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

Hao Wang, Lixin Zhang (), Huan Wang, Xue Hu, Jiawei Zhao, Fenglei Zhu and Xun Wu
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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
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

Agriculture, 2023, vol. 13, issue 12, 1-14

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.

Keywords: water and manure EC regulation; Wild Horse Optimizer; fuzzy neural network; lagging system (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
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