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  • The paper deals with the problem of modelling nonlinear processes using the Local Model Network (LMN). The idea is based on development of the local linear models for the whole operating range of the controlled process. The nonlinear plant is then approximated by a set of locally valid submodels, which are smoothly connected using the validity function. For saving the computational load, linear model is obtained by interpolating these linear models at each sample instant and then used in GPC framework to calculate the future behaviour of the process. It avoids the time consuming quadratic optimization calculation, which is normally necessary in nonlinear predictive control. The proposed approach is applied to a simulated nonlinear pH neutralization process.
  • The paper deals with the problem of modelling nonlinear processes using the Local Model Network (LMN). The idea is based on development of the local linear models for the whole operating range of the controlled process. The nonlinear plant is then approximated by a set of locally valid submodels, which are smoothly connected using the validity function. For saving the computational load, linear model is obtained by interpolating these linear models at each sample instant and then used in GPC framework to calculate the future behaviour of the process. It avoids the time consuming quadratic optimization calculation, which is normally necessary in nonlinear predictive control. The proposed approach is applied to a simulated nonlinear pH neutralization process. (en)
Title
  • Control of pH neutralization process using multiple models and predictive control
  • Control of pH neutralization process using multiple models and predictive control (en)
skos:prefLabel
  • Control of pH neutralization process using multiple models and predictive control
  • Control of pH neutralization process using multiple models and predictive control (en)
skos:notation
  • RIV/70883521:28140/09:63508773!RIV10-GA0-28140___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GP102/09/P243)
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
  • 308261
http://linked.open...ai/riv/idVysledku
  • RIV/70883521:28140/09:63508773
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Predictive control; Multiple models; Modelling; pH neutralization (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [F85D57880CBA]
http://linked.open...v/mistoKonaniAkce
  • Innsbruck
http://linked.open...i/riv/mistoVydani
  • Calgary
http://linked.open...i/riv/nazevZdroje
  • Proceeding of the 28th IASTED International Conference on Modelling, Identification 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...iv/tvurceVysledku
  • Bobál, Vladimír
  • Chalupa, Petr
  • Novák, Jakub
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • ACTA Press
https://schema.org/isbn
  • 978-0-88986-781-9
http://localhost/t...ganizacniJednotka
  • 28140
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