Dynamic Adaptation Gain Design and Tuning for Threat Discrimination
Kangkang Zhang, Kaiwen Chen, Marios M. Polycarpou, Thomas Parisini · 2024
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 obscured 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.