Authors

Yan Ma, Conghao Wang, Liang He, Quan Ouyang, Danwei Wang

Abstract

Current studies struggle to achieve multiobjective optimization for overactuated electric vehicles. This article proposes a cohesive hierarchical architecture that integrates coordinated control with velocity optimization, enabling tracking of desired planar motions while ensuring ride comfort and energy efficiency. First, a velocity optimization strategy based on the deep deterministic policy gradient is developed to achieve comfortable and energy-efficient driving. Desired vehicle states are generated by a reference model. The stability of the coordinated control is then formulated as a tracking problem for these desired vehicle states. To account for parameter uncertainties and external disturbances, a port-Hamiltonian model is employed to describe nonlinear planar motions incorporating four-wheel dynamics of overactuated electric vehicles. A robust passivity-based control method with guaranteed stability is developed using this model to track the desired vehicle states. Furthermore, to reduce sensor costs and improve measurement reliability, a real-time nonlinear observer is designed to accurately estimate lateral velocity. The stability of the overall system is analyzed considering estimation errors. Finally, simulation and experimental results demonstrate the effectiveness of the proposed method compared to existing approaches across various driving scenarios.

Citation

  • Journal: IEEE/ASME Transactions on Mechatronics
  • Year: 2026
  • Volume: 31
  • Issue: 3
  • Pages: 2586–2598
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • DOI: 10.1109/tmech.2025.3628660

BibTeX

@article{Ma_2026,
  title={{Passivity-Based Collaborative Control With Velocity Optimization for Overactuated Electric Vehicles}},
  volume={31},
  ISSN={1941-014X},
  DOI={10.1109/tmech.2025.3628660},
  number={3},
  journal={IEEE/ASME Transactions on Mechatronics},
  publisher={Institute of Electrical and Electronics Engineers (IEEE)},
  author={Ma, Yan and Wang, Conghao and He, Liang and Ouyang, Quan and Wang, Danwei},
  year={2026},
  pages={2586--2598}
}

Download the bib file

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