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Titlebook: Introduction to Discrete Event Systems; Christos G. Cassandras,Stéphane Lafortune Textbook 19991st edition Springer-Verlag US 1999 Algebra

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Languages and Automata,fferential or difference equations. Our first task, therefore, in studying DES is to develop appropriate models, which both adequately describe the behavior of these systems and provide a framework for analytical techniques to meet the goals of design, control, and performance evaluation.
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Supervisory Control, must be modified by feedback control in order to achieve a given set of .. This is reminiscent of the feedback control loop introduced in Chapter 1, in Section 1.2.8. Let us assume that the given DES is modeled by automaton ., where the state space of . need not be finite. Let . be the event set of
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Petri Nets,As we will see, Petri nets are related to automata in the sense that they also explicitly represent the transition function of DES. Like an automaton, a Petri net is a device that manipulates events according to certain rules. One of its features is that it includes explicit conditions under which a
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Timed Models,ces {.., ..,...} or state sequences {.., ..,...}, but they must include some form of timing information. For example, let, .., . = 1, 2,..., denote the time instant when the kth state transition occurs (with to given); then a timed sample path of a DES may be described by the sequence {(.., ..), (..
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Markov Chains,ocks to tick at varying speeds, we also provided an extension to the basic GSMP. In addition, we introduced the Poisson process as a basic building block for a class of stochastic DES which possess the Markov (memoryless) property. Thus, we obtained the class of stochastic processes known as ., whic
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Sensitivity Analysis and Concurrent Estimation,less of the modeling framework adopted. It would also be natural to wonder whether there are any properties at all in DES that we can exploit in our effort to develop mathematical techniques for analysis, design, and control.
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