Region of attraction analysis of nonlinear stochastic systems using Polynomial Chaos Expansion

TL;DR

This paper introduces a novel method using Polynomial Chaos expansions to estimate the region of attraction for nonlinear stochastic systems, linking deterministic and stochastic stability properties through optimization techniques.

Contribution

It develops a new approach combining Polynomial Chaos and sum-of-squares programming to estimate the stochastic system's ROA from its deterministic PC expansion.

Findings

Successfully estimates ROA for stochastic systems

Connects stochastic moments with Lyapunov stability

Demonstrates effectiveness through two examples

Abstract

A method is presented to estimate the region of attraction (ROA) of stochastic systems with finite second moment and uncertainty-dependent equilibria. The approach employs Polynomial Chaos (PC) expansions to represent the stochastic system by a higher-dimensional set of deterministic equations. We first show how the equilibrium point of the deterministic formulation provides the stochastic moments of an uncertainty-dependent equilibrium point of the stochastic system. A connection between the boundedness of the moments of the stochastic system and the Lyapunov stability of its PC expansion is then derived. Defining corresponding notions of a ROA for both system representations, we show how this connection can be leveraged to recover an estimate of the ROA of the stochastic system from the ROA of the PC expanded system. Two optimization programs, obtained from sum-of-squares programming techniques, are provided to compute inner estimates of the ROA. The first optimization program uses the Lyapunov stability arguments to return an estimate of the ROA of the PC expansion. Based on this result and user specifications on the moments for the initial conditions, the second one employs the shown connection to provide the corresponding ROA of the stochastic system. The method is demonstrated by two examples.