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  • Possible contribution and limitations of model predictive controller are discussed for special case of thermostatic bath temperature control. The control potential is restricted because of constrained heating and cooling manipulated variables. The aim of the paper is to find out which knowledge and requirements can improve control and try to compare predictive controller with some traditional control approaches for given specific thermal process. Base predictive controller uses information about MIMO system dynamics and constrains. The control response of such a controller is unfortunately similar to known simple approaches. Control with predictive controller became beneficial after additional information (future set-point knowledge) and other requirements (minimal energy cost ? optimal steady state energy consumption) were used.
  • Possible contribution and limitations of model predictive controller are discussed for special case of thermostatic bath temperature control. The control potential is restricted because of constrained heating and cooling manipulated variables. The aim of the paper is to find out which knowledge and requirements can improve control and try to compare predictive controller with some traditional control approaches for given specific thermal process. Base predictive controller uses information about MIMO system dynamics and constrains. The control response of such a controller is unfortunately similar to known simple approaches. Control with predictive controller became beneficial after additional information (future set-point knowledge) and other requirements (minimal energy cost ? optimal steady state energy consumption) were used. (en)
Title
  • Control of a simple constrained MIMO system with steady state optimization - case study
  • Control of a simple constrained MIMO system with steady state optimization - case study (en)
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  • Control of a simple constrained MIMO system with steady state optimization - case study
  • Control of a simple constrained MIMO system with steady state optimization - case study (en)
skos:notation
  • RIV/00216275:25530/09:00009500!RIV10-MSM-25530___
http://linked.open...avai/riv/aktivita
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  • Z(MSM0021627505)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 308250
http://linked.open...ai/riv/idVysledku
  • RIV/00216275:25530/09:00009500
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  • Mathematical model; MIMO control; constrains; predictive controller; steady state optimization (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [7F1D1E4E58D1]
http://linked.open...v/mistoKonaniAkce
  • Innsbruck, Austria
http://linked.open...i/riv/mistoVydani
  • Calgary
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 28th IASTED International Conference on Modelling, Identification and Control
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http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Dušek, František
  • Honc, Daniel
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
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  • ACTA Press
https://schema.org/isbn
  • 978-0-88986-781-9
http://localhost/t...ganizacniJednotka
  • 25530
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