Authors

Liangcheng Cai, Deqing Huang

Abstract

In whole operation procedure, trajectory tracking control for heavy haul train with the time-varying desired speed is investigated in this paper. Taking full advantage of the heavy haul train’s facilities, a proportional-integral (PI) control method based on the real-time speed error and the real-time displacement is proposed to represent the dynamic model of heavy haul train as port-Hamiltonian (PH) system framework, which implements the interactive feature of the in-train force and the damping feature of the basic resistance force effectively. Due to the proposed method, the real-time speed of heavy haul train asymptotically tracks the time-varying desired speed and the real-time displacement is small in whole operation procedure. Since the basic resistance force is utilized to construct the non-negative definite matrix and heavy haul train is represented as PH system, the proposed method is also effective for trajectory tracking control of heavy haul train subjected to the uncertain and time-varying basic resistances without parameter adaption or estimation. In short, a universal tracking control scheme is presented for heavy haul train subjected to the aforementioned different operative conditions. Finally, simulations confirm the validity and advantage of the proposed method.

Citation

  • Journal: IEEE Transactions on Vehicular Technology
  • Year: 2024
  • Volume: 73
  • Issue: 11
  • Pages: 16225–16237
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • DOI: 10.1109/tvt.2024.3419755

BibTeX

@article{Cai_2024,
  title={{Trajectory Tracking Control of Heavy Haul Train in Whole Operation Procedure}},
  volume={73},
  ISSN={1939-9359},
  DOI={10.1109/tvt.2024.3419755},
  number={11},
  journal={IEEE Transactions on Vehicular Technology},
  publisher={Institute of Electrical and Electronics Engineers (IEEE)},
  author={Cai, Liangcheng and Huang, Deqing},
  year={2024},
  pages={16225--16237}
}

Download the bib file

References

  • 2021 statistical bulletin. (2022)
  • Chang, C., Wang, C., Chen, B. & Li, L. A Study of a Numerical Analysis Method for the Wheel-Rail Wear of a Heavy-Haul Train. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit vol. 224 473–482 (2010) – 10.1243/09544097jrrt341
  • Hay, Railroad Engineering (1991)
  • Kull, R. C. Wabtec ECP system update. Proceedings of the 2001 IEEE/ASME Joint Railroad Conference (Cat. No.01CH37235) 129–134 doi:10.1109/rrcon.2001.921756 – 10.1109/rrcon.2001.921756
  • Wei, W., Zhang, Y., Zhang, J. & Zhao, X. Influence of quick release valve on braking performance and coupler force of heavy haul train. Railway Engineering Science vol. 31 153–161 (2023) – 10.1007/s40534-022-00301-1
  • International Standard IEC (2007)
  • Chou, M., Xia, X. & Kayser, C. Modelling and model validation of heavy-haul trains equipped with electronically controlled pneumatic brake systems. Control Engineering Practice vol. 15 501–509 (2007) – 10.1016/j.conengprac.2006.09.006
  • Modeling and Control of Heavy-Haul Trains [Applications of Control]. IEEE Control Systems vol. 31 18–31 (2011) – 10.1109/mcs.2011.941403
  • Zhang, W., Li, W., Fan, Y. & Cao, Y. Influence of Cyclic Pneumatic Brake on the Longitudinal Dynamics of Heavy-Haul Combined Trains. IEEE Transactions on Intelligent Transportation Systems vol. 25 2545–2557 (2024) – 10.1109/tits.2023.3321416
  • Zhuan, X. & Xia, X. Cruise control scheduling of heavy haul trains. IEEE Transactions on Control Systems Technology vol. 14 757–766 (2006) – 10.1109/tcst.2006.872506
  • Chou, M. & Xia, X. Optimal cruise control of heavy-haul trains equipped with electronically controlled pneumatic brake systems. Control Engineering Practice vol. 15 511–519 (2007) – 10.1016/j.conengprac.2006.09.007
  • Howlett, P. G., Pudney, P. J. & Vu, X. Local energy minimization in optimal train control. Automatica vol. 45 2692–2698 (2009) – 10.1016/j.automatica.2009.07.028
  • McClanachan, M. & Cole, C. Current train control optimization methods with a view for application in heavy haul railways. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit vol. 226 36–47 (2011) – 10.1177/0954409711406352
  • Bai, Y., Ho, T. K., Mao, B., Ding, Y. & Chen, S. Energy-Efficient Locomotive Operation for Chinese Mainline Railways by Fuzzy Predictive Control. IEEE Transactions on Intelligent Transportation Systems vol. 15 938–948 (2014) – 10.1109/tits.2013.2292712
  • Zhang, L. & Zhuan, X. Development of an Optimal Operation Approach in the MPC Framework for Heavy-Haul Trains. IEEE Transactions on Intelligent Transportation Systems vol. 16 1391–1400 (2015) – 10.1109/tits.2014.2364178
  • Su, S., She, J., Li, K., Wang, X. & Zhou, Y. A Nonlinear Safety Equilibrium Spacing-Based Model Predictive Control for Virtually Coupled Train Set Over Gradient Terrains. IEEE Transactions on Transportation Electrification vol. 8 2810–2824 (2022) – 10.1109/tte.2021.3134669
  • Wang, X., Li, S. & Tang, T. Robust efficient cruise control for heavy haul train via the state-dependent intermittent control. Nonlinear Analysis: Hybrid Systems vol. 38 100918 (2020) – 10.1016/j.nahs.2020.100918
  • Wang, X., Su, S., Cao, Y., Qin, L. & Liu, W. Robust Cruise Control for the Heavy Haul Train Subject to Disturbance and Actuator Saturation. IEEE Transactions on Intelligent Transportation Systems vol. 24 8003–8013 (2023) – 10.1109/tits.2023.3264238
  • Gao, K., Huang, Z., Wang, J., Peng, J. & Liu, W. Decentralized control of heavy-haul trains with input constraints and communication delays. Control Engineering Practice vol. 21 420–427 (2013) – 10.1016/j.conengprac.2012.12.010
  • Wang, X., Li, S., Tang, T., Wang, X. & Xun, J. Intelligent operation of heavy haul train with data imbalance: A machine learning method. Knowledge-Based Systems vol. 163 36–50 (2019) – 10.1016/j.knosys.2018.08.015
  • Huang, D., Chen, Y., Meng, D. & Sun, P. Adaptive Iterative Learning Control for High-Speed Train: A Multi-Agent Approach. IEEE Transactions on Systems, Man, and Cybernetics: Systems vol. 51 4067–4077 (2021) – 10.1109/tsmc.2019.2931289
  • Li, S., Yang, L. & Gao, Z. Coordinated cruise control for high-speed train movements based on a multi-agent model. Transportation Research Part C: Emerging Technologies vol. 56 281–292 (2015) – 10.1016/j.trc.2015.04.016
  • Yu, W., Huang, D., Wang, Q. & Cai, L. Distributed Event-Triggered Iterative Learning Control for Multiple High-Speed Trains With Switching Topologies: A Data-Driven Approach. IEEE Transactions on Intelligent Transportation Systems vol. 24 10818–10829 (2023) – 10.1109/tits.2023.3277452
  • Dong, H., Ning, B., Cai, B. & Hou, Z. Automatic Train Control System Development and Simulation for High-Speed Railways. IEEE Circuits and Systems Magazine vol. 10 6–18 (2010) – 10.1109/mcas.2010.936782
  • Knorn, S., Donaire, A., Agüero, J. C. & Middleton, R. H. Passivity-based control for multi-vehicle systems subject to string constraints. Automatica vol. 50 3224–3230 (2014)10.1016/j.automatica.2014.10.038
  • van der Schaft, A. L2-Gain and Passivity Techniques in Nonlinear Control. Communications and Control Engineering (Springer International Publishing, 2017). doi:10.1007/978-3-319-49992-5 – 10.1007/978-3-319-49992-5
  • Cai, L., He, Z. & Hu, H. A New Load Frequency Control Method of Multi-Area Power System via the Viewpoints of Port-Hamiltonian System and Cascade System. IEEE Transactions on Power Systems vol. 32 1689–1700 (2017)10.1109/tpwrs.2016.2605007
  • Khalil, Nonlinear Systems (2002)
  • Cai, L., He, Y. & Wu, M. On the effects of desired damping matrix and desired Hamiltonian function in the matching equation for Port–Hamiltonian systems. Nonlinear Dynamics vol. 72 91–99 (2012)10.1007/s11071-012-0693-7
  • She, J.-H., Fang, M., Ohyama, Y., Hashimoto, H. & Wu, M. Improving Disturbance-Rejection Performance Based on an Equivalent-Input-Disturbance Approach. IEEE Transactions on Industrial Electronics vol. 55 380–389 (2008) – 10.1109/tie.2007.905976
  • Zuo, Z., Song, J., Tian, B. & Basin, M. Robust Fixed-Time Stabilization Control of Generic Linear Systems With Mismatched Disturbances. IEEE Transactions on Systems, Man, and Cybernetics: Systems vol. 52 759–768 (2022) – 10.1109/tsmc.2020.3010221
  • Wu, H.-N., Feng, S., Liu, Z.-Y. & Guo, L. Disturbance observer based robust mixed H2/H∞ fuzzy tracking control for hypersonic vehicles. Fuzzy Sets and Systems vol. 306 118–136 (2017) – 10.1016/j.fss.2016.02.002
  • Wang, X., Zhu, L., Wang, H., Tang, T. & Li, K. Robust Distributed Cruise Control of Multiple High-Speed Trains Based on Disturbance Observer. IEEE Transactions on Intelligent Transportation Systems vol. 22 267–279 (2021) – 10.1109/tits.2019.2956162
  • Li, Y., Zhao, Y., Liu, W. & Hu, J. Adaptive Fuzzy Predefined-Time Control for Third-Order Heterogeneous Vehicular Platoon Systems With Dead Zone. IEEE Transactions on Industrial Informatics vol. 19 9525–9534 (2023) – 10.1109/tii.2022.3221220
  • Li, Y., Dong, S. & Li, K. Fuzzy Adaptive Finite-Time Event-Triggered Control of Time-Varying Formation for Nonholonomic Multirobot Systems. IEEE Transactions on Intelligent Vehicles vol. 9 725–737 (2024) – 10.1109/tiv.2023.3304064
  • Wu, G., Chen, G., Zhang, H. & Huang, C. Fully Distributed Event-Triggered Vehicular Platooning With Actuator Uncertainties. IEEE Transactions on Vehicular Technology vol. 70 6601–6612 (2021) – 10.1109/tvt.2021.3086824