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Adaptive Decentralized Neural Network Tracking Control for Uncertain Interconnected Nonlinear Systems With Input

Haibin Sun, Linlin Hou, Guangdeng Zong

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    Summary
    This summary is machine-generated.

    This study develops adaptive decentralized tracking control for nonlinear systems with unknown delays and functions. The new method ensures system stability and signal boundedness for complex control challenges.

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    Area of Science:

    • Control Systems Engineering
    • Nonlinear Dynamics
    • Artificial Intelligence in Control

    Background:

    • Decentralized control systems face challenges with unknown dynamics, time-delays, and input quantization.
    • Interconnected nonlinear systems require robust control strategies to ensure stability and performance.
    • Adaptive control is crucial for systems with partially or fully unknown parameters.

    Purpose of the Study:

    • To design an adaptive decentralized tracking controller for interconnected nonlinear systems.
    • To address challenges posed by unknown time-delays, unknown functions, and input quantization.
    • To ensure uniform ultimate boundedness of all signals in the closed-loop system.

    Main Methods:

    • Utilized the backstepping design technique combined with neural network approximations.
    • Employed a sliding-mode differentiator to estimate virtual control law derivatives, simplifying the controller.
    • Applied Lyapunov stability analysis and graph theory to prove system stability.

    Main Results:

    • Successfully constructed an adaptive decentralized tracking controller.
    • Demonstrated that all closed-loop system signals are uniformly ultimately bounded.
    • Validated the controller's effectiveness through an inverted pendulum system simulation.

    Conclusions:

    • The proposed adaptive decentralized tracking control strategy is effective for complex nonlinear systems.
    • The integration of neural networks and sliding-mode differentiators offers a robust solution for systems with uncertainties.
    • The method provides a theoretical guarantee of stability and practical applicability.