Asymptotic Tracking of a Class of Uncertain Nonlinear Systems in Strict-Feedback Form

Abstract

Recently adaptive neural network controllers for a class of high uncertain nonlinear systems in strict-feedback form have been developed using the backstepping technique. The resulting closed-loop system is shown to be ultimately uniformly stable with bounded tracking error. In this paper, we will present a modified design technique, which guarantees the convergence of the tracking error to zero.