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TECHNICAL PAPERS

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

Abstract

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
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