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Data-Driven Multiagent Systems Consensus Tracking Using Model Free Adaptive Control.

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    This study introduces a data-driven adaptive control method for multiagent systems, enabling agents with unknown dynamics to achieve consensus tracking using only input-output data, even with switching communication. This approach ensures reliable trajectory following.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Multiagent systems often face challenges with unknown dynamics and communication uncertainties.
    • Achieving consensus tracking is crucial for coordinated behavior in distributed systems.
    • Existing methods may require precise system models or full trajectory access.

    Purpose of the Study:

    • To develop a data-driven distributed control strategy for consensus tracking in multiagent systems.
    • To address systems with unknown nonlinear dynamics and fixed or switching communication topologies.
    • To enable consensus tracking using only local input-output data.

    Main Methods:

    • Utilized a distributed Model-Free Adaptive Control (MFAC) approach.
    • Applied dynamical linearization based on the pseudo partial derivative for each agent.
    • Developed a novel algorithm for consensus tracking without explicit system identification.

    Main Results:

    • Successfully reduced consensus error for both time-invariant and time-varying trajectories.
    • Demonstrated that consensus tracking can be achieved solely from input-output data.
    • Validated the proposed method's effectiveness through simulation examples.

    Conclusions:

    • The proposed distributed MFAC method effectively achieves data-driven consensus tracking for multiagent systems.
    • This approach offers a robust solution for systems with unknown dynamics and complex communication structures.
    • The reliance on input-output data simplifies implementation and broadens applicability.