lqr

Linear-Quadratic Regulator (LQR) design.

📝 Syntax

• [K, S, P] = lqr(sys, Q, R, N)

• [K, S, P] = lqr(A, B, Q, R, N)

📥 Input argument

• sys - LTI model

• Q - State-cost weighted matrix

• R - Input-cost weighted matrix

• N - Optional cross term matrix: 0 by default.

• A - State matrix: n x n matrix.

• B - Input-to-state matrix: n x m matrix.

📤 Output argument

• K - Optimal gain: row vector.

• S - Solution of the Algebraic Riccati Equation.

• p - Poles of the closed-loop system: column vector.

📄 Description

In the context of continuous-time state-space matrices A and B, the command [K, S, P] = lqr(A, B, Q, R, N) computes the optimal gain matrix K, the solution S to the associated algebraic Riccati equation, and the closed-loop poles P.

This syntax is applicable exclusively to continuous-time models.

When applied to a continuous-time or discrete-time state-space model represented by sys, the command [K, S, P] = lqr(sys, Q, R, N) computes the optimal gain matrix K, the solution S to the associated algebraic Riccati equation, and the closed-loop poles P.

The weight matrices Q and R govern the importance of states and inputs, and the cross term matrix N is zero by default when not specified.

💡 Example

A = [-0.313 56.7 0; -0.0139 -0.426 0; 0 56.7 0];
B = [0.232; 0.0203; 0];
C = [0 0 1];
D = 1;
Ts = 1.2;
sys1 = ss(A, B, C, D, Ts);
sys2 = ss(A, B, C, D);

P = 2;
Q = P * C' * C;
R = 2;
[K1, S1, e1] = lqr(sys1, Q, R)
[K2, S2, e2] = lqr(sys2, Q, R)

🔗 See also

care, dare, lqe.

🕔 History

Version	📄 Description
1.0.0	initial version