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    This study introduces a novel method for multiagent systems (MASs) with discontinuous constraints, enabling optimal consensus control. The approach transforms constraints, simplifying controller design and ensuring system stability.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Robotics

    Background:

    • Multiagent systems (MASs) often face challenges with discontinuous state constraints, limiting traditional control methods.
    • Existing barrier function-based backstepping techniques are not directly applicable to discontinuous constraints.

    Purpose of the Study:

    • To address the optimal consensus control problem for MASs with discontinuous constraints.
    • To develop a novel technique for handling discontinuous constraint boundaries in MASs.
    • To simplify controller design and remove feasibility conditions.

    Main Methods:

    • A constraint boundary reconstruction technique using switch-like functions to convert discontinuous boundaries to continuous ones.
    • An improved coordinate transformation incorporating barrier functions and distributed event-triggered estimators.
    • A modified reinforcement learning strategy using neural networks (NNs) with prediction error and revised terms.

    Main Results:

    • The proposed technique strictly proves that transformed constraints guarantee original constraint fulfillment.
    • The improved coordinate transformation eliminates the feasibility condition, simplifying controller design.
    • The modified reinforcement learning strategy effectively resolves the optimal consensus problem for MASs.

    Conclusions:

    • The developed method provides an effective solution for optimal consensus control in MASs with discontinuous constraints.
    • Lyapunov stability theory confirms the stability of the MASs under the proposed control strategy.
    • Simulation results validate the practical effectiveness of the proposed approach.