System Identification of a Moored ASV with Recessed Moon Pool via Deterministic and Bayesian Hankel-DMDc

2025-11-05

AI Summary

Small autonomous surface vehicles (ASVs) with embedded moonpools exhibit strong nonlinear responses under moored conditions due to internal fluid sloshing, rendering conventional system identification methods ineffective. Method: This study proposes a hybrid Hankel dynamic mode decomposition framework—HDMDc and its Bayesian extension (BHDMDc)—integrating deterministic modeling with Bayesian inference to quantify epistemic uncertainty via hyperparameter estimation. Contribution/Results: HDMDc is applied for the first time to multi-sea-state generalization validation. The Bayesian formulation significantly enhances model robustness and credibility. Experimental results demonstrate that the data-driven model accurately predicts six-degree-of-freedom motion responses under both regular and irregular wave excitations, effectively mitigating nonlinear hydrodynamic interference from the moonpool. The approach advances methodology for complex moored dynamics modeling and offers tangible engineering applicability.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Mixture of Experts (MoE)
• Cognitive Modeling & Cognitive Systems: Applications

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This study addresses the system identification of a small autonomous surface vehicle (ASV) under moored conditions using Hankel dynamic mode decomposition with control (HDMDc) and its Bayesian extension (BHDMDc). Experiments were carried out on a Codevintec CK-14e ASV in the towing tank of CNR-INM, under both irregular and regular head-sea wave conditions. The ASV under investigation features a recessed moon pool, which induces nonlinear responses due to sloshing, thereby increasing the modelling challenge. Data-driven reduced-order models were built from measurements of vessel motions and mooring loads. The HDMDc framework provided accurate deterministic predictions of vessel dynamics, while the Bayesian formulation enabled uncertainty-aware characterization of the model response by accounting for variability in hyperparameter selection. Validation against experimental data demonstrated that both HDMDc and BHDMDc can predict the vessel's response to unseen regular and irregular wave excitations. In conclusion, the study shows that HDMDc-based ROMs are a viable data-driven alternative for system identification, demonstrating for the first time their generalization capability for a sea condition different from the training set, achieving high accuracy in reproducing vessel dynamics.

Problem

• Modeling moored autonomous surface vehicle dynamics with nonlinear moon pool effects
• Developing data-driven reduced-order models using deterministic and Bayesian methods
• Predicting vessel response to unseen wave conditions with uncertainty quantification

Innovation

• Used Hankel dynamic mode decomposition with control
• Applied Bayesian extension for uncertainty quantification
• Built data-driven reduced-order models from measurements