About: Efficient Q-learning modification aplied on active magnetic bearing control

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	The paper is focused on use of Q-learning for active magnetic bearing (AMB) control. Q-learning belongs to the reinforcement learning methods which are the part of real time machine learning approaches. The essence of proposed method is in separating the Q-learning into two phases – prelearning phase, which use mathematical model of real system and tutorage phase, which works with the real system and is used for further improvement of Q-values found during the prelearning phase. Proposed learning method, used in prelearning phase, proved to be highly efficient. Controller based on Q-learning show better results (regarding the number of successful trials) than referential PID controller after only 1000 table passes. The control quality criterion results are comparable. The policy found by learning also shows high robustness against errors of system variables observations, even only very simple reinforcement function in shape of simple reduced penalty is used. The paper is focused on use of Q-learning for active magnetic bearing (AMB) control. Q-learning belongs to the reinforcement learning methods which are the part of real time machine learning approaches. The essence of proposed method is in separating the Q-learning into two phases – prelearning phase, which use mathematical model of real system and tutorage phase, which works with the real system and is used for further improvement of Q-values found during the prelearning phase. Proposed learning method, used in prelearning phase, proved to be highly efficient. Controller based on Q-learning show better results (regarding the number of successful trials) than referential PID controller after only 1000 table passes. The control quality criterion results are comparable. The policy found by learning also shows high robustness against errors of system variables observations, even only very simple reinforcement function in shape of simple reduced penalty is used. (en)
Title	Efficient Q-learning modification aplied on active magnetic bearing control Efficient Q-learning modification aplied on active magnetic bearing control (en)
skos:prefLabel	Efficient Q-learning modification aplied on active magnetic bearing control Efficient Q-learning modification aplied on active magnetic bearing control (en)
skos:notation	RIV/00216305:26210/04:PU45638!RIV11-MSM-26210___
http://linked.open...avai/riv/aktivita	V Z
http://linked.open...avai/riv/aktivity	V, Z(MSM 261100009), Z(MSM 262100024)
http://linked.open...iv/cisloPeriodika	2
http://linked.open...vai/riv/dodaniDat	2011
http://linked.open...aciTvurceVysledku	Krejsa, Jiří Březina, Tomáš
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoké učení technické v Brně / Fakulta strojního inženýrství
http://linked.open...dnocenehoVysledku	562241
http://linked.open...ai/riv/idVysledku	RIV/00216305:26210/04:PU45638
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Reinforcement learning, Q-learning, Active magnetic bearing (en)
http://linked.open.../riv/klicoveSlovo	Reinforcement learning Q-learning Active magnetic bearing
http://linked.open...odStatuVydavatele	CZ - Česká republika
http://linked.open...ontrolniKodProRIV	[A2CFB115B0AF]
http://linked.open...i/riv/nazevZdroje	Inženýrská mechanika - Engineering Mechanics
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...UplatneniVysledku	2004
http://linked.open...v/svazekPeriodika	11/2004
http://linked.open...iv/tvurceVysledku	Březina, Tomáš Krejsa, Jiří
http://linked.open...n/vavai/riv/zamer	http://linked.opendata.cz/resource/domain/vavai/zamer/MSM%20262100024 http://linked.opendata.cz/resource/domain/vavai/zamer/MSM%20261100009
issn	1210-2717
number of pages	14 (xsd:int)
http://localhost/t...ganizacniJednotka	26210

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