DocumentCode
184140
Title
Convex identification of models for asymmetric hysteresis
Author
Hedegard, Marcus ; Wik, Torsten
Author_Institution
Dept. of Signals & Syst., Chalmers Univ. of Technol., Goteborg, Sweden
fYear
2014
fDate
4-6 June 2014
Firstpage
4753
Lastpage
4758
Abstract
The generalized Prandtl-Ishlinskii model (GPI) of hysteresis has a wide applicability, partly because of its capability of modelling highly asymmetric hysteresis. A disadvantage of the GPI models compared to, for example the Modified Prandtl-Ishlinskii models, has been that they have had to be identified using parametric non convex methods. Recently though, a method for non-parametric convex identification for an extended and more general GPI model was described, giving all model functions. Here, the method, which was based on input discrete equations, is briefly presented in terms of the corresponding input continuous equations. This extended model corresponds to a Preisach model and an explicit expression for this relation is derived. The method is directly applicable to data consisting of first order reversal curves, but in an application to an electrical substation equipment, it is shown that other kinds of data can also be used. The method gives significantly closer fit to this data than previous studies, and it demonstrates that non-equal left and right envelope functions are optimal.
Keywords
concave programming; convex programming; identification; modelling; GPI model; Preisach model; electrical substation equipment; first order reversal curve; generalized Prandtl-Ishlinskii model; highly asymmetric hysteresis; input continuous equation; input discrete equation; model function; modified Prandtl-Ishlinskii model; nonparametric convex identification; parametric nonconvex method; Data models; Equations; Hysteresis; Mathematical model; Relays; Substations; Turning; Identification; Modeling and simulation; Nonlinear systems;
fLanguage
English
Publisher
ieee
Conference_Titel
American Control Conference (ACC), 2014
Conference_Location
Portland, OR
ISSN
0743-1619
Print_ISBN
978-1-4799-3272-6
Type
conf
DOI
10.1109/ACC.2014.6858953
Filename
6858953
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