Document Type
Thesis
Date of Award
5-31-2024
Degree Name
Master of Science in Electrical Engineering - (M.S.)
Department
Electrical and Computer Engineering
First Advisor
MengChu Zhou
Second Advisor
Marcos Netto
Third Advisor
Philip Pong
Abstract
Petri Nets (PNs) are a well-established framework for modeling and analyzing complex systems with interacting, concurrent processes. This thesis extends traditional Petri Net methodologies by integrating Dynamic Mode Decomposition (DMD), a technique originally developed for fluid dynamics, to analyze Continuous Petri Nets (CPNs). By applying DMD to CPN marking evolution, this research constructs a reduced-order model that captures its dynamics through dynamic modes and eigenvalues, enabling prediction of future markings without detailed knowledge of transition firings or underlying deterministic models. The principal contribution of this research is extending the kit of tools available for analysis of CPN dynamics, providing insights into CPN stability and boundedness through the eigenvalues of the DMD operator. Through a series of case studies on established CPN models from PN literature, this work showcases the effectiveness of the DMD operator in forecasting and reconstructing marking evolutions. This work advances the theoretical framework of Petri Nets and opens avenues for future research in integrating data-driven analysis techniques with traditional modeling tools. This thesis is structured to first lay down the theoretical underpinnings of Petri Nets and Dynamic Mode Decomposition, followed by a discussion on the methodology and application of DMD to CPNs, culminating in a presentation of results and conclusions that highlight the potential of this integrated approach.
Recommended Citation
Kale, Aditya, "Marking estimation in petri nets using dynamic mode decomposition" (2024). Theses. 2586.
https://digitalcommons.njit.edu/theses/2586
