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Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners
Liang, Shiyun1; Xi, Ruidong1; Xiao, Xiao2; Yang, Zhixin1
2022-03-01
Source PublicationMicromachines
Volume13Issue:3
Abstract

The 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 strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (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 1 µm error in simulations and actual experiments, which shows the sterling performance and the accuracy improvement of the controller.

KeywordDeep Deterministic Policy Gradient Disturbance Observer Micropositioners Reinforcement Learning
DOI10.3390/mi13030458
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaChemistry ; Science & Technology - Other Topics ; Instruments & Instrumentation ; Physics
WOS SubjectChemistry, Analytical ; Nanoscience & Nanotechnology ; Instruments & Instrumentation ; Physics, Applied
WOS IDWOS:000774082500001
Scopus ID2-s2.0-85127392167
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Cited Times [WOS]:1   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionFaculty of Science and Technology
Corresponding AuthorYang, Zhixin
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering, University of Macau, 999078, Macao
2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Liang, Shiyun,Xi, Ruidong,Xiao, Xiao,et al. Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners[J]. Micromachines,2022,13(3).
APA Liang, Shiyun,Xi, Ruidong,Xiao, Xiao,&Yang, Zhixin.(2022).Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners.Micromachines,13(3).
MLA Liang, Shiyun,et al."Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners".Micromachines 13.3(2022).
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