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    This study introduces an event-triggered sampling strategy for discrete-time multiagent systems (MASs). This method achieves asymptotic consensus with dynamic quantization, reducing required network bandwidth compared to periodic sampling.

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    Area of Science:

    • Control Theory
    • Networked Systems
    • Robotics

    Background:

    • Multiagent systems (MASs) face challenges in achieving consensus due to quantized communication and limited bandwidth.
    • Event-triggered sampling and dynamic quantization are crucial for efficient information exchange in MASs.

    Purpose of the Study:

    • To investigate the asymptotic consensus problem for discrete-time MASs under event-triggered sampling.
    • To develop a co-designed event-triggering strategy and dynamic quantization method to ensure consensus and save network bandwidth.

    Main Methods:

    • A model-based event-triggering strategy is proposed, co-designed with a dynamic quantization method.
    • The strategy bounds the state estimation error and control input, utilizing information from event-triggering instants.

    Main Results:

    • A sufficient bit-rate condition for asymptotic consensus is derived, dependent on agent dynamics and network topology.
    • The proposed strategy achieves lower bit rates than conventional periodic sampling while ensuring MAS consensus.

    Conclusions:

    • The developed event-triggering strategy effectively ensures asymptotic consensus in MASs with quantized communication.
    • The findings demonstrate significant advantages in bandwidth reduction compared to traditional sampling methods.