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首页> 外文期刊>IEEE Transactions on Automatic Control >On Most Permissive Observers in Dynamic Sensor Activation Problems
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On Most Permissive Observers in Dynamic Sensor Activation Problems

机译：动态传感器激活问题中的大多数允许观察者

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摘要

We consider the problem of dynamic sensor activation for fault diagnosis of discrete event systems modeled by finite state automata under the constraint that any fault must be diagnosed within no more than $K + 1$ events after its occurrence, a property called $K$ -diagnosability. We begin by defining an appropriate notion of information state for the problem and defining dynamic versions of the projection operator and information state evolution. We continue by showing that the problem can be reduced to that of state disambiguation. Then we define the most permissive observer (MPO) structure that contains all the solutions to the problem, and we prove results showing that maintaining the $K$-diagnosability property is equivalent to satisfying the extended specification of the state disambiguation problem. We then prove a monotonicity property of the extended specification, and show that this allows us to reduce our information state, which in turn allows us to significantly reduce the complexity of our solution. Putting all of our results together, we obtain a MPO with a size complexity of $O(2^{vert Xvert }(K+2)^{vert Xvert }2^{vert Evert })$, compared with $O(2^{vert Xvert ^{2} cdot K cdot 2^{vert Evert }})$ for the previous approach, where $X$ and $E$ are, respectively, the sets of states and events of the automaton to diagnose. Finally, we provide an algorithm for constructing the most permissive observer and demonstrate its scalability through simulation.
机译：我们考虑了动态传感器激活问题，该问题用于有限状态自动机建模的离散事件系统的故障诊断，其约束条件是在故障发生后，必须在不超过$ K + 1 $个事件内对任何故障进行诊断，该属性称为$ K $-可诊断性。我们首先为问题定义适当的信息状态概念，并定义投影算子和信息状态演化的动态版本。我们继续通过表明问题可以减少到国家消除歧义的问题。然后，我们定义包含该问题所有解决方案的最宽松观察者（MPO）结构，并证明结果表明，维持$ K $ -diagnosability属性等同于满足状态消歧问题的扩展规范。然后，我们证明了扩展规范的单调性，并表明这使我们能够减少信息状态，从而使我们能够显着降低解决方案的复杂性。综合我们所有的结果，我们得到一个MPO，其大小复杂度为$ O（2 ^ {vert Xvert}（K + 2）^ {vert Xvert} 2 ^ {vert Evert}）$ ^ {vert Xvert ^ {2} cdot K cdot 2 ^ {vert Evert}}）$用于前一种方法，其中$ X $和$ E $分别是要诊断的自动机的状态和事件集。最后，我们提供了一种构造最宽松观察者的算法，并通过仿真演示了它的可伸缩性。

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