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    Area of Science:

    • Control Systems Engineering
    • Robotics
    • Artificial Intelligence

    Background:

    • Multiagent systems (MASs) face challenges in cooperative tracking due to unknown dynamics.
    • Denial-of-service (DoS) attacks and communication delays disrupt system performance.

    Purpose of the Study:

    • To develop a resilient cooperative tracking method for nonlinear MASs under DoS attacks.
    • To address communication delays inherent in hierarchical control architectures.

    Main Methods:

    • A hierarchical cooperative resilient learning method combining a distributed resilient observer and decentralized learning controller.
    • A resilient model-free adaptive control (MFAC) strategy to mitigate DoS attacks and communication delays.
    • Design of a virtual reference signal for agents to estimate time-varying references under DoS attacks.

    Main Results:

    • The developed decentralized MFAC algorithm enables agents to track reference signals using only local information.
    • The method effectively withstands the impact of communication delays and DoS attacks.
    • Simulation results validate the proposed approach's effectiveness.

    Conclusions:

    • The resilient learning method ensures robust cooperative tracking in nonlinear MASs.
    • The MFAC approach provides a viable solution for systems operating under adversarial conditions.
    • This work contributes to secure and reliable autonomous systems.