Reference : Hysteresis identification using nonlinear state-space models
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Aerospace & aeronautics engineering
http://hdl.handle.net/2268/193191
Hysteresis identification using nonlinear state-space models
English
Noël, Jean-Philippe mailto [Université de Liège > Département d'aérospatiale et mécanique > Laboratoire de structures et systèmes spatiaux >]
Esfahani, Alireza [Vrije Universiteit Brussel - VUB > > > >]
Kerschen, Gaëtan mailto [Université de Liège > Département d'aérospatiale et mécanique > Laboratoire de structures et systèmes spatiaux >]
Schoukens, Johan [Vrije Universiteit Brussel - VUB > > > >]
Jan-2016
Proceedings of the International Modal Analysis Conference (IMAC) XXXIV
No
No
International
International Modal Analysis Conference (IMAC) XXXIV
du 25 janvier au 28 janvier 2016
Society for Experimental Mechanics (SEM)
Orlando
FL
[en] Hysteresis ; dynamic nonlinearity ; nonlinear system identification ; black-box method ; 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.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers ; Professionals ; Students
http://hdl.handle.net/2268/193191
10.1007/978-3-319-29739-2_30

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