利用卡尔曼滤波求取直升机气动导数的应用研究

本文主要介绍利用低通滤波、卡尔曼滤波及最小二乘法求取直升机气动导数的方法。本方法的特点是,通过低通滤波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至最小,最后用最小二乘法求得直升机的气动导数,为了提高卡尔曼滤波的准确度,用最小二乘法由试验数据求取直升机的气动导数作为卡尔曼滤波时的初始导数。计算结果表明,该方法可使试验数据中包含的噪声大大减小,误差带减少70％以上,而计算工作量又远远小于最大似然法。

APPLICATION OF KALMAN FILTERING TECHNIQUE TO AERODYNAMIC DERIVATIVES FOR A HELICOPTER

This paper describes a method for extracting the aerodynamic derivatives of a helicopter from flight data by means of low pass filtering, Kalman filtering and least square techniques. The method can be summaried as follows:a ) The high frequency effects from the rotor are eliminated by low pass filtering, and then measurement noise and process noise statistics are obtained.b) By using Kalman filter, the random noise is minimized and the bias error is eliminated, but the derivatives are not identified yet.c) The derivatives are identified from raw data by least square technique. They serve as initial values for Kalman filtering.d) The final derivatives are extracted from Kalman filtering data by least square technique.The method presented requires considerably less computation than the maximum likelihood method of reference 1 ]. It is more accurate than the least square technique and the least square technique with Kalman filtering data of reference 1 ]. This method cut down more than 70 percent of error in comparison with the least square technique.