Adaptive control of three-tank-system: Comparison of two methods

Kubalčík, Marek; Bobál, Vladimír

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.
utb.fulltext.projects	MSM 7088352101
utb.fulltext.projects	MSM 1M0567