Cart (Loading....) | Create Account
Close category search window
 

Recurrent wavelet-based Elman neural network control for multi-axis motion control stage using linear ultrasonic motors

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $31
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Lin, F.-J. ; Dept. of Electr. Eng., Nat. Central Univ., Chungli, Taiwan ; Kung, Y.-S. ; Chen, S.-Y. ; Liu, Y.-H.

A novel recurrent wavelet-based Elman neural network (RWENN) control system is proposed in this study to control the mover position of a multi-axis motion control stage using linear ultrasonic motors (LUSMs) for the tracking of various contours. First, the structure and operating principles of the LUSMs are introduced briefly. Since the dynamic characteristics and motor parameters of the LUSMs are non-linear and time varying, the RWENN is proposed to control the mover of the X-Y-- motion control stage to track various contours precisely using a direct decentralised control strategy. In the proposed RWENN, each hidden neuron employs a different wavelet function as an activation function. Moreover, the recurrent connective weights are added in the RWENN. Therefore compared with the conventional Elman neural network (ENN), both the precision and time of convergence are improved. Furthermore, the on-line learning algorithm based on the supervised gradient descent method and the convergence analysis of the tracking error using a discrete-type Lyapunov function of the RWENN are developed. Finally, some experimental results of various contours tracking show that the tracking performance of the RWENN is significantly improved compared with the ENN.

Published in:

Electric Power Applications, IET  (Volume:4 ,  Issue: 5 )

Date of Publication:

May 2010

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.