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Research on Methods of Active Steering Control Based on Receding Horizon Control

Author

Listed:
  • Jiwei Feng

    (School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China)

  • Chunjiang Bao

    (School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China)

  • Jian Wu

    (School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China
    State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China)

  • Shuo Cheng

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China)

  • Guangfei Xu

    (School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China)

  • Shifu Liu

    (School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China)

Abstract

Active steering technology is a key technology for automatic driving vehicles to achieve route tracking and obstacle avoidance and risk avoidance, and its performance will affect the stability control of the vehicle. For solving the stability control issues of vehicles, which have uncertainty in model and robustness in system, this paper proposes an active steering control method based on the receding horizon control model. It calculates the optimal control law by this method by using the real-time vehicle state so that it can compensate for the uncertainty caused by model mismatch, interference, etc. The design of the controller is implemented by using the yaw rate deviation of the vehicle as the input of the receding horizon linear quadratic controller model and then inputting the calculated superposition angle into the vehicle model in real time. We built a Simulink control model to implement co-simulation with CarSim to verify the control effect of the controller. In addition, we built a steering hardware-in-the-loop platform based on the LabVIEW RT system. The experimental results show that the active steering system adopting a receding horizon control method had better system robustness and robust stability.

Suggested Citation

  • Jiwei Feng & Chunjiang Bao & Jian Wu & Shuo Cheng & Guangfei Xu & Shifu Liu, 2018. "Research on Methods of Active Steering Control Based on Receding Horizon Control," Energies, MDPI, vol. 11(9), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2243-:d:165948
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    References listed on IDEAS

    as
    1. Jian Wu & Shuo Cheng & Binhao Liu & Congzhi Liu, 2017. "A Human-Machine-Cooperative-Driving Controller Based on AFS and DYC for Vehicle Dynamic Stability," Energies, MDPI, vol. 10(11), pages 1-18, October.
    2. Wu, Jian & Wang, Xiangyu & Li, Liang & Qin, Cun'an & Du, Yongchang, 2018. "Hierarchical control strategy with battery aging consideration for hybrid electric vehicle regenerative braking control," Energy, Elsevier, vol. 145(C), pages 301-312.
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    Cited by:

    1. Jinhong Sun & Xiangdang Xue & Ka Wai Eric Cheng, 2019. "Fuzzy Sliding Mode Wheel Slip Ratio Control for Smart Vehicle Anti-Lock Braking System," Energies, MDPI, vol. 12(13), pages 1-22, June.
    2. Pengwei Wang & Song Gao & Liang Li & Binbin Sun & Shuo Cheng, 2019. "Obstacle Avoidance Path Planning Design for Autonomous Driving Vehicles Based on an Improved Artificial Potential Field Algorithm," Energies, MDPI, vol. 12(12), pages 1-14, June.

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