- Chinese Core Journal
- Chinese Science Citation Database (CSCD) Source journal
- Journal of Citation Report of Chinese S&T Journals (Core Edition)
| Citation: |
CEN Zhenzhao, YUE Xuejun, WANG Linhui, et al. Design and test of self-adaptive variable spray system of UAV based on neural network PID[J]. Journal of South China Agricultural University, 2019, 40(4): 100-108. DOI: 10.7671/j.issn.1001-411X.201811017
|
The change of flight speed in constant flow spraying process of traditional plant protection UAVs causes nonuniform pesticide application, and the common control algorithms cannot meet the requirements of the real-time and stability that UAVs variable spray system needs. To solve these problems, we designed a self-adaptive variable spray system of UAV based on neural network PID.
The flight speed of UAV was measured by wind pressure transmitter. According to the flight speed, we used pulse width modulation (PWM) for self-adaptive variable spray. At the same time, we measured the actual spray flow with the flow sensor and adjusted the spray flow with PID control algorithm based on BP neural network. We used MATLAB to construct PID control algorithm with BP neural network and compared with PID, fuzzy PID and neural PID control algorithms. In the field experiment, we compared and analyzed the effects of constant spray and variable spray based on changing flight speed. We used water-sensitive paper to obtain the distribution of droplet deposition, and then evaluated the uniformity of deposition distribution from the whole area, flight direction, and spray bar direction respectively.
The simulation result of algorithms indicated that comparing with PID, fuzzy PID and neuron PID, the rise time of step response for BP neural network PID was 28.57%, 84.73% and 31.03% shorter respectively, the average error of sinusoidal tracking was 63.01%, 87.03% and 0.58% lower respectively, the average error of square wave tracking was 74.00%, 79.53% and 6.80% lower respectively. Additionally, the BP neural network PID had strong robustness, 0 static error, and lower overshoot (1.20%). The comparison of the spray tests showed that this system can automatically adjust the spray flow according to flight speed. The average deviation between the actual flow and the set flow was 8.43%. Based on the testing results of the water sensitive paper, variable spray decreased the coefficient of variation of droplet deposition in the whole area by 26.25% and in the spray bar direction by 18.79% on average compared with constant spray.
The research results can provide a basis for the application of variable spray technology in precision agricultural aviation.
| [1] |
邱白晶, 闫润, 马靖, 等. 变量喷雾技术研究进展分析[J]. 农业机械学报, 2015, 46(3): 59-72.
|
| [2] |
沈成杰. 变量喷雾系统设计及喷雾流量控制特性试验研究[D]. 镇江: 江苏大学, 2009.
|
| [3] |
何雄奎. 改变我国植保机械和施药技术严重落后的现状[J]. 农业工程学报, 2004, 20(1): 13-15. doi: 10.3321/j.issn:1002-6819.2004.01.003
|
| [4] |
王林惠, 甘海明, 岳学军, 等. 基于图像识别的无人机精准喷雾控制系统的研究[J]. 华南农业大学学报, 2016, 37(6): 23-30.
|
| [5] |
魏新华, 蒋杉, 张进敏, 等. 脉宽调制间歇喷雾变量喷施系统施药量控制[J]. 农业机械学报, 2013, 44(2): 87-92.
|
| [6] |
邱白晶, 李佐鹏, 吴昊, 等. 变量喷雾装置响应性能的试验研究[J]. 农业工程学报, 2007, 23(11): 148-152. doi: 10.3321/j.issn:1002-6819.2007.11.026
|
| [7] |
SHAHEMABADI A R, MOAYED M J. An algorithm for pulsed activation of solenoid valves for variable rate application of agricultural chemicals[C]// International Symposium on Information Technology 2008, Malaysia: Putra University, Department of Electrical and Instrumentation Engineering, 2008: 1-3.
|
| [8] |
徐兴, 徐胜, 刘永鑫, 等. 小型无人机机载农药变量喷洒系统设计[J]. 广东农业科学, 2014, 41(9): 207-210. doi: 10.3969/j.issn.1004-874X.2014.09.048
|
| [9] |
LEBEAU F, EL B L, DESTAIN M F, et al. Improvement of spray deposit homogeneity using a PWM spray controller to compensate horizontal boom speed variations[J]. Comput Electron Agr, 2004, 43(2): 149-161.
|
| [10] |
FRITZ B K, HOFFMANN W C, BAGLEY W E. Effects of spray mixtures on droplet size under aerial application conditions and implications on drift[J]. Appl Eng Agric, 2010, 26(1): 21-29.
|
| [11] |
王大帅, 张俊雄, 李伟, 等. 植保无人机动态变量施药系统设计与试验[J]. 农业机械学报, 2017, 48(5): 86-93.
|
| [12] |
郭娜, 胡静涛. 基于Smith−模糊PID控制的变量喷药系统设计及试验[J]. 农业工程学报, 2014, 30(8): 56-64. doi: 10.3969/j.issn.1002-6819.2014.08.007
|
| [13] |
白如月, 汪小旵, 鲁伟, 等. 施药机器人对行施药系统的设计与试验[J]. 华南农业大学学报, 2018, 39(5): 101-109.
|
| [14] |
DAI A, ZHOU X, LIU X. Design and simulation of a genetically optimized fuzzy immune PID controller for a novel grain dryer[J]. IEEE Access, 2017, 5(99): 14981-14990.
|
| [15] |
蔡祥, MARTIN W, MALTE D, et al. 喷嘴直接注入式农药喷洒系统控制方法研究[J]. 农业机械学报, 2016, 47(11): 100-105. doi: 10.6041/j.issn.1000-1298.2016.11.013
|
| [16] |
汪少华, 窦辉, 孙晓强, 等. 电控空气悬架车高调节与整车姿态控制研究[J]. 农业机械学报, 2015, 46(10): 335-342. doi: 10.6041/j.issn.1000-1298.2015.10.045
|
| [17] |
玄子玉. 喷雾机同步喷雾控制系统的研究[D]. 哈尔滨: 东北农业大学, 2009.
|
| [18] |
涂川川, 朱凤武, 李铁. BP神经网络PID控制器在温室温度控制中的研究[J]. 中国农机化, 2012(2): 151-154. doi: 10.3969/j.issn.1006-7205.2012.02.041
|
| [19] |
谢炜. 基于BP神经网络PID算法的多电机同步控制研究[D]. 沈阳: 沈阳工业大学, 2017.
|
| [20] |
李小峰, 于慧彬. 基于BP神经网络的船舶航向智能PID控制研究[J]. 山东科学, 2018, 31(4): 8-14.
|
| [21] |
张利君. 基于动态PWM变量喷雾系统的雾量分布均匀性研究[D]. 杭州: 浙江大学, 2017.
|
| [22] |
陈盛德, 兰玉彬, 李继宇, 等. 植保无人机航空喷施作业有效喷幅的评定与试验[J]. 农业工程学报, 2017, 33(7): 82-90.
|
| [23] |
蒋焕煜, 周鸣川, 李华融, 等. PWM变量喷雾系统动态雾滴分布均匀性实验[J]. 农业机械学报, 2015, 46(3): 73-77.
|
| [24] |
蒋焕煜, 张利君, 刘光远, 等. 基于PWM变量喷雾的单喷头动态雾量分布均匀性实验[J]. 农业机械学报, 2017, 48(4): 41-46.
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: