Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2013, Vol. 36 ›› Issue (3): 16-19.doi: 10.13190/jbupt.201303.16.005

• Papers • Previous Articles     Next Articles

Compensation Control for Model-Free Dynamic Friction Using Self-Recurrent Wavelet Neural Networks

CHU Ming, CHEN Gang, JIA Qing-xuan, SUN Han-xu   

  1. Automation School,Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2012-08-27 Online:2013-06-30 Published:2013-06-30

Abstract:

An intelligence control algorithm for friction compensation of low-speed servo system is proposed based on self-recurrent wavelet neural networks. There’s of no necessary to predict the system dynamic model parameters,and the high-precision compensation of nonlinear friction is realized by using few neurons and iterations through only position feedback. Lyapunov stability analysis shows the bounded convergence of tracking error and network weights. Also the servo experiments from a robot joint show that the servo positioning accuracy can be greatly improved by introducing the proposed compensation algorithm.

Key words: model-free, friction compensation, self-recurrent wavelet neural networks, intelligence control

CLC Number: