针对传统获取四旋翼无人机关键参数的时域方法存在的对环境适应性不强, 计算量大, 风洞试验成本高、周期长等问题, 提出了基于UKF数据预处理与CIFER组合的辨识算法。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进行无迹卡尔曼滤波处理, 再将其应用到频域辨识工具CIFER软件包中, 对于模型中不易确定的关键参数进行辨识。结果表明, 经过数据预处理的数据辨识得到的结果在低频阶段跟踪性能好, 曲线吻合程度较好, 关键参数可信度高。

There are some limitations of existing methods for obtaining the key parameters of quad-rotor UAVs in the time domain. For instance, they are not adaptive to varied environment and need a large amount of computation, along with high costs and long operating time in wind tunnel test. This paper presents an identification algorithm combining UKF data pre-processing with CIFER algorithm. Firstly, the system model of the quad-rotor UAV under hovering conditions is built. Then, the measured data of the quad-rotor UAV platform is pre-processed by UKF to get rid of the noise, which is then applied to CIFER software package to identify the uncertain key parameters of the model. The result shows that the result obtained from identification of pre-processed data has a better tracking performance at the low-frequency stage, the curves fit well, and the key parameters have high credibility.