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Nonlinear system identification with an integrable continuous time nonlinear ARX model

Abstract : Beside the wide success of discrete time models in system identification, some advantages of continuous time models have also been acknowledged, in particular, the ability of fully benefiting from fast sampling devices. This paper proposes a continuous time black-box model structure for nonlinear system identification, together with an efficient model estimation method. This model structure belongs to the class of continuous time nonlinear ARX (AutoRegressive with eXogenous input) models, with the particularity of being integrable. By applying techniques of adaptive observer, models of the proposed structure can be efficiently estimated from input-output data, without requiring computing the time derivatives of the data. The proposed model structure and estimation method are illustrated through the identification of a magneto-rheological fluid damper system.
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https://hal.inria.fr/hal-00776949
Contributor : Qinghua Zhang <>
Submitted on : Wednesday, January 16, 2013 - 3:14:51 PM
Last modification on : Friday, May 25, 2018 - 12:02:05 PM

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Qinghua Zhang. Nonlinear system identification with an integrable continuous time nonlinear ARX model. Journal Européen des Systèmes Automatisés (JESA), Lavoisier, 2012, 46 (6-7), pp.691-710. ⟨10.3166/jesa.46.691-710⟩. ⟨hal-00776949⟩

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