Robust Tracking Control of 6 DOF Manipulator using Artificial Neural Network based Integral Sliding Mode Control

Abstract

This paper proposes a robust tracking control method for a six degree-of-freedom manipulator using artificial neural network based integral sliding mode control. The proposed method is designed to consider all of the actual manipulator's weight, length, and moment of inertia, while maintaining the robustness against the disturbance and the uncertainty of the system model. The trajectory tracking error can be effectively reduced by using the proposed method even in this situation. Both the simulation and experimental results are provided to verify the validity of the proposed method.