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تسجيل مستخدم جديدDiagnosability of Fuzzy Discrete Event Systems
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In order to more effectively cope with the real-world problems of vagueness, {it fuzzy discrete event systems} (FDESs) were proposed recently, and the supervisory control theory of FDESs was developed. In view of the importance of failure diagnosis, in this paper, we present an approach of the failure diagnosis in the framework of FDESs. More specifically: (1) We formalize the definition of diagnosability for FDESs, in which the observable set and failure set of events are {it fuzzy}, that is, each event has certain degree to be observable and unobservable, and, also, each event may possess different possibility of failure occurring. (2) Through the construction of observability-based diagnosers of FDESs, we investigate its some basic properties. In particular, we present a necessary and sufficient condition for diagnosability of FDESs. (3) Some examples serving to illuminate the applications of the diagnosability of FDESs are described. To conclude, some related issues are raised for further consideration.
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The supervisory control theory of fuzzy discrete event systems (FDESs) for fuzzy language equivalence has been developed. However, in a way, language equivalence has limited expressiveness. So if the given specification can not be expressed by langua
ge equivalence, then the control for language equivalence does not work. In this paper, we further establish the supervisory control theory of FDESs for fuzzy simulation equivalence whose expressiveness is stronger than that of fuzzy language equivalence. First, we formalize the notions of fuzzy simulation and fuzzy simulation equivalence between two FDESs. Then we present a method for deciding whether there is a fuzzy simulation or not. In addition, we also show several basic properties of fuzzy simulation relations. Afterwards, we put forward the notion of fuzzy simulation-based controllability, and particularly show that it serves as a necessary and sufficient condition for the existence of the fuzzy supervisors of FDESs. Moreover, we study the range control problem of FDESs. Some examples are given to illustrate the main results obtained.
Recently, the diagnosability of {it stochastic discrete event systems} (SDESs) was investigated in the literature, and, the failure diagnosis considered was {it centralized}. In this paper, we propose an approach to {it decentralized} failure diagnos
is of SDESs, where the stochastic system uses multiple local diagnosers to detect failures and each local diagnoser possesses its own information. In a way, the centralized failure diagnosis of SDESs can be viewed as a special case of the decentralized failure diagnosis presented in this paper with only one projection. The main contributions are as follows: (1) We formalize the notion of codiagnosability for stochastic automata, which means that a failure can be detected by at least one local stochastic diagnoser within a finite delay. (2) We construct a codiagnoser from a given stochastic automaton with multiple projections, and the codiagnoser associated with the local diagnosers is used to test codiagnosability condition of SDESs. (3) We deal with a number of basic properties of the codiagnoser. In particular, a necessary and sufficient condition for the codiagnosability of SDESs is presented. (4) We give a computing method in detail to check whether codiagnosability is violated. And (5) some examples are described to illustrate the applications of the codiagnosability and its computing method.
It is well known that type-1 fuzzy sets (T1 FSs) have limited capabilities to handle some data uncertainties directly, and type-2 fuzzy sets (T2 FSs) can cover the shortcoming of T1 FSs to a certain extent. Fuzzy discrete event systems (FDESs) were p
roposed based on T1 FSs theory. Hence, FDES may not be a satisfactory model to characterize some high-uncertainty systems. In this paper, we propose a new model, called as bi-fuzzy discrete event systems (BFDESs), by combining classical DESs theory and T2 FSs theory. Then, we consider the supervisory control problem of BFDESs. The bi-fuzzy controllability theorem and nonblocking bi-fuzzy controllability theorem are demonstrated. Also, an algorithm for checking the bi-fuzzy controllability condition is presented. In addition, two controllable approximations to an uncontrollable language are investigated in detail. An illustrative example is provided to show the applicability and the advantages of BFDESs model.
An epistemic model for decentralized discrete-event systems with non-binary control is presented. This framework combines existing work on conditional control decisions with existing work on formal reasoning about knowledge in discrete-event systems.
The novelty in the model presented is that the necessary and sufficient conditions for problem solvability encapsulate the actions that supervisors must take. This direct coupling between knowledge and action -- in a formalism that mimics natural language -- makes it easier, when the problem conditions fail, to determine how the problem requirements should be revised.
We study supervisor localization for real-time discrete-event systems (DES) in the Brandin-Wonham framework of timed supervisory control. We view a real-time DES as comprised of asynchronous agents which are coupled through imposed logical and tempor
al specifications; the essence of supervisor localization is the decomposition of monolithic (global) control action into local control strategies for these individual agents. This study extends our previous work on supervisor localization for untimed DES, in that monolithic timed control action typically includes not only disabling action as in the untimed case, but also ``clock preempting action which enforces prescribed temporal behavior. The latter action is executed by a class of special events, called ``forcible events; accordingly, we localize monolithic preemptive action with respect to these events. We demonstrate the new features of timed supervisor localization with a manufacturing cell case study, and discuss a distributed control implementation.
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