Control of processes with multiple steady states using MPC and RBF neural networks

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος	el
dc.contributor.author	Σαρίμβεης, Χαράλαμπος	el
dc.date.accessioned	2015-05-14T18:47:17Z
dc.date.available	2015-05-14T18:47:17Z
dc.date.issued	2015-05-14
dc.identifier.uri	http://hdl.handle.net/11400/10420
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.sciencedirect.com	el
dc.subject	Predictive control
dc.subject	Radial basis functions
dc.subject	Neural networks (Computer science)
dc.subject	μοντέλο πρόβλεψης ελέγχου
dc.subject	ακτινική συνάρτηση βάσης
dc.subject	νευρωνικά δίκτυα
dc.subject	πολλαπλές καταστάσεις ηρεμίας
dc.subject	multiple steady states
dc.title	Control of processes with multiple steady states using MPC and RBF neural networks	en
heal.type	journalArticle
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρονική
heal.classification	Technology
heal.classification	Electrical engineering
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρονική
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://skos.um.es/unescothes/C01311
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh92000169
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh2002004691
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh90001937
heal.identifier.secondary	DOI: 10.1016/B978-0-444-53711-9.50140-1
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Ηλεκτρονικών Μηχανικών Τ.Ε.	el
heal.publicationDate	2011
heal.bibliographicCitation	Alexandridis, A. & Sarimveis, H. (2011). Control of processes with multiple steady states using MPC and RBF neural networks. "Computer Aided Chemical Engineering". 29: p. 698-702	en
heal.abstract	This work presents a new methodology for controlling processes that exhibit multiple steady states. The proposed approach is based on a Model Predictive Control (MPC) framework, where the dynamics of the process are modeled by a Radial Basis Function (RBF) neural network. The innovative non-symmetric fuzzy means algorithm is employed in order to train the RBF network. The proposed methodology is applied to the control of a non-isothermal Continuous Stirred Tank Reactor (CSTR) that exhibits three steady state points. The results show that the proposed controller can drive the CSTR through the entire operating region, including the unstable steady state point, around which the control task is rather challenging.	en
heal.journalName	Computer Aided Chemical Engineering	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false