Linear fractional LPV model identification from local experiments: an $H_{\infty}$-based optimization technique

Abstract : In this paper, a new identification technique is introduced to estimate a linear fractional representation of a linear parameter-varying (LPV) system from local experiments by using a dedicated non-smooth optimization procedure. More precisely, the developed approach consists in estimating the parameters of an LPV state-space model from local fully-parameterized identified state-space models through the non-smooth optimization of a specific H∞-based criterion. The method presented in this paper results directly in an LPV model whose parametric matrices can be rational functions of the scheduling variables without any interpolation step (required usually by the local approach) and without writing the local fully-parameterized LTI state-space models with respect to a coherent basis. A numerical example is used to illustrate the performance of the suggested technique.
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https://hal-unilim.archives-ouvertes.fr/hal-00940057
Contributeur : Olivier Prot <>
Soumis le : vendredi 31 janvier 2014 - 11:50:34
Dernière modification le : mardi 13 août 2019 - 11:10:03

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Daniel Vizer, Guillaume Mercère, Olivier Prot, Edouard Laroche, Marco Lovera. Linear fractional LPV model identification from local experiments: an $H_{\infty}$-based optimization technique. IEEE Conference on Decision and Control, Dec 2013, Florence, Italy. pp.4559-4564, ⟨10.1109/CDC.2013.6760594⟩. ⟨hal-00940057⟩

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