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Robust Neurooptimal Control for a Robot via Adaptive Dynamic Programming.

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    This study introduces a novel robot tracking control optimization using neural networks (NNs) and adaptive dynamic programming. The method enhances robustness against unknown nonlinear perturbations, ensuring stable robot movement.

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    Area of Science:

    • Robotics
    • Control Systems Engineering
    • Artificial Intelligence

    Background:

    • Robot tracking control is crucial for many applications.
    • Unknown nonlinear perturbations challenge control system robustness.
    • Existing methods may require initial stabilizing control, limiting applicability.

    Purpose of the Study:

    • To optimize robot tracking control for improved robustness against unknown nonlinear perturbations.
    • To develop a control strategy that does not require initial stabilizing control.
    • To utilize adaptive dynamic programming and neural networks for robust control.

    Main Methods:

    • An auxiliary system is introduced to approximate the robot's optimal control.
    • Neural networks (NNs) approximate the Hamilton-Jacobi-Isaacs equation solution.
    • Adaptive critic design and gradient descent train NNs using a novel updating law.
    • Lyapunov stability theory is used to prove system stability.

    Main Results:

    • The proposed control method effectively improves robustness against unknown nonlinear perturbations.
    • Simulation studies demonstrate the successful implementation and effectiveness of the adaptive control strategy.
    • The control system ensures that all error signals are uniformly ultimately bounded.

    Conclusions:

    • The developed adaptive dynamic programming approach offers a robust solution for robot tracking control.
    • The method successfully overcomes the limitation of requiring initial stabilizing control.
    • The NN-based controller provides a stable and effective means to handle nonlinear perturbations in robotic systems.