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Further investigations on friction compensation using a neuro-genetic based hybrid framework
Author(s)
Chaiyaratana, N.
Boonlong, K.
Date Issued
December 1, 2001
Type
Conference Paper
Abstract
This paper presents further investigations into the use of a neuro-genetic based hybrid framework within a model-based friction compensation scheme in a closed-loop robotic system. The hybrid framework is composed of a number of neural network modules and a genetic algorithm module. The neural networks are used to perform a function approximation task while the role of the genetic algorithm is to search for an optimal combination between different neural structures during the generalisation process. In the previous work, the genetic algorithm has successfully located an optimal combination between radial-basis function networks and multilayer perceptrons and that between radial-basis function networks and modular networks. The extension presented in this paper covers the modification on the genetic algorithm to include two additional genetic operators: fitness scaling and diversity control operators. In addition, the search for an optimal combination between different neural structures is also extended to the case of the combination between radial-basis function networks, multilayer perceptrons and modular networks. The simulation results indicate that the friction compensation performance is further improved after the genetic algorithm and the search space has been modified. This helps to reveal the full potential of the hybrid framework in the friction compensation task.
Citation
Annual Conference of the North American Fuzzy Information Processing Society NAFIPS, 5, 2772-2777, 2001