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    This study introduces adaptive decentralized tracking control for complex nonlinear systems with asymmetric constraints. The novel approach ensures all signals, including tracking errors, remain bounded, enhancing system stability.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear Dynamics
    • Artificial Intelligence in Control

    Background:

    • Existing adaptive decentralized tracking control methods struggle with strongly interconnected nonlinear systems, especially those with asymmetric time-varying constraints.
    • Assumptions regarding interconnection terms, such as upper functions and structural restrictions, limit current control designs.

    Purpose of the Study:

    • To develop an adaptive decentralized tracking control strategy for strongly interconnected nonlinear systems featuring asymmetric time-varying constraints.
    • To overcome limitations imposed by assumptions on interconnection terms and state constraints in control design.

    Main Methods:

    • Utilized properties of Gaussian functions within Radial Basis Function (RBF) neural networks to approximate unknown interconnection terms.
    • Introduced a nonlinear state-dependent function (NSDF) and a novel coordinate transformation to eliminate conservative steps and feasibility conditions.
    • Employed adaptive control techniques for decentralized tracking.

    Main Results:

    • Successfully removed the conservative step converting state constraints into tracking error boundaries.
    • Eliminated the feasibility condition for virtual controllers.
    • Proved that all system signals, including original and new tracking errors, are bounded.
    • Demonstrated the effectiveness and benefits of the proposed control scheme through simulations.

    Conclusions:

    • The proposed adaptive decentralized tracking control scheme effectively handles strongly interconnected nonlinear systems with asymmetric constraints.
    • The method enhances system stability by ensuring bounded tracking errors and signals.
    • The approach offers a significant advancement over existing control strategies for complex nonlinear systems.