Fuzzy neuro controller for a two-link rigid-flexible manipulator system

Abstract

This paper deals with the tracking control problem of a manipulator system with unknown and changing dynamics. In this study, a fuzzy logic controller (FLC) in the feedback configuration is proposed, and an efficient dynamic recurrent neural network (DRNN) in the feedforward configuration is developed. The DRNN, which possesses the ability of approaching arbitrary nonlinear function, is utilized to approximate the inverse dynamics of the robotic manipulator system. Based on the outputs of the FLC, parameter updating equations are derived for the adaptive DRNN model. The analysis of the stability of the system is also carried out. Finally, comparisons between fuzzy control and the proposed controller are carried out. The results demonstrate remarkable performance of the proposed controller for the two-link flexible manipulator system.