Date of Award
5-2026
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Ocean Engineering and Marine Sciences
First Advisor
J. Travis Hunsucker
Second Advisor
Seong Hyeon Hong
Third Advisor
Stephen Wood
Fourth Advisor
Richard B. Aronson
Abstract
This work proposes a residual hybrid MMG-GP model to address the growing trend in marine autonomy requiring ship maneuvering predictions that are both accurate and reliable across a range of operational conditions. The model combines a Maneuvering Modeling Group (MMG) model with a Gaussian Process (GP) corrective term to combine the strengths of both physics and data-driven methods. The hybrid MMG-GP model and the individual MMG and equivalent black-box GP model are compared using positional and energy error metrics for a 35° turning circle and a 10°/10° zigzag maneuver. Training data are varied in duration and rudder excitation frequency to examine the effects of common system identification limitations across 3200 independent samples. The MMG-GP demonstrated consistent improvements over the MMG model and achieved an average error reduction of 58.2% compared to its black-box counterpart, with larger gains when trained on limited and low-excitation data. Results indicate that a physics model corrected via machine learning is a simple yet effective method for improving maneuvering modeling applications.
Recommended Citation
Milgram, Alex Robert, "Residual Hybrid Approach to Ship Maneuvering and Power Prediction: MMG Model Corrected via Gaussian Process Regression" (2026). Theses and Dissertations. 1630.
https://repository.fit.edu/etd/1630