Full-Order Sliding Mode Control Algorithm for Robot Manipulators Using an Adaptive Radial Basis Function Neural Network

Abstract

In this paper, a full-order sliding mode tracking control system is developed for industrial robots. First, to dismiss the effects of perturbations and uncertainties, while to improve faster response time and to eliminate the singularity, a full-order sliding function is selected. Next, to reach the prescribed tracking path and to remove the chattering, a control method is designed for robot manipulators by using a combination of full-order sliding function and a continuous adaptive control term. Additionally, the unknown dynamic model of the robot is estimated by adopting a radial basis function neural network. Due to the combination of these methodologies, the proposed controller can run free of exact robot dynamics. The suggested controller provides strong properties of high tracking accuracy and quick response with minimum tracking errors. In simulation analysis, the simulated performances verify high effectiveness of the proposed controller in trajectory tracking control of a 3-DOF robot manipulator.