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

Daniel Vizer; Guillaume Mercère; Olivier Prot; Edouard Laroche; Marco Lovera

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

Daniel Vizer ¹ Guillaume Mercère ¹ Olivier Prot ² Edouard Laroche ³ Marco Lovera ⁴

1 LIAS - Laboratoire d'Informatique et d'Automatique pour les Systèmes

2 XLIM-DMI - DMI

XLIM - XLIM

3 LSIIT - Laboratoire des Sciences de l'Image, de l'Informatique et de la Télédétection

4 Dipartimento di Elettronica e Informazione

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.

Type de document :

Communication dans un congrès

Domaine :

Mathématiques [math] / Optimisation et contrôle [math.OC]

Liste complète des métadonnées

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

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

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

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