Volume 24 Issue 5
Oct.  2024
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2024  >  24(5): 362-378
LI Zhong-qi, ZHONG Ling-yu, YANG Hui. Distributed cooperative predictive control of virtual coupled trains[J]. Journal of Traffic and Transportation Engineering, 2024, 24(5): 362-378. doi: 10.19818/j.cnki.1671-1637.2024.05.023
Citation: LI Zhong-qi, ZHONG Ling-yu, YANG Hui. Distributed cooperative predictive control of virtual coupled trains[J]. Journal of Traffic and Transportation Engineering, 2024, 24(5): 362-378. doi: 10.19818/j.cnki.1671-1637.2024.05.023
shu

Distributed cooperative predictive control of virtual coupled trains

doi: 10.19818/j.cnki.1671-1637.2024.05.023
  • 1.

    School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang 330013, Jiangxi, China

  • 2.

    State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure, East China Jiaotong University, Nanchang 330013, Jiangxi, China

Funds:

National Natural Science Foundation of China 52472342

National Natural Science Foundation of China 52162048

National Natural Science Foundation of China 61991404

National Natural Science Foundation of China 62363011

More Information
  • Author Bio:

    LI Zhong-qi(1975-), male, professor, PhD, lzq0828@163.com

  • Received Date: 2024-05-12
    Available Online: 2024-12-20
  • Publish Date: 2024-10-25
    Abstract
  • To improve the cooperative tracking efficiency and stability of virtual coupled trains, a multi-train interactive cooperative tracking control method was proposed based on the distributed model predictive control (DMPC). A state-space model of virtual coupled leader-follower trains bidirectional topology was established based on the unit train dynamics analysis, so as to improve the limitation of unidirectional topology and make the communication structure more stable. The improved DMPC algorithm was designed by introducing the neighboring system state information into the cost index function and weighting it with the self-state information. Under the constraints of running velocity limit, distance limit, and control quantity limit, the optimal control quantity and state quantity of trains were obtained by solving the improved cost index function, the distributed cooperative control of virtual coupled trains was realized, and the feasibility and closed-loop stability of the algorithm were theoretically proven. The semi-physical simulation system for train tracking and running in the laboratory was used for simulation. The virtual coupled trains consisting of four CRH380A unit trains were controlled to track a specified velocity curve and compared the proposed algorithm with other traditional algorithms. Simulation results indicate that under different initial conditions, the distance and velocity errors of virtual coupled trains can converge after 300 s, the control output can meet the requirements of passenger comfort, and each unit train can still maintain a stable coupled formation after receiving the velocity adjustment instruction. The root mean square errors of velocity and distance of virtual coupled trains obtained by the proposed method are 3.32×10-8 km·h-1 and 6.11×10-7 m, respectively, which are 99.99% lower than traditional methods. Therefore, the control and tracking performance of the proposed method is superior to that of traditional control methods, and the sampling time simulation duration of each unit train can be guaranteed within 3 ms, meeting the requirements of high-speed train control system.

     

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