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dc.contributor.authorKlemets, Jonatan Ralf Axel
dc.contributor.authorHovd, Morten
dc.date.accessioned2019-01-05T13:09:46Z
dc.date.available2019-01-05T13:09:46Z
dc.date.created2019-01-03T16:52:39Z
dc.date.issued2018
dc.identifier.citationIFAC-PapersOnLine. 2018, 51 (18), 458-463.nb_NO
dc.identifier.issn2405-8963
dc.identifier.urihttp://hdl.handle.net/11250/2579332
dc.description.abstractSelf-optimizing control focuses on minimizing loss for processes in the presence of disturbances by holding selected controlled variables at constant set-points. A measurement combination can be found, using the Null-space method, which further reduces the loss. Since self-optimizing control focuses on the steady-state operation, little attention has been put on the dynamic performance when selecting measurement combinations. In this work, an iterative LMI approach is combined with the sparsity promoting weighted l1-norm, to find a measurement subset together with PI controllers for the Null-space method. The measurement combination and the controllers are designed such that, the dynamic response is improved when the process is facing disturbances. The proposed method is illustrated on a Petlyuk column case study.nb_NO
dc.language.isoengnb_NO
dc.publisherElseviernb_NO
dc.titleController Design and Sparse Measurement Selection in Self-optimizing controlnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber458-463nb_NO
dc.source.volume51nb_NO
dc.source.journalIFAC-PapersOnLinenb_NO
dc.source.issue18nb_NO
dc.identifier.doihttps://doi.org/10.1016/j.ifacol.2018.09.382
dc.identifier.cristin1649924
dc.description.localcode© 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.nb_NO
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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