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Description
  • In this work the structure of the network consisting of local models is optimized via iterative algorithm. The initial position of the local models in the operating space is obtained using the clustering algorithm. Clustering of dynamic data is used to unable multiple local data regions to be identified as a function of similarity between the dynamic data within the local data regions. The number of clusters is further reduced by merging clusters together based the modelling performance and similarity criterion. The reduced model is then used in the GPC framework as a linear approximation of the process in the current operating point. Simple structure of the local model network enables its linearization along the future trajectory. The approach is illustrated by a simulation study of a continuous stirred tank reactor.
  • In this work the structure of the network consisting of local models is optimized via iterative algorithm. The initial position of the local models in the operating space is obtained using the clustering algorithm. Clustering of dynamic data is used to unable multiple local data regions to be identified as a function of similarity between the dynamic data within the local data regions. The number of clusters is further reduced by merging clusters together based the modelling performance and similarity criterion. The reduced model is then used in the GPC framework as a linear approximation of the process in the current operating point. Simple structure of the local model network enables its linearization along the future trajectory. The approach is illustrated by a simulation study of a continuous stirred tank reactor. (en)
Title
  • Optimization of the local model network structure for predictive control
  • Optimization of the local model network structure for predictive control (en)
skos:prefLabel
  • Optimization of the local model network structure for predictive control
  • Optimization of the local model network structure for predictive control (en)
skos:notation
  • RIV/70883521:28140/11:43865504!RIV12-GA0-28140___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GP102/09/P243)
http://linked.open...iv/cisloPeriodika
  • 1
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 218774
http://linked.open...ai/riv/idVysledku
  • RIV/70883521:28140/11:43865504
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Predictive control; Optimization; Multiple models; Modelling (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • AT - Rakouská republika
http://linked.open...ontrolniKodProRIV
  • [0803B8D1F05C]
http://linked.open...i/riv/nazevZdroje
  • Modelling, Indentification and Control
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 31
http://linked.open...iv/tvurceVysledku
  • Bobál, Vladimír
  • Chalupa, Petr
  • Novák, Jakub
issn
  • 1025-8973
number of pages
http://localhost/t...ganizacniJednotka
  • 28140
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