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This study introduces an adaptive neural network control for underactuated surface ships. The method ensures course tracking stability, validated through simulations.

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Area of Science:

  • Naval Architecture and Marine Engineering
  • Control Systems Engineering
  • Artificial Intelligence in Engineering

Background:

  • Underactuated surface ships present complex control challenges due to limited control inputs.
  • Accurate control parameter estimation is crucial for effective course-keeping and trajectory following.
  • Existing control methods may struggle with unknown dynamics and uncertainties in ship maneuvering.

Purpose of the Study:

  • To develop and validate a novel adaptive neural network control strategy for underactuated surface ship course control.
  • To address the challenge of unknown system parameters in the control design.
  • To rigorously prove the stability and convergence of the proposed control system.

Main Methods:

  • Utilizing neural networks for online estimation of unknown control parameters.
  • Implementing an adaptive technique to update neural network weights.
  • Applying Lyapunov stability theory to demonstrate uniform stability of course tracking errors.
  • Conducting simulation experiments to verify the control system's performance.

Main Results:

  • The proposed adaptive neural network control effectively estimates unknown ship dynamics.
  • Uniform stability of course tracking errors is theoretically proven.
  • Simulation results demonstrate the practical effectiveness and robustness of the control method.

Conclusions:

  • The adaptive neural network approach offers a viable solution for underactuated surface ship course control.
  • The integration of Lyapunov stability theory provides a strong theoretical foundation for the control design.
  • The validated method shows promise for enhancing the maneuverability and operational safety of surface vessels.