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

Aggelogiannaki, E; Sarimveis, H; Alexandridis, A

dc.contributor.author	Aggelogiannaki, E	en
dc.contributor.author	Sarimveis, H	en
dc.contributor.author	Alexandridis, A	en
dc.date.accessioned	2014-03-01T02:49:59Z
dc.date.available	2014-03-01T02:49:59Z
dc.date.issued	2005	en
dc.identifier.issn	14746670	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34847
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-79960735194&partnerID=40&md5=47f54b9d21ba2deb0c1bec25484e6569	en
dc.subject	Adaptation	en
dc.subject	Heuristic searches	en
dc.subject	Model based control	en
dc.subject	Multiobjective optimization	en
dc.subject	Radial base function networks	en
dc.subject.other	Adaptation	en
dc.subject.other	Heuristic searches	en
dc.subject.other	Model based control	en
dc.subject.other	Multi objective	en
dc.subject.other	Radial base function	en
dc.subject.other	Adaptive control systems	en
dc.subject.other	Automation	en
dc.subject.other	Control	en
dc.subject.other	Evolutionary algorithms	en
dc.subject.other	Model predictive control	en
dc.subject.other	Neural networks	en
dc.subject.other	Predictive control systems	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Multiobjective optimization	en
dc.title	A prioritized multiobjective MPC configuration using adaptive RBF networks and evolutionary computation	en
heal.type	conferenceItem	en
heal.publicationDate	2005	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. Copyright © 2005 IFAC.	en
heal.journalName	IFAC Proceedings Volumes (IFAC-PapersOnline)	en
dc.identifier.volume	16	en
dc.identifier.spage	150	en
dc.identifier.epage	155	en