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    Area of Science:

    • Control Theory
    • Nonlinear Systems
    • Artificial Intelligence

    Background:

    • Decentralized control is crucial for complex systems.
    • Event-triggered control (ETC) reduces communication load.
    • Existing methods often assume zero equilibrium points, limiting applicability.

    Purpose of the Study:

    • To develop a novel decentralized event-triggered control (ETC) scheme.
    • To address continuous-time nonlinear systems with matched interconnections and non-zero equilibrium points.
    • To ensure system stability using reinforcement learning.

    Main Methods:

    • A theorem representing the decentralized ETC law as optimal ETC laws for subsystems.
    • A reinforcement learning (RL)-based method to solve Hamilton-Jacobi-Bellman equations.
    • Utilizing critic-only networks, gradient descent, and concurrent learning for weight tuning.

    Main Results:

    • The proposed RL-based method relaxes the persistence of excitation condition.
    • Critic network weights are uniformly ultimately bounded.
    • The decentralized ETC law guarantees uniform ultimate boundedness for the entire system.

    Conclusions:

    • The novel decentralized ETC scheme is effective for nonlinear interconnected systems.
    • The RL-based approach provides a robust method for designing optimal ETC laws.
    • The strategy is validated through simulations of interconnected inverted pendulums.