Neural Network Based Adaptive Chaotification of Uncertain Robot Manipulators Incorporating Motor Dynamics
Yang Li, Yuxiang Wu · IOP Conference Series Materials Science and Engineering · 2018
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.