Fault diagnosis and fault-tolerant control for non-Gaussian non-linear stochastic systems using a rational square-root approximation model
Fault diagnosis and fault-tolerant control for non-Gaussian non-linear stochastic systems using a rational square-root approximation model
复制标题
使用有理平方根近似模型对非高斯非线性随机系统进行故障诊断和容错控制
DOI：
10.1049/iet-cta.2012.0466
复制
发表时间：
2013-01-01
影响因子：
2.6
通讯作者：
Wang, Hong
中科院分区：
文献类型：
--
作者：
Yao, Lina;Qin, Jifeng;Wang, Hong
随机分布控制系统的故障检测与诊断的目的是利用测量的输入和系统输出的概率密度函数（PDF）来获取系统的故障信息。在本文中，有理平方根B样条模型被用来表示输出PDF和输入之间的动态。其次，这是由一个新的设计的非线性神经网络的故障诊断（FD）算法，以诊断故障的动态部分，这样的系统。利用李雅普诺夫稳定性定理对故障检测与诊断阶段产生的误差动态系统进行了收敛性分析。最后，基于故障分布信息，设计了一种基于比例积分跟踪控制的容错控制方案，使故障后的概率密度函数仍能跟踪给定的分布。最后给出了一个仿真例子来说明所提算法的有效性。
The purpose of the fault detection and diagnosis of stochastic distribution control systems is to use the measured input and the system output probability density functions (PDFs) to obtain the fault information of the system. In this paper, the rational square-root B-spline model is used to represent the dynamics between the output PDF and the input. This is then followed by the novel design of a non-linear neural network observer-based fault diagnosis (FD) algorithm so as to diagnose the fault in the dynamic part of such systems. Convergency analysis is performed for the error dynamic system raised from the fault detection and diagnosis phase using the Lyapunov stability theorem. Finally, based on the FD information, a new fault-tolerant control based on proportional integral tracking control scheme is designed to make the post-fault PDF still track the given distribution. A simulated example is given to illustrate the efficiency of the proposed algorithms.