TRAJECTORY CONTROL OF ROBOTIC MANIPULATORS BY USING A FEEDBACK-ERROR-LEARNING NEURAL-NETWORK

This paper presents a neural network based control strategy for the tr ajectory control of robot manipulators. The neural network learns the inverse dynamics of a robot manipulator without any a priori knowledge of the manipulator inertial parameters nor any a priori knowledge of the equation of dynamics. A two step feedback-error-learning process i s proposed. Strategies for selection of the training trajectories and difficulties with on-line training are discussed. Simulation of a two degree of freedom serial link manipulator shows the effectiveness of t he proposed method. Experiments are performed on a two degree of freed om, direct drive manipulator. The experimental results are very good.