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Adaptive Learning Control for Cooperation of Two Robots Manipulating a Rigid Object with Model Uncertainties

Published online by Cambridge University Press:  09 March 2009

Dong Sun and
F Xiaolun Shi Yunhui Liu
Dong Sun
Affiliation:
Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong, Shatin N.T. (Hong Kong).
F Xiaolun Shi Yunhui Liu
Affiliation:
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong (Hong Kong).
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Summary

In this paper, an adaptive learning (A-L) control scheme is proposed for cooperation of two manipulators handling a rigid object with model uncertainties. For robots performing repetitive cooperating tasks, their operations are decomposed into two modes: the single operational mode and the repetitive operational mode on which the A-L controller is based. In the single operational mode, the controller is a learning based adaptive controller in which the robotic parameters are updated by using the information of the previous operation. In the repetitive operational mode, the controller is a model-based iterative learning controller. The advantages of the A-L controller lie in the fact that it can improve the transient performance as robots repeat operations at a high speed of the learning convergence. Simulation results ascertain that the A-L algorithm is effective in controlling two cooperated robots with model uncertainties.

Keywords

Two robotsCooperationAdaptive controlRigid objectLearning controlUncertainty.

Information

Type
Article
Information
Robotica , Volume 14 , Issue 4 , July 1996, pp. 365 - 373
Copyright
Copyright © Cambridge University Press 1996

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