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# lqry

Form linear-quadratic (LQ) state-feedback regulator with output weighting

## Syntax

[K,S,e] = lqry(sys,Q,R,N)

## Description

Given the plant

$\begin{array}{l}\stackrel{˙}{x}=Ax+Bu\\ y=Cx+Du\end{array}$

or its discrete-time counterpart, lqry designs a state-feedback control

$u=-Kx$

that minimizes the quadratic cost function with output weighting

$J\left(u\right)={\int }_{0}^{\infty }\left({y}^{T}Qy+{u}^{T}Ru+2{y}^{T}Nu\right)dt$

(or its discrete-time counterpart). The function lqry is equivalent to lqr or dlqr with weighting matrices:

$\left[\begin{array}{cc}\overline{Q}& \overline{N}\\ {\overline{N}}^{T}& \overline{R}\end{array}\right]=\left[\begin{array}{cc}{C}^{T}& 0\\ {D}^{T}& I\end{array}\right]\left[\begin{array}{cc}Q& N\\ {N}^{T}& R\end{array}\right]\left[\begin{array}{cc}C& D\\ 0& I\end{array}\right]$

[K,S,e] = lqry(sys,Q,R,N) returns the optimal gain matrix K, the Riccati solution S, and the closed-loop eigenvalues e = eig(A-B*K). The state-space model sys specifies the continuous- or discrete-time plant data (A, B, C, D). The default value N=0 is assumed when N is omitted.

## Examples

See LQG Design for the x-Axis for an example.

## Limitations

The data $A,B,\overline{Q},\overline{R},\overline{N}$ must satisfy the requirements for lqr or dlqr.