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    Summary

    This study introduces a data-based method for multiagent systems with complex dynamics and cooperation-antagonism networks. It achieves bipartite consensus and output convergence using adaptive learning and a heterogeneous linear data model.

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    Area of Science:

    • Control Theory
    • Networked Systems
    • Artificial Intelligence

    Background:

    • Multiagent systems face challenges with heterogeneous dynamics, nonlinear structures, and cooperation-antagonism.
    • Existing methods struggle with complex agent behaviors and uncertain parameters.

    Purpose of the Study:

    • To develop a data-based approach for achieving output consensus in complex multiagent systems.
    • To address challenges posed by heterogeneous dynamics, nonlinear structures, and cooperation-antagonism networks.

    Main Methods:

    • A heterogeneous linear data model (LDM) is proposed to handle nonlinear, nonaffine agent structures.
    • An adaptive update algorithm estimates uncertain parameters for unknown dynamics and model structures.
    • An adaptive learning consensus protocol is designed for cooperation-antagonism networks using signed graphs.

    Main Results:

    • Bipartite consensus is proven for structurally balanced graphs.
    • Convergence of agent outputs to zero is proven for structurally unbalanced graphs.
    • The data-based method demonstrates effectiveness without explicit model information.

    Conclusions:

    • The proposed method effectively achieves consensus in complex multiagent systems.
    • Adaptive learning and data-driven modeling enhance adaptability to uncertainties.
    • This approach offers a robust solution for real-world multiagent system applications.