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    This study introduces neuroadaptive impulsive control for uncertain multiagent systems, enabling consensus through intermittent communication and neural network-based adaptation for improved efficiency.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Consensus problems in uncertain multiagent systems are critical for coordinated behavior.
    • Existing control schemes often require continuous communication, leading to high energy costs.
    • Dynamic uncertainties in multiagent systems pose significant challenges for achieving consensus.

    Purpose of the Study:

    • To develop novel neuroadaptive impulsive control schemes for uncertain multiagent systems.
    • To address consensus challenges by utilizing intermittent communication and neural network-based adaptive control.
    • To design and compare two distinct control schemes: one with continuous-time information and another with sampled information.

    Main Methods:

    • Neuroadaptive impulsive control schemes are proposed, integrating neural networks for uncertainty adaptation.
    • Two schemes are designed: one using continuous-time information with impulsive feedback, and another using sampled information executed at impulsive instants.
    • Analysis of estimation and consensus achievement under specific conditions, validated by numerical simulations.

    Main Results:

    • Both proposed control schemes demonstrate the potential to achieve estimation and consensus with errors.
    • The sampled-information-based scheme offers reduced energy costs for communication and control.
    • Numerical simulations, including a practical system example, validate the effectiveness of the developed control strategies.

    Conclusions:

    • Neuroadaptive impulsive control is a viable approach for addressing consensus in uncertain multiagent systems.
    • The sampled-information scheme provides an energy-efficient alternative, albeit requiring auxiliary systems.
    • The study confirms that consensus can be achieved under specific conditions, offering a pathway for practical applications.