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Adaptive Neurodynamic Approach to Multiple Constrained Distributed Resource Allocation.

Linhua Luan, Sitian Qin

    IEEE Transactions on Neural Networks and Learning Systems
    |June 13, 2023
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    Summary
    This summary is machine-generated.

    This study introduces an adaptive neurodynamic approach for multiagent systems to solve complex distributed resource allocation problems (DRAPs). The method ensures optimal team cost minimization under general constraints while reducing communication load.

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    Area of Science:

    • Control Theory
    • Optimization
    • Artificial Intelligence

    Background:

    • Distributed resource allocation problems (DRAPs) are common in multiagent systems.
    • Existing methods struggle with complex constraints like coupled inequalities and private sets.
    • Efficient communication strategies are crucial for large-scale multiagent systems.

    Purpose of the Study:

    • To design an adaptive neurodynamic approach for solving nonsmooth DRAPs.
    • To handle affine-coupled equality, coupled inequality, and private set constraints.
    • To improve system communication efficiency and ensure convergence.

    Main Methods:

    • Utilizing an adaptive neurodynamic approach with auxiliary variables for consensus on Lagrange multipliers.
    • Employing a penalty method for adaptive control to manage private set constraints.
    • Applying Lyapunov stability theory to analyze convergence and exclude the Zeno phenomenon.
    • Introducing an event-triggered mechanism to reduce communication burden.

    Main Results:

    • The proposed neurodynamic approach effectively solves DRAPs with general constraints.
    • Convergence of the adaptive neurodynamic approach is proven using Lyapunov stability theory.
    • The event-triggered mechanism reduces communication load without compromising convergence.
    • The Zeno phenomenon is successfully excluded in the event-triggered system.

    Conclusions:

    • The adaptive neurodynamic approach provides an effective solution for complex DRAPs in multiagent systems.
    • The method is robust to various constraints and enhances communication efficiency.
    • Demonstrated effectiveness through numerical examples and a virtual 5G system application.