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BŘEZINA, T. KREJSA, J. VĚCHET, S.
Originální název
Improvement of Q-learning Used for Control of AMB
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
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 to 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 model controlled by referential PID controller.
Klíčová slova
Control, Q-learning, Active Magnetic Bearing
Autoři
BŘEZINA, T.; KREJSA, J.; VĚCHET, S.
Rok RIV
2003
Vydáno
24. 9. 2003
Místo
Košice, Slovak Republik
ISBN
80-89061-77-X
Kniha
Electrical Drives and Power Electronics 2003
Edice
Neuveden
Číslo edice
Strany od
51
Strany do
54
Strany počet
4
BibTex
@inproceedings{BUT8152, author="Tomáš {Březina} and Jiří {Krejsa} and Stanislav {Věchet}", title="Improvement of Q-learning Used for Control of AMB", booktitle="Electrical Drives and Power Electronics 2003", year="2003", series="Neuveden", number="Neuveden", pages="4", address="Košice, Slovak Republik", isbn="80-89061-77-X" }