The project is developing novel architectures for control and diagnosis of complex cyber-­‐physical  systems  subject to stringent performance  requirements  in  terms  of safety, resilience, and adaptivity. These ever-­‐increasing demands necessitate the use of    formal    model-­‐based    approaches    to    synthesize    provably-­‐correct    feedback controllers.

Complex, networked, distributed cyber-physical systems (CPSs) are emerging in many safety-critical application domains such as aerospace and automotive. Design of such systems heavily relies on insights and experiences of engineers as principled design methodologies that can cope with the complexity of these systems are lacking. As a result, extensive testing and fine-tuning is required to ensure that the final product satisfies the design objectives.

This project is developing theoretical foundations and computational algorithms for synthesizing higher-level supervisory and information-acquisition control logic in cyber-physical systems that expend or replenish their resources while interacting with the environment. On the one hand, qualitative requirements capture the safety requirements that are imposed on the system as it operates. On the other hand, quantitative requirements capture resource constraints in the context of energy- aware systems.

Our research addresses urgent challenges in high confidence testing of automotive systems due to on-going and anticipated introduction of advanced, connected, and autonomous vehicle technologies.  We pursue the development of tools for maneuver and data optimization to determine test trajectories and scenarios to facilitate vehicle testing.   Our approaches exploit game theoretic traffic interaction modeling to inform in-traffic relevant trajectories, model-free optimization to identify trajectories falsifying time domain specifications, and the development of Smart Black Box