变质量负载四旋翼无人机抗风性研究  

Research on Wind Resistance of Quad-Rotor UAV with Varied Mass Load

作　　者：段捷 周春桂[1] 张肖江 刘芷汐 DUAN Jie;ZHOU Chun-gui;ZHANG Xiao-jiang;LIU Zhi-xi(North University of China,Taiyuan Shanxi 030051,China)

机构地区：[1]中北大学,山西太原030051

出　　处：《计算机仿真》2024年第4期43-49,共7页Computer Simulation

基　　金：中北大学研究生科技立项资助课题(20221804)。

摘　　要：四旋翼无人机已越来越多的应用于农业植保方面,针对其随着农药量的减少以及外部风扰影响下导致系统的飞行性能的问题,设计了一种积分反步控制法与串级RBF神经网络PID控制相结合的控制方案。方案具有内外环结构,在外环采用积分反步法,对水平位置和高度进行控制;内环采用串级RBF神经网络PID控制,依靠神经网络自学习性对PID参数动态整定实现对姿态角的控制。仿真结果表明,与常规PID控制相比,该方案随外界干扰的波动范围更小,误差更小,证明了该方案的抗干扰性更强。Quadcopter drones have been increasingly applied in agricultural plant protection.In response to the problem of reduced pesticide levels and external wind disturbances affecting the flight performance of the system,a control scheme combining integral backstepping control method and cascade RBF neural network PID control has been designed.The horizontal position and height were controlled by integral back stepping control method in the outer loop;In the inner loop of the control method,the cascade RBF neural network PID control which relies on the selflearning of neural network to realize the dynamic tuning of PID parameters was adopted to control the attitude angle.The simulation results show that compared with the conventional PID control,the control method of this paper has smaller fluctuation range with external interference,and the error are smaller,which proves that it has stronger ability in interference suppression.

关 键 词：四旋翼无人机 变质量负载 积分反步控制 神经网络控制 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]