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Data-Driven Adaptive Consensus Learning From Network Topologies.

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    This study introduces a data-driven adaptive learning consensus protocol for nonlinear multiagent systems. It enhances consensus performance by learning from system dynamics and network topology without needing a system model.

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    Area of Science:

    • Control Systems Engineering
    • Networked Systems
    • Artificial Intelligence

    Background:

    • Consensus learning is crucial for coordinating multiagent systems (MASs).
    • Existing methods often rely on precise system models, limiting applicability.
    • Network topology significantly impacts consensus performance in MASs.

    Purpose of the Study:

    • To develop a data-driven consensus learning protocol for nonlinear nonaffine MASs.
    • To address challenges posed by network topologies and system uncertainties.
    • To improve consensus performance without system model dependence.

    Main Methods:

    • A linear spatial dynamic relationship (LSDR) was formulated to model agent interactions.
    • A data-driven adaptive learning consensus protocol (DDALCP) was proposed.
    • Parametric and nonlinear uncertainties were managed using an estimator and observer.

    Main Results:

    • The DDALCP effectively learns from both internal agent dynamics and network topology.
    • Robustness was improved by estimating system uncertainties.
    • The protocol demonstrated strong learning capabilities, enhancing consensus performance.

    Conclusions:

    • The proposed data-driven method achieves consensus in MASs without model knowledge.
    • Considering both temporal and spatial dynamics leads to superior consensus performance.
    • The approach offers a robust and adaptable solution for networked control systems.