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    This study introduces event-triggered distributed average tracking (ETDAT) for nonlinear multiagent systems. Novel adaptive-gain algorithms improve practical applications by reducing communication needs.

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    Area of Science:

    • Control Theory
    • Networked Systems
    • Nonlinear Dynamics

    Background:

    • Distributed average tracking (DAT) is crucial for coordinating multiagent systems.
    • Existing DAT methods often require continuous communication, limiting practical applications.
    • Lipschitz-type nonlinearities are common in real-world systems but pose control challenges.

    Purpose of the Study:

    • To develop novel event-triggered distributed average tracking (ETDAT) algorithms for Lipschitz-type nonlinear multiagent systems.
    • To address the need for more practical and communication-efficient control strategies.
    • To investigate both static and adaptive-gain ETDAT algorithms.

    Main Methods:

    • Utilizing a state-dependent gain design approach combined with an event-triggered mechanism.
    • Developing two distinct ETDAT algorithms: static and adaptive-gain.
    • Implementing a fully distributed adaptive-gain algorithm that avoids global network topology information.

    Main Results:

    • Successfully designed and analyzed static and adaptive-gain ETDAT algorithms.
    • Demonstrated the practical advantages of event-triggered strategies in nonlinear multiagent systems.
    • Validated the effectiveness of the adaptive-gain ETDAT algorithms through simulations on a Watts-Strogatz small-world network.

    Conclusions:

    • Event-triggered strategies can be effectively integrated into DAT for nonlinear multiagent systems.
    • Adaptive-gain ETDAT algorithms offer a practical, fully distributed solution without requiring global network information.
    • The proposed methods enhance the applicability of distributed control in real-world engineering scenarios.