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Updated: Mar 7, 2026

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control
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Distributed Optimal Leader-Following Consensus Control of MAS Under Input Saturation: A Stackelberg Game Approach.

Haitao Wang, Qingshan Liu, Ju H Park

    IEEE Transactions on Cybernetics
    |March 5, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study presents a novel approach for optimal state observation and leader-following consensus in nonlinear multiagent systems (MAS) using a Stackelberg game and fuzzy reinforcement learning, ensuring system stability.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Game Theory

    Background:

    • Multiagent systems (MAS) face challenges in state observation and consensus with unknown dynamics and limited leader state accessibility.
    • Input saturation in nonlinear MAS complicates control design and state estimation.

    Purpose of the Study:

    • To achieve optimal state observation and leader-following consensus for nonlinear MAS with input saturation under a Stackelberg game framework.
    • To develop a robust control strategy addressing unknown system dynamics and partial leader state information.

    Main Methods:

    • A distributed estimation algorithm for leader state estimation.
    • A game-based observer design considering bidirectional dynamics interaction.
    • Fuzzy reinforcement learning to approximate unknown dynamics and derive optimal controllers.
    • Lyapunov stability analysis to guarantee uniform ultimate boundedness of closed-loop signals.

    Main Results:

    • An optimal auxiliary controller for the observer and an optimal consensus controller were developed.
    • The fuzzy reinforcement learning approach successfully approximated unknown dynamics.
    • All closed-loop signals were proven to be uniformly ultimately bounded, confirming system stability.

    Conclusions:

    • The proposed approach effectively achieves optimal state observation and leader-following consensus in complex nonlinear MAS.
    • The integration of Stackelberg game theory and fuzzy reinforcement learning offers a powerful framework for robust control design.
    • Simulation results validate the practical applicability and performance of the developed methodology.