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兵工学报 ›› 2020, Vol. 41 ›› Issue (8): 1600-1612.doi: 10.3969/j.issn.1000-1093.2020.08.015

• 论文 • 上一篇    下一篇

固定翼无人机多工况聚类及工况匹配

梁少军1,2, 张世荣2, 郑幸1, 林冬生1   

  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

摘要: 固定翼无人机(UAV)执行任务全程可分为多个工况,对UAV进行工况分析是故障诊断的前提。基于UAV操控原理,选取横向、纵向、速度控制回路的9个状态变量(升降舵偏角、左副翼舵偏角、右副翼舵偏角3个执行器输出数据以及航向角、俯仰角、倾斜角、高度、空速、缸温6个传感器输出数据)表征UAV实时工作状态。根据UAV数据特征提出共享近邻改进的密度聚类(SNN-DBSCAN*)算法,用于划分UAV工况。结合数据剪切率、满意曲线概念得到适合SNN-DBSCAN*算法的拐点启发式参数优化方法。提出独立成分分析与支持向量机(ICA-SVM)融合算法用于在线工况匹配,其中独立成分分析(ICA)算法旨在对UAV各变量数据进行特征提取和重构,以提高匹配模型的抗干扰能力。UAV实飞数据结果表明:SNN-DBSCAN*算法可在不增加先验知识基础上合理地划分UAV工况,ICA-SVM匹配模型可以获得满意的工况匹配准确率,且对UAV的变量偏差有较好的抵御能力。

关键词: 固定翼无人机, 多工况聚类, 工况匹配, 共享近邻, 密度聚类, 独立成分分析, 模式匹配

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.

Key words: fixed-wingunmannedairvehicle, multi-workingconditionclustering, workingconditionmatching, sharednearestneighbor, densityclustering, independentcomponentanalysis, patternmatching

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