Neural-variable structure controller with PID compensation for robot tracking

One of the most important manipulator operations is the control of the manipulator to track a given trajectory. Most commercial robot systems currently are equipped with conventional PID controllers due to their simplicity in structure and ease of design. Using PID control, however, it is difficult to achieve a desired tracking control performance since the dynamic equations of a mechanical manipulator are tightly coupled, highly nonlinear and uncertain. In order to improve the tracking control performance under uncertainty, This work presents a new hybrid control scheme for manipulator, which consists of a neural network controller, a sliding mode controller and PID compensation controller. In this control architecture, it will be shown that the robotic manipulator can track precisely both the trained and untrained trajectories. Simulation examples are included to illustrate the validity of the proposed method.