Nonlinear model predictive control for distributed parameter systems using data driven artificial neural network models

Aggelogiannaki, E; Sarimveis, H

dc.contributor.author	Aggelogiannaki, E	en
dc.contributor.author	Sarimveis, H	en
dc.date.accessioned	2014-03-01T01:28:52Z
dc.date.available	2014-03-01T01:28:52Z
dc.date.issued	2008	en
dc.identifier.issn	00981354	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19007
dc.subject	Distributed parameter systems	en
dc.subject	Model predictive control	en
dc.subject	Radial basis function neural networks	en
dc.subject	Singular value decomposition	en
dc.subject.other	Distributed computer systems	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Singular value decomposition	en
dc.subject.other	Distributed parameter systems (DPS)	en
dc.subject.other	Spatial behavior	en
dc.subject.other	Model predictive control	en
dc.subject.other	Distributed computer systems	en
dc.subject.other	Model predictive control	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Singular value decomposition	en
dc.title	Nonlinear model predictive control for distributed parameter systems using data driven artificial neural network models	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.compchemeng.2007.05.002	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.compchemeng.2007.05.002	en
heal.publicationDate	2008	en
heal.abstract	In this work the radial basis function neural network architecture is used to model the dynamics of Distributed Parameter Systems (DPSs). Two pure data driving schemes which do not require knowledge of the governing equations are described and compared. In the first method, the neural network methodology generates the full model of the system that is able to predict the process outputs at any spatial point. Past values of the process inputs and the coordinates of the specific location provide the input information to the model. The second method uses empirical basis functions produced by the Singular Value Decomposition (SVD) on the snapshot matrix to describe the spatial behavior of the system, while the neural network model is used to estimate only the temporal coefficients. The models produced by both methods are then implemented in Model Predictive Control (MPC) configurations, suitable for constrained DPSs. The accuracies of the modeling methodologies and the efficiencies of the proposed MPC formulations are tested in a tubular reactor and produce encouraging results. © 2007 Elsevier Ltd. All rights reserved.	en
heal.journalName	Computers and Chemical Engineering	en
dc.identifier.doi	10.1016/j.compchemeng.2007.05.002	en
dc.identifier.volume	32	en
dc.identifier.issue	6	en
dc.identifier.spage	1233	en
dc.identifier.epage	1245	en