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Model predictive control of greenhouse climate using RBF models

dc.contributor.authorFerreira, P. M.
dc.contributor.authorRuano, Antonio
dc.date.accessioned2013-02-04T14:43:04Z
dc.date.available2013-02-04T14:43:04Z
dc.date.issued2006
dc.date.updated2013-01-28T16:23:40Z
dc.description.abstractThis paper presents the methodology and simulation results regarding the application of model predictive control to greenhouse environmental control and crop production optimisation. External climate (solar radiation, temperature and humidity) and greenhouse environmental (temperature and humidity) predictive models are built using radial basis function neural networks. Multi-Objective Genetic Algorithms were applied to select the input-output structure of models. The control actions over the prediction horizon are selected using a branch-andbound algorithm minimising a cost function. A tomato crop was grown in order to collect data to develop a crop production model to enable production optimisation in the proposed control strategy.por
dc.identifier.citationFerreira, P. M.; Ruano, A. E. Model predictive control of greenhouse climate using RBF models, Trabalho apresentado em Global Education Techology Symposium (GETS 2006), In Proceedings of the Global Education Techology Symposium (GETS 2006), Faro, 2006.por
dc.identifier.otherAUT: ARU00698;
dc.identifier.urihttp://hdl.handle.net/10400.1/2206
dc.language.isoengpor
dc.peerreviewedyespor
dc.titleModel predictive control of greenhouse climate using RBF modelspor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.conferencePlaceFaropor
oaire.citation.endPage2por
oaire.citation.startPage1por
oaire.citation.titleGlobal Education Techology Symposium (GETS 2006)por
person.familyNameRuano
person.givenNameAntonio
person.identifier.orcid0000-0002-6308-8666
person.identifier.ridB-4135-2008
person.identifier.scopus-author-id7004284159
rcaap.rightsrestrictedAccesspor
rcaap.typeconferenceObjectpor
relation.isAuthorOfPublication13813664-b68b-40aa-97a9-91481a31ebf2
relation.isAuthorOfPublication.latestForDiscovery13813664-b68b-40aa-97a9-91481a31ebf2

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