Neural networks for modelling and control of a non-linear dynamic system

Murray-Smith, R., Neumerkel, D. and Sbarbaro-Hofer, D. (1992) Neural networks for modelling and control of a non-linear dynamic system. In: IEEE International Symposium on Intelligent Control, Glasgow, Scotland, 11-13 August 1992, pp. 404-409. ISBN 0780305469 (doi: 10.1109/ISIC.1992.225125)

Abstract

The authors describe the use of neural nets to model and control a nonlinear second-order electromechanical model of a drive system with varying time constants and saturation effects. A model predictive control structure is used. This is compared with a proportional-integral (PI) controller with regard to performance and robustness against disturbances. Two feedforward network types, the multilayer perceptron and radial-basis-function nets, are used to model the system. The problems involved in the transfer of connectionist theory to practice are discussed.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Murray-Smith, Professor Roderick
• Authors: Murray-Smith, R., Neumerkel, D., and Sbarbaro-Hofer, D.
• Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• ISBN: 0780305469
• Copyright Holders: Copyright © 1992 Institute of Electrical and Electronics Engineers (IEEE)
• First Published: First published in Proceedings of the 1992 IEEE International Symposium on Intelligent Control
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher