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Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
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Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity.

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    This study introduces a new adaptive neural control method for nonlinear systems with hysteresis and unmeasured states. The approach ensures tracking error convergence and system stability, overcoming common control challenges.

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    Area of Science:

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

    Background:

    • Adaptive neural control is crucial for complex nonlinear systems.
    • Hysteresis and unmeasured states present significant challenges in control design.
    • Existing methods often struggle with these combined nonlinearities.

    Purpose of the Study:

    • To develop an adaptive neural output-feedback controller for nonlinear systems with hysteretic output.
    • To address the issue of unmeasured states in such systems.
    • To guarantee prescribed tracking error constraints and system stability.

    Main Methods:

    • A modified Bouc-Wen model is used to represent output hysteresis.
    • Neural networks and Nussbaum-type functions are integrated using novel lemmas.
    • Barrier Lyapunov functions ensure tracking error constraints, and a robust filter handles unmeasured states.

    Main Results:

    • The proposed controller guarantees the convergence of tracking errors.
    • Semiglobal uniform ultimate boundedness of all closed-loop signals is achieved.
    • Simulation results validate the effectiveness of the neural control algorithm.

    Conclusions:

    • The developed adaptive neural control strategy effectively manages nonlinear systems with hysteresis and unmeasured states.
    • The method provides robust performance and stability guarantees.
    • This work offers a significant advancement in adaptive control for challenging nonlinear systems.