An application of particle filter for FDI oriented change detection and bounded parameter estimation

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dc.contributor.authorCofré Torres Patricio Esteban
dc.contributor.authorCipriano, Aldo
dc.date.accessioned2022-05-13T19:15:14Z
dc.date.available2022-05-13T19:15:14Z
dc.date.issued2007
dc.description.abstractIn their original formulations, state estimation schemes such as Kalman Filter, do not allow the incorporation of prior information on their physical bounds. This results in a certain probability of generating estimates that are physically impossible. Also, the Gaussian assumption in conventional schemes produces a trade-off between estimation error and estimation speed. This paper presents a solution based on a particle filter for which a bounded a priori parameter distribution is chosen. It is shown that a Beta distribution with hard bounds and adaptive estimation variance can overcome both drawbacks, thus achieving a lower fault detection time delay without increasing the estimation error, compared with the Extended Kalman Filter.
dc.fuente.origenIEEE
dc.identifier.doi10.23919/ECC.2007.7068891
dc.identifier.isbn978-3952417386
dc.identifier.urihttps://doi.org/10.23919/ECC.2007.7068891
dc.identifier.urihttps://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7068891
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/63857
dc.information.autorucEscuela de ingeniería ; Cofré Torres Patricio Esteban ; S/I ; 16193
dc.information.autorucEscuela de ingeniería ; Cipriano Zamorano, Aldo ; S/I ; 99102
dc.language.isoen
dc.nota.accesoContenido parcial
dc.publisherIEEE
dc.relation.ispartofEuropean Control Conference (2007 : Kos, Grecia)
dc.rightsacceso restringido
dc.subjectParticle filters
dc.subjectParameter estimation
dc.subjectEstimation error
dc.subjectState estimation
dc.subjectKalman filters
dc.subjectFault detection
dc.titleAn application of particle filter for FDI oriented change detection and bounded parameter estimationes_ES
dc.typecomunicación de congreso
sipa.codpersvinculados16193
sipa.codpersvinculados99102
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