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    This study introduces a novel distributed consensus tracking controller for nonlinear multiagent systems. It overcomes state constraints and uncertainties using adaptive backstepping and neural networks, enhancing control performance.

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    Area of Science:

    • Control Theory
    • Nonlinear Systems
    • Multiagent Systems

    Background:

    • Existing control methods for constrained nonlinear multiagent systems often require strict feasibility conditions.
    • Consensus tracking in multiagent systems typically necessitates Lipschitz conditions, limiting applicability.
    • Handling internal uncertainties and external disturbances simultaneously poses a significant challenge.

    Purpose of the Study:

    • To propose a distributed consensus tracking controller for nonlinear multiagent systems with time-varying asymmetric full-state constraints.
    • To remove the feasibility condition and Lipschitz condition limitations found in prior research.
    • To develop a robust control scheme that addresses internal uncertainties and external disturbances.

    Main Methods:

    • Utilized nonlinear mapping function (NMF)-based state reconstruction to eliminate feasibility conditions.
    • Employed an adaptive command-filtered backstepping framework to remove the Lipschitz condition.
    • Integrated a neural network-based function approximator (NN-FAP) with a finite-time smooth disturbance observer (DOB) for uncertainty handling.

    Main Results:

    • Successfully removed the need for feasibility conditions and Lipschitz conditions in constrained consensus tracking.
    • Developed a composite learning scheme combining NN-FAP and DOB for simultaneous handling of internal and external uncertainties.
    • Demonstrated enhanced learning excitation for the neural network using online historical data.
    • Achieved finite-time observation of external disturbances even with unknown system dynamics.

    Conclusions:

    • The proposed controller effectively achieves distributed consensus tracking for nonlinear multiagent systems under challenging constraints and uncertainties.
    • The novel combination of NMF, adaptive backstepping, NN-FAP, and DOB offers a robust and generalizable control strategy.
    • The study provides a complete controller design, stability analysis, and simulation validation.