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dc.contributor.authorAbdollahpouri, Mohammad
dc.contributor.authorHaring, Mark
dc.contributor.authorJohansen, Tor Arne
dc.contributor.authortakacs, gergely
dc.contributor.authorRohal-Ilkiv, B
dc.date.accessioned2017-12-13T12:13:50Z
dc.date.available2017-12-13T12:13:50Z
dc.date.created2017-12-09T14:49:17Z
dc.date.issued2017
dc.identifier.issn2405-8963
dc.identifier.urihttp://hdl.handle.net/11250/2471152
dc.description.abstractDealing with nonlinear dynamics in conventional estimation methods like the extended Kalman filter (EKF) is challenging, since they are not guaranteed to have global convergence, and their instability can arise by selecting a poor initial guess. Recently, a double Kalman filter (DKF) has been proposed, where two stages of estimation are considered using cascade stability theory in the continuous time domain. The first stage guarantees global convergence through the use of a globally valid linear time-varying model transformation, but leads to sub-optimal accuracy in the presence of noise. The global model transformation is applicable to a class of nonlinear systems, where its state can be explicitly derived through a mapping of previous measurements and disturbances. Furthermore, the second stage compensates the lost performance using the estimate from the first stage via local linearization. Here, we derive the stability analysis of this globally convergent method in discrete time using a Lyapunov approach. Different Kalman filters are compared via simulation to validate the benefit of using DKF for nonlinear state and parameter estimation.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.titleNonlinear State and Parameter Estimation using Discrete-Time Double Kalman Filternb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.journalIFAC-PapersOnLinenb_NO
dc.identifier.doi10.1016/j.ifacol.2017.08.1661
dc.identifier.cristin1525172
dc.relation.projectNorges forskningsråd: 250725nb_NO
dc.relation.projectNorges forskningsråd: 223254nb_NO
dc.relation.projectEC/FP7/607957nb_NO
dc.description.localcode© 2017. This is the authors’ accepted and refereed manuscript to the article. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/nb_NO
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal