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Alternative approach to optimization in model predictive control using hill climbing algorithm

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dc.title Alternative approach to optimization in model predictive control using hill climbing algorithm en
dc.contributor.author Antoš, Jan
dc.contributor.author Kubalčík, Marek
dc.relation.ispartof Annals of DAAAM and Proceedings of the International DAAAM Symposium
dc.identifier.issn 1726-9679 Scopus Sources, Sherpa/RoMEO, JCR
dc.identifier.isbn 978-3-902734-07-5
dc.date.issued 2015
utb.relation.volume 2015-January
dc.citation.spage 856
dc.citation.epage 864
dc.event.title 26th DAAAM International Symposium on Intelligent Manufacturing and Automation, DAAAM 2015
dc.event.location Zadar
utb.event.state-en Croatia
utb.event.state-cs Chorvatsko
dc.event.sdate 2015-10-21
dc.event.edate 2015-10-24
dc.type conferenceObject
dc.language.iso en
dc.publisher Danube Adria Association for Automation and Manufacturing, DAAAM
dc.identifier.doi 10.2507/26th.daaam.proceedings.119
dc.relation.uri http://doi.org/10.2507/26th.daaam.proceedings.045
dc.subject Evolutionary Algorithm en
dc.subject Hill Climbing en
dc.subject Optimization en
dc.subject Predictive Control en
dc.subject Simulation en
dc.description.abstract The term predictive control designates a class of control methods suitable for control of various kinds of systems. One of the major advantages of predictive control is its ability to do on-line constraints handling in a systematic way. The predictive control is based on the prediction of a system behavior using a model. Based on this prediction, it is possible to optimize the systems behavior by utilization of a cost function. Each of control variables may be limited thus creating a specific subspace within a cost function. This problem is computationally complex and must be solved in each sampling period by optimization algorithms. Various kinds of algorithms may be applied. This contribution is focused on an alternative approach to optimization by implementation of Hill Climbing algorithm. The motivation for this concept is an effort to find algorithms suitable for reduction of computational expenses. These algorithms might be applied for control of systems with faster dynamics. en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1006765
utb.identifier.obdid 43874297
utb.identifier.scopus 2-s2.0-84987681461
utb.source d-scopus
dc.date.accessioned 2016-12-22T16:19:07Z
dc.date.available 2016-12-22T16:19:07Z
dc.rights Attribution-NonCommercial 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc/4.0/
dc.rights.access openAccess
utb.contributor.internalauthor Antoš, Jan
utb.contributor.internalauthor Kubalčík, Marek
utb.fulltext.affiliation Jan Antos, Marek Kubalcik Department of Process Control, Faculty of Applied Informatics, Tomas Bata University in Zlín, nám. T. G. Masaryka 5555, 760 01 Zlín, Czech Republic
utb.fulltext.dates -
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.
utb.fulltext.projects IGA/CebiaTech/2015/026
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