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Related Experiment Videos

Modeling admissible behavior using event signals.

Luz Pinzon1, Mohsen A Jafari, Hans-Michael Hanisch

  • 1Bell Laboratories, Lucent Technologies, San Francisco, CA 94104, USA.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 16, 2004
PubMed
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This study presents a method to model admissible system behavior using safe Petri nets (PN). The approach avoids complex state enumeration by analyzing the structure of a combined plant and specification model.

Area of Science:

  • Discrete Event Systems
  • Formal Methods
  • Control Theory

Background:

  • Discrete event systems (DES) require modeling for admissible behavior analysis.
  • Petri nets (PN) offer a graphical framework for DES modeling.
  • Controllable and uncontrollable transitions complicate behavior analysis.

Purpose of the Study:

  • To develop a method for obtaining a model of admissible behavior for DES.
  • To utilize safe Petri net (PN) models for system representation.
  • To integrate sequential specifications with the system model.

Main Methods:

  • Representing the system using a safe Petri net (PN) model.
  • Modeling sequential specifications with a state machine.
  • Combining the plant and specification models using net condition/event systems.

Related Experiment Videos

  • Analyzing the structure of the combined model to determine admissible behavior.

    • Main Results:

    • A method is presented to derive admissible system behavior directly from the combined model's structure.
    • The approach avoids the computational burden of complete state enumeration.
    • The method is applicable to systems with controllable and uncontrollable transitions.

    Conclusions:

    • The proposed method offers an efficient way to model admissible behavior in DES.
    • Leveraging the structure of combined PN models simplifies behavior analysis.
    • This technique provides a valuable tool for the design and verification of complex systems.