基于优化VARIMA的无人机发动机飞行参数预测模型  

Flight Parameter Prediction Model of UAV Engine Based on Optimized VARIMA

作　　者：王凤芹 耿宝亮 杜晶 Wang Fengqin;Geng Baoliang;Du Jing(College of Basic Sciences for Aviation,Naval Aviation University,Yantai 264001,China;College of Combat Service,Naval Aviation University,Yantai 264001,China)

机构地区：[1]海军航空大学航空基础学院,烟台264001 [2]海军航空大学作战勤务学院,烟台264001

出　　处：《兵工自动化》2022年第10期45-50,共6页Ordnance Industry Automation

基　　金：大学自主立项科研课题(2021HDZZLX012)。

摘　　要：为了预测无人机发动机飞行参数值,建立一种多参数关联的向量求和自回归移动平均模型(vector auto regression integrated moving average,VARIMA)。模型以无人机飞行数据作为输入,利用模拟退火算法对VARIMA模型参数进行优化,构建多源飞行参数关联的数据模式,利用构建好的数据模式实现状态参数的预测。选取无人机多次飞行的飞参数据进行实验。实验结果表明:优化后的VARIMA预测模型预测性能好,比优化前的预测用时节省了0.23 s。In order to predict the flight parameters of UAV engine,a vector auto regression integrated moving average(VARIMA)model with multi-parameter correlation was established.The model takes the UAV flight data as the input,uses the simulated annealing algorithm to optimize the VARIMA mode l parameters,constructs the data pattern of multi-source flight parameters association,and uses the constructed data pattern to realize the prediction of state parameters.The flight data of multiple flights of UAV are selected for the experiment.The ex perimental results show that the optimized VARIMA prediction model has good prediction performance,and the prediction time is saved by 0.23 s compared with that before optimization.

关 键 词：飞行参数预测 无人机健康管理 求和自回归移动平均模型 向量自回归模型 时间序列分析算法 

分 类 号：V247.5[航空宇航科学与技术—飞行器设计]