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.
Trustworthy operation of next-generation complex power grid critical infrastructures requires mathematical and practical verification solutions to guarantee the correct infrastructural functionalities. This project develops the foundations of theoretical modeling, synthesis and real-world deployment of a formal and scalable controller code verifier for programmable logic controllers (PLCs) in cyber-physical settings. PLCs are widely used for control automation in industrial control systems.
Until now, the "cyber" component of automobiles has consisted of control algorithms and associated software for vehicular subsystems designed to achieve one or more performance, efficiency, reliability, comfort, or safety (PERCS) goals, primarily based on short-term intrinsic vehicle sensor data. However, there exist many extrinsic factors that can affect the degree to which these goals can be achieved.
Shared hardware resources like caches and translation look aside buffers (TLBs) introduce timing unpredictability for real-time systems. We propose techniques to mitigate unpredictabil- ity for multicore systems. The TLB improves the performance of the system by caching the virtual page to physical frame mapping. But TLBs present a source of unpredictability for real-rime systems. Standard heap allocated regions do not provide guarantees on the TLB set that will hold a particular page translation.
Multicore platforms have the potential of revolutionizing the capabilities of embedded cyber-physical systems but lack predictability in execution time due to shared resources. Safety-critical systems require such predictability for certification. This research aims at resolving this multicore "predictability problem.'' It will develop methods that enable to share hardware resources to be allocated and provide predictability, including support for real-time operating systems, middleware, and associated analysis tools.
