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control.matlab.dlqe — Python Control Systems Library 0.10.2 documentation

control.matlab.dlqe(A, G, C, QN, RN[, N])[source]

Discrete-time linear quadratic estimator (Kalman filter).

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, G, C2D array_like

Dynamics, process noise (disturbance), and output matrices.

QN, RN2D array_like

Process and sensor noise covariance matrices.

NN2D array, optional

Cross covariance matrix (not yet supported).

methodstr, 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:

L2D array

Kalman estimator gain.

P2D array

Solution to Riccati equation.

E1D 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)                     

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