固定翼无人机多工况聚类及工况匹配

梁少军;张世荣;郑幸;林冬生

兵工学报 ›› 2020, Vol. 41 ›› Issue (8) : 1600-1612.

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  • 主管：中国科学技术协会

    主编：许毅达

    ISSN 1000-1093

    　

  • 主办：中国兵工学会

    出版：《兵工学报》 编辑部

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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

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
作者信息 +

Multiple Working Condition Clustering and Matching of Fixed Wing UAV

  • LIANG Shaojun1,2， ZHANG Shirong2， ZHENG Xing1， LIN Dongsheng1
Author information +
文章历史 +

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。

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.

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

Key words

fixed-wingunmannedairvehicle / multi-workingconditionclustering / workingconditionmatching / sharednearestneighbor / densityclustering / independentcomponentanalysis / patternmatching

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梁少军， 张世荣， 郑幸， 林冬生. 固定翼无人机多工况聚类及工况匹配. 兵工学报. 2020, 41(8): 1600-1612 https://doi.org/10.3969/j.issn.1000-1093.2020.08.015
LIANG Shaojun， ZHANG Shirong， ZHENG Xing， LIN Dongsheng. Multiple Working Condition Clustering and Matching of Fixed Wing UAV. Acta Armamentarii. 2020, 41(8): 1600-1612 https://doi.org/10.3969/j.issn.1000-1093.2020.08.015

基金

陆军装备“十三五”军内科研重点项目（LJ20182B050054）

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