Design and test of self-adaptive variable spray system of UAV based on neural network PID

CEN Zhenzhao; YUE Xuejun; WANG Linhui; LING Kangjie; CHENG Ziyao; LU Yang

doi:10.7671/j.issn.1001-411X.201811017

Journal of South China Agricultural University > 2019 > 40(4): 100-108. > DOI: 10.7671/j.issn.1001-411X.201811017

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

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Design and test of self-adaptive variable spray system of UAV based on neural network PID

College of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China

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Received Date: November 07, 2018
Available Online: May 17, 2023

Graphical Abstract

Abstract

Abstract

Objective
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.

Method
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.

Result
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.

Conclusion
The research results can provide a basis for the application of variable spray technology in precision agricultural aviation.
- UAV,
- wind pressure transmitter,
- self-adaption variable spray,
- neural network PID

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References (24)

References

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Design and test of self-adaptive variable spray system of UAV based on neural network PID

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