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Consensus of Continuous-Time Linear Multiagent Systems With Discrete Measurements.

Xiaoling Wang, Guo-Ping Jiang, Housheng Su

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    Researchers developed a robust consensus protocol for uncertain continuous-time multiagent systems (MASs) using discrete-time measurements. This method ensures reliable coordination in networked systems, even with data-sampled information.

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    Area of Science:

    • Control Systems Engineering
    • Networked Systems
    • Robotics

    Background:

    • Multiagent systems (MASs) face challenges in achieving consensus due to inherent uncertainties and discrete-time measurement limitations.
    • Achieving robust consensus is critical for coordinated behavior in distributed systems, especially when dealing with noisy or sampled data.

    Purpose of the Study:

    • To propose a distributed output-feedback protocol for robust consensus in continuous-time linear MASs with uncertainty.
    • To address the challenge of integrating discrete-time measurement information into a continuous-time control framework.
    • To provide sufficient conditions for achieving robust consensus in MASs networked over directed graphs.

    Main Methods:

    • Development of a distributed output-feedback protocol incorporating an output error correction term with discrete-time measurements.
    • Design of a concrete algorithm for constructing the necessary feedback matrices.
    • Application of the delay-input approach to derive conditions for robust consensus.

    Main Results:

    • The proposed output-feedback protocol effectively handles uncertainty and discrete-time measurements in MASs.
    • Sufficient conditions for robust consensus were established for systems operating over directed network topologies.
    • Numerical simulations validated the theoretical findings and demonstrated the protocol's efficacy.

    Conclusions:

    • The study successfully presents a method for achieving robust consensus in continuous-time linear MASs despite uncertainties and discrete-time data.
    • The developed protocol and theoretical conditions offer a valuable framework for designing reliable coordinated control systems.
    • The findings are applicable to various networked systems requiring robust and coordinated decision-making.