报告人简介：

张康康，现为南京航空航天大学教授，博士生导师，国家高层次青年人才（海外）。先后在河南工业大学、东北大学、南京航空航天大学获得学士、硕士和博士学位。2019至2022年，他在塞浦路斯大学KIOS研究中心担任博士后研究员，导师为Marios Polycarpou 教授，2022至2025年于英国伦敦帝国理工学院控制与电力研究组担任玛丽·居里学者博士后研究员，导师为Thomas Parisini 教授。曾受邀访问塞浦路斯大学、肯特大学、瑞典皇家理工学院（KTH）、乌普萨拉大学（Uppsala University）和香港城市大学。在控制领域已发表30多篇期刊和会议论文。其研究方向涵盖复杂信息物理系统信息与物理威胁分析、诊断与控制，及其在无人飞行器系统的应用。其博士学位论文曾获中国自动化学会优秀博士学位论文奖。

报告摘要：

Considering potential threats to cyber-physical systems such as component faults and stealthy cyber-attacks, an adaptive observer-based threat discrimination method is proposed for identifying the occurring threat type. Typically, stealthy attacks have only weak effects easily inundated by disturbances on the system outputs. To solve this problem, a parameter adaptation algorithm based on a newly designed dynamic adaptive gain generator is proposed, aiming at improving the sensitivity of the adaptive threat discrimination scheme to potential threats. Only the strictly positive real condition of the proposed gain generator sufficiently ensures the stability of the adaptive observer error system. A moment-matching method is then developed to determine the proper parameters of the gain generator, allowing for the improvement of the sensitivity of the threat discriminators. A numerical example to demonstrate the effectiveness of the proposed methodology is presented.