作者：姜舒涵，庞涛，张新悦，周彦君，王萌谋，曹湘雨，梁靖贤，应安青（四川农业大学机电学院，四川 雅安 625000）
摘 要：【目的】喷施无人机具有作业效率高、成本低、安全性高等特点，但当前喷施无人机控制仍存在精度低、稳定性差等问题。【方法】课题组设计了一种基于粒子群优化的喷施无人机PID控制方法，通过建立喷施无人机水泵系统数学模型，详细分析了PID控制、GA算法优化PID控制、基于PSO算法的PID控制的系统设计。采用MATLAB软件构建仿真系统，在Simulink中搭建仿真模型，对基于PID、GA-PID、PSO-PID控制算法的系统进行目标量分别为500 rad/s、1 400 rad/s、2 500 rad/s的仿真实验，并对三个不同目标量控制算法性能进行了比较。【结果】PID的平均调节时间为2.688 s，GA-PID的平均调节时间为2.396 s，PSO-PID的平均调节时间为1.037 s；PID的平均超调量为21.52%，GA-PID的平均超调量为6.25%，PSO-PID的平均超调量为2.83%。【结论】基于粒子群优化的PID控制响应时间短，超调量小，稳定性强，动态性能好，能够满足喷施控制需求，可应用于喷施无人机控制系统，可有效提高喷施效率。
(School of Mechanical and Electrical Engineering, Sichuan Agricultural University, Sichuan Ya’an 625000)
Abstract: [Objective] Spraying UAV has the characteristics of high work efficiency, low cost, and high safety, but there are still problems with low accuracy and poor stability in current spraying drone control. [Method] The research group designed a PID control method of spraying UAV based on particle swarm optimization. Through establishing the mathematical model of spraying UAV water pump system, the system design of PID control, GA algorithm optimized PID control, and PSO algorithm based PID control was analyzed in detail. A simulation system was constructed using MATLAB software, and a simulation model was built in Simulink. Simulation experiments were conducted on systems based on PID, GA-PID, and PSO-PID control algorithms with target quantities of 500 rad/s, 1 400 rad/s, and 2 500 rad/s, respectively. The performance of three different target quantity control algorithms was compared. [Result] The average adjustment time of PID is 2.688 s, the average adjustment time of GA-PID is 2.396 s, and the average adjustment time of PSO-PID is 1.037 s. The average overshoot of PID is 21.52%, the average overshoot of GA-PID is 6.25%, and the average overshoot of PSO-PID is 2.83%. [Conclusion] PID control based on particle swarm optimization has short response time, small overshoot, strong stability and good dynamic performance, which can meet the demand of spraying control, can be applied to the spraying UAV control system, and can effectively improve spraying efficiency.
Keywords: particle swarm optimization; PID; spraying UAV; automatic control