Deep Learning Based Semi-Supervised Control for Vertical Security of Maglev Vehicle With Guaranteed Bounded Airgap

doi:10.1109/TITS.2020.3045319

CORC > 力学研究所 > 中国科学院力学研究所 > 流固耦合系统力学重点实验室(2012-)

	Deep Learning Based Semi-Supervised Control for Vertical Security of Maglev Vehicle With Guaranteed Bounded Airgap
	Sun, Yougang 3,4; Xu, Junqi 3; Wu H(吴晗)2; Lin, Guobin 3; Mumtaz, Shahid 1
刊名	IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
	2021-07-01
卷号	22 期号:7 页码:4431-4442
关键词	Extended state observer intelligent control nonlinear dynamics state constrains vertical security
ISSN号	1524-9050
DOI	10.1109/TITS.2020.3045319
通讯作者	Xu, Junqi(xujunqi@tongji.edu.cn) ; Wu, Han(wuhan@imech.ac.cn)
英文摘要	The vertical security problem of maglev train is challenging for nonlinearity, external disturbances, unmeasurable airgap velocity and constrained output. To solve this problem, a semi-supervised controller based on deep belief network (DBN) algorithm is proposed in the presence of unknown external disturbances. Firstly, the extended state observer (ESO) is designed to ensure fast convergence of observation errors with high enough estimation precision. An output-constrained controller is designed by backstepping method, and the estimated value of ESO is introduced to ensure that the output airgap is constrained within a bounded range. Then, the stability of this method is proved based on the symmetric Barrier Lyapunov function. Subsequently, a semi-supervised controller is presented based on DBN algorithm and the output-constrained controller. The numerical simulation results show that this method can effectively deal with unmeasurable airgap velocity and generalized external disturbances, and guarantee the vertical security with output airgap within a bounded range. Finally, experiments are implemented on a full-scale maglev vehicle and the experimental results demonstrate that the developed deep learning controller can ensure the vertical security.
分类号	一类
资助项目	National Natural Science Foundation of China[51905380] ; National Natural Science Foundation of China[51805522] ; China Postdoctoral Science Foundation[2019M651582] ; China Postdoctoral Science Foundation[2020T130475]
WOS关键词	BREATHING HYPERSONIC VEHICLES ; SLIDING MODE CONTROL ; NEURAL APPROXIMATION ; NONLINEAR-SYSTEMS ; NETWORKS ; OBSERVER
WOS研究方向	Engineering ; Transportation
语种	英语
WOS记录号	WOS:000673518500048
资助机构	National Natural Science Foundation of China ; China Postdoctoral Science Foundation
其他责任者	Xu, Junqi ; Wu, Han
内容类型	期刊论文
源URL	[http://dspace.imech.ac.cn/handle/311007/87067]
专题	力学研究所_流固耦合系统力学重点实验室(2012-)
作者单位	1.Inst Telecomunicacoes, P-3810193 Aveiro, Portugal 2.Chinese Acad Sci, Inst Mech, Key Lab Mech Fluid Solid Coupling Syst, Beijing 100190, Peoples R China; 3.Tongji Univ, Natl Maglev Transportat Engn R&D Ctr, Shanghai 201804, Peoples R China; 4.Tongji Univ, Inst Rail Transit, Shanghai 201804, Peoples R China;
推荐引用方式 GB/T 7714	Sun, Yougang,Xu, Junqi,Wu H,et al. Deep Learning Based Semi-Supervised Control for Vertical Security of Maglev Vehicle With Guaranteed Bounded Airgap[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021,22(7):4431-4442.
APA	Sun, Yougang,Xu, Junqi,吴晗,Lin, Guobin,&Mumtaz, Shahid.(2021).Deep Learning Based Semi-Supervised Control for Vertical Security of Maglev Vehicle With Guaranteed Bounded Airgap.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,22(7),4431-4442.
MLA	Sun, Yougang,et al."Deep Learning Based Semi-Supervised Control for Vertical Security of Maglev Vehicle With Guaranteed Bounded Airgap".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22.7(2021):4431-4442.