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ROMANSY 11 pp 347-354 | Cite as

Using Backpropagation Algorithm for Neural Adaptive Control: Experimental Validation on an Industrial Mobile Robot

  • P. Henaff
  • S. Delaplace
Part of the International Centre for Mechanical Sciences book series (CISM, volume 381)

Abstract

This paper presents an original method in the use of neural networks and backpropagation algorithm to learn control of robotics systems. The originality consists to express the control objective as a criterion of which the gradient is backpropagating through the network instead of the classical quadratic error used in standard backpropagation. This technic allows on-line learning that is impossible to do with standard backpropagation. Experimental validation is realised by the position and the orientation control of a faster industrial mobile robot. Results show the feasability of the method, and particularly establish that on-line learning scheme permit to refine the weights of the network in front of the kinematics constraints of the robot.

Keywords

Control Objective Real Robot Neural Controller Wheel Velocity Human Teacher 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 1997

Authors and Affiliations

  • P. Henaff
    • 1
  • S. Delaplace
    • 2
  1. 1.University of Paris 6VélizyFrance
  2. 2.Versailles Saint-Quentin UniversityVélizyFrance

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