Rejection of parametric model errors in a recursive Kalman filter for virtual sensing applications - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2024

Rejection of parametric model errors in a recursive Kalman filter for virtual sensing applications

Résumé

The performance of the Kalman filter is often hindered by the discrepancies between a model used to realize the filter and the true model of the data-generating system. While some methods to account for those errors exists, the majority is restricted to Luenberger's observers. The objective of this work is to develop a Kalman filter where the effect of parametric model errors is rejected from the state and observation equations before the development of the state estimate, and without the explicit knowledge of the error terms. For this purpose, errors in a physical parameter of a parametrized mechanical system are modelled as additive terms in the state and observation equations by means of first-order perturbation analysis. The rejection step is achieved with an injection of a shaped output nullifying the effect of the added terms. A state observer is then derived under the assumption that the injected shaped outputs and the system states are uncorrelated. The performance of the proposed approach is illustrated on an experimental data of a plate system.
Fichier principal
Vignette du fichier
EWSHM24__error_rejection-1.pdf (700.58 Ko) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04639385 , version 1 (09-07-2024)

Licence

Identifiants

Citer

Szymon Gres, Konstantinos E Tatsis, Michael Döhler, Vasilis Dertimanis, Eleni Chatzi. Rejection of parametric model errors in a recursive Kalman filter for virtual sensing applications. EWSHM 2024 - 11th European Workshop on Structural Health Monitoring, Jun 2024, Potsdam, Germany. pp.1-8, ⟨10.58286/29694⟩. ⟨hal-04639385⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More