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针对微小型无人直升机传感器工作环境恶劣,易出现性能不稳定并引发故障,且受噪声干扰较大的问题,提出了基于强跟踪滤波器(STF)和小波阈值去噪相结合的故障诊断方法。利用强跟踪滤波理论,将系统的参数扩展为状态变量,构造故障观测器,得到系统状态与参数的联合估计,同时采用小波阈值去噪方法对进入滤波器中的量测信息进行实时去噪处理,提高估计精度,实现了故障的实时诊断。通过微小型无人直升机在悬停飞行状态下的仿真实验,证明了该方法的有效性。
Aiming at the problem of poor working environment, unstable performance and fault caused by small unmanned helicopter sensor, and the problem of large noise interference, a fault diagnosis method based on strong tracking filter (STF) and wavelet threshold denoising . Using strong tracking filter theory, the parameters of the system are extended to state variables, fault observers are constructed, and the joint estimation of system states and parameters is obtained. At the same time, the wavelet transform threshold denoising method is used to denoise the measurement information entering the filter in real time , Improve the estimation accuracy, real-time fault diagnosis. The simulation experiment of the miniature unmanned helicopter in hovering flight state proves the effectiveness of the method.