Intelligent Algorithm Optimization of Liquid Manure Spreading Control

Wang, Pengjun; Chen, Yongsheng; Xu, Binxing; Wu, Aibing; Fu, Jingjing; Chen, Mingjiang; Ma, Biao

doi:10.3390/agriculture13020278

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…Pengju Wang et al [5] addressed the issue of low precision in the fertilization control device for tractor-trailer applications and proposed a genetic algorithm-optimized Back Propagation Neural Network (BP) PID control algorithm. Test results show an average relative error of 1.07% in liquid manure flow and an actual response time of 2.85 s. However, in practical operation, the system requires a longer time to compute optimal parameters, thus affecting the real-time performance of the device.…”

Section: Introductionmentioning

confidence: 99%

Design and Optimization of Precision Fertilization Control System Based on Hybrid Optimized Fractional-Order PID Algorithm

Wang,

Zhang,

et al. 2023

Processes

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In order to mitigate time-varying, lag, and nonlinearity impacts on fertilization systems and achieve precise control of liquid conductivity, we propose a novel hybrid-optimized fractional-order proportional-integral-derivative (PID) algorithm. This algorithm utilizes a fuzzy algorithm to tune the five parameters of the fractional-order PID algorithm, employs the Smith predictor for structural optimization, and utilizes Wild Horse Optimizer, improved by genetic algorithms, to optimize fuzzy rules. We conducted MATLAB simulations, precision experiments, and stability tests on this controller. MATLAB simulation results, along with precision experiment results, indicate that compared to PID controllers, Smith predictor-optimized PID controllers, and fuzzy-tuned fractional-order PID controllers, the proposed controller has the narrowest steady-state conductivity range, the shortest settling time, and the lowest overshoot, showcasing excellent overall dynamic performance. Stability test results demonstrate that the controller maintains stable operation under different pressure conditions. Therefore, this control system from our study achieves superior control effectiveness, providing a viable approach for the control of nonlinear time-delay systems.

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Section: Introductionmentioning

confidence: 99%

Design and Optimization of Precision Fertilization Control System Based on Hybrid Optimized Fractional-Order PID Algorithm

Wang,

Zhang,

et al. 2023

Processes

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“…Pengjun Wang et al [6] proposed a Back-Propagation (BP) Neural Network PID (Proportional-Integral-Derivative) control algorithm based on Genetic Algorithm (GA) optimization. Their simulation results indicated that this control algorithm exhibited excellent stability, a short response time, and minimal overshoot, achieving precise fertilization effects.…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Zhang,

Wang

et al. 2023

Agriculture

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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.

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“…Two papers described precise identification and positioning systems ("eyes"); these studies applied the improved YOLO algorithm to the recognition of tea and apples [18,19]. Two papers described sensitive decision-making and control systems ("brain") [20,21]; these studies use intelligent control algorithms, such as BP neural network algorithms, to control actuating components or for fault diagnosis. These papers address harvesting components, traveling mechanisms, sensing systems and decision-making algorithms for a wide range of crop harvesters, including grains, vegetables and fruits.…”

mentioning

confidence: 99%