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Modified hildreth’s method applied in multivariable model predictive control

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dc.title Modified hildreth’s method applied in multivariable model predictive control en
dc.contributor.author Kubalčík, Marek
dc.contributor.author Bobál, Vladimír
dc.contributor.author Barot, Tomáš
dc.relation.ispartof Lecture Notes in Electrical Engineering
dc.identifier.issn 1876-1100 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-319-91333-9
dc.date.issued 2019
utb.relation.volume 505
dc.citation.spage 75
dc.citation.epage 81
dc.event.title 3rd Conference on Innovation, Engineering and Entrepreneurship, Regional HELIX 2018
dc.event.location Guimarães
utb.event.state-en Portugal
utb.event.state-cs Portugalsko
dc.event.sdate 2018-06-27
dc.event.edate 2018-06-29
dc.type conferenceObject
dc.language.iso en
dc.publisher Springer Verlag
dc.identifier.doi 10.1007/978-3-319-91334-6_11
dc.relation.uri https://link.springer.com/chapter/10.1007/978-3-319-91334-6_11
dc.subject Algorithm-complexity en
dc.subject Hildreth‘s method en
dc.subject Model predictive control en
dc.subject Multivariable control en
dc.subject Quadratic programming en
dc.description.abstract A significantly important part of model predictive control (MPC) with constraints are algorithms of numerical optimization. Reduction of the computational complexity of the optimization methods has been widely researched. The reason is that in certain cases of predictive control of fast dynamics processes an optimization algorithm may not be feasible within the sampling period time. This situation occurs particularly when requirements on control are more complex, e.g. in the multivariable control. Hildreth’s method based on the dual-problem-optimization-principles has been widely applied and implemented in model predictive control. However, modifications of this method are not widely described in context of model predictive control. This paper proposes a modification of Hildreth’s method, which reduces the computational complexity of the algorithm, and its application in the multivariable predictive control. © 2019, Springer International Publishing AG, part of Springer Nature. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1008073
utb.identifier.obdid 43878622
utb.identifier.scopus 2-s2.0-85048550857
utb.source d-scopus
dc.date.accessioned 2018-07-27T08:47:42Z
dc.date.available 2018-07-27T08:47:42Z
utb.contributor.internalauthor Kubalčík, Marek
utb.contributor.internalauthor Bobál, Vladimír
utb.fulltext.affiliation Marek Kubalcik 1, Vladimir Bobal 1, Tomas Barot 2 1 Department of Process Control, Faculty of Applied Informatics, Tomas Bata University in Zlín, Nad Stráněmi 4511, 760 05 Zlín, Czech Republic {kubalcik,bobal}@utb.cz 2 Department of Mathematics with Didactics, Faculty of Education, University of Ostrava, Mlynska 5, 701 03 Ostrava, Czech Republic Tomas.Barot@osu.cz
utb.fulltext.dates -
utb.scopus.affiliation Department of Process Control, Faculty of Applied Informatics, Tomas Bata University in Zlín, Nad Stráněmi 4511, Zlín, Czech Republic; Department of Mathematics with Didactics, Faculty of Education, University of Ostrava, Mlynska 5, Ostrava, Czech Republic
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.faculty Faculty of Applied Informatics
utb.fulltext.ou Department of Process Control
utb.fulltext.ou Department of Process Control
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