A System Identification Approach to Motion Model Based on Full-Scale Ship Maneuvering Data.

Area of Science:

• Naval Architecture and Marine Engineering
• System Identification
• Control Theory

Background:

• Accurate ship motion modeling is crucial for safe and efficient maritime operations.
• Traditional models often struggle with the complex nonlinear dynamics of full-scale ship maneuvering.
• System identification (SI) offers a data-driven approach to model complex systems.

Purpose of the Study:

• To develop and validate a nonlinear integrating ship motion model for full-scale vessels.
• To apply system identification principles for robust parameter estimation.
• To assess the model's accuracy and applicability in real-world scenarios.

Main Methods:

• Collection of maneuvering data from full-scale ship experiments.
• Development of a nonlinear integrating ship motion model with 21 parameters.
• Parameter estimation using a batch least-squares (BLS) algorithm based on an error criterion.

Main Results:

• Successful identification and establishment of a 21-parameter nonlinear integrating ship motion model.
• Accurate estimation of model parameters using the BLS method.
• Validation of the model's feasibility and accuracy through a case study comparing simulated and trial trajectories.

Conclusions:

• The proposed system identification approach is effective for modeling ship maneuvering motion.
• The developed nonlinear integrating model provides a reliable tool for predicting ship dynamics.
• This methodology enhances the understanding and control of full-scale ship movements.