Title :
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
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;
Conference_Titel :
Robotics and Automation, 2006. ICRA 2006. Proceedings 2006 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-9505-0
DOI :
10.1109/ROBOT.2006.1642267
