Radial basis function network-based adaptive tracking control for robot manipulators

Author

Wang, Hong-rui ; Zhu, Qi-guang ; Chen, Ying

Author_Institution

Inst. of Electron. & Commun. Eng., Hebei Univ., Baoding, China

Volume

1

fYear

2004

fDate

26-29 Aug. 2004

Firstpage

510

Abstract

A radial basis function (RBF) network-based adaptive tracking control scheme is proposed for robot manipulators. A RBF network is used to generate control input signals that are similar to the control inputs of adaptive control using liner reparameterization of the robot manipulator. A sliding model control term is used to eliminate the effects of the network inherent approximation errors and external disturbance. The asymptotic stability of the control system is established using Lyapunov theorem. Simulations are given for a two-link robot in the end of the paper, and validate the control arithmetic.

Keywords

Lyapunov methods; adaptive control; asymptotic stability; manipulators; neurocontrollers; radial basis function networks; tracking; variable structure systems; Lyapunov theorem; adaptive tracking control; asymptotic stability; liner reparameterization; radial basis function network; robot manipulator; sliding model control; two-link robot; Adaptive control; Adaptive systems; Approximation error; Asymptotic stability; Manipulators; Programmable control; Radial basis function networks; Robot control; Signal generators; Sliding mode control;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on

Print_ISBN

0-7803-8403-2

Type

conf

DOI

10.1109/ICMLC.2004.1380744

Filename

1380744