Evolutionary method for designing and learning control structure of a wheelchair

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.