A real-time tracking controller for piezoelectric actuators based on reinforcement learning and inverse compensation

doi:10.1016/j.scs.2021.102822

	A real-time tracking controller for piezoelectric actuators based on reinforcement learning and inverse compensation
	Qin, Shijie ; Cheng, Long1
发表期刊	SUSTAINABLE CITIES AND SOCIETY
ISSN	2210-6707
	2021-06-01
卷号	69 页码:9
通讯作者	Cheng, Long(long.cheng@ia.ac.cn)
摘要	Nanotechnology is a promising technology and has been widely applied for sustainable smart cities. As the fundamental devices for nanotechnology, piezoelectric actuators (PEAs) have gained wide attention in precision manufacturing because of the advantages of rapid response, large mechanical force and high resolution. However, the inherent nonlinearities of PEAs hinder wide applications for nano-positioning and high-precision manipulation. To eliminate these nonlinearities, various control methods have been proposed, while the optimal control of PEAs is considered rarely. Inspired by the reinforcement learning, adaptive dynamic programming (ADP) is proposed to solve the optimal tracking control problem of PEAs. In this paper, a controller based on reinforcement learning and inverse compensation is designed for the tracking control of PEAs. The experiments on the PEA platform are designed to verify the effectiveness of the proposed method. Comparisons with some representative controllers have demonstrated that the proposed controller has a better control performance.
关键词	Piezoelectric actuators Real-time tracking control Reinforcement learning Adaptive dynamic programming Hysteresis compensation
DOI	10.1016/j.scs.2021.102822
关键词[WOS]	MODEL-PREDICTIVE CONTROL ; HYSTERESIS ; PIEZOSTAGE
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62025307] ; National Natural Science Foundation of China[U1913209] ; National Natural Science Foundation of China[61873268] ; Beijing Municipal Natural Science Foundation, China[JQ19020]
项目资助者	National Natural Science Foundation of China ; Beijing Municipal Natural Science Foundation, China
WOS研究方向	Construction & Building Technology ; Science & Technology - Other Topics ; Energy & Fuels
WOS类目	Construction & Building Technology ; Green & Sustainable Science & Technology ; Energy & Fuels
WOS记录号	WOS:000689066400008
出版者	ELSEVIER
七大方向——子方向分类	智能控制
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/45903
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Cheng, Long
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Control & Management Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Qin, Shijie,Cheng, Long. A real-time tracking controller for piezoelectric actuators based on reinforcement learning and inverse compensation[J]. SUSTAINABLE CITIES AND SOCIETY,2021,69:9.
APA	Qin, Shijie,&Cheng, Long.(2021).A real-time tracking controller for piezoelectric actuators based on reinforcement learning and inverse compensation.SUSTAINABLE CITIES AND SOCIETY,69,9.
MLA	Qin, Shijie,et al."A real-time tracking controller for piezoelectric actuators based on reinforcement learning and inverse compensation".SUSTAINABLE CITIES AND SOCIETY 69(2021):9.