Fault diagnosis and minimum entropy fault tolerant control for non-Gaussian singular stochastic distribution systems using square-root approximation
Fault diagnosis and minimum entropy fault tolerant control for non-Gaussian singular stochastic distribution systems using square-root approximation
复制标题
使用平方根近似的非高斯奇异随机分布系统的故障诊断和最小熵容错控制
DOI：
10.1504/ijmic.2015.072620
复制
发表时间：
2015-01-01
影响因子：
0.7
通讯作者：
Wang, Aiping
中科院分区：
文献类型：
--
作者：
Yao, Lina;Guan, Yacun;Wang, Aiping
本文针对非高斯奇异随机分布控制（SDC）系统提出了故障诊断（FD）和最小熵容错控制（FTC）算法。与一般SDC系统不同的是，在奇异SDC系统中，权向量与控制输入之间的关系用奇异状态空间模型来表达，这增加了故障诊断和容错控制的设计难度。进行非奇异状态变换，将奇异动态系统转变为微分代数系统。迭代学习观察器（ILO）是为故障估计而设计的。当事先未知目标概率密度函数（PDF）时，将熵的概念引入非高斯随机分布系统的容错控制中。基于估计的故障信息，通过最小化关于受到均值约束的熵的性能函数来重新配置控制器。重新配置的控制器可以使故障后SDC系统的输出仍然具有最小的不确定性，从而导致非高斯奇异SDC系统的熵FTC最小。计算机仿真验证了故障诊断和最小熵FTC算法的有效性。
The fault diagnosis (FD) and minimum entropy fault tolerant control (FTC) algorithms are proposed for non-Gaussian singular stochastic distribution control (SDC) systems in this paper. Different from general SDC systems, in singular SDC systems, the relationship between the weight vector and the control input is expressed by a singular state space model, which increases the difficulty in the design of fault diagnosis and fault tolerant control. A non-singular state transformation is made to transform the singular dynamic system into a differential-algebraic system. An iterative learning observer (ILO) is designed for fault estimation. The concept of entropy is introduced to fault tolerant control of the non-Gaussian stochastic distribution system when the target probability density function (PDF) is not known in advance. Based on the estimated fault information, the controller is reconfigured by minimising the performance function with regard to the entropy subjected to mean constraint. The reconfigured controller can make the output of the post-fault SDC system still have minimum uncertainty, leading to minimum entropy FTC of the non-Gaussian singular SDC system. Computer simulations are given to demonstrate the validity of the fault diagnosis and minimum entropy FTC algorithms.