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Neuro-Adaptive Control With Given Performance Specifications for Strict Feedback Systems Under Full-State

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    This study introduces a neural network (NN)-based control method for strict feedback systems. The approach ensures finite-time convergence of tracking errors within constraints, achieving precise control performance.

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence in Control
    • Nonlinear System Analysis

    Background:

    • Strict feedback systems present challenges in achieving precise tracking control under state constraints.
    • Existing methods often struggle to meet stringent performance specifications like finite-time convergence and bounded overshoot simultaneously.
    • The integration of neural networks (NNs) offers potential for adaptive and robust control solutions.

    Purpose of the Study:

    • To develop a novel neural network (NN)-based tracking control strategy for strict feedback systems.
    • To address the challenge of full-state constraints while ensuring finite-time convergence of tracking errors.
    • To achieve predefined performance specifications, including bounded overshoot and adjustable residual error.

    Main Methods:

    • A back-stepping design incorporating barrier Lyapunov functions (BLFs) is employed to handle state constraints.
    • Two consecutive error transformations, utilizing behavior-shaping and asymmetric scaling functions, are introduced.
    • A single symmetric BLF is utilized to manage asymmetric output constraints, simplifying stability analysis.

    Main Results:

    • The proposed NN-based controller guarantees finite-time convergence of the tracking error to a prescribed region.
    • The control method ensures the error converges to a smaller, adjustable residual set with limited overshoot.
    • All internal signals, including neural network inputs, are proven to be bounded, ensuring system stability.

    Conclusions:

    • The developed control method effectively addresses tracking control problems in strict feedback systems with full-state constraints.
    • The integration of BLFs and novel error transformations provides a feasible approach to meet demanding performance specifications.
    • Both theoretical analysis and numerical simulations confirm the efficacy and benefits of the proposed NN-based control strategy.