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dc.contributor.authorJavalera Rincón, Valeria
dc.contributor.authorMorcego Seix, Bernardo
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2013-05-14T12:34:16Z
dc.date.available2013-05-14T12:34:16Z
dc.date.created2010
dc.date.issued2010
dc.identifier.citationJavalera, V.; Morcego, B.; Puig, V. Negotiation and learning in distributed MPC of large scale systems. A: American Control Conference. "Proceedings of the 2010 American Control Conference". Baltimore: IEEE Press. Institute of Electrical and Electronics Engineers, 2010, p. 3168-3173.
dc.identifier.isbn978-1-4244-7425-7
dc.identifier.urihttp://hdl.handle.net/2117/19206
dc.description.abstractA key issue in distributed MPC control of Large Scale Systems (LSS) is how shared variables among the different MPC controller in charge of controlling each system partition (subsystems) are handled. When these connections represent control variables, the distributed control has to be consistent for both subsystems and the optimal value of these variables will have to accomplish a common goal. In order to achieve this, the present work combines ideas from Distributed Artificial Intelligence (DAI), Reinforcement Learning (RL) and Model Predictive Control (MPC) in order to provide an approach based on negotiation, cooperation and learning techniques. Results of the application of this approach to a small drinking water network show that the resulting trajectories of the levels in tanks (control variables) can be acceptable compared to the centralized solution. The application to a real network (the Barcelona case) is currently under development.
dc.format.extent6 p.
dc.language.isoeng
dc.publisherIEEE Press. Institute of Electrical and Electronics Engineers
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses::Gestió i direcció
dc.subject.lcshProduction management
dc.subject.otherpredictive control PARAULES AUTOR:cooperative systems
dc.subject.otherdistributed control
dc.subject.othermodel predictive control
dc.subject.othermulti agent systems
dc.subject.othernegotiation
dc.subject.otherreinforcement learning
dc.titleNegotiation and learning in distributed MPC of large scale systems
dc.typeConference report
dc.subject.lemacProducció -- Direcció i administració
dc.contributor.groupUniversitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Control theory::Predictive control
dc.relation.publisherversionhttp://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05530986
dc.rights.accessOpen Access
local.identifier.drac4408179
dc.description.versionPostprint (author’s final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/224168/EU/Decentralized and Wireless Control of Large-Scale Systems/WIDE
local.citation.authorJavalera, V.; Morcego, B.; Puig, V.
local.citation.contributorAmerican Control Conference
local.citation.pubplaceBaltimore
local.citation.publicationNameProceedings of the 2010 American Control Conference
local.citation.startingPage3168
local.citation.endingPage3173


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