Efficient Q-learning modification aplied on active magnetic bearing control

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F04%3APU45638" target="_blank" >RIV/00216305:26210/04:PU45638 - isvavai.cz</a>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient Q-learning modification aplied on active magnetic bearing control

Popis výsledku v původním jazyce

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 theQ-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 methodd, 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.

Název v anglickém jazyce

Efficient Q-learning modification aplied on active magnetic bearing control

Popis výsledku anglicky

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 theQ-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 methodd, 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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2004

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Inženýrská mechanika - Engineering Mechanics

ISSN

1210-2717

e-ISSN

—

Svazek periodika

11/2004

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

14

Strana od-do

83-96

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—