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

Alexandridis, A; Sarimveis, H

dc.contributor.author	Alexandridis, A	en
dc.contributor.author	Sarimveis, H	en
dc.date.accessioned	2014-03-01T02:43:26Z
dc.date.available	2014-03-01T02:43:26Z
dc.date.issued	2005	en
dc.identifier.issn	0001-1541	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31412
dc.subject	Adaptive control	en
dc.subject	Digester control	en
dc.subject	Model predictive control	en
dc.subject	Nonlinear control	en
dc.subject	Radial basis function networks	en
dc.subject.classification	Engineering, Chemical	en
dc.subject.other	Adaptive control systems	en
dc.subject.other	Mathematical models	en
dc.subject.other	Perturbation techniques	en
dc.subject.other	Predictive control systems	en
dc.subject.other	Process control	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Digester control	en
dc.subject.other	Model predictive control	en
dc.subject.other	Nonlinear adaptive model	en
dc.subject.other	Nonlinear control	en
dc.subject.other	Nonlinear systems	en
dc.subject.other	adaptive control	en
dc.subject.other	neural network	en
dc.subject.other	predictive control	en
dc.title	Nonlinear adaptive model predictive control based on self-correcting neural network models	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1002/aic.10505	en
heal.identifier.secondary	http://dx.doi.org/10.1002/aic.10505	en
heal.language	English	en
heal.publicationDate	2005	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. (c) 2005 American Institute of Chemical Engineers.	en
heal.publisher	JOHN WILEY & SONS INC	en
heal.journalName	AIChE Journal	en
dc.identifier.doi	10.1002/aic.10505	en
dc.identifier.isi	ISI:000231237200016	en
dc.identifier.volume	51	en
dc.identifier.issue	9	en
dc.identifier.spage	2495	en
dc.identifier.epage	2506	en