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    This study introduces a novel data-driven method for multiagent systems (MASs) to achieve consensus without needing a system model. The direct distributed iterative learning control (DirDILC) method simplifies analysis and advances consensus control theory.

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    Area of Science:

    • Control Engineering
    • Artificial Intelligence
    • Systems Science

    Background:

    • Multiagent systems (MASs) often require complex models for consensus control.
    • Existing methods for achieving consensus in MASs can be computationally intensive and rely on explicit system dynamics.
    • Developing model-free approaches is crucial for enhancing the adaptability and applicability of MASs.

    Purpose of the Study:

    • To develop a novel, direct, model-free learning control protocol for achieving consensus in MASs.
    • To formulate consensus dynamics using a nonlinear autoregressive moving average (NARMA) function.
    • To establish a data-driven framework for analyzing and designing consensus control protocols.

    Main Methods:

    • Designed a nonlinear autoregressive moving average (NARMA) function to represent consensus dynamics.
    • Constructed a consensus performance-related iterative linear data model (CPiLDM) for a data-driven reformulation.
    • Developed a direct distributed iterative learning control (DirDILC) method using optimization based on the CPiLDM.
    • Proved convergence directly for a virtual NARMA consensus system, independent of agent dynamics.

    Main Results:

    • Successfully formulated consensus dynamics using NARMA and CPiLDM, enabling a model-free approach.
    • The developed DirDILC method achieves consensus without relying on explicit agent models or system identification.
    • Convergence analysis was simplified by focusing on the virtual NARMA consensus system.
    • Demonstrated a purely data-driven control strategy for MAS consensus.

    Conclusions:

    • The DirDILC method offers a significant advancement in consensus control for MASs by eliminating the need for explicit models.
    • This model-free, data-driven approach simplifies analysis and enhances the practical application of consensus control.
    • The study paves the way for more adaptable and efficient multiagent coordination strategies.