The scientific project of the ToSyMA team aims to design methodological tools capable of ensuring fault tolerance, supervision, and operational safety of cooperative and distributed dynamic systems. The team’s expertise is focused on system diagnosis, including fault detection, localization, and estimation (Fault Detection and Isolation - FDI), as well as the design of fault-tolerant multi-sensor fusion methods (Fault Tolerant Fusion - FTF) with integrity supervision, and fault-tolerant control laws (Fault Tolerant Control - FTC). Our goal is to develop FDI, FTC, and FTF methods for complex distributed dynamic systems, integrating interactions between diagnosis, multi-sensor fusion, and control in the presence of faults to ensure a global level of operational safety and system security. The diagnostic methods developed can be based on established models or be entirely model-independent, utilizing data-driven approaches and machine learning techniques, including deep learning, to enhance system safety. In our approach, we seek to merge these two paradigms, exploiting the best of each to improve system performance.

The research conducted by the team is organized around three methodological and theoretical thematic axes:

• System diagnosis, state observation, fault tolerant control, and prognosis;
• Fault-tolerant multi-sensor data fusion for localization;
• Cybersecurity and operational safety.