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Latent variable based model predictive control: Ensuring validity of predictions

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Latent variable based model predictive control: Ensuring validity of predictions

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dc.contributor.author Laurí Pla, David es_ES
dc.contributor.author Sanchís Saez, Javier es_ES
dc.contributor.author Martínez Iranzo, Miguel Andrés es_ES
dc.contributor.author Hilario Caballero, Adolfo es_ES
dc.date.accessioned 2014-07-11T09:07:22Z
dc.date.issued 2013-01
dc.identifier.issn 0959-1524
dc.identifier.uri http://hdl.handle.net/10251/38718
dc.description.abstract This paper presents a methodology to constrain the optimisation problem in LV-MPC so that validity of predictions can be ascertained. LV-MPC is a model-based predictive control methodology implemented in the space of the latent variables and is based on a linear predictor. Provided real processes are non-linear, there is model-process mismatch, and under tight control, the predictor can be used for extrapolation. Extrapolation leads to bad predictions which deteriorates control performance, hence the interest in validity of predictions. In the proposed approach first two validity indicators on predictions are defined. The novelty in the two indicators proposed is they neglect past data, and so validity of predictions is ascertained in terms of future moves which are actually the degrees of freedom in the optimisation. Second, the indicators are introduced in the optimisation as constraints. Provided the indicators are quadratic, recursive optimisation with linearised constraints is implemented. A MIMO example shows how ensuring validity of predictions neglecting past data can improve closed-loop performance, specially under tight control outside the identification region. (C) 2012 Elsevier Ltd. All rights reserved. es_ES
dc.description.sponsorship The first author is recipient of a fellowship from the Spanish Ministry of Science and Innovation (FPU AP2007-04549). This paper is partially funded by projects DPI2008-02133/DPI, TIN2011-28082 and PROMETEO/2012/028. The authors gratefully acknowledge reviewers' comments. en_EN
dc.language Inglés es_ES
dc.publisher International Federation of Automatic Control (IFAC) es_ES
dc.relation Spanish Ministry of Science and Innovation [FPU AP2007-04549] es_ES
dc.relation [DPI2008-02133/DPI] es_ES
dc.relation [TIN2011-28082] es_ES
dc.relation [PROMETEO/2012/028] es_ES
dc.relation.ispartof Journal of Process Control es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Data-driven es_ES
dc.subject Model predictive control es_ES
dc.subject Latent variable es_ES
dc.subject Prediction es_ES
dc.subject Control relevant identification es_ES
dc.subject Validity of predictions es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Latent variable based model predictive control: Ensuring validity of predictions es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.jprocont.2012.11.001
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Automática e Informática Industrial - Institut Universitari d'Automàtica i Informàtica Industrial es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.description.bibliographicCitation Laurí Pla, D.; Sanchís Saez, J.; Martínez Iranzo, MA.; Hilario Caballero, A. (2013). Latent variable based model predictive control: Ensuring validity of predictions. Journal of Process Control. 23(1):12-22. doi:10.1016/j.jprocont.2012.11.001 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.1016/j.jprocont.2012.11.001 es_ES
dc.description.upvformatpinicio 12 es_ES
dc.description.upvformatpfin 22 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 23 es_ES
dc.description.issue 1 es_ES
dc.relation.senia 231901


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