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

该项目的目标是提供一个科学基础，通过开发控制合成工具来理解和利用物理系统，人工智能/网络人类代理及其环境之间的相互作用，以在现实世界的不确定性下推理安全和安保。这种网络物理系统包括许多维持现代社会的重要基础设施（例如，运输系统、电力分配）通常是安全关键的。如果受到损害，可能会对受控制的实体和操作或使用它们的人造成严重损害，并造成重大经济损失。然而，除了其他不确定性来源（例如，测量误差）使现有的安全和安保保护失效，而没有学习的鲁棒解决方案可能过于保守。这些挑战表明需要设计新型计算工具，以确保网络物理系统在现实世界的不确定性下具有强大的安全性，而不会牺牲性能。该项目包括与教育和推广相结合的研究活动，以吸引学生和行业合作伙伴了解安全和安全对计算相关技术的重要性。为了实现网络物理系统的学习辅助鲁棒安全和安全，该项目将开发数学基础和基于集合成员和学习方法的控制综合算法，用于不确定性量化，安全/抗攻击估计和安全设计控制。研究奋进将产生新的科学基础，代表：1）从基于机器学习和/或物理学的模型的不确定性的常规平均或随机表征到使用混合包含的集合成员资格表示的转变，2）从安全点估计器设计到具有中间人攻击模型/策略的运行时学习的安全集合成员资格估计器的转变，以及3）从具有未妥协状态反馈的固定安全设计控制算法到具有从运行时数据学习的攻击弹性输出反馈设计的进展。总之，这些贡献为网络物理安全和保障的学习辅助控制综合奠定了基础，为广泛的网络物理系统提供了非保守的安全解决方案，包括主要应用于自该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的评估被认为值得支持。影响审查标准。

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.