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    This study introduces a predictive event-triggered mechanism for heterogeneous linear multiagent systems, significantly reducing communication costs by minimizing data transmissions for achieving output consensus.

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    Area of Science:

    • Control Theory
    • Networked Systems
    • Robotics

    Background:

    • Heterogeneous linear multiagent systems present challenges in achieving coordinated behavior due to agent diversity.
    • Traditional consensus protocols often incur high communication overhead.
    • Event-triggered mechanisms aim to reduce communication by only transmitting data when necessary.

    Purpose of the Study:

    • To investigate the output consensus problem in heterogeneous linear multiagent systems.
    • To propose a novel predictive event-triggered consensus protocol.
    • To reduce communication costs while ensuring system stability and consensus.

    Main Methods:

    • Development of an event-triggered consensus protocol incorporating an internal reference model for each agent.
    • Prediction of internal reference model inputs based on estimated state differences between neighboring agents.
    • Analysis of system dynamics on directed graphs to derive consensus conditions.
    • Mathematical formulation to ensure the absence of Zeno behavior.

    Main Results:

    • A necessary and sufficient condition for achieving output consensus in the investigated system is established.
    • The proposed predictive event-triggered mechanism significantly reduces the number of event-triggered times compared to traditional methods.
    • Demonstrated reduction in communication costs among agents.
    • Proven avoidance of Zeno behavior for all agents.

    Conclusions:

    • The proposed predictive event-triggered consensus protocol effectively addresses the output consensus problem in heterogeneous linear multiagent systems.
    • The method offers substantial savings in communication resources.
    • The approach is validated through numerical examples, confirming its practical applicability and effectiveness.