UM  > Faculty of Science and Technology  > DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Status已發表Published
Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners
Liang, S.; Xi, R.; Xiao, X.; Yang, Z. X.
2022-03-01
Source PublicationMicromachines
ISSN2072-666X
Pages1-10
AbstractThe motion control of high-precision electromechanitcal systems, such as micropositioners,is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems,this work investigates a disturbance observer-based deep reinforcement learning control strategyto realize high robustness and precise tracking performance. Reinforcement learning has showngreat potential as optimal control scheme, however, its application in micropositioning systems is stillrare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policygradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improve the transient response speed. In addition, an adaptive sliding mode disturbance observer(ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The micropositioner controlled by the proposed algorithm can track the target path precisely with less than 1μm error in simulations and actual experiments, which shows the sterling performance and the accuracy.
Keywordmicropositioners reinforcement learning disturbance observer deep deterministic policy gradient
Language英語English
The Source to ArticlePB_Publication
PUB ID63192
Document TypeJournal article
CollectionDEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Corresponding AuthorYang, Z. X.
Recommended Citation
GB/T 7714
Liang, S.,Xi, R.,Xiao, X.,et al. Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners[J]. Micromachines,2022:1-10.
APA Liang, S.,Xi, R.,Xiao, X.,&Yang, Z. X..(2022).Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners.Micromachines,1-10.
MLA Liang, S.,et al."Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners".Micromachines (2022):1-10.
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