Robust Adaptive Dynamic Programming for Optimal Nonlinear Control Design

TL;DR

This paper develops a robust adaptive dynamic programming approach for optimal control of uncertain nonlinear systems, integrating nonlinear control tools to address dynamic uncertainties and unmodeled dynamics.

Contribution

It introduces a novel robust-ADP framework that extends ADP to uncertain nonlinear systems by combining modern nonlinear control techniques.

Findings

Developed a practical learning algorithm for robust-ADP.

Applied the method successfully to a sensorimotor control problem.

Addressed a gap in ADP literature regarding uncertainties.

Abstract

This paper studies the robust optimal control design for uncertain nonlinear systems from a perspective of robust adaptive dynamic programming (robust-ADP). The objective is to fill up a gap in the past literature of ADP where dynamic uncertainties or unmodeled dynamics are not addressed. A key strategy is to integrate tools from modern nonlinear control theory, such as the robust redesign and the backstepping techniques as well as the nonlinear small-gain theorem, with the theory of ADP. The proposed robust-ADP methodology can be viewed as a natural extension of ADP to uncertain nonlinear systems. A practical learning algorithm is developed in this paper, and has been applied to a sensorimotor control problem.