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Neural-Network-Based Event-Triggered Consensus Tracking for Nonlinear Heterogeneous Multi-Agent Systems Against Denial-of-Service Attacks
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DOI：10.1002/rnc.70646.png)
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Abstract
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This article proposes a distributed resilient control framework to ensure consensus tracking for nonlinear heterogeneous multi-agent systems (NHMAS) under aperiodic denial-of-service (DoS) attacks. First, a resilient observer is introduced to estimate the leader's state considering that not all agents have direct access to the leader's information. This observer incorporates a local asynchronous dynamic event-triggered mechanism that effectively mitigates the impact of DoS attacks. Second, an event-driven estimator is designed to reconstruct the unmeasured states. To address unknown nonlinear dynamics, an adaptive output-feedback compensator based on neural networks is presented and only updated at event-triggered instants. Finally, a distributed control framework is developed to achieve consensus tracking for NHMAS in the presence of DoS attacks. Theoretical analysis demonstrates that the closed-loop system guarantees resilient consensus despite DoS attacks, and Zeno behavior is rigorously excluded by ensuring a strictly positive minimum inter-event time. Comparative simulations validate that the proposed framework effectively enhances system resilience, accelerates convergence, and reduces computational overhead. Experiments on a group of unmanned ground vehicles further verify the advantages of the proposed method.
Keywords:
distributed consensus tracking
DoS attacks
event-triggered control
neural networks
nonlinear heterogeneous multi-agent systems
unmanned ground vehicles
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