摘要
针对飞行器采用离散数据进行气动力建模的辨识需求,发展了一种基于气动力数据集的气动偏差建模与辨识方法。该方法利用离散数据可高效描述气动力非线性变化的特点,采用依据极大似然准则的牛顿-拉夫逊法,将气动力数据集与真实气动力的偏差作为辨识对象,利用线性模型进行气动偏差建模与辨识,避免了传统多项式气动力模型在辨识非线性气动力时参数过多、不便对离散气动力数据修正等问题。通过仿真飞行试验,对比了飞行器受扰动激励后的辨识拟合弹道与飞行仿真弹道,并进行了全局攻角下的飞行器阻力系数、升力系数和俯仰力矩系数辨识修正结果和真实值的对比。结果表明:辨识拟合弹道与真实飞行弹道基本重合,辨识修正后的气动力与真实值吻合较好,所建立的方法是正确、有效的,具有良好的实用性。
Aiming at the identification requirement of aerodynamic modeling of aircraft using discrete data,an aerodynamic deviation modeling and identification method based on aerodynamic data set is developed.This method uses discrete data to efficiently describe the characteristics of nonlinear variation of aerodynamic force.The Newton-Raphson method based on maximum likelihood criterion is adopted.The deviation between aerodynamic data set and real aerodynamic force is taken as the identification object.The linear model is used for modeling and identification of aerodynamic deviations,which avoids the problems of too many parameters in the traditional polynomial aerodynamic model when identifying nonlinear aerodynamic forces and the inconvenience of correcting discrete aerodynamic data.Through simulation flight tests,the identification trajectory fitting and flight simulation trajectory after disturbance excitation of the aircraft are compared,and the identification correction results of the aircraft drag coefficient,lift coefficient and pitching moment coefficient are compared with the real values under the global angle of attack.The results show that the identification of the fitted trajectory basically coincides with the real flight trajectory,and the aerodynamic force after identification and correction is in good agreement with the real value.The established method is correct,effective,and has good practicability.
作者
曹德一
张翔
张元静
Cao Deyi;Zhang Xiang;Zhang Yuanjing(Beijing Electro-mechanical Engineering Institute,Beijing 100074,China)
出处
《战术导弹技术》
北大核心
2021年第3期7-13,共7页
Tactical Missile Technology
关键词
气动力数据集
气动力偏差
建模与辨识
极大似然方法
aerodynamic data set
aerodynamic deviation
modeling and identification
maximum likelihood estimation method