Robust nonlinear H∞ control of hyperbolic distributed parameter systems

Aggelogiannaki, E; Sarimveis, H

dc.contributor.author	Aggelogiannaki, E	en
dc.contributor.author	Sarimveis, H	en
dc.date.accessioned	2014-03-01T01:31:49Z
dc.date.available	2014-03-01T01:31:49Z
dc.date.issued	2009	en
dc.identifier.issn	09670661	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19940
dc.subject	H∞ control	en
dc.subject	Hyperbolic distributed parameter systems	en
dc.subject	Radial basis function neural networks	en
dc.subject	Robust control	en
dc.subject	Thermal systems	en
dc.subject.other	Control laws	en
dc.subject.other	Controlled variables	en
dc.subject.other	Conventional controls	en
dc.subject.other	Empirical models	en
dc.subject.other	Hyperbolic distributed parameter systems	en
dc.subject.other	Non-linear models	en
dc.subject.other	Non-linear state	en
dc.subject.other	Nonlinear h	en
dc.subject.other	Process inputs	en
dc.subject.other	Radial basis function neural networks	en
dc.subject.other	Robust h	en
dc.subject.other	Spatial locations	en
dc.subject.other	Thermal systems	en
dc.subject.other	Attitude control	en
dc.subject.other	Distributed computer systems	en
dc.subject.other	Distributed parameter networks	en
dc.subject.other	Identification (control systems)	en
dc.subject.other	Intelligent control	en
dc.subject.other	Neural networks	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Robust control	en
dc.subject.other	Systems engineering	en
dc.subject.other	Vibration control	en
dc.subject.other	Distributed parameter control systems	en
dc.title	Robust nonlinear H∞ control of hyperbolic distributed parameter systems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.conengprac.2008.11.005	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.conengprac.2008.11.005	en
heal.publicationDate	2009	en
heal.abstract	A radial basis function (RBF) neural network model is developed for the identification of hyperbolic distributed parameter systems (DPSs). The empirical model is based only on process input-output data and is used for the estimation of the controlled variables at multiple spatial locations. The produced nonlinear model is transformed to a nonlinear state-space formulation, which in turn is used for deriving a robust H∞ control law. The proposed methodology is applied to a long duct for the flow-based control of temperature distribution. The performance of the proposed method is illustrated by comparing it with conventional control strategies. © 2008 Elsevier Ltd. All rights reserved.	en
heal.journalName	Control Engineering Practice	en
dc.identifier.doi	10.1016/j.conengprac.2008.11.005	en
dc.identifier.volume	17	en
dc.identifier.issue	6	en
dc.identifier.spage	723	en
dc.identifier.epage	732	en