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  • Active magnetic bearing (AMB) is perspective design element; however AMB itself is unstable and must be stabilized by feedback control loop. Artificial intelligence methods, which use real time machine learning, can be used for the proposition of new control methods, which either improve the AMB control, or require less complex control electronics. The paper is focused on use of reinforcement learning version called Q-learning. As the conventional Q-learning architectures learning process is too slow too be practical for real control tasks, the paper proposes improvement of Q-learning by partitioning the learning process into two phases: prelearning phase and tutorage phase. Prelearning phase requires computational model but is highly efficient, tutorage phase uses conventional real time Q-learning and assumes the interaction with the real system. To demonstrate the qualities of developed controllers the performance of AMB model controlled by such controller is compared with the performance of AMB mod
  • Active magnetic bearing (AMB) is perspective design element; however AMB itself is unstable and must be stabilized by feedback control loop. Artificial intelligence methods, which use real time machine learning, can be used for the proposition of new control methods, which either improve the AMB control, or require less complex control electronics. The paper is focused on use of reinforcement learning version called Q-learning. As the conventional Q-learning architectures learning process is too slow too be practical for real control tasks, the paper proposes improvement of Q-learning by partitioning the learning process into two phases: prelearning phase and tutorage phase. Prelearning phase requires computational model but is highly efficient, tutorage phase uses conventional real time Q-learning and assumes the interaction with the real system. To demonstrate the qualities of developed controllers the performance of AMB model controlled by such controller is compared with the performance of AMB mod (en)
Title
  • IMPROVEMENT OF Q-LEARNING USED FOR CONTROL OF AMB
  • IMPROVEMENT OF Q-LEARNING USED FOR CONTROL OF AMB (en)
skos:prefLabel
  • IMPROVEMENT OF Q-LEARNING USED FOR CONTROL OF AMB
  • IMPROVEMENT OF Q-LEARNING USED FOR CONTROL OF AMB (en)
skos:notation
  • RIV/00216305:26210/03:PU37863!RIV/2004/MSM/262104/N
http://linked.open.../vavai/riv/strany
  • 51-54
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM 262100024)
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
  • 609994
http://linked.open...ai/riv/idVysledku
  • RIV/00216305:26210/03:PU37863
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Control, Q-learning, Active Magnetic Bearing (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [685185DE4195]
http://linked.open...v/mistoKonaniAkce
  • Hotel Permon, The High Tatras
http://linked.open...i/riv/mistoVydani
  • Košice, Slovak Republik
http://linked.open...i/riv/nazevZdroje
  • Electrical Drives and Power Electronics 2003
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...ocetUcastnikuAkce
http://linked.open...nichUcastnikuAkce
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Březina, Tomáš
  • Krejsa, Jiří
  • Věchet, Stanislav
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
  • Neuveden
https://schema.org/isbn
  • 80-89061-77-X
http://localhost/t...ganizacniJednotka
  • 26210
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