• 论文 •

### 固定翼无人机多工况聚类及工况匹配

1. （1.陆军工程大学 军械士官学校， 湖北 武汉 430075； 2.武汉大学 电气与自动化学院， 湖北 武汉 430072）
• 收稿日期:2019-08-02 修回日期:2019-08-02 上线日期:2020-09-23
• 通讯作者: 张世荣（1975—），男，副教授，硕士生导师，博士后 E-mail:srzhang@whu.edu.cn
• 作者简介:梁少军（1987—），男，讲师，硕士。E-mail: sjliang@whu.edu.cn
• 基金资助:
陆军装备“十三五”军内科研重点项目（LJ20182B050054）

### Multiple Working Condition Clustering and Matching of Fixed Wing UAV

LIANG Shaojun1,2， ZHANG Shirong2， ZHENG Xing1， LIN Dongsheng1

1. （1.School of Ordnance Sergeant, Army Engineering University, Wuhan 430075, Hubei, China；2.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, Hubei, China）
• Received:2019-08-02 Revised:2019-08-02 Online:2020-09-23

Abstract: The whole mission of fixed-wing UAV can be divided into multiple working conditions, and the analysis of UAV working conditions is the precondition of fault diagnosis. According to the control principle of UAV, 9 variables (deflection angle of elevator, rudder deflection angle of left and right ailerons, course angle, elevation angle, angle of inclination, height, airospeed, and cylinder temperature) of the horizontal, vertical and speed control loops are selected to represent the real-time working conditons of UAV. For the characteristic of UAV data set, a modified density clustering algorithm coupled with shared nearest neighbor is proposed, which is notated as SNN-DBSCAN* to classify UAV working conditions. An inflexion heuristic parameter optimization approach combined with data shear rate and satisfied curves is specifically proposed for SNN-DBSCAN*. An independent component analysis and support vector machine fusion algorithm which is notated as ICA-SVM is proposed for condition matching of UAV. ICA is intentionally employed for feature extraction and reconstruction of the UAV variables so as to improve the disturbance resisting capacity of the condition matching algorithm. The test result of the real UAV flight data set shows that the SNN-DBSCAN* algorithm can reasonably classify UAV working conditions without increasing prior knowledge, and a satisfied matching accuracy can be achieved with ICA-SVM model which shows good disturbance resisting capacity to the deviations of the UAV variables.