Lyapunov-Based Attack Detection and Mitigation with Variable Control Gain

Achieving the desired rate of convergence (RoC) is an important aspect while designing the stabilizing controllers. Particularly, in multi-Agent systems where the control law relies on the relative information among neighboring agents, it is challenging to maintain the desired RoC due to the vulnerability of the communication channels to malicious attacks. This paper addresses this issue for the multi-Agent consensus in both leaderless and leader-follower configurations while considering the presence of false data injection (FDI) attack on actuator. The paper proposes a model-free attack detection method, which relies on exponential stability and continuous monitoring of the Lyapunov function. An attack is detected when the system violates the desired decay function, and it is mitigated by leveraging the time-varying nature of the control gain. Different from actuator saturation that might also occur due to an attack, the issue of control saturation is addressed. The results are verified with numerical simulations.