A prioritized multiobjective MPC configuration using adaptive RBF networks and evolutionary computation

dc.contributor.author	Αγγελογιαννάκη, Ελένη	el
dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.date.accessioned	2015-06-04T13:02:57Z
dc.date.available	2015-06-04T13:02:57Z
dc.date.issued	2015-06-04
dc.identifier.uri	http://hdl.handle.net/11400/15057
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.ifac.org/	en
dc.subject	Adaptation
dc.subject	Heuristic searches
dc.subject	Model based control
dc.subject	Multiobjective optimization
dc.subject	Radial base function networks
dc.subject	προσαρμογή
dc.subject	Μοντέλο βασισμένο στον έλεγχο
dc.subject	Πολυκριτηριακή βελτιστοποίηση
dc.title	A prioritized multiobjective MPC configuration using adaptive RBF networks and evolutionary computation	en
heal.type	conferenceItem
heal.classification	Technology
heal.classification	Electrical engineering
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρολογία Μηχανολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://zbw.eu/stw/descriptor/18426-4
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρολογία Μηχανολογία
heal.language	en
heal.access	campus
heal.publicationDate	2005
heal.bibliographicCitation	Aggelogiannaki, E., Sarimveis, H.K. & Alexandridis, A.P. (2005) A prioritized multiobjective MPC configuration using adaptive RBF networks and evolutionary computation, In: Proceedings of the 16th Triennial World Congress of International Federation of Automatic Control, IFAC 2005. Prague, Czech Republic. 3-4 July, 2005. [online] 16. p. 150-155. Available from: http://www.ifac.org/	en
heal.abstract	in this work a prioritized multiobjective model predictive control configuration for nonlinear processes is proposed. The process is modeled by an adaptive radial basis function neural network so that modifications through time can be identified. The different control targets are formulated in a multiobjective optimization problem which is solved using a prioritized evolutionary algorithm. The request for adequate information in order to adapt the dynamics of the model is considered as the top priority objective. The algorithm is tested through the control of a pH reactor and the results are in favor of the proposed methodology.	en
heal.publisher	IFAC	en
heal.fullTextAvailability	true
heal.conferenceName	16th Triennial World Congress of International Federation of Automatic Control, IFAC 2005	en
heal.conferenceItemType	poster