首页> 外文会议>International Federation of Automatic Control Symposium on Fault Detection, Supervision and Safety of Technical Processes >FAULT DIAGNOSTIC FILTERING USING STOCHASTIC DISTRIBUTIONS IN NONLINEAR GENERALIZED H_∞ SETTING
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FAULT DIAGNOSTIC FILTERING USING STOCHASTIC DISTRIBUTIONS IN NONLINEAR GENERALIZED H_∞ SETTING

机译:非线性通用H_∞设置中随机分布的故障诊断滤波

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摘要

A fault diagnosis problem is considered by using output probability density functions (PDFs) for stochastic time-delayed systems in the continuous time domain. For such systems, a B-spline approximation is used to model the output PDFs and the approximation coefficients (i.e., the weights) are then dynamically linked with the control input in the form of a weighting system. The modelling errors and system uncertainties resulting from both the B-spline expansion and the weighting system are merged into the system disturbances and the established weighting system is also subjected tononlinearities, uncertainties and time delays. The generalized H_∞ optimization is applied to the fault diagnosis problem with the non-zero initial condition and the truncated norms. An LMI-based fault diagnostic filtering (FDF) method is presented such that the fault can be estimated and the disturbances can be attenuated. Simulations are given to demonstrate the efficiency of the proposed approach.
机译:通过使用连续时域中的随机时间延迟系统的输出概率密度函数(PDF)考虑故障诊断问题。对于这样的系统,使用B样条近似来模拟输出PDF和近似系数(即,权重),然后以加权系统的形式与控制输入动态地链接。由B样条膨胀和加权系统产生的建模误差和系统不确定性被合并到系统扰动中,并且建立的加权系统也受到吨度线性,不确定性和时间延迟。通过非零初始条件和截断规范应用广泛的H_‖优化应用于故障诊断问题。提出了基于LMI的故障诊断滤波(FDF)方法,使得可以估计故障,并且可以衰减干扰。给出了仿真展示了所提出的方法的效率。

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