A Lyapunov-based design of robust control for a robot using neural networks

Author

Barambones, O.

Author_Institution

Dpto. Ingenieria de Sistemas y Automaitica E.U.I.T.I Bilbao, Pais Vasco Univ., Bilbao, Spain

Volume

1

fYear

2002

fDate

2002

Firstpage

333

Abstract

An adaptive neural control scheme for mechanical manipulators is presented. The design basically consists of a neural controller which implements a feedback linearization control law for a generic manipulator with unknown parameters, and a sliding-mode control which robustifies the design and compensates for the neural approximation errors. The neural updating law is obtained based on the Liapunov design to guarantees the closed-loop stability. Moreover, this scheme attains a good trajectory tracking with a small transient under the robot dynamical uncertainties.

Keywords

Lyapunov methods; adaptive control; closed loop systems; control system synthesis; error compensation; linearisation techniques; manipulators; neurocontrollers; recurrent neural nets; robust control; variable structure systems; Lyapunov-based design; adaptive neural control scheme; closed-loop stability; feedback linearization control law; mechanical manipulators; neural approximation error compensation; neural networks; robot dynamical uncertainties; robust control; sliding-mode control; trajectory tracking; unknown parameters; Adaptive control; Adaptive systems; Linear feedback control systems; Manipulators; Neural networks; Neurofeedback; Programmable control; Robots; Robust control; Sliding mode control;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Applications, 2002. Proceedings of the 2002 International Conference on

Print_ISBN

0-7803-7386-3

Type

conf

DOI

10.1109/CCA.2002.1040208

Filename

1040208