control.lqr

control.lqr(A, B, Q, R[, N])

Linear quadratic regulator design

The lqr() function computes the optimal state feedback controller that minimizes the quadratic cost

J = \int_0^\infty (x' Q x + u' R u + 2 x' N u) dt

The function can be called with either 3, 4, or 5 arguments:

  • lqr(sys, Q, R)

  • lqr(sys, Q, R, N)

  • lqr(A, B, Q, R)

  • lqr(A, B, Q, R, N)

where sys is an LTI object, and A, B, Q, R, and N are 2d arrays or matrices of appropriate dimension.

Parameters

  • A (2D array) – Dynamics and input matrices

  • B (2D array) – Dynamics and input matrices

  • sys (LTI (StateSpace or TransferFunction)) – Linear I/O system

  • Q (2D array) – State and input weight matrices

  • R (2D array) – State and input weight matrices

  • N (2D array, optional) – Cross weight matrix

Returns

  • K (2D array (or matrix)) – State feedback gains

  • S (2D array (or matrix)) – Solution to Riccati equation

  • E (1D array) – Eigenvalues of the closed loop system

See also

lqe

Notes

The return type for 2D arrays depends on the default class set for state space operations. See use_numpy_matrix().

Examples

>>> K, S, E = lqr(sys, Q, R, [N])
>>> K, S, E = lqr(A, B, Q, R, [N])