control.matlab.lqe
- control.matlab.lqe(A, G, C, QN, RN[, NN])[source]
Linear quadratic estimator design (Kalman filter) for continuous-time systems. Given the system
with unbiased process noise w and measurement noise v with covariances
The lqe() function computes the observer gain matrix L such that the stationary (non-time-varying) Kalman filter
produces a state estimate x_e that minimizes the expected squared error using the sensor measurements y. The noise cross-correlation NN is set to zero when omitted.
The function can be called with either 3, 4, 5, or 6 arguments:
L, P, E = lqe(sys, QN, RN)
L, P, E = lqe(sys, QN, RN, NN)
L, P, E = lqe(A, G, C, QN, RN)
L, P, E = lqe(A, G, C, QN, RN, NN)
where sys is an LTI object, and A, G, C, QN, RN, and NN are 2D arrays or matrices of appropriate dimension.
- Parameters
A (2D array_like) – Dynamics, process noise (disturbance), and output matrices
G (2D array_like) – Dynamics, process noise (disturbance), and output matrices
C (2D array_like) – Dynamics, process noise (disturbance), and output matrices
sys (LTI (StateSpace or TransferFunction)) – Linear I/O system, with the process noise input taken as the system input.
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 currently implemented.
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)
Notes
If the first argument is an LTI object, then this object will be used to define the dynamics, noise and output matrices. Furthermore, if the LTI object corresponds to a discrete time system, the
dlqe()
function will be called.Examples
>>> L, P, E = lqe(A, G, C, QN, RN) >>> L, P, E = lqe(A, G, C, Q, RN, NN)