湍流空气中基于EKF的路径跟踪方法

C. Grillo, F. Montano
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引用次数: 4

摘要

提出了扩展卡尔曼滤波(EKF)的一种创新应用,在湍流空气中实现精确的路径跟踪和足够的干扰抑制。调整过程同时采用两种不同的EKF:第一个估计阵风干扰,第二个估计修改后的飞机参数。第一个过滤器,通过使用在湍流空气中收集的测量数据,估计飞机状态和风的成分。第二种方法是利用估计的扰动,得到能够抑制扰动的指令律。第二次EKF的预报器使用估计的风分量来求解湍流空气中的运动方程。此外,在预测器中引入了一组未知的稳定性和控制参数(包含控制器的位移)。这一组包含修正的空气动力系数。这些是通过在基本的衍生物上添加全新的衍生物或合成增量而得到的。因此,上述飞机参数的未知值增强了飞机的状态。该滤波器通过使用由期望的飞行路径变量组成的一组测量值来估计增强状态。这样,就可以利用假设的未知稳定性和包含控制位移的控制导数来获得命令律。因此,所得到的控制律,无论是与扰动的特性还是与期望的飞行路径有关,都是自适应的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An EKF Based Method for Path Following in Turbulent Air
An innovative use of the Extended Kalman Filter (EKF) is proposed to perform both accurate path following and adequate disturbance rejection in turbulent air. The tuned up procedure employs simultaneously two different EKF: the first one estimates gust disturbances, the second one estimates modified aircraft parameters. The first filter, by using measurements gathered in turbulent air, estimates both aircraft states and wind components. The second one, by using the estimated disturbances, obtains command laws that are able to reject disturbances. The predictor of the second EKF uses the estimated wind components to solve motion equations in turbulent air. Besides a set of unknown stability and control parameters (containing displacements of the controls) is introduced into the predictor. This set contains modified aerodynamic coefficients. These ones are obtained by adding entirely new derivatives or synthetic increments to basic ones. Therefore, the above aforementioned unknown values of aircraft parameters augment the aircraft’s state. The filter estimates the augmented state by using a set of measurements formed by the desired flight path variables. In this way, it is possible to obtain the command laws by using the postulated unknown stability and control derivatives, which contain the control displacements. Therefore, the obtained control laws, related to either the characteristics of the disturbance or the desired flight path, are adaptive.
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