We consider a generalized form of the conventional decentralized control architecture for discreteevent systems where the control actions of a set of supervisors can be``fused'' using both union and intersection of enabled events. Namely, the supervisors agree a priori on choosing``fusion by union'' for certain controllable events and``fusion by intersection'' for certain other controllable events. We show that under this architecture, a larger class of languages can be achieved than before since a relaxed version of the notion of co-observability appears in the necessary and suf®cient conditions for the existence of supervisors. The computational complexity of verifying these new conditions is studied. A method of partitioning the controllable events between``fusion by union'' and``fusion by intersection'' is presented. The algebraic properties of co-observability in the context of this architecture are presented. We show that appropriate combinations of fusion rules with corresponding decoupled local decision rules guarantee the safety of the closed-loop behavior with respect to a given speci®cation that is not co-observable. We characterize an``optimal'' combination of fusion rules among those combinations guaranteeing the safety of the closed-loop behavior. In addition, a simple supervisor synthesis technique generating the in®mal pre®x-closed controllable and co-observable superlanguage is presented.
Decentralized diagnosis of discrete event systems has received a lot of attention to deal with distributed systems or with systems that may be too large to be diagnosed by one centralized site. This paper casts the problem of decentralized diagnosis in a new hierarchical framework. A key feature is the exploitation of different local decisions together with appropriate rules for their fusion. This includes local diagnosis decisions that can be interpreted as "conditional decisions." Under this new framework, a series of new decentralized architectures are defined and studied. The properties of their corresponding notions of decentralized diagnosability are characterized and their relationship with existing work described. Corresponding verification algorithms are also presented and on-line diagnosis strategies discussed.
We investigate diagnosability of stochastic discrete-event systems where the observation of certain events is unreliable, that is, there are non-zero probabilities of the misdetection and misclassification of events based on faulty sensor readings. Such sensor unreliability is unavoidable in applications such as nuclear energy generation. We propose the notions of uA-and uAA-diagnosability for stochastic automata and demonstrate their relationship with the concepts of A-and AA-diagnosabilty defined in [1]. We extend the concept of the stochastic diagnoser to the unreliable observation paradigm and find conditions for uA-and uAA-diagnosability.
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