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murrayrmmurrayrm
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Merge pull request #800 from murrayrm/benchmarks-24Aug2022
Update benchmarks to help with optimal control tuning
2 parents ed4ff84 + 54de2a3 commit 4d89991

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9 files changed

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‎benchmarks/README

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Original file line numberDiff line numberDiff line change
@@ -11,7 +11,7 @@ you can use the following command from the root directory of the repository:
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PYTHONPATH=`pwd` asv run --python=python
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You can also run benchmarks against specific commits usuing
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You can also run benchmarks against specific commits using
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asv run <range>
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‎benchmarks/flatsys_bench.py

Lines changed: 71 additions & 13 deletions
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@@ -11,6 +11,10 @@
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import control.flatsys as flat
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import control.optimal as opt
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#
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# System setup: vehicle steering (bicycle model)
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#
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# Vehicle steering dynamics
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def vehicle_update(t, x, u, params):
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# Get the parameters for the model
@@ -67,11 +71,28 @@ def vehicle_reverse(zflag, params={}):
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# Define the time points where the cost/constraints will be evaluated
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timepts = np.linspace(0, Tf, 10, endpoint=True)
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def time_steering_point_to_point(basis_name, basis_size):
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if basis_name == 'poly':
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basis = flat.PolyFamily(basis_size)
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elif basis_name == 'bezier':
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basis = flat.BezierFamily(basis_size)
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#
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# Benchmark test parameters
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#
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basis_params = (['poly', 'bezier', 'bspline'], [8, 10, 12])
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basis_param_names = ["basis", "size"]
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def get_basis(name, size):
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if name == 'poly':
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basis = flat.PolyFamily(size, T=Tf)
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elif name == 'bezier':
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basis = flat.BezierFamily(size, T=Tf)
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elif name == 'bspline':
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basis = flat.BSplineFamily([0, Tf/2, Tf], size)
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return basis
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#
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# Benchmarks
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#
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def time_point_to_point(basis_name, basis_size):
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basis = get_basis(basis_name, basis_size)
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# Find trajectory between initial and final conditions
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traj = flat.point_to_point(vehicle, Tf, x0, u0, xf, uf, basis=basis)
@@ -80,13 +101,16 @@ def time_steering_point_to_point(basis_name, basis_size):
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x, u = traj.eval([0, Tf])
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np.testing.assert_array_almost_equal(x0, x[:, 0])
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np.testing.assert_array_almost_equal(u0, u[:, 0])
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np.testing.assert_array_almost_equal(xf, x[:, 1])
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np.testing.assert_array_almost_equal(uf, u[:, 1])
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np.testing.assert_array_almost_equal(xf, x[:, -1])
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np.testing.assert_array_almost_equal(uf, u[:, -1])
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time_point_to_point.params = basis_params
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time_point_to_point.param_names = basis_param_names
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time_steering_point_to_point.params = (['poly', 'bezier'], [6, 8])
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time_steering_point_to_point.param_names = ["basis", "size"]
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def time_steering_cost():
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def time_point_to_point_with_cost(basis_name, basis_size):
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basis = get_basis(basis_name, basis_size)
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# Define cost and constraints
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traj_cost = opt.quadratic_cost(
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vehicle, None, np.diag([0.1, 1]), u0=uf)
@@ -95,13 +119,47 @@ def time_steering_cost():
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traj = flat.point_to_point(
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vehicle, timepts, x0, u0, xf, uf,
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cost=traj_cost, constraints=constraints, basis=flat.PolyFamily(8)
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cost=traj_cost, constraints=constraints, basis=basis,
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)
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# Verify that the trajectory computation is correct
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x, u = traj.eval([0, Tf])
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np.testing.assert_array_almost_equal(x0, x[:, 0])
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np.testing.assert_array_almost_equal(u0, u[:, 0])
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np.testing.assert_array_almost_equal(xf, x[:, 1])
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np.testing.assert_array_almost_equal(uf, u[:, 1])
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np.testing.assert_array_almost_equal(xf, x[:, -1])
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np.testing.assert_array_almost_equal(uf, u[:, -1])
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time_point_to_point_with_cost.params = basis_params
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time_point_to_point_with_cost.param_names = basis_param_names
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def time_solve_flat_ocp_terminal_cost(method, basis_name, basis_size):
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basis = get_basis(basis_name, basis_size)
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# Define cost and constraints
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traj_cost = opt.quadratic_cost(
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vehicle, None, np.diag([0.1, 1]), u0=uf)
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term_cost = opt.quadratic_cost(
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vehicle, np.diag([1e3, 1e3, 1e3]), None, x0=xf)
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constraints = [
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opt.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ]
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# Initial guess = straight line
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initial_guess = np.array(
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[x0[i] + (xf[i] - x0[i]) * timepts/Tf for i in (0, 1)])
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traj = flat.solve_flat_ocp(
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vehicle, timepts, x0, u0, basis=basis, initial_guess=initial_guess,
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trajectory_cost=traj_cost, constraints=constraints,
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terminal_cost=term_cost, minimize_method=method,
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)
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# Verify that the trajectory computation is correct
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x, u = traj.eval([0, Tf])
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np.testing.assert_array_almost_equal(x0, x[:, 0])
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np.testing.assert_array_almost_equal(xf, x[:, -1], decimal=2)
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time_solve_flat_ocp_terminal_cost.params = tuple(
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[['slsqp', 'trust-constr']] + list(basis_params))
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time_solve_flat_ocp_terminal_cost.param_names = tuple(
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['method'] + basis_param_names)

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