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dc.contributor.authorAbdollahpouri, Mohammad
dc.contributor.authorQuirynen, Rien
dc.contributor.authorHaring, Mark
dc.contributor.authorJohansen, Tor Arne
dc.contributor.authorTakács, Gergely
dc.contributor.authorDiehl, Moritz
dc.contributor.authorRohal'-Ilkiv, Boris
dc.date.accessioned2017-12-18T07:52:44Z
dc.date.available2017-12-18T07:52:44Z
dc.date.created2017-12-11T12:20:17Z
dc.date.issued2017
dc.identifier.issn0020-7179
dc.identifier.urihttp://hdl.handle.net/11250/2472271
dc.description.abstractMoving horizon estimation (MHE) solves a constrained dynamic optimisation problem. Including nonlinear dynamics into an optimal estimation problem generally comes at the cost of tackling a non-convex optimisation problem. Here, a particular model formulation is proposed in order to convexify a class of nonlinear MHE problems. It delivers a linear time-varying (LTV) model that is globally equivalent to the nonlinear dynamics in a noise-free environment, hence the optimisation problem becomes convex. On the other hand, in the presence of unknown disturbances, the accuracy of the LTV model degrades and this results in a less accurate solution. For this purpose, some assumptions are imposed and a homotopy-based approach is proposed in order to transform the problem from convex to non-convex, where the sequential implementation of this technique starts with solving the convexified MHE problem. Two simulation studies validate the efficiency and optimality of the proposed approach with unknown disturbances.nb_NO
dc.language.isoengnb_NO
dc.publisherTaylor & Francisnb_NO
dc.relation.urihttps://doi.org/10.1080/00207179.2017.1406150
dc.titleA homotopy-based moving horizon estimationnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.journalInternational Journal of Controlnb_NO
dc.identifier.doi10.1080/00207179.2017.1406150
dc.identifier.cristin1525599
dc.relation.projectDet europeiske økonomiske samarbeidsområde (EØS): SK06-II- 01-004nb_NO
dc.relation.projectEC/FP7/607957nb_NO
dc.relation.projectEU/259166nb_NO
dc.relation.projectNorges forskningsråd: 250275nb_NO
dc.relation.projectNorges forskningsråd: 223254nb_NO
dc.relation.projectEU/642682nb_NO
dc.description.localcodeThis is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Control on 08 Dec 2017, available online:http://www.tandfonline.com/doi/full/10.1080/00207179.2017.1406150 . Locked until 08 December 2018 due to copyright restrictionsnb_NO
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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