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Receding-Horizon Control of Constrained Switched Systems with Neural Networks as Parametric Function Approximators

dc.date.accessioned2023-01-04T10:42:56Z
dc.date.available2023-01-04T10:42:56Z
dc.date.issued2022-11-21
dc.identifierdoi:10.17170/kobra-202301047295
dc.identifier.urihttp://hdl.handle.net/123456789/14328
dc.description.sponsorshipGefördert im Rahmen des Projekts DEAL
dc.language.isoeng
dc.rightsNamensnennung 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectModel approximationeng
dc.subjectNeural networkseng
dc.subjectOptimizationeng
dc.subjectOnline controleng
dc.subjectReinforcement learningeng
dc.subjectStabilityeng
dc.subject.ddc600
dc.titleReceding-Horizon Control of Constrained Switched Systems with Neural Networks as Parametric Function Approximatorseng
dc.typeAufsatz
dcterms.abstractThis work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach.eng
dcterms.accessRightsopen access
dcterms.creatorMarkolf, Lukas
dcterms.creatorStursberg, Olaf
dcterms.extent16 Seiten
dcterms.extent16 Seiten
dc.relation.doidoi:10.1007/s42979-022-01442-0
dc.subject.swdModellprädiktive Regelungger
dc.subject.swdNeuronales Netzger
dc.subject.swdOptimierungger
dc.subject.swdApproximationger
dc.subject.swdModellger
dc.type.versionpublishedVersion
dcterms.source.identifiereissn:2661-8907
dcterms.source.issueissue 1
dcterms.source.journalSN Computer Scienceeng
dcterms.source.volumeVolume 4
kup.iskupfalse
dcterms.source.articlenumber62


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Namensnennung 4.0 International
Except where otherwise noted, this item's license is described as Namensnennung 4.0 International