Represents the system as the continuous-time, first order differential equation $\dot{x} = A x + B u$
. Continuous-time StateSpace
systems inherit additional functionality from the lti
class.
Changing the value of properties that are not part of the StateSpace
system representation (such as zeros
or :None:None:`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 StateSpace
class can be instantiated with 1 or 3 arguments. The following gives the number of input arguments and their interpretation:
1:
ltisystem: (StateSpace,TransferFunctionorZerosPolesGain)4: array_like: (A, B, C, D)
Continuous-time Linear Time Invariant system in state-space form.
>>> from scipy import signal
>>> 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)See :
... print(sys) StateSpaceContinuous( array([[0, 1], [0, 0]]), array([[0], [1]]), array([[1, 0]]), array([[0]]), dt: None )
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