Adaptive control based neural network sliding mode approach for two links robot

Abstract

In order to improve the control accuracy of the robot manipulator, the sliding mode control combined with the adaptive neural network (ANNSMC) is proposed. Sliding mode control (SMC) is a nonlinear control recognized for its efficiency, easy tuning and implementation, accuracy and robustness. However, higher amplitude of chattering is produced due to the higher switching gain to handle the large uncertainties. For the purpose of reducing this gain, the uncertain parts of the system are estimated using neural network (NN) with on-line training using back propagation (BP) technique. The results of the online interconnection weights between the input and the hidden layers and between the hidden and the output layers are injected offline in order to improve the network performance in term of the convergence speed. In order to reduce the response time caused by the online training, the obtained output and input weights are updated using the adaptive laws derived from the Lyapunov stability approachthe proposed control ANNSMC has improved the convergence speed with 41.13% for the first link and 40.15% for the second link comparing to NNSMC. The simulation result illustrates the performance of the proposed approach by using MATLAB and the control action suggested did not manifest any chattering behavior.