Neural Network Based Adaptive Chaotification of Uncertain Robot Manipulators Incorporating Motor Dynamics

3rd International Conference on Automation, Control and Robotics Engineering

工业技术;计算机科学;无线电电子学

Li, Yang^1 ; Wu, Yuxiang^1

School of Automation Science and Engineering, South China University of Technology, Guangzhou, Guangdong

510641, China^1

关键词: Adaptive radial basis function neural network;    Lyapunov stability theorem;    Nonchaotic systems;    Nonlinear functions;    Robot manipulator;    Two-link rigid robots;    Uncertain robot manipulators;    Velocity tracking;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/428/1/012054/pdf
DOI  :  10.1088/1757-899X/428/1/012054

来源: IOP

【 摘 要 】

Chaotification refers to the problem of generating chaos from an originally non-chaotic system by using a control law. In this paper, an adaptive Radial Basis Function Neural Network (RBF NN) control method is proposed to realize the chaotification of uncertain robot manipulators incorporating motor dynamics. In order to achieve velocity tracking of robot manipulators, the velocity field is introduced as a chaos reference field, and the adaptive RBF NN is used to approximate the unknown nonlinear function of the system. Finally, the uniformly ultimately boundedness of all signals in the closed-loop system is proved via Lyapunov stability theorem, and the effectiveness and feasibility of the proposed control method is verified through the simulation on two-link rigid robot manipulators.