control.dlqe
- control.dlqe(A, G, C, QN, RN[, N])[source]
Linear quadratic estimator design (Kalman filter) for discrete-time systems. Given the system
with unbiased process noise w and measurement noise v with covariances
The dlqe() function computes the observer gain matrix L such that the stationary (non-time-varying) Kalman filter
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 (2D array_like) – Dynamics and noise input matrices
G (2D array_like) – Dynamics and noise input matrices
QN (2D array_like) – Process and sensor noise covariance matrices
RN (2D array_like) – Process and sensor noise covariance matrices
NN (2D array, optional) – 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’.
- Returns
L (2D array) – Kalman estimator gain
P (2D array) – Solution to Riccati equation
E (1D array) – Eigenvalues of estimator poles eig(A - L C)
Examples
>>> L, P, E = dlqe(A, G, C, QN, RN) >>> L, P, E = dlqe(A, G, C, QN, RN, NN)