复杂风场环境下的多旋翼无人机编队故障检测方法  

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

作　　者：王小艺[1] 刘迪一 于家斌[1] 何卓昀 赵峙尧 WANG Xiao-yi;LIU Di-yi;YU Jia-bin;HE Zhuo-yun;ZHAO Zhi-yao(School of Artificial Intelligence,Beijing Technology and Business University,Beijing 100048,China)

机构地区：[1]北京工商大学人工智能学院,北京100048

出　　处：《吉林大学学报（工学版）》2023年第3期823-831,共9页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(61903008)。

摘　　要：针对多旋翼无人机编队故障检测研究的不足,提出了一种在复杂风场飞行环境下多旋翼无人机编队的故障检测方法。结合多旋翼动态行为和风扰动建模,基于卡尔曼滤波算法对编队中各多旋翼的状态变量进行实时估计,并通过概率统计的方法提取故障特征,进而建立辨识器模型进行故障检测,最终得到编队中的故障机辨识结果。在仿真验证环节,搭建了基于RflySim飞行仿真平台的湍流风与有色阵风叠加的复杂风场模型,分别对多旋翼的螺旋桨、加速度计、GPS、电池进行故障注入和编队飞行模拟,通过多组仿真验证了本文方法的有效性。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.

关 键 词：故障检测 多旋翼无人机编队 复杂风场 卡尔曼滤波 RflySim平台 

分 类 号：V240.2[航空宇航科学与技术—飞行器设计]