Using Backpropagation Algorithm for Neural Adaptive Control: Experimental Validation on an Industrial Mobile Robot - Archive ouverte HAL Access content directly
Book Sections Year : 1997

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

(1) , (1)
1
Henaff Patrick
Stéphane Delaplace
  • Function : Author
  • PersonId : 879478

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.
Fichier principal
Vignette du fichier
Romansy97.pdf (943.5 Ko) Télécharger le fichier
Origin : Explicit agreement for this submission

Dates and versions

hal-01843719 , version 1 (08-11-2022)

Identifiers

  • HAL Id : hal-01843719 , version 1

Cite

Henaff Patrick, Stéphane Delaplace. Using Backpropagation Algorithm for Neural Adaptive Control: Experimental Validation on an Industrial Mobile Robot. Morecki, A., Bianchi, G., Rzymkowski, C ROMANSY 11. International Centre for Mechanical Sciences Springer, Vienna. Part of the International Centre for Mechanical Sciences book series, 381 (347-354), 1997. ⟨hal-01843719⟩
7 View
0 Download

Share

Gmail Facebook Twitter LinkedIn More