Adaptive Neural Network Sliding Mode Control For Electrically-Driven Robot Manipulators

Abstract

In this paper a method for neural network sliding mode control design (NNS) is proposed for the robust tracking control of the electrically-driven two-links robot manipulators. The aim of this study is to overcome some shortcomings of the standard sliding mode controller (SMC) such as the produced higher amplitude of chattering, due to the higher switching gain required in the presence of large uncertainties. In the proposed NNS, the sliding mode control with a boundary layer approach is combined with the neural network (NN) to control the electrically-driven two-links robot. The NN is used for the prediction of the model unknown parts and hence it enables a lower switching gain to be used in the presence of large uncertainties. The stability is shown by the Lyapunov Theory and the control action used did not exhibit any chattering behavior. As a result, a high-precision position tracking performance is obtained without any oscillatory behavior. The effectiveness of the designed NNS is illustrated by simulations.