IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i5p747-d1392182.html
   My bibliography  Save this article

Design and Optimization of Power Shift Tractor Starting Control Strategy Based on PSO-ELM Algorithm

Author

Listed:
  • Yu Qian

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

  • Lin Wang

    (State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 470139, China)

  • Zhixiong Lu

    (College of Engineering, Nanjing Agricultural University, Nanjing 210031, China)

Abstract

Power shift tractors have been widely used in agricultural tractors in recent years because of their advantages of uninterrupted power during shifting, high transmission efficiency and high stability. As one of the indispensable driving states of the power shift tractor, the starting process requires a small impact and a starting speed that meets the driver’s requirements. In this paper, aiming at such contradictory requirements, the starting control strategy of a power shift tractor is formulated with the goal of starting quality and the driver’s intention. Firstly, the identification characteristics of the driver under three starting intentions are obtained by a real vehicle test. An extreme learning machine with fast identification speed and short training time is used to establish the basic driver’s intention identification model. For the instability of the identification results of the Extreme Learning Machine (ELM), the particle swarm optimization algorithm (PSO) is used to optimize the ELM. The optimized extreme learning machine model has an accuracy of 96.891% for driver’s intention identification. The wet clutch is an important part of the power shift gearbox. In this paper, the starting control strategy knowledge base of the starting clutch is established by a combination of bench tests and simulation tests. Through the fuzzy algorithm, the driver’s intention is combined with the starting control strategy. Different drivers’ intentions will affect the comprehensive evaluation model of the clutch (the single evaluation index of the clutch is: the maximum sliding power, the sliding power, the speed stability time, the impact degree), thus affecting the final choice of the starting clutch control strategy considering the driver’s intention. On this basis, this paper studies and establishes the MPC starting controller for the power shift gearbox. Compared with the linear control strategy, the PSO-ELM-fuzzy weight starting strategy proposed in this paper can reduce the maximum sliding friction power by 45%, the sliding friction power by 69.45%, and the speed stabilization time by 0.11 s. The effectiveness of the starting control strategy considering the driver’s intention proposed in this paper to improve the starting quality of the power shift tractor is verified.

Suggested Citation

  • Yu Qian & Lin Wang & Zhixiong Lu, 2024. "Design and Optimization of Power Shift Tractor Starting Control Strategy Based on PSO-ELM Algorithm," Agriculture, MDPI, vol. 14(5), pages 1-21, May.
  • Handle: RePEc:gam:jagris:v:14:y:2024:i:5:p:747-:d:1392182
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/5/747/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/14/5/747/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhou, Xingyu & Qin, Datong & Hu, Jianjun, 2017. "Multi-objective optimization design and performance evaluation for plug-in hybrid electric vehicle powertrains," Applied Energy, Elsevier, vol. 208(C), pages 1608-1625.
    2. Cheng, Zhun, 2023. "High nonlinearity of BEV's stepped automatic transmission design objectives and its optimal solution by a novel ISA-RSA," Energy, Elsevier, vol. 282(C).
    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.

      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:14:y:2024:i:5:p:747-:d:1392182. 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.