# LMIs in Control/pages/H-infinity filtering

LMIs in Control/pages/H-infinity filtering

For systems that have disturbances, filtering can be used to reduce the effects of these disturbances. Described on this page is a method of attaining a filter that will reduce the effects of the disturbances as completely as possible. To do this, we look to find a set of new coefficient matrices that describe the filtered system. The process to achieve such a new system is described below. The H-infinity-filter tries to minimize the maximum magnitude of error.

## The System

For the application of this LMI, we will look at linear systems that can be represented in state space as

{\begin{aligned}{\dot {x}}&=Ax+Bw,x(0)=x_{0}\\y&=Cx+Dw\\z&=Lx\end{aligned}}

where $x\in R^{n},y\in R^{l},z\in R^{m}$  represent the state vector, the measured output vector, and the output vector of interest, respectively, $w\in R^{p}$  is the disturbance vector, and $A,B,C,D$  and $L$  are the system matrices of appropriate dimension.

To further define: $x$  is $\in R^{n}$  and is the state vector, $A$  is $\in R^{n*n}$  and is the state matrix, $B$  is $\in R^{n*r}$  and is the input matrix, $w$  is $\in R^{r}$  and is the exogenous input, $C$  is $\in R^{m*n}$  and is the output matrix, $D$  and $L$  are $\in R^{m*r}$  and are feedthrough matrices, and $y$  and $z$  are $\in R^{m}$  and are the output and the output of interest, respectively.

## The Data

The data are $w$  (the disturbance vector), and $A,B,C,D$  and $L$  (the system matrices). Furthermore, the $A$  matrix is assumed to be stable

## The Optimization Problem

We need to design a filter that will eliminate the effects of the disturbances as best we can. For this, we take a filter of the following form:

{\begin{aligned}{\dot {\sigma }}&=A_{f}\sigma +B_{f}\sigma ,\sigma (0)=\sigma _{0}\\{\hat {z}}&=C_{f}\sigma +D_{f}y,\end{aligned}}

where $\sigma \in R^{n}$  is the state vector, ${\hat {z}}\in R^{m}$  is the estimation vector of z, and $A_{f},B_{f},C_{f},andD_{f}$  are the coefficient matrices of appropriate dimensions.

Note that the combined complete system can be represented as

{\begin{aligned}{\dot {x}}_{e}&={\tilde {A}}x_{e}+{\tilde {B}}w,x_{e}(0)=x_{e0}\\{\tilde {z}}&={\tilde {C}}x_{e}+{\tilde {D}}w,\end{aligned}}

where ${\tilde {z}}=z-{\hat {z}}\in R^{m}$  is the estimation error,

{\begin{aligned}x_{e}={\begin{bmatrix}x\\\sigma \end{bmatrix}}\\\end{aligned}}

is the state vector of the system, and ${\tilde {A}},{\tilde {B}},{\tilde {C}},{\tilde {D}}$  are the coefficient matrices, defined as:

{\begin{aligned}{\tilde {A}}={\begin{bmatrix}A&0\\B_{f}C&A_{f}\end{bmatrix}},{\tilde {B}}={\begin{bmatrix}B\\B_{f}D\end{bmatrix}},\\{\tilde {C}}={\begin{bmatrix}L-D_{f}C&-C_{f}\end{bmatrix}},{\tilde {D}}=-D_{f}D\end{aligned}}

In other words, for the system defined above we need to find $A_{f},B_{f},C_{f},andD_{f}$  such that

{\begin{aligned}|G_{{\tilde {z}}w}(s)|_{\inf }<\gamma ,\end{aligned}}

where $\gamma$  is a positive constant, and

{\begin{aligned}G_{{\tilde {z}}w}(s)={\tilde {C}}(sI-{\tilde {A}})^{-1}{\tilde {B}}+{\tilde {D}}.\end{aligned}}

## The LMI: H-inf Filtering

The solution can be obtained by finding matrices $R,X,M,N,Z,D_{f}$  that obey the following LMIs:

{\begin{aligned}X&>0\\R-X&>0\\{\begin{bmatrix}RA+A^{T}R+ZC+C^{T}Z^{T}&*&*&*\\M^{T}+ZC+XA&M^{T}+M&*&*\\B^{T}R+D^{T}Z^{T}&B^{T}X+D^{T}Z^{T}&-\gamma I&*\\L-D_{f}C&-N&-D_{f}D&-\gamma I\end{bmatrix}}&<0\\\end{aligned}}

## Conclusion:

To find the corresponding filter, use the optimized matrices from the solution to find:

$A_{f}=X^{-1}M,B_{f}=X^{-1}Z,C_{f}=N,D_{f}=D_{f}$

These matrices can then be used to produce ${\tilde {A}},{\tilde {B}},{\tilde {C}},{\tilde {D}}$  to construct the filter described above, that will best eliminate the disturbances of the system.

## Implementation

This implementation requires Yalmip and Sedumi.