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dc.contributor.authorJanatianghadikolaei, Nima
dc.contributor.authorSharma, Roshan
dc.date.accessioned2024-04-17T10:57:42Z
dc.date.available2024-04-17T10:57:42Z
dc.date.created2023-06-16T13:54:53Z
dc.date.issued2023
dc.identifier.citationJanatian, N., & Sharma, R. (2023). A robust model predictive control with constraint modification for gas lift allocation optimization. Journal of Process Control, 128, Artikkel 102996.en_US
dc.identifier.issn0959-1524
dc.identifier.urihttps://hdl.handle.net/11250/3126995
dc.description.abstractThis paper presents a novel approach for implementing a robust real-time optimization framework under the presence of parametric uncertainty. Conservativeness is an inevitable drawback of a robust control approach. Therefore we aimed to provide a simple and efficient method to mitigate the conservativeness while the robust fulfillment of the constraints is still preserved. The proposed method in this paper is based on the worst-case realization of the uncertainties, however, with constraint modification. The mismatch between measured and predicted output is used directly to modify the active constraint in the optimization problem. The superiority of the method in terms of conservativeness and computational time has been demonstrated in comparison with the other robust optimization counterparts, such as traditional min–max and multi-stage MPC. The promising advantage of the proposed method is that not only it reduces the conservativeness significantly, but also the computational price for this achievement is considerably cheaper than closed-loop optimization methods such as multi-stage MPC.en_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA robust model predictive control with constraint modification for gas lift allocation optimizationen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2023 The Author(s).en_US
dc.source.volume128en_US
dc.source.journalJournal of Process Controlen_US
dc.identifier.doihttps://doi.org/10.1016/j.jprocont.2023.102996
dc.identifier.cristin2155300
dc.relation.projectNorges forskningsråd: 308817en_US
dc.source.articlenumber102996en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal