LMIs in Control/pages/MatrixNormMinimization

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LMI for Matrix Norm Minimization

This problem is a slight generalization of the eigenvalue minimization problem for a matrix. Calculating norm of a matrix is necessary in designing an H2 or an H optimal controller for linear time-invariant systems. In those cases, we need to compute the norm of the matrix of the closed-loop system. Moreover, we desire to design the controller so as to minimize the closed-loop matrix norm.

The System

Assume that we have a matrix function of variables x:

A(x)=A0+A1x1+...+Anxn

where Ai,i=1,2,...,n are symmetric matrices.

The Data

The symmetric matrices Ai (A0,A1,...,An) are given.

The Optimization Problem

The optimization problem is to find the variables x=[x1x2...xn] in order to minimize the following cost function:

J(x)=||A(x)||2

where J(x) is the cost function and ||.||2 indicates the norm of the matrix function A.

According to Lemma 1.1 in LMI in Control Systems Analysis, Design and Applications (page 10), the following statements are equivalent:

ATAt2I0[tIAATtI]0

The LMI: LMI for matrix norm minimization

This optimization problem can be converted to an LMI problem.

The mathematical description of the LMI formulation can be written as follows:

mints.t.[tIA(x)A(x)TtI]0

Conclusion:

As a result, the variables xi,i=1,2,...,n after solving this LMI problem and we obtain t that is the norm of matrix function A(x).

Implementation

A link to Matlab codes for this problem in the Github repository:

https://github.com/asalimil/LMI-for-Matrix-Norm-Minimization

LMI for Matrix Norm Minimization

LMI for Generalized Eigenvalue Problem

LMI for Maximum Singular Value of a Complex Matrix

LMI for Matrix Positivity

A list of references documenting and validating the LMI.

  • [1] - LMI in Control Systems Analysis, Design and Applications


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