An online algorithm for minimal sensor activation in discrete event systems

Supervisory control of discrete event systems under total and partial observation

In this paper, we investigate how to quantitatively evaluate the performance of state estimation in a discrete event system. We adopt stochastic automaton to quantitatively describe a discrete event system. With the stochastic automaton, we can calculate the probability of occurrence of any event sequence. We say that an event sequence (with sufficient length) is detectable if we can determine the current state and subsequent states of the system after the occurrence of the sequence. We then define the sum of probabilities of all detectable event sequences as a quantitative state estimation indicator. This indicator is the limit when the length of sequence goes to infinite. In order to calculate the limit, we augment the discrete event system into a larger automaton which includes the information of the discrete event system and all its state estimates. The augmented automaton is also a stochastic automaton and can be converted into a Markov Chain by removing all the event labels. The calculation of the limit is then translated into the calculation of the sum of probabilities of some states in the Markov Chain, which can be done efficiently.

Verification of initial-state opacity in security applications of discrete event systems

Most of the research on supervisory control of discrete event systems considers a supervisor which takes a control action according to only event sequences. This paper proposes a new class of supervisors which assign a control pattern based on partial observations of both events and states. The authors consider the case that control specifications are given in terms of formal languages. The authors derive necessary and sufficient conditions for the existence of their proposed supervisor. Then it is shown that their supervisor is more powerful than an event based supervisor considered in Cieslak et al. (1988) and Lin and Wonham (1988). Moreover, the authors study a sublanguage of a given language for which their supervisor exists.

In the conventional framework for failure diagnosis of partially observed discrete event systems, it is assumed that, for each event, the corresponding output symbol is determined uniquely. However, this assumption does not hold in discrete event systems such as a mobile system, where an output symbol depends on not only an event but also a state at which the event occurs. In this paper, we model such discrete event systems by Mealy automata with nondeterministic output functions, and consider a decentralized failure diagnosis problem that requires any occurrence of a failure should be detected by at least one local diagnoser within a uniformly bounded number of steps. We present a necessary and sufficient condition for the existence of a decentralized diagnoser. We then present an algorithm for verifying this condition. Moreover, we present a method for synthesizing local online diagnosers.

In order to make it possible to effectively represent deterministic uncertainties and vagueness as well as the human subjective observation and judgement inherent to many real-world problems, especially those in biomedicine, we introduce, in this paper, fuzzy states and fuzzy events and generalize (crisp) discrete event systems (DES) to fuzzy DES. The largely graph-based current framework of the crisp DES is unsuitable for the expansion, and we have thus reformulated it using state vectors and event transition matrices which can be extended to fuzzy vectors and matrices by allowing their elements to take values between 0 and 1. To measure information related to fuzzy DES, we generalize the crisp DES observability. The new observability allows one to determine whether or not the system output observed is sufficient for decision making. Finally, we extend the optimal control of DES to fuzzy DES. The new fuzzy DES theory is consistent with the existing theory, both at the conceptual and the computation levels, in that the former contains the latter as a special case when the memberships must be either 0 or 1. Numerical examples are provided to illustrate the theoretical development.

Stochastic Flow Models (SFMs) form a class of hybrid systems used as abstractions of complex Discrete Event Systems (DES) for the purpose of deriving performance sensitivity estimates through Infinitesimal Perturbation Analysis (IPA) techniques when these cannot be applied to the original DES. In this paper, we establish explicit connections between gradient estimators obtained through a SFM and those obtained in the underlying DES, thus providing analytical evidence for the effectiveness of these estimators which has so far been limited to empirical observations. We consider DES for which analytical expressions of IPA (or finite difference) estimators are available, specifically G/G/1 and G/G/1/K queueing systems. In the case of the G/G/1 system, we show that, when evaluated on the same sample path of the underlying DES, the IPA gradient estimators of states, event times, and various performance metrics derived through SFMs are, under certain conditions, the same as those of the associated DES or their expected values are asymptotically the same under large traffic rates. For G/G/1/K systems without and with feedback, we show that SFM-based derivative estimates capture basic properties of finite difference estimates evaluated on a sample path of the underlying DES.

We are interested in a class of optimal control problems for discrete event systems (DES). We adopt the formalism of supervisory control theory and model the system as a finite state machine (FSM). Our control problem is characterized by the presence of uncontrollable as well as unobservable events, the notion of occurrence and control costs for events and a worst-case objective function. We first derive an observer for the partially unobservable FSM, which allows us to construct an approximation of the unobservable trajectory costs. We define the performance measure on this observer rather than on the original FSM itself. Further, we use the algorithm of Sengupta and Lafortune (1998) to synthesize an optimal submachine of the observer. This submachine leads to the desired supervisor for the system.

The simulation of cyber-physical systems involves modular heterogeneous systems. When embedding continuous subsystems in a discrete event system, in a classic approach the different subsystems use the same communication points and wait for each other. The approach presented in this paper uses predictions for every single continuous subsystem. That way the continuous subsystems can be used as discrete components in a discrete event system. This concept is implemented with FMUs (Functional Mock-up Units) generated with OpenModelica and the Discrete Event domain of Ptolemy II as a proof of concept. A model is implemented using this environment and compared to another implementation that uses only Ptolemy II. The results show the better scalability and shorter runtime of the presented approach compared to the pure Ptolemy II approach.

This paper is concerned with fault detection and location of discrete event systems (DES) modeled using interpreted Petri nets (IPN). The approach held deals with IPN models in which the marking is partially known. A diagnosis scheme that tracks the DES inputs and outputs is proposed; it allows detecting and locating an unexpected behavior within the DES. The diagnoser handles a monitoring model, expressed also as an IPN, which is computed from the DES model through an induced conservative marking law.

formula omitted]-diagnosability for active on-line diagnosis in discrete event systems

In the conventional supervisory control framework for discrete event systems (DESs) with partial event observation, it is assumed that, for each event, the corresponding output symbol is determined uniquely. However, this assumption does not hold in DESs such as a system with sensor errors and a mobile system, where an output symbol depends on not only an event but also a state at which the event occurs. In this technical note, we model such a DES by a Mealy automaton with a nondeterministic output function. We consider a supervisor, called the anti-permissive supervisor, that assigns its control action based on an anti-permissive policy. We introduce a notion of AP-achievability to characterize a class of languages achievable by the anti-permissive supervisor, and discuss the existence of a nonblocking supervisor.

Secret inference and attacktability analysis of discrete event systems

This paper aims to introduce a facility operation modeling and simulation mainly based on a discrete event system modeling scheme. Facilities have become so much complex that its operational status cannot be estimated by simple calculations. A facility can consist of a lot of unit process and a lot of transportation between consecutive processes, which may be modelled as a time driven system or an event driven system. However, the transportation between processes can usually be considered as a discrete event system and not a continuous system. Therefore, in macro-scale, a facility operation can be simulated on the basis of a discrete event system modeling if we do not consider how an individual process behaves. This study introduces the current status of the studies on an operation modeling and simulation regarding typical nuclear facilities along with some instances. Also, this paper provides insights about how a discrete event system can be applied to a nuclear facility. These kinds of modeling and simulation are very important because they can contribute to a facility design in terms of an optimal process and an equipment layout, a generic understanding for regulators, stakeholders and operators, a system diagnosis through by comparing measurement data with simulation results and furthermore it can contribute to an efficient operation scheduling.

Generalized detectability for discrete event systems