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Negotiation and learning in distributed MPC of large scale systems
dc.contributor.author | Javalera Rincón, Valeria |
dc.contributor.author | Morcego Seix, Bernardo |
dc.contributor.author | Puig Cayuela, Vicenç |
dc.contributor.other | Institut de Robòtica i Informàtica Industrial |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2013-05-14T12:34:16Z |
dc.date.available | 2013-05-14T12:34:16Z |
dc.date.created | 2010 |
dc.date.issued | 2010 |
dc.identifier.citation | Javalera, 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.isbn | 978-1-4244-7425-7 |
dc.identifier.uri | http://hdl.handle.net/2117/19206 |
dc.description.abstract | A 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.extent | 6 p. |
dc.language.iso | eng |
dc.publisher | IEEE Press. Institute of Electrical and Electronics Engineers |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Economia i organització d'empreses::Gestió i direcció |
dc.subject.lcsh | Production management |
dc.subject.other | predictive control PARAULES AUTOR:cooperative systems |
dc.subject.other | distributed control |
dc.subject.other | model predictive control |
dc.subject.other | multi agent systems |
dc.subject.other | negotiation |
dc.subject.other | reinforcement learning |
dc.title | Negotiation and learning in distributed MPC of large scale systems |
dc.type | Conference report |
dc.subject.lemac | Producció -- Direcció i administració |
dc.contributor.group | Universitat Politècnica de Catalunya. SIC - Sistemes Intel·ligents de Control |
dc.contributor.group | Universitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.inspec | Classificació INSPEC::Control theory::Predictive control |
dc.relation.publisherversion | http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05530986 |
dc.rights.access | Open Access |
local.identifier.drac | 4408179 |
dc.description.version | Postprint (author’s final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/EC/FP7/224168/EU/Decentralized and Wireless Control of Large-Scale Systems/WIDE |
local.citation.author | Javalera, V.; Morcego, B.; Puig, V. |
local.citation.contributor | American Control Conference |
local.citation.pubplace | Baltimore |
local.citation.publicationName | Proceedings of the 2010 American Control Conference |
local.citation.startingPage | 3168 |
local.citation.endingPage | 3173 |