Stability model verification and control strategy optimization of train platoon based on stochastic priced timed game

LU Wan-li; LYU Ji-dong; GAO Jin-jin; CHAI Ming; LIU Hong-jie; TANG Tao; LI Dan-yong; SONG Dong-liang

doi:10.19818/j.cnki.1671-1637.2023.02.020

Volume 23 Issue 2

Apr. 2023

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Article Navigation > Journal of Traffic and Transportation Engineering > 2023 > 23(2): 273-286

LU Wan-li, LYU Ji-dong, GAO Jin-jin, CHAI Ming, LIU Hong-jie, TANG Tao, LI Dan-yong, SONG Dong-liang. Stability model verification and control strategy optimization of train platoon based on stochastic priced timed game[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 273-286. doi: 10.19818/j.cnki.1671-1637.2023.02.020

Citation:

LU Wan-li, LYU Ji-dong, GAO Jin-jin, CHAI Ming, LIU Hong-jie, TANG Tao, LI Dan-yong, SONG Dong-liang. Stability model verification and control strategy optimization of train platoon based on stochastic priced timed game[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 273-286. doi: 10.19818/j.cnki.1671-1637.2023.02.020

Citation:

LU Wan-li, LYU Ji-dong, GAO Jin-jin, CHAI Ming, LIU Hong-jie, TANG Tao, LI Dan-yong, SONG Dong-liang. Stability model verification and control strategy optimization of train platoon based on stochastic priced timed game[J]. Journal of Traffic and Transportation Engineering, 2023, 23(2): 273-286. doi: 10.19818/j.cnki.1671-1637.2023.02.020

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Stability model verification and control strategy optimization of train platoon based on stochastic priced timed game

doi: 10.19818/j.cnki.1671-1637.2023.02.020

1.
National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Beijing 100044, China
2.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044 China
3.
School of Electronic Information Engineering, Beijing Jiaotong University, Beijing 100044, China
4.
Suzhou CRRC Construction Engineering Co., Ltd., Wuxi 214105, Jiangsu, China

Funds:

National Natural Science Foundation of China 52272329

Fundamental Research Funds for the Central Universities 2022JBXT000

Fundamental Research Funds for the Central Universities 2021YJS018

Fundamental Research Funds for the Central Universities 2019JBM009

Natural Science Foundation of Beijing L211019

Natural Science Foundation of Beijing L201004

Natural Science Foundation of Beijing L181005

Science and Technology Research and Development Project of China State Railway Group Co., Ltd. L2021G003

More Information

Author Bio:
LU Wan-li(1996-), male, doctoral student, luwanli@bjtu.edu.cn;

LYU Ji-dong(1981-), male, professor, PhD, jdlv@bjtu.edu.cn.
Received Date: 2022-10-29
Available Online: 2023-05-09
Publish Date: 2023-04-25

Abstract

Abstract

In order to ensure the safe operation of the train platoon and improve the platoon stability, the problems of stability model verification and control strategy optimization of the train platoon were studied. The train platoon based on train-train communication adopted three control strategies including constant distance interval, constant time interval, and variable time interval. A platoon control model consisting of a leader train and following trains was established by using the stochastic priced timed game(SPTG) automata, and the stability of the platoon model was analyzed. Under the premise of ensuring the safety of train operation, the relative position difference, relative speed difference, and time interval difference of the trains were taken as the cost function, and a control strategy set was obtained by the SPTG automata model of the platoon. The optimal driving strategy of the platoon was obtained by the Q-Learning method, and the safety and stability of the platoon operation were verified. In addition, the train operation tracking scenario was utilized to analyze the stability of the platoon. Simulation results show that through formal verification, the safety of the train platoon under the three control strategies is guaranteed. For SPTG theory, covariance optimization control, and Q-Learning method compared with PID control, the maximum errors of platoon stability under the constant distance interval strategy reduce to 0.19%, 0.18% and 0.11%, respectively, the maximum errors of platoon stability under the constant time interval strategy reduce to 30.21%, 10.34%, and 9.24%, respectively, and the maximum errors of platoon stability under the variable time interval strategy are 118.37%, 56.12%, and 39.80%, respectively. Therefore, the SPTG theory using the Q-Learning method can improve the stability of the train platoon under the premise of safety.
- traffic information engineering,
- train control system,
- stochastic priced timed game automata,
- stability,
- model verification,
- control strategy optimization

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References(33)

References

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Stability model verification and control strategy optimization of train platoon based on stochastic priced timed game

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