基于改进SPBO优化算法的飞机气动参数辨识  

Aerodynamic Parameter Identification of Aircraft Based on Improved SPBO

作　　者：耿宏 段振亚 GEN Hong;DUAN Zhenya(College of Electronic Information and Automation,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学电子信息与自动化学院,天津300300

出　　处：《兵器装备工程学报》2021年第10期176-181,共6页Journal of Ordnance Equipment Engineering

基　　金：中央高校基本科研业务费项目(3122017046);中美绿色航线合作基金项目(GH201661279)。

摘　　要：在利用QAR数据辨识飞机气动参数过程中,针对传统极大似然法对初值选取敏感的问题,提出了一种基于改进学生心理学(ISPBO)优化算法的气动参数辨识方法。该算法在SPBO算法基础上,采用Logistic映射初始化,增强初值分布的均匀性,设计双曲函数动态调整最佳学生的迭代步长,降低对初值选取范围要求,依据极大似然判据,将飞机模型输出状态量与QAR观测矢量的差值转化为目标函数,反复迭代使目标函数最小化求取最优气动参数。辨识结果表明,该方法解决了传统极大似然法对初值选取敏感问题。In the process of using QAR data to identify aircraft aerodynamic parameters,aimed at the sensitivity of traditional maximum likelihood method to initial value selection,an aerodynamic parameter identification method based on improved student psychology(ISPBO)optimization algorithm was proposed.Based on the SPBO algorithm,the algorithm used logistic mapping initialization to enhance the uniformity of the initial value distribution,designed a hyperbolic function to dynamically adjust the iterative step size of the best students,and reduced the requirement for the selection range of the initial value.According to the maximum likelihood criterion,the difference between the output state of the aircraft model and the QAR observation vector was transformed into the objective function,and the objective function was iterated repeatedly to minimize the optimal aerodynamic parameters.The identification results show that this method can solve the problem that the traditional maximum likelihood method is sensitive to the initial value.

关 键 词：气动参数 初值 学生心理学算法 极大似然法 LOGISTIC映射 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]