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    This study introduces fault-tolerant consensus control for nonlinear multiagent systems facing actuator faults and network issues. Novel neural network-based adaptive control ensures system stability and bounded consensus despite disturbances.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Multiagent systems are susceptible to actuator faults and network disturbances, compromising control performance.
    • Existing control strategies often struggle with unknown nonlinear dynamics and simultaneous fault conditions.

    Purpose of the Study:

    • To develop a robust fault-tolerant consensus control scheme for nonlinear multiagent systems.
    • To address challenges posed by actuator faults and disturbed/faulty communication networks.

    Main Methods:

    • Utilizing neural network (NN) and adaptive control techniques for estimating unknown system dynamics and fault bounds.
    • Designing a novel NN-based adaptive observer to detect faulty network signals.
    • Implementing adaptive control strategies for fault-tolerant consensus.

    Main Results:

    • Successful estimation of unknown state-dependent boundaries for nonlinear dynamics and actuator faults.
    • Effective observation of faulty transformation signals within networks using the NN-based observer.
    • Demonstrated bounded consensus in a closed-loop multiagent system under fault conditions.

    Conclusions:

    • The proposed adaptive distributed consensus control schemes effectively guarantee bounded consensus.
    • The methods are validated on a multiagent system of nonlinear forced pendulums, showing robustness to faults and disturbances.