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Adaptive Control of Three-Tank-System: Comparison of Two Methods

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dc.title Adaptive Control of Three-Tank-System: Comparison of Two Methods en
dc.contributor.author Kubalčík, Marek
dc.contributor.author Bobál, Vladimír
dc.relation.ispartof 2008 Mediterranean Conference on Control Automation, Vols 1-4
dc.identifier.isbn 978-1-4244-2504-4
dc.date.issued 2008
dc.citation.spage 81
dc.citation.epage 86
dc.event.title 16th Mediterranean Conference on Control and Automation
dc.event.location Ajaccio
utb.event.state-en France
utb.event.state-cs Francie
dc.event.sdate 2008-06-25
dc.event.edate 2008-06-27
dc.type conferenceObject
dc.language.iso en
dc.publisher The Institute of Electrical and Electronics Engineers (IEEE) en
dc.identifier.doi 10.1109/MED.2008.4601981
dc.relation.uri http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4601981
dc.subject multivariable control en
dc.subject self-tuning control en
dc.subject polynomial methods en
dc.subject pole assignment en
dc.subject predictive control en
dc.description.abstract This paper compares two different methods applied to adaptive control of a real multivariable laboratory system of three interconnected tanks. In first case, a controller based on polynomial methods was used. The second method is based on model predictive control (MPC) approach. Both methods are based on a same model of the controlled process. Both controllers were realized as self - tuning controllers with on - tine recursive identification of an ARX model of the controlled process. Results of real-time experiments are also included and quality of control achieved by both methods is compared and discussed. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1001896
utb.identifier.obdid 43855568
utb.identifier.scopus 2-s2.0-52949127337
utb.identifier.wok 000261534400014
utb.source d-wok
dc.date.accessioned 2011-08-09T07:34:10Z
dc.date.available 2011-08-09T07:34:10Z
utb.contributor.internalauthor Kubalčík, Marek
utb.contributor.internalauthor Bobál, Vladimír
utb.fulltext.affiliation Marek Kubalcik, Vladimir Bobal Department of Process Control, Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stranemi 4511, 760 05 Zlín, Czech Republic, Tel.: +420 57 6035198, E-mail: kubalcik@fai.utb.cz
utb.fulltext.dates -
utb.fulltext.references [1] Kucera, V., “Stochastic Multivariable Control: a Polynomial approach”, IEEE Trans. of Automatic Control, vol. 5, 1980, pp. 913 – 919. [2] Kucera, V., Analysis and Design of Discrete Linear Control Systems. Academia, Prague, 1991. [3] Camacho E. F., Bordons C., Model Predictive Control. SpringerVerlag, London, 2004. [4] Bitmead R. R., Gevers M., Hertz, V., Adaptive Optimal Control. The Thinking Man’s GPC. Prentice Hall, Englewood Cliffs, New Persey, 1990. [5] Landau, I. D., Lozano, R., M’Saad, M., Adaptive Control. Springer - Verlag, Berlin, 1998. [6] Bobal, J., Bőhm , J., Fessl, J., Machacek J., Adaptive Control. Springer - Verlag, London, 2005. [7] AMIRA-DTS2000, Laboratory setup three tank system. Amira Gmbh, Duisburg, 1996. [8] Maciejowski J.M., Predictive Control with Constraints. Prentice Hall, London, 2002. [9] Nelles, O., Nonlinear System Identification. Springer-Verlag, Berlin, 2001. [10] Bittanti S., Bolzern P. and Campi M., “Convergence and Exponential Convergence of Identification Algorithms with Directional Forgetting Factor”, Automatica, Vol. 26, No. 5, 1990, pp. 929 – 932. [11] Kulhavy, R., “Restricted Exponential Forgetting in Real – Time Identification”, Automatica, Vol. 23, 1987, pp. 589 – 600.
utb.fulltext.sponsorship This work was supported by the Ministry of Education of the Czech Republic under grants No. MSM 7088352101 and No. 1M0567.
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