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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Robotics

    Background:

    • Multiagent systems (MASs) are crucial in various applications.
    • Achieving consensus in nonlinear MASs with uncertainties is challenging.
    • Actuator faults and intermittent communication hinder system performance.

    Purpose of the Study:

    • To develop a novel consensus protocol for nonlinear MASs with unknown actuator faults.
    • To address challenges posed by sensor-triggered mechanisms and intermittent data.
    • To enhance the robustness and fault tolerance of MASs.

    Main Methods:

    • A leader-follower consensus protocol utilizing a sensor-triggered mechanism.
    • A neural estimation algorithm for fault estimation.
    • A signal decomposition and compensation strategy for intermittent data.
    • A resilient fault management mechanism for unknown actuator faults.
    • Nonlinear filters with compensation terms to avoid design complexity.

    Main Results:

    • The proposed sensor event-triggered mechanism involves sampling and information transmission.
    • A strategy effectively balances intermittent sensor data with real system inputs.
    • A fault management mechanism addresses unknown actuator faults in followers.
    • The backstepping design complexity is mitigated using nonlinear filters.
    • Simulation results validate the effectiveness of the developed control protocol.

    Conclusions:

    • The novel control protocol successfully achieves prespecified performance consensus in nonlinear MASs.
    • The approach demonstrates resilience against unknown actuator faults and intermittent communication.
    • The method offers a robust solution for complex nonlinear multiagent systems.