A dynamic recurrent neural network-based controller for a 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 and an efficient dynamic recurrent neural network (DRNN) in the feedforward configuration are proposed. The DRNN, which possesses the ability of approaching arbitrary nonlinear function, is applied 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, extensive simulations are conducted under different conditions. Results demonstrate the remarkable performance of the proposed controller. It can successfully identify the inverse dynamics of the flexible manipulator system and perform accurate tracking for a given trajectory.