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Dynamic Event-Sampled Control of Interconnected Nonlinear Systems Using Reinforcement Learning.

Xiong Yang, Mengmeng Xu, Qinglai Wei

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    This summary is machine-generated.

    We introduce a novel decentralized dynamic event-based control strategy for nonlinear systems. This approach uses reinforcement learning and neural networks to ensure system stability and improve control performance.

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    Area of Science:

    • Control Theory
    • Nonlinear Systems
    • Reinforcement Learning

    Background:

    • Decentralized control strategies are crucial for complex interconnected systems.
    • Event-based control offers advantages in reducing communication and computation load.
    • Stability analysis of nonlinear systems with interconnections presents significant challenges.

    Purpose of the Study:

    • To develop a decentralized dynamic event-based control strategy for nonlinear systems with matched interconnections.
    • To design a critic-only reinforcement learning architecture for deriving optimal event-based control policies.
    • To ensure the stability of the closed-loop system and the convergence of neural network weights.

    Main Methods:

    • A dynamic event-based sampling mechanism utilizing system states and differential equations.
    • A critic-only reinforcement learning architecture to solve event-based Hamilton-Jacobi-Bellman equations.
    • Gradient descent with concurrent learning for updating critic neural network weights.
    • Lyapunov's approach for stability analysis.

    Main Results:

    • The decentralized controller is proven to be composed of optimal event-based control policies of nominal subsystems.
    • Asymptotic stability of closed-loop nominal subsystems is guaranteed.
    • Uniformly ultimate boundedness stability of critic neural networks' weight estimation errors is demonstrated.
    • Simulations validate the effectiveness of the proposed control strategy.

    Conclusions:

    • The proposed decentralized dynamic event-based control strategy is effective for nonlinear systems.
    • The critic-only reinforcement learning approach successfully derives optimal control policies.
    • The method ensures system stability and reliable performance in interconnected nonlinear plants.