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Combination of evolutionary and gradient optimization techniques in model predictive control

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dc.title Combination of evolutionary and gradient optimization techniques in model predictive control en
dc.contributor.author Antoš, Jan
dc.contributor.author Kubalčík, Marek
dc.relation.ispartof International Journal of Mathematical Models and Methods in Applied Sciences
dc.identifier.issn 1998-0140 Scopus Sources, Sherpa/RoMEO, JCR
dc.date.issued 2016
utb.relation.volume 10
dc.citation.spage 34
dc.citation.epage 41
dc.type article
dc.language.iso en
dc.publisher North Atlantic University Union (NAUN)
dc.relation.uri http://naun.org/cms.action?id=12152
dc.subject Control process en
dc.subject Evolutionary algorithms en
dc.subject Gradients algorithms en
dc.subject Model predictive control en
dc.subject Optimization en
dc.description.abstract Model predictive control (MPC) designates a control method based on the model. This method is suitable for controlling of various kinds of systems. The basic principle is to calculate the future behaviour of a system and to use this prediction for the optimization of a control process. The optimization problem must be then solved in each sampling period. One of the advantages of MPC is its ability to do online constraints handling systematically. These constraints may, however, cause that the optimization problem is more complex. In this case, some iterative algorithms must be applied in order to solve this problem effectively. This paper is focus on the combination of the optimization techniques. The basic idea is to combine the advantages of gradient and evolutionary algorithms. © 2016, North Atlantic University Union NAUN. All rights reserved. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1006814
utb.identifier.obdid 43875818
utb.identifier.scopus 2-s2.0-85000399439
utb.source j-scopus
dc.date.accessioned 2017-02-28T15:11:29Z
dc.date.available 2017-02-28T15:11:29Z
utb.ou CEBIA-Tech
utb.contributor.internalauthor Antoš, Jan
utb.contributor.internalauthor Kubalčík, Marek
utb.fulltext.affiliation Jan Antos and Marek Kubalcik Jan Antos is with the Tomas Bata University in Zlín, Faculty of Applied Informatics, Nám. T. G. Masaryka 5555, 760 05 Zlín (e-mail: antos@fai.utb.cz). Marek Kubalcik is with the Tomas Bata University in Zlín, Faculty of Applied Informatics, Nám. T. G. Masaryka 5555, 760 05 Zlín (corresponding author to provide phone: +420 57-603-5198; e-mail: kubalcik@fai.utb.cz).
utb.fulltext.dates -
utb.fulltext.references [1] E. F. Camacho, C. Bordons, Model Predictive Control, SpringerVerlag, London, 2004. [2] M. Morari, J. H. Lee, Model predictive control: past, present and future. Computers and Chemical Engineering, 23, 1999, 667-682. [3] R. R. Bitmead, M. Gevers, V. Hertz, Adaptive Optimal Control. The Thinking Man’s GPC, Prentice Hall, Englewood Cliffs, New Jersey, 1990. [4] P. Thitiyasook, P. Kittisupakorn, “Model Predictive Control of a Batch Reactor with Membrane – Based Separation,” in Proc. 7th WSEAS Int. Conf. on Signal Processing, Robotics and Automation (ISPRA ’08), University of Cambridge, UK, 2008, pp. 88-92. [5] R. Balan, O. Hancu, S. Stan, C. Lapusan, R. Donca, “Application of a Model Based Predictive Control Algorithm for Building Temperature Control,” in Proc. 3rd WSEAS Int. Conf. on Energy Planning, Energy Saving, Environmental Education (EPESE ’09), Tenerife, Spain, 2009, pp. 97-101. [6] Z. Ju, W. Wanliang, “Synthesis of Explicit Model Predictive Control System with Feasible Region Shrinking,” in Proc. 8th WSEAS Int. Conf. on Robotics, Control and Manufacturing Technology (ROCOM’08), Hangzhon, China, 2008, pp. 80-85. [7] D. W. Clarke, C. Mohtadi, P. S. Tuffs, Generalized predictive control, part I: the basic algorithm. Automatica, 23, 1987, 137-148. [8] D. W. Clarke, C. Mohtadi, P. S. Tuffs, Generalized predictive control, part II: extensions and interpretations. Automatica, 23, 1987, 149-160. [9] G.M. Lee, N.N. Tam, and N.D. Yen, Quadratic Programming and Affine Variational Inequalities:A Qualitative Study, Springer, 2005. [10] D.G. Luenberger and Y. Ye, Linear and nonlinear programming, 3rd ed. New York: Springer, 2008. [11] T. Back, D. B. Fogel, Z. Michalewicz, Handbook of evolutionary algorithms, Oxford: Oxford University Press, 1997 [12] Z. Muhammad, Z.M. Yusoff, M.H.F. Rahiman, M.N. Taib, Steam temperature control for steam distillation pot using model predictive control, Signal Processing and its Applications (CSPA), 2012 IEEE 8th International Colloquium on, 2012, 474-479. [13] M. Rau, D. Schroder, Model predictive control with nonlinear state space models, Advanced Motion Control, 2002. 7th International Workshop on, 2002, 136-141. [14] G.I. Suárez, O.A. Ortiz, P.M. Aballay, N.H. Aros, Adaptive neural model predictive control for the grape juice concentration process, Industrial Technology (ICIT), 2010 IEEE International Conference on, 2010, 57-63 [15] G. Tan, H. Hao, Y. Wang, Real time turning flow estimation based on model predictive control, Information Technology and Artificial Intelligence Conference (ITAIC), 2011 6th IEEE Joint International, 1, 2011, 356-360. [16] H.G. Han; X.L. Wu; J.F. Qiao, Real-Time Model Predictive Control Using a Self-Organizing Neural Network, Neural Networks and Learning Systems, IEEE Transactions on, 24, 9, 2013, 1425-1436. [17] Z. Dostál, Optimal Quadratic Programming Algorithms: With Applications to Variational Inequalities, New York: Springer, 2009.
utb.fulltext.sponsorship Authors are thankful to Internal Grant Agency (IGA/CebiaTech/2015/026) of Tomas Bata University in Zlín, Czech Republic for financial support.
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