Title: Constructing an overall dynamical model for a system with changing design parameter properties

Authors: Hua-Liang Wei, Zi-Qiang Lang, Stephen A. Billings

Addresses: Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK. ' Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK. ' Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield S1 3JD, UK

Abstract: This study considers the identification problem for a class of non-linear parameter-varying systems associated with the following scenario: the system behaviour depends on some specifically prescribed parameter properties, which are adjustable. To understand the effect of the varying parameters, several different experiments, corresponding to different parameter properties, are carried out and different data sets are collected. The objective is to find, from the available data sets, a common parameter-dependent model structure that best fits the adjustable parameter properties for the underlying system. An efficient Common Model Structure Selection (CMSS) algorithm, called the Extended Forward Orthogonal Regression (EFOR) algorithm, is proposed to select such a common model structure. Two examples are presented to illustrate the application and the effectiveness of the new identification approach.

Keywords: forward orthogonal regression; nonlinear system identification; parameter-dependent models; CMSS; common model structure selection; overall model; dynamic modelling.

DOI: 10.1504/IJMIC.2008.022014

International Journal of Modelling, Identification and Control, 2008 Vol.5 No.2, pp.93 - 104

Published online: 16 Dec 2008 *

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