Synthesis of Adaptive Robust Controller Based on Neural Network
for Industrial Robot Manipulator
Authors: Lê Văn Chương, Đặng Thái Sơn, Mai Thế Anh, Dương Đình Tú, Đinh Văn Nam, Phan Văn Dư, Hồ Sỹ Phương, Tạ Hùng Cường, Phan Văn Vỹ
The 7th International Conference and Exhibition on Control and Automation
: : 130
Publishing year: 5/2024
This article presents a method for synthesizing an adaptive robust controller based on a neural network for industrial robot manipulators with n degrees of freedom (n-DOF) in the case of uncertain changes in the robot's dynamics parameters and the impact of external disturbances. The uncertainty components are approximated by an RBF neural network with weights adjusted by adaptive control law to improve the ability to track a desired trajectory. In addition, a robust control component is added to eliminate the effects of approximation errors and unknown external disturbances affecting the system. Apply the proposed controller to the PUMA 560 industrial robot manipulator model; simulation results on Matlab Simulink software show that the robot control system proposed by the article has high quality, adaptability, and good interference resistance.
Adaptive control; Robust control; RBF neural network; Robot manipulator.
