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Robust nonlinear H∞ control of hyperbolic distributed parameter systems

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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


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