A unified framework for deterministic and probabilistic D-stability analysis of uncertain polynomial matrices

TL;DR

This paper introduces a unified framework for analyzing the D-stability of uncertain polynomial matrices, combining deterministic and probabilistic approaches using convex optimization and moment theory.

Contribution

It presents a novel method that verifies D-stability for polynomially parameterized matrices under broad conditions and incorporates probabilistic analysis with minimal assumptions.

Findings

Provides sufficient conditions for robust D-stability of polynomial matrices.

Formulates probabilistic D-stability analysis with partial information on parameter distributions.

Uses convex optimization and moment relaxations to solve stability verification problems.

Abstract

Many problems in systems and control theory can be formulated in terms of robust D-stability analysis, which aims at verifying if all the eigenvalues of an uncertain matrix lie in a given region D of the complex plane. Robust D-stability analysis is an NP-hard problem and many polynomial-time algorithms providing either sufficient or necessary conditions for an uncertain matrix to be robustly D-stable have been developed in the past decades. Despite the vast literature on the subject, most of the contributions consider specific families of uncertain matrices, mainly with interval or polytopic uncertainty. In this work, we present a novel approach providing sufficient conditions to verify if a family of matrices, whose entries depend polynomially on some uncertain parameters, is robustly D-stable. The only assumption on the stability region D is that its complement is a semialgebraic set described by polynomial constraints, which comprises the main important cases in stability analysis. Furthermore, the D-stability analysis problem is formulated in a probabilistic framework. In this context, the uncertain parameters characterizing the considered family of matrices are described by a set of non a priori specified probability measures. Only the support and some of the moments (e.g., expected values) are assumed to be known and, among all possible probability measures, we seek the one which provides the minimum probability of D-stability. The robust and the probabilistic D-stability analysis problems are formulated in a unified framework, and relaxations based on the theory of moments are used to solve the D-stability analysis problem through convex optimization. Application to robustness and probabilistic analysis of dynamical systems is discussed.