IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i5p786-d1397925.html
   My bibliography  Save this article

Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System

Author

Listed:
  • Huibin Zhu

    (Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Tao Huang

    (Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Lizhen Bai

    (Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China)

  • Wenkai Zhang

    (Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China)

Abstract

In order to improve the obstacle avoidance control performance and anti-interference ability of a stubble breaking device of a no-tillage planter, a back-propagation neural network (BPNN)-optimized fuzzy active disturbance rejection control (ADRC) controller was designed to optimize the control performance of a servo motor. Firstly, a negative feedback mathematical model was established for the obstacle avoidance control system. Then, the nonlinear state error feedback (NLSEF) parameters in the fuzzy ADRC were intelligently optimized by the BPNN algorithm. In this way, a fuzzy ADRC controller based on BPNN optimization was formed to optimize the control process of a servo motor. Matlab/Simulink (R2022b) was used to complete the simulation model design and parameter adjustment. Consequently, the response time was 0.089 s using the BPNN fuzzy ADRC controller, which was shorter than the 0.303 s of the ADRC controller and the 0.100 s of the fuzzy ADRC controller. The overshoot was 0.1% using a BPNN fuzzy ADRC controller, which was less than the 2% of the ADRC controller and the 1% of the fuzzy ADRC controller. After noise signal interference was introduced into the control system, the regression steady state time of the BPNN fuzzy ADRC controller was 0.22 s, which was shorter than the 0.56 s of the ADRC controller and the 0.45 s of the fuzzy ADRC controller. A hardware-in-the-loop simulation experimental platform of the obstacle avoidance control system was constructed. The experiment results show that the servo motor control system has a fast dynamic response, small steady-state error and strong anti-interference ability for obstacle avoidance at the target height. Then, the control system error was within the allowable range. The servo motor control effect of the BPNN fuzzy ADRC was better than the ADRC and fuzzy ADRC. This optimized servo motor control method can provide a reference for improving the obstacle avoidance control effect problem of no-tillage seeders in stubble breaking operations on rocky desertification areas.

Suggested Citation

  • Huibin Zhu & Tao Huang & Lizhen Bai & Wenkai Zhang, 2024. "Optimizing Active Disturbance Rejection Control for a Stubble Breaking and Obstacle Avoiding Control System," Agriculture, MDPI, vol. 14(5), pages 1-17, May.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:5:p:786-:d:1397925
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/5/786/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/5/786/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fengping Li & Zhengya Zhang & Antonios Armaou & Yao Xue & Sijia Zhou & Yuqing Zhou, 2018. "Study on ADRC Parameter Optimization Using CPSO for Clamping Force Control System," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-8, April.
    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:786-:d:1397925. 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.