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Neural-Network-Based Predefined-Time Adaptive Consensus in Nonlinear Multi-Agent Systems With Switching Topologies.

Yanzheng Zhu, Zuo Wang, Hongjing Liang

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    Summary
    This summary is machine-generated.

    This study introduces a new control strategy for multi-agent systems with unknown dynamics and switching topologies. The adaptive consensus control ensures tracking errors converge within a user-defined time, enhancing system predictability.

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    Area of Science:

    • Control Theory
    • Robotics
    • Artificial Intelligence

    Background:

    • Multi-agent systems (MAS) often face challenges with unknown nonlinear dynamics and dynamic network structures (switching topologies).
    • Achieving consensus (agreement) in MAS is crucial for coordinated tasks but complicated by system uncertainties.
    • Existing control methods may not offer precise control over the convergence time for consensus.

    Purpose of the Study:

    • To develop a predefined-time adaptive consensus control strategy for multi-agent systems.
    • To simultaneously address unknown nonlinear dynamics and switching topologies.
    • To enable adjustable convergence times for tracking errors.

    Main Methods:

    • Utilizing neural network (NN) approximation to handle unknown nonlinear dynamics.
    • Implementing time-varying functions (TVFs) for adjustable decay rates.
    • Applying Lyapunov stability theory to guarantee bounded and convergent tracking errors.

    Main Results:

    • A novel predefined-time consensus control strategy was successfully developed.
    • The proposed method effectively handles unknown nonlinearities and switching topologies.
    • Simulation results validated the feasibility and effectiveness of the control scheme, demonstrating adjustable convergence times.

    Conclusions:

    • The developed strategy provides precise control over consensus convergence time in MAS.
    • The approach offers robustness against unknown system dynamics and network changes.
    • This work advances the field of adaptive control for complex multi-agent systems.