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Detectability of Discrete Event Systems with Dynamic Event Observation.

Shaolong Shu¹, Feng Lin

¹School of Electronics and Information Engineering Tongji University, Shanghai, China.

Systems & Control Letters

|February 18, 2010

Summary

This summary is machine-generated.

This study introduces dynamic event observation for discrete event systems, moving beyond static assumptions. It defines new detectability types and methods for analyzing system states under changing observation conditions.

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Area of Science:

Control Systems Engineering
Computer Science
Information Theory

Background:

Traditional discrete event systems analysis assumes static event observability, where events are always observable once detected.
Practical systems, like sensor networks, often exhibit dynamic event observation, where event detectability varies with system state.
This limitation hinders accurate state determination in complex, real-world systems.

Purpose of the Study:

To generalize the concept of event observation from static to dynamic.
To introduce and define four novel types of detectability for discrete event systems under dynamic observation.
To develop efficient methods for analyzing system detectability with dynamic event observation.

Main Methods:

Formalizing dynamic event observation and its implications for system state determination.
Defining four distinct classes of detectability tailored to dynamic observation scenarios.
Developing an observer with exponential complexity for checking detectabilities.
Proposing a polynomial-complexity detector for efficiently verifying strong detectabilities.

Main Results:

The study successfully generalizes static event observation to dynamic event observation for discrete event systems.
Four new types of detectability are formally defined, expanding the analytical framework.
Efficient algorithms are presented, including a polynomial-time detector for strong detectabilities, mitigating computational complexity.
Methods for active observation are explored, focusing on minimal policies to maintain detectability.

Conclusions:

Dynamic event observation is crucial for accurately analyzing modern discrete event systems.
The proposed detectability framework and efficient algorithms provide practical tools for system analysis and design.
Findings enable the development of more robust and reliable systems, particularly in sensor networks and similar applications.