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Updated: Jan 17, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

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Fully-Distributed Neural-Network-Based Approaches for Monotonic Game With Finite-Time Disturbance Rejection.

Jianing Chen, Sichen Qian, Chuangyin Dang

    IEEE Transactions on Cybernetics
    |September 24, 2025
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    Summary
    This summary is machine-generated.

    This study introduces a distributed neural network for finding variational generalized Nash equilibria in complex games with dynamic players. It enhances robustness and eliminates parameter predesign for improved performance in multi-agent systems.

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    Area of Science:

    • Control Theory
    • Game Theory
    • Artificial Intelligence

    Background:

    • Variational generalized Nash equilibrium (vGNE) seeking is crucial for multi-agent systems with coupling constraints.
    • Existing methods often require parameter predesign and lack robustness against disturbances.
    • Dynamical players introduce complexities in achieving equilibrium in general monotonic games.

    Purpose of the Study:

    • To develop a distributed neural network for seeking vGNE in general monotonic games with multiple coupling constraints.
    • To address challenges posed by high-order dynamics and parameter predesign requirements.
    • To enhance the robustness and full distribution of the neural network against external disturbances.

    Main Methods:

    • Designed a distributed vGNE-seeking neural network (vGSNN) using a high-pass filter to convert high-order dynamics to second-order.
    • Proposed an adaptive weight controller to remove the need for fixed parameter predesign, enabling full distribution.
    • Incorporated a sliding-mode controller for finite-time disturbance rejection and maintained full distribution.

    Main Results:

    • The proposed vGSNN successfully transforms high-order dynamics into manageable second-order dynamics.
    • Adaptive weights eliminated the need for parameter predesign, achieving full distribution of the network.
    • The sliding-mode controller ensured finite-time disturbance rejection, enhancing robustness.
    • Effectiveness was validated through an uncrewed aerial vehicle (UAV) swarm game simulation.

    Conclusions:

    • The developed vGSNN offers an effective, distributed solution for vGNE seeking in complex monotonic games.
    • The adaptive and sliding-mode control strategies enhance robustness and reduce design complexity.
    • The approach is validated in a practical scenario, demonstrating its applicability to real-world multi-agent systems.