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.
|Number of pages||15|
|Journal||International Journal of Robust and Nonlinear Control|
|State||Published - Apr 2022|
Bibliographical noteFunding Information:
China Postdoctoral Science Foundation, 2021M692783; National Natural Science Foundation of China, U1934221; U21A20169; Sichuan Science and Technology Program, 2020YFQ0057; 2021JDJQ0012 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.
© 2022 John Wiley & Sons Ltd.
- barrier composite energy function
- 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