| 基于遗传算法的液肥变量施肥控制系统 |
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| 引用本文： | 田敏,白金斌,李江全.基于遗传算法的液肥变量施肥控制系统[J].农业工程学报,2021,37(17):21-30. |
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| 作者姓名： | 田敏 白金斌 李江全 |
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| 作者单位： | 石河子大学机械电气工程学院，石河子 832003 |
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| 基金项目： | 国家自然科学基金项目（61962053）；石河子大学高层次人才科研启动资金项目（RCZK2018C39） |
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| 摘 要： | 为解决大田牵引式液肥施肥机的变量施肥作业精度不高、施肥流量不均匀以及肥料浪费问题,该研究针对液肥变量施肥控制系统,基于遗传算法的模糊PID(Proportion Integral Derivative)对电动比例阀的控制过程进行优化。首先对牵引式液肥变量施肥机的控制过程进行分析,建立液肥变量施肥控制系统的负反馈控制模型。根据控制系统要求,将模糊控制规则进行染色体编码,通过选择、交叉、变异等遗传算子对模糊控制规则进行仿真寻优,得到最优模糊控制规则表。依据得到的最优模糊控制规则对模糊PID控制器进行设置,并通过MATLAB软件进行仿真分析,结果表明,基于遗传算法的模糊PID控制的响应时间为4.86 s,小于传统PID控制的8.4 s和模糊PID控制的7.32 s。搭建试验平台进行液肥变量施肥控制系统流量控制的稳定性试验和变量控制试验,得到传统PID、模糊PID以及基于遗传算法的模糊PID在系统稳定运行时流量控制的相对误差分别为5.19%、3.40%、1.14%,响应时间分别为5.19、4.12、3.21 s,基于遗传算法的模糊PID较传统PID的相对误差减少了4.05个百分点,响应时间减少了1.98 s;基于遗传算法的模糊PID较模糊PID的相对误差减少了2.26个百分点,响应时间减少了0.91 s。基于遗传算法的模糊PID对液肥流量的控制效果优于传统PID和模糊PID,本文控制方法为变量施肥的研究提供了一种可行方案。
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| 关 键 词： | 控制系统 遗传算法 模糊控制 变量施肥 液肥 |
| 收稿时间： | 2021/4/10 0:00:00 |
| 修稿时间： | 2021/7/8 0:00:00 |
Variable rate fertilization control system for liquid fertilizer based on genetic algorithm |
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| Tian Min,Bai Jinbin,Li Jiangquan.Variable rate fertilization control system for liquid fertilizer based on genetic algorithm[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(17):21-30. |
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| Authors: | Tian Min Bai Jinbin Li Jiangquan |
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| Institution: | College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China |
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| Abstract: | Variable rate fertilization has generally been implemented in the field traction liquid fertilizer applicator under a variety of soil and tillage conditions in recent years. However, it is very necessary to improve the precision, even fertilization, and fertilizer saving during operation in modern mechanized agriculture. In this research, a novel fuzzy PID control was proposed using genetic algorithm (GA) for the variable rate fertilization control system of liquid fertilizer. Firstly, a closed-loop negative feedback model was established for the control system of liquid fertilizer variable rate fertilization, thereby to obtain the transfer function Gr(s) of control process. The control process was also optimized, according to the structure of traction variable liquid fertilizer applicator and the composition of electrical components. Among them, the control object was mainly an electric proportional valve in the control system. The feedback channel was read by the flow meter, and then transferred the electric signal to the controller. Specifically, the controller was implemented to compare the flow reading with the vehicle speed and the amount of fertilizer required for the current field. The obtained data was converted into the control signal, and then output to the electric proportional valve, so as to realize the negative feedback control of system. Some models were established for the traditional, fuzzy, and GA-based fuzzy PID control, according to the requirements of control system. Particularly, the fuzzy PID control model was first established before the GA-based fuzzy PID control model. The input quantity of fuzzy controller was set as the error and the error rate of change, while the output quantity was set as the compensation value of three parameters in the PID controller, where each input and output quantity was set to 7 fuzzy language values. Therefore, there were 49 fuzzy control rules in total. Subsequently, the fuzzy control rules were chromosome-coded within GA. The chromosomes of fuzzy control rules were then simulated and optimized to obtain the optimal fuzzy control rule table using genetic operators, such as selection, crossover, mutation. Correspondingly, the fuzzy PID controller was further set, according to the optimal fuzzy control rules. Finally, MATLAB software was also selected to simulate the traditional, fuzzy, and GA-based fuzzy PID control. Consequently, the response time of electric proportional valve control was 4.86 s for the variable rate fertilization control system using the GA-based fuzzy PID control, which was significantly shorter than the 8.4 s of the traditional PID control, and the fuzzy PID control of 7.32 s. An experimental platform was constructed to carry out the stability and variable control experiment for the flow control in the control system of liquid fertilizer variable rate fertilization. In addition, the flow error was measured during operation in the fertilization stability experiment. The average relative errors of control system were 5.01%, 3.41%, and 1.24%, respectively, corresponding to traditional PID control, fuzzy and GA-base fuzzy PID control during stable operation. The signal was collected and recorded for the actual vehicle speed change in the variable control experiment. The inputs were the collected vehicle speed signal to the controller through a signal generator, thereby to measure the flow when the vehicle speed changed. Consequently, the actual response times were 5.19, 4.12, and 3.21 s, respectively, corresponding to the three control modes. More importantly, the GA-based and fuzzy PID control reduced the average relative error of 3.77% and 2.17%, respectively, compared with traditional, when controlling the flow of variable rate fertilization control system. Additionally, the actual response time of GA-based and fuzzy PID reduced by 1.98 and 0.91s, compared with the traditional PID control. Anyway, the GA-based fuzzy PID control presented better response time to flow control in the variable rate fertilization control system than traditional and fuzzy PID control, indicating better operational stability. |
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| Keywords: | control systems genetic algorithms fuzzy control variable rate fertilization liquid fertilizer |
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