基于PSO算法的某型飞机起飞阶段参数辨识及性能分析

Parameter identification and performance analysis of a certain aircrafttaking off stage based on PSO algorithm

摘要:

飞机性能分析是飞行训练评估的重要组成部分具有重要的应用价值。结合某型军用战机起飞阶段飞参数据,研究了利用粒子群算法(PSO)进行参数辨识并进行飞机性能分析的问题。首先通过动力学建立了起飞性能数学模型；然后将动力学方程转化为以速度的平方为输入量,速度增量为输出量的状态方程,利用PSO进行识别并得到了待辨识的参数,并具有较高的精度；最后将辨识的参数代入动力学方程针对影响起飞性能的起飞质量和温度进行了分析,得到在极端条件下飞机起飞性能。可以为日后选择最佳性能飞机作战出动提供决策参考。

Abstract:

Aircraft performance analysis is an important part of flight training evaluation and has important application value. Combining with the flight parameter data of a military fighter during takeoff, the problem of parameter identification and aircraft performance analysis by particle swarm optimization (PSO) is studied. First, the mathematical model of take-off performance is established by dynamics, and then the dynamic equation is transformed into a state equation with the square of the velocity as the input and the increment of the velocity as the output. The parameters which are identified are identified by PSO and have high accuracy. Finally, the parameters of the identified parameters are replaced by the kinetic equation. The takeoff quality and temperature affecting take-off performance are analyzed to get the takeoff performance under extreme conditions. It can provide decision-making reference for selecting the best performance aircraft operation in the future.