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Research and Design of Improved Wild Horse Optimizer-Optimized Fuzzy Neural Network PID Control Strategy for EC Regulation of Cotton Field Water and Fertilizer Systems

Author

Listed:
  • Hao Wang

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China)

  • Lixin Zhang

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China
    Bingtuan Energy Development Institute, Shihezi University, Shihezi 832003, China)

  • Huan Wang

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China)

  • Xue Hu

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China)

  • Jiawei Zhao

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China)

  • Fenglei Zhu

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China)

  • Xun Wu

    (College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China)

Abstract

Xinjiang is the largest cotton-producing region in China, but it faces a severe shortage of water resources. According to relevant studies, the cotton yield does not significantly decrease under appropriate limited water conditions. Therefore, this paper proposes a water and fertilizer integrated control system to achieve water and fertilizer conservation in the process of cotton field cultivation. This paper designs a fuzzy neural network Proportional–Integral–Derivative controller based on the improved Wild Horse Optimizer to address the water and fertilizer integrated control system’s time-varying, lag, and non-linear characteristics. The controller precisely controls fertilizer electrical conductivity (EC) by optimizing parameters through an improved Wild Horse Optimizer for the initial weights from the normalization layer to the output layer, the initial center values of membership functions, and the initial base width of membership functions in the fuzzy neural network. The performance of the controller is validated through MATLAB simulation and experimental tests. The results indicate that, compared with conventional PID controllers and fuzzy PID controllers, this controller exhibits excellent control accuracy and robustness, effectively achieving precise fertilization.

Suggested Citation

  • Hao Wang & Lixin Zhang & Huan Wang & Xue Hu & Jiawei Zhao & Fenglei Zhu & Xun Wu, 2023. "Research and Design of Improved Wild Horse Optimizer-Optimized Fuzzy Neural Network PID Control Strategy for EC Regulation of Cotton Field Water and Fertilizer Systems," Agriculture, MDPI, vol. 13(12), pages 1-14, November.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:12:p:2176-:d:1284453
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    References listed on IDEAS

    as
    1. Wenming Chen & Lianglong Hu & Gongpu Wang & Jianning Yuan & Guocheng Bao & Haiyang Shen & Wen Wu & Zicheng Yin, 2023. "Design of 4UM-120D Electric Leafy Vegetable Harvester Cutter Height off the Ground Automatic Control System Based on Incremental PID," Agriculture, MDPI, vol. 13(4), pages 1-18, April.
    2. Lei Chen & Yikai Zhao & Yunpeng Ma & Bingjie Zhao & Changzhou Feng, 2023. "Improving Wild Horse Optimizer: Integrating Multistrategy for Robust Performance across Multiple Engineering Problems and Evaluation Benchmarks," Mathematics, MDPI, vol. 11(18), pages 1-35, September.
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