An Adaptive Control of Manipulator Based on RBF Neural Network Approximation

Abstract

When the manipulator performs the operation task, there are modeling errors and the influence of external disturbance, which is easy to lead to the large tracking error of the manipulator end trajectory. Firstly, according to the structure of the manipulator, the dynamic model of the manipulator is established. Then RBF neural network and self -adaptation are introduced. Compared with the traditional error function, the sliding mode function is introduced in the algorithm, which can ensure the system to approach the desired trajectory quickly. The neural network used has the ability to estimate the uncertainty of the system and reduce the bad influence of interference on the system. Adaptive law and robust term are also introduced to improve the performance of the system. Finally, Lyapunov function is used to prove the stability of the system, and MATLAB/SIMULINK simulation software is used to carry out simulation experiments. Simulation results show that the algorithm has a good effect on disturbance suppression, and the end tracking accuracy is also improved.