A novel Robust Adaptive Control Using RFWNNs and Backstepping for Industrial Robot Manipulators with Dead-Zone

Abstract

This paper proposes a novel robust adaptive-backstepping-recurrent-fuzzy-wavelet-neural-networks controller (ABRFWNNs) based on dead zone compensator for Industrial Robot Manipulators (IRMs) in order to improve high correctness of the position tracking control with the presence of the unknown dynamics, and disturbances. To deal on the unknown dynamics of the robot system problems, the proposed controller used recurrent-fuzzy-wavelet-neural-networks (RFWNNs) to approximate the unknown dynamics. The online adaptive control training laws and estimation of the dead-zone are determined by Lyapunov stability theory and the approximation theory. In this method, the robust sliding-mode-control (SMC) is constructed to optimize parameter vectors, solve the approximation error and higher order terms. Therefore, the stability, robustness, and desired tracking performance of ABRFWNNs for IRMs are guaranteed. The simulations and experiments performed on three-link IRMs are provided in comparison with fuzzy-wavelet-neural-networks (FWNNs) and proportional-integral-derivative (PID) to demonstrate the robustness and effectiveness of the ARBFWNNs.