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A stable adaptive force/position controller for a C5 parallel robot: a neural network approach

Published online by Cambridge University Press:  17 January 2012

B. Achili ,
B. Daachi ,
Y. Amirat ,
A. Ali-Cherif  and
M. E. Daâchi
B. Achili*
Affiliation:
Computer Science Lab., LIASD-University of Paris 8, 2, rue de la liberté, 93526 Saint Denis Cedex, France
B. Daachi
Affiliation:
Images, Signals and Intelligent Systems Lab. (LISSI), University of Paris East Créteil 12, 120-122, rue Paul Armangot, 94400 Vitry/Seine, France
Y. Amirat
Affiliation:
Images, Signals and Intelligent Systems Lab. (LISSI), University of Paris East Créteil 12, 120-122, rue Paul Armangot, 94400 Vitry/Seine, France
A. Ali-Cherif
Affiliation:
Computer Science Lab., LIASD-University of Paris 8, 2, rue de la liberté, 93526 Saint Denis Cedex, France
M. E. Daâchi
Affiliation:
Department of Electronics, University of Sétif, Algeria
*
*Corresponding author. E-mail: [email protected]
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Summary

This paper presents an adaptive force/position controller for a parallel robot executing constrained motions. This controller, based on an MLPNN (or Multi-Layer Perceptron Neural Network), does not require the inverse dynamic model of the robot to derive the control law. A neural identification of the dynamic model of the robot is proposed to determine the principal components of the MLPNN input vector. The latter is used to compensate the dynamic effects arising from the robot–environment interaction and its parameters are adjusted according to an adaptation law based on the Lyapunov-analysis methodology. The proposed controller is evaluated experimentally on the C5 parallel robot. This method is capable of tracking accurately the force/position trajectories and its stability robustness is proved.

Keywords

Adaptive controlNeural networkStability analysisParallel robot
Type
Articles
Information
Robotica , Volume 30 , Issue 7 , December 2012 , pp. 1177 - 1187
Copyright
Copyright © Cambridge University Press 2012

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