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This chapter addresses the event-triggered observer-based security complete consensusComplete consensus and fault detection problem for nonlinear multi-agent systems (MASs) under external disturbances and stochastic false data injection attacks (FDIAs) in a directed communication network. The randomly occurring FDIAs are modeled by Bernoulli-distributed random variables. Then, an observer-based event-triggered control strategy is developed, relying on local measurements and information from neighboring agents, while ensuring that Zeno behaviorZeno behavior is avoided. Interestingly, the observer errors are first regarded as disturbanceDisturbance, and then attenuated by \({H}_{\infty }\) norm bounds together with the external disturbances. Additionally, the same measurement information used by the state observers is employed to construct residuals with adaptive thresholds to detect faults in any agent. The accuracy of the observer and the fault detection performance are enhanced by a disturbanceDisturbance compensation mechanism. Finally, simulation results demonstrate the effectiveness and advantages of the proposed strategy.

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\(H_\infty \) Anti-disturbance Control and Fault Detection of MASs Under FDIAs

  • 章节

  • 发表日期：

    2026年

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  • Xiang-Gui Guo,
  • Pei-Ming Liu,
  • Dong-Yu Zhang,
  • Jian-Liang Wang,
  • Lei Guo

摘要

This chapter addresses the event-triggered observer-based security complete consensusComplete consensus and fault detection problem for nonlinear multi-agent systems (MASs) under external disturbances and stochastic false data injection attacks (FDIAs) in a directed communication network. The randomly occurring FDIAs are modeled by Bernoulli-distributed random variables. Then, an observer-based event-triggered control strategy is developed, relying on local measurements and information from neighboring agents, while ensuring that Zeno behaviorZeno behavior is avoided. Interestingly, the observer errors are first regarded as disturbanceDisturbance, and then attenuated by \({H}_{\infty }\) norm bounds together with the external disturbances. Additionally, the same measurement information used by the state observers is employed to construct residuals with adaptive thresholds to detect faults in any agent. The accuracy of the observer and the fault detection performance are enhanced by a disturbanceDisturbance compensation mechanism. Finally, simulation results demonstrate the effectiveness and advantages of the proposed strategy.