An adaptive proportional-derivative control method for robot manipulator

This research presents an improved control method for the robot manipulator system based on the proportional-derivative technique and neural networks. In the proposed strategy, the proportional-derivative controller based on the filtered tracking error technique has been modified such that the proportional-derivative gain parameters are adaptively updated. Similar to the conventional intelligent control methods, the neural networks approximator is applied to relax the unknown dynamics of the robot control system. In addition, the compensator-typed robust controller is also considered to eliminate inevitable approximating errors and unknown disturbances of the control system. By using the Lyapunov stability theorem for the proposed control design procedure, the tracking control and stability are guaranteed. The comparative simulation results will provide clearly the evident to prove effectiveness of the proposed approach.