Detail publikačního výsledku

Efficient Q-learning modification aplied on active magnetic bearing control

BŘEZINA, T., KREJSA, J.

Originální název

Efficient Q-learning modification aplied on active magnetic bearing control

Anglický název

Efficient Q-learning modification aplied on active magnetic bearing control

Druh

Článek recenzovaný mimo WoS a Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

Reinforcement learning, Q-learning, Active magnetic bearing

Klíčová slova v angličtině

Reinforcement learning, Q-learning, Active magnetic bearing

Autoři

BŘEZINA, T., KREJSA, J.

Rok RIV

2011

Vydáno

01.11.2004

Nakladatel

Association for engineering mechanics, Czech Republic

ISSN

1210-2717

Periodikum

Inženýrská mechanika - Engineering Mechanics

Svazek

11/2004

Číslo

2

Stát

Česká republika

Strany od

83

Strany počet

14

BibTex

@article{BUT45424,
  author="Tomáš {Březina} and Jiří {Krejsa}",
  title="Efficient Q-learning modification aplied on active magnetic bearing control",
  journal="Inženýrská mechanika - Engineering Mechanics",
  year="2004",
  volume="11/2004",
  number="2",
  pages="14",
  issn="1210-2717"
}