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dc.contributor.authorKlemets, Jonatan Ralf Axel
dc.contributor.authorHovd, Morten
dc.date.accessioned2018-03-06T14:05:06Z
dc.date.available2018-03-06T14:05:06Z
dc.date.created2018-01-11T08:45:14Z
dc.date.issued2017
dc.identifier.isbn978-1-5090-1572-6
dc.identifier.urihttp://hdl.handle.net/11250/2488954
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. The loss can further be reduced by controlling measurement combinations to constant values. Two methods for finding appropriate measurement combinations are the Null-space and the Exact local method. Both approaches offer sets with an infinite number of solutions that give the same loss. Since self-optimizing control is mainly concerned with minimizing the steady-state loss, little attention has been put on the dynamic performance when selecting measurement combinations.nb_NO
dc.language.isoengnb_NO
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)nb_NO
dc.relation.ispartofProceedings of the 2017 Asian Control Conference
dc.titleAn Iterative LMI Approach to Controller Design and Measurement Selection in Self-Optimizing Controlnb_NO
dc.typeChapternb_NO
dc.description.versionacceptedVersionnb_NO
dc.identifier.doi10.1109/ASCC.2017.8287629
dc.identifier.cristin1540424
dc.description.localcode© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.nb_NO
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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