复杂风场环境下的多旋翼无人机编队故障检测方法

doi:10.13229/j.cnki.jdxbgxb20221264

Abstract

Abstract:

A fault detection method for multirotor UAV formation in complex wind field flight environments was proposed to address the shortage of multirotor UAV formation fault detection research. Combining multirotor dynamic behavior and wind disturbance modelling， the state variables of each multirotor in the formation were estimated in real time based on the Kalman filter algorithm， and the fault features were extracted by means of probabilistic statistics. Then， an identification model was built for fault identification， and the final result was obtained for the identification of the faulty aircraft in the formation. In the simulation validation session， a complex wind field model with superimposed turbulent wind and colored gusts was built based on the RflySim flight simulation platform. In order to assess the efficiency of the approach， based on this platform， the propellers， accelerometers， GPS and batteries of the multirotor are injected into the aircraft， and flight simulations of the multirotor UAV formation are carried out.

Key words: fault detection, multirotor UAV formation, complex wind field, Kalman filter, RflySim platform

CLC Number:

V240.2

Xiao-yi WANG,Di-yi LIU,Jia-bin YU,Zhuo-yun HE,Zhi-yao ZHAO. Fault detection method of multirotor unmanned aerial vehicle formation in complex wind field environment[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 823-831.

Figures/Tables 13

Table 1

Wind field model parameters"

参数	含义	取值
$v w i n d$	风相对于地面的速度	不超过最大风力等级
$b v$	机体系下的机体速度	不超过最大飞行速度
$R e b$	姿态旋转矩阵（地球系到机体系）	$R 3 × 3$
$c d$	机体的空气阻力系数	$R +$
$p w i n d$	机体系下风力的作用点	机身重心或负载附近

Table 1

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Table 2

Identification parameters"

参数	数值	参数	数值
$Δ t$	10	$? 1$	0.6
$γ$	20	$? 2$	0.4
$ξ$	0.04

Table 2

Table 3

Fault occurrence scenario for 1st simulation"

时间段/ $s$	故障机编号	故障类型
［0，10］	-	无故障
（10，20］	1	螺旋桨故障
（20，30］	-	无故障
（30，40］	2	加速度计故障
（40，50］	-	无故障
（50，60］	3	GPS故障
（60，70］	1	电池故障

Table 3

Table 4

Fault occurrence scenario for 2nd simulation"

时间段/ $s$	故障参数/%	时间段/ $s$	故障参数/%
［0，10］	100	（50，60］	75
（10，20］	95	（60，70］	70
（20，30］	90	（70，80］	65
（30，40］	85	（80，90］	60
（40，50］	80

Table 4

Fig.6

Fig.7

Fig.8

Fig.9

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Fault detection method of multirotor unmanned aerial vehicle formation in complex wind field environment

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