摘要
在航空航天工业中,气动参数辨识广泛应用于飞机气动性能测试,随着飞机性能、操纵要求的提高,以及在线辨识实时性的需要,对气动参数辨识的精度、速度有了越来越高的要求。该文对扩展卡尔曼滤波模型(EKF)、无损卡尔曼滤波模型(UKF)、飞行器气动参数辨识模型进行理论分析。而后依据固定翼飞机飞行数据,结合二维飞行器运动模型,分别应用EKF算法、UFK算法对气动参数进行辨识,对两者的辨识过程和结果进行比较,为飞行器气动参数辨识中滤波算法的选择提供借鉴。
In the aerospace industry, aerodynamic parameter identification is widely used in aircraft aerodynamic performance tests. As the improvement of aircraft performance, control requirements and the need for real-time online identification, demands of aerodynamic parameter identification precision and speed are getting higher and higher. In this paper, EKF, UKF and aerodynamic parameter identification model of theories in the former part were analyzed. Combining with 2D motion model of the aircraft, the performance of these two recursive parameter estimation algorithms for aerodynamic parameter estimation from flight data of fixed wing aircraft was compared, getting reference for the choice of filter algorithm in aerodynamic parameters identification.
出处
《中国测试》
CAS
北大核心
2013年第5期102-106,共5页
China Measurement & Test
关键词
气动参数辨识
扩展卡尔曼滤波
无损卡尔曼滤波
飞行器运动模型
aerodynamic parameter identification
extended Kalman filter
unscented Kalman filter
aircraft motion model
