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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.accessioned2010-09-30T12:36:25Z
dc.date.available2010-09-30T12:36:25Z
dc.date.issued2010
dc.identifier.citationJavalera Rincón, V.; Morcego Seix, B.; Puig Cayuela, V. Distributed MPC for large scale systems using agent-based reinforcement learning. A: IFAC Symposium on Large Scale Systems Theory and Applications. "Proceedings of 12th IFAC Symposium on Large-Scale Systems: Theory and Applications". 2010, p. 1-6. ISBN 978-2-915913-26-2.
dc.identifier.isbn978-2-915913-26-2
dc.identifier.urihttp://hdl.handle.net/2117/9208
dc.description.abstractIn the present work, distributed control and artificial intelligence are combined in a control architecture for Large Scale Systems (LSS). The aim of this architecture is to provide a general structure and methodology to perform optimal control in networked distributed environments where multiple dependencies between sub-systems are found. Often these dependencies or connections represent control variables so the distributed control has to be consistent for both subsystems and the optimal value of these variables has to accomplish a common goal. The aim of the research described in this paper is to exploit the attractive features of MPC (meaningful objective functions and constraints) in a distributed implementation combining learning techniques to perform the negotiation of these variables in a cooperative Multi Agent environment and over a Multi Agent platform to provide speed, scalability, and computational effort reduction. This approach is based on negotiation, cooperation and learning. Results of the application of this architecture 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.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Optimització
dc.subject.lcshControl theory
dc.subject.lcshMathematical optimization
dc.subject.lcshPredictive control
dc.subject.otherDistributed control Distributed architectures MPC Learning Multi-agent systems
dc.titleDistributed MPC for large scale systems using agent-based reinforcement learning
dc.typeConference report
dc.subject.lemacControl predictiu
dc.subject.lemacOptimització matemàtica
dc.subject.lemacControl, Teoria de
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.subject.inspecClassificació INSPEC::Control theory
dc.subject.inspecClassificació INSPEC::Optimisation
dc.subject.inspecClassificació INSPEC::Control theory::Predictive control
dc.relation.publisherversionhttp://www.ifacpapersonline.com
dc.rights.accessOpen Access
local.identifier.drac3103438
dc.description.versionPreprint
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/224168/EU/Decentralized and Wireless Control of Large-Scale Systems/WIDE
local.citation.contributorIFAC Symposium on Large Scale Systems Theory and Applications
local.citation.publicationNameProceedings of 12th IFAC Symposium on Large-Scale Systems: Theory and Applications
local.citation.startingPage1
local.citation.endingPage6


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