A Novel Iterative Learning Approach for Tracking Control of High-Speed Trains Subject to Unknown Time-Varying Delay

Chen, Yong; Huang, Deqing; Li, Yanan; Feng, Xiaoyun

doi:10.1109/tase.2020.3041952

Cited by 31 publications

(17 citation statements)

References 39 publications

(52 reference statements)

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“…Based on the lifted data in (11), the matrices 8) can be obtained in a least-squares sense, i.e., they are obtained by solving the following optimization problems ( 13) and (14).…”

Section: B Koopman Model Of High-speed Trainmentioning

confidence: 99%

“…However, for solving the least-squares problem ( 13), the analytical solution ( 15) is not the preferred method in practice. Particularly, for larger data sets with I N + 2, it is better to the normal equations associated to (13) and (14). The normal equations are described by V=HG,…”

Section: B Koopman Model Of High-speed Trainmentioning

confidence: 99%

“…Figures [13][14] show the acceleration tracking performance and the control input of four compared methods. From Figure 13(b), the acceleration tracking error of UKMPC is less than those of other three MPC methods.…”

Section: ) Changeable Operating Conditionmentioning

confidence: 99%

“…In [11], using the explicit train operation data, three data-driven models including a linear regression-based model, a nonlinear regression-based model, and a deep neural network based model were constructed and compared, while the train operation controller was not designed. In [12]- [14], based on the repetitive operation pattern of the high-speed train, iterative learning control methods were proposed to improve the train's trajectory tracking accuracy. However, the trained iterative learning controller would be ineffective if the desired trajectory changes [15].…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains

et al. 2021

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Automatic train operation systems of high-speed trains are critical to guarantee operational safety, comfort, and parking accuracy. However, implementing optimal automatic operation control is challenging due to the train's uncertain dynamics and actuator saturation. To address this issue, this paper develops a data-driven Koopman model based predictive control method for automatic train operation systems. The proposed control scheme is designed within a data-driven framework. First, using operational data of trains and the Koopman operator, an explicit linear Koopman model is built to characterize the train dynamics. Then, a model predictive controller is designed based on the Koopman model under comfort and actuator constraints. Furthermore, an online update mechanism for the Koopman model is developed to cope with the changing dynamic characteristics of trains, which reduces the accumulation errors and improves control performance. Stability analysis of the closed-loop control system is provided. Comparative simulation results validate the effectiveness of the proposed control approach.

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“…Based on the lifted data in (11), the matrices 8) can be obtained in a least-squares sense, i.e., they are obtained by solving the following optimization problems ( 13) and (14).…”

Section: B Koopman Model Of High-speed Trainmentioning

confidence: 99%

Section: B Koopman Model Of High-speed Trainmentioning

confidence: 99%

Section: ) Changeable Operating Conditionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains

et al. 2021

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“…Iterative learning control (ILC) is an effective technique used for achieving desired tracking performance of the control system with uncertain dynamics by repetitive operation [17]-2 [19]. It can be considered as a multipass control process where the transient performance is repetitively improved over fixed iteration durations.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning-Based Robotic Controller With Prescribed Human–Robot Interaction Force

Maqsood

Huang

Yang

et al. 2022

IEEE Trans. Automat. Sci. Eng.

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

Huang

Chen

et al. 2022

Intl J Robust & Nonlinear

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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.

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A Novel Iterative Learning Approach for Tracking Control of High-Speed Trains Subject to Unknown Time-Varying Delay

Abstract: Xiaoyun (2020) A novel iterative learning approach for tracking control of high-speed trains subject to unknown time-varying delay. IEEE Transactions on Automation Science and Engineering. pp. 1-9.

Cited by 31 publications

References 39 publications

Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains

Data-Driven Koopman Model Predictive Control for Optimal Operation of High-Speed Trains

Iterative Learning-Based Robotic Controller With Prescribed Human–Robot Interaction Force

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

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