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Decentralized stabilization for a class of continuous-time nonlinear interconnected systems using online learning

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    Summary

    A new decentralized control strategy stabilizes nonlinear large-scale systems using neural networks. This online learning approach enhances stability by adjusting isolated subsystem controllers for interconnected systems.

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    Area of Science:

    • Control Theory
    • Artificial Intelligence
    • Systems Engineering

    Background:

    • Large-scale systems present significant control challenges due to their interconnected and nonlinear nature.
    • Decentralized control strategies are crucial for managing complexity and ensuring stability in such systems.
    • Existing methods often struggle with real-time adaptation and handling interconnections effectively.

    Purpose of the Study:

    • To develop a novel decentralized control strategy for stabilizing continuous-time nonlinear interconnected large-scale systems.
    • To utilize a neural-network-based online learning optimal control approach for enhanced adaptability.
    • To establish a method for designing controllers that account for system interconnections.

    Main Methods:

    • Designing optimal controllers for isolated subsystems with cost functions considering interconnection bounds.
    • Proving that decentralized control can be achieved by augmenting isolated subsystem policies with feedback gains.
    • Employing an online policy iteration algorithm to solve Hamilton-Jacobi-Bellman equations.
    • Utilizing critic neural networks for approximate cost function and control policy derivation.

    Main Results:

    • A novel decentralized control strategy is developed and proven effective for stabilizing the target systems.
    • The dynamics of critic neural network estimation errors are shown to be uniformly and ultimately bounded, ensuring stability.
    • The proposed method successfully demonstrates stabilization through simulation examples.

    Conclusions:

    • The developed neural-network-based online learning optimal control approach provides an effective decentralized strategy for nonlinear interconnected large-scale systems.
    • The method ensures system stability by appropriately adjusting controllers based on interconnection dynamics.
    • The approach offers a robust framework for real-time control and adaptation in complex systems.