Dynamic Event-Triggering Surrounding Control for Multi-USVs Under FDI Attacks via Adaptive Dynamic Programming

This paper investigates the surrounding control problem of multiple unmanned surface vehicles (USVs) against false data injection (FDI) attacks and proposes a learning-based prescribed performance control (PPC) integrated with a dynamic event-triggering (DET) mechanism. First, a predefined-time observer (PTO) is designed to estimate the injected false data. Then, the constrained surrounding tracking error of multi-USVs is first formulated based on an exponential prescribed performance function. To facilitate the control law design, the constrained surrounding problem is transformed into an unconstrained space using a hyperbolic tangent function. Based on adaptive dynamic programming (ADP) and the DET mechanism, a prescribed performance time-varying surrounding control scheme is developed. Finally, the effectiveness and superiority of the proposed control strategy are demonstrated through rigorous theoretical analysis and simulation experiments, while Zeno behavior in the event-triggered mechanism is excluded.