Hybridisation of neural networks and a genetic algorithm for friction compensation

Abstract

This paper presents the use of neural networks and a genetic algorithm within a model-based friction compensation scheme in a closed-loop robotic system. Unlike previous works which concentrate on using a single type of neural network to model friction function, three types of neural network, namely a multilayer perceptron, a radial-basis function network and a modular network, are used in the modelling task. The genetic algorithm is then used to find the optimal combination between the radial-basis function network and the multilayer perceptron and that between the radial-basis function network and the modular network. The simulation results indicate that the genetic algorithm has successfully found the combinations of the neural networks which results in a better compensation performance than using only one type of neural network. As a result of using both neural networks and a genetic algorithm in this application, an idea of a task hybridisation between neural networks and a genetic algorithm for use in a control system is also effectively demonstrated.