NEURAL NETWORK SLIDING-MODE-PID CONTROLLER DESIGN FOR ELECTRICALLY DRIVEN ROBOT MANIPULATORS

This paper addresses a neural-network-based chattering free sliding mode control (SMC) for robot manipulators including structured and unstructured uncertainties in both manipulator and actuator dynamics by incorporating a PID outer loop. The main idea is that the robustness property of SMC and good response characteristics of PID are combined to achieve more acceptable performance. Uncertainties in the robot dynamics and actuator models are compensated by a two-layer neural network. External disturbance and approximation error are counteracted by robust signal with adaptive gain. The stability of closed-loop system is guaranteed by developed control scheme. Finally, the proposed methodology is applied to a two-link elbow robot as a case of study. The simulation results show the effectiveness of the method and its robustness to uncertainties and disturbances.