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Design of a neural internal model control system for a robot

Published online by Cambridge University Press:  18 April 2001

D. T. Pham  and
Şahin Yildirim
D. T. Pham
Affiliation:
Intelligent Systems Research Laboratory, Systems Engineering Division, School of Engineering, University of Wales Cardiff, Newport Road, P.O. Box 688, Cardiff CF24 3TE (UK)
Şahin Yildirim
Affiliation:
Intelligent Systems Research Laboratory, Systems Engineering Division, School of Engineering, University of Wales Cardiff, Newport Road, P.O. Box 688, Cardiff CF24 3TE (UK)
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Abstract

This paper describes the design of an Internal Model Control (IMC) system for a planar two-degree-of-freedom robot. IMC was investigated as an alternative to the basic inverse control scheme which is difficult to implement. The proposed IMC system consisted of a forward internal neural model of the robot, a neural controller and a conventional feedback controller, all of which were realised easily. Both the neural model and the neural controller were based on recurrent networks which were trained using the backpropagation (BP) algorithm. The paper presents the results obtained with two types of recurrent networks as well as a conventional PID system.

Keywords

Internal model controlRecurrent neural networkDiagonal recurrent networkBackpropagationRobot control

Information

Type
Research Article
Information
Robotica , Volume 18 , Issue 5 , September 2000 , pp. 505 - 512
Copyright
© 2000 Cambridge University Press

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