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This research investigates optimal control methods to secure multi-agent systems from FDI attacks, highlighting significant findings on error management.
This paper investigates event-triggered optimal control for human-in-the-loop (HiTL) multi-agent systems (MASs) facing false data injection (FDI) attacks. An approach is introduced to enhance the security of sensor measurements against sparse attacks by employing a secure preselector, while also devising a state observer to estimate the system state, which is not directly measurable. Introducing an event-driven mechanism for solution transformation involves establishing a Hamilton-Jacobi-Bellman (HJB) equation, while employing a critic network along-side an experience replay technique to approximate it. Lyapunov stability theory is employed to derive an event-triggered condition ensuring the boundedness of consensus error and critic network weight error.
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Xu et al. (2024) studied this question.
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