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Evolutionary-games approach for distributed predictive control involving resource allocation

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dc.creatorBarreiro-Gomez J.spa
dc.creatorObando Bravo, Germán Dariospa
dc.creatorOcampo-Martinez C.spa
dc.creatorQuijano N.spa
dc.date.accessioned2020-05-26T00:10:44Z
dc.date.available2020-05-26T00:10:44Z
dc.date.created2019spa
dc.description.abstractThis study proposes a distributed model predictive control (DMPC) scheme based on population games for a system formed by a set of sub-systems. In addition to considering independent operational constraints for each sub-system, the controller addresses a coupled constraint that involves the sum of all control inputs. This constraint models an upper bound on the total amount of energy supplied to the plant. The proposed approach does not need a centralised coordinator when having a coupled constraint involving all the decision variables. The proposed methodology, which takes advantage of evolutionary game theory concepts, provides an optimal solution for the described problem. Moreover, it is shown that the methodology has plug- and-play features, i.e. for each already designed local MPC controller nothing changes when more sub-systems are added/ removed to/from the global constrained control problem. Furthermore, the stability analysis of the proposed DMPC scheme is presented. © The Institution of Engineering and Technology 2019eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1049/iet-cta.2018.5716
dc.identifier.issn17518644
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/24254
dc.language.isoengspa
dc.publisherInstitution of Engineering and Technologyspa
dc.relation.citationEndPage782
dc.relation.citationIssueNo. 6
dc.relation.citationStartPage772
dc.relation.citationTitleIET Control Theory and Applications
dc.relation.citationVolumeVol. 13
dc.relation.ispartofIET Control Theory and Applications, ISSN:17518644, Vol.13, No.6 (2019); pp. 772-782spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85064574798&doi=10.1049%2fiet-cta.2018.5716&partnerID=40&md5=46e02c2ee357991ada97828e6ff538b9spa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordGame theoryspa
dc.subject.keywordModel predictive controlspa
dc.subject.keywordPredictive control systemsspa
dc.subject.keywordConstrained controlsspa
dc.subject.keywordCoupled constraintsspa
dc.subject.keywordDecision variablesspa
dc.subject.keywordDistributed Model predictive Controlspa
dc.subject.keywordDistributed predictive controlspa
dc.subject.keywordEvolutionary game theoryspa
dc.subject.keywordOperational constraintsspa
dc.subject.keywordStability analysisspa
dc.subject.keywordControllersspa
dc.titleEvolutionary-games approach for distributed predictive control involving resource allocationspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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