LMIs in Control/pages/Optimal Output Feedback H2 LMI

Optimal Output Feedback $H_2$ LMI

Similar to state feedback, output feedback is necessary when information about the output is not known. Often techniques such as Kalman filtering are implemented to tackle this problem. The method below, however, does not use a filtering technique and instead uses a combination of LMI constraints to perform the output feedback as well as find the minimal bound on the $H_2$ norm of the system. $H_2$ is often done using more classical tools such as Riccati equations. More recently LMI techniques have been created to solve problems such as full state feedback or output feedback as seen below.

The System

The system is represented using the 9-matrix notation shown below.

$\left[\begin{array}{c}\dot{x}\\ z\\ y\end{array}\right]=\left[\begin{array}{ccc}A& B_1& B_2\\ C_1& D_{11}& D_{12}\\ C_2& D_{21}& D_{22}\end{array}\right]\left[\begin{array}{c}x\\ w\\ u\end{array}\right]$

where $x(t)\in {ℝ}^n$ is the state, $z(t)\in {ℝ}^p$ is the regulated output, $y(t)\in {ℝ}^q$ is the sensed output, $w(t)\in {ℝ}^r$ is the exogenous input, and $u(t)\in {ℝ}^m$ is the actuator input, at any $t\in ℝ$.

The Data

$A$, $B_1$, $B_2$, $C_1$, $C_2$, $D_{11}$, $D_{12}$, $D_{21}$, $D_{22}$ are known.

The LMI: Optimal Output Feedback $H_2$ Control LMI

The following are equivalent.

1) There exists a $\hat{K}=\left[\begin{array}{cc}{A}_K& B_K\\ C_K& D_K\end{array}\right]$ such that $||S(K,P)|{|}_{H_2}<\gamma$

2) There exists $X_1$, $Y_1$, $Z$, $A_n$, $B_n$, $C_n$, $D_n$ such that

$\left[\begin{array}{ccc}A{Y}_1+Y_1{A}^{\text{T}}+B_2{C}_n+C_n{B}_2^{\text{T}}& {*}^{\text{T}}& {*}^{\text{T}}\\ A^{\text{T}}+A_n+(B_2{D}_n{C}_2{)}^{\text{T}}& X_{1}A+A^{\text{T}}+B_n{C}_2+C_2^{\text{T}}{B}_n^{\text{T}}& {*}^{\text{T}}\\ (B_1+B_2{D}_n{D}_{21}{)}^{\text{T}}& (X_1{B}_1+B_n{D}_{21}{)}^{\text{T}}& -\gamma I\end{array}\right]<0$

$\left[\begin{array}{ccc}{Y}_1& {*}^T& {*}^T\\ I& X_1& {*}^T\\ C_1{Y}_1+D_{12}{C}_n& C_1+D_{12}{D}_n{C}_2& Z\end{array}\right]>0$

$D_{11}+D_{12}{D}_n{D}_{21}=0$

$\text{trace}(Z)<{\gamma }^2$

Conclusion:

The above LMI determines the the upper bound $\gamma$ on the H2 norm. In addition to this the controller $\hat{K}(A_K,B_K,C_K,D_K)$ can also be recovered.

$D_K=(I+D_{K2}{D}_{22}{)}^{-1}{D}_{K2}$

$B_K=B_{K2}(I+D_{22}{D}_K)$

$C_K=(I+D_K{D}_{22})C_{K2}$

$A_K=A_{K2}-B_K(I+D_{22}{D}_K{)}^{-1}{D}_{22}{C}_K$

where,

$\left[\begin{array}{cc}{A}_{K2}& B_{K2}\\ C_{K2}& D_{K2}\end{array}\right]={\left[\begin{array}{cc}{X}_2& X_1{B}_2\\ 0& I\end{array}\right]}^{-1}[\left[\begin{array}{cc}{A}_n& B_n\\ C_n& D_n\end{array}\right]-\left[\begin{array}{cc}{X}_{1}A{Y}_1& 0\\ 0& 0\end{array}\right]]{\left[\begin{array}{cc}{Y}_2^T& 0\\ C_2{Y}_1& I\end{array}\right]}^{-1}$

for any full-rank $X_2$ and $Y_2$ such that

$\left[\begin{array}{cc}{X}_1& X_2\\ X_2^T& X_3\end{array}\right]={\left[\begin{array}{cc}{Y}_1& Y_2{B}_2\\ Y_2^T& Y_3\end{array}\right]}^{-1}$.

Implementation

This implementation requires Yalmip and Sedumi. https://github.com/eoskowro/LMI/blob/master/OF_H2.m

Related LMIs

Optimal Output Feedback Hinf

External Links

A list of references documenting and validating the LMI.

• LMI Methods in Optimal and Robust Control - A course on LMIs in Control by Matthew Peet.
• LMI Properties and Applications in Systems, Stability, and Control Theory - A List of LMIs by Ryan Caverly and James Forbes.
• LMIs in Systems and Control Theory - A downloadable book on LMIs by Stephen Boyd.
• LMIs in Control Systems: Analysis, Design and Applications - by Guang-Ren Duan and Hai-Hua Yu, CRC Press, Taylor & amp; Francis Group, 2013, Section 6.1.1 and Table 6.1 pp. 166–170, 192.
• A Course in Robust Control Theory: a Convex Approach, - by Geir E. Dullerud and Fernando G. Paganini, Springer, 2011, Section 2.2.3, pp. 71-73.