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Use of MATLAB environment for simulation and control of CSTR

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dc.title Use of MATLAB environment for simulation and control of CSTR en
dc.contributor.author Vojtěšek, Jiří
dc.contributor.author Dostál, Petr
dc.relation.ispartof International Journal of Mathematics and Computers in Simulation
dc.identifier.issn 1998-0159 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2011
utb.relation.volume 5
utb.relation.issue 6
dc.citation.spage 528
dc.citation.epage 535
dc.type article
dc.language.iso en
dc.relation.uri http://www.naun.org/multimedia/NAUN//mcs/17-248.pdf
dc.subject adaptive control en
dc.subject MATLAB en
dc.subject modeling en
dc.subject recursive identification en
dc.subject simulation en
dc.description.abstract This contribution presents the usability of the mathematical software MATLAB ® (MATrix LABoratory) in the field of simulation of the steady-state, dynamic behaviour and adaptive control of the Continuous Stirred Tank Reactor (CSTR). These types of chemical reactors belong to the class of nonlinear lumped-parameters systems mathematical model of which is described by one or more Ordinary Differential Equations (ODEs). The simple iteration method was used for steady-state analysis of the system while the Runge-Kutta's method was employed for the numerical solution of the set of ODE. Both methods are simple, provides sufficient results and they are easily programmable which was important in our case. The presented adaptive approach used for controlling of the system provides sufficient results although the system has negative properties from the control point of view. The benefit of this paper can be found in the simulation program made in MATLAB with the use of Graphical User Interface (GUI) that provides user possibilities to examine simulations without changing of the program code. en
utb.faculty Faculty of Applied Informatics
utb.faculty University Institute
dc.identifier.uri http://hdl.handle.net/10563/1002624
utb.identifier.rivid RIV/70883521:28140/11:43866437!RIV12-MSM-28140___
utb.identifier.rivid RIV/70883521:28610/11:43866437!RIV12-MSM-28610___
utb.identifier.obdid 43866545
utb.identifier.scopus 2-s2.0-80055048679
utb.source j-scopus
dc.date.accessioned 2012-02-10T13:15:19Z
dc.date.available 2012-02-10T13:15:19Z
utb.ou Centre of Polymer Systems
utb.contributor.internalauthor Vojtěšek, Jiří
utb.contributor.internalauthor Dostál, Petr
utb.fulltext.affiliation Jiri Vojtesek, Petr Dostal J. Vojtesek and P. Dostal are with Department of Process Controll, Faculty of Applied Informatics, Tomas Bata University in Zlin, Czech Republic (phone: +420576035199; fax: +420576032716; e-mail: {vojtesek,dostalp}@fai.utb.cz). Jiri Vojtesek (Ph.D.) was born in Zlin, Czech Republic in 1979 and studied at the Tomas Bata University in Zlin. where he got his master degree in chemical and process engineering in 2002. He has finished his Ph.D. focused on Modern control methods for chemical reactors in 2007. He now works as a Senior Lecturer at Department of Process Control, Faculty of Applied Informatics, Tomas Bata University in Zlin. Petr Dostal (prof.) studied at the Technical University of Pardubice. He obtained his PhD. degree in Technical Cybernetics in 1979 and he became professor in Process Control in 2000. His research interest are modeling and simulation of continuous-time chemical processes. polynomial methods. optimal. adaptive and robust control. He works as a professor and head of the Department of Process Control, Faculty of Applied Informatics, Tomas Bata University in Zlin.
utb.fulltext.dates Manuscript received August 9, 2011.
utb.fulltext.references [1] P. Dostal, V. Bobal, F. Gazdos, Simulation of nonlinear adaptive control of a continuous stirred tank reactor, International Journal of Mathematics and Computers in Simulation ,Volume 5, Issue 4, 2011, Pages 370-377 [2] J. Ingham, I. J. Dunn; E. Heinzle, J. E. Přenosil, Chemical Engineering Dynamics. An Introduction to Modeling and Computer Simulation. Second. Completely Revised Edition. VCH Verlagsgesellshaft. Weinheim, 2000 [3] Y. Saad, Iterative Methods for Sparse Linear Systems. Society for Industrial & Applied, 2003 [4] F. L. Severance, System Modeling and Simulation: An Introduction. John Wiley & Sons 2001 [5] J. H. Mathews, K. K. Fink, Numerical Methods Using Matlab. PrenticeHall 2004 [6] L. Pekar, R. Prokop, Stabilization of a delayed system by a proportional controller,International Journal of Mathematical Models and Methods in Applied Sciences 4 (4), 2010, pp. 282-290 [7] D. Samek, D. Manas, Artificial neural networks in artificial time series prediction benchmark, International Journal of Mathematical Models and Methods in Applied Sciences 5 (6), 2011, pp. 1085-1093 [8] V. Bobal, J. Böhm, J. Fessl, J. Machacek, Digital Self-tuning Controllers: Algorithms. Implementation and Applications. Advanced Textbooks in Control and Signal Processing. Springer-Verlag London Limited, 2005. [9] V. Kucera, Diophantine equations in control – A survey. Automatica. 29. 1361-1375, 1993 [10] MathWorks - MATLAB and Simulink for Technical Computing, official webpage. Available: http://www.mathworks.com (URL) [11] Matusu R. A software tool for algebraic design of interval systems control, International Journal of Computational Science and Engineering 5 (3-4), 2010, pp. 262-268 [12] Brancik, L., Sevcik, B. Time-domain simulation of nonuniform multiconductor transmission lines in Matlab, International Journal of Mathematics and Computers in Simulation 5 (2), 2011, pp. 77-84 [13] R. Gao, A. O’dywer, E. Coyle, A Non-linear PID Controller for CSTR Using Local Model Networks. Proc. of 4th World Congress on Intelligent Control and Automation. Shanghai. P. R. China. 3278-3282, 2002. [14] J. Vojtesek, P. Dostal, Effect of External Linear Model’s Order on Adaptive Control of CSTR. In: Proceedings of the IFAC workshop on Adaptation and Learning in Control and Signal Processing ALCOSP 2010. Antalya, Turkey. [15] D. L. Stericker, N. K. Sinha, Identification of continuous-time systems from samples of input-output data using the -operator. Control-Theory and Advanced Technology, vol. 9, 1993, 113-125 [16] S. Mukhopadhyay, A. G. Patra , G. P. Rao, New class of discrete-time models for continuos-time system. International Journal of Control, vol.55, 1992, 1161-1187 [17] G. P. Rao, H. Unbehauen, Identification of continuous-time systems. IEEE Process-Control Theory Application., 152, 2005, 185-220
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utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.ou Department of Process Control
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