四旋翼无人机姿态异常感知数据生成方法  

Anomalous attitude sensing data generation method for quadrotor unmanned aerial vehicle

作　　者：刘连胜[1] 张哲彦 王志亮 彭宇[1] Liu liansheng;Zhang Zlieyan;Wang Zhiliang;Peng Yu(Department of Test and Control Engineering,Harbin Imtitute of Technology,Harbin J50080,China;Department of Precision Instrument,Tsinghua University,Beijing 100084,China;Test Center,AECC Harbin Dong an Engine,Co.,Ltd.,Harbin 150060,China)

机构地区：[1]哈尔滨工业大学测控工程系,哈尔滨150080 [2]清华大学精密仪器系,北京100084 [3]中国航发哈尔滨东安发动机有限公司试验中心,哈尔滨150060

出　　处：《仪器仪表学报》2020年第4期58-67,共10页Chinese Journal of Scientific Instrument

基　　金：国家自然科学基金(61803121,61701131);中国博士后科学基金(2019M651277)项目资助。

摘　　要：为解决四旋翼无人机飞控系统因缺少异常感知数据而难以评估其运行状态的问题,提出一种基于物理仿真模型生成异常姿态数据的方法。首先,通过定义四旋翼无人机的运动坐标系,结合牛顿-欧拉公式建立无人机运动方程,设计飞控系统控制回路,利用Simulink软件构建飞控系统物理仿真模型,为生成异常感知数据提供实验环境。其次,在利用实际姿态感知数据验证仿真模型可用的基础上,通过异常注入来生成姿态的异常数据。最后,以基于主成分分析的异常检测方法为例,评估生成异常感知数据的应用效果。实验结果表明,所提方法能够有效地生成恒偏差和漂移两种异常姿态感知数据,基于主成分分析方法开展的异常检测结果:误检率处于2%~74%,正确率处于73%~97%。因此,所提的感知数据生方法可为改进检测方法性能提供相应的数据支撑。In order to solve the problem that due to the lack of anomalous sensing data, it is difficult for the quadrotor unmanned aerial vehicle(UAV) to implement the motion condition assessment of flight control system, this paper proposes a method to generate anomalous attitude data based on physical simulation model. Firstly, the moving coordinate system of quadrotor unmanned aerial vehicle is defined and Newton-Eulerian formula is used to establish the UAV motion equation. In this way, the control loop of the flight control system is designed. The physical simulation model of the flight control system is established with Simulink software, which provides the experiment environment for generating anomalous sensing data. Secondly, the real attitude sensing data of the quadrotor UAV are utilized to verify the applicability of the simulation model, and through abnormal injection the anomalous attitude data are generated. Finally, taking the anomaly detection method based on principle component analysis(PCA) as an example, the application effect of the generated anomalous sensing data is evaluated. Experiment results show that the method proposed in this paper can effectively generate two kinds of anomalous attitude sensing data with constant bias and drift. The anomaly detection results of PCA method show that false positive ratio is 2%~74% and accuracy is 73%~94%. Therefore, the proposed sensing data generation method can provide the corresponding data support for improving the performance of anomaly detection method.

关 键 词：无人机 飞控系统 物理模型 姿态感知 异常检测 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术] TH-39[自动化与计算机技术—计算机科学与技术]