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

职业：实现网络物理安全的非保守学习辅助鲁棒性

• 批准号: 2313814
• 负责人: Sze Zheng Yong
• 金额: $ 50.18万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313814&HistoricalAwards=false
• 关键词: CAREER; Towards; Non; Conservative; Learning

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.

该项目的目标是通过开发控制综合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提供科学基础，以推理现实世界不确定性下的安全性。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（例如交通系统、电力分配），通常对安全至关重要。如果受到损害，可能会对受控物理实体以及操作或使用它们的人员造成严重损害以及重大经济损失。然而，除了其他不确定性来源（例如测量误差）之外，真实系统与不完美的系统模型之间的模型不匹配会禁用现有的安全和安保保护，而无需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颖的计算工具，以保证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，而不牺牲性能。该项目包括与教育和外展相结合的研究活动，以使学生和行业合作伙伴认识到计算相关技术的安全和保障的重要性。为了实现网络物理系统的学习辅助稳健安全和保障，该项目将开发基于集合成员资格和学习方法的数学基础和控制综合算法，用于不确定性量化、安全/攻击弹性估计和安全设计控制。该研究工作将产生新颖的科学基础，代表：1）从基于机器学习和/或物理的模型的不确定性的传统平均或随机表征到使用混合包含的集合成员表示的转变，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估计器的转变，以及3）从固定的设计安全控制的进展 具有不受影响的状态反馈的算法到具有攻击弹性的输出反馈设计，并从运行时数据中学习。这些贡献共同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非保守的安全解决方案，包括用于驱动研究项目的自动驾驶汽车的主要应用。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影响审查标准进行评估，被认为值得支持。

项目成果

Guaranteed State Estimation via Direct Polytopic Set Computation for Nonlinear Discrete-Time Systems

通过非线性离散时间系统的直接多面集计算保证状态估计

• DOI: 10.1109/lcsys.2021.3138355
• 发表时间: 2022
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Khajenejad, Mohammad;Shoaib, Fatima;Yong, Sze Zheng
• 通讯作者: Yong, Sze Zheng

Robust Data-Driven Control Barrier Functions for Unknown Continuous Control Affine Systems

未知连续控制仿射系统的鲁棒数据驱动控制屏障函数

• DOI: 10.1109/lcsys.2023.3235958
• 发表时间: 2023
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Jin, Zeyuan;Khajenejad, Mohammad;Yong, Sze Zheng
• 通讯作者: Yong, Sze Zheng

Stability Control of Autonomous Ground Vehicles Using Control-Dependent Barrier Functions

使用控制相关障碍函数的自主地面车辆稳定性控制

• DOI: 10.1109/tiv.2021.3058064
• 发表时间: 2021
• 期刊: IEEE Transactions on Intelligent Vehicles
• 影响因子: 8.2
• 作者: Huang, Yiwen;Yong, Sze Zheng;Chen, Yan
• 通讯作者: Chen, Yan

Guaranteed State Estimation via Indirect Polytopic Set Computation for Nonlinear Discrete-Time Systems

通过非线性离散时间系统的间接多面集计算保证状态估计

• DOI: 10.1109/cdc45484.2021.9683626
• 发表时间: 2021
• 期刊: IEEE Conference on Decision and Control
• 作者: Khajenejad, Mohammad;Shoaib, Fatima;Yong, Sze Zheng
• 通讯作者: Yong, Sze Zheng

Simultaneous state and unknown input set‐valued observers for quadratically constrained nonlinear dynamical systems

• DOI: 10.1002/rnc.6163
• 发表时间: 2020-01
• 期刊: International Journal of Robust and Nonlinear Control
• 影响因子: 3.9
• 作者: Mohammad Khajenejad;Sze Zheng Yong