基于STF和小波阈值去噪的无人直升机故障诊断  

Fault Diagnosis for Unmanned Helicopter Based on STF and Wavelet Threshold Denoising

作　　者：张国超[1,2] 李平[1] 涂望明[1] 孟庆志[1] 

机构地区：[1]浙江大学工业控制研究所 [2]96263部队

出　　处：《控制工程》2013年第3期517-520,525,共5页Control Engineering of China

基　　金：国家973重点基础研究发展规划项目(2009CB320603);国家自然科学基金项目(61004066

摘　　要：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 that the sensors in the mini unmanned helicopter （MUH） are most prone to faults caused by bad conditions and strong noise, a method of fault diagnosis combining strong tracking filter （STF） with wavelet threshold denoising is proposed. Based on the theory of STF, the method can estimate the states and parameters by constructing the fault observer in which the parameters of the system are expanded to state variables. Meanwhile, the estimate precision is improved as wavelet threshold denoising method is used to denoise the measurement information of the filter in real time. The method can realize real-time diagnosis once faults occur. The effectiveness of the proposed method is proved by the simulation when MUH is in the state of hovering.

关 键 词：微小型无人直升机 小波阈值去噪 强跟踪滤波器 量测噪声 故障诊断 参数估计 悬停飞行 

分 类 号：TP27[自动化与计算机技术—检测技术与自动化装置]