It conducts research in embedded and hybrid physical systems. These systems are characterized by mixed discrete/continuous behaviors that are effectively described using the theory of hybrid systems. From new theoretical results, we construct tools to support the modeling and analysis of hybrid systems in the context of embedded system realizations. Specifically, we develop capabilities for
- Model-based fault detection and isolation (FDI) and diagnosis
- Data-driven Methods for Fault Diagnosis and Anomaly detection
- Model Predictive and Reinforcement Learning approaches to Fault-Tolerant control
- Model- and data-driven approaches for system-level prognostics
- Integrated systems health management (ISHM) of high availability and mission-critical engineering applications.
The research summary gives an overview of our research objectives and core strengths. We have applied our research in a number of projects, both in a laboratory setting and in collaboration with government and industry sponsors for real-world problems.
In the past, we have worked on projects that include the development of Advanced Life Support System (ALS) technology for human space exploration, fuel transfer systems for aircraft, the secondary sodium cooling loop for nuclear reactors, and the cooling system for automobile engines.
Our current research focuses on Anomaly Detection in Complex Systems using Data-Driven methods, Building Energy Management for Smart Buildings applications, and Systems Wide safety of Unmanned Aerial Vehicles and Urban Air Mobility systems.