`scipy.signal._ltisys:StateSpace`

source: build-install/usr/lib/python3.14/site-packages/scipy/signal/_ltisys.py:1495

Signature

class StateSpace(*system, dt = None)

Members

Summary

Linear Time Invariant system in state-space form.

Extended Summary

Represents the system as the continuous-time, first order differential equation $\overset{x}{˙} = A x + B u$ or the discrete-time difference equation $x [k + 1] = A x [k] + B u [k]$ . StateSpace systems inherit additional functionality from the lti, respectively the dlti classes, depending on which system representation is used.

Parameters

*system : arguments

The StateSpace class can be instantiated with 1 or 4 arguments. The following gives the number of input arguments and their interpretation:

1: lti or dlti system: (StateSpace, TransferFunction or ZerosPolesGain)
4: array_like: (A, B, C, D)

dt : float, optional

Sampling time [s] of the discrete-time systems. Defaults to None (continuous-time). Must be specified as a keyword argument, for example, dt=0.1.

Notes

If the parameter system is a tuple (A, B, C, D) with four state space matrices, then those matrices are converted into two-dimensional arrays of the same dtype by calling abcd_normalize. The resulting dtype will be based on NumPy's dtype promotion rules, except in the case where each of A, B, C, and D has integer dtype, in which case the resulting dtype will be the default floating point dtype of float64.

Changing the value of properties that are not part of the StateSpace system representation (such as zeros or poles) is very inefficient and may lead to numerical inaccuracies. It is better to convert to the specific system representation first. For example, call sys = sys.to_zpk() before accessing/changing the zeros, poles or gain.

The tutorial_signal_state_space_representation section of the user_guide presents the corresponding definitions of continuous-time and disrcete time state space systems.

Examples

from scipy import signal
import numpy as np
a = np.array([[0, 1], [0, 0]])
b = np.array([[0], [1]])
c = np.array([[1, 0]])
d = np.array([[0]])

✓

sys = signal.StateSpace(a, b, c, d)
print(sys)

✓

StateSpaceContinuous(
array([[0., 1.],
       [0., 0.]]),
array([[0.],
       [1.]]),
array([[1., 0.]]),
array([[0.]]),
dt: None
)

sys.to_discrete(0.1)

✓

StateSpaceDiscrete(
array([[1. , 0.1],
       [0. , 1. ]]),
array([[0.005],
       [0.1  ]]),
array([[1., 0.]]),
array([[0.]]),
dt: 0.1
)

a = np.array([[1, 0.1], [0, 1]])
b = np.array([[0.005], [0.1]])

✓

signal.StateSpace(a, b, c, d, dt=0.1)

✓

StateSpaceDiscrete(
array([[1. , 0.1],
       [0. , 1. ]]),
array([[0.005],
       [0.1  ]]),
array([[1., 0.]]),
array([[0.]]),
dt: 0.1
)

Aliases

scipy.signal.StateSpace