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dc.contributor.authorKrishnamoorthy, Dinesh
dc.contributor.authorFoss, Bjarne Anton
dc.contributor.authorSkogestad, Sigurd
dc.date.accessioned2019-03-28T12:02:06Z
dc.date.available2019-03-28T12:02:06Z
dc.date.created2018-10-24T19:49:57Z
dc.date.issued2018
dc.identifier.citationIFAC-PapersOnLine. 2018, 51 (18), 351-356.nb_NO
dc.identifier.issn2405-8963
dc.identifier.urihttp://hdl.handle.net/11250/2592195
dc.description.abstractIn this paper, we consider the decomposition of scenario-based model predictive control problem. Scenario MPC explicitly considers the concept of recourse by representing the evolution of uncertainty by a discrete scenario tree, which can result in large optimization problems. Due to the inherent nature of the scenario tree, the problem can be decomposed into each scenario. The different subproblems are only coupled via the non-anticipativity constraints which ensures that the first control input is the same for all the scenarios. This constraint is relaxed in the dual decomposition approaches, which may lead to infeasibility of the non-anticipativity constraints if the master problem does not converge within the required time. In this paper, we present an alternative approach using primal decomposition which ensures feasibility of the non-anticipativity constraints throughout the iterations. The proposed method is demonstrated using gas-lift optimization as case study.nb_NO
dc.language.isoengnb_NO
dc.publisherInternational Federation of Automatic Control (IFAC)nb_NO
dc.titleA Distributed Algorithm for Scenario-based Model Predictive Control using Primal Decomposition *nb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.pagenumber351-356nb_NO
dc.source.volume51nb_NO
dc.source.journalIFAC-PapersOnLinenb_NO
dc.source.issue18nb_NO
dc.identifier.doi10.1016/j.ifacol.2018.09.325
dc.identifier.cristin1623271
dc.relation.projectNorges forskningsråd: 237893nb_NO
dc.description.localcode© 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd.nb_NO
cristin.unitcode194,66,30,0
cristin.unitcode194,63,25,0
cristin.unitnameInstitutt for kjemisk prosessteknologi
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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