Neural Network Global Sliding Mode PID Control for Robot Manipulators

Abstract

Abstract—This paper presents a neural network global PID-sliding mode control method for the tracking control of robot manipulators with bounded uncertainties. A certain sliding mode controller with PID sliding function is developed. In this controller, the switching gain is tuned by a RBF neural network on the reachable condition of sliding mode. Thus, the effect of chattering can be alleviated. Moreover, global sliding mode is realized by designing an exponential dynamic sliding function. Mathematical proof of the stability and convergence of the control system is given. Simulation results demonstrate that the chattering and the steady state errors are eliminated and satisfactory trajectory tracking is achieved.