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    This study presents a data-driven control method for multiagent systems (MASs) to achieve bipartite formation. The approach uses a novel function and dynamic event-triggered communication, reducing data load and ensuring stability.

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    Area of Science:

    • Control Theory
    • Multiagent Systems
    • Data-Driven Control

    Background:

    • Challenges in distributed formation control for nonlinear discrete-time multi-input-multi-output multiagent systems (MASs) with unknown dynamics and quantized information.
    • Need for robust control strategies that handle cooperative and competitive interactions within MASs.
    • Limitations of traditional consensus problems in addressing complex formation tasks.

    Purpose of the Study:

    • To develop a fully distributed, data-driven bipartite formation control method for MASs.
    • To address unknown system dynamics and quantized communication constraints.
    • To reduce communication burden and enhance convergence speed.

    Main Methods:

    • Development of a distributed combined measurement error function (DCMEF) to convert bipartite formation into consensus problems.
    • Establishment of a distributed compact form dynamic linearization model using input-output data, removing the need for strongly connected topologies.
    • Implementation of a logarithmic quantization scheme and a dynamic event-triggered communication mechanism.

    Main Results:

    • A novel data-driven fully distributed dynamic event-triggered bipartite formation control method is proposed.
    • Rigorous proof of convergence for the proposed control method.
    • Validation of effectiveness through simulation studies and hardware experiments.

    Conclusions:

    • The proposed method effectively achieves bipartite formation control in complex MASs under challenging conditions.
    • The dynamic event-triggered mechanism significantly reduces communication load while maintaining performance.
    • The data-driven approach offers a practical solution for real-world applications of distributed MASs.