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Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability

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  • Zhou, Yang
  • Wang, Meng
  • Ahn, Soyoung

Abstract

In this paper, a serial distributed model predictive control (MPC) approach for connected automated vehicles (CAVS) is developed with local stability (disturbance dissipation over time) and multi-criteria string stability (disturbance attenuation through a vehicular string). Two string stability criteria are considered within the proposed MPC: (i) the l∞-norm string stability criterion for attenuation of the maximum disturbance magnitude and (ii) l2-norm string stability criterion for attenuation of disturbance energy. The l∞-norm string stability is achieved by formulating constraints within the MPC based on the future states of the leading CAV, and the l2-norm string stability is achieved by proper weight matrix tuning over a robust positive invariant set. For rigor, mathematical proofs for asymptotical local stability and multi-criteria string stability are provided. Simulation experiments verify that the distributed serial MPC proposed in this study is effective for disturbance attenuation and performs better than traditional MPC without stability guarantee.

Suggested Citation

  • Zhou, Yang & Wang, Meng & Ahn, Soyoung, 2019. "Distributed model predictive control approach for cooperative car-following with guaranteed local and string stability," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 69-86.
    • Handle: RePEc:eee:transb:v:128:y:2019:i:c:p:69-86
    DOI: 10.1016/j.trb.2019.07.001
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    1. Zhou, Yang & Ahn, Soyoung, 2019. "Robust local and string stability for a decentralized car following control strategy for connected automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 125(C), pages 175-196.
    2. Gong, Siyuan & Du, Lili, 2018. "Cooperative platoon control for a mixed traffic flow including human drive vehicles and connected and autonomous vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 116(C), pages 25-61.
    3. Nowakowski, Christopher & Shladover, Steven E & Lu, Xiao-Yun & Thompson, Deborah & Kailas, Aravind, 2015. "Cooperative Adaptive Cruise Control (CACC) for Truck Platooning: Operational Concept Alternatives," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt7jf9n5wm, Institute of Transportation Studies, UC Berkeley.
    4. Gong, Siyuan & Shen, Jinglai & Du, Lili, 2016. "Constrained optimization and distributed computation based car following control of a connected and autonomous vehicle platoon," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 314-334.
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    Citations

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    Cited by:

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    6. Zhou, Yang & Zhong, Xinzhi & Chen, Qian & Ahn, Soyoung & Jiang, Jiwan & Jafarsalehi, Ghazaleh, 2023. "Data-driven analysis for disturbance amplification in car-following behavior of automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    7. Xiao Xiao & Yunlong Zhang & Xiubin Bruce Wang & Shu Yang & Tianyi Chen, 2021. "Hierarchical Longitudinal Control for Connected and Automated Vehicles in Mixed Traffic on a Signalized Arterial," Sustainability, MDPI, vol. 13(16), pages 1-17, August.
    8. Li, Chao & Zhao, Xiaomei & Xie, Dongfan, 2022. "Steady-state performance and dynamic performance of heterogeneous platoons under a connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    9. Wu, Zhibei & Sun, Jitao & Xu, Ruihua, 2021. "Consensus-based connected vehicles platoon control via impulsive control method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
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    11. Cui, Bo-Yuan & Zhang, Geng & Ma, Qing-Lu, 2021. "A stable velocity control strategy for a discrete-time car-following model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
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    14. Liu, Chunyu & Sheng, Zihao & Chen, Sikai & Shi, Haotian & Ran, Bin, 2023. "Longitudinal control of connected and automated vehicles among signalized intersections in mixed traffic flow with deep reinforcement learning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).

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