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  • The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. The new approach to analysis of on-line identification methods based on one-step-ahead prediction clears up their sensitivity to disturbances in control loop. On one hand faster disturbance rejection due to short sampling period can be an advantage but on the other hand it brings us some practical problems. Particularly, quantization error and finite numerical precision of industrial controller must be considered in the real process control. We concentrate our attention on dealing with adverse effects that work on real-time identification of process, especially quantization. It is shown; that a neural network applied to on-line identification process produces more stable solution in the rapid sampling domain.
  • The use of short sampling period in adaptive control has not been described properly when controlling the real process by adaptive controller. The new approach to analysis of on-line identification methods based on one-step-ahead prediction clears up their sensitivity to disturbances in control loop. On one hand faster disturbance rejection due to short sampling period can be an advantage but on the other hand it brings us some practical problems. Particularly, quantization error and finite numerical precision of industrial controller must be considered in the real process control. We concentrate our attention on dealing with adverse effects that work on real-time identification of process, especially quantization. It is shown; that a neural network applied to on-line identification process produces more stable solution in the rapid sampling domain. (en)
Title
  • Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period
  • Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period (en)
skos:prefLabel
  • Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period
  • Adaptive Controllers by Using Neural Network Based Identification for Short Sampling Period (en)
skos:notation
  • RIV/00216305:26220/06:PU63746!RIV10-MSM-26220___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA102/06/1132), Z(MSM0021630503)
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
  • 464131
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26220/06:PU63746
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Adaptive Controllers, Neural Networks for Identification, Comparison of Identifications methods, Rapid Sampling Domain (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [E1C46B3F3E51]
http://linked.open...v/mistoKonaniAkce
  • Singapore
http://linked.open...i/riv/mistoVydani
  • Singapore
http://linked.open...i/riv/nazevZdroje
  • 9th International Conference on Control, Automation, Robotics and Vision, IEEE ICARCV2006
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
  • Pivoňka, Petr
  • Veleba, Václav
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • Nanyang Technological University
https://schema.org/isbn
  • 1-4244-0342-1
http://localhost/t...ganizacniJednotka
  • 26220
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