Generalized dynamic fuzzy neural network-based tracking control of robot manipulators

Author

Wen, Shu-Huan ; Zhu, Qi-guang

Author_Institution

Inst. of Electr. Eng., Yanshan Univ., Qinhuangdao, China

Volume

2

fYear

2004

fDate

26-29 Aug. 2004

Firstpage

812

Abstract

A robust adaptive control based on generalized dynamic fuzzy neural network (GD-FNN) is presented for robot manipulators. Fuzzy control rules can be generated or deleted automatically according to their significance to the control system, and no predefined fuzzy rules are required. Using radial basis function neural network (RBFNN) the learning speed is very fast. 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 the control arithmetic is validated.

Keywords

Lyapunov methods; adaptive control; asymptotic stability; fuzzy control; fuzzy neural nets; manipulators; neurocontrollers; position control; radial basis function networks; robust control; Lyapunov theorem; asymptotic stability; dynamic fuzzy neural network-based tracking control; fuzzy control rules; radial basis function neural network; robot manipulators; robust adaptive control; Adaptive control; Automatic control; Control systems; Fuzzy control; Fuzzy neural networks; Manipulator dynamics; Neural networks; Robot control; Robotics and automation; Robust 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.2004.1382297

Filename

1382297