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首页> 外文期刊>Control Theory & Applications, IET >Fault detection for a class of non-linear networked control systems in the presence of Markov sensors assignment with partially known transition probabilities
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Fault detection for a class of non-linear networked control systems in the presence of Markov sensors assignment with partially known transition probabilities

机译:在具有部分已知跃迁概率的马尔可夫传感器分配的情况下,对一类非线性网络控制系统进行故障检测

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摘要

In this study, the problem of fault detection for a class of discrete-time non-linear networked control systems is investigated. An event modelled as a Markov chain taking matrix values in a certain set with partially known transition probabilities is utilised to characterise the phenomenon of the sensors assignment. A full-order mode-dependent fault detection filter is constructed and the corresponding fault detection problem is converted into an filtering problem of a Markov jump system with partially known transition probabilities. Sufficient conditions for the existence of the fault detection filter are formulated as a linear matrix inequality-based convex optimisation problem. If the convex optimisation problem has a feasible solution, the corresponding fault detection filter parameters are determined. A numerical example with four cases of transition probability matrices is presented to show the usefulness of the developed method.
机译:本文研究了一类离散时间非线性网络控制系统的故障检测问题。利用事件建模为马尔可夫链的事件,该事件采用具有部分已知的转移概率的某个集合中的矩阵值来表征传感器分配的现象。构造了一个与全模相关的故障检测滤波器,并将相应的故障检测问题转换为具有部分已知跃迁概率的马尔可夫跳跃系统的滤波问题。将故障检测滤波器存在的充分条件公式化为基于线性矩阵不等式的凸优化问题。如果凸优化问题具有可行的解决方案,则确定相应的故障检测滤波器参数。给出了带有四种情况下的转移概率矩阵的数值示例,以证明该方法的有效性。

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