Key research themes
1. How does algebraic and automata theory provide foundational models and analysis tools for discrete event systems?
Research in this area focuses on formal mathematical frameworks to model, analyze, and control discrete event systems (DES). This includes the use of max-plus algebra, automata theory, and Petri nets to capture synchronization, event timing, and system states. Such algebraic and automata-theoretic models enable rigorous system representation, verification, and performance analysis critical for industrial and cyber-physical applications.
2. What methods exist for fault diagnosis, prognosis, and supervisory control to ensure safety and reliability in discrete event systems?
This research theme explores advanced diagnostic and predictive control methods for fault-tolerant operation of DES. The focus is on identifying fault occurrences (diagnosis), anticipating faults before occurrence (prognosis), and designing supervisory control strategies for safety. It covers modeling intermittent faults, online fault prognosis, and integrating these into supervisory control to proactively maintain system safety.
3. How can real-time and hierarchical event stream models improve schedulability analysis and event density characterization in discrete event systems?
This theme investigates advanced event stream representations for real-time embedded systems, incorporating dependencies and hierarchical structures to accurately model event timing and interrelationships. These improved models enable tighter bounds on event densities and more precise schedulability and feasibility analyses, which support resource optimization and system performance guarantees in hard real-time environments.
4. What are effective identification and reconstruction methods for event behavior and state estimation in discrete event systems under partial observability?
This theme covers identification of DES models from observed data, reconstructing unobserved or incomplete event logs, and state estimation in systems with shared resources and asynchronous observations. It addresses challenges due to partial event observability, unrecorded events, and the interactions among concurrent cases, enabling accurate performance analysis, root cause diagnosis, and systems modeling.