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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 control for nonlinear pure-feedback systems with high-order sliding mode observer.

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    This study introduces a novel adaptive control for nonlinear pure-feedback systems, avoiding complex backstepping. It uses a new state transformation and a sliding mode observer for accurate state estimation and reliable tracking control.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear Dynamics
    • Artificial Intelligence

    Background:

    • Traditional control schemes for pure-feedback systems often rely on the backstepping technique.
    • Backstepping can lead to complexity explosion and circular issues in control design.

    Purpose of the Study:

    • To develop a novel adaptive control design for nonlinear pure-feedback systems that circumvents the need for backstepping.
    • To address the challenge of unknown state estimation in these systems.
    • To achieve robust and computationally efficient tracking control.

    Main Methods:

    • A novel state transformation converts the pure-feedback system into a canonical system suitable for output-feedback control.
    • A high-order sliding mode observer is employed for finite-time estimation of unknown states.
    • Two adaptive neural controllers are proposed, one with a robust term and another with a simplified scalar weight update.

    Main Results:

    • The proposed method successfully avoids the backstepping technique, mitigating complexity and circular issues.
    • Finite-time convergence of the observer error is guaranteed.
    • Both adaptive neural controllers achieve stable closed-loop systems and converge tracking errors to a small neighborhood around zero.

    Conclusions:

    • The novel adaptive control strategy is effective for nonlinear pure-feedback systems.
    • The approach offers improved computational efficiency and reliability, validated by simulations and experiments on a servo motor system.