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rdf:type
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Description
| - The paper is focused on the control of active magnetic bearing using improved version of Q-learning. The improvement subsists in separating the Q-learning into two phases – efficient prelearning phase, which uses mathematical model of real system, and tutorage phase working with the real system and used for further improvement. Q-learning based controller is compared with PID controller and shows better results regarding the percentage of successful trials. When tutorage is applied the Q-learning based controllers show better results also in terms of control quality criterion. The policy found by learning express high robustness against errors of system variables observations.
- The paper is focused on the control of active magnetic bearing using improved version of Q-learning. The improvement subsists in separating the Q-learning into two phases – efficient prelearning phase, which uses mathematical model of real system, and tutorage phase working with the real system and used for further improvement. Q-learning based controller is compared with PID controller and shows better results regarding the percentage of successful trials. When tutorage is applied the Q-learning based controllers show better results also in terms of control quality criterion. The policy found by learning express high robustness against errors of system variables observations. (en)
- The paper is focused on the control of active magnetic bearing using improved version of Q-learning. The improvement subsists in separating the Q-learning into two phases – efficient prelearning phase, which uses mathematical model of real system, and tutorage phase working with the real system and used for further improvement. Q-learning based controller is compared with PID controller and shows better results regarding the percentage of successful trials. When tutorage is applied the Q-learning based controllers show better results also in terms of control quality criterion. The policy found by learning express high robustness against errors of system variables observations. (cs)
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Title
| - ACTIVE MAGNETIC BEARING CONTROL THROUGH Q-LEARNING
- ACTIVE MAGNETIC BEARING CONTROL THROUGH Q-LEARNING (en)
- ACTIVE MAGNETIC BEARING CONTROL THROUGH Q-LEARNING (cs)
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skos:prefLabel
| - ACTIVE MAGNETIC BEARING CONTROL THROUGH Q-LEARNING
- ACTIVE MAGNETIC BEARING CONTROL THROUGH Q-LEARNING (en)
- ACTIVE MAGNETIC BEARING CONTROL THROUGH Q-LEARNING (cs)
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skos:notation
| - RIV/00216305:26210/03:PU34278!RIV11-MSM-26210___
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
| - V, Z(MSM 261100009), Z(MSM 262100024)
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/00216305:26210/03:PU34278
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - Q-learning, control, active magnetic bearing (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...ontrolniKodProRIV
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http://linked.open...v/mistoKonaniAkce
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http://linked.open...i/riv/mistoVydani
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http://linked.open...i/riv/nazevZdroje
| - Dynamics of Machines 2003
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...UplatneniVysledku
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http://linked.open...iv/tvurceVysledku
| - Březina, Tomáš
- Krejsa, Jiří
- Kratochvíl, Ctirad
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http://linked.open...vavai/riv/typAkce
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http://linked.open.../riv/zahajeniAkce
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http://linked.open...n/vavai/riv/zamer
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number of pages
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http://purl.org/ne...btex#hasPublisher
| - Ústav termomechaniky AV ČR
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https://schema.org/isbn
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http://localhost/t...ganizacniJednotka
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