Online adaptive control of robot manipulators using dynamic fuzzy neural networks

Author

Gao, Yang ; Er, Mengt Joo ; Leithead, W.E. ; Leith, D.J.

Author_Institution

Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore

Volume

6

fYear

2001

fDate

2001

Firstpage

4828

Abstract

This paper presents a robust adaptive fuzzy neural controller suitable for motion control of a multi-link robot manipulator. The proposed controller has the following salient features: (1) the dynamic fuzzy neural networks structure, i.e. fuzzy control rules, can be generated or deleted automatically; (2) adaptive learning; (3) online learning of the robot dynamics; (4) fast learning speed; and (5) fast convergence of tracking error. The global stability of the system is established using the Lyapunov approach. Computer simulation studies of a two-link robot manipulator demonstrate that an excellent tracking performance can be achieved under external disturbances

Keywords

Lyapunov methods; adaptive control; fuzzy control; fuzzy neural nets; learning (artificial intelligence); manipulator dynamics; neurocontrollers; real-time systems; stability; tracking; Lyapunov method; adaptive control; dynamic fuzzy neural networks; dynamics; fuzzy logic; learning; robot manipulators; stability; tracking; Adaptive control; Automatic control; Fuzzy control; Manipulator dynamics; Motion control; Motion planning; Programmable control; Robotics and automation; Robots; Robust control;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2001. Proceedings of the 2001

Conference_Location

Arlington, VA

ISSN

0743-1619

Print_ISBN

0-7803-6495-3

Type

conf

DOI

10.1109/ACC.2001.945747

Filename

945747