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A new neural network control technique for robot manipulators*

Published online by Cambridge University Press:  09 March 2009

Seul Jung  and
T. C. Hsia
Seul Jung
Affiliation:
Robotics Research Laboratory, Department of Electrical and Computer Engineering, University of California, Davis, CA 95616 (USA)
T. C. Hsia
Affiliation:
Robotics Research Laboratory, Department of Electrical and Computer Engineering, University of California, Davis, CA 95616 (USA)
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Summary

A new neural network (NN) control technique for robot manipulators is introduced in this paper. The fundamental robot control technique is the model-based computed-torque control which is subjected to performance degradation due. to model uncertainty. NN controllers have been traditionally used to generate a compensating joint torque to account for the effects of the uncertainties. The proposed NN control approach is conceptually different in that it is aimed at prefiltering the desired joint trajectories before they are used to command the computed-torque-controlled robot system (the plant) to counteract performance degradation due to plant uncertainties. In this framework, the NN controller serves as the inverse model of the plant, which can be trained on-line using joint tracking error. Several variations of this basic technique is introduced; Back-propagation training algorithms for the NN controller have been developed. Simulation results have demonstrated the excellent tracking performance of the proposed control technique.

Keywords

Neural networkControlRobotsComputed torque.
Type
Articles
Information
Robotica , Volume 13 , Issue 5 , September 1995 , pp. 477 - 484
Copyright
Copyright © Cambridge University Press 1995

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