Hysteresis identification using nonlinear state-space models

[en] Most studies tackling hysteresis identification in the technical literature follow white-box approaches, i.e. they rely on the assumption that measured data obey a specific hysteretic model. Such an assumption may be a hard requirement to handle in real applications, since hysteresis is a highly individualistic nonlinear behaviour. The present paper adopts a black-box approach based on nonlinear state-space models to identify hysteresis dynamics. This approach is shown to provide a general framework to hysteresis identification, featuring flexibility and parsimony of representation. Nonlinear model terms are constructed as a multivariate polynomial in the state variables, and parameter estimation is performed by minimising weighted least-squares cost functions. Technical issues, including the selection of the model order and the polynomial degree, are discussed, and model validation is achieved in both broadband and sine conditions. The study is carried out numerically by exploiting synthetic data generated via the Bouc-Wen equations.

Disciplines :

Aerospace & aeronautics engineering

Author, co-author :

Noël, Jean-Philippe ; Université de Liège > Département d'aérospatiale et mécanique > Laboratoire de structures et systèmes spatiaux

Esfahani, Alireza; Vrije Universiteit Brussel - VUB

Kerschen, Gaëtan ; Université de Liège > Département d'aérospatiale et mécanique > Laboratoire de structures et systèmes spatiaux

Schoukens, Johan; Vrije Universiteit Brussel - VUB

Language :

English

Title :

Hysteresis identification using nonlinear state-space models

Publication date :

January 2016

Event name :

International Modal Analysis Conference (IMAC) XXXIV

Event organizer :

Society for Experimental Mechanics (SEM)

Event place :

Orlando, United States - Florida

Event date :

du 25 janvier au 28 janvier 2016

Audience :

International

Main work title :

Proceedings of the International Modal Analysis Conference (IMAC) XXXIV

Funders :

F.R.S.-FNRS - Fonds de la Recherche Scientifique [BE]

Available on ORBi :

since 11 February 2016

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