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    This study introduces a reinforcement learning (RL) control framework for nonlinear switched systems, enabling preset performance like convergence time and accuracy. It overcomes unmeasurable states and complex switching for practical applications.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • System Dynamics

    Background:

    • Nonlinear switched interconnected systems present significant control challenges due to unmeasurable states and complex switching behaviors.
    • Existing control methods often struggle with guaranteed performance and adaptability to system variations.

    Purpose of the Study:

    • To develop a reinforcement learning (RL)-based control framework for nonlinear switched interconnected systems.
    • To guarantee predefined performance metrics, including convergence time and accuracy.
    • To address challenges posed by unmeasurable states and group average dwell time switching mechanisms.

    Main Methods:

    • Reconstruction of system equations targeting nonlinear and interconnected terms, approximated by neural networks (NNs).
    • Design of an NN-based switching state observer for estimating unmeasurable states.
    • Development of a distributed optimal controller using a backstepping framework with a performance transformation function integrated into the cost function.
    • Approximation of the control law via an identifier-actor-critic architecture.
    • Generalization of group average dwell time-based stability analysis for optimal control.

    Main Results:

    • The proposed framework effectively handles unmeasurable states and group average dwell time switching.
    • Convergence time and accuracy can be preset through parameter configuration.
    • The approach demonstrates enhanced extensibility and practicality compared to existing methods.
    • Simulation examples validate the effectiveness and superiority of the proposed method.

    Conclusions:

    • The developed RL-based control framework provides a robust and adaptable solution for nonlinear switched interconnected systems.
    • The method achieves guaranteed performance and overcomes key limitations of prior approaches.
    • This work offers significant potential for real-world applications requiring precise and reliable control.