Neural Network-Based an Adaptive Discrete-Time Global Sliding Mode Control Scheme

Abstract

An adaptive discrete-time global sliding mode control scheme based on neural network reaching law for a class of discrete linear uncertain systems is proposed in this paper. Parameters ε and δ were determined previously in the conventional reaching law method, but they are regulated adaptively by a feedforward neural network(FNN) in this paper. Simulation results shown that all advantages of the reaching law are retained, meanwhile the dynamic features and robustness of the control system are improved effectively through shortening the reaching phase and chattering phenomenon is eliminated.