基于优化VARIMA 的无人机发动机飞行参数预测模型

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。

Flight Parameter Prediction Model of UAV Engine Based on Optimized VARIMA

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 model 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 experimental 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.