IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i4p871-d1124241.html
   My bibliography  Save this article

Research on Path Tracking of Articulated Steering Tractor Based on Modified Model Predictive Control

Author

Listed:
  • Baocheng Zhou

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Xin Su

    (Beijing Institute of Space Launch Technology, Beijing 100076, China)

  • Hongjun Yu

    (Beijing Institute of Space Launch Technology, Beijing 100076, China)

  • Wentian Guo

    (Beijing Institute of Space Launch Technology, Beijing 100076, China)

  • Qing Zhang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

Abstract

With the development of agricultural mechanization and information technology, automatic navigation tractors are becoming a more common piece of farm equipment. The accuracy of automatic navigation tractor path tracking has become critical for maximizing efficiency and crop yield. Aiming at improving path tracking control accuracy and the real-time performance of the traditional model predictive control (MPC) algorithm, the study proposed an adaptive time-domain parameter with MPC in the path tracking control of the articulated steering tractor. Firstly, the kinematics model of the articulated steering tractor was established, as well as the multi-body dynamics model by RecurDyn. Secondly, the genetic algorithm was combined with MPC. The genetic algorithm was used to calculate the optimal time domain parameters under real-time vehicle speed, vehicle posture and road conditions, and the adaptive MPC was realized. Then, path tracking simulations were conducted by combining RecurDyn and Simulink under different path types. Compared with the traditional MPC algorithm under the three paths of U-shaped, figure-eight-shaped and complex curves, the maximum lateral deviations of the modified MPC algorithm were reduced by 59.0%, 24.9% and 13.2%, respectively. At the same time, the average lateral deviation was reduced by 72%, 43.5% and 20.3%, respectively. Finally, the real path tracking tests of the articulated steering tractor were performed. The test results indicated that under the three path tracking conditions of straight line, front wheel steering and articulated steering, the maximum lateral deviation of the modified MPC algorithm was reduced by 67.8%, 44.7% and 45.1% compared with the traditional MPC. The simulation analysis and real tractor tests verified the proposed MPC algorithm, considering the adaptive time-domain parameter has a smaller deviation and can quickly eliminate the deviation and maintain tracking stability.

Suggested Citation

  • Baocheng Zhou & Xin Su & Hongjun Yu & Wentian Guo & Qing Zhang, 2023. "Research on Path Tracking of Articulated Steering Tractor Based on Modified Model Predictive Control," Agriculture, MDPI, vol. 13(4), pages 1-21, April.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:4:p:871-:d:1124241
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/4/871/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/4/871/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yourui Huang & Jiahao Fu & Shanyong Xu & Tao Han & Yuwen Liu, 2022. "Research on Integrated Navigation System of Agricultural Machinery Based on RTK-BDS/INS," Agriculture, MDPI, vol. 12(8), pages 1-14, August.
    2. Hafiz Md-Tahir & Jumin Zhang & Yong Zhou & Muhammad Sultan & Fiaz Ahmad & Jun Du & Amman Ullah & Zawar Hussain & Junfang Xia, 2023. "Engineering Design, Kinematic and Dynamic Analysis of High Lugs Rigid Driving Wheel, a Traction Device for Conventional Agricultural Wheeled Tractors," Agriculture, MDPI, vol. 13(2), pages 1-21, February.
    3. Jinming Zheng & Lin Wang & Xiaochan Wang & Yinyan Shi & Zhenyu Yang, 2023. "Parameter Calibration of Cabbages ( Brassica oleracea L.) Based on the Discrete Element Method," Agriculture, MDPI, vol. 13(3), pages 1-17, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dongdong Li & Rongyang Wang & Yingpeng Zhu & Jianneng Chen & Guofeng Zhang & Chuanyu Wu, 2024. "Calibration of Simulation Parameters for Fresh Tea Leaves Based on the Discrete Element Method," Agriculture, MDPI, vol. 14(1), pages 1-14, January.
    2. Kang Zheng & Xueguan Zhao & Changjie Han & Yakai He & Changyuan Zhai & Chunjiang Zhao, 2023. "Design and Experiment of an Automatic Row-Oriented Spraying System Based on Machine Vision for Early-Stage Maize Corps," Agriculture, MDPI, vol. 13(3), pages 1-22, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:4:p:871-:d:1124241. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.