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    This study introduces a simplified reinforcement learning (RL) framework using minimal neural networks (NNs) for high-order nonlinear systems. The novel approach reduces computational complexity and guarantees system stability without needing persistent excitation (PE).

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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Nonlinear Dynamics

    Background:

    • Optimal tracking control for high-order uncertain nonlinear systems is computationally demanding.
    • Existing reinforcement learning (RL) methods often require numerous neural networks (NNs) and complex recursive designs.
    • A critical issue in simplified RL is the potential for invalid Lyapunov stability analysis due to vanishing eigenvalues.

    Purpose of the Study:

    • To develop a simplified reinforcement learning (RL) framework with minimal neural networks (NNs) for high-order uncertain nonlinear systems.
    • To overcome the computational complexity and theoretical limitations of existing RL-based control strategies.
    • To ensure rigorous stability guarantees without relying on persistent excitation (PE) conditions.

    Main Methods:

    • Leveraged high-order fully actuated (HOFA) system theory to reformulate system dynamics into a compact normal form.
    • Developed a unified, nonrecursive controller design utilizing only three neural networks (NNs), irrespective of system order.
    • Introduced a novel critic-actor weight update law to circumvent problematic correlation matrices, ensuring stability analysis validity.

    Main Results:

    • The proposed method significantly reduces computational complexity by using a fixed number of NNs (three) regardless of system order.
    • The novel weight update law rigorously guarantees semiglobal uniform ultimate boundedness of the closed-loop system.
    • Simulation results demonstrate superior effectiveness and computational efficiency compared to existing control methods.

    Conclusions:

    • The simplified RL framework offers a computationally efficient and practically implementable solution for optimal tracking control of high-order nonlinear systems.
    • The approach successfully addresses theoretical deficiencies in existing simplified RL strategies, providing robust stability guarantees.
    • This work paves the way for broader application of advanced RL techniques in complex control systems.