Adaptive Sliding Mode Disturbance Observer and Deep Reinforcement Learning Based Motion Control for Micropositioners

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 Publication	Micromachines
ISSN	2072-666X
Pages	1-10
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 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.
Keyword	micropositioners reinforcement learning disturbance observer deep deterministic policy gradient
Language	英語English
The Source to Article	PB_Publication
PUB ID	63192
Document Type	Journal article
Collection	DEPARTMENT OF ELECTROMECHANICAL ENGINEERING
Corresponding Author	Yang, 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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