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Ressources Électroniques
Non asymptotic estimation methods (2020)
Non asymptotic estimation methods : a focus on the volterra and modulating functions approaches [document électronique] / Rania Tafat, Auteur ; Messaoud Chakir, Directeur de thèse . - [S.l.] : [s.n.], 2020 . - 1 fichier PDF (6.3Mo) : ill.
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Automatique : Alger, École Nationale Polytechnique : 2020
Bibliogr. f. 101 - 108
Langues : Anglais (eng)
Mots-clés : Non-asymptotic estimators
Volterra observers
Modulating functions based method.Index. décimale : PA01620 Résumé :
In this work, we present two non-asymptotic integration transform based estimation methods: the Volterra and modulating functions approaches. We explain the design and reproduce both of the robust Volterra observer of a biased sinusoidal signal and Volterra differentiator. We contribute to the Volterra differentiator by constructing a novel bivariate kernel functions family in order to extend the approach to the noisy scenario and obtain promising results. We also propose a novel type of pseudo-modulating functions that are randomized, relax the differentiability condition and test them on a simple ODE parameter estimation in both noise-free and noisy cases where we obtain a maximum error of 5%. At last, we use the modulating functions based method to estimate the arterial blood flow and Windkessel 2-Element parameter first with analytically generated blood pressure and then using a database and conclude by underlying the data-sensitivity of the method.
