CAREER: Towards Non-Conservative Learning-Aided Robustness for Cyber-Physical Safety and Security

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The goal of this project is to provide a scientific basis to understand and leverage the interaction among physical systems, artificial intelligence/cyber-human agents and their environment through the development of control synthesis tools to reason about safety and security under real-world uncertainties. Such cyber-physical systems, which include many vital infrastructures that sustain modern society (e.g., transportation systems, electric power distribution) are usually safety-critical. If compromised, serious harm to the controlled physical entities and the people operating or utilizing them as well as significant economic losses can result. However, model mismatches between the real system and an imperfect model of the system, in addition to other sources of uncertainties (e.g., measurement errors) disable existing safety and security protection, while robust solutions without learning may be overly conservative. These challenges demonstrate the need to design novel computational tools that can guarantee robust safety and security of cyber-physical systems under real-world uncertainties without sacrificing performance. The project includes research activities that are integrated with education and outreach to engage students and industry partners to appreciate the importance of safety and security for computing-related technologies.To enable learning-aided robust safety and security for cyber-physical systems, this project will develop mathematical foundations and control synthesis algorithms based on set-membership and learning approaches for uncertainty quantification, secure/attack-resilient estimation and safe-by-design control. The research endeavor will produce novel scientific foundations representing: 1) a shift from the conventional average or stochastic characterization of uncertainty of machine learning- and/or physics-based models to a set-membership representation using hybrid inclusion, 2) a transition from secure point estimator designs to secure set-membership estimators with run-time learning of man-in-the-middle attack models/strategies, and 3) a progression from fixed safe-by-design control algorithms with uncompromised state feedback to attack-resilient output feedback designs with learning from run-time data. Together, these contributions lay the foundations in learning-aided control synthesis for cyber-physical safety and security, enabling non-conservative safe and secure solutions for a broad range of cyber-physical systems, including the main application to self-driving cars used to drive the research program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.