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Hysteresis Identification Using Nonlinear State-Space Models

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Nonlinear Dynamics, Volume 1

Abstract

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.

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Acknowledgements

This work was supported in part by the Fund for Scientific Research (FWO-Vlaanderen), by the Flemish Government (Methusalem), by the Belgian Government through the Inter university Poles of Attraction (IAP VII) Program, and by the ERC advanced grant SNLSID, under contract 320378.

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Authors and Affiliations

  1. ELEC Department, Vrije Universiteit Brussel, Brussels, Belgium

    J. P. Noël, A. F. Esfahani & J. Schoukens

  2. Aerospace and Mechanical Engineering Department, Space Structures and Systems Laboratory, University of Liège, Liège, Belgium

    G. Kerschen

Authors
  1. J. P. Noël
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  2. A. F. Esfahani
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  3. G. Kerschen
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  4. J. Schoukens
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Correspondence to J. P. Noël .

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Editors and Affiliations

  1. Liege, Belgium

    Gaetan Kerschen

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© 2016 The Society for Experimental Mechanics, Inc.

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Noël, J.P., Esfahani, A.F., Kerschen, G., Schoukens, J. (2016). Hysteresis Identification Using Nonlinear State-Space Models. In: Kerschen, G. (eds) Nonlinear Dynamics, Volume 1. Conference Proceedings of the Society for Experimental Mechanics Series. Springer, Cham. https://doi.org/10.1007/978-3-319-29739-2_30

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  • DOI: https://doi.org/10.1007/978-3-319-29739-2_30

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  • Online ISBN: 978-3-319-29739-2

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