Adaptive Sliding Mode Control for Robot Manipulators Based on Neural Network

Abstract

A neural network based adaptive sliding model controller is proposed for robot manipulators. This control scheme integrates the theory of VSS and the nonlinear mapping of neural network. A key feature of this scheme is that the prior knowledge of the upper bound of the system uncertainties is not required. A RBF neural network is used to adaptively learn the unknown bounds of system uncertainties, and then the output of the neural network estimator is used to adjust the switching gain. This new controller can guarantee the asymptotic convergence of the tracking error to zero.