RBF Neural Network Adaptive Compensation Control for Robotic Arms

When the robotic arm operates in some scenes with high accuracy requirements, it usually suffers from some unknown interference from the external environment, which affects the operating accuracy of the robotic arm. In order to deal with this kind of situation, this paper proposes a method for adaptive compensation of interference terms based on rbf neural network. This control method improves the adaptability and robustness of the system by improving the nonlinear dynamic characteristic compensation of modeling errors in order to cope with complex and changeable operating environments. This article is approved by (LiyaPunov theory) verified the convergence of the entire control system. It realizes the trajectory tracking control of the system and improves the accuracy and robustness. Then use Matlab/Simulink software for simulation analysis to verify This proves the rationality of the designed control method.