Support vector network enhanced adaptive friction compensation

Author

Wang, G.L. ; Li, Y.F. ; Bi, D.X.

Author_Institution

Dept. of Electron. & Commun. Eng., Sun Yat-Sen Univ., Guangzhou

fYear

2006

fDate

15-19 May 2006

Firstpage

3699

Lastpage

3704

Abstract

This paper explores the notation of support vector networks, a new paradigm of combining support vector regression (SVR) parametrization with adaptive neural mechanism, in friction compensation for servo-motion systems. The contribution of this work is twofold. The first is to develop an enhanced adaptive friction compensator via SVR parametrization; the second is to present an analysis that shows the evidences of the performance improvement and practical usefulness enhancement due to SVR parametrization. The experimental study was conducted to validate the proposed method

Keywords

adaptive control; compensation; friction; mechanical variables control; neurocontrollers; servomechanisms; support vector machines; adaptive friction compensation; adaptive neural mechanism; servo-motion systems; support vector network; support vector regression parametrization; Adaptive control; Adaptive systems; Estimation error; Friction; Haptic interfaces; Manufacturing; Neural networks; Programmable control; Sun; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1050-4729

Print_ISBN

0-7803-9505-0

Type

conf

DOI

10.1109/ROBOT.2006.1642267

Filename

1642267