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Design and Analysis of a Novel Distributed Gradient Neural Network for Solving Consensus Problems in a Predefined

Lin Xiao, Lei Jia, Jianhua Dai

    IEEE Transactions on Neural Networks and Learning Systems
    |July 29, 2022
    PubMed
    Summary

    A new distributed gradient neural network (DGNN) achieves predefined-time convergence for multiagent systems. This novel model enhances consensus problem-solving with neighbor-only communication and faster convergence rates.

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    Area of Science:

    • Control Systems
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Multiagent systems (MASs) often face consensus problems, requiring coordinated behavior.
    • Existing gradient neural networks (GNNs) for optimization may lack efficiency in distributed settings.

    Purpose of the Study:

    • Propose a novel distributed gradient neural network (DGNN) for solving consensus problems in MASs.
    • Achieve predefined-time convergence (PTC) for enhanced performance.

    Main Methods:

    • Developed a nonfully connected DGNN model where neurons only require neighbor information.
    • Proved convergence and asymptotic stability using Lyapunov theory.
    • Designed three novel nonlinear activation functions to accelerate convergence to PTC under relaxed conditions.

    Main Results:

    • The DGNN model demonstrates effective consensus achievement in MASs.
    • Rigorous theoretical proof confirms the predefined-time convergence capability.
    • Computer simulations validate the model's effectiveness and PTC, especially with nonlinear activation functions.

    Conclusions:

    • The proposed DGNN with nonlinear activation functions offers an effective solution for consensus problems in MASs.
    • The model's nonfully connected nature and PTC capability are significant advancements.
    • Feasibility is demonstrated through a directional consensus case and connectivity analysis.