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On Consensus Control of Uncertain Multiagent Systems Based on Two Types of Interval Observers.

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    This study addresses multiagent robust consensus with uncertain models. New methods ensure state bounds and achieve consensus by minimizing errors, enhancing design flexibility.

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    Area of Science:

    • Control Theory
    • Systems Engineering
    • Robotics

    Background:

    • Multiagent systems often face challenges due to model uncertainties and parameter variations.
    • Achieving robust consensus is critical for coordinated behavior in distributed systems.
    • Existing methods may impose constraints that limit design flexibility.

    Purpose of the Study:

    • To investigate the multiagent robust consensus problem under interval-bounded model uncertainties.
    • To develop novel methods for robust consensus that enhance design freedom.
    • To ensure state values remain within specified bounds and errors converge to zero.

    Main Methods:

    • Construction of upper and lower dynamic systems based on positive system theory.
    • Application of the Lyapunov stability principle to guarantee error convergence.
    • Development of local and neighborhood protocols using novel parametric matrices.

    Main Results:

    • Guaranteed bounds for state values using constructed dynamic systems.
    • Convergence of observation and consensus errors to zero, enabling state reconstruction.
    • Successful achievement of robust consensus under model uncertainties.
    • Elimination of the Metzler constraint on the error matrix.

    Conclusions:

    • The proposed methods effectively solve the multiagent robust consensus problem under model uncertainties.
    • The approach enhances design flexibility by introducing novel parametric matrices.
    • The theoretical results are validated through numerical simulations, confirming the effectiveness of the protocols.