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Description
  • V příspěvku je popsáno řízení aktivního magnetického ložiska pomocí navrženého lineárního regulátoru s paralelní nelineární kompenzací. Parametry kompenzace jsou učeny pomocí metody CARLA (Continuous Action Reinforcement Learning Automata). Je zde popsán vliv parametrů učení na průběh učení. Vliv parametrů je dále ověřen pomocí simulační studie. Je ukázáno, že vhodnou volbou parametrů lze dosáhnout zlepšení řízení. (cs)
  • The active magnetic bearing control through analytically designed linear PD regulator, with parallel nonlinear compensation represented by automatic approximator is described in this contribution. Coefficient (parameter) values come from actions of Continuous Action Reinformcement Learning Automata (CARLAs). Influence of CARLAs parameters to learning is discussed. Parameters influence is proved by simulation study. It is shown that learning improvement can be reached by selecting appropriate parameters of learning.
  • The active magnetic bearing control through analytically designed linear PD regulator, with parallel nonlinear compensation represented by automatic approximator is described in this contribution. Coefficient (parameter) values come from actions of Continuous Action Reinformcement Learning Automata (CARLAs). Influence of CARLAs parameters to learning is discussed. Parameters influence is proved by simulation study. It is shown that learning improvement can be reached by selecting appropriate parameters of learning. (en)
Title
  • Self-learning controller of active magnetic bearing based on CARLA method
  • Učící se řídící člen aktivního magnetického ložiska založený na metodě CARLA (cs)
  • Self-learning controller of active magnetic bearing based on CARLA method (en)
skos:prefLabel
  • Self-learning controller of active magnetic bearing based on CARLA method
  • Učící se řídící člen aktivního magnetického ložiska založený na metodě CARLA (cs)
  • Self-learning controller of active magnetic bearing based on CARLA method (en)
skos:notation
  • RIV/00216305:26210/07:PU64498!RIV07-MSM-26210___
http://linked.open.../vavai/riv/strany
  • 6-9
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM0021630518)
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
  • 449315
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26210/07:PU64498
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • active magnetic bearing control, continuous action reinforcement learning automata (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • PL - Polská republika
http://linked.open...ontrolniKodProRIV
  • [0723967BABAB]
http://linked.open...i/riv/nazevZdroje
  • Pomiary Automatyka Kontrola
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 2007
http://linked.open...iv/tvurceVysledku
  • Březina, Tomáš
  • Pulchart, Jaroslav
  • Turek, Milan
http://linked.open...n/vavai/riv/zamer
issn
  • 0032-4140
number of pages
http://localhost/t...ganizacniJednotka
  • 26210
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