Estimating the Region of Attraction Using Polynomial Optimization: a Converse Lyapunov Result

TL;DR

This paper introduces an iterative SOS programming method to estimate the region of attraction for polynomial systems, proving the existence of polynomial Lyapunov functions that approximate the true region arbitrarily well.

Contribution

It provides a theoretical proof of the existence of polynomial Lyapunov functions that approximate the maximal Lyapunov function, enabling better estimation of the region of attraction.

Findings

Proved existence of polynomial Lyapunov functions approximating the true region of attraction.

Demonstrated convergence of the iterative SOS method with a numerical example.

Established a link between smooth Lyapunov functions and polynomial approximations.

Abstract

In this paper, we propose an iterative method for using SOS programming to estimate the region of attraction of a polynomial vector field, the conjectured convergence of which necessitates the existence of polynomial Lyapunov functions whose sublevel sets approximate the true region of attraction arbitrarily well. The main technical result of the paper is the proof of existence of such a Lyapunov function. Specifically, we use the Hausdorff distance metric to analyze convergence and in the main theorem demonstrate that the existence of an $n$-times continuously differentiable maximal Lyapunov function implies that for any $\epsilon>0$, there exists a polynomial Lyapunov function and associated sub-level set which together prove stability of a set which is within $\epsilon$ Hausdorff distance of the true region of attraction. The proposed iterative method and probably convergence is illustrated with a numerical example.