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Related Experiment Video

Updated: May 17, 2025

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Event-Triggered Model-Free Adaptive Formation Constrained Control for Nonlinear Heterogeneous Multiagent Systems.

Weiming Zhang, Dezhi Xu, Yujian Ye

    IEEE Transactions on Cybernetics
    |April 25, 2025
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    Summary

    This study introduces an event-triggered control for unknown multiagent systems (MAS) to enhance formation tracking accuracy while minimizing computational costs. The novel approach uses data-driven methods for model reconfiguration and control design.

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    Area of Science:

    • Control Systems Engineering
    • Robotics
    • Artificial Intelligence

    Background:

    • Multiagent systems (MAS) face challenges in formation control due to unknown dynamics and non-linearities.
    • Balancing formation tracking accuracy with computational cost is critical for practical MAS applications.

    Purpose of the Study:

    • To develop an event-triggered formation control strategy for unknown nonaffine nonlinear heterogeneous MAS.
    • To simultaneously improve formation tracking accuracy and reduce computational burden.

    Main Methods:

    • A dynamic prescribed boundary-based event-triggered mechanism for flexible adjustment of control objectives.
    • An observer-based pseudo gradient estimation algorithm for data-driven model reconfiguration.
    • An event-triggered constrained control strategy incorporating a data-driven anti-windup compensator and a fractional order terminal sliding mode controller.

    Main Results:

    • The proposed mechanism effectively balances formation tracking accuracy and computational cost.
    • The data-driven approach successfully reconfigures system models using only input/output data.
    • The developed controller enhances formation tracking accuracy and robustness, validated by simulations and experiments.

    Conclusions:

    • The novel event-triggered formation control strategy is effective for unknown nonaffine nonlinear heterogeneous MAS.
    • The approach offers a practical solution for improving MAS performance in terms of accuracy and efficiency.
    • The method is validated through comprehensive simulations and hard-in-the-loop experiments on distributed energy storage systems.