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Robust Sliding Mode-Based Learning Control for MIMO Nonlinear Nonminimum Phase System in General Form.

Xiaoxiang Hu, Changhua Hu, Xiaosheng Si

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    This study introduces a sliding mode-based learning controller for complex nonlinear systems with unknown uncertainties. The proposed fuzzy logic controller ensures system stability and effective tracking control, validated in aircraft simulations.

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    Area of Science:

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

    Background:

    • Tracking control of multi-input multi-output (MIMO) nonlinear nonminimum phase systems presents significant challenges due to parameter uncertainties and unmodeled dynamics.
    • Existing control strategies often struggle with systems where prior information about these uncertainties is unknown.

    Purpose of the Study:

    • To propose a robust sliding mode-based learning controller for MIMO nonlinear nonminimum phase systems with unknown uncertainties.
    • To guarantee closed-loop system stability under both exact and uncertain model conditions.
    • To enhance control performance by mitigating the effects of parameter uncertainties and unmodeled dynamics.

    Main Methods:

    • Development of a sliding mode-based learning controller incorporating a fuzzy logic system.
    • Design of a specific sliding surface and a learning control law.
    • Validation through numerical simulations on a vertical takeoff and landing (VTOL) aircraft model.

    Main Results:

    • The proposed controller ensures guaranteed stability for the closed-loop system, even in the presence of unknown parameter uncertainties and unmodeled dynamics.
    • The fuzzy logic system effectively compensates for system uncertainties, improving tracking control performance.
    • Simulation results demonstrate the controller's effectiveness in a practical application (VTOL aircraft).

    Conclusions:

    • The sliding mode-based learning controller with fuzzy logic is a viable and effective solution for robust tracking control of complex nonlinear systems.
    • The approach successfully addresses challenges posed by unknown uncertainties and unmodeled dynamics, offering improved stability and performance.
    • The validated effectiveness in VTOL aircraft control highlights its potential for real-world applications.