An approach for accurate stopping of high-speed train by using model predictive control

Liu, Xiaoyu; Xun, Jing; Ning, Bin; Liu, Yuan

doi:10.1109/itsc.2019.8917237

Cited by 9 publications

(9 citation statements)

References 15 publications

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“…FIGURE 12 Result analysis for the three kinds of scheduling measures under different scheduling measures, respectively. We find that the total cumulative delays for 𝜏 c (6, 9) is 13 min less than 𝜏 a (6, 9) and 34 min less than 𝜏 b (6,9).…”

Section: Discussionmentioning

confidence: 99%

“…The distribution of train delays in each station can be obtained in Figure 12. Figure 12(a)-(c) displays the distribution of cumulative delays, maximum delays, number of delayed trains FIGURE 12 Result analysis for the three kinds of scheduling measures under different scheduling measures, respectively. We find that the total cumulative delays for c (6, 9) is 13 min less than a (6,9) and 34 min less than b (6,9).…”

Section: As Shown Inmentioning

confidence: 99%

“…Constraint (12) ensures that trains can run according to the scheduled timetables without delay when there is no disturbance, and it indicates the non-negativity of train delays.…”

Section: Timetable Constraintsmentioning

confidence: 99%

“…Due to track occupancy restrictions and the interaction between trains, a delayed train could cause a domino impact on adjacent trains. An analytical delay propagation model is helpful to timetable planners to investigate the robustness of timetables, and dispatchers to predict the effects of delays and obtain appropriate rescheduling measures [7][8][9][10][11][12][13]. Hence it is important to provide an exploratory analysis of train delay propagation to improve the traffic management.…”

Section: Introductionmentioning

confidence: 99%

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Scheduling‐measure dependent modelling of delay propagation on a single railway line: A matrix transformation approach

et al. 2021

IET Intelligent Trans Sys

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This paper proposes an analytical delay propagation model for single railway lines based on the max-plus algebra theory. The scheduling measures taken by dispatchers, including re-timing and re-ordering, will be incorporated into our delay propagation model using a matrix transformation method. An analysis of delay propagation under some typical emergencies such as segment blockages and train speed limitation is performed. Numerical simulations show that the proposed train delay propagation model can predict emergencyinduced train delays under different scheduling strategies, thus may give a guidance to improve the traffic management. In the high-speed railway train system, the scheduling measures taken by dispatchers, such as re-timing and re-ordering, can be formulated as a delay propagation model using a matrix transformation method.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Discussionmentioning

confidence: 99%

Section: As Shown Inmentioning

confidence: 99%

“…Constraint (12) ensures that trains can run according to the scheduled timetables without delay when there is no disturbance, and it indicates the non-negativity of train delays.…”

Section: Timetable Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Scheduling‐measure dependent modelling of delay propagation on a single railway line: A matrix transformation approach

et al. 2021

IET Intelligent Trans Sys

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“…The multi-particle model is currently mostly aimed at the research of heavy-duty trains. The single-particle model is currently the most commonly used model for describing train operation control systems [26]. This paper uses single particle dynamic model for research.…”

Section: Train Modelmentioning

confidence: 99%

Research on Train Dynamic Coupling Strategy Based on Distributed Model Predictive Control

Yang

Yan

2022

J. Phys.: Conf. Ser.

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This paper mainly studies the problem of dynamic coupling under multiple optimization objectives and multiple constraints. Virtual coupling (VC), as a new train operation mode, effectively improves the operating efficiency of the line, but reduces the safety margin between trains, and requires higher control accuracy. As an important scenario of virtual coupling, dynamic formation has not formed a set of applicable train control strategies. Aiming at the dynamic train formation scenario of virtual coupling, propose a control architecture based on distributed model predictive control (DMPC). Numerical simulation using Matlab verifies the effectiveness of the algorithm in this paper. The simulation results show that the train is under the condition of satisfying the operating constraints. Compared with PID control, the method in this paper is more effective and more accurate.

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Intelligent train stopping control for railways: A deep learning approach

Chen

Yin

Ning³

2023

IET Intelligent Trans Sys

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Station parking accuracy is an important indicator for the automatic control of high‐speed trains; however, it is subject to many influencing factors, such as the characteristics of high nonlinearity and large time delays in the train control model, time‐variant humidity, and uncertain weather conditions, leading to unsatisfying performance with existing feedback control algorithms. This study first proposes an intelligent train stopping control approach based on deep learning for high‐speed railways. By collecting a large amount of historical train operation data from the Beijing–Shenyang high‐speed railway, three data‐driven models are developed for train stopping control. The first model is based on a deep‐layered feedforward neural network (DNN), which predicts the exact train stopping position with dynamic train running states (position, velocity etc.) as input. Taking advantage of the physical train control models used in practice, the DNN to a convolutional neural network (CNN) is then improved by customizing the convolutional layers of the CNN. To overcome the issues arising from the incompleteness of data samples, a few‐shot convolutional neural network (FSCNN) is further developed to enhance the prediction performance of the CNN. Compared with that of the current method used in practice, the simulation experiments show that the train station parking error can be decreased by 38.6%, 42.8%, and 49.2% by our developed DNN, CNN, and FSCNN, respectively.

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An approach for accurate stopping of high-speed train by using model predictive control

Cited by 9 publications

References 15 publications

Scheduling‐measure dependent modelling of delay propagation on a single railway line: A matrix transformation approach

Scheduling‐measure dependent modelling of delay propagation on a single railway line: A matrix transformation approach

Research on Train Dynamic Coupling Strategy Based on Distributed Model Predictive Control

Intelligent train stopping control for railways: A deep learning approach

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