针对现有的四旋翼无人机频域参数辨识方法存在的不易兼顾频带宽度与精度, 且辨识过程效率较低等问题, 提出了一种改进的频域辨识算法。首先, 建立了悬停条件下四旋翼无人机的系统模型, 然后对基于四旋翼无人机平台的实测数据采用组合窗处理得到频率响应, 再将模式搜索中的K-平均聚类算法应用于辨识过程, 得到四旋翼无人机状态空间模型。结果表明, 该方法兼顾辨识精度与复杂度, 辨识得到的模型频率响应能与四旋翼无人机实际模型较好地拟合。

The problems of existing ways in identifying the key parameters of quad-rotor UAVs in the frequency domain are that it is hard to guarantee the bandwidth and accuracy at the same time and the identification process is inefficient. To solve these problems, an improved identification algorithm in the frequency domain was proposed. Firstly, the system model of the quad-rotor UAVs under hovering conditions was built. Then, based on the measured data obtained from the quad-rotor UAV platform, the frequency response was obtained by the composite-window disposal. Finally, a kind of pattern search algorithm, K-means clustering algorithm, was applied to the identification process, and the state-space model of the quad-rotor UAVs was obtained. The result showed that, the proposed method could balance the accuracy and complexity of the identification, and the frequency response of the identified model could fit the actual model of the quad-rotor UAVs well.