Fuzzy Neural Network PID Strategy Based on PSO Optimization for pH Control of Water and Fertilizer Integration

Zhou, Runmeng; Zhang, Lixin; Fu, Changxin; Wang, Huan; Meng, Zihao; Du, Chanchan; Shan, Yongchao; Bu, Haoran

doi:10.3390/app12157383

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…In the actual fertilization process, the fertilizer mother liquor and irrigation water are added to the mixing tank first, and the pH value measured at this time is alkaline, then the acid tank solenoid valve is opened, and the pH value in the mixing tank is made to meet the requirements for cotton growth by controlling the opening time of the acid tank solenoid valve. An extensive literature review revealed that the most suitable pH values for cotton growth are between 6 and 7 [13].The process of mixing mother liquor, acid and irrigation water can be regarded as the process of acid-base neutralization [12].This process can consist of a dynamic equation describing the state variables and a static pH equation describing the neutralization titration. Assume that the volume V of the liquid before and after mixing in the mixing tank remains the same, and the mother liquor, water and acid are mixed uniformly.…”

Section: Description Of Water and Fertilizer Ph Adjustment Process An...mentioning

confidence: 99%

“…Considering the characteristics of the fertilizer pH control system, the mathematical model of the system needs to be obtained. According to the fertilizer application characteristics and the complexity of the system, this paper selects the first-order inertia plus delay link transfer function to describe the mathematical model of the fertilizer application control system [13].The model equation is as follows：…”

Section: System Model Constructionmentioning

confidence: 99%

“…Equation (13) shows that if the input language value of PH deviation value 𝑒 is A and the input language value of PH deviation rate of change 𝛥𝑒is B, the output language value of proportional gain Kp is C, the output language value of integral gain Ki is D, and the output language value of differential gain Kd is E by fuzzy rule inference [27].The total fuzzy control rule R is the concatenation of 49 multiple multidimensional conditional statements. In order to improve the sensitivity of the system, the input and output quantities of the system fuzzy controller are all triangular-type subordination functions.…”

Section: Fuzzy Algorithm Optimized Pid Controller Designmentioning

confidence: 99%

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Research on water and fertilizer PH control strategy of automatic fertilizer application system in cotton field

Wang

Zhang

et al. 2023

Preprint

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Xinjiang, China's largest cotton-producing region, suffers from a severe shortage of water resources. Cotton is a highly adapted crop to limited water conditions and no significant decreases in cotton yield have been observed under appropriate limited water conditions. Therefore, water and fertilizer integration technology has become the fundamental way out for the development of the cotton industry in Xinjiang. Time-varying, hysteresis and non-linearity in automatic fertilizer application systems for cotton fields. To address this problem, a fuzzy PID controller based on genetic algorithm optimisation is studied and designed in this paper. Optimisation of the control parameters of the PID is achieved through the optimisation of the proportionality factor in the fuzzy control algorithm by means of a genetic algorithm to complete the precise control of the fertilizer pH value. Development of an automatic fertilizer control system based on a real-time fuzzy control system using MATLAB and PLC controllers with OPC technology. The performance of the controller was verified experimentally and the results showed that the controller has good control accuracy and robustness compared with conventional PID controllers and fuzzy PID controllers, achieving the effect of precise fertilizer application.

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Section: Description Of Water and Fertilizer Ph Adjustment Process An...mentioning

confidence: 99%

Section: System Model Constructionmentioning

confidence: 99%

Section: Fuzzy Algorithm Optimized Pid Controller Designmentioning

confidence: 99%

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Research on water and fertilizer PH control strategy of automatic fertilizer application system in cotton field

Wang

Zhang

et al. 2023

Preprint

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“…When the pH of the fermentation material is lower than 5.5, methane production during the fermentation process will be inhibited, leading to a decrease in biogas production. Therefore, in order to increase the biogas production of dry straw fermentation material, it is necessary to control the pH of the material and establish a rapidly adjustable environmental pH control system [4,5], which is of great significance for improving fermentation efficiency and success rates.…”

Section: Introductionmentioning

confidence: 99%

T-S Fuzzy Algorithm Optimized by Genetic Algorithm for Dry Fermentation pH Control

Wang

Shen

et al. 2023

Processes

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In the process of anaerobic dry fermentation to produce biogas, maintaining a suitable pH in the environment is more conducive to the degradation of crop straw. When the pH in the fermentation environment is too low, the process of anaerobic digestion by anaerobic bacteria is inhibited. Therefore, it is necessary to quickly adjust the pH. In this work, we studied the control technology of a pH regulation system and then constructed a T-S fuzzy controller. Upon simplifying the T-S fuzzy controller, the system delay time was reduced, and two genetic algorithms with different fitness performance indicators were used to optimize the T-S fuzzy control. The simulation experiment in this study was designed through simulation software, and the results show that the improved control method has a fast regulation ability. Finally, on-site experiments were conducted using the four control methods under the acidification conditions set in the experimental device. The results show that the control method used in this study to improve performance by integrating the error sum of squares has a short control time and small oscillation and overshoot, and it can better regulate the environmental pH to achieve appropriate conditions when acidification occurs during anaerobic dry fermentation.

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Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients

Watanabe,

2024

Appl Intell

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In contemporary particle swarm optimization (PSO) algorithms, to efficiently explore global optimum solutions, it is common practice to set the inertia weight to monotonically decrease over time for stability, while allowing the two acceleration coefficients, representing cognitive and social factors, to adopt decreasing or increasing functions over time, including random variations. However, there has been little discussion on a unified design approach for these time-varying acceleration coefficients. This paper presents a unified methodology for designing monotonic decreasing or increasing functions to construct nonlinear time-varying inertia weight and two acceleration coefficients in PSO, along with a control strategy for exploring global optimum solutions. We first construct time-varying coefficients by linearly amplifying well-posed monotonic functions that decrease or increase over normalized time. Here, well-posed functions ensure satisfaction of specified conditions at the initial and terminal points of the search process. However, many of the functions employed thus far only satisfy well-posedness at either the initial or terminal points of the search time, prompting the proposal of a method to adjust them to virtually meet specified initial or terminal points. Furthermore, we propose a crossing strategy where the developed cognitive and social acceleration coefficients intersect within the search time interval, effectively guiding the search process by pre-determining crossing values and times. The performance of our Nonlinear Crossing Strategy-based Particle Swarm Optimization (NCS-PSO) is evaluated using the CEC2014 (Congress on Evolutionary Computation in 2014) benchmark functions. Through comprehensive numerical comparisons and statistical analyses, we demonstrate the superiority of our approach over seven conventional algorithms. Additionally, we validate our approach, particularly in a drone navigation scenario, through an example of optimal 3D path planning. These contributions advance the field of PSO optimization techniques, providing a robust approach to addressing complex optimization problems.

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Fuzzy Neural Network PID Strategy Based on PSO Optimization for pH Control of Water and Fertilizer Integration

Cited by 4 publications

References 26 publications

Research on water and fertilizer PH control strategy of automatic fertilizer application system in cotton field

Research on water and fertilizer PH control strategy of automatic fertilizer application system in cotton field

T-S Fuzzy Algorithm Optimized by Genetic Algorithm for Dry Fermentation pH Control

Nonlinear crossing strategy-based particle swarm optimizations with time-varying acceleration coefficients

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