Research on Virtual Coupled Train Control Method Based on GPC &amp; VAPF

CAO Yuan; YANG Yaran; MA Lianchuan; WEN Jiakun

doi:10.1049/cje.2021.00.241

Volume 31 Issue 5

Sep. 2022

Turn off MathJax

Article Contents

Article Navigation > Chinese Journal of Electronics > 2022 > 31(5): 897-905

CAO Yuan, YANG Yaran, MA Lianchuan, et al., “Research on Virtual Coupled Train Control Method Based on GPC & VAPF,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 897-905, 2022, doi: 10.1049/cje.2021.00.241

Citation:

CAO Yuan, YANG Yaran, MA Lianchuan, et al., “Research on Virtual Coupled Train Control Method Based on GPC & VAPF,” Chinese Journal of Electronics, vol. 31, no. 5, pp. 897-905, 2022, doi: 10.1049/cje.2021.00.241

Citation:

PDF( 8707 KB)

Research on Virtual Coupled Train Control Method Based on GPC & VAPF

doi: 10.1049/cje.2021.00.241

1.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
2.
National Engineering Research Center of Rail Transportation Operation and Control System,Beijing Jiaotong University, Beijing 100044, China
3.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Funds: This work was supported by the National Key R&D Program of China (2021YFF0501102), the National Natural Science Foundation of China (U1934219), and the National Science Fund for Excellent Young Scholars (52022010).

More Information

Author Bio:
received the B.S. degree in electric engineering and automation from Dalian Jiaotong University and Ph.D. degree in traffic information engineering and control from Beijing Jiaotong University in 2004 and 2011 respectively, where he is now a Professor. Since 2006, he has participated in many engineering practice, especially in the signal and communication system of railway. He has taken part in several key national research projects in the field of high-speed train control system. His research interests include intelligent control and health management in railway system. (Email: ycao@bjtu.edu.cn)

is now pursuing the B.S. degree in traffic information engineering and control. Her research interests include formal verification and implementation of train control system

was born in 1970, and received the B.S. degree in electromagnetic field engineering from Xidian University and M.S degree in communication engineering from Beijing Jiaotong University in 1992 and 1995, respectively. And now he is an Associate Professor of Beijing Jiaotong University. Since 1995, he has participated in many engineering practice, especially in the signal and communication system of urban rail transit, high-speed railway. His research interests include the intelligent control of train control system

(corresponding author) is now pursuing the B.S. degree in traffic information engineering and control. His research interests include formal verification and implementation of train control system. (Email: 18120268@bjtu.edu.cn)
Received Date: 2021-07-18
Accepted Date: 2022-02-11

Available Online: 2022-03-12

Publish Date: 2022-09-05

Abstract

Abstract

Improving transportation efficiency is an eternal research hotspot in rail transit system. In recent years, the train operation control method based on virtual coupling has attracted the attention of many scholars. The method of train coordination and anti-collision control is not only the key to realize the virtual coupling of train, but also the key to ensure the safety of train operation. Therefore, based on the existing research, a virtual coupled train dynamics model with nonlinear dynamics is established. Then, the parameters of the operation process model of the nonlinear virtual coupled train are identified by the recursive least squares method based on real-time data, which is applied to the variable parameter artificial potential field (VAPF) for parameter identification. A fusion controller based on feature-based generalized model prediction (GPC) and VAPF is used to control the virtual coupled train and prevent collision. Finally, the validity of the proposed method is verified by using real high-speed railway data.
- Virtual coupling train control system,
- Generalized model predictive control,
- Nonlinear system,
- Artificial potential field (APF)

FullText(HTML)

References(36)

References

[1]	Y. Cao, Y. Sun, G. Xie, et al., “Fault diagnosis of train plug door based on a hybrid criterion for IMFs selection and fractional wavelet package energy entropy,” IEEE Transactions on Vehicular Technology, vol.68, no.8, pp.7544–7551, 2019. doi: 10.1109/TVT.2019.2925903
[2]	J. Xun, M. Chen, B. Ning, et al., “Train tracking performance measurement under virtual coupling in subway,” Journal of Beijing Jiaotong University, vol.43, no.1, pp.96–103, 2019.
[3]	Y. Cao, L. Ma, and Y. Zhang, “Application of fuzzy predictive control technology in automatic train operation,” Cluster Computing, vol.22, no.6, pp.14135–14144, 2019.
[4]	S. Zhu and R. Song, “Theoretical analysis on combinatorial control of the trains velocities system,” Journal of the China Railway Society, vol.19, no.6, pp.10–16, 1997.
[5]	W. Wang, X. Na, D. Cao, et al., “Decision-making in driver-automation shared control: A review and perspectives,” IEEE/CAA Journal of Automatica Sinica, vol.7, no.5, pp.1289–1307, 2020.
[6]	U. Bock and J. U. Varchmin, “Improvement of line capacity by using ‘virtually coupled train formations’,” https://www.researchgate.net/publication/294612127_Improvement_of_line_capacity_by_using_virtually_coupled_train_formations, 1999.
[7]	Y. Zhang, C. Hong, Y. Cao, et al., “Safety mechanism design and verification of safety computer parallel program,” Chinese Journal of Electronics, vol.27, no.6, pp.1163–1169, 2018. doi: 10.1049/cje.2018.02.016
[8]	Y. Cao, L. Ma, S. Xiao, et al., “Standard analysis for transfer delay in CTCS-3,” Chinese Journal of Electronics, vol.26, no.5, pp.1057–1063, 2017. doi: 10.1049/cje.2017.08.024
[9]	Y. Sun, G. Xie, Y. Cao, et al., “Strategy for fault diagnosis on train plug doors using audio sensors,” Sensors (Basel, Switzerland), vol.19, no.1, article no.3, 2018.
[10]	L. Chen, N. Qin, X. Dai, et al., “Fault diagnosis of high-speed train bogie based on capsule network,” IEEE Transactions on Instrumentation and Measurement, vol.69, no.9, pp.6203–6211, 2020. doi: 10.1109/TIM.2020.2968161
[11]	I. Mitchell, E. Goddard, F. Montes, et al., “ERTMS Level 4 train convoys or virtual coupling,” IRSE News, vol.219, pp.14–15, 2016.
[12]	C. Williams, ”The next ETCS level?” in Proceedings of 2016 IEEE International Conference on Intelligent Rail Transportation, Birmingham, UK, pp.75–79, 2016.
[13]	T. Schumann, “Increase of capacity on the shinkansen high-speed line using virtual coupling,” International Journal of Transport Development and Integration, vol.1, no.4, pp.666–676, 2017. doi: 10.2495/TDI-V1-N4-666-676
[14]	J. Park, B. Lee, and Y. Eun, “Virtual coupling of railway vehicles: Gap reference for merge and separation, robust control, and position measurement,” IEEE Transactions on Intelligent Transportation Systems, vol.23, no.2, pp.1085–1096, 2020.
[15]	J. Felez, Y. Kim, and F. Borrelli, “A model predictive control approach for virtual coupling in railways,” IEEE Transactions on Intelligent Transportation Systems, vol.20, no.7, pp.2728–2739, 2019. doi: 10.1109/TITS.2019.2914910
[16]	J. Goikoetxea, “Roadmap towards the wireless virtual coupling of trains,” in Proceedings of the 10th International Workshop on Communication Technologies for Vehicles-Volume 9669, pp.3–9, 2016.
[17]	H. Q. Le, A. Lehner, and S. Sand, “Performance analysis of ITS-G5 for dynamic train coupling application,” Lecture Notes in Computer Science, vol.9066, Cham: Springer International Publishing, pp.129–140, 2015.
[18]	U. Bock and G. Bikker, “Design and development of a future freight train concept – ‘virtually coupled train formations’,” IFAC Proceedings Volumes, vol.33, no.9, pp.395–400, 2000. doi: 10.1016/S1474-6670(17)38176-4
[19]	H. Chen, B. Jiang, W. Chen, et al., “Edge computing-aided framework of fault detection for traction control systems in high-speed trains,” IEEE Transactions on Vehicular Technology, vol.69, no.2, pp.1309–1318, 2020. doi: 10.1109/TVT.2019.2957962
[20]	Y. Cao, J. Wen, and L. Ma, “Tracking and collision avoidance of virtual coupling train control system,” Future Generation Computer Systems, vol.120, pp.76–90, 2021. doi: 10.1016/j.future.2021.02.014
[21]	S. Iwnicki, Handbook of Railway Vehicle Dynamics, CRC Press, Boca Raton, USA, 2006.
[22]	Q. Wu, M. Spiryagin, and C. Cole, “Longitudinal train dynamics: An overview,” Vehicle System Dynamics, vol.54, no.12, pp.1688–1714, 2016. doi: 10.1080/00423114.2016.1228988
[23]	J. Song and W. Wei, “Validity of simulation model of heavy haul train longitudinal dynamics,” Journal of Dalian Jiaotong University, vol.40, no.3, pp.23–29, 2019.
[24]	Y. Sun, Y. Cao, M. Zhou, et al., “A hybrid method for life prediction of railway relays based on multi-layer decomposition and RBFNN,” IEEE Access, vol.7, pp.44761–44770, 2019. doi: 10.1109/ACCESS.2019.2906895
[25]	D. Huang, S. Li, N. Qin,et al., “Fault diagnosis of high-speed train Bogie based on the improved-CEEMDAN and 1-D CNN algorithms,” IEEE Transactions on Instrumentation and Measurement, vol.70, pp.1–11, 2021. doi: 10.1109/tim.2020.3047922
[26]	H. Chen, J. Wu, B. Jiang, et al., “A modified neighborhood preserving embedding-based incipient fault detection with applications to small-scale cyber-physical systems,” ISA Transactions, vol.104, pp.175–183, 2020. doi: 10.1016/j.isatra.2019.08.022
[27]	Z. J. Sun, “Nonlinear relationship based on range quadratic loss function,” Applied Mathematics and Nonlinear Sciences, vol.5, no.2, pp.483–492, 2020. doi: 10.2478/amns.2020.2.00024
[28]	X. Zhang, L. Xu, F. Ding, et al., “Combined state and parameter estimation for a bilinear state space system with moving average noise,” Journal of the Franklin Institute, vol.355, no.6, pp.3079–3103, 2018. doi: 10.1016/j.jfranklin.2018.01.011
[29]	X. Zhang, F. Ding, F. E. Alsaadi, et al., “Recursive parameter identification of the dynamical models for bilinear state space systems,” Nonlinear Dynamics, vol.89, no.4, pp.2415–2429, 2017. doi: 10.1007/s11071-017-3594-y
[30]	Y. Cao, Y. Zhang, T. Wen, et al., “Research on dynamic nonlinear input prediction of fault diagnosis based on fractional differential operator equation in high-speed train control system,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol.29, no.1, article no.013130, 2019.
[31]	T. Bächle, S. Hentzelt, and K. Graichen, “Nonlinear model predictive control of a magnetic levitation system,” Control Engineering Practice, vol.21, no.9, pp.1250–1258, 2013. doi: 10.1016/j.conengprac.2013.04.009
[32]	X. Zhang, F. Ding, L. Xu, et al., “State filtering‐based least squares parameter estimation for bilinear systems using the hierarchical identification principle,” IET Control Theory & Applications, vol.12, no.12, pp.1704–1713, 2018.
[33]	C. Zhao, W. Wang, S. Li, et al., “Influence of cut-In maneuvers for an autonomous car on surrounding drivers: Experiment and analysis,” IEEE Transactions on Intelligent Transportation Systems, vol.21, no.6, pp.2266–2276, 2020. doi: 10.1109/TITS.2019.2914795
[34]	X. Zhang, F. Ding, L. Xu, et al., “Highly computationally efficient state filter based on the delta operator,” International Journal of Adaptive Control and Signal Processing, vol.33, no.6, pp.875–889, 2019. doi: 10.1002/acs.2995
[35]	K. Huang, S. Wen, W. Wang, et al., “Erythrocyte membrane coated nanoparticle-based control releasing hydrogen sulfide system protects ischemic myocardium,” Nanomedicine, vol.16, no.6, pp.465–480, 2021. doi: 10.2217/nnm-2020-0404
[36]	H. Chen, B. Jiang, T. Zhang, et al., “Data-driven and deep learning-based detection and diagnosis of incipient faults with application to electrical traction systems,” Neurocomputing, no.396, pp.429–437, 2020.