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On fluidization of discrete event models: observation and control of continuous Petri nets

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Abstract

As a preliminary overview, this work provides first a broad tutorial on the fluidization of discrete event dynamic models, an efficient technique for dealing with the classical state explosion problem. Even if named as continuous or fluid, the relaxed models obtained are frequently hybrid in a technical sense. Thus, there is plenty of room for using discrete, hybrid and continuous model techniques for logical verification, performance evaluation and control studies. Moreover, the possibilities for transferring concepts and techniques from one modeling paradigm to others are very significant, so there is much space for synergy. As a central modeling paradigm for parallel and synchronized discrete event systems, Petri nets (PNs) are then considered in much more detail. In this sense, this paper is somewhat complementary to David and Alla (2010). Our presentation of fluid views or approximations of PNs has sometimes a flavor of a survey, but also introduces some new ideas or techniques. Among the aspects that distinguish the adopted approach are: the focus on the relationships between discrete and continuous PN models, both for untimed, i.e., fully non-deterministic abstractions, and timed versions; the use of structure theory of (discrete) PNs, algebraic and graph based concepts and results; and the bridge to Automatic Control Theory. After discussing observability and controllability issues, the most technical part in this work, the paper concludes with some remarks and possible directions for future research.

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Notes

  1. Think, for example, in models of atoms based on wave-particle duality or the uncertainty principle (Bernard Pullman, The Atom in the History of Human Thought, Oxford Univ. Press, 1998).

  2. Before electronic computers became a reality, it is clear that the foundations of Theoretical Computer Science go back to the 1930s with the works of Alan Turing. Moreover, in 1928 Claude Shannon applied Boolean Algebra while developing Switching Theory. Even some years before, in the electromechanical domain, Leonardo Torres Quevedo’s chess player was driven by an automata. Furthermore, it is clear that, for example, the pioneering works of Markov and Erlang belong to the first decades of the XX century. Nevertheless, Automata Theory or Queueing Theory (Operations Research in a broader sense), are identifiable bodies of literature defined by the foundational research of the 1950’s and 1960’s.

  3. For each place in the trap, the minimum weight of the input arcs is greater than or equal to the minimum weight of its output arcs, i.e., ∀ p ∈ Θ such that  ∙  p ≠ ∅ it holds that \(\mathop{min}_{t_i\in{\,\!^\bullet{p}}} {{\boldsymbol{Post}}}[p,t_i]\geq \mathop{min}_{t_o\in{{p}^\bullet}} {{\boldsymbol{Pre}}}[p,t_o]\).

  4. For simulation purposes, in the state equation for \(\boldsymbol{m}_{k+1}\), the noise \(\boldsymbol{v}_k\) is added only if \(\boldsymbol{m}_{k+1}\geq 0\). This means that very close to boundaries the system may be kept as deterministic. In fact, if the system is crowded, i.e., \(\boldsymbol{m}_0\) is big, the probability of getting \(\boldsymbol{m}_{k+1} \not \geq 0\) is very low.

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Acknowledgements

This work benefits from the comments of the following colleagues and friends: R. David (INP de Grenoble), A. Giua (Univ. di Cagliari), E. Jiménez (Univ. de La Rioja), A. Ramírez (Cinvestav, Guadalajara, Mx) and J. Zaytoon (Univ. de Reims-Champagne-Ardennes). All of them and the anonymous reviewers are gratefully acknowledged. This work has been partially supported by the European Community’s Seventh Framework Program under project DISC (Grant Agreement n. INFSO-ICT-224498), by CICYT - FEDER grant DPI2010-20413 and by Fundación Aragón I+D.

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Silva, M., Júlvez, J., Mahulea, C. et al. On fluidization of discrete event models: observation and control of continuous Petri nets. Discrete Event Dyn Syst 21, 427–497 (2011). https://doi.org/10.1007/s10626-011-0116-9

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