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Related Experiment Videos

Event-Triggered RNN-Based Resilient Model Predictive Consensus Control for Nonlinear Multiagent Systems Under DoS

Abdolah RoshanaeeDeh, Iman Sharifi

    IEEE Transactions on Cybernetics
    |March 3, 2026
    PubMed
    Summary
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    This study introduces a resilient consensus controller for nonlinear multiagent systems (MASs) that can withstand denial-of-service (DoS) attacks. The novel event-triggered, recurrent neural network-based model predictive consensus controller (RNN-MPCC) ensures reliable operation even under network disruptions.

    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Network Security

    Background:

    • Multiagent systems (MASs) face challenges in maintaining consensus under network attacks.
    • Denial-of-service (DoS) attacks disrupt communication, degrading system performance and reliability.
    • Existing control strategies often lack robustness against dynamic and persistent network threats.

    Purpose of the Study:

    • To develop a resilient consensus control strategy for nonlinear MASs against DoS attacks.
    • To design an event-triggered controller that minimizes communication overhead while ensuring stability.
    • To integrate data-driven prediction with robust control for enhanced system resilience.

    Main Methods:

    • A recurrent neural network (RNN) was employed for data-driven prediction of agent dynamics with guaranteed error bounds.

    Related Experiment Videos

  • A DoS-aware communication model was integrated to manage attack impacts on consensus.
  • An event-triggered, model predictive consensus controller (RNN-MPCC) was developed, optimizing a cost function over a receding horizon.
  • A minimum interevent time was enforced to prevent Zeno behavior in the event-triggered system.

    • Main Results:

    • The proposed RNN-MPCC effectively achieved resilient consensus in nonlinear MASs under DoS attacks.
    • The event-triggered approach reduced communication load and computational demands.
    • Simulations on a multi-unmanned aerial vehicle (UAV) network validated the controller's performance.
    • The controller successfully handled varying attack frequencies and durations.

    Conclusions:

    • The developed RNN-MPCC offers a robust solution for achieving consensus in MASs facing DoS attacks.
    • Event-triggered control combined with RNN prediction enhances system resilience and efficiency.
    • This approach provides a foundation for secure and reliable distributed control systems.