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fixed StateSpace for complex inputs #468

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fixed StateSpace for complex inputs #468

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8 changes: 4 additions & 4 deletions control/statesp.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,7 +70,7 @@

# Define module default parameter values
_statesp_defaults = {
'statesp.use_numpy_matrix': True,
'statesp.use_numpy_matrix': False,
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Please don't change the default setting here. We have a road map in mind when this should happen and it should be independent of the support for complex types.

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That said, actually #438 takes complex inputs into account already!

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Ok, I will change this back

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That said, actually #438 takes complex inputs into account already!

Oh, I see, should I close my PR?

'statesp.default_dt': None,
'statesp.remove_useless_states': True,
}
Expand Down Expand Up @@ -98,11 +98,11 @@ def _ssmatrix(data, axis=1):
# Convert the data into an array or matrix, as configured
# If data is passed as a string, use (deprecated?) matrix constructor
if config.defaults['statesp.use_numpy_matrix']:
arr = np.matrix(data, dtype=float)
arr = np.matrix(data, dtype=complex)
elif isinstance(data, str):
arr = np.array(np.matrix(data, dtype=float))
arr = np.array(np.matrix(data, dtype=complex))
else:
arr = np.array(data, dtype=float)
arr = np.array(data, dtype=complex)
ndim = arr.ndim
shape = arr.shape

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42 changes: 42 additions & 0 deletions examples/diagonalize_space_state.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,42 @@
from matplotlib import pyplot as plt

import numpy as np
from scipy.linalg import eig
from control import step_response, StateSpace
# Example of diagonalization of StateSpace representation
# In this example the diagonalized system has complex values on the matrices

def diagonalize(A, B, C, D):
eigenvectors_matrix = eig(A)[1]
A = np.matmul(np.matmul(np.linalg.inv(eigenvectors_matrix), A), eigenvectors_matrix)
B = np.matmul(np.linalg.inv(eigenvectors_matrix), B)
C = np.matmul(C, eigenvectors_matrix)
print(A)
print(B)
print(C)
print(D)
return A, B, C, D


def main():
A = np.array([[0, 0, 1000], [0.1, 0, 0], [0, 0.5, 0.5]])
B = np.array([[0], [0], [1]])
C = np.array([[0, 0, 1]])
D = np.array([[0]])

Aw, Bw, Cw, Dw = diagonalize(A, B, C, D)

system = StateSpace(A, B, C, D, 1)
system_w = StateSpace(Aw, Bw, Cw, Dw, 1)

length = 20
plt.figure(figsize=(14, 9), dpi=100)
response_w = step_response(system_w, T=np.arange(length))
response = step_response(system, T=np.arange(length))
plt.plot(response[0], response[1], color="red", label="System")
plt.plot(response_w[0], response_w[1], color="blue", label="System W")
plt.xticks(np.arange(length))
plt.legend()
plt.savefig("diagonalization.png")

main()