A Learning Control Scheme Based on Neural Networks for Repeatable Robot Trajectory Tracking

This paper presents an iterative learning controller using neural network (NN) for the robot trajectory tracking control. Basic control configuration is briefly presented and a new weight-tuning algorithm of NN is proposed with a dead-zone technique. Theoretical proof is given which shows that our modified algorithm guarantees the convergence of NN estimation error in the presence of disturbance. The simulation study demonstrates that the proposed weight-tuning algorithm is robust and less sensitive to noise compared to the standard backpropagation (BP) algorithm in identifying the robot inverse dynamics. Moreover, the simulation results also shows that the proposed NN learning control scheme can greatly reduce tracking errors as the iteration number increases.