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dc.contributor.authorBradford, Eric
dc.contributor.authorImsland, Lars Struen
dc.contributor.authorZhang, Dongda
dc.contributor.authorChanona del Rio, Ehecatl Antonio
dc.date.accessioned2020-06-02T07:40:45Z
dc.date.available2020-06-02T07:40:45Z
dc.date.created2020-05-23T14:52:44Z
dc.date.issued2020
dc.identifier.issn0098-1354
dc.identifier.urihttps://hdl.handle.net/11250/2656081
dc.description.abstractNonlinear model predictive control (NMPC) is one of the few control methods that can handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) present a powerful tool to identify the required plant model and quantify the residual uncertainty of the plant-model mismatch. It is crucial to consider this uncertainty, since it may lead to worse control performance and constraint violations. In this paper we propose a new method to design a GP-based NMPC algorithm for finite horizon control problems. The method generates Monte Carlo samples of the GP offline for constraint tightening using back-offs. The tightened constraints then guarantee the satisfaction of chance constraints online. Advantages of our proposed approach over existing methods include fast online evaluation, consideration of closed-loop behaviour, and the possibility to alleviate conservativeness by considering both online learning and state dependency of the uncertainty. The algorithm is verified on a challenging semi-batch bioprocess case study.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.urihttps://doi.org/10.1016/j.compchemeng.2020.106844
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleStochastic data-driven model predictive control using gaussian processesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.volume139en_US
dc.source.journalComputers and Chemical Engineeringen_US
dc.identifier.doi10.1016/j.compchemeng.2020.106844
dc.identifier.cristin1812238
dc.relation.projectEC/H2020/675215en_US
dc.description.localcodeDOI:10.1016/j.compchemeng.2020.106844. j.compchemeng.2020.106844 0098-1354/© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode2


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