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    This study introduces a neural-network (NN)-based consensus control for nonlinear multiagent systems (MASs) with input constraints. An event-triggered observer and actor-critic NN scheme ensure system stability and performance.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Networked Systems

    Background:

    • Investigating consensus control for discrete-time nonlinear multiagent systems (MASs) with input constraints is crucial for coordinated system behavior.
    • Smart sensors collect relative measurements for local tracking errors, enabling decentralized control strategies.

    Purpose of the Study:

    • To develop a neural-network (NN)-based consensus control strategy for nonlinear MASs under input constraints.
    • To design an NN-based observer for reconstructing local tracking errors using relative measurements and an event-triggered mechanism.
    • To implement an actor-critic NN scheme for realizing the optimal control policy while minimizing a nonquadratic cost function.

    Main Methods:

    • A local nonquadratic cost function is introduced to assess control performance under input constraints.
    • An NN-based observer with an event-triggered mechanism and a time-dependent threshold is designed to estimate local tracking errors.
    • A novel adaptive tuning law updates NN weights for the event-triggered mechanism.
    • An actor-critic NN scheme with online learning is employed to implement the control policy.

    Main Results:

    • The consensus condition for the MAS is mathematically established, ensuring coordinated system behavior.
    • The boundedness of estimation errors for actor and critic NN weights is proven.
    • The impact of the event-triggered mechanism on the local cost is analyzed, with its upper bound derived compared to time-triggered systems.

    Conclusions:

    • The proposed NN-based observer and actor-critic control scheme effectively addresses the consensus control problem for discrete-time nonlinear MASs with input constraints.
    • The event-triggered mechanism offers potential advantages in terms of control cost compared to traditional time-triggered approaches.
    • Simulation results validate the practical applicability and performance of the developed controller design.