Trajectory Control of Robotic Manipulators by Using a Feedback-Error-Learning Neural Network
This paper presents a neural network based control strategy for the trajectory 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 is 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 the proposed method. Experiments are performed on a two degree of freedom, direct drive manipulator. The experimental results are very good.
|Keywords||control, Neural networks, Robot, Trajectory tracking|
Hamavand, Z. (Zaryab), & Schwartz, H.M. (1995). Trajectory Control of Robotic Manipulators by Using a Feedback-Error-Learning Neural Network. Robotica, 13(5), 449–459. doi:10.1017/S0263574700018282