Nonlinear adaptive model predictive control based on self-correcting neural network models

dc.contributor.author	Αλεξανδρίδης, Αλέξανδρος Π.	el
dc.contributor.author	Σαρίμβεης, Χαράλαμπος Κ.	el
dc.date.accessioned	2015-06-04T13:09:22Z
dc.date.available	2015-06-04T13:09:22Z
dc.date.issued	2015-06-04
dc.identifier.uri	http://hdl.handle.net/11400/15058
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://eu.wiley.com/	en
dc.subject	Adaptive control
dc.subject	Digester control
dc.subject	Model predictive control
dc.subject	Nonlinear control
dc.subject	Radial basis function networks
dc.subject	Προσαρμοζόμενος έλεγχος
dc.subject	Μοντέλο πρόβλεψης ελέγχου
dc.subject	Μη γραμμικός έλεγχος
dc.title	Nonlinear adaptive model predictive control based on self-correcting neural network models	en
heal.type	journalArticle
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.identifier.secondary	DOI: 10.1002/aic.10505
heal.language	en
heal.access	campus
heal.publicationDate	2005-09
heal.bibliographicCitation	ALEXANDRIDIS, A.P. & SARIMVEIS, H.K. (2005). Nonlinear adaptive model predictive control based on self-correcting neural network models. AIChE Journal. [online] 51 (9). p. 2495-2506. Available from: http://eu.wiley.com/	en
heal.abstract	Two major issues in process control are the nonlinearities and variations with time that are observed in the dynamics of the processes. In most cases these problems are confronted by robust linear controllers, which are frequently retuned to take into account changes in the operating region or the system dynamics. Obviously, the performance of these controllers is limited by the degree of nonlinearities and the frequency of process variations. In this paper we present a new model predictive control (MPC) framework that can deal with both these issues. The proposed methodology is based on a nonlinear dynamic radial basis function (RBF) model of the process that is able to correct itself as new information about the process dynamics becomes available. The adaptive training algorithm that is used is able to update both the structure and the parameters of the RBF model. The typical formulation of the on-line optimization problem is augmented by a persistent excitation condition that guarantees that enough perturbation is introduced to the system by the control moves. The proposed MPC framework is applied on the digester control problem and proves to be superior to other MPC configurations.	en
heal.publisher	Wiley	en
heal.journalName	AIChE Journal	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true