A New Adaptive Neuro-Fuzzy Controller for Trajectory Tracking of Robot Manipulators

Abstract

In this paper, an adaptive control method for trajectory tracking of robot manipulators, based on new neuro-fuzzy modelling is presented. The proposed control scheme uses a three-layer neural fuzzy network (NFN) to estimate system uncertainties. The function of robot system dynamics is first modelled by a fuzzy system, which in the sequel is approximated by a combination of high order neural networks (HONNs). The overall representation is linear in respect to the unknown NN weights leading to weight adaptation laws that ensure stability and convergence to unique global minimum of the error functional. Due to the adaptive neurofuzzy modelling, the proposed controller is independent of robot dynamics, since the free parameters of the neuro-fuzzy controller are adaptively updated to cope with changes in the system and the environment. Adaptation laws for the network parameters are derived, which ensure network convergence and stable control. A weight hopping technique is also introduced to ensure that the estimated weights stay within pre-specified bounds. The simulation results show very good approximation performance of the proposed representation as compared with a simple NN approximator and very good tracking abilities under disturbance torque compared to conventional computed torque PD control.