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RESEARCH PAPERS: Design and Synthesis

Learning Control of Robot Manipulators

[+] Author and Article Information
Roberto Horowitz

Department of Mechanical Engineering, University of California at Berkeley, Berkeley, CA 94720

J. Dyn. Sys., Meas., Control 115(2B), 402-411 (Jun 01, 1993) (10 pages) doi:10.1115/1.2899080 History: Received February 15, 1993; Online March 17, 2008

Abstract

Learning control encompasses a class of control algorithms for programmable machines such as robots which attain, through an iterative process, the motor dexterity that enables the machine to execute complex tasks. In this paper we discuss the use of function identification and adaptive control algorithms in learning controllers for robot manipulators. In particular, we discuss the similarities and differences between betterment learning schemes, repetitive controllers and adaptive learning schemes based on integral transforms. The stability and convergence properties of adaptive learning algorithms based on integral transforms are highlighted and experimental results illustrating some of these properties are presented.

Copyright © 1993 by The American Society of Mechanical Engineers
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