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dc.contributor.authorSauder, Thomas Michel
dc.contributor.authorMarelli, Stefano
dc.contributor.authorSørensen, Asgeir Johan
dc.date.accessioned2019-01-04T18:17:56Z
dc.date.available2019-01-04T18:17:56Z
dc.date.created2019-01-04T14:42:24Z
dc.date.issued2019-03
dc.identifier.citationAutomatica. 2019, 101 111-119.nb_NO
dc.identifier.issn0005-1098
dc.identifier.urihttp://hdl.handle.net/11250/2579304
dc.description.abstractCyber–physicalempirical methods consist in partitioning a dynamical system under study into a set of physical and numerical substructures that interact in real-time through a control system. In this paper, we define and investigate the fidelity of such methods, that is their capacity to generate systems whose outputs remain close to those of the original system under study. In practice, fidelity is jeopardized by uncertain and heterogeneous artefacts originating from the control system, such as actuator dynamics, time delays and measurement noise. We present a computationally efficient method, based on surrogate modelling and active learning techniques, to (1) verify that a cyber–physical empirical setup achieves probabilistic robust fidelity, and (2) to derive fidelity bounds, which translate to absolute requirements to the control system. For verification purposes, the method is first applied to the study of a simple mechanical system. Its efficiency is then demonstrated on a more complex problem, namely the active truncation of slender marine structures, in which the substructures’ dynamics cannot be described by an analytic solution.nb_NO
dc.description.sponsorshipThis work was supported by the Research Council of Norway through the Centres of Excellence funding scheme, Project number 223254 - AMOS, and through the project 254845/O80 “Real-Time Hybrid Model Testing for Extreme Marine Environments”. The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Angelo Alessandri under the direction of Editor Thomas Parisini.nb_NO
dc.language.isoengnb_NO
dc.publisherElsevier Ltdnb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectCyber–physical empirical methodnb_NO
dc.subjectFidelitynb_NO
dc.subjectArtefactsnb_NO
dc.subjectProbabilistic robustnessnb_NO
dc.subjectAdaptive krigingnb_NO
dc.titleProbabilistic robust design of control systems for high-fidelity cyber–physical testingnb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.rights.holder© 2018 Elsevier Ltd. All rights reserved.nb_NO
dc.source.pagenumber111-119nb_NO
dc.source.volume101nb_NO
dc.source.journalAutomaticanb_NO
dc.identifier.doi10.1016/j.automatica.2018.11.040
dc.identifier.cristin1650575
dc.relation.projectNorges forskningsråd: 223254nb_NO
dc.relation.projectNorges forskningsråd: 254845nb_NO
cristin.unitcode7566,9,0,0
cristin.unitnameSkip og havkonstruksjoner
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode2


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal