Adaptive Tracking Control for Nonlinear Systems Under False Data Injection Attacks via Intermittent State Triggering

This article investigates the adaptive tracking control strategy for a class of nonlinear systems subjected to false data injection (FDI) attacks, incorporating an improved event-triggered mechanism. A significant breakthrough of this study lies in the challenge that, after FDI, not all states of the system can be utilized for stability design, thereby making it more complicated to achieve tracking control. This article eliminates the restrictive assumption, required in some existing results, that the attack signal at the first step must be known. Instead, we propose to estimate the tracking error directly. This approach not only facilitates the tracking control of nonlinear systems but also enhances the generalizability and practical applicability of the solution. To conserve system resources, an improved event-triggered condition is proposed that utilizes the triggered attacked-output. Consequently, the controllers and adaptive laws are implemented using the sampled states rather than continuous real-time states, thereby minimizing unnecessary computations and communications. By constructing Lyapunov functions, the proposed control strategy ensures that all signals in the closed-loop system are globally bounded. Finally, the simulation results are displayed to validate the effectiveness of the proposed control strategy.