Stable adaptive control for robot trajectory tracking using neural networks

Abstract

Existing stable adaptive control approaches using neural networks have been developed mostly in continuous time systems for robot trajectory tracking. This paper investigates the discrete time case. A novel scheme for integrating a neural network (NN) approach with an adaptive implementation of the sliding mode control with the sector is developed. The sliding mode control with the sector serves two purposes, one is to provide the global stability of the closed loop system, the other is to improve the tracking performance. The whole system stability and tracking error convergence are proved by Lyapunov techniques which yield a NN weight tuning algorithm.