Iterative learning control for high-speed trains with velocity and displacement constraints

Deqing Huang; Tengfei Huang; Chunrong Chen; Na Qin; Xu Jin; Qingyuan Wang; Yong Chen

doi:10.1002/rnc.5984

Iterative learning control for high-speed trains with velocity and displacement constraints

Deqing Huang, Tengfei Huang, Chunrong Chen, Na Qin, Xu Jin, Qingyuan Wang, Yong Chen

Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

7 Scopus citations

Abstract

In this article, a novel iterative learning control (ILC) scheme is presented for the operation control of high-speed train (HST), where the velocity and displacement of HST are strictly limited to ensure safety and comfort. The model of HST constructed in the article is practical in the sense that both parametric and nonparametric uncertainties of system are addressed simultaneously. Backstepping design with the newly proposed barrier Lyapunov function is incorporated in analysis to ensure the uniform convergence of the state tracking error and that the constraint requirements on velocity and displacement would not be violated during the whole operation process. In the end, a simulation study is presented to demonstrate the efficacy of the proposed ILC law.

Original language	English
Pages (from-to)	3647-3661
Number of pages	15
Journal	International Journal of Robust and Nonlinear Control
Volume	32
Issue number	6
DOIs	https://doi.org/10.1002/rnc.5984
State	Published - Apr 2022

Bibliographical note

Funding Information:
China Postdoctoral Science Foundation, 2021M692783; National Natural Science Foundation of China, U1934221; U21A20169; Sichuan Science and Technology Program, 2020YFQ0057; 2021JDJQ0012 Funding information

Funding Information:
The work by Deqing Huang, Tengfei Huang, Chunrong Chen, Na Qin, Qingyuan Wang, and Yong Chen was partially supported by the National Natural Science Foundation of China under Grants U1934221, U21A20169, the Sichuan Science and Technology Program under Grant 2021JDJQ0012, 2020YFQ0057, and the China Postdoctoral Science Foundation under Grant 2021M692783.

Publisher Copyright:
© 2022 John Wiley & Sons Ltd.

Keywords

barrier composite energy function
constraint
high-speed train
iterative learning control

ASJC Scopus subject areas

Control and Systems Engineering
Chemical Engineering (all)
Biomedical Engineering
Aerospace Engineering
Mechanical Engineering
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

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10.1002/rnc.5984

Cite this

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title = "Iterative learning control for high-speed trains with velocity and displacement constraints",

abstract = "In this article, a novel iterative learning control (ILC) scheme is presented for the operation control of high-speed train (HST), where the velocity and displacement of HST are strictly limited to ensure safety and comfort. The model of HST constructed in the article is practical in the sense that both parametric and nonparametric uncertainties of system are addressed simultaneously. Backstepping design with the newly proposed barrier Lyapunov function is incorporated in analysis to ensure the uniform convergence of the state tracking error and that the constraint requirements on velocity and displacement would not be violated during the whole operation process. In the end, a simulation study is presented to demonstrate the efficacy of the proposed ILC law.",

keywords = "barrier composite energy function, constraint, high-speed train, iterative learning control",

author = "Deqing Huang and Tengfei Huang and Chunrong Chen and Na Qin and Xu Jin and Qingyuan Wang and Yong Chen",

note = "Funding Information: China Postdoctoral Science Foundation, 2021M692783; National Natural Science Foundation of China, U1934221; U21A20169; Sichuan Science and Technology Program, 2020YFQ0057; 2021JDJQ0012 Funding information Funding Information: The work by Deqing Huang, Tengfei Huang, Chunrong Chen, Na Qin, Qingyuan Wang, and Yong Chen was partially supported by the National Natural Science Foundation of China under Grants U1934221, U21A20169, the Sichuan Science and Technology Program under Grant 2021JDJQ0012, 2020YFQ0057, and the China Postdoctoral Science Foundation under Grant 2021M692783. Publisher Copyright: {\textcopyright} 2022 John Wiley & Sons Ltd.",

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doi = "10.1002/rnc.5984",

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AU - Huang, Deqing

AU - Huang, Tengfei

AU - Chen, Chunrong

AU - Qin, Na

AU - Jin, Xu

AU - Wang, Qingyuan

AU - Chen, Yong

N1 - Funding Information: China Postdoctoral Science Foundation, 2021M692783; National Natural Science Foundation of China, U1934221; U21A20169; Sichuan Science and Technology Program, 2020YFQ0057; 2021JDJQ0012 Funding information Funding Information: The work by Deqing Huang, Tengfei Huang, Chunrong Chen, Na Qin, Qingyuan Wang, and Yong Chen was partially supported by the National Natural Science Foundation of China under Grants U1934221, U21A20169, the Sichuan Science and Technology Program under Grant 2021JDJQ0012, 2020YFQ0057, and the China Postdoctoral Science Foundation under Grant 2021M692783. Publisher Copyright: © 2022 John Wiley & Sons Ltd.

PY - 2022/4

Y1 - 2022/4

N2 - In this article, a novel iterative learning control (ILC) scheme is presented for the operation control of high-speed train (HST), where the velocity and displacement of HST are strictly limited to ensure safety and comfort. The model of HST constructed in the article is practical in the sense that both parametric and nonparametric uncertainties of system are addressed simultaneously. Backstepping design with the newly proposed barrier Lyapunov function is incorporated in analysis to ensure the uniform convergence of the state tracking error and that the constraint requirements on velocity and displacement would not be violated during the whole operation process. In the end, a simulation study is presented to demonstrate the efficacy of the proposed ILC law.

AB - In this article, a novel iterative learning control (ILC) scheme is presented for the operation control of high-speed train (HST), where the velocity and displacement of HST are strictly limited to ensure safety and comfort. The model of HST constructed in the article is practical in the sense that both parametric and nonparametric uncertainties of system are addressed simultaneously. Backstepping design with the newly proposed barrier Lyapunov function is incorporated in analysis to ensure the uniform convergence of the state tracking error and that the constraint requirements on velocity and displacement would not be violated during the whole operation process. In the end, a simulation study is presented to demonstrate the efficacy of the proposed ILC law.

KW - barrier composite energy function

KW - constraint

KW - high-speed train

KW - iterative learning control

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JF - International Journal of Robust and Nonlinear Control

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Iterative learning control for high-speed trains with velocity and displacement constraints

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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