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dc.contributor.authorGrosso Pérez, Juan Manuel
dc.contributor.authorOcampo-Martínez, Carlos
dc.contributor.authorPuig Cayuela, Vicenç
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2013-10-04T08:54:22Z
dc.date.available2013-10-04T08:54:22Z
dc.date.created2013
dc.date.issued2013
dc.identifier.citationGrosso, J.; Ocampo-Martinez, C.A.; Puig, V. Learning-based tuning of supervisory model predictive control for drinking water networks. "Engineering applications of artificial intelligence", 2013, vol. 26, núm. 7, p. 1741-1750.
dc.identifier.issn0952-1976
dc.identifier.urihttp://hdl.handle.net/2117/20298
dc.description.abstractThis paper presents a constrained Model Predictive Control (MPC) strategy enriched with soft-control techniques as neural networks and fuzzy logic, to incorporate self-tuning capabilities and reliability aspects for the management of drinking water networks (DWNs). The control system architecture consists in a multilayer controller with three hierarchical layers: learning and planning layer, supervision and adaptation layer, and feedback control layer. Results of applying the proposed approach to the Barcelona DWN show that the quasi-explicit nature of the proposed adaptive predictive controller leads to improve the computational time, especially when the complexity of the problem structure can vary while tuning the receding horizons.
dc.format.extent10 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subjectÀrees temàtiques de la UPC::Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Enginyeria sanitària
dc.subject.lcshDrinking water networks
dc.subject.lcshDrinking water -- Spain -- Barcelona
dc.subject.otherDrinking water networks
dc.subject.otherFuzzy-logic
dc.subject.otherModel predictive control
dc.subject.otherMultilayer controller
dc.subject.otherNeural networks
dc.subject.otherSelf-tuning
dc.titleLearning-based tuning of supervisory model predictive control for drinking water networks
dc.typeArticle
dc.subject.lemacAigua potable -- Abastament -- Control automàtic
dc.contributor.groupUniversitat Politècnica de Catalunya. SAC - Sistemes Avançats de Control
dc.identifier.doi10.1016/j.engappai.2013.03.003
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0952197613000390
dc.rights.accessOpen Access
local.identifier.drac12466849
dc.description.versionPreprint
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/318556/EU/Efficient Integrated Real-time Monitoring and Control of Drinking Water Networks/EFFINET
dc.relation.projectidinfo:eu-repo/grantAgreement/MICINN//DPI2009-13744/ES/Analisis Y Diseño De Estrategidas De Control Optimo Distribuido Aplicadas A La Gestion De Sistemas De Agua De Gran Escala/
local.citation.authorGrosso, J.; Ocampo-Martinez, C.A.; Puig, V.
local.citation.publicationNameEngineering applications of artificial intelligence
local.citation.volume26
local.citation.number7
local.citation.startingPage1741
local.citation.endingPage1750


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