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

TL;DR

This paper develops and validates data-driven reduced-order models using Hankel-DMDc and Bayesian Hankel-DMDc for a moored ASV with a recessed moon pool, demonstrating accurate predictions and uncertainty quantification under various wave conditions.

Contribution

It introduces the first application of HDMDc and BHDMDc to model a moored ASV with nonlinear sloshing effects, showing their effectiveness in generalizing to unseen sea conditions.

Findings

HDMDc provides accurate deterministic vessel dynamics predictions.

BHDMDc quantifies uncertainty in model responses.

Models successfully predict vessel behavior under new wave conditions.

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.