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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Systems Engineering

    Background:

    • Multiagent systems (MASs) face challenges including external disturbances, unmeasurable states, and prescribed constraints.
    • Existing optimal control methods often struggle with asymmetric input saturation and initial error conditions.

    Purpose of the Study:

    • To develop an adaptive optimal consensus control strategy for MASs.
    • To address unmeasurable states, external disturbances, and prescribed constraints.
    • To overcome limitations of existing methods regarding input saturation and initial conditions.

    Main Methods:

    • A composite observer using neural networks (NNs) estimates unmeasurable states and disturbances.
    • An improved prescribed performance control (PPC) technique guarantees consensus error within prescribed boundaries.
    • A simplified reinforcement learning (RL) algorithm establishes NN updating laws, resolving asymmetric input saturation via an auxiliary system.

    Main Results:

    • The proposed method effectively estimates unknown states and disturbances.
    • Consensus error is confined within prescribed performance limits, eliminating initial condition issues.
    • Asymmetric input saturation is successfully managed, and all closed-loop system signals are proven to be bounded.

    Conclusions:

    • The developed adaptive optimal consensus control strategy enhances MAS performance under complex conditions.
    • The integration of NNs, PPC, and RL offers a robust solution for practical MAS applications.
    • Simulation results validate the effectiveness and stability of the proposed control method.