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BIBO stability of continuous and discrete -time systems01:24

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Event-Triggered Reinforcement Learning-Based Adaptive Tracking Control for Completely Unknown Continuous-Time

Xinxin Guo, Weisheng Yan, Rongxin Cui

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    Summary
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    This study introduces event-triggered adaptive control using reinforcement learning for nonlinear systems. The method enhances stability and reduces computational load, validated on an autonomous underwater vehicle.

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    Area of Science:

    • Robotics
    • Control Systems
    • Artificial Intelligence

    Background:

    • Nonlinear systems require advanced control strategies to handle unknown dynamics and disturbances.
    • Traditional control methods can be computationally intensive and require frequent data transmission.

    Purpose of the Study:

    • To develop an event-triggered adaptive tracking control for continuous-time nonlinear systems.
    • To reduce communication and computational costs using a novel dead-zone event-triggered condition.
    • To ensure system stability and bounded errors through rigorous theoretical analysis.

    Main Methods:

    • Event-triggered reinforcement learning framework.
    • Utilization of critic and action neural networks for approximation.
    • Design of a dead-zone event-triggered condition.
    • Theoretical stability analysis of the closed-loop system.

    Main Results:

    • The developed controller ensures uniform ultimate boundedness of weight errors and filtered tracking error.
    • The closed-loop system stability is rigorously proven.
    • Reduced communication and computational burden demonstrated by the event-triggered approach.

    Conclusions:

    • The proposed event-triggered reinforcement learning-based adaptive control is effective for nonlinear systems.
    • The method offers a computationally efficient and stable control solution.
    • Successful simulation on an autonomous underwater vehicle model validates the controller's performance.