LMIs in Control/pages/LMI for Generalized eigenvalue problem

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LMI for Generalized Eigenvalue Problem

Technically, the generalized eigenvalue problem considers two matrices, like A and B, to find the generalized eigenvector, x, and eigenvalues, λ, that satisfies Ax=λBx. If the matrix B is an identity matrix with the proper dimension, the generalized eigenvalue problem is reduced to the eigenvalue problem.

The System

Assume that we have three matrice functions which are functions of variables x=[x1x2...xn]Tn as follows:

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

B(x)=B0+B1x1+...+Bnxn

C(x)=C0+C1x1+...+Cnxn

where are Ai, Bi, and Ci (i=1,2,...,n) are the coefficient matrices.

The Data

The A(x), B(x), and C(x) are matrix functions of appropriate dimensions which are all linear in the variable x and Ai, Bi, Ci are given matrix coefficients.

The Optimization Problem

The problem is to find x=[x1x2...xn] such that:

A(x)<λB(x), B(x)>0, and C(x)<0 are satisfied and λ is a scalar variable.

The LMI: LMI for Schur stabilization

A mathematical description of the LMI formulation for the generalized eigenvalue problem can be written as follows:

minλs.t.A(x)<λB(x)B(x)>0C(x)<0

Conclusion:

The solution for this LMI problem is the values of variables x such that the scalar parameter, λ, is minimized. In practical applications, many problems involving LMIs can be expressed in the aforementioned form. In those cases, the objective is to minimize a scalar parameter that is involved in the constraints of the problem.

Implementation

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

https://github.com/asalimil/LMI-for-Schur-Stability

LMI for Generalized Eigenvalue Problem

LMI for Matrix Norm Minimization

LMI for Maximum Singular Value of a Complex Matrix

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


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