Authors

Yunfei Yin, Yuanlong Wei, Zejiao Dong, Mengqi Xue, Sergio Vazquez, Ligang Wu

Abstract

To overcome the heterogeneous dynamics and unreliable communication which adversely effect the stable control for connected and automated vehicle platoons, a new intelligent control approach, named virtual order-degradation interconnection and damping assignment (VO-IDA), is proposed in this article. First, the internal stability of vehicle platoons is ed into a class of tracking control problems for general chained integral systems. By converting the chained integral system into standard closed-loop port-controlled Hamiltonian form, VO-IDA achieves asymptotic tracking through the integration of backstepping order degradation and virtual stabilization control techniques. This conversion effectively eliminates the dependence on preceding vehicular acceleration as well. Second, under heterogeneous dynamics, explicit stable domains of control parameters are provided to ensure the attenuation of string stability for vehicle platoons via Laplace transform. Furthermore, a linear-proportional relationship between heterogeneous and homogeneous dynamics regarding spacing error ratio is uncovered. Leveraging this relationship, a modified multiobjective genetic algorithm is employed to online explore target locations within stable domains, enabling VO-IDA to conduct stable and precise control under heterogeneous dynamics. Comparative experiments verify the superiority of this approach.

Citation

  • Journal: IEEE Internet of Things Journal
  • Year: 2024
  • Volume: 11
  • Issue: 22
  • Pages: 36257–36271
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • DOI: 10.1109/jiot.2024.3467265

BibTeX

@article{Yin_2024,
  title={{V2V-Based Cooperative Control of Heterogeneous CAV Platoons: An Intelligent VO-IDA Approach}},
  volume={11},
  ISSN={2372-2541},
  DOI={10.1109/jiot.2024.3467265},
  number={22},
  journal={IEEE Internet of Things Journal},
  publisher={Institute of Electrical and Electronics Engineers (IEEE)},
  author={Yin, Yunfei and Wei, Yuanlong and Dong, Zejiao and Xue, Mengqi and Vazquez, Sergio and Wu, Ligang},
  year={2024},
  pages={36257--36271}
}

Download the bib file

References