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H∞ Cluster Formation Control of Networked Multiagent Systems With Stochastic Sampling.

Lang Ma, Yu-Long Wang, Qing-Long Han

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    This study introduces H∞ cluster formation control for multiagent systems (MAS) with network sampling. It develops criteria to ensure agents achieve distinct cluster formations despite external disturbances, enhancing system robustness.

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    Area of Science:

    • Control Theory
    • Networked Systems
    • Robotics

    Background:

    • Multiagent systems (MAS) require coordinated control for complex tasks.
    • Networked environments introduce challenges like stochastic sampling and external disturbances.
    • Achieving distinct formations within agent clusters is crucial for specialized applications.

    Purpose of the Study:

    • To develop H∞ cluster formation control for MAS under stochastic sampling.
    • To address external disturbances affecting individual agents.
    • To derive criteria for achieving desired formations within and between agent clusters.

    Main Methods:

    • Agents are partitioned into clusters based on directed communication topology.
    • H∞ control theory is applied to handle stochastic sampled-data and external disturbances.
    • A stochastic system framework is used to derive H∞ cluster formation criteria.

    Main Results:

    • An H∞ cluster formation criterion is derived for MAS with external disturbance.
    • A simplified criterion is obtained for disturbance-free MAS.
    • Performance analysis confirms the effectiveness of the proposed control design.

    Conclusions:

    • The proposed H∞ control strategy effectively manages cluster formation in MAS with stochastic sampling.
    • The derived criteria ensure robust formation control despite external disturbances.
    • The methods are validated, demonstrating practical applicability in networked multiagent systems.