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Autonomous berthing path tracking of a 4-DOF ship under nonlinear model predictive control.

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Summary

This study presents a nonlinear model predictive control (NMPC) approach for precise unmanned surface ship berthing in harsh seas. The method significantly improves path tracking and control accuracy, ensuring safer autonomous operations.

Keywords:
4-DOF ship dynamic modelAutonomous berthingPredictive controlRough sea conditionsShip path tracking

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Area of Science:

  • Marine engineering
  • Robotics
  • Control systems

Background:

  • Unmanned surface ships (USS) face significant challenges in path tracking and control during berthing, especially in severe maritime environments.
  • Traditional control methods often struggle with the complex dynamics and environmental variations encountered during autonomous operations.

Purpose of the Study:

  • To develop an intelligent and accurate berthing system for USS using nonlinear model predictive control (NMPC).
  • To enhance the precision of ship berthing control by dynamically adapting to the ship's state and environmental conditions.

Main Methods:

  • A 4-DOF (four-degree-of-freedom) ship berthing path tracking method based on NMPC was proposed.
  • The Fossen dynamic model (4-DOF: sway, surge, yaw, roll) was established, incorporating moving horizon estimation (MHE).
  • A real-time NMPC system was designed to predict trajectories and optimize control inputs.

Main Results:

  • Simulations in the Port of Hamburg demonstrated the algorithm's effectiveness.
  • Track error was maintained below 2 meters.
  • Berthing position error was reduced to only 0.3 meters.

Conclusions:

  • The proposed NMPC-based approach offers superior efficiency, generalization, and suitability for autonomous ship berthing.
  • It significantly improves attitude control compared to traditional PID and standard NMPC methods, especially in rough seas.
  • Provides a theoretical basis and practical recommendations for enhancing the reliability of autonomous ship berthing.