【作者】 吴康；

【导师】 韩波； 李平；

【作者基本信息】 浙江大学 ， 控制理论与控制工程， 2010， 硕士

【摘要】 微小型无人直升机(Mini unmanned helicopter,MUH)由于其独特的飞行性能和使用价值,成为目前自主控制机器人领域的研究热点,对其安全可靠性的要求也在不断提高。而保证MUH安全飞行的前提则是机载传感器系统的可靠性。由于机载传感器的工作环境恶劣,容易引起传感器性能不稳定并引发故障。在航空领域,传感器故障后果十分严重。因此,基于提高MUH传感器系统安全性和可靠性的考虑,对于MUH传感器故障诊断技术的研究尤为重要。本文以浙江大学“玉泉之翼”微小型无人直升机传感器系统作为研究对象。提出了运用小波变换以及支持向量机的方法对其进行故障诊断,诊断方法简便有效,能够基本解决在实验过程中所遇到的传感器故障问题,有效地保证了实验的顺利进行。本文的主要工作及贡献如下:1.系统介绍了目前国内外微小型无人直升机传感器故障诊断领域的研究现状,分析了主流的技术方法与研究手段,指出了面临的主要问题与发展趋势。2.介绍了MUH系统的硬件构建以及低成本机载传感器的选择,并说明了基于互补滤波器的传感器多级融合方法。该方法能够互补融合不同频域特性的传感器,具有良好的静态及动态精度。3.运用小波变换的故障诊断方法对MUH传感器系统进行故障诊断。根据信号输出能量的不同,小波的多尺度分辨特性能够及时地检测出传感器的异常状态,有效地进行传感器的故障诊断。4.采用支持向量机(SVM)的方法对MUH传感器系统进行故障诊断。通过LS-SVM的方法辨识了MUH的非线性回归模型,并构建残差生成器。其后,运用SVM分类的方法构建故障分类器,对故障进行有效地分类。5.提出了将回归型支持向量机(SVR)与小波变换(DWT)相结合的MUH传感器故障检测与分离方法。通过采用离线训练,在线应用的方式,运用SVR建立系统的动态模型,将输出结果与实际系统输出相比较构建残差生成器检测传感器故障并在此基础上采用小波多分辨率分析的方法分解不同传感器的输出信号并提取故障特征,从而实现对故障传感器的分离。更多还原

【Abstract】 Extensive research on Mini unmanned helicopter (MUH) has been carried out around the world as a result of the growing autonomous control technology. Its unique flight performance and great value in use have become new focal points of latest research. The requirements for its security and reliability are also increasing. To ensure the flight safety, the reliability of airborne sensor system serves as a premise. Airborne sensor, the basic component of flight control system, is one of the parts that are most prone to faults. In the domain of aviation, once the sensor comes across failure and outputs the incorrect data, the consequences will be very serious. As for that, the research on fault diagnosis of MUH sensor system has become a highly urgent task to increase its security and reliability.In this paper, the main research object is the sensor system of "Wings of Yuquan" MUH. The fault diagnosis methods by using wavelet transform and support vector machine are proposed. These simple methods are proved to be effective in solving basic sensor failure problems occurred during the research process; therefore can guarantee flight experiments go smoothly. The main contributions of this work are as follows:1. The history and current research status of the fault diagnosis of airborne sensors system on MUH are systematically reviewed.2. Introducing the hardware architecture design, sensor selection and information fusion and filtering. A hierarchical filtering structure is adopted for sensor fusion, in which complementary filters are applied to fuse information from sensors with different frequency characteristics.3. Wavelet transform method is introduced into the fault diagnosis of sensors on MUH. According to the variation of output signal energy, any abnormal state of sensor output signals can be discovered promptly by identifying multi-scale wavelet features, and subsequently to detect and isolate the fault sensors.4. A new method based on Support vector machine is proposed to detect the fault of MUH airborne sensors system. Least square support vector machine (LS-SVM) is applied to compute the SVM model, then a residual generator is constructed to detect faults. Furthermore, SVM is also used to build the Fault Classifier.5. A new fault detection and isolation method based on Support Vector Regression (SVR) combined with Discrete Wavelet Transform (DWT) method is presented in this paper. With its strong capabilities in self learning and nonlinear mapping, SVR is used to build a residual generator to detect faults. Then, DWT is used to isolate the faulty sensor.更多还原