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    Area of Science:

    • Control Systems Engineering
    • Artificial Intelligence
    • Robotics

    Background:

    • Unknown nonlinear systems in discrete time present significant control challenges.
    • Traditional H2 control methods struggle with system uncertainties and modeling errors.
    • Neural networks offer a powerful tool for approximating complex system dynamics.

    Purpose of the Study:

    • To develop an H2 tracking control strategy for unknown discrete-time nonlinear systems.
    • To enhance the robustness and tracking accuracy of neural H2 control against modeling errors.
    • To provide theoretical guarantees for the stability and convergence of the proposed control approach.

    Main Methods:

    • Utilizing a discrete-time recurrent neural network to model the unknown nonlinear system.
    • Applying H2 tracking control based on the derived neural model.
    • Integrating reinforcement learning and an additional neural approximator to mitigate neural modeling errors.
    • Proving the stability of the neural identifier and the H2 tracking controller.

    Main Results:

    • The proposed method demonstrates improved tracking accuracy and robustness in H2 control.
    • Theoretical analysis confirms the stability of the neural identifier and controller.
    • The approach's convergence is mathematically established.
    • Successful validation on a pan and tilt robot and a surge tank system.

    Conclusions:

    • The combined approach of neural networks and reinforcement learning provides an effective solution for H2 control of unknown discrete-time nonlinear systems.
    • The method offers enhanced performance and robustness compared to traditional techniques.
    • The theoretical underpinnings ensure reliable and stable control system operation.