Data-Driven Robust Stabilization with RobustDomain of Attraction Estimate for Nonlinear Discrete-Time Systems

TL;DR

This paper introduces a data-driven nonlinear robust control approach using Lyapunov functions to stabilize uncertain discrete-time systems and estimate their robust domain of attraction, addressing limitations of linear control methods.

Contribution

It proposes a new Lyapunov-based robust control method that estimates the RDOA for nonlinear systems considering non-affine nonlinearities, using a data-driven approach.

Findings

Established a sufficient condition for robust stabilization and RDOA estimation.

Developed a data-driven method for estimating robust negative-definite domains.

Provided a controller design framework based on RDOA estimation.

Abstract

Nonlinear robust control is pursued by overcoming the drawback of linear robust control that it ignores available information about existing nonlinearities and the resulting controllers may be too conservative, especially when the nonlinearities are significant. However, most existing nonlinear robust control approaches just consider the affine nonlinear nominal model and thereby ignore available information about existing non-affine nonlinearities. When the general nonlinear nominal model is considered, the robust domain of attraction (RDOA) of closed-loops requires extensive investigation because it is hard to achieve the global stabilization. In this paper, we propose a new nonlinear robust control method based on Lyapunov function to stabilize a discrete-time uncertain system and to estimate the RDOA of closed-loops. First, a sufficient condition for robust stabilization of all plants in a plant set and estimation of the RDOA of all closed-loops is proposed. Then, to tackle the non-affine nonlinearities, a data-driven method of estimating the robust negative-definite domains (RNDD) is presented, and based on it the estimation of the RDOA of closed-loops and the resulting controller design are also given.