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A delay-resistant cloud supported control model for Optimizing vehicle platooning operation

Author

Listed:
  • Liu, Ying
  • Xu, Qing
  • Wang, Guangwei
  • Liu, Yi
  • Cai, Mengchi
  • Chen, Chaoyi
  • Wang, Jianqiang
  • Yin, Guodong

Abstract

The cloud supported system can effectively optimize vehicle platooning operation due to its centralized control mode in the cloud, but due to its wireless transmission characteristics and the complexity of the mixed traffic environment, the controlled traffic units will inevitably suffer from time delays and outside disturbances, which can lead to serious safety issues. To address the problem of platooning stable operation under stochastic road slope and bi-directional time-varying delay, a novel delay-resistant cloud supported control model is proposed in this paper. First, the mixed vehicle platoon system under the vehicle–road-cloud integrated architecture is established, considering the influence of driving intentions’ uncertainty of human-driven vehicles (HDVs), random variations of road slope, and bi-direction time-varying delay. Second, an exponential mean-square stable delay-dependent controller is designed to stabilize the cloud supported platoon system subject on the basis of robust H∞ approach and Lyapunov-Krasovskii theorem. In addition, the inner-vehicle stability of time-delay mixed platoon system is analyzed using the enhanced free weighting matrix (EFWM) approach along with the improved cone complementarity linearization (ICCL) algorithm. Third, a L2 string stability criterion is defined to inhibit the increasement of perturbances as they propagate along the platoon. Finally, real traffic data as well as different driving conditions are adopted to verify the control performance of the presented method. Compared to traditional vehicle platoon control method, the presented controller can achieve better disturbance suppression and tracking performance under stochastic interferences and bi-direction time-varying delay, the distance error between adjacent vehicles is less than 0.44 m at low and medium speeds.

Suggested Citation

  • Liu, Ying & Xu, Qing & Wang, Guangwei & Liu, Yi & Cai, Mengchi & Chen, Chaoyi & Wang, Jianqiang & Yin, Guodong, 2025. "A delay-resistant cloud supported control model for Optimizing vehicle platooning operation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 194(C).
    • Handle: RePEc:eee:transe:v:194:y:2025:i:c:s1366554524005192
    DOI: 10.1016/j.tre.2024.103928
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    References listed on IDEAS

    as
    1. El Mehdi Aboulkacem & François Combes, 2023. "A micro-economic model of the market uptake of truck platooning," Post-Print hal-04446372, HAL.
    2. Chen, Yan & Huang, Zhenhua & Ai, Hongshan & Guo, Xingkun & Luo, Fan, 2021. "The Impact of GIS/GPS Network Information Systems on the Logistics Distribution Cost of Tobacco Enterprises," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
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    4. Aboulkacem, El Mehdi & Combes, François, 2023. "A micro-economic model of the market uptake of truck platooning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 178(C).
    Full references (including those not matched with items on IDEAS)

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