Performance Improvement of Industrial Robot Trajectory Tracking Using Adaptive-Learning Scheme

[+] Author and Article Information
Dong Sun, James K. Mills

Laboratory for Nonlinear Systems Control, Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, Ontario, Canada M5S 3G8

J. Dyn. Sys., Meas., Control 121(2), 285-292 (Jun 01, 1999) (8 pages) doi:10.1115/1.2802467 History: Received May 05, 1998; Revised February 19, 1999; Online December 03, 2007


More and more industrial robot operations demand high-accuracy trajectory performance which may not be achievable by using conventional PID control. This paper describes a new adaptive control method with a learning ability in the repetitive tasks, called the Adaptive-Learning (A-L) scheme. The method is based on the proposed theory of two operational modes: the single operational mode and the repetitive operational mode. In the single operational mode, the control is an adaptive control with a new parameter adaptation law using information from the previous trials. In the repetitive operational mode, the control is a model-based iterative learning control. The advantage of the A-L scheme lies in the ability to guarantee convergence in both modes. Theoretical analysis and experimental evaluation on a commercial robot demonstrate the effectiveness of the A-L scheme in controlling an industrial robot manipulator.

Copyright © 1999 by The American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.





Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In