Robust adaptive neural network optimal control for a class of uncertain nonlinear systems

Abstract

This study is concerned with the robust adaptive neural network optimal control problem for a class of nonlinear systems with parameter uncertainties and external disturbances. By using neural network (NN) to approximate the unknown nonlinear function within a suitable compact set, a novel adaptive neural network optimal controller with the adjustable parameter updated laws is designed. Furthermore, based on the Lyapunov stability theory, it is shown that the proposed robust adaptive optimal strategy can guarantee that the closed-loop system is asymptotically stable, and the performance of the design shows excellent consistency. Finally, simulation results are provided to illustrate the effectiveness of the proposed robust adaptive optimal control scheme.