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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Distributed nonlinear multiagent systems (MASs) present significant control challenges due to unknown dynamics and communication delays.
    • Existing control strategies often struggle with the complexity and scale of modern MASs.

    Purpose of the Study:

    • To develop a robust control strategy for group coordinated control in nonlinear MASs.
    • To address the challenges of unknown system dynamics and communication network delays.
    • To enable effective coordination in large-scale networked distributed multigroup-agent systems (ND-MGASs).

    Main Methods:

    • A novel networked model-free adaptive predictive control (NMFAPC) strategy is proposed.
    • The NMFAPC combines networked predictive control theory with model-free adaptive control.
    • Agents are modeled using time-varying data models relying solely on system measurement data for adaptive learning.
    • A simultaneous analysis method for stability and consensus in ND-MGASs is presented.

    Main Results:

    • The NMFAPC strategy effectively achieves group coordinated control for nonlinear MASs with unknown dynamics.
    • The proposed method actively compensates for communication network delays.
    • Numerical simulations and experimental examples validate the strategy's effectiveness and practicability.
    • The study demonstrates a viable solution for coordinating large-scale nonlinear MASs.

    Conclusions:

    • The NMFAPC strategy offers a powerful tool for controlling complex, large-scale nonlinear multiagent systems.
    • This research contributes to advancing model-free adaptive control in networked systems.
    • The findings pave the way for enhanced coordination and performance in distributed intelligent systems.