Dynamic output feedback RBF neural network sliding mode control for robust tracking and model following

Abstract

A dynamic output feedback radial basis function (RBF) neural network sliding mode control (SMC) scheme is proposed to realize the problem of robust tracking and model following for a class of uncertain time-delay systems. The algorithm is based on dynamic output feedback SMC, RBF neural network and adaptive control. The design of sliding surface and the existence of sliding mode have been addressed. The proposed robust tracking controller guarantees the stability of overall closed-loop system and achieves zero-tracking error in the presence of state delays, input delays, time-varying parameter uncertainties and external disturbances. Moreover, the knowledge of upper bound of uncertainties is not required, and chattering phenomenon is eliminated. Both theoretical analysis and illustrative examples demonstrate the validity of the proposed scheme.