63I1Qqcsdu2UJJDpYIu9OnKYRMrablqlWAp6c3Fct7/33giy9qLQIbFkbibtYs0oAffAAkJQHr19MDIJEVE0PCqn59SgLx9SWtdOsWCbUbN+ianZxMpe5MQa2mroZXrxq3fVAQCb2QEBJ6kZE0Ji8vqqYRGEjVW3JzKWE2Oxu4dcsLdeoAn39u2tgYPUhdinJyTNsvK4vK5ahU3MLSRWFhp4/duylguEMHeVLO/fw4vo4xG1UVM44Qotq62rbXt14iISEB8+bNq3UcbtlZiEYWBlxaD5x7mCw1dkz//iTstmwBXn7ZvGPk5pKlLj+fEgzXrRXw9Lz7OXbuTHXhTMDTE3j0UeoEdugQtZDevp0ST2/dArZtM/5Y7u4kuBo1omWTJiS+GjYkC1yDBiTK/PxIHObnkyi8fp3OJT1u3qTH7dskztLS6F40J4ceyck0VmMJC2Nh9//t3X10U/X9B/B3WvoADIolQNtBoUUHTtBhgbaMI1RcKeNhOGU4Z4EJKDgmFlHBOVr0aH86kE1kgjwU3TjixsPQA/LgbFEHqDgYlJbyZKHQ1lLEFvCYUvr9/fEhTR+SNClJ7s3N+3VOzk2Te5NPSL7kk+/9fj9fj4iMlO0337h3nHV8XZcujusmkqExsbOnqEjOmYwaxVpCpBmz2Yzg4OBmvXMVFRXNeuWsoqKi7O7fpk0bdG641FQDri7LN2WuGVM+nYa6Le8Bzx9pXMxWh+69V3rIPvpIkhZ3l2muqwOmTJHavj16AP9Y+jXCfvpzWdN16NAbis1kAgYPlsuCBdKb9r//yXJtlZWScF25IvMQQkKkB69zZ0naunWT34k9e7r3vW02yxK1LVFKErqKCskpKipkYubZs9JLaLFIklhdLQljp07Se9e5s+Qi1rlndINuNLHjadiAxcTOHk8WKX7uOaC8XBLE68WWiVwRGhqKhIQE7Nq1C/fee2/97bt27cIvHKxjnJycjPfff7/RbTt37sTAgQMdjq9za1m+rCwEbdkCrF8P/OEPMkVUp370I+l0z8+XSZuTJ7t3/F/+IqtshYYCG9ZUo8uDP5PM67HHgAMHPLqeWliYLdHTmslkG29HGmptYjdkiIyb8Kv6OuRJLHdijyfXi928WYpdeaomHgWUOXPmYNWqVVizZg0KCwuRkZGBM2fOYMaMGQCkt23SpEn1+8+YMQOnT5/GnDlzUFhYiDVr1mD16tWYO3euZwL6yU+kiJtSsuSRzlmLE7tbMSI/H5g/X64veeUqBmf9XJK66Gip6abHRXLJWPr2BV54AXjySfeO69RJln/Te00i8hr22NljTewqK+W8g6u9GU0pBRQXy3VXzoEQNTFx4kRcuHABzz//PMrKytCvXz9s27YNPa9X+i8rK2tU0y4uLg7btm1DRkYGli1bhpiYGLz22mu4z5PLL2Rl2Ub9f/WVrseP3ncfsHAhsGOHFBJuuHiCIxYL8JvfyHb0aIWZn/9W1lft1EkeSMevlwwkNlbO+BC5yaSUsfprq6urERERgaqqKnRsuA6iO5SS6WgWiywH1tr/yCsqZECMySSDU1qbIJLmPPK58hMuvdaRI2Wk/zPPyBILOqUU0KcPcPy4nD2eOLHlY+bOlVmrXboAh6f9Bd2yn5Aeup07pS4JtUqgtCHNX+e6dTIAMi2NP0IMxJ3PFU/F2mMyeWacnbW3LiaGSR0Zy8yZUgDNbNY6EqdMJpnVCshqEC15/31J6gBg1ayDktQBwNKlTOrI9/LzZbkXd2rbLFki40Dz870XF+kaEztHPDHOjkuJkVGNHSvVcD01ds+LrL10mzfLmvaOnD5tm2AxezYwLqM3MGmSJLEzZ3o/UNLciy++iCFDhqBdu3bo1KmT1uFIjZ3hw+XMkausnRHW7zAKOEzsHHnnHZnzP2FC6x+D4+vIqIKDZbqoHxgwQJpxXZ2saW9v8Mnly7LPxYsyM/WVVyAD8t56S3rrKCDU1NRgwoQJmKmXRN7dmbFXr9qK4TOxC1hM7Bz54Q+lOJOra/zYU1EhW/bYkVFduwZs3SrrjenYK68A4eFAbq703DX03XfSAfnFF1Li4925XyC0TZ1tB86ADRgLFy5ERkYG+vfvr3Uowt3Errxcfrm0acOCggGMiZ03LV4sXQHuTlcn8hcPPwyMGSMlQHSsVy/bWeMnngA+/FC+/woKgPHjgbw86aD74I970GtiolQ3vnpVu4CJAFtid/Gia/tbT8NGRwNB/HoPVHznHSkoAGbMsBWzai3roopERtSvn2ytC8nq2Lx5slpDSQnws59Jp/xtt8mSXu3aAdtWlSLxhTGS8cXEcDkmconFYkF1dXWji8e422PH8XUEJnaOffONLHj4j39oHQmRfg0bJttPPpFBbDrWvj2wdy/w+ONyWrasTM5YjR0L5H7wPYb+3xjbILs//1nrcMlDsrKyYDKZnF7279/f6sfPzs5GRERE/cXecnytxsSOWoEFih2xNs6zZ+ULy91u7bIyID1d1jVatuzGxuoR6dWAAZIxffMNcOQIoJexSQ5ER8tSYc8+C+zfDyQmAubOCph6fZkwsxnYsIHliQxk1qxZeOCBB5zu0+sGJri5utZyq7ib2D34oK1NUsBiYudITIwkczU1MssoOtq9448fB/79byl5wqSOjCokRNam3LVLTsfqPLGz6tYNGD36+h8rVwE5OdLe33nH9qOODMFsNsPsxXqLbq217K6775ZuZVcXETabZTkxCmg8FetISIitO7vBkk0us9Ydio/3XExEemQ9Hbt7t7ZxtEZ5uRStA2Rdznvu0TYe0tSZM2dw8OBBnDlzBteuXcPBgwdx8OBBXL58WZuA7rpLlhVLTdXm+ckvscfOmdhYGWl95oycs3GHtfxD796ej4tIT6yLjX/8sUw88Kce6qgoSUjffVdmV1BAW7BgAd566636vwcMGAAAyM3NxfDhwzWKyg2vvioDSCdOBDp31joa0ggTO2diY2Xxb/bYETk2aBCwaJEtwfM3gwbJhQLe2rVrsXbtWq3DsLFYgGPHgCtXgKQk5/sqBSxYIPumpjKxC2BM7JyJjZVtWZn7xzKxo0ARHu5/tRovXJDLj36kdSREjp0+Ddx+u5TM+vZb5/tWV0tSB8gYcQpYHGPnzNNPS2NatMj9Y3kqlki/nntOJnqsWKF1JESOWWfFVlUBtbXO97WWOunUSQozUsBiYudMZGTrigt//71tnBF77CgQXLwIrFlzfZFVnTt2DFi5Uma833qr1tEQOdapk+16Sz12rGFH1zGx84bwcCmRcukSV52gwPDNN8DUqTLGp6WeBa0995yscTt6tP+OC6TA0KaN7TukpVp2TOzoOl0ldsXFxZg6dSri4uLQtm1b9O7dG5mZmaipqdEmoNpa4LHHZC1M69gFd/zgB56PiUiP4uLk824d7K1X+/cD//yn9KhnZ2sdDVHLXC1SzMSOrtNVYnf06FHU1dVhxYoVOHLkCJYsWYLly5fj2Wef1SagNm2AdeuArVul7AkR2RcUZCtO/L//aRuLM9a1nx96yG+KKVOAu+km2TKxIxfpKrFLS0tDTk4OUlNTER8fj3HjxmHu3LnYtGmTdkFZZ8a6U/Jk4UJZZXzLFu/ERKRHt98u20OHtI3Dkd27gQ8/lOLjzz+vdTRErnG1x+6Pf5Q1m6dM8XpIpG+6L3dSVVWFSOsHWws9ewL5+TLt3FV79sgXyK9/7b24iPTmjjtkq9ceu5MnZQ3NSZOAG1gblMin0tOB4cNtP5wciY52f+lLMiRdJ3YnT57E0qVLsXjxYof7WCwWWCyW+r+rq6s9G0RreuxYw44Ckd577B5+GBg7VusoiNwzaZLWEZCf8cmp2KysLJhMJqeX/fv3NzqmtLQUaWlpmDBhAqZNm+bwsbOzsxEREVF/6eHpBbzdTexqa4HiYrnOGnYUSKxj1s6dk+K/etSli1yIjKSmBnjmGanLePWq1tGQxnyS2M2aNQuFhYVOL/369avfv7S0FCkpKUhOTsabb77p9LHnz5+Pqqqq+kuJpyc5uJvYnT0ryV1oKKt/0w25ePEi0tPT63+0pKen49sWallNmTKl2Y+mpJaWIvKUjh2BvDygvFxfyxkdOAB8+qksuUTkb6qrgcOHgcJCx/ucOSM1JDMyZNIfBTSffALMZjPMZrNL+547dw4pKSlISEhATk4OgoKc555hYWEICwvzRJj2WRO7lgauWllXnOjVCwgO9kpIFBgefPBBnD17Ftu3bwcAPPLII0hPT8f777/v9DjrJCSr0NBQr8bZyLBhvnsuV82fD+zYIV98Tz2ldTRE7tm4UYYRjBoFbNtmf5+Gw3+sxfEpYOkqtS8tLcXw4cMRGxuLRYsW4fz58/X3RUVFaRPU4MFS8dvVQsPHj8v25pu9FhIZX2FhIbZv3459+/YhMTERALBy5UokJyejqKgIffr0cXhsWFiYdu1Fbw4dkqQuKAi47z6toyFyn7X321nnAsd1UwO6Sux27tyJEydO4MSJE+jevXuj+5RWp1FCQ+XiKosFMJuBH//YezGR4e3duxcRERH1SR0AJCUlISIiAnv27HGa2OXl5aFr167o1KkThg0bhhdffBFdu3b1Rdgyvm7ZMuDyZeC113zznM5Y13m+/35+6ZF/siZ2FRWO9/nqK9nyM07QWR27KVOmQCll9+I3Zs8Gzp8HXnpJ60jIj5WXl9tNxrp27Yry8nKHx40aNQrr1q3DRx99hMWLF+OLL77A3Xff3WjmeFMWiwXV1dWNLq1msciKDitWyIBuLZWUAO+8I9d5Cpb8lXU4UEmJLIVnD3vsqAFdJXa6tWqVjG9Yv971Y0JCvBcP+S13Zoib7IyVUUrZvd1q4sSJGD16NPr164exY8figw8+wLFjx7B161aHx3h0ZnlcnAxbqKkBCgpa/zie8PrrMpFp+HBg4EBtYyFqrZgY+T6prbWtLtEUEztqgImdKwoKgO3bgX37tI6E/JyrM8SjoqLw9ddfNzv+/Pnz6Natm8vPFx0djZ49e+K4deynHR6dWW4yAXfeKdcPHGj949woiwVYs0auz56tXRxENyo42NZrZy2l1RQTO2pAV2PsdMs6Xs7ZdHMA+PhjYPJkWU6shTItFJhcnSGenJyMqqoqfP755xg8eDAA4LPPPkNVVRWGDBni8vNduHABJSUliHZSkd7jM8vvvBPIzQX++1/gt7/13OO646uvgA4dZHzsmDHaxEDkKb16ScWF4mLgrrsa36eUlEM5dYq1UwkAe+xcc+utsm0psTt8WBpeWZnXQyJju/XWW5GWlobp06dj37592LdvH6ZPn44xY8Y0mjjRt29fbN68GQBw+fJlzJ07F3v37kVxcTHy8vIwduxYmM1m3Hvvvb4L3tpj99//+u45m+rbFzhxQtbOZF0v8neTJ8vYVWvbashkArp3l4TPm6W/yG/wfzxXWBO7khLg0iXpCbDnyBHZ3nabb+IiQ1u3bh0ef/xxpKamAgDGjRuH119/vdE+RUVFqKqqAgAEBwfj8OHDePvtt/Htt98iOjoaKSkpePfdd9HB0WfWG6xfPgcPymBvreo5BgXx1BQZQ3q61hGQH2Fi54rISCAqSirqHz0KDBpkfz8mduRBkZGR+Pvf/+50n4Yzxtu2bYsdO3Z4O6yW3XIL0K6d9JSdO2cbH+Qrhw5Jj50vCzMTaWXLFmDvXmDkSCAlRetoSAd4KtZV1l47RzP9lLIldg2WRyMKOMHBwLFjwMWLvk/qLBZgxAg5NdXS0Akif3H1KpCfD+za1fy+bduAl1+W5fyIwB471/34xzLL7/Jl+/dXVMjC50FB0ltAFMh++ENtnnfDBqCyUhK7W27RJgYiTzt/HujfX340ff9943GjnBFLTbDHzlWLF8uSLr/7nf378/NlGx8PtG3ru7iIyOaNN2Q7fTonTZBxREXJxIhr12Ssd0NcdYKaYGLnqrCwlhdXTkqSC1Ggq6wEJk4EEhJkmIIvHD4M/Oc/0qsxbZpvnpPIF4KCgJ495XrDWna1tcDp03KdiR1dx5+0njJihFyISFaf+Ne/ZAWKkyeBm2/2/nMuXy7b8eOlWj+RkcTFydjVhond2bOS3IWFAU5qVVJgYY+dOyZNknE7hw9rHQmRvoWEAImJcn37du8/X3U18Le/yfWZM73/fES+1quXbK2nXgH50WS9L4hf5yT4SXDHiRNysc5+taqulvF3RGQzbpxst2xpeV+lgE2bZMWK2lr3n2vnTqkx2bcvcPfd7h9PpHfWxK5hj511bPftt/s6GtIxJnbusNan+/zzxre/8w7QpQvw2GO+j4lIr37xC9nm5QHffut8X5MJ+Pe/JSnr2hXIyJDTuK66/36Ztb5iRctjYYn8UVycbBv22M2eLWPsXnhBm5hIl5jYuWPUKNlu2tR4QPj27UBdnXYlHoj06JZbpExQba3U2mrJ3LlA585S/+7Pf5axct995/rz/eQnzdfRJDKKQYOAl14Cnn668e2xsUCDZQaJmNi5Iy1NKuqfPg3s3y+31dRIT4P1fiKyGT9etvZOxx49Kj0OV67I33FxwNdfSy26tm2BDz4AUlNlqIMj5eW2Ol5ERhYfD8yfD4wdq3UkpHNM7NzRrh0wZoxc/+c/Zbt3r4zt6dIFGDBAu9iI9Gj8eCms2nTx8qtXgYceAl57DZgzx3Z7cDBw331SYT8iQsqX3H+/7N9Uba08Rp8+wNKlXn0ZRLrzzDPyfZSbq3UkpDNM7Nw1YYJsN2yQ07HWtTlTUzkriaipQYNk7dZnnml8+4IFwJdfAjfdBGRmNj/upz8FPvxQfkwdPgycOdN8n2efld7ysDCukUk3rLi4GFOnTkVcXBzatm2L3r17IzMzEzXujPX0NqWkjNC4ccDGjcDWrUBVldZRkc6wjp27Ro2SoqujR8tU8w0b5HaehiVyTings8+A1auBVavktuXLHdecGzhQTuHecoutOCsgwx+WLwf+9Cf5e80ars9MN+zo0aOoq6vDihUrcPPNNyM/Px/Tp0/HlStXsGjRIq3DE9XVwNSptioMwcH8UUPNMLFzV/v2tvF1+/YBx48DHToAI0dqGxeR3r36qkyQAGTmanY28KtfOT/mnnsa//3UU7JsmHVc3lNPtfwYRC5IS0tDWoMf6PHx8SgqKsIbb7yhn8QuIkLazaOPyt+JiXIbUQM8d3gjkpKAhQulnEOXLlpHQ6RfStkmOURGykzypqdnW1JUJLNlr1yRKvuvvCJfckReUlVVhcjISK3DaGzaNGDwYLlurdRA1AB77G7UggVaR0CkfyYTsGwZkJ4us1+7dXP/MeLjpU5daKiMdQ0L83ycRNedPHkSS5cuxeLFi53uZ7FYYLFY6v+udjaL2xOCgmSIwvr1wCOPePe5yC+xx46IfCcpqXVJHSDLlD38sMyEZVJHLsrKyoLJZHJ62W8dXnNdaWkp0tLSMGHCBEybNs3p42dnZyMiIqL+0qNHD2++HBEVBTzxhEwuImrCpFTDSrv+r7q6GhEREaiqqkLHjh21DocMIpA+V4H0Wsl3tPpcVVZWorKy0uk+vXr1Qnh4OABJ6lJSUpCYmIi1a9ciqIVqB/Z67Hr06MH2Qx7lTvvhqVgiIjIss9kMs9ns0r7nzp1DSkoKEhISkJOT02JSBwBhYWEIYw8y6QgTOyIiCnilpaUYPnw4YmNjsWjRIpw/f77+vqioKA0jI3IPEzsiIgp4O3fuxIkTJ3DixAl079690X0GG7FEBsfJE0REFPCmTJkCpZTdC5E/MVyPnbURen3KOQUU6+cpEP6TZxsibwiUNsT2Q97gTvsxXGJ36dIlAPDNlHMKOJcuXUKEwSu9sw2RNxm9DbH9kDe50n4MV+6krq4OpaWl6NChA0wmU6P7rNPQS0pKDD0Nna/T85RSuHTpEmJiYlyaKefPAr0NBcJrBHz/OgOlDQV6+wEC43Xquf0YrscuKCio2cDXpjp27GjYD1tDfJ2eZeRehobYhkQgvEbAt68zENoQ249NILxOPbYf4/5sIiIiIgowTOyIiIiIDCKgEruwsDBkZmYavko4Xyd5SyD8mwfCawQC53XqSaD8mwfC69TzazTc5AkiIiKiQBVQPXZERERERsbEjoiIiMggmNgRERERGQQTOyIiIiKDMFxi99e//hVxcXEIDw9HQkICPvnkE6f77969GwkJCQgPD0d8fDyWL1/uo0hbJzs7G4MGDUKHDh3QtWtXjB8/HkVFRU6PycvLg8lkanY5evSoj6J2X1ZWVrN4o6KinB7jb++lHrH9NMf2Q+5gG2rO39qQ37cfZSDr169XISEhauXKlaqgoEDNnj1btW/fXp0+fdru/qdOnVLt2rVTs2fPVgUFBWrlypUqJCREbdiwwceRu27kyJEqJydH5efnq4MHD6rRo0er2NhYdfnyZYfH5ObmKgCqqKhIlZWV1V9qa2t9GLl7MjMz1W233dYo3oqKCof7++N7qTdsP/ax/ZCr2Ibs87c25O/tx1CJ3eDBg9WMGTMa3da3b181b948u/s//fTTqm/fvo1ue/TRR1VSUpLXYvS0iooKBUDt3r3b4T7WRnXx4kXfBXaDMjMz1R133OHy/kZ4L7XG9mMf2w+5im3IPn9rQ/7efgxzKrampgZffvklUlNTG92empqKPXv22D1m7969zfYfOXIk9u/fj6tXr3otVk+qqqoCAERGRra474ABAxAdHY0RI0YgNzfX26HdsOPHjyMmJgZxcXF44IEHcOrUKYf7GuG91BLbD9uPv7+XWmMbMlYb8uf2Y5jErrKyEteuXUO3bt0a3d6tWzeUl5fbPaa8vNzu/rW1taisrPRarJ6ilMKcOXMwdOhQ9OvXz+F+0dHRePPNN7Fx40Zs2rQJffr0wYgRI/Dxxx/7MFr3JCYm4u2338aOHTuwcuVKlJeXY8iQIbhw4YLd/f39vdQa2w/bjz+/l3rANmScNuTv7aeNz5/Ry0wmU6O/lVLNbmtpf3u369GsWbNw6NAhfPrpp07369OnD/r06VP/d3JyMkpKSrBo0SLcdddd3g6zVUaNGlV/vX///khOTkbv3r3x1ltvYc6cOXaP8ef3Ui/Yfppj+9H/e6knbEPN+Vsb8vf2Y5geO7PZjODg4Ga/jCoqKppl0lZRUVF292/Tpg06d+7stVg94fe//z3ee+895Obmonv37m4fn5SUhOPHj3shMu9o3749+vfv7zBmf34v9YDtxz1sP9QU25B7/KkN+Vv7MUxiFxoaioSEBOzatavR7bt27cKQIUPsHpOcnNxs/507d2LgwIEICQnxWqw3QimFWbNmYdOmTfjoo48QFxfXqsc5cOAAoqOjPRyd91gsFhQWFjqM2R/fSz1h+3EP2w81xTbkHn9qQ37XfjSYsOE11qnmq1evVgUFBeqJJ55Q7du3V8XFxUoppebNm6fS09Pr97dOUc7IyFAFBQVq9erVup9qPnPmTBUREaHy8vIaTcX+7rvv6vdp+jqXLFmiNm/erI4dO6by8/PVvHnzFAC1ceNGLV6CS5588kmVl5enTp06pfbt26fGjBmjOnToYKj3Um/YfgTbj3+8l3rENiT8vQ35e/sxVGKnlFLLli1TPXv2VKGhoerOO+9sNAV78uTJatiwYY32z8vLUwMGDFChoaGqV69e6o033vBxxO4BYPeSk5NTv0/T1/nyyy+r3r17q/DwcHXTTTepoUOHqq1bt/o+eDdMnDhRRUdHq5CQEBUTE6N++ctfqiNHjtTfb4T3Uo/Yfth+/OW91Cu2If9vQ/7efkxKXR/hR0RERER+zTBj7IiIiIgCHRM7IiIiIoNgYkdERERkEEzsiIiIiAyCiR0RERGRQTCxIyIiIjIIJnZEREREBsHEjoiIiMggmNgRERERGQQTOyIiIiKDYGJHREREZBBM7IiIiIgM4v8BQRH/oouT8EYAAAAASUVORK5CYII=\n", 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", 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" ] @@ -932,6 +1014,14 @@ "plot_state_comparison(timepts, mhe_resp.states, lqr_resp.states)" ] }, + { + "cell_type": "markdown", + "id": "d94b5d23-e482-440e-b449-4cd905d6269e", + "metadata": {}, + "source": [ + "We see that while the estimates eventually converge to the correct values, the initial estimates for the state trajectory are not close to the actual values. This is in large part due to the fact that we started far from an equilibrium point for the closed loop system. We can see an improved response if we change the control problem to start at the origin and then move to the final point, allowing the MHE estimator to have an initial estimate that is closer to the actual state." + ] + }, { "cell_type": "code", "execution_count": 24, @@ -939,7 +1029,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Resimulate starting at the origin and moving to the \"initial\" condition\n", + "# Resimulate starting at the origin and moving to the original initial condition\n", "uvec = [x0, ue, V, W*0]\n", "lqr_resp = ct.input_output_response(lqr_clsys, timepts, uvec, xe)\n", "U = lqr_resp.outputs[6:8] # controller input signals\n", @@ -954,7 +1044,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -964,6 +1054,7 @@ } ], "source": [ + "# Create a new optimal estimation problem with a slightly shorter horizon\n", "mhe_timepts = timepts[0:8]\n", "oep = opt.OptimalEstimationProblem(\n", " dsys, mhe_timepts, traj_cost, terminal_cost=init_cost,\n", @@ -978,12 +1069,12 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "4158e922", + "cell_type": "markdown", + "id": "29d5d904-f6bc-463b-8e53-c0b5fbbeeded", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "We see now that the MHE estimtor is able to quickly converge to values that are close to the actual state and maintain a very good estimate throughout the trajectory." + ] } ], "metadata": { @@ -1002,7 +1093,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.13.1" } }, "nbformat": 4, diff --git a/examples/mrac_siso_lyapunov.py b/examples/mrac_siso_lyapunov.py index 60550a8d9..52dc2610c 100644 --- a/examples/mrac_siso_lyapunov.py +++ b/examples/mrac_siso_lyapunov.py @@ -42,27 +42,27 @@ def adaptive_controller_state(_t, xc, uc, params): """Internal state of adaptive controller, f(t,x,u;p)""" - + # Parameters gam = params["gam"] signB = params["signB"] - + # Controller inputs r = uc[0] xm = uc[1] x = uc[2] - + # Controller states # x1 = xc[0] # kr # x2 = xc[1] # kx - + # Algebraic relationships e = xm - x # Controller dynamics d_x1 = gam*r*e*signB d_x2 = gam*x*e*signB - + return [d_x1, d_x2] def adaptive_controller_output(_t, xc, uc, params): @@ -72,7 +72,7 @@ def adaptive_controller_output(_t, xc, uc, params): r = uc[0] #xm = uc[1] x = uc[2] - + # Controller state kr = xc[0] kx = xc[1] @@ -112,7 +112,7 @@ def adaptive_controller_output(_t, xc, uc, params): Tend = 100 dt = 0.1 -# Define simulation time +# Define simulation time t_vec = np.arange(0, Tend, dt) # Define control reference input diff --git a/examples/mrac_siso_mit.py b/examples/mrac_siso_mit.py index f901478cb..a821b65d0 100644 --- a/examples/mrac_siso_mit.py +++ b/examples/mrac_siso_mit.py @@ -42,11 +42,10 @@ def adaptive_controller_state(t, xc, uc, params): """Internal state of adaptive controller, f(t,x,u;p)""" - + # Parameters gam = params["gam"] Am = params["Am"] - Bm = params["Bm"] signB = params["signB"] # Controller inputs @@ -59,7 +58,7 @@ def adaptive_controller_state(t, xc, uc, params): # x2 = xc[1] # kr x3 = xc[2] # # x4 = xc[3] # kx - + # Algebraic relationships e = xm - x @@ -78,11 +77,11 @@ def adaptive_controller_output(t, xc, uc, params): r = uc[0] # xm = uc[1] x = uc[2] - + # Controller state kr = xc[1] kx = xc[3] - + # Control law u = kx*x + kr*r @@ -118,7 +117,7 @@ def adaptive_controller_output(t, xc, uc, params): Tend = 100 dt = 0.1 -# Define simulation time +# Define simulation time t_vec = np.arange(0, Tend, dt) # Define control reference input diff --git a/examples/phase_plane_plots.py b/examples/phase_plane_plots.py index b3b2a01c3..44a47a29c 100644 --- a/examples/phase_plane_plots.py +++ b/examples/phase_plane_plots.py @@ -5,9 +5,8 @@ # using the phaseplot module. Most of these figures line up with examples # in FBS2e, with different display options shown as different subplots. -import time import warnings -from math import pi, sqrt +from math import pi import matplotlib.pyplot as plt import numpy as np @@ -15,6 +14,9 @@ import control as ct import control.phaseplot as pp +# Set default plotting parameters to match ControlPlot +plt.rcParams.update(ct.rcParams) + # # Example 1: Dampled oscillator systems # @@ -35,16 +37,18 @@ def damposc_update(t, x, u, params): ct.phase_plane_plot(damposc, [-1, 1, -1, 1], 8, ax=ax1) ax1.set_title("boxgrid [-1, 1, -1, 1], 8") -ct.phase_plane_plot(damposc, [-1, 1, -1, 1], ax=ax2, gridtype='meshgrid') -ax2.set_title("meshgrid [-1, 1, -1, 1]") +ct.phase_plane_plot(damposc, [-1, 1, -1, 1], ax=ax2, plot_streamlines=True, + gridtype='meshgrid') +ax2.set_title("streamlines, meshgrid [-1, 1, -1, 1]") ct.phase_plane_plot( - damposc, [-1, 1, -1, 1], 4, ax=ax3, gridtype='circlegrid', dir='both') -ax3.set_title("circlegrid [0, 0, 1], 4, both") + damposc, [-1, 1, -1, 1], 4, ax=ax3, plot_streamlines=True, + gridtype='circlegrid', dir='both') +ax3.set_title("streamlines, circlegrid [0, 0, 1], 4, both") ct.phase_plane_plot( damposc, [-1, 1, -1, 1], ax=ax4, gridtype='circlegrid', - dir='reverse', gridspec=[0.1, 12], timedata=5) + plot_streamlines=True, dir='reverse', gridspec=[0.1, 12], timedata=5) ax4.set_title("circlegrid [0, 0, 0.1], reverse") # @@ -67,17 +71,19 @@ def invpend_update(t, x, u, params): ax1.set_title("default, 5") ct.phase_plane_plot( - invpend, [-2*pi, 2*pi, -2, 2], gridtype='meshgrid', ax=ax2) -ax2.set_title("meshgrid") + invpend, [-2*pi, 2*pi, -2, 2], gridtype='meshgrid', ax=ax2, + plot_streamlines=True) +ax2.set_title("streamlines, meshgrid") ct.phase_plane_plot( invpend, [-2*pi, 2*pi, -2, 2], 1, gridtype='meshgrid', - gridspec=[12, 9], ax=ax3, arrows=1) -ax3.set_title("denser grid") + gridspec=[12, 9], ax=ax3, arrows=1, plot_streamlines=True) +ax3.set_title("streamlines, denser grid") ct.phase_plane_plot( invpend, [-2*pi, 2*pi, -2, 2], 4, gridspec=[6, 6], - plot_separatrices={'timedata': 20, 'arrows': 4}, ax=ax4) + plot_separatrices={'timedata': 20, 'arrows': 4}, ax=ax4, + plot_streamlines=True) ax4.set_title("custom") # @@ -102,21 +108,22 @@ def oscillator_update(t, x, u, params): try: ct.phase_plane_plot( oscillator, [-1.5, 1.5, -1.5, 1.5], 1, gridtype='meshgrid', - dir='forward', ax=ax2) + dir='forward', ax=ax2, plot_streamlines=True) except RuntimeError as inst: - axs[0,1].text(0, 0, "Runtime Error") + ax2.text(0, 0, "Runtime Error") warnings.warn(inst.__str__()) -ax2.set_title("meshgrid, forward, 0.5") +ax2.set_title("streamlines, meshgrid, forward, 0.5") ax2.set_aspect('equal') -ct.phase_plane_plot(oscillator, [-1.5, 1.5, -1.5, 1.5], ax=ax3) +ct.phase_plane_plot(oscillator, [-1.5, 1.5, -1.5, 1.5], ax=ax3, + plot_streamlines=True) pp.streamlines( oscillator, [-0.5, 0.5, -0.5, 0.5], dir='both', ax=ax3) -ax3.set_title("outer + inner") +ax3.set_title("streamlines, outer + inner") ax3.set_aspect('equal') ct.phase_plane_plot( - oscillator, [-1.5, 1.5, -1.5, 1.5], 0.9, ax=ax4) + oscillator, [-1.5, 1.5, -1.5, 1.5], 0.9, ax=ax4, plot_streamlines=True) pp.streamlines( oscillator, np.array([[0, 0]]), 1.5, gridtype='circlegrid', gridspec=[0.5, 6], dir='both', ax=ax4) @@ -141,8 +148,9 @@ def saddle_update(t, x, u, params): ax1.set_title("default") ct.phase_plane_plot( - saddle, [-1, 1, -1, 1], 0.5, gridtype='meshgrid', ax=ax2) -ax2.set_title("meshgrid") + saddle, [-1, 1, -1, 1], 0.5, plot_streamlines=True, gridtype='meshgrid', + ax=ax2) +ax2.set_title("streamlines, meshgrid") ct.phase_plane_plot( saddle, [-1, 1, -1, 1], gridspec=[16, 12], ax=ax3, @@ -150,9 +158,9 @@ def saddle_update(t, x, u, params): ax3.set_title("vectorfield") ct.phase_plane_plot( - saddle, [-1, 1, -1, 1], 0.3, + saddle, [-1, 1, -1, 1], 0.3, plot_streamlines=True, gridtype='meshgrid', gridspec=[5, 7], ax=ax4) -ax3.set_title("custom") +ax4.set_title("custom") # # Example 5: Internet congestion control @@ -172,6 +180,7 @@ def _congctrl_update(t, x, u, params): return np.append( c / x[M] - (rho * c) * (1 + (x[:-1]**2) / 2), N/M * np.sum(x[:-1]) * c / x[M] - c) + congctrl = ct.nlsys( _congctrl_update, states=2, inputs=0, params={'N': 60, 'rho': 2e-4, 'c': 10}) @@ -203,7 +212,7 @@ def _congctrl_update(t, x, u, params): ax3.set_title("vector field") ct.phase_plane_plot( - congctrl, [2, 6, 200, 300], 100, + congctrl, [2, 6, 200, 300], 100, plot_streamlines=True, params={'rho': 4e-4, 'c': 20}, ax=ax4, plot_vectorfield={'gridspec': [12, 9]}) ax4.set_title("vector field + streamlines") diff --git a/examples/plot_gallery.py b/examples/plot_gallery.py index 272de3d8e..d7876d78f 100644 --- a/examples/plot_gallery.py +++ b/examples/plot_gallery.py @@ -102,7 +102,6 @@ def invpend_update(t, x, u, params): invpend = ct.nlsys(invpend_update, states=2, inputs=1, name='invpend') ct.phase_plane_plot( invpend, [-2*pi, 2*pi, -2, 2], 5, - gridtype='meshgrid', gridspec=[5, 8], arrows=3, plot_separatrices={'gridspec': [12, 9]}, params={'m': 1, 'l': 1, 'b': 0.2, 'g': 1}) @@ -120,12 +119,13 @@ def invpend_update(t, x, u, params): # root locus with create_figure("Root locus plot") as fig: - ax1, ax2 = fig.subplots(2, 1) + ax_array = ct.pole_zero_subplots(2, 1, grid=[True, False]) + ax1, ax2 = ax_array[:, 0] sys1 = ct.tf([1, 2], [1, 2, 3], name='sys1') sys2 = ct.tf([1, 0.2], [1, 1, 3, 1, 1], name='sys2') - ct.root_locus_plot([sys1, sys2], grid=True, ax=ax1) - ct.root_locus_plot([sys1, sys2], grid=False, ax=ax2) - print(" -- BUG: should have 2 x 1 array of plots") + ct.root_locus_plot([sys1, sys2], ax=ax1) + ct.root_locus_plot([sys1, sys2], ax=ax2) + plt.suptitle("Root locus plots (w/ specified axes)", fontsize='medium') # sisotool with create_figure("sisotool"): @@ -146,7 +146,7 @@ def invpend_update(t, x, u, params): # time response with create_figure("time response"): timepts = np.linspace(0, 10) - + U = np.vstack([np.sin(timepts), np.cos(2*timepts)]) resp1 = ct.input_output_response(sys_mimo1, timepts, U) diff --git a/examples/pvtol-nested-ss.py b/examples/pvtol-nested-ss.py index f53ac70f1..e8542a828 100644 --- a/examples/pvtol-nested-ss.py +++ b/examples/pvtol-nested-ss.py @@ -10,7 +10,6 @@ import os import matplotlib.pyplot as plt # MATLAB plotting functions -from control.matlab import * # MATLAB-like functions import numpy as np import math import control as ct @@ -23,12 +22,12 @@ c = 0.05 # damping factor (estimated) # Transfer functions for dynamics -Pi = tf([r], [J, 0, 0]) # inner loop (roll) -Po = tf([1], [m, c, 0]) # outer loop (position) +Pi = ct.tf([r], [J, 0, 0]) # inner loop (roll) +Po = ct.tf([1], [m, c, 0]) # outer loop (position) # Use state space versions -Pi = tf2ss(Pi) -Po = tf2ss(Po) +Pi = ct.tf2ss(Pi) +Po = ct.tf2ss(Po) # # Inner loop control design @@ -40,10 +39,10 @@ # Design a simple lead controller for the system k, a, b = 200, 2, 50 -Ci = k*tf([1, a], [1, b]) # lead compensator +Ci = k*ct.tf([1, a], [1, b]) # lead compensator # Convert to statespace -Ci = tf2ss(Ci) +Ci = ct.tf2ss(Ci) # Compute the loop transfer function for the inner loop Li = Pi*Ci @@ -51,49 +50,49 @@ # Bode plot for the open loop process plt.figure(1) -bode(Pi) +ct.bode(Pi) # Bode plot for the loop transfer function, with margins plt.figure(2) -bode(Li) +ct.bode(Li) # Compute out the gain and phase margins #! Not implemented # (gm, pm, wcg, wcp) = margin(Li); # Compute the sensitivity and complementary sensitivity functions -Si = feedback(1, Li) +Si = ct.feedback(1, Li) Ti = Li*Si # Check to make sure that the specification is met plt.figure(3) -gangof4(Pi, Ci) +ct.gangof4(Pi, Ci) # Compute out the actual transfer function from u1 to v1 (see L8.2 notes) # Hi = Ci*(1-m*g*Pi)/(1+Ci*Pi); -Hi = parallel(feedback(Ci, Pi), -m*g*feedback(Ci*Pi, 1)) +Hi = ct.parallel(ct.feedback(Ci, Pi), -m*g*ct.feedback(Ci*Pi, 1)) plt.figure(4) plt.clf() -bode(Hi) +ct.bode(Hi) # Now design the lateral control system a, b, K = 0.02, 5, 2 -Co = -K*tf([1, 0.3], [1, 10]) # another lead compensator +Co = -K*ct.tf([1, 0.3], [1, 10]) # another lead compensator # Convert to statespace -Co = tf2ss(Co) +Co = ct.tf2ss(Co) # Compute the loop transfer function for the outer loop Lo = -m*g*Po*Co plt.figure(5) -bode(Lo, display_margins=True) # margin(Lo) +ct.bode(Lo, display_margins=True) # margin(Lo) # Finally compute the real outer-loop loop gain + responses L = Co*Hi*Po -S = feedback(1, L) -T = feedback(L, 1) +S = ct.feedback(1, L) +T = ct.feedback(L, 1) # Compute stability margins #! Not yet implemented @@ -101,7 +100,7 @@ plt.figure(6) plt.clf() -out = ct.bode(L, logspace(-4, 3), initial_phase=-math.pi/2) +out = ct.bode(L, np.logspace(-4, 3), initial_phase=-math.pi/2) axs = ct.get_plot_axes(out) # Add crossover line to magnitude plot @@ -111,7 +110,7 @@ # Nyquist plot for complete design # plt.figure(7) -nyquist(L) +ct.nyquist(L) # set up the color color = 'b' @@ -126,10 +125,10 @@ # 'EdgeColor', color, 'FaceColor', color); plt.figure(9) -Yvec, Tvec = step(T, linspace(1, 20)) +Yvec, Tvec = ct.step_response(T, np.linspace(1, 20)) plt.plot(Tvec.T, Yvec.T) -Yvec, Tvec = step(Co*S, linspace(1, 20)) +Yvec, Tvec = ct.step_response(Co*S, np.linspace(1, 20)) plt.plot(Tvec.T, Yvec.T) #TODO: PZmap for statespace systems has not yet been implemented. @@ -142,7 +141,7 @@ # Gang of Four plt.figure(11) plt.clf() -gangof4(Hi*Po, Co, linspace(-2, 3)) +ct.gangof4(Hi*Po, Co, np.linspace(-2, 3)) if 'PYCONTROL_TEST_EXAMPLES' not in os.environ: plt.show() diff --git a/examples/pvtol-outputfbk.ipynb b/examples/pvtol-outputfbk.ipynb index 7d8bc8529..bc999c140 100644 --- a/examples/pvtol-outputfbk.ipynb +++ b/examples/pvtol-outputfbk.ipynb @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "id": "544525ab", "metadata": {}, "outputs": [], @@ -30,7 +30,7 @@ "metadata": {}, "source": [ "## System definition\n", - "We consider a (planar) vertical takeoff and landing aircraf model:\n", + "We consider a (planar) vertical takeoff and landing aircraft model:\n", "\n", "![PVTOL diagram](https://murray.cds.caltech.edu/images/murray.cds/7/7d/Pvtol-diagram.png)\n", "\n", @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 2, "id": "ffafed74", "metadata": {}, "outputs": [ @@ -71,15 +71,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "Object: pvtol\n", - "Inputs (2): F1, F2, \n", - "Outputs (6): x0, x1, x2, x3, x4, x5, \n", - "States (6): x0, x1, x2, x3, x4, x5, \n", + ": pvtol\n", + "Inputs (2): ['F1', 'F2']\n", + "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "Parameters: ['m', 'J', 'r', 'g', 'c']\n", "\n", - "Object: pvtol_noisy\n", - "Inputs (7): F1, F2, Dx, Dy, Nx, Ny, Nth, \n", - "Outputs (6): x0, x1, x2, x3, x4, x5, \n", - "States (6): x0, x1, x2, x3, x4, x5, \n" + "Update: \n", + "Output: \n", + "\n", + "Forward: \n", + "Reverse: \n", + "\n", + ": pvtol_noisy\n", + "Inputs (7): ['F1', 'F2', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", + "Outputs (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "States (6): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']\n", + "\n", + "Update: \n", + "Output: \n" ] } ], @@ -117,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 3, "id": "1e1ee7c9", "metadata": {}, "outputs": [], @@ -143,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 4, "id": "3647bf15", "metadata": {}, "outputs": [ @@ -151,10 +161,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Object: sys[3]\n", - "Inputs (5): x0, x1, x2, F1, F2, \n", - "Outputs (6): xh0, xh1, xh2, xh3, xh4, xh5, \n", - "States (42): x[0], x[1], x[2], x[3], x[4], x[5], x[6], x[7], x[8], x[9], x[10], x[11], x[12], x[13], x[14], x[15], x[16], x[17], x[18], x[19], x[20], x[21], x[22], x[23], x[24], x[25], x[26], x[27], x[28], x[29], x[30], x[31], x[32], x[33], x[34], x[35], x[36], x[37], x[38], x[39], x[40], x[41], \n" + ": sys[1]\n", + "Inputs (5): ['x0', 'x1', 'x2', 'F1', 'F2']\n", + "Outputs (6): ['xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "States (42): ['x[0]', 'x[1]', 'x[2]', 'x[3]', 'x[4]', 'x[5]', 'x[6]', 'x[7]', 'x[8]', 'x[9]', 'x[10]', 'x[11]', 'x[12]', 'x[13]', 'x[14]', 'x[15]', 'x[16]', 'x[17]', 'x[18]', 'x[19]', 'x[20]', 'x[21]', 'x[22]', 'x[23]', 'x[24]', 'x[25]', 'x[26]', 'x[27]', 'x[28]', 'x[29]', 'x[30]', 'x[31]', 'x[32]', 'x[33]', 'x[34]', 'x[35]', 'x[36]', 'x[37]', 'x[38]', 'x[39]', 'x[40]', 'x[41]']\n", + "\n", + "Update: \n", + "Output: at 0x13771f7e0>\n" ] } ], @@ -209,7 +222,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "id": "9787db61", "metadata": {}, "outputs": [ @@ -217,10 +230,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "Object: control\n", - "Inputs (14): xd[0], xd[1], xd[2], xd[3], xd[4], xd[5], ud[0], ud[1], xh0, xh1, xh2, xh3, xh4, xh5, \n", - "Outputs (2): F1, F2, \n", - "States (0): \n", + ": sys[2]\n", + "Inputs (14): ['xd[0]', 'xd[1]', 'xd[2]', 'xd[3]', 'xd[4]', 'xd[5]', 'ud[0]', 'ud[1]', 'xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "Outputs (2): ['F1', 'F2']\n", + "States (0): []\n", "\n", "A = []\n", "\n", @@ -228,20 +241,72 @@ "\n", "C = []\n", "\n", - "D = [[-3.16227766e+00 -1.31948924e-07 8.67680175e+00 -2.35855555e+00\n", - " -6.98881806e-08 1.91220852e+00 1.00000000e+00 0.00000000e+00\n", - " 3.16227766e+00 1.31948924e-07 -8.67680175e+00 2.35855555e+00\n", - " 6.98881806e-08 -1.91220852e+00]\n", - " [-1.31948923e-06 3.16227766e+00 -2.32324805e-07 -2.36396241e-06\n", - " 4.97998224e+00 7.90913288e-08 0.00000000e+00 1.00000000e+00\n", - " 1.31948923e-06 -3.16227766e+00 2.32324805e-07 2.36396241e-06\n", - " -4.97998224e+00 -7.90913288e-08]]\n", - " \n", + "D = [[-3.16227766e+00 -1.31948922e-07 8.67680175e+00 -2.35855555e+00\n", + " -6.98881821e-08 1.91220852e+00 1.00000000e+00 0.00000000e+00\n", + " 3.16227766e+00 1.31948922e-07 -8.67680175e+00 2.35855555e+00\n", + " 6.98881821e-08 -1.91220852e+00]\n", + " [-1.31948921e-06 3.16227766e+00 -2.32324826e-07 -2.36396240e-06\n", + " 4.97998224e+00 7.90913276e-08 0.00000000e+00 1.00000000e+00\n", + " 1.31948921e-06 -3.16227766e+00 2.32324826e-07 2.36396240e-06\n", + " -4.97998224e+00 -7.90913276e-08]] \n", + "\n", + ": sys[3]\n", + "Inputs (13): ['xd[0]', 'xd[1]', 'xd[2]', 'xd[3]', 'xd[4]', 'xd[5]', 'ud[0]', 'ud[1]', 'Dx', 'Dy', 'Nx', 'Ny', 'Nth']\n", + "Outputs (14): ['x0', 'x1', 'x2', 'x3', 'x4', 'x5', 'F1', 'F2', 'xh0', 'xh1', 'xh2', 'xh3', 'xh4', 'xh5']\n", + "States (48): ['pvtol_noisy_x0', 'pvtol_noisy_x1', 'pvtol_noisy_x2', 'pvtol_noisy_x3', 'pvtol_noisy_x4', 'pvtol_noisy_x5', 'sys[1]_x[0]', 'sys[1]_x[1]', 'sys[1]_x[2]', 'sys[1]_x[3]', 'sys[1]_x[4]', 'sys[1]_x[5]', 'sys[1]_x[6]', 'sys[1]_x[7]', 'sys[1]_x[8]', 'sys[1]_x[9]', 'sys[1]_x[10]', 'sys[1]_x[11]', 'sys[1]_x[12]', 'sys[1]_x[13]', 'sys[1]_x[14]', 'sys[1]_x[15]', 'sys[1]_x[16]', 'sys[1]_x[17]', 'sys[1]_x[18]', 'sys[1]_x[19]', 'sys[1]_x[20]', 'sys[1]_x[21]', 'sys[1]_x[22]', 'sys[1]_x[23]', 'sys[1]_x[24]', 'sys[1]_x[25]', 'sys[1]_x[26]', 'sys[1]_x[27]', 'sys[1]_x[28]', 'sys[1]_x[29]', 'sys[1]_x[30]', 'sys[1]_x[31]', 'sys[1]_x[32]', 'sys[1]_x[33]', 'sys[1]_x[34]', 'sys[1]_x[35]', 'sys[1]_x[36]', 'sys[1]_x[37]', 'sys[1]_x[38]', 'sys[1]_x[39]', 'sys[1]_x[40]', 'sys[1]_x[41]']\n", + "\n", + "Subsystems (3):\n", + " * ['x0', 'x1', 'x2', 'x3', 'x4', 'x5']>\n", + " * ['F1',\n", + " 'F2']>\n", + " * ['xh0', 'xh1',\n", + " 'xh2', 'xh3', 'xh4', 'xh5']>\n", + "\n", + "Connections:\n", + " * pvtol_noisy.F1 <- sys[2].F1\n", + " * pvtol_noisy.F2 <- sys[2].F2\n", + " * pvtol_noisy.Dx <- Dx\n", + " * pvtol_noisy.Dy <- Dy\n", + " * pvtol_noisy.Nx <- Nx\n", + " * pvtol_noisy.Ny <- Ny\n", + " * pvtol_noisy.Nth <- Nth\n", + " * sys[2].xd[0] <- xd[0]\n", + " * sys[2].xd[1] <- xd[1]\n", + " * sys[2].xd[2] <- xd[2]\n", + " * sys[2].xd[3] <- xd[3]\n", + " * sys[2].xd[4] <- xd[4]\n", + " * sys[2].xd[5] <- xd[5]\n", + " * sys[2].ud[0] <- ud[0]\n", + " * sys[2].ud[1] <- ud[1]\n", + " * sys[2].xh0 <- sys[1].xh0\n", + " * sys[2].xh1 <- sys[1].xh1\n", + " * sys[2].xh2 <- sys[1].xh2\n", + " * sys[2].xh3 <- sys[1].xh3\n", + " * sys[2].xh4 <- sys[1].xh4\n", + " * sys[2].xh5 <- sys[1].xh5\n", + " * sys[1].x0 <- pvtol_noisy.x0\n", + " * sys[1].x1 <- pvtol_noisy.x1\n", + " * sys[1].x2 <- pvtol_noisy.x2\n", + " * sys[1].F1 <- sys[2].F1\n", + " * sys[1].F2 <- sys[2].F2\n", "\n", - "Object: xh5\n", - "Inputs (13): xd[0], xd[1], xd[2], xd[3], xd[4], xd[5], ud[0], ud[1], Dx, Dy, Nx, Ny, Nth, \n", - "Outputs (14): x0, x1, x2, x3, x4, x5, F1, F2, xh0, xh1, xh2, xh3, xh4, xh5, \n", - "States (48): pvtol_noisy_x0, pvtol_noisy_x1, pvtol_noisy_x2, pvtol_noisy_x3, pvtol_noisy_x4, pvtol_noisy_x5, sys[3]_x[0], sys[3]_x[1], sys[3]_x[2], sys[3]_x[3], sys[3]_x[4], sys[3]_x[5], sys[3]_x[6], sys[3]_x[7], sys[3]_x[8], sys[3]_x[9], sys[3]_x[10], sys[3]_x[11], sys[3]_x[12], sys[3]_x[13], sys[3]_x[14], sys[3]_x[15], sys[3]_x[16], sys[3]_x[17], sys[3]_x[18], sys[3]_x[19], sys[3]_x[20], sys[3]_x[21], sys[3]_x[22], sys[3]_x[23], sys[3]_x[24], sys[3]_x[25], sys[3]_x[26], sys[3]_x[27], sys[3]_x[28], sys[3]_x[29], sys[3]_x[30], sys[3]_x[31], sys[3]_x[32], sys[3]_x[33], sys[3]_x[34], sys[3]_x[35], sys[3]_x[36], sys[3]_x[37], sys[3]_x[38], sys[3]_x[39], sys[3]_x[40], sys[3]_x[41], \n" + "Outputs:\n", + " * x0 <- pvtol_noisy.x0\n", + " * x1 <- pvtol_noisy.x1\n", + " * x2 <- pvtol_noisy.x2\n", + " * x3 <- pvtol_noisy.x3\n", + " * x4 <- pvtol_noisy.x4\n", + " * x5 <- pvtol_noisy.x5\n", + " * F1 <- sys[2].F1\n", + " * F2 <- sys[2].F2\n", + " * xh0 <- sys[1].xh0\n", + " * xh1 <- sys[1].xh1\n", + " * xh2 <- sys[1].xh2\n", + " * xh3 <- sys[1].xh3\n", + " * xh4 <- sys[1].xh4\n", + " * xh5 <- sys[1].xh5\n" ] } ], @@ -292,20 +357,18 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "id": "c2583a0e", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -368,30 +429,42 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "id": "c5f24119", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<>:16: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:16: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:16: SyntaxWarning: invalid escape sequence '\\h'\n", + "<>:16: SyntaxWarning: invalid escape sequence '\\h'\n", + "/var/folders/3h/8vlrqzts6wnd_p5xvy01zclc0000gn/T/ipykernel_62492/1696903767.py:16: SyntaxWarning: invalid escape sequence '\\h'\n", + " [h1, h2, h3, h4], ['$x$', '$y$', '$\\hat{x}$', '$\\hat{y}$'],\n", + "/var/folders/3h/8vlrqzts6wnd_p5xvy01zclc0000gn/T/ipykernel_62492/1696903767.py:16: SyntaxWarning: invalid escape sequence '\\h'\n", + " [h1, h2, h3, h4], ['$x$', '$y$', '$\\hat{x}$', '$\\hat{y}$'],\n" + ] + }, { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 18, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -427,20 +500,26 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "id": "3b6a1f1c", "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/murray/Library/CloudStorage/Dropbox/macosx/src/python-control/murrayrm/control/statefbk.py:788: UserWarning: cannot verify system output is system state\n", + " warnings.warn(\"cannot verify system output is system state\")\n" + ] + }, { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -503,7 +582,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.13.1" } }, "nbformat": 4, diff --git a/examples/pvtol.py b/examples/pvtol.py index 4f92f12fa..bc826a564 100644 --- a/examples/pvtol.py +++ b/examples/pvtol.py @@ -64,8 +64,6 @@ def _pvtol_flat_forward(states, inputs, params={}): F1, F2 = inputs # Use equations of motion for higher derivates - x1ddot = (F1 * cos(theta) - F2 * sin(theta)) / m - x2ddot = (F1 * sin(theta) + F2 * cos(theta) - m * g) / m thddot = (r * F1) / J # Flat output is a point above the vertical axis @@ -110,7 +108,6 @@ def _pvtol_flat_reverse(zflag, params={}): J = params.get('J', 0.0475) # inertia around pitch axis r = params.get('r', 0.25) # distance to center of force g = params.get('g', 9.8) # gravitational constant - c = params.get('c', 0.05) # damping factor (estimated) # Given the flat variables, solve for the state theta = np.arctan2(-zflag[0][2], zflag[1][2] + g) @@ -185,10 +182,6 @@ def _windy_update(t, x, u, params): def _noisy_update(t, x, u, params): # Get the inputs F1, F2, Dx, Dy = u[:4] - if u.shape[0] > 4: - Nx, Ny, Nth = u[4:] - else: - Nx, Ny, Nth = 0, 0, 0 # Get the system response from the original dynamics xdot, ydot, thetadot, xddot, yddot, thddot = \ @@ -196,7 +189,6 @@ def _noisy_update(t, x, u, params): # Get the parameter values we need m = params.get('m', 4.) # mass of aircraft - J = params.get('J', 0.0475) # inertia around pitch axis # Now add the disturbances xddot += Dx / m @@ -219,7 +211,6 @@ def _noisy_output(t, x, u, params): def pvtol_noisy_A(x, u, params={}): # Get the parameter values we need m = params.get('m', 4.) # mass of aircraft - J = params.get('J', 0.0475) # inertia around pitch axis c = params.get('c', 0.05) # damping factor (estimated) # Get the angle and compute sine and cosine diff --git a/examples/python-control_tutorial.ipynb b/examples/python-control_tutorial.ipynb new file mode 100644 index 000000000..4d718b050 --- /dev/null +++ b/examples/python-control_tutorial.ipynb @@ -0,0 +1,1267 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "numerous-rochester", + "metadata": {}, + "source": [ + "# Python Control Systems Library (python-control) Tutorial\n", + "\n", + "This Jupyter notebook contains an introduction to the basic operations in the Python Control Systems Library (python-control), a Python package for control system design. The tutorial consists of two examples:\n", + "\n", + "* Example 1: Open loop analysis of a coupled mass spring system\n", + "* Example 2: Trajectory tracking for a kinematic car model" + ] + }, + { + "cell_type": "markdown", + "id": "9531972e-c4b8-4a87-87d8-d83a01d4271f", + "metadata": {}, + "source": [ + "## Initialization\n", + "\n", + "The python-control package can be installed using `pip` or from conda-forge. The code below will import the control toolbox either from your local installation or via pip. We use the prefix `ct` to access control toolbox commands:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "macro-vietnamese", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "python-control 0.10.1.dev324+g2fd3802a.d20241218\n" + ] + } + ], + "source": [ + "# Import the packages needed for the examples included in this notebook\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Import the python-control package\n", + "try:\n", + " import control as ct\n", + " print(\"python-control\", ct.__version__)\n", + "except ImportError:\n", + " %pip install control\n", + " import control as ct" + ] + }, + { + "cell_type": "markdown", + "id": "distinct-communist", + "metadata": {}, + "source": [ + "### Installation notes\n", + "\n", + "If you get an error importing the `control` package, it may be that it is not in your current Python path. You can fix this by setting the PYTHONPATH environment variable to include the directory where the python-control package is located. If you are invoking Jupyter from the command line, try using a command of the form\n", + "\n", + " PYTHONPATH=/path/to/python-control jupyter notebook\n", + " \n", + "If you are using [Google Colab](https://colab.research.google.com), use the following command at the top of the notebook to install the `control` package:\n", + "\n", + " %pip install control\n", + "\n", + "(The import code above automatically runs this command if needed.)\n", + " \n", + "For the examples below, you will need version 0.10.0 or higher of the python-control toolbox. You can find the version number using the command\n", + "\n", + " print(ct.__version__)" + ] + }, + { + "cell_type": "markdown", + "id": "5dad04d8", + "metadata": {}, + "source": [ + "### More information on Python, NumPy, python-control\n", + "\n", + "* [Python tutorial](https://docs.python.org/3/tutorial/)\n", + "* [NumPy tutorial](https://numpy.org/doc/stable/user/quickstart.html)\n", + "* [NumPy for MATLAB users](https://numpy.org/doc/stable/user/numpy-for-matlab-users.html)\n", + "* [Python Control Systems Library (python-control) documentation](https://python-control.readthedocs.io/en/latest/)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "1c619183", + "metadata": { + "id": "qMVGK15gNQw2" + }, + "source": [ + "## Example 1: Open loop analysis of a coupled mass spring system\n", + "\n", + "Consider the spring mass system below:\n", + "\n", + "
\n", + "\n", + "We wish to analyze the time and frequency response of this system using a variety of python-control functions for linear systems analysis.\n", + "\n", + "### System dynamics\n", + "\n", + "The dynamics of the system can be written as\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + " m \\ddot{q}_1 &= -2 k q_1 - c \\dot{q}_1 + k q_2, \\\\\n", + " m \\ddot{q}_2 &= k q_1 - 2 k q_2 - c \\dot{q}_2 + ku\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "or in state space form:\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + " \\dfrac{dx}{dt} &= \\begin{bmatrix}\n", + " 0 & 0 & 1 & 0 \\\\\n", + " 0 & 0 & 0 & 1 \\\\[0.5ex]\n", + " -\\dfrac{2k}{m} & \\dfrac{k}{m} & -\\dfrac{c}{m} & 0 \\\\[0.5ex]\n", + " \\dfrac{k}{m} & -\\dfrac{2k}{m} & 0 & -\\dfrac{c}{m}\n", + " \\end{bmatrix} x\n", + " + \\begin{bmatrix}\n", + " 0 \\\\ 0 \\\\[0.5ex] 0 \\\\[1ex] \\dfrac{k}{m}\n", + " \\end{bmatrix} u.\n", + "\\end{aligned}\n", + "$$\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9f86a07f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": coupled spring mass\n", + "Inputs (1): ['u[0]']\n", + "Outputs (2): ['q1', 'q2']\n", + "States (4): ['x[0]', 'x[1]', 'x[2]', 'x[3]']\n", + "\n", + "A = [[ 0. 0. 1. 0. ]\n", + " [ 0. 0. 0. 1. ]\n", + " [-4. 2. -0.1 0. ]\n", + " [ 2. -4. 0. -0.1]]\n", + "\n", + "B = [[0.]\n", + " [0.]\n", + " [0.]\n", + " [2.]]\n", + "\n", + "C = [[1. 0. 0. 0.]\n", + " [0. 1. 0. 0.]]\n", + "\n", + "D = [[0.]\n", + " [0.]]\n" + ] + } + ], + "source": [ + "# Define the parameters for the system\n", + "m, c, k = 1, 0.1, 2\n", + "# Create a linear system\n", + "A = np.array([\n", + " [0, 0, 1, 0],\n", + " [0, 0, 0, 1],\n", + " [-2*k/m, k/m, -c/m, 0],\n", + " [k/m, -2*k/m, 0, -c/m]\n", + "])\n", + "B = np.array([[0], [0], [0], [k/m]])\n", + "C = np.array([[1, 0, 0, 0], [0, 1, 0, 0]])\n", + "D = 0\n", + "\n", + "sys = ct.ss(A, B, C, D, outputs=['q1', 'q2'], name=\"coupled spring mass\")\n", + "print(sys)" + ] + }, + { + "cell_type": "markdown", + "id": "1941fba0", + "metadata": { + "id": "YmH87LEXWo1U" + }, + "source": [ + "### Initial response\n", + "\n", + "The `initial_response` function can be used to compute the response of the system with no input, but starting from a given initial condition. This function returns a response object, which can be used for plotting." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "195a3289", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "response = ct.initial_response(sys, X0=[1, 0, 0, 0])\n", + "cplt = response.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "3e48c1df", + "metadata": { + "id": "Y4aAxYvZRBnD" + }, + "source": [ + "If you want to play around with the way the data are plotted, you can also use the response object to get direct access to the states and outputs." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "705cac47", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the outputs of the system on the same graph, in different colors\n", + "t = response.time\n", + "x = response.states\n", + "plt.plot(t, x[0], 'b', t, x[1], 'r')\n", + "plt.legend(['$x_1$', '$x_2$'])\n", + "plt.xlim(0, 50)\n", + "plt.ylabel('States')\n", + "plt.xlabel('Time [s]')\n", + "plt.title(\"Initial response from $x_1 = 1$, $x_2 = 0$\");" + ] + }, + { + "cell_type": "markdown", + "id": "b136ca77", + "metadata": { + "id": "Cou0QVnkTou9" + }, + "source": [ + "There are also lots of options available in `initial_response` and `.plot()` for tuning the plots that you get." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3d127338", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for X0 in [[1, 0, 0, 0], [0, 2, 0, 0], [1, 2, 0, 0], [0, 0, 1, 0], [0, 0, 2, 0]]:\n", + " response = ct.initial_response(sys, T=20, X0=X0)\n", + " response.plot(label=f\"{X0=}\")" + ] + }, + { + "cell_type": "markdown", + "id": "c09ccc24", + "metadata": { + "id": "b3VFPUBKT4bh" + }, + "source": [ + "### Step response\n", + "\n", + "Similar to `initial_response`, you can also generate a step response for a linear system using the `step_response` function, which returns a time response object:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "91364e84", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cplt = ct.step_response(sys).plot()" + ] + }, + { + "cell_type": "markdown", + "id": "3bd1f5be", + "metadata": { + "id": "iHZR1Q3IcrFT" + }, + "source": [ + "We can analyze the properties of the step response using the `stepinfo` command:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "00fe1ab8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Input 0, output 0 rise time = 0.6153902252990775 seconds\n", + "\n" + ] + }, + { + "data": { + "text/plain": [ + "[[{'RiseTime': 0.6153902252990775,\n", + " 'SettlingTime': 89.02645259326653,\n", + " 'SettlingMin': -0.13272845655369417,\n", + " 'SettlingMax': 0.9005994876222034,\n", + " 'Overshoot': 170.17984628666102,\n", + " 'Undershoot': 39.81853696610825,\n", + " 'Peak': 0.9005994876222034,\n", + " 'PeakTime': 2.3589958636464634,\n", + " 'SteadyStateValue': 0.33333333333333337}],\n", + " [{'RiseTime': 0.6153902252990775,\n", + " 'SettlingTime': 73.6416969607896,\n", + " 'SettlingMin': 0.2276019820782241,\n", + " 'SettlingMax': 1.13389337710215,\n", + " 'Overshoot': 70.08400656532254,\n", + " 'Undershoot': 0.0,\n", + " 'Peak': 1.13389337710215,\n", + " 'PeakTime': 6.564162403190159,\n", + " 'SteadyStateValue': 0.6666666666666665}]]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "step_info = ct.step_info(sys)\n", + "print(\"Input 0, output 0 rise time = \",\n", + " step_info[0][0]['RiseTime'], \"seconds\\n\")\n", + "step_info" + ] + }, + { + "cell_type": "markdown", + "id": "4c43d03c", + "metadata": { + "id": "F8KxXwqHWFab" + }, + "source": [ + "Note that by default the inputs are not included in the step response plot (since they are a bit boring), but you can change that:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9e0eaa51", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "stepresp = ct.step_response(sys)\n", + "cplt = stepresp.plot(plot_inputs=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "03cdf207", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the inputs on top of the outputs\n", + "cplt = stepresp.plot(plot_inputs='overlay')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "5cc0e76c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "stepresp.time.shape=(1348,)\n", + "stepresp.inputs.shape=(1, 1, 1348)\n", + "stepresp.states.shape=(4, 1, 1348)\n", + "stepresp.outputs.shape=(2, 1, 1348)\n" + ] + } + ], + "source": [ + "# Look at the \"shape\" of the step response\n", + "print(f\"{stepresp.time.shape=}\")\n", + "print(f\"{stepresp.inputs.shape=}\")\n", + "print(f\"{stepresp.states.shape=}\")\n", + "print(f\"{stepresp.outputs.shape=}\")" + ] + }, + { + "cell_type": "markdown", + "id": "beecce7f", + "metadata": { + "id": "FDfZkyk1ly0T" + }, + "source": [ + "### Forced response\n", + "\n", + "To compute the response to an input, using the convolution equation, we can use the `forced_response` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "33d8291a", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "T = np.linspace(0, 50, 500)\n", + "U1 = np.cos(T)\n", + "U2 = np.sin(3 * T)\n", + "\n", + "resp1 = ct.forced_response(sys, T, U1)\n", + "resp2 = ct.forced_response(sys, T, U2)\n", + "resp3 = ct.forced_response(sys, T, U1 + U2)\n", + "\n", + "# Plot the individual responses\n", + "resp1.sysname = 'U1'; resp1.plot(color='b')\n", + "resp2.sysname = 'U2'; resp2.plot(color='g')\n", + "resp3.sysname = 'U1 + U2'; resp3.plot(color='r');" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "10a05cb1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show that the system response is linear\n", + "cplt = resp3.plot(label=\"G(U1 + U2)\")\n", + "cplt.axes[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--', label=\"G(U1) + G(U2)\")\n", + "cplt.axes[1, 0].plot(resp1.time, resp1.outputs[1] + resp2.outputs[1], 'k--')\n", + "cplt.axes[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--')\n", + "cplt.axes[0, 0].legend(loc='upper right', fontsize='x-small');" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c03f2556", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Show that the forced response from non-zero initial condition is not linear\n", + "X0 = [1, 0, 0, 0]\n", + "resp1 = ct.forced_response(sys, T, U1, X0=X0)\n", + "resp2 = ct.forced_response(sys, T, U2, X0=X0)\n", + "resp3 = ct.forced_response(sys, T, U1 + U2, X0=X0)\n", + "\n", + "cplt = resp3.plot(label=\"G(U1 + U2)\")\n", + "cplt.axes[0, 0].plot(resp1.time, resp1.outputs[0] + resp2.outputs[0], 'k--', label=\"G(U1) + G(U2)\")\n", + "cplt.axes[1, 0].plot(resp1.time, resp1.outputs[1] + resp2.outputs[1], 'k--')\n", + "cplt.axes[2, 0].plot(resp1.time, resp1.inputs[0] + resp2.inputs[0], 'k--')\n", + "cplt.axes[0, 0].legend(loc='upper right', fontsize='x-small');" + ] + }, + { + "cell_type": "markdown", + "id": "891204fe", + "metadata": { + "id": "mo7hpvPQkKke" + }, + "source": [ + "### Frequency response" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "b2966a99", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Manual computation of the frequency response\n", + "resp = ct.input_output_response(sys, T, np.sin(1.35 * T))\n", + "\n", + "cplt = resp.plot(\n", + " plot_inputs='overlay', \n", + " legend_map=np.array([['lower left'], ['lower left']]),\n", + " label=[['q1', 'u[0]'], ['q2', None]])" + ] + }, + { + "cell_type": "markdown", + "id": "75fa2659", + "metadata": { + "id": "muqeLlJJ6s8F" + }, + "source": [ + "The magnitude and phase of the frequency response is controlled by the transfer function,\n", + "\n", + "$$\n", + "G(s) = C (sI - A)^{-1} B + D\n", + "$$\n", + "\n", + "which can be computed using the `ss2tf` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "443764af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ": u to q1, q2\n", + "Inputs (1): ['u[0]']\n", + "Outputs (2): ['q1', 'q2']\n", + "\n", + "Input 1 to output 1:\n", + "\n", + " 4\n", + " -------------------------------------\n", + " s^4 + 0.2 s^3 + 8.01 s^2 + 0.8 s + 12\n", + "\n", + "Input 1 to output 2:\n", + "\n", + " 2 s^2 + 0.2 s + 8\n", + " -------------------------------------\n", + " s^4 + 0.2 s^3 + 8.01 s^2 + 0.8 s + 12\n" + ] + } + ], + "source": [ + "try:\n", + " G = ct.ss2tf(sys, name='u to q1, q2')\n", + "except ct.ControlMIMONotImplemented:\n", + " # Create SISO transfer functions, in case we don't have slycot\n", + " G = ct.ss2tf(sys[0, 0], name='u to q1')\n", + "print(G)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "fd2df9a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G(1.35j)=array([[3.33005647-2.70686327j],\n", + " [3.80831226-2.72231858j]])\n", + "Gain: [[4.29143157]\n", + " [4.681267 ]]\n", + "Phase: [[-0.6825322 ]\n", + " [-0.62061375]] ( [[-39.10621449]\n", + " [-35.55854848]] deg)\n" + ] + } + ], + "source": [ + "# Gain and phase for the simulation above\n", + "from math import pi\n", + "val = G(1.35j)\n", + "print(f\"{G(1.35j)=}\")\n", + "print(f\"Gain: {np.absolute(val)}\")\n", + "print(f\"Phase: {np.angle(val)}\", \" (\", np.angle(val) * 180/pi, \"deg)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "bf710831", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "G(0)=array([[0.33333333+0.j],\n", + " [0.66666667+0.j]])\n", + "Final value of step response: 0.33297541813724874\n" + ] + } + ], + "source": [ + "# Gain and phase at s = 0 (= steady state step response)\n", + "print(f\"{G(0)=}\")\n", + "print(\"Final value of step response:\", stepresp.outputs[0, 0, -1])" + ] + }, + { + "cell_type": "markdown", + "id": "5108e6c6", + "metadata": { + "id": "I9eFoXm92Jgj" + }, + "source": [ + "The frequency response across all frequencies can be computed using the `frequency_response` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "41429d56", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "freqresp = ct.frequency_response(sys)\n", + "cplt = freqresp.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "5ec3b52c", + "metadata": { + "id": "pylQb07G2cqe" + }, + "source": [ + "By default, frequency responses are plotted using a \"Bode plot\", which plots the log of the magnitude and the (linear) phase against the log of the forcing frequency.\n", + "\n", + "You can also call the Bode plot command directly, and change the way the data are presented:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "456ad3a9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cplt = ct.bode_plot(sys, overlay_outputs=True)" + ] + }, + { + "cell_type": "markdown", + "id": "190f59c6", + "metadata": { + "id": "I_LTjP2J6gqx" + }, + "source": [ + "Note the \"dip\" in the frequency response for $q_2$ at frequency 2 rad/sec, which corresponds to a \"zero\" of the transfer function." + ] + }, + { + "cell_type": "markdown", + "id": "2f27f767-e012-45f9-8b76-cc040cfc89e2", + "metadata": {}, + "source": [ + "## Example 2: Trajectory tracking for a kinematic vehicle model\n", + "\n", + "This example illustrates the use of python-control to model, analyze, and design nonlinear control systems.\n", + "\n", + "We make use of a simple model for a vehicle navigating in the plane, known as the \"bicycle model\". The kinematics of this vehicle can be written in terms of the contact point $(x, y)$ and the angle $\\theta$ of the vehicle with respect to the horizontal axis:\n", + "\n", + "\n", + "\n", + " \n", + " \n", + "\n", + "
\n", + "$$\n", + "\\large\\begin{aligned}\n", + " \\dot x &= \\cos\\theta\\, v \\\\\n", + " \\dot y &= \\sin\\theta\\, v \\\\\n", + " \\dot\\theta &= \\frac{v}{l} \\tan \\delta\n", + "\\end{aligned}\n", + "$$\n", + "
\n", + "\n", + "The input $v$ represents the velocity of the vehicle and the input $\\delta$ represents the turning rate. The parameter $l$ is the wheelbase." + ] + }, + { + "cell_type": "markdown", + "id": "novel-geology", + "metadata": {}, + "source": [ + "### System Definiton\n", + "\n", + "We define the dynamics of the system that we are going to use for the control design. The dynamics of the system will be of the form\n", + "\n", + "$$\n", + "\\dot x = f(x, u), \\qquad y = h(x, u)\n", + "$$\n", + "\n", + "where $x$ is the state vector for the system, $u$ represents the vector of inputs, and $y$ represents the vector of outputs.\n", + "\n", + "The python-control package allows definition of input/output systems using the `InputOutputSystem` class and its various subclasess, including the `NonlinearIOSystem` class that we use here. A `NonlinearIOSystem` object is created by defining the update law ($f(x, u)$) and the output map ($h(x, u)$), and then calling the factory function `ct.nlsys`.\n", + "\n", + "For the example in this notebook, we will be controlling the steering of a vehicle, using a \"bicycle\" model for the dynamics of the vehicle. A more complete description of the dynamics of this system are available in [Example 3.11](https://fbswiki.org/wiki/index.php/System_Modeling) of [_Feedback Systems_](https://fbswiki.org/wiki/index.php/FBS) by Astrom and Murray (2020)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "sufficient-douglas", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the update rule for the system, f(x, u)\n", + "# States: x, y, theta (postion and angle of the center of mass)\n", + "# Inputs: v (forward velocity), delta (steering angle)\n", + "def vehicle_update(t, x, u, params):\n", + " # Get the parameters for the model\n", + " a = params.get('refoffset', 1.5) # offset to vehicle reference point\n", + " b = params.get('wheelbase', 3.) # vehicle wheelbase\n", + " maxsteer = params.get('maxsteer', 0.5) # max steering angle (rad)\n", + "\n", + " # Saturate the steering input\n", + " delta = np.clip(u[1], -maxsteer, maxsteer)\n", + " alpha = np.arctan2(a * np.tan(delta), b)\n", + "\n", + " # Return the derivative of the state\n", + " return np.array([\n", + " u[0] * np.cos(x[2] + alpha), # xdot = cos(theta + alpha) v\n", + " u[0] * np.sin(x[2] + alpha), # ydot = sin(theta + alpha) v\n", + " (u[0] / a) * np.sin(alpha) # thdot = v sin(alpha) / a\n", + " ])\n", + "\n", + "# Define the readout map for the system, h(x, u)\n", + "# Outputs: x, y (planar position of the center of mass)\n", + "def vehicle_output(t, x, u, params):\n", + " return x\n", + "\n", + "# Default vehicle parameters (including nominal velocity)\n", + "vehicle_params={'refoffset': 1.5, 'wheelbase': 3, 'velocity': 15, \n", + " 'maxsteer': 0.5}\n", + "\n", + "# Define the vehicle steering dynamics as an input/output system\n", + "vehicle = ct.nlsys(\n", + " vehicle_update, vehicle_output, states=3, name='vehicle',\n", + " inputs=['v', 'delta'], outputs=['x', 'y', 'theta'], params=vehicle_params)" + ] + }, + { + "cell_type": "markdown", + "id": "intellectual-democrat", + "metadata": {}, + "source": [ + "### Open loop simulation\n", + "\n", + "After these operations, the `vehicle` object references the nonlinear model for the system. This system can be simulated to compute a trajectory for the system. Here we command a velocity of 10 m/s and turn the wheel back and forth at one Hertz." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "likely-hindu", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the time interval that we want to use for the simualation\n", + "timepts = np.linspace(0, 10, 1000)\n", + "\n", + "# Define the inputs\n", + "U = [\n", + " 10 * np.ones_like(timepts), # velocity\n", + " 0.1 * np.sin(timepts * 2*np.pi) # steering angle\n", + "]\n", + "\n", + "# Simulate the system dynamics, starting from the origin\n", + "response = ct.input_output_response(vehicle, timepts, U, 0)\n", + "time, outputs, inputs = response.time, response.outputs, response.inputs" + ] + }, + { + "cell_type": "markdown", + "id": "dutch-charm", + "metadata": {}, + "source": [ + "We can plot the results using standard `matplotlib` commands:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "piano-algeria", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Create a figure to plot the results\n", + "fig, ax = plt.subplots(2, 1)\n", + "\n", + "# Plot the results in the xy plane\n", + "ax[0].plot(outputs[0], outputs[1])\n", + "ax[0].set_xlabel(\"$x$ [m]\")\n", + "ax[0].set_ylabel(\"$y$ [m]\")\n", + "\n", + "# Plot the inputs\n", + "ax[1].plot(timepts, U[0])\n", + "ax[1].set_ylim(0, 12)\n", + "ax[1].set_xlabel(\"Time $t$ [s]\")\n", + "ax[1].set_ylabel(\"Velocity $v$ [m/s]\")\n", + "ax[1].yaxis.label.set_color('blue')\n", + "\n", + "rightax = ax[1].twinx() # Create an axis in the right\n", + "rightax.plot(timepts, U[1], color='red')\n", + "rightax.set_ylim(None, 0.5)\n", + "rightax.set_ylabel(r\"Steering angle $\\phi$ [rad]\")\n", + "rightax.yaxis.label.set_color('red')\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "id": "alone-worry", + "metadata": {}, + "source": [ + "Notice that there is a small drift in the $y$ position despite the fact that the steering wheel is moved back and forth symmetrically around zero. Exercise: explain what might be happening." + ] + }, + { + "cell_type": "markdown", + "id": "portable-rubber", + "metadata": {}, + "source": [ + "### Linearize the system around a trajectory\n", + "\n", + "We choose a straight path along the $x$ axis at a speed of 10 m/s as our desired trajectory and then linearize the dynamics around the initial point in that trajectory." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "surprising-algorithm", + "metadata": {}, + "outputs": [], + "source": [ + "# Create the desired trajectory \n", + "Ud = np.array([10 * np.ones_like(timepts), np.zeros_like(timepts)])\n", + "Xd = np.array([10 * timepts, 0 * timepts, np.zeros_like(timepts)])\n", + "\n", + "# Now linizearize the system around this trajectory\n", + "linsys = vehicle.linearize(Xd[:, 0], Ud[:, 0])" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "protecting-committee", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0.])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check on the eigenvalues of the open loop system\n", + "np.linalg.eigvals(linsys.A)" + ] + }, + { + "cell_type": "markdown", + "id": "trying-stereo", + "metadata": {}, + "source": [ + "We see that all eigenvalues are zero, corresponding to a single integrator in the $x$ (longitudinal) direction and a double integrator in the $y$ (lateral) direction." + ] + }, + { + "cell_type": "markdown", + "id": "pressed-delta", + "metadata": {}, + "source": [ + "### Compute a state space (LQR) control law\n", + "\n", + "We can now compute a feedback controller around the trajectory. We choose a simple LQR controller here, but any method can be used." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "auburn-caribbean", + "metadata": {}, + "outputs": [], + "source": [ + "# Compute LQR controller\n", + "K, S, E = ct.lqr(linsys, np.diag([1, 1, 1]), np.diag([1, 1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "independent-lafayette", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-1. +0.j , -5.06896878+2.76385399j,\n", + " -5.06896878-2.76385399j])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check on the eigenvalues of the closed loop system\n", + "np.linalg.eigvals(linsys.A - linsys.B @ K)" + ] + }, + { + "cell_type": "markdown", + "id": "handmade-moral", + "metadata": {}, + "source": [ + "The closed loop eigenvalues have negative real part, so the closed loop (linear) system will be stable about the operating trajectory." + ] + }, + { + "cell_type": "markdown", + "id": "handy-virgin", + "metadata": {}, + "source": [ + "### Create a controller with feedforward and feedback\n", + "\n", + "We now create an I/O system representing the control law. The controller takes as an input the desired state space trajectory $x_\\text{d}$ and the nominal input $u_\\text{d}$. It outputs the control law\n", + "\n", + "$$\n", + "u = u_\\text{d} - K(x - x_\\text{d}).\n", + "$$" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "negative-scope", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the output rule for the controller\n", + "# States: none (=> no update rule required)\n", + "# Inputs: z = [xd, ud, x]\n", + "# Outputs: v (forward velocity), delta (steering angle)\n", + "def control_output(t, x, z, params):\n", + " # Get the parameters for the model\n", + " K = params.get('K', np.zeros((2, 3))) # nominal gain\n", + " \n", + " # Split up the input to the controller into the desired state and nominal input\n", + " xd_vec = z[0:3] # desired state ('xd', 'yd', 'thetad')\n", + " ud_vec = z[3:5] # nominal input ('vd', 'deltad')\n", + " x_vec = z[5:8] # current state ('x', 'y', 'theta')\n", + " \n", + " # Compute the control law\n", + " return ud_vec - K @ (x_vec - xd_vec)\n", + "\n", + "# Define the controller system\n", + "control = ct.nlsys(\n", + " None, control_output, name='control',\n", + " inputs=['xd', 'yd', 'thetad', 'vd', 'deltad', 'x', 'y', 'theta'], \n", + " outputs=['v', 'delta'], params={'K': K})" + ] + }, + { + "cell_type": "markdown", + "id": "affected-motor", + "metadata": {}, + "source": [ + "Because we have named the signals in both the vehicle model and the controller in a compatible way, we can use the autoconnect feature of the `interconnect()` function to create the closed loop system." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "stock-regression", + "metadata": {}, + "outputs": [], + "source": [ + "# Build the closed loop system\n", + "vehicle_closed = ct.interconnect(\n", + " (vehicle, control),\n", + " inputs=['xd', 'yd', 'thetad', 'vd', 'deltad'],\n", + " outputs=['x', 'y', 'theta']\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "hispanic-monroe", + "metadata": {}, + "source": [ + "### Closed loop simulation\n", + "\n", + "We now command the system to follow a trajectory and use the linear controller to correct for any errors. \n", + "\n", + "The desired trajectory is a given by a longitudinal position that tracks a velocity of 10 m/s for the first 5 seconds and then increases to 12 m/s and a lateral position that varies sinusoidally by $\\pm 0.5$ m around the centerline. The nominal inputs are not modified, so that feedback is required to obtained proper trajectory tracking." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "american-return", + "metadata": {}, + "outputs": [], + "source": [ + "Xd = np.array([\n", + " 10 * timepts + 2 * (timepts-5) * (timepts > 5), \n", + " 0.5 * np.sin(timepts * 2*np.pi), \n", + " np.zeros_like(timepts)\n", + "])\n", + "\n", + "Ud = np.array([10 * np.ones_like(timepts), np.zeros_like(timepts)])\n", + "\n", + "# Simulate the system dynamics, starting from the origin\n", + "resp = ct.input_output_response(\n", + " vehicle_closed, timepts, np.vstack((Xd, Ud)), 0)\n", + "time, outputs = resp.time, resp.outputs" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "indirect-longitude", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot the results in the xy plane\n", + "plt.plot(Xd[0], Xd[1], 'b--') # desired trajectory\n", + "plt.plot(outputs[0], outputs[1]) # actual trajectory\n", + "plt.xlabel(\"$x$ [m]\")\n", + "plt.ylabel(\"$y$ [m]\")\n", + "plt.ylim(-1, 2)\n", + "\n", + "# Add a legend\n", + "plt.legend(['desired', 'actual'], loc='upper left')\n", + "\n", + "# Compute and plot the velocity\n", + "rightax = plt.twinx() # Create an axis in the right\n", + "rightax.plot(Xd[0, :-1], np.diff(Xd[0]) / np.diff(timepts), 'r--')\n", + "rightax.plot(outputs[0, :-1], np.diff(outputs[0]) / np.diff(timepts), 'r-')\n", + "rightax.set_ylim(0, 13)\n", + "rightax.set_ylabel(\"$x$ velocity [m/s]\")\n", + "rightax.yaxis.label.set_color('red')" + ] + }, + { + "cell_type": "markdown", + "id": "weighted-directory", + "metadata": {}, + "source": [ + "We see that there is a small error in each axis. By adjusting the weights in the LQR controller we can adjust the steady state error (try it!)" + ] + }, + { + "cell_type": "markdown", + "id": "03b1fd75-579c-47da-805d-68f155957084", + "metadata": {}, + "source": [ + "## Computing environment" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "280d8d0e-38bc-484c-8ed5-fd6a7f2b56b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Control version: 0.10.1.dev324+g2fd3802a.d20241218\n", + "Slycot version: 0.6.0\n", + "NumPy version: 2.2.0\n" + ] + } + ], + "source": [ + "print(\"Control version:\", ct.__version__)\n", + "if ct.slycot_check():\n", + " import slycot\n", + " print(\"Slycot version:\", slycot.__version__)\n", + "else:\n", + " print(\"Slycot version: not installed\")\n", + "print(\"NumPy version:\", np.__version__)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/repr_gallery.ipynb b/examples/repr_gallery.ipynb new file mode 100644 index 000000000..56962210e --- /dev/null +++ b/examples/repr_gallery.ipynb @@ -0,0 +1,1336 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "639f45ae-0ee8-426e-9d52-a7b9bb95d45a", + "metadata": {}, + "source": [ + "# System Representation Gallery\n", + "\n", + "This Jupyter notebook creates different types of systems and generates a variety of representations (`__repr__`, `__str__`) for those systems that can be used to compare different versions of python-control. It is mainly intended for uses by developers to make sure there are no unexpected changes in representation formats, but also has some interesting examples of different choices in system representation." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "c4b80abe-59e4-4d76-a81c-6979a583e82d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'0.10.1.dev324+g2fd3802a.d20241218'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "import control as ct\n", + "import control.flatsys as fs\n", + "\n", + "ct.__version__" + ] + }, + { + "cell_type": "markdown", + "id": "035ebae9-7a4b-4079-8111-31f6c493c77c", + "metadata": {}, + "source": [ + "## Text representations\n", + "\n", + "The code below shows what the output in various formats will look like in a terminal window." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "eab8cc0b-3e8a-4df8-acbd-258f006f44bb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "============================================================================\n", + " Default repr\n", + "============================================================================\n", + "\n", + "StateSpace: sys_ss, dt=0:\n", + "-------------------------\n", + "StateSpace(\n", + "array([[ 0., 1.],\n", + " [-4., -5.]]),\n", + "array([[0.],\n", + " [1.]]),\n", + "array([[-1., 1.]]),\n", + "array([[0.]]),\n", + "name='sys_ss', states=2, outputs=1, inputs=1)\n", + "----\n", + "\n", + "StateSpace: sys_dss, dt=0.1:\n", + "----------------------------\n", + "StateSpace(\n", + "array([[ 0.98300988, 0.07817246],\n", + " [-0.31268983, 0.59214759]]),\n", + "array([[0.00424753],\n", + " [0.07817246]]),\n", + "array([[-1., 1.]]),\n", + "array([[0.]]),\n", + "dt=0.1,\n", + "name='sys_dss', states=2, outputs=1, inputs=1)\n", + "----\n", + "\n", + "StateSpace: stateless, dt=None:\n", + "-------------------------------\n", + "StateSpace(\n", + "array([], shape=(0, 0), dtype=float64),\n", + "array([], shape=(0, 2), dtype=float64),\n", + "array([], shape=(2, 0), dtype=float64),\n", + "array([[1., 0.],\n", + " [0., 1.]]),\n", + "dt=None,\n", + "name='stateless', states=0, outputs=2, inputs=['u0', 'u1'])\n", + "----\n", + "\n", + "TransferFunction: sys_ss$converted, dt=0:\n", + "-----------------------------------------\n", + "TransferFunction(\n", + "array([ 1., -1.]),\n", + "array([1., 5., 4.]),\n", + "name='sys_ss$converted', outputs=1, inputs=1)\n", + "----\n", + "\n", + "TransferFunction: sys_dss_poly, dt=0.1:\n", + "---------------------------------------\n", + "TransferFunction(\n", + "array([ 0.07392493, -0.08176823]),\n", + "array([ 1. , -1.57515746, 0.60653066]),\n", + "dt=0.1,\n", + "name='sys_dss_poly', outputs=1, inputs=1)\n", + "----\n", + "\n", + "TransferFunction: sys[3], dt=0:\n", + "-------------------------------\n", + "TransferFunction(\n", + "array([1]),\n", + "array([1, 0]),\n", + "outputs=1, inputs=1)\n", + "----\n", + "\n", + "TransferFunction: sys_mtf_zpk, dt=0:\n", + "------------------------------------\n", + "TransferFunction(\n", + "[[array([ 1., -1.]), array([0.])],\n", + " [array([1, 0]), array([1, 0])]],\n", + "[[array([1., 5., 4.]), array([1.])],\n", + " [array([1]), array([1, 2, 1])]],\n", + "name='sys_mtf_zpk', outputs=2, inputs=2)\n", + "----\n", + "\n", + "FrequencyResponseData: sys_ss$converted$sampled, dt=0:\n", + "------------------------------------------------------\n", + "FrequencyResponseData(\n", + "array([[[-0.24365959+0.05559644j, -0.19198193+0.1589174j ,\n", + " 0.05882353+0.23529412j, 0.1958042 -0.01105691j,\n", + " 0.0508706 -0.07767156j]]]),\n", + "array([ 0.1 , 0.31622777, 1. , 3.16227766, 10. ]),\n", + "name='sys_ss$converted$sampled', outputs=1, inputs=1)\n", + "----\n", + "\n", + "FrequencyResponseData: sys_dss_poly$sampled, dt=0.1:\n", + "----------------------------------------------------\n", + "FrequencyResponseData(\n", + "array([[[-0.24337799+0.05673083j, -0.18944184+0.16166381j,\n", + " 0.07043649+0.23113479j, 0.19038528-0.04416494j,\n", + " 0.00286505-0.09595906j]]]),\n", + "array([ 0.1 , 0.31622777, 1. , 3.16227766, 10. ])\n", + "dt=0.1,\n", + "name='sys_dss_poly$sampled', outputs=1, inputs=1)\n", + "----\n", + "\n", + "FrequencyResponseData: sys_mtf_zpk$sampled, dt=0:\n", + "-------------------------------------------------\n", + "FrequencyResponseData(\n", + "array([[[-0.24365959 +0.05559644j, -0.19198193 +0.1589174j ,\n", + " 0.05882353 +0.23529412j, 0.1958042 -0.01105691j,\n", + " 0.0508706 -0.07767156j],\n", + " [ 0. +0.j , 0. +0.j ,\n", + " 0. +0.j , 0. +0.j ,\n", + " 0. +0.j ]],\n", + "\n", + " [[ 0. +0.1j , 0. +0.31622777j,\n", + " 0. +1.j , 0. +3.16227766j,\n", + " 0. +10.j ],\n", + " [ 0.01960592 +0.09704931j, 0.16528926 +0.23521074j,\n", + " 0.5 +0.j , 0.16528926 -0.23521074j,\n", + " 0.01960592 -0.09704931j]]]),\n", + "array([ 0.1 , 0.31622777, 1. , 3.16227766, 10. ]),\n", + "name='sys_mtf_zpk$sampled', outputs=2, inputs=2)\n", + "----\n", + "\n", + "NonlinearIOSystem: sys_nl, dt=0:\n", + "--------------------------------\n", + " ['y[0]']>\n", + "----\n", + "\n", + "NonlinearIOSystem: sys_dnl, dt=0.1:\n", + "-----------------------------------\n", + " ['y[0]'], dt=0.1>\n", + "----\n", + "\n", + "InterconnectedSystem: sys_ic, dt=0:\n", + "-----------------------------------\n", + " ['y[0]', 'y[1]']>\n", + "----\n", + "\n", + "LinearICSystem: sys_ic, dt=0:\n", + "-----------------------------\n", + " ['y[0]', 'y[1]']>\n", + "----\n", + "\n", + "FlatSystem: sys_fs, dt=0:\n", + "-------------------------\n", + " ['y[0]']>\n", + "----\n", + "\n", + "FlatSystem: sys_fsnl, dt=0:\n", + "---------------------------\n", + " ['y[0]']>\n", + "----\n", + "\n", + "============================================================================\n", + " Default str (print)\n", + "============================================================================\n", + "\n", + "StateSpace: sys_ss, dt=0:\n", + "-------------------------\n", + ": sys_ss\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "States (2): ['x[0]', 'x[1]']\n", + "\n", + "A = [[ 0. 1.]\n", + " [-4. -5.]]\n", + "\n", + "B = [[0.]\n", + " [1.]]\n", + "\n", + "C = [[-1. 1.]]\n", + "\n", + "D = [[0.]]\n", + "----\n", + "\n", + "StateSpace: sys_dss, dt=0.1:\n", + "----------------------------\n", + ": sys_dss\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "States (2): ['x[0]', 'x[1]']\n", + "dt = 0.1\n", + "\n", + "A = [[ 0.98300988 0.07817246]\n", + " [-0.31268983 0.59214759]]\n", + "\n", + "B = [[0.00424753]\n", + " [0.07817246]]\n", + "\n", + "C = [[-1. 1.]]\n", + "\n", + "D = [[0.]]\n", + "----\n", + "\n", + "StateSpace: stateless, dt=None:\n", + "-------------------------------\n", + ": stateless\n", + "Inputs (2): ['u0', 'u1']\n", + "Outputs (2): ['y[0]', 'y[1]']\n", + "States (0): []\n", + "dt = None\n", + "\n", + "A = []\n", + "\n", + "B = []\n", + "\n", + "C = []\n", + "\n", + "D = [[1. 0.]\n", + " [0. 1.]]\n", + "----\n", + "\n", + "TransferFunction: sys_ss$converted, dt=0:\n", + "-----------------------------------------\n", + ": sys_ss$converted\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "\n", + " s - 1\n", + " -------------\n", + " s^2 + 5 s + 4\n", + "----\n", + "\n", + "TransferFunction: sys_dss_poly, dt=0.1:\n", + "---------------------------------------\n", + ": sys_dss_poly\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "dt = 0.1\n", + "\n", + " 0.07392 z - 0.08177\n", + " ----------------------\n", + " z^2 - 1.575 z + 0.6065\n", + "----\n", + "\n", + "TransferFunction: sys[3], dt=0:\n", + "-------------------------------\n", + ": sys[3]\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "\n", + " 1\n", + " -\n", + " s\n", + "----\n", + "\n", + "TransferFunction: sys_mtf_zpk, dt=0:\n", + "------------------------------------\n", + ": sys_mtf_zpk\n", + "Inputs (2): ['u[0]', 'u[1]']\n", + "Outputs (2): ['y[0]', 'y[1]']\n", + "\n", + "Input 1 to output 1:\n", + "\n", + " s - 1\n", + " ---------------\n", + " (s + 1) (s + 4)\n", + "\n", + "Input 1 to output 2:\n", + "\n", + " s\n", + " -\n", + " 1\n", + "\n", + "Input 2 to output 1:\n", + "\n", + " 0\n", + " -\n", + " 1\n", + "\n", + "Input 2 to output 2:\n", + "\n", + " s\n", + " ---------------\n", + " (s + 1) (s + 1)\n", + "----\n", + "\n", + "FrequencyResponseData: sys_ss$converted$sampled, dt=0:\n", + "------------------------------------------------------\n", + ": sys_ss$converted$sampled\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "\n", + "Freq [rad/s] Response\n", + "------------ ---------------------\n", + " 0.100 -0.2437 +0.0556j\n", + " 0.316 -0.192 +0.1589j\n", + " 1.000 0.05882 +0.2353j\n", + " 3.162 0.1958 -0.01106j\n", + " 10.000 0.05087 -0.07767j\n", + "----\n", + "\n", + "FrequencyResponseData: sys_dss_poly$sampled, dt=0.1:\n", + "----------------------------------------------------\n", + ": sys_dss_poly$sampled\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "dt = 0.1\n", + "\n", + "Freq [rad/s] Response\n", + "------------ ---------------------\n", + " 0.100 -0.2434 +0.05673j\n", + " 0.316 -0.1894 +0.1617j\n", + " 1.000 0.07044 +0.2311j\n", + " 3.162 0.1904 -0.04416j\n", + " 10.000 0.002865 -0.09596j\n", + "----\n", + "\n", + "FrequencyResponseData: sys_mtf_zpk$sampled, dt=0:\n", + "-------------------------------------------------\n", + ": sys_mtf_zpk$sampled\n", + "Inputs (2): ['u[0]', 'u[1]']\n", + "Outputs (2): ['y[0]', 'y[1]']\n", + "\n", + "Input 1 to output 1:\n", + "\n", + " Freq [rad/s] Response\n", + " ------------ ---------------------\n", + " 0.100 -0.2437 +0.0556j\n", + " 0.316 -0.192 +0.1589j\n", + " 1.000 0.05882 +0.2353j\n", + " 3.162 0.1958 -0.01106j\n", + " 10.000 0.05087 -0.07767j\n", + "\n", + "Input 1 to output 2:\n", + "\n", + " Freq [rad/s] Response\n", + " ------------ ---------------------\n", + " 0.100 0 +0.1j\n", + " 0.316 0 +0.3162j\n", + " 1.000 0 +1j\n", + " 3.162 0 +3.162j\n", + " 10.000 0 +10j\n", + "\n", + "Input 2 to output 1:\n", + "\n", + " Freq [rad/s] Response\n", + " ------------ ---------------------\n", + " 0.100 0 +0j\n", + " 0.316 0 +0j\n", + " 1.000 0 +0j\n", + " 3.162 0 +0j\n", + " 10.000 0 +0j\n", + "\n", + "Input 2 to output 2:\n", + "\n", + " Freq [rad/s] Response\n", + " ------------ ---------------------\n", + " 0.100 0.01961 +0.09705j\n", + " 0.316 0.1653 +0.2352j\n", + " 1.000 0.5 +0j\n", + " 3.162 0.1653 -0.2352j\n", + " 10.000 0.01961 -0.09705j\n", + "----\n", + "\n", + "NonlinearIOSystem: sys_nl, dt=0:\n", + "--------------------------------\n", + ": sys_nl\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "States (2): ['x[0]', 'x[1]']\n", + "Parameters: ['a', 'b']\n", + "\n", + "Update: \n", + "Output: \n", + "----\n", + "\n", + "NonlinearIOSystem: sys_dnl, dt=0.1:\n", + "-----------------------------------\n", + ": sys_dnl\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "States (2): ['x[0]', 'x[1]']\n", + "dt = 0.1\n", + "\n", + "Update: \n", + "Output: \n", + "----\n", + "\n", + "InterconnectedSystem: sys_ic, dt=0:\n", + "-----------------------------------\n", + ": sys_ic\n", + "Inputs (2): ['r[0]', 'r[1]']\n", + "Outputs (2): ['y[0]', 'y[1]']\n", + "States (2): ['proc_nl_x[0]', 'proc_nl_x[1]']\n", + "\n", + "Subsystems (2):\n", + " * ['y[0]', 'y[1]']>\n", + " * ['y[0]', 'y[1]']>\n", + "\n", + "Connections:\n", + " * proc_nl.u[0] <- ctrl_nl.y[0]\n", + " * proc_nl.u[1] <- ctrl_nl.y[1]\n", + " * ctrl_nl.u[0] <- -proc_nl.y[0] + r[0]\n", + " * ctrl_nl.u[1] <- -proc_nl.y[1] + r[1]\n", + "\n", + "Outputs:\n", + " * y[0] <- proc_nl.y[0]\n", + " * y[1] <- proc_nl.y[1]\n", + "----\n", + "\n", + "LinearICSystem: sys_ic, dt=0:\n", + "-----------------------------\n", + ": sys_ic\n", + "Inputs (2): ['r[0]', 'r[1]']\n", + "Outputs (2): ['y[0]', 'y[1]']\n", + "States (2): ['proc_x[0]', 'proc_x[1]']\n", + "\n", + "Subsystems (2):\n", + " * ['y[0]', 'y[1]']>\n", + " * ['y[0]', 'y[1]'], dt=None>\n", + "\n", + "Connections:\n", + " * proc.u[0] <- ctrl.y[0]\n", + " * proc.u[1] <- ctrl.y[1]\n", + " * ctrl.u[0] <- -proc.y[0] + r[0]\n", + " * ctrl.u[1] <- -proc.y[1] + r[1]\n", + "\n", + "Outputs:\n", + " * y[0] <- proc.y[0]\n", + " * y[1] <- proc.y[1]\n", + "\n", + "A = [[-2. 3.]\n", + " [-1. -5.]]\n", + "\n", + "B = [[-2. 0.]\n", + " [ 0. -3.]]\n", + "\n", + "C = [[-1. 1.]\n", + " [ 1. 0.]]\n", + "\n", + "D = [[0. 0.]\n", + " [0. 0.]]\n", + "----\n", + "\n", + "FlatSystem: sys_fs, dt=0:\n", + "-------------------------\n", + ": sys_fs\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "States (2): ['x[0]', 'x[1]']\n", + "\n", + "Update: ['y[0]']>>\n", + "Output: ['y[0]']>>\n", + "\n", + "Forward: \n", + "Reverse: \n", + "----\n", + "\n", + "FlatSystem: sys_fsnl, dt=0:\n", + "---------------------------\n", + ": sys_fsnl\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "States (2): ['x[0]', 'x[1]']\n", + "\n", + "Update: \n", + "Output: \n", + "\n", + "Forward: \n", + "Reverse: \n", + "----\n", + "\n", + "============================================================================\n", + " repr_format='info'\n", + "============================================================================\n", + "\n", + "StateSpace: sys_ss, dt=0:\n", + "-------------------------\n", + " ['y[0]']>\n", + "----\n", + "\n", + "StateSpace: sys_dss, dt=0.1:\n", + "----------------------------\n", + " ['y[0]'], dt=0.1>\n", + "----\n", + "\n", + "StateSpace: stateless, dt=None:\n", + "-------------------------------\n", + " ['y[0]', 'y[1]'], dt=None>\n", + "----\n", + "\n", + "TransferFunction: sys_ss$converted, dt=0:\n", + "-----------------------------------------\n", + " ['y[0]']>\n", + "----\n", + "\n", + "TransferFunction: sys_dss_poly, dt=0.1:\n", + "---------------------------------------\n", + " ['y[0]'], dt=0.1>\n", + "----\n", + "\n", + "TransferFunction: sys[3], dt=0:\n", + "-------------------------------\n", + " ['y[0]']>\n", + "----\n", + "\n", + "TransferFunction: sys_mtf_zpk, dt=0:\n", + "------------------------------------\n", + " ['y[0]', 'y[1]']>\n", + "----\n", + "\n", + "FrequencyResponseData: sys_ss$converted$sampled, dt=0:\n", + "------------------------------------------------------\n", + " ['y[0]']>\n", + "----\n", + "\n", + "FrequencyResponseData: sys_dss_poly$sampled, dt=0.1:\n", + "----------------------------------------------------\n", + " ['y[0]'], dt=0.1>\n", + "----\n", + "\n", + "FrequencyResponseData: sys_mtf_zpk$sampled, dt=0:\n", + "-------------------------------------------------\n", + " ['y[0]', 'y[1]']>\n", + "----\n", + "\n", + "NonlinearIOSystem: sys_nl, dt=0:\n", + "--------------------------------\n", + " ['y[0]']>\n", + "----\n", + "\n", + "NonlinearIOSystem: sys_dnl, dt=0.1:\n", + "-----------------------------------\n", + " ['y[0]'], dt=0.1>\n", + "----\n", + "\n", + "InterconnectedSystem: sys_ic, dt=0:\n", + "-----------------------------------\n", + " ['y[0]', 'y[1]']>\n", + "----\n", + "\n", + "LinearICSystem: sys_ic, dt=0:\n", + "-----------------------------\n", + " ['y[0]', 'y[1]']>\n", + "----\n", + "\n", + "FlatSystem: sys_fs, dt=0:\n", + "-------------------------\n", + " ['y[0]']>\n", + "----\n", + "\n", + "FlatSystem: sys_fsnl, dt=0:\n", + "---------------------------\n", + " ['y[0]']>\n", + "----\n", + "\n", + "============================================================================\n", + " iosys.repr_show_count=False\n", + "============================================================================\n", + "\n", + "StateSpace: sys_ss, dt=0:\n", + "-------------------------\n", + "StateSpace(\n", + "array([[ 0., 1.],\n", + " [-4., -5.]]),\n", + "array([[0.],\n", + " [1.]]),\n", + "array([[-1., 1.]]),\n", + "array([[0.]]),\n", + "name='sys_ss')\n", + "----\n", + "\n", + "StateSpace: sys_dss, dt=0.1:\n", + "----------------------------\n", + "StateSpace(\n", + "array([[ 0.98300988, 0.07817246],\n", + " [-0.31268983, 0.59214759]]),\n", + "array([[0.00424753],\n", + " [0.07817246]]),\n", + "array([[-1., 1.]]),\n", + "array([[0.]]),\n", + "dt=0.1,\n", + "name='sys_dss')\n", + "----\n", + "\n", + "StateSpace: stateless, dt=None:\n", + "-------------------------------\n", + "StateSpace(\n", + "array([], shape=(0, 0), dtype=float64),\n", + "array([], shape=(0, 2), dtype=float64),\n", + "array([], shape=(2, 0), dtype=float64),\n", + "array([[1., 0.],\n", + " [0., 1.]]),\n", + "dt=None,\n", + "name='stateless', inputs=['u0', 'u1'])\n", + "----\n", + "\n", + "LinearICSystem: sys_ic, dt=0:\n", + "-----------------------------\n", + " ['y[0]', 'y[1]']>\n", + "----\n", + "\n", + "============================================================================\n", + " xferfcn.display_format=zpk, str (print)\n", + "============================================================================\n", + "\n", + "TransferFunction: sys_ss$converted, dt=0:\n", + "-----------------------------------------\n", + ": sys_ss$converted\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "\n", + " s - 1\n", + " ---------------\n", + " (s + 1) (s + 4)\n", + "----\n", + "\n", + "TransferFunction: sys_dss_poly, dt=0.1:\n", + "---------------------------------------\n", + ": sys_dss_poly\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "dt = 0.1\n", + "\n", + " 0.07392 z - 0.08177\n", + " ----------------------\n", + " z^2 - 1.575 z + 0.6065\n", + "----\n", + "\n", + "TransferFunction: sys[3], dt=0:\n", + "-------------------------------\n", + ": sys[3]\n", + "Inputs (1): ['u[0]']\n", + "Outputs (1): ['y[0]']\n", + "\n", + " 1\n", + " -\n", + " s\n", + "----\n", + "\n", + "TransferFunction: sys_mtf_zpk, dt=0:\n", + "------------------------------------\n", + ": sys_mtf_zpk\n", + "Inputs (2): ['u[0]', 'u[1]']\n", + "Outputs (2): ['y[0]', 'y[1]']\n", + "\n", + "Input 1 to output 1:\n", + "\n", + " s - 1\n", + " ---------------\n", + " (s + 1) (s + 4)\n", + "\n", + "Input 1 to output 2:\n", + "\n", + " s\n", + " -\n", + " 1\n", + "\n", + "Input 2 to output 1:\n", + "\n", + " 0\n", + " -\n", + " 1\n", + "\n", + "Input 2 to output 2:\n", + "\n", + " s\n", + " ---------------\n", + " (s + 1) (s + 1)\n", + "----\n", + "\n" + ] + } + ], + "source": [ + "# Grab system definitions (and generate various representations as text)\n", + "from repr_gallery import *" + ] + }, + { + "cell_type": "markdown", + "id": "19f146a3-c036-4ff6-8425-c201fba14ec7", + "metadata": {}, + "source": [ + "## Jupyter notebook (HTML/LaTeX) representations\n", + "\n", + "The following representations are generated using the `_html_repr_` method in selected types of systems. Only those systems that have unique displays are shown." + ] + }, + { + "cell_type": "markdown", + "id": "16ff8d11-e793-456a-bf27-ae4cc0dd1e3b", + "metadata": {}, + "source": [ + "### Continuous time state space systems" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c1ca661d-10f3-45be-8619-c3e143bb4b4c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<StateSpace sys_ss: ['u[0]'] -> ['y[0]']>\n", + "$$\n", + "\\left[\\begin{array}{rllrll|rll}\n", + "0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "-4\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&-5\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\hline\n", + "-1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "$$" + ], + "text/plain": [ + "StateSpace(\n", + "array([[ 0., 1.],\n", + " [-4., -5.]]),\n", + "array([[0.],\n", + " [1.]]),\n", + "array([[-1., 1.]]),\n", + "array([[0.]]),\n", + "name='sys_ss', states=2, outputs=1, inputs=1)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys_ss" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b10b4db3-a8c0-4a2c-a19d-a09fef3dc25f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<StateSpace sys_ss: ['u[0]'] -> ['y[0]']>\n", + "$$\n", + "\\begin{array}{ll}\n", + "A = \\left[\\begin{array}{rllrll}\n", + "0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "-4\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&-5\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "&\n", + "B = \\left[\\begin{array}{rll}\n", + "0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "\\\\\n", + "C = \\left[\\begin{array}{rllrll}\n", + "-1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "&\n", + "D = \\left[\\begin{array}{rll}\n", + "0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "\\end{array}\n", + "$$" + ], + "text/plain": [ + "StateSpace(\n", + "array([[ 0., 1.],\n", + " [-4., -5.]]),\n", + "array([[0.],\n", + " [1.]]),\n", + "array([[-1., 1.]]),\n", + "array([[0.]]),\n", + "name='sys_ss', states=2, outputs=1, inputs=1)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with ct.config.defaults({'statesp.latex_repr_type': 'separate'}):\n", + " display(sys_ss)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0537f6fe-a155-4c49-be7c-413608a03887", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<StateSpace sys_ss: ['u[0]'] -> ['y[0]']>\n", + "$$\n", + "\\begin{array}{ll}\n", + "A = \\left[\\begin{array}{rllrll}\n", + " 0.&\\hspace{-1em}0000&\\hspace{-1em}\\phantom{\\cdot}& 1.&\\hspace{-1em}0000&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + " -4.&\\hspace{-1em}0000&\\hspace{-1em}\\phantom{\\cdot}& -5.&\\hspace{-1em}0000&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "&\n", + "B = \\left[\\begin{array}{rll}\n", + " 0.&\\hspace{-1em}0000&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + " 1.&\\hspace{-1em}0000&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "\\\\\n", + "C = \\left[\\begin{array}{rllrll}\n", + " -1.&\\hspace{-1em}0000&\\hspace{-1em}\\phantom{\\cdot}& 1.&\\hspace{-1em}0000&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "&\n", + "D = \\left[\\begin{array}{rll}\n", + " 0.&\\hspace{-1em}0000&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "\\end{array}\n", + "$$" + ], + "text/plain": [ + "StateSpace(\n", + "array([[ 0., 1.],\n", + " [-4., -5.]]),\n", + "array([[0.],\n", + " [1.]]),\n", + "array([[-1., 1.]]),\n", + "array([[0.]]),\n", + "name='sys_ss', states=2, outputs=1, inputs=1)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with ct.config.defaults({\n", + " 'statesp.latex_repr_type': 'separate',\n", + " 'statesp.latex_num_format': '8.4f'}):\n", + " display(sys_ss)" + ] + }, + { + "cell_type": "markdown", + "id": "fa75f040-633d-401c-ba96-e688713d5a2d", + "metadata": {}, + "source": [ + "### Stateless state space system" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5a71e38c-9880-4af7-82e0-49f074653e94", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<StateSpace stateless: ['u0', 'u1'] -> ['y[0]', 'y[1]'], dt=None>\n", + "$$\n", + "\\left[\\begin{array}{rllrll}\n", + "1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "$$" + ], + "text/plain": [ + "StateSpace(\n", + "array([], shape=(0, 0), dtype=float64),\n", + "array([], shape=(0, 2), dtype=float64),\n", + "array([], shape=(2, 0), dtype=float64),\n", + "array([[1., 0.],\n", + " [0., 1.]]),\n", + "dt=None,\n", + "name='stateless', states=0, outputs=2, inputs=['u0', 'u1'])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys_ss0" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7ddbd638-9338-4204-99bc-792f35e14874", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<StateSpace stateless: ['u0', 'u1'] -> ['y[0]', 'y[1]'], dt=None>\n", + "$$\n", + "\\begin{array}{ll}\n", + "D = \\left[\\begin{array}{rllrll}\n", + "1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "\\end{array}\n", + "$$" + ], + "text/plain": [ + "StateSpace(\n", + "array([], shape=(0, 0), dtype=float64),\n", + "array([], shape=(0, 2), dtype=float64),\n", + "array([], shape=(2, 0), dtype=float64),\n", + "array([[1., 0.],\n", + " [0., 1.]]),\n", + "dt=None,\n", + "name='stateless', states=0, outputs=2, inputs=['u0', 'u1'])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with ct.config.defaults({'statesp.latex_repr_type': 'separate'}):\n", + " display(sys_ss0)" + ] + }, + { + "cell_type": "markdown", + "id": "06c5d470-0768-4628-b2ea-d2387525ed80", + "metadata": {}, + "source": [ + "### Discrete time state space system" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e7b9b438-28e3-453e-9860-06ff75b7af10", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<StateSpace sys_dss: ['u[0]'] -> ['y[0]'], dt=0.1>\n", + "$$\n", + "\\left[\\begin{array}{rllrll|rll}\n", + "0.&\\hspace{-1em}983&\\hspace{-1em}\\phantom{\\cdot}&0.&\\hspace{-1em}0782&\\hspace{-1em}\\phantom{\\cdot}&0.&\\hspace{-1em}00425&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "-0.&\\hspace{-1em}313&\\hspace{-1em}\\phantom{\\cdot}&0.&\\hspace{-1em}592&\\hspace{-1em}\\phantom{\\cdot}&0.&\\hspace{-1em}0782&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\hline\n", + "-1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "$$" + ], + "text/plain": [ + "StateSpace(\n", + "array([[ 0.98300988, 0.07817246],\n", + " [-0.31268983, 0.59214759]]),\n", + "array([[0.00424753],\n", + " [0.07817246]]),\n", + "array([[-1., 1.]]),\n", + "array([[0.]]),\n", + "dt=0.1,\n", + "name='sys_dss', states=2, outputs=1, inputs=1)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys_dss" + ] + }, + { + "cell_type": "markdown", + "id": "7719e725-9d38-4f2a-a142-0ebc090e74e4", + "metadata": {}, + "source": [ + "### \"Stateless\" state space system" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "938fd795-f402-4491-b2c9-eb42c458e1e1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<StateSpace stateless: ['u0', 'u1'] -> ['y[0]', 'y[1]'], dt=None>\n", + "$$\n", + "\\left[\\begin{array}{rllrll}\n", + "1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "$$" + ], + "text/plain": [ + "StateSpace(\n", + "array([], shape=(0, 0), dtype=float64),\n", + "array([], shape=(0, 2), dtype=float64),\n", + "array([], shape=(2, 0), dtype=float64),\n", + "array([[1., 0.],\n", + " [0., 1.]]),\n", + "dt=None,\n", + "name='stateless', states=0, outputs=2, inputs=['u0', 'u1'])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys_ss0" + ] + }, + { + "cell_type": "markdown", + "id": "c620f1a1-40ff-4320-9f62-21bff9ab308e", + "metadata": {}, + "source": [ + "### Continuous time transfer function" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e364e6eb-0cfa-486a-8b95-ff9c6c41a091", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<TransferFunction sys_ss\\$converted: ['u[0]'] -> ['y[0]']>\n", + "$$\\dfrac{s - 1}{s^2 + 5 s + 4}$$" + ], + "text/plain": [ + "TransferFunction(\n", + "array([ 1., -1.]),\n", + "array([1., 5., 4.]),\n", + "name='sys_ss$converted', outputs=1, inputs=1)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys_tf" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "af9959fd-90eb-4287-93ee-416cd13fde50", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<TransferFunction sys_ss\\$converted: ['u[0]'] -> ['y[0]']>\n", + "$$\\dfrac{s - 1}{(s + 1) (s + 4)}$$" + ], + "text/plain": [ + "TransferFunction(\n", + "array([ 1., -1.]),\n", + "array([1., 5., 4.]),\n", + "name='sys_ss$converted', outputs=1, inputs=1)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with ct.config.defaults({'xferfcn.display_format': 'zpk'}):\n", + " display(sys_tf)" + ] + }, + { + "cell_type": "markdown", + "id": "7bf40707-f84c-4e19-b310-5ec9811faf42", + "metadata": {}, + "source": [ + "### MIMO transfer function" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b2db2d1c-893b-43a1-aab0-a5f6d059f3f9", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/murray/miniconda3/envs/python3.13-slycot/lib/python3.13/site-packages/scipy/signal/_filter_design.py:1112: BadCoefficients: Badly conditioned filter coefficients (numerator): the results may be meaningless\n", + " b, a = normalize(b, a)\n", + "/Users/murray/miniconda3/envs/python3.13-slycot/lib/python3.13/site-packages/scipy/signal/_filter_design.py:1116: RuntimeWarning: invalid value encountered in divide\n", + " b /= b[0]\n" + ] + }, + { + "data": { + "text/html": [ + "<TransferFunction sys_mtf_zpk: ['u[0]', 'u[1]'] -> ['y[0]', 'y[1]']>\n", + "$$\\begin{bmatrix}\\dfrac{s - 1}{(s + 1) (s + 4)}&\\dfrac{0}{1}\\\\\\dfrac{s}{1}&\\dfrac{s}{(s + 1) (s + 1)}\\\\ \\end{bmatrix}$$" + ], + "text/plain": [ + "TransferFunction(\n", + "[[array([ 1., -1.]), array([0.])],\n", + " [array([1, 0]), array([1, 0])]],\n", + "[[array([1., 5., 4.]), array([1.])],\n", + " [array([1]), array([1, 2, 1])]],\n", + "name='sys_mtf_zpk', outputs=2, inputs=2)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys_mtf # SciPy generates a warning due to 0 numerator in 1, 2 entry" + ] + }, + { + "cell_type": "markdown", + "id": "ef78a05e-9a63-4e22-afae-66ac8ec457c2", + "metadata": {}, + "source": [ + "### Discrete time transfer function" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "365c9b4a-2af3-42e5-ae5d-f2d7d989104b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<TransferFunction sys_dss_poly: ['u[0]'] -> ['y[0]'], dt=0.1>\n", + "$$\\dfrac{0.07392 z - 0.08177}{z^2 - 1.575 z + 0.6065}$$" + ], + "text/plain": [ + "TransferFunction(\n", + "array([ 0.07392493, -0.08176823]),\n", + "array([ 1. , -1.57515746, 0.60653066]),\n", + "dt=0.1,\n", + "name='sys_dss_poly', outputs=1, inputs=1)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys_dtf" + ] + }, + { + "cell_type": "markdown", + "id": "b49fa8ab-c3af-48d1-b160-790c9f4d3c6e", + "metadata": {}, + "source": [ + "### Linear interconnected system" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "78d21969-4615-4a47-b449-7a08138dc319", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<LinearICSystem sys_ic: ['r[0]', 'r[1]'] -> ['y[0]', 'y[1]']>\n", + "$$\n", + "\\left[\\begin{array}{rllrll|rllrll}\n", + "-2\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&3\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&-2\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "-1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&-5\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&-3\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\hline\n", + "-1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "1\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}&0\\phantom{.}&\\hspace{-1em}&\\hspace{-1em}\\phantom{\\cdot}\\\\\n", + "\\end{array}\\right]\n", + "$$" + ], + "text/plain": [ + " ['y[0]', 'y[1]']>" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sys_lic" + ] + }, + { + "cell_type": "markdown", + "id": "bee65cd5-d9b5-46af-aee5-26a6a4679939", + "metadata": {}, + "source": [ + "### Non-HTML capable system representations" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e5486349-2bd3-4015-ad17-a5b8e8ec0447", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "FrequencyResponseData(\n", + "array([[[-0.24365959+0.05559644j, -0.19198193+0.1589174j ,\n", + " 0.05882353+0.23529412j, 0.1958042 -0.01105691j,\n", + " 0.0508706 -0.07767156j]]]),\n", + "array([ 0.1 , 0.31622777, 1. , 3.16227766, 10. ]),\n", + "name='sys_ss$converted$sampled', outputs=1, inputs=1)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " ['y[0]']>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " ['y[0]', 'y[1]']>" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + " ['y[0]']>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for sys in [sys_frd, sys_nl, sys_ic, sys_fs]:\n", + " display(sys)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/repr_gallery.py b/examples/repr_gallery.py new file mode 100644 index 000000000..27755b59e --- /dev/null +++ b/examples/repr_gallery.py @@ -0,0 +1,156 @@ +# repr-galler.py - different system representations for comparing versions +# RMM, 30 Dec 2024 +# +# This file creates different types of systems and generates a variety +# of representations (__repr__, __str__) for those systems that can be +# used to compare different versions of python-control. It is mainly +# intended for uses by developers to make sure there are no unexpected +# changes in representation formats, but also has some interesting +# examples of different choices in system representation. + +import numpy as np + +import control as ct +import control.flatsys as fs + +# +# Create systems of different types +# +syslist = [] + +# State space (continuous and discrete time) +sys_ss = ct.ss([[0, 1], [-4, -5]], [0, 1], [-1, 1], 0, name='sys_ss') +sys_dss = sys_ss.sample(0.1, name='sys_dss') +sys_ss0 = ct.ss([], [], [], np.eye(2), name='stateless', inputs=['u0', 'u1']) +syslist += [sys_ss, sys_dss, sys_ss0] + +# Transfer function (continuous and discrete time) +sys_tf = ct.tf(sys_ss) +sys_dtf = ct.tf(sys_dss, name='sys_dss_poly', display_format='poly') +sys_gtf = ct.tf([1], [1, 0]) +syslist += [sys_tf, sys_dtf, sys_gtf] + +# MIMO transfer function (continuous time only) +sys_mtf = ct.tf( + [[sys_tf.num[0][0].tolist(), [0]], [[1, 0], [1, 0] ]], + [[sys_tf.den[0][0].tolist(), [1]], [[1], [1, 2, 1]]], + name='sys_mtf_zpk', display_format='zpk') +syslist += [sys_mtf] + +# Frequency response data (FRD) system (continuous and discrete time) +sys_frd = ct.frd(sys_tf, np.logspace(-1, 1, 5)) +sys_dfrd = ct.frd(sys_dtf, np.logspace(-1, 1, 5)) +sys_mfrd = ct.frd(sys_mtf, np.logspace(-1, 1, 5)) +syslist += [sys_frd, sys_dfrd, sys_mfrd] + +# Nonlinear system (with linear dynamics), continuous time +def nl_update(t, x, u, params): + return sys_ss.A @ x + sys_ss.B @ u + +def nl_output(t, x, u, params): + return sys_ss.C @ x + sys_ss.D @ u + +nl_params = {'a': 0, 'b': 1} + +sys_nl = ct.nlsys( + nl_update, nl_output, name='sys_nl', params=nl_params, + states=sys_ss.nstates, inputs=sys_ss.ninputs, outputs=sys_ss.noutputs) + +# Nonlinear system (with linear dynamics), discrete time +def dnl_update(t, x, u, params): + return sys_ss.A @ x + sys_ss.B @ u + +def dnl_output(t, x, u, params): + return sys_ss.C @ x + sys_ss.D @ u + +sys_dnl = ct.nlsys( + dnl_update, dnl_output, dt=0.1, name='sys_dnl', + states=sys_ss.nstates, inputs=sys_ss.ninputs, outputs=sys_ss.noutputs) + +syslist += [sys_nl, sys_dnl] + +# Interconnected system +proc = ct.ss([[0, 1], [-4, -5]], np.eye(2), [[-1, 1], [1, 0]], 0, name='proc') +ctrl = ct.ss([], [], [], [[-2, 0], [0, -3]], name='ctrl') + +proc_nl = ct.nlsys(proc, name='proc_nl') +ctrl_nl = ct.nlsys(ctrl, name='ctrl_nl') +sys_ic = ct.interconnect( + [proc_nl, ctrl_nl], name='sys_ic', + connections=[['proc_nl.u', 'ctrl_nl.y'], ['ctrl_nl.u', '-proc_nl.y']], + inplist=['ctrl_nl.u'], inputs=['r[0]', 'r[1]'], + outlist=['proc_nl.y'], outputs=proc_nl.output_labels) +syslist += [sys_ic] + +# Linear interconnected system +sys_lic = ct.interconnect( + [proc, ctrl], name='sys_ic', + connections=[['proc.u', 'ctrl.y'], ['ctrl.u', '-proc.y']], + inplist=['ctrl.u'], inputs=['r[0]', 'r[1]'], + outlist=['proc.y'], outputs=proc.output_labels) +syslist += [sys_lic] + +# Differentially flat system (with implicit dynamics), continuous time (only) +def fs_forward(x, u): + return np.array([x[0], x[1], -4 * x[0] - 5 * x[1] + u[0]]) + +def fs_reverse(zflag): + return ( + np.array([zflag[0][0], zflag[0][1]]), + np.array([4 * zflag[0][0] + 5 * zflag[0][1] + zflag[0][2]])) + +sys_fs = fs.flatsys( + fs_forward, fs_reverse, name='sys_fs', + states=sys_nl.nstates, inputs=sys_nl.ninputs, outputs=sys_nl.noutputs) + +# Differentially flat system (with nonlinear dynamics), continuous time (only) +sys_fsnl = fs.flatsys( + fs_forward, fs_reverse, nl_update, nl_output, name='sys_fsnl', + states=sys_nl.nstates, inputs=sys_nl.ninputs, outputs=sys_nl.noutputs) + +syslist += [sys_fs, sys_fsnl] + +# Utility function to display outputs +def display_representations( + description, fcn, class_list=(ct.InputOutputSystem, )): + print("=" * 76) + print(" " * round((76 - len(description)) / 2) + f"{description}") + print("=" * 76 + "\n") + for sys in syslist: + if isinstance(sys, tuple(class_list)): + print(str := f"{type(sys).__name__}: {sys.name}, dt={sys.dt}:") + print("-" * len(str)) + print(fcn(sys)) + print("----\n") + +# Default formats +display_representations("Default repr", repr) +display_representations("Default str (print)", str) + +# 'info' format (if it exists and hasn't already been displayed) +if getattr(ct.InputOutputSystem, '_repr_info_', None) and \ + ct.config.defaults.get('iosys.repr_format', None) and \ + ct.config.defaults['iosys.repr_format'] != 'info': + with ct.config.defaults({'iosys.repr_format': 'info'}): + display_representations("repr_format='info'", repr) + +# 'eval' format (if it exists and hasn't already been displayed) +if getattr(ct.InputOutputSystem, '_repr_eval_', None) and \ + ct.config.defaults.get('iosys.repr_format', None) and \ + ct.config.defaults['iosys.repr_format'] != 'eval': + with ct.config.defaults({'iosys.repr_format': 'eval'}): + display_representations("repr_format='eval'", repr) + +# Change the way counts are displayed +with ct.config.defaults( + {'iosys.repr_show_count': + not ct.config.defaults['iosys.repr_show_count']}): + display_representations( + f"iosys.repr_show_count={ct.config.defaults['iosys.repr_show_count']}", + repr, class_list=[ct.StateSpace]) + +# ZPK format for transfer functions +with ct.config.defaults({'xferfcn.display_format': 'zpk'}): + display_representations( + "xferfcn.display_format=zpk, str (print)", str, + class_list=[ct.TransferFunction]) diff --git a/examples/secord-matlab.py b/examples/secord-matlab.py index 6cef881c1..53fe69e6f 100644 --- a/examples/secord-matlab.py +++ b/examples/secord-matlab.py @@ -3,7 +3,8 @@ import os import matplotlib.pyplot as plt # MATLAB plotting functions -from control.matlab import * # MATLAB-like functions +import numpy as np +from control.matlab import ss, step, bode, nyquist, rlocus # MATLAB-like functions # Parameters defining the system m = 250.0 # system mass @@ -24,7 +25,7 @@ # Bode plot for the system plt.figure(2) -mag, phase, om = bode(sys, logspace(-2, 2), plot=True) +mag, phase, om = bode(sys, np.logspace(-2, 2), plot=True) plt.show(block=False) # Nyquist plot for the system @@ -32,7 +33,7 @@ nyquist(sys) plt.show(block=False) -# Root lcous plot for the system +# Root locus plot for the system rlocus(sys) if 'PYCONTROL_TEST_EXAMPLES' not in os.environ: diff --git a/examples/sisotool_example.py b/examples/sisotool_example.py index 6453bec74..44d7c0443 100644 --- a/examples/sisotool_example.py +++ b/examples/sisotool_example.py @@ -10,24 +10,24 @@ #%% import matplotlib.pyplot as plt -from control.matlab import * +import control as ct # first example, aircraft attitude equation -s = tf([1,0],[1]) +s = ct.tf([1,0],[1]) Kq = -24 T2 = 1.4 damping = 2/(13**.5) omega = 13**.5 H = (Kq*(1+T2*s))/(s*(s**2+2*damping*omega*s+omega**2)) plt.close('all') -sisotool(-H) +ct.sisotool(-H) #%% # a simple RL, with multiple poles in the origin plt.close('all') H = (s+0.3)/(s**4 + 4*s**3 + 6.25*s**2) -sisotool(H) +ct.sisotool(H) #%% @@ -43,4 +43,4 @@ plt.close('all') H = (b0 + b1*s + b2*s**2) / (a0 + a1*s + a2*s**2 + a3*s**3) -sisotool(H) +ct.sisotool(H) diff --git a/examples/slycot-import-test.py b/examples/slycot-import-test.py index 2df9b5b23..9c92fd2dc 100644 --- a/examples/slycot-import-test.py +++ b/examples/slycot-import-test.py @@ -5,7 +5,7 @@ """ import numpy as np -from control.matlab import * +import control as ct from control.exception import slycot_check # Parameters defining the system @@ -17,12 +17,12 @@ A = np.array([[1, -1, 1.], [1, -k/m, -b/m], [1, 1, 1]]) B = np.array([[0], [1/m], [1]]) C = np.array([[1., 0, 1.]]) -sys = ss(A, B, C, 0) +sys = ct.ss(A, B, C, 0) # Python control may be used without slycot, for example for a pole placement. # Eigenvalue placement w = [-3, -2, -1] -K = place(A, B, w) +K = ct.place(A, B, w) print("[python-control (from scipy)] K = ", K) print("[python-control (from scipy)] eigs = ", np.linalg.eig(A - B*K)[0]) diff --git a/examples/steering-gainsched.py b/examples/steering-gainsched.py index 88eed9a95..36dafd617 100644 --- a/examples/steering-gainsched.py +++ b/examples/steering-gainsched.py @@ -46,10 +46,10 @@ def vehicle_output(t, x, u, params): return x # return x, y, theta (full state) # Define the vehicle steering dynamics as an input/output system -vehicle = ct.NonlinearIOSystem( +vehicle = ct.nlsys( vehicle_update, vehicle_output, states=3, name='vehicle', - inputs=('v', 'phi'), - outputs=('x', 'y', 'theta')) + inputs=('v', 'phi'), outputs=('x', 'y', 'theta'), + params={'wheelbase': 3, 'maxsteer': 0.5}) # # Gain scheduled controller @@ -89,10 +89,12 @@ def control_output(t, x, u, params): return np.array([v, phi]) # Define the controller as an input/output system -controller = ct.NonlinearIOSystem( +controller = ct.nlsys( None, control_output, name='controller', # static system inputs=('ex', 'ey', 'etheta', 'vd', 'phid'), # system inputs - outputs=('v', 'phi') # system outputs + outputs=('v', 'phi'), # system outputs + params={'longpole': -2, 'latpole1': -1/2 + sqrt(-7)/2, + 'latpole2': -1/2 - sqrt(-7)/2, 'wheelbase': 3} ) # @@ -113,7 +115,7 @@ def trajgen_output(t, x, u, params): return np.array([vref * t, yref, 0, vref, 0]) # Define the trajectory generator as an input/output system -trajgen = ct.NonlinearIOSystem( +trajgen = ct.nlsys( None, trajgen_output, name='trajgen', inputs=('vref', 'yref'), outputs=('xd', 'yd', 'thetad', 'vd', 'phid')) @@ -156,10 +158,13 @@ def trajgen_output(t, x, u, params): inplist=['trajgen.vref', 'trajgen.yref'], inputs=['yref', 'vref'], - # System outputs + # System outputs outlist=['vehicle.x', 'vehicle.y', 'vehicle.theta', 'controller.v', 'controller.phi'], - outputs=['x', 'y', 'theta', 'v', 'phi'] + outputs=['x', 'y', 'theta', 'v', 'phi'], + + # Parameters + params=trajgen.params | vehicle.params | controller.params, ) # Set up the simulation conditions @@ -220,9 +225,10 @@ def trajgen_output(t, x, u, params): # Create the gain scheduled system controller, _ = ct.create_statefbk_iosystem( vehicle, (gains, points), name='controller', ud_labels=['vd', 'phid'], - gainsched_indices=['vd', 'theta'], gainsched_method='linear') + gainsched_indices=['vd', 'theta'], gainsched_method='linear', + params=vehicle.params | controller.params) -# Connect everything together (note that controller inputs are different +# Connect everything together (note that controller inputs are different) steering = ct.interconnect( # List of subsystems (trajgen, controller, vehicle), name='steering', @@ -235,7 +241,7 @@ def trajgen_output(t, x, u, params): ['controller.x', 'vehicle.x'], ['controller.y', 'vehicle.y'], ['controller.theta', 'vehicle.theta'], - ['controller.vd', ('trajgen', 'vd', 0.2)], # create error + ['controller.vd', ('trajgen', 'vd', 0.2)], # create some error ['controller.phid', 'trajgen.phid'], ['vehicle.v', 'controller.v'], ['vehicle.phi', 'controller.phi'] @@ -245,10 +251,13 @@ def trajgen_output(t, x, u, params): inplist=['trajgen.vref', 'trajgen.yref'], inputs=['yref', 'vref'], - # System outputs + # System outputs outlist=['vehicle.x', 'vehicle.y', 'vehicle.theta', 'controller.v', 'controller.phi'], - outputs=['x', 'y', 'theta', 'v', 'phi'] + outputs=['x', 'y', 'theta', 'v', 'phi'], + + # Parameters + params=steering.params ) # Plot the results to compare to the previous case diff --git a/examples/steering-optimal.py b/examples/steering-optimal.py index d9bad608e..80ad671f6 100644 --- a/examples/steering-optimal.py +++ b/examples/steering-optimal.py @@ -79,14 +79,14 @@ def plot_lanechange(t, y, u, yf=None, figure=None): plt.xlabel("t [sec]") plt.ylabel("steering [rad/s]") - plt.suptitle("Lane change manuever") + plt.suptitle("Lane change maneuver") plt.tight_layout() plt.show(block=False) # # Optimal control problem # -# Perform a "lane change" manuever over the course of 10 seconds. +# Perform a "lane change" maneuver over the course of 10 seconds. # # Initial and final conditions diff --git a/examples/stochresp.ipynb b/examples/stochresp.ipynb index 74d744b0f..dda6bb501 100644 --- a/examples/stochresp.ipynb +++ b/examples/stochresp.ipynb @@ -284,7 +284,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.13.1" } }, "nbformat": 4, diff --git a/examples/type2_type3.py b/examples/type2_type3.py index 52e0645e2..f0d79dc51 100644 --- a/examples/type2_type3.py +++ b/examples/type2_type3.py @@ -4,9 +4,10 @@ import os import matplotlib.pyplot as plt # Grab MATLAB plotting functions -from control.matlab import * # MATLAB-like functions -from numpy import pi -integrator = tf([0, 1], [1, 0]) # 1/s +import control as ct +import numpy as np + +integrator = ct.tf([0, 1], [1, 0]) # 1/s # Parameters defining the system J = 1.0 @@ -29,20 +30,20 @@ # System Transfer Functions # tricky because the disturbance (base motion) is coupled in by friction -closed_loop_type2 = feedback(C_type2*feedback(P, friction), gyro) +closed_loop_type2 = ct.feedback(C_type2*ct.feedback(P, friction), gyro) disturbance_rejection_type2 = P*friction/(1. + P*friction+P*C_type2) -closed_loop_type3 = feedback(C_type3*feedback(P, friction), gyro) +closed_loop_type3 = ct.feedback(C_type3*ct.feedback(P, friction), gyro) disturbance_rejection_type3 = P*friction/(1. + P*friction + P*C_type3) # Bode plot for the system plt.figure(1) -bode(closed_loop_type2, logspace(0, 2)*2*pi, dB=True, Hz=True) # blue -bode(closed_loop_type3, logspace(0, 2)*2*pi, dB=True, Hz=True) # green +ct.bode(closed_loop_type2, np.logspace(0, 2)*2*np.pi, dB=True, Hz=True) # blue +ct.bode(closed_loop_type3, np.logspace(0, 2)*2*np.pi, dB=True, Hz=True) # green plt.show(block=False) plt.figure(2) -bode(disturbance_rejection_type2, logspace(0, 2)*2*pi, Hz=True) # blue -bode(disturbance_rejection_type3, logspace(0, 2)*2*pi, Hz=True) # green +ct.bode(disturbance_rejection_type2, np.logspace(0, 2)*2*np.pi, Hz=True) # blue +ct.bode(disturbance_rejection_type3, np.logspace(0, 2)*2*np.pi, Hz=True) # green if 'PYCONTROL_TEST_EXAMPLES' not in os.environ: plt.show() diff --git a/examples/vehicle.py b/examples/vehicle.py index b316ceced..f89702d4e 100644 --- a/examples/vehicle.py +++ b/examples/vehicle.py @@ -3,7 +3,6 @@ import numpy as np import matplotlib.pyplot as plt -import control as ct import control.flatsys as fs # @@ -84,7 +83,7 @@ def _vehicle_output(t, x, u, params): states=('x', 'y', 'theta')) # -# Utility function to plot lane change manuever +# Utility function to plot lane change maneuver # def plot_lanechange(t, y, u, figure=None, yf=None): @@ -107,5 +106,5 @@ def plot_lanechange(t, y, u, figure=None, yf=None): plt.xlabel("t [sec]") plt.ylabel("steering [rad/s]") - plt.suptitle("Lane change manuever") + plt.suptitle("Lane change maneuver") plt.tight_layout() diff --git a/pyproject.toml b/pyproject.toml index f3df75f1d..db70b8f48 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,7 +40,7 @@ dynamic = ["version"] packages = ["control"] [project.optional-dependencies] -test = ["pytest", "pytest-timeout"] +test = ["pytest", "pytest-timeout", "ruff", "numpydoc"] slycot = [ "slycot>=0.4.0" ] cvxopt = [ "cvxopt>=1.2.0" ] @@ -56,3 +56,14 @@ addopts = "-ra" filterwarnings = [ "error:.*matrix subclass:PendingDeprecationWarning", ] + +[tool.ruff] + +# TODO: expand to cover all code +include = ['control/**.py', 'benchmarks/*.py', 'examples/*.py'] + +[tool.ruff.lint] +select = [ + 'F', # pyflakes + # todo: add more as needed +]