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Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear

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    This study introduces a novel model-free adaptive control scheme for multiagent systems (MASs) to achieve accurate bipartite formation tracking. The event-triggered approach enhances communication efficiency and ensures reliable performance for unknown nonlinear systems.

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    Area of Science:

    • Control Systems Engineering
    • Robotics
    • Artificial Intelligence

    Background:

    • Multiagent systems (MASs) require sophisticated control for coordinated tasks.
    • Event-triggered mechanisms are crucial for efficient communication in MASs.
    • Nonlinear discrete-time systems present significant control challenges.

    Purpose of the Study:

    • To address the event-triggering time-varying trajectory bipartite formation tracking problem in unknown nonaffine nonlinear discrete-time MASs.
    • To develop a model-free adaptive control scheme that does not require prior knowledge of system dynamics.
    • To enhance communication resource utilization and system flexibility through an observer-based event-triggering mechanism.

    Main Methods:

    • Utilizing the pseudo-partial-derivative technique to derive an equivalent linear data model.
    • Proposing an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme.
    • Implementing an observer-based event-triggering mechanism with a dead-zone operator for improved flexibility.

    Main Results:

    • The proposed algorithm rigorously proves convergence, reducing tracking errors to a small range around zero.
    • The control scheme effectively achieves time-varying trajectory bipartite formation tracking for unknown nonlinear MASs.
    • Simulation studies validate the control approach's effectiveness and suitability for homogeneous MASs.

    Conclusions:

    • The developed model-free adaptive control scheme offers a robust solution for bipartite formation tracking in complex MASs.
    • The event-triggered mechanism optimizes communication, making the system more efficient and flexible.
    • The approach is validated for both general and homogeneous unknown nonlinear discrete-time MASs.