Friday , September 21 2018

Evolutionary Method for Designing and Learning Control Structure of a Wheelchair

Imen Ben Omrane
Institut National des Sciences Appliquees et de Technologie INSAT, Centre Urbain Nord BP
676, Tunis, 1080, TUNISIA

Abderrazak Chatti
Institut National des Sciences Appliquees et de Technologie INSAT, Centre Urbain Nord BP
676, Tunis, 1080, TUNISIA

Pierre Borne
Ecole Centrale de Lille ECLille, Cite Scientifique Villeneuve-d’Ascq
Lille, 59650, FRANCE

Abstract:

This article describes an aspect of evolutionary robotics for trajectory tracking. We will combine genetic algorithms with neural networks for modelling and controlling a wheelchair for disabled people. The interest of the hybridization of Neural Networks (NN) with Evolutionary Algorithms (EA) in robotics is based on the observation that a local search by a gradient descent method is replaced by a global search performed by EA. The gradient descent methods are subject to variations in performance due to the initial position of the NN, which sometimes leads to a convergence towards local minima. In contrast, the proposed evolutionary methods provide a global research of both the structure and the weights of the neural net. The control structure used for robot trajectory tracking control is based on the Internal Model Control (IMC) which direct neural model was learned with our new EA.

Keywords:

Evolutionary Robotics, trajectory tracking, evolutionary algorithms, Neural Networks, direct neural model, mobile robots, wheelchairs, Internal Model Control.

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CITE THIS PAPER AS:
Imen BEN OMRANE, Abderrazak CHATTI, Pierre BORNE, Evolutionary Method for Designing and Learning Control Structure of a Wheelchair, Studies in Informatics and Control, ISSN 1220-1766, vol. 21 (2), pp. 155-164, 2012.

https://doi.org/10.24846/v21i2y201205