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State-space predictive control of inverted pendulum model

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dc.title State-space predictive control of inverted pendulum model en
dc.contributor.author Rušar, Lukáš
dc.contributor.author Krhovják, Adam
dc.contributor.author Talaš, Stanislav
dc.contributor.author Bobál, Vladimír
dc.relation.ispartof Proceedings - 31st European Conference on Modelling and Simulation, ECMS 2017
dc.identifier.isbn 978-0-9932440-4-9
dc.date.issued 2017
dc.citation.spage 384
dc.citation.epage 390
dc.event.title 31st European Conference on Modelling and Simulation, ECMS 2017
dc.event.location Budapest
utb.event.state-en Hungary
utb.event.state-cs Maďarsko
dc.event.sdate 2017-05-23
dc.event.edate 2017-05-26
dc.type conferenceObject
dc.language.iso en
dc.publisher European Council for Modelling and Simulation
dc.identifier.doi 10.7148/2017-0384
dc.relation.uri http://www.scs-europe.net/dlib/2017/2017-0384.htm
dc.relation.uri http://www.scs-europe.net/dlib/2017/ecms2017acceptedpapers/0384-mct_ECMS2017_0050.pdf
dc.subject predictive control en
dc.subject state-space en
dc.subject inverted pendulum en
dc.subject predictor-corrector en
dc.description.abstract This paper presents a possible way to control the a very fast nonlinear systems. The system of the inverted pendulum was chosen as an exemplar process. This is an example of the nonlinear single-input multi-output process with a sampling period in order of milliseconds. The state-space predictive control was chosen as a control method and the system is described by CARIMA model. The whole process of the controller design is described in this paper. That includes a description of the inverted pendulum nonlinear mathematical model and its linearization, the inference of the output values prediction and the control signal calculation. The control signal is calculated by predictor-corrector method. The results compare several optimization methods to achieve the fastest calculation of the control signal. All of the simulation was done in Matlab. © ECMS Zita Zoltay Paprika, Péter Horák, Kata Váradi,Péter Tamás Zwierczyk, Ágnes Vidovics-Dancs, János Péter Rádics (Editors). en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1007254
utb.identifier.obdid 43876783
utb.identifier.scopus 2-s2.0-85021814308
utb.identifier.wok 000404420000058
utb.source d-scopus
dc.date.accessioned 2017-09-03T21:40:06Z
dc.date.available 2017-09-03T21:40:06Z
dc.description.sponsorship Ministry of Education of the Czech Republic under grant IGA [IGA/FAI/2016/009]
utb.contributor.internalauthor Rušar, Lukáš
utb.contributor.internalauthor Krhovják, Adam
utb.contributor.internalauthor Talaš, Stanislav
utb.contributor.internalauthor Bobál, Vladimír
utb.fulltext.affiliation Lukáš Rušar, Adam Krhovják, Stanislav Talaš and Vladimír Bobál Department of process control Faculty of applied informatics, Tomas Bata university in Zlin Nad Stráněmi 4511, Zlin 76005, Czech Republic E-mail: [email protected]
utb.fulltext.dates -
utb.fulltext.references Albertos Pérez P. and Sala A. 2004. Multivariable Control Systems: an Engineering Approach. Springer. London. Amira. 2000. PS600 Laboratory Experiment Inverted Pendulum. Amira GmbH, Duisburg. Bars R.; R. Haber and U. Schmitz. 2011. Predictive control in process engineering: From the basics to the applications. Weinhaim: Willey-VCH Verlag. Bobál, V. 2008, Adaptive and predictive control. vol. 1. Zlin, Tomas Bata University in Zlin. Camacho E.F. and C. Bordons. 2004. Model predictive control, Springer Verlag, London. Chalupa P. and V. Bobál. 2008. "Modelling and Predictive Control of Inverted Pendulum". In: Proceedings 22nd European Conference on Modelling and Simulation. pp. 531-537. Fikar M. and J. Mikleš. 2008. Process modelling, optimisation and control, Springer-Verlag, Berlin. Hangos K.M.; Bokor J. and Szederkényi G. 2004. Analysis and Control of Nonlinear Process Systems. Springer. London. Maciejowski J.M. 2002. Predictive control with constraints, Prentice Hall, London. Nocedal J. and S. Wright. 2000. Numerical optimisation second edition. Springer, New York. Rossiter J.A. 2003. Model based predictive control: a practical approach, CRC Press. Wang L. 2009. Model predictive control system design and implementation using MATLAB, Springer Verlag, London. Wright S. 1997 Primal-dual interior point methods. Philadelphia: Society for Industrial and Applied Mathematics.
utb.fulltext.sponsorship This article was created with support of the Ministry of Education of the Czech Republic under grant IGA reg. n. IGA/FAI/2016/009.
utb.scopus.affiliation Department of Process Control, Faculty of Applied Informatics, Tomas Bata University in Zlin, Nad Stráněmi 4511, Zlin, Czech Republic
utb.fulltext.projects IGA/FAI/2016/009
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