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本文主要介绍利用频率法、卡尔曼滤波和最大似然法求取气动导数的方法。方法的特点是,通过频率法使旋翼高频成份的影响减至最小,同时求取试验数据的测量噪声和过程噪声,然后通过卡尔曼滤波使试验数据包含的随机噪声减小,最后使用最大似然法使试验数据包含的随机噪声进一步减小,并求取最终的气动导数。计算结果表明,该方法可使试验数据中包含的噪声减至最小,复合相关系数提高到0.95以上,特征根更接近真值,其准确度优于最小二乘法、频率法、卡尔曼滤波方法和最大似然法,它保留了使用低通滤波数据的卡尔曼滤波方法的优点,克服了原方法振荡模态频率偏低的缺点,适合于各种直升机的导数识别。
This article mainly introduces the method of using the frequency method, Kalman filter and maximum likelihood method to get the aerodynamic derivatives. The method is characterized by minimizing the influence of the high frequency components of the rotor by the frequency method, obtaining the measurement noise and the process noise of the test data at the same time, reducing the random noise included in the test data by using Kalman filtering, and finally using the maximum likelihood However, the random noise contained in the test data is further reduced and the final aerodynamic derivative is obtained. The results show that this method can minimize the noise contained in the experimental data, the composite correlation coefficient is increased to above 0.95 and the eigenvalue is closer to the true value. The accuracy of the method is better than the least square method, the frequency method and the Kalman filter method Maximum likelihood method, which retains the advantages of Kalman filtering method using low-pass filtered data to overcome the shortcomings of the original method low oscillation mode frequency suitable for a variety of helicopter derivative identification.