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Distributed Optimization Based on a Multiagent System Disturbed by General Noise.

Huaguang Zhang, Fei Teng, Qiuye Sun

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    This study analyzes distributed optimization in multiagent systems (MAS) with general noise disturbances. We developed conditions for noise-to-state (NOS) stability, minimizing estimation errors for more accurate agent disturbance modeling.

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    Area of Science:

    • Control Theory
    • Optimization
    • Multiagent Systems

    Background:

    • Distributed optimization problems are crucial for multiagent systems (MAS).
    • Accurate modeling of disturbances, such as general noise, is essential for MAS performance.
    • Existing models may not fully capture the complexities of noise in MAS.

    Purpose of the Study:

    • To analyze a distributed optimization problem for a continuous-time MAS under general noise.
    • To develop improved theoretical results for noise-to-state (NOS) stability analysis.
    • To derive less conservative conditions for estimating the minimum error between optimal solutions and NOS stable states.

    Main Methods:

    • Consideration of a continuous-time multiagent system (MAS) with general noise.
    • Application of improved theoretical results from random differential equations for noise-to-state (NOS) stability analysis.
    • Development of sufficient conditions using linear matrix inequality (LMI) for reduced conservatism.

    Main Results:

    • The noise-to-state (NOS) stability of the MAS with general noise is analyzed.
    • Sufficient conditions in the form of linear matrix inequality (LMI) are established with reduced conservatism.
    • A method is provided to obtain the minimum estimation error by selecting appropriate distributed optimization parameters.

    Conclusions:

    • The proposed approach accurately models disturbances in MAS using general noise.
    • The derived NOS stability conditions and LMI-based methods offer less conservative results.
    • The study provides a framework for minimizing estimation errors in distributed optimization for MAS under noise.