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Data-Driven Predictive Control Applied to Gear Shifting for Heavy-Duty Vehicles

Author

Listed:
  • Xinxin Zhao

    (School of Mechanical Engineering, University of Science & Technology Beijing, Beijing 100083, China)

  • Zhijun Li

    (School of Mechanical Engineering, University of Science & Technology Beijing, Beijing 100083, China)

Abstract

In this paper, the data-driven predictive control method is applied to the clutch speed tracking control for the inertial phase of the shift process. While the clutch speed difference changes according to the predetermined trajectory, the purpose of improving the shift quality is achieved. The data-driven predictive control is implemented by combining the subspace identification with the model predictive control. Firstly, the predictive factors are constructed from the input and output data of the shift process via subspace identification, and then the factors are applied to a prediction equation. Secondly, an optimization function is deduced by taking the tracking error and the increments of inputs into accounts. Finally, the optimal solutions are solved through quadratic programming algorithm in Matlab software, and the future inputs of the system are obtained. The control algorithm is applied to the upshift process of an automatic transmission, the simulation results show that the algorithm is in good performance and satisfies the practical requirements.

Suggested Citation

  • Xinxin Zhao & Zhijun Li, 2018. "Data-Driven Predictive Control Applied to Gear Shifting for Heavy-Duty Vehicles," Energies, MDPI, vol. 11(8), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2139-:d:164090
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    Cited by:

    1. Zhao, Chen & Zu, Bingfeng & Xu, Yuliang & Wang, Zhen & Zhou, Jianwei & Liu, Lina, 2020. "Design and analysis of an engine-start control strategy for a single-shaft parallel hybrid electric vehicle," Energy, Elsevier, vol. 202(C).

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