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Trajectory control of robotic manipulators by using a feedback-error-learning neural network

Published online by Cambridge University Press:  09 March 2009

Zaryab Hamavand  and
Howard M. Schwartz
Zaryab Hamavand
Affiliation:
Department of Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, (Canada) KIS5B6
Howard M. Schwartz
Affiliation:
Department of Systems and Computer Engineering, Carleton University, 1125 Colonel By Drive, Ottawa, Ontario, (Canada) KIS5B6
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Summary

This paper presents a neural network based control strategy for the trajectory control of robot manipulators. The neural network learns the inverse dynamics of a robot manipulator without any a priori knowledge of the manipulator inertial parameters nor any a priori knowledge of the equation of dynamics. A two step feedback-error-learning process is proposed. Strategies for selection of the training trajectories and difficulties with on-line training are discussed.

Keywords

Neural networksTrajectory trackingRobot control
Type
Articles
Information
Robotica , Volume 13 , Issue 5 , September 1995 , pp. 449 - 459
Copyright
Copyright © Cambridge University Press 1995

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References

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