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Predictive control of the heat exchanger using local model network

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dc.title Predictive control of the heat exchanger using local model network en
dc.contributor.author Novák, Jakub
dc.contributor.author Bobál, Vladimír
dc.relation.ispartof MED: 2009 17th Mediterranean Conference on Control & Automation, Vols 1-3
dc.identifier.isbn 978-1-4244-4684-1
dc.date.issued 2009
dc.citation.spage 657
dc.citation.epage 662
dc.event.title 17th Mediterranean Conference on Control and Automation
dc.event.location Thessaloniki
utb.event.state-en Greece
utb.event.state-cs Řecko
dc.event.sdate 2009-06-24
dc.event.edate 2009-06-26
dc.type conferenceObject
dc.language.iso en
dc.publisher The Institute of Electrical and Electronics Engineers (IEEE) en
dc.identifier.doi 10.1109/MED.2009.5164618
dc.relation.uri http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5164618
dc.subject Multiple model control en
dc.subject Predictive control en
dc.subject Local Model Networks en
dc.description.abstract The paper deals with the problem of modeling and control of nonlinear processes using the Local Model Network (LMN). The idea is based on development of the local linear models for the whole operating range of the controlled process. The nonlinear plant is then approximated by a set of locally valid sub-models, which are smoothly connected using the validity function. For saving the computational load, linear model is obtained by interpolating these linear models at each sample instant and then used in Model Predictive Control (MPC) framework to calculate the future behavior of the process. The approach is verified in a real-time control of Multifunction Process Control Teaching System (MPCTS) - the Armfield PCT 40. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1001824
utb.identifier.rivid RIV/70883521:28140/09:63508772!RIV10-GA0-28140___
utb.identifier.obdid 43859142
utb.identifier.wok 000280699600114
utb.source d-wok
dc.date.accessioned 2011-08-09T07:34:02Z
dc.date.available 2011-08-09T07:34:02Z
utb.contributor.internalauthor Novák, Jakub
utb.contributor.internalauthor Bobál, Vladimír
utb.fulltext.affiliation Jakub Novak Tomas Bata University in Zlin Faculty of Applied Informatics Zlin, Czech Republic jnovak@fai.utb.cz Vladimir Bobal Tomas Bata University in Zlin Faculty of Applied Informatics Zlin, Czech Republic bobal@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.references [1] M. Mahfouf, and D.A. Linkens, “Non-linear generalized predictive control (NLGPC) applied to muscle relaxant anaesthesia,” International Journal of Control, vol 71, 1998, pp. 239–257. [2] R. Murray-Smith, and T.A. Johansen, Multiple Model Approaches to Modelling and Control.. Taylor and Francis, London ,1997. [3] G. Gregorčič and G. Lightbody, “Nonlinear system identification: From multiple-model networksnext term to Gaussian processes,” Engineering Applications of Artificial Intelligence, vol. 21, October 2008, pp. 1035-1055. [4] Z.K. Xue, and S.Y. Li, “Multi-model modelling and predictive control based on local model networks,” Control and Intelligent Systems archive, vol. 34, 2006, pp. 105 - 112. [5] O. Hecker, O.Nelles, and O. Moseler, “Nonlinear System Identification and Predictive Control of a Heatexchanger based on linear fuzzy models,” Proceedings of teh American Control Conference, Vol. 5, 1997, pp. 3294-3298. [6] S.K. Sharma, S. McLoone, and G.W.Irwin, “Genetic Algorithms for Local Model and Local Controller Network Design,” Proceedings of the American Control Conference, vol. 2, 2002, pp. 1693-1698. [7] T.A. Johansen, and B.A. Foss, “Identification of non-linear system structure and parameters using regime decomposition,” Automatica, Vol. 2, 1995, pp. 321-326. [8] J. Abonyi, T.Chovan, and T. Szeifert, “Identification of Nonlinear Systems using Gaussian Mixture of Local Models,” Hungarian Journal of Industry Chemistry, vol. 29, 2001, pp. 139–134.
utb.fulltext.sponsorship The work has been supported by Ministry of Education of the Czech Republic under the grant 102/09/P243.
utb.fulltext.projects MSM 102/09/P243
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