The University of Southampton
University of Southampton Institutional Repository

Modelling and Control of Nonlinear, Operating Point dependent Systems via Associative Memory Networks

Record type: Article

This paper presents a novel approach to the modelling and control of a specific class of nonlinear systems whose parameters are unknown nonlinear functions of the measurable operating points. An associative memory network is used to identify each nonlinear function, whose inputs are the measurable operating points and output being the estimated value of the parameter. Two different cases are considered; the first being those systems where the networks can exactly model the nonlinear functions, whereas the second case considers those systems which can only approximate the nonlinear functions to a known accuracy. The first type of system is referred to as a matching system and the second is called a mismatching system. During the modelling phase, the weights for each network are trained in parallel using the normalised back-propagation algorithm for matching systems, and the modified recursive least squares algorithm for mismatching systems. It has been shown that these algorithms, together with Goodwin's technical lemma lead to a stable d-step-ahead control scheme for matching systems and a pole assignment control strategy for mismatching systems.

Full text not available from this repository.

Citation

Wang, H., Brown, M. and Harris, C.J. (1996) Modelling and Control of Nonlinear, Operating Point dependent Systems via Associative Memory Networks J. Dynamics and Control, 6, (2), pp. 199-218.

More information

Published date: April 1996
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 250220
URI: http://eprints.soton.ac.uk/id/eprint/250220
PURE UUID: 7923a2ce-4670-4635-8384-dfe8a77a951f

Catalogue record

Date deposited: 04 May 1999
Last modified: 18 Jul 2017 10:43

Export record

Contributors

Author: H. Wang
Author: M. Brown
Author: C.J. Harris

University divisions


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×