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    This study addresses iterative learning averaging consensus in multiagent systems facing uncertainties and faults. A novel adaptive fault-tolerant control strategy ensures system stability despite binary communications and noise.

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    Area of Science:

    • Control Engineering
    • Systems Science
    • Networked Systems

    Background:

    • Multiagent systems (MAS) face challenges like system uncertainties, actuator faults, and noisy binary communications.
    • Achieving averaging consensus in MAS under these conditions is critical for coordinated behavior.

    Purpose of the Study:

    • To develop a fault-tolerant averaging consensus control scheme for MAS with system uncertainties and binary-valued communications.
    • To design a novel two-iteration-scale framework for estimation and control.

    Main Methods:

    • A new two-iteration-scale framework alternating estimation and control was designed.
    • Agents estimate neighbor states using empirical measurements within dwell intervals.
    • An adaptive iterative learning fault-tolerant control scheme was developed using own and estimated states.

    Main Results:

    • The proposed framework effectively handles system uncertainties and actuator faults.
    • The adaptive control scheme ensures averaging consensus is achieved.
    • Stability of the closed-loop system was rigorously proven.

    Conclusions:

    • The developed adaptive iterative learning fault-tolerant averaging consensus control strategy is effective.
    • Numerical simulations validated the control strategy's performance in complex MAS environments.