# LMIs in Control/Click here to continue/Controller synthesis/Quadratic Polytopic Full State Feedback Optimal H2 Control

LMIs in Control/Click here to continue/Controller synthesis/Quadratic Polytopic Full State Feedback Optimal H2 Control

## Quadratic Polytopic Full State Feedback Optimal $H_{2}$ Control

For a system having polytopic uncertainties, Full State Feedback is a control technique that attempts to place the system's closed-loop system poles in specified locations based on performance specifications given, such as requiring stability or bounding the overshoot of the output. By minimizing the $H_{2}$ norm of this system we are minimizing the effect noise has on the system as part of the performance specifications.

## The System

Consider System with following state-space representation.
    ${\begin{aligned}{\dot {x}}(t)&=Ax(t)+B_{1}q(t)+B_{2}w(t)\\p(t)&=C_{1}x(t)+D_{11}q(t)+D_{12}w(t)\\z(t)&=C_{2}x(t)+D_{21}q(t)+D_{22}w(t)\\\end{aligned}}$
where $x\in \mathbb {R} ^{m}$ , $q\in \mathbb {R} ^{n}$ , $w\in \mathbb {R} ^{g}$, $A\in \mathbb {R} ^{mxm}$, $B_{1}\in \mathbb {R} ^{mxn}$, $B_{2}\in \mathbb {R} ^{mxg}$, $p\in \mathbb {R} ^{p}$ , $C_{1}\in \mathbb {R} ^{pxm}$, $D_{11}\in \mathbb {R} ^{pxn}$, $D_{12}\in \mathbb {R} ^{pxg}$, $z\in \mathbb {R} ^{s}$, $C_{2}\in \mathbb {R} ^{sxm}$, $D_{21}\in \mathbb {R} ^{sxn}$ , $D_{22}\in \mathbb {R} ^{sxg}$ for any $t\in \mathbb {R}$.
Add uncertainty to system matrices
    $A,B_{1},B_{2},C_{1},C_{2},D_{11},D_{12}$
New state-space representation
    ${\begin{aligned}{\dot {x}}(t)&=(A+A_{i})x(t)+(B_{1}+B_{i})q(t)+(B_{2}+B_{i})w(t)\\p(t)&=(C_{1}+C_{i})x(t)+(D_{11}+D_{i})q(t)+(D_{12}+D_{i})w(t)\\z(t)&=C_{2}x(t)+D_{21}q(t)+D_{22}w(t)\\\end{aligned}}$

## The Data

The matrices necessary for this LMI are

## The Optimization Problem:

Recall the closed-loop in state feedback is:
$S(P,K)=$
       ${\begin{aligned}{\begin{bmatrix}A+B_{22}F&&B_{1}\\C_{1}+D_{12}F&&D_{11}\end{bmatrix}}\\\end{aligned}}$
This problem can be formulated as $H_{2}$ optimal state-feedback, where K is a controller gain matrix.

## The LMI: An LMI for Quadratic Polytopic $H_{2}$ Optimal

State-Feedback Control
$||S(P(\Delta ),K(0,0,0,F))||_{H_{2}}\leq \gamma$
$X>0$
       ${\begin{aligned}{\begin{bmatrix}AX+B_{2}Z+XA^{T}+Z^{T}B_{2}^{T}&&B_{1}\\B_{1}^{T}&&-I\end{bmatrix}}+{\begin{bmatrix}A_{i}X+B_{2,i}Z+XA_{i}^{T}+Z^{T}B_{2,I}^{T}&&B_{1,i}\\B_{1,i}^{T}&&0\end{bmatrix}}<0\quad i=1,......,k\end{aligned}}$
       ${\begin{aligned}{\begin{bmatrix}X&&(C_{1}X+D_{12}Z)^{T}\\C_{1}X+D_{12}Z&&W\end{bmatrix}}+{\begin{bmatrix}0&&(C_{1,i}X+D_{12,i}Z)^{T}\\C_{1,i}X+D_{12,i}Z&&0\end{bmatrix}}>0\quad i=1,......,k\end{aligned}}$
       ${\begin{aligned}\\TraceW<\gamma \end{aligned}}$

## Conclusion:

The $H_{2}$ Optimal State-Feedback Controller is recovered by $F=ZX^{-1}$

## Implementation:

https://github.com/JalpeshBhadra/LMI/blob/master/H2_optimal_statefeedback_controller.m

## Related LMIs

$H_{2}$ Optimal State-Feedback Controller

## External Links

* 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.