基于实测数据的气动辨识方法研究和对比

Aerodynamic parameter estimation algorithms based on measured data

气动参数辨识是检验飞行器的真实气动特性与设计值的匹配性的重要方法。研究分析基于理论计算的方法和基于增广的扩展卡尔曼滤波算法,通过实测数据,对比2种辨识方法的估计结果,得到了扩展卡尔曼滤波法可以有效降低实测数据中的噪声影响,获取更加精确的估计结果的结论。同时结果证明实际辨识结果与设计值之间还存在一定误差,为后续修改设计气动参数值提供了依据。

Aerodynamic parameter estimation is an important way to find the matches between the real aerodynamic characteristics and the designed ones. The theoretical method and the Augmented Extended Kalman Filter(AEKF) is studied and analyzed. By comparing the estimated results with the experimental data, the Extended Kalman Filter algorithm could reduce the influence of noises to get more precious estimation. And the result shows the difference between the real and the designed parameters, which provides a basis for future aerodynamic parameter design.