System Identification of a Moored ASV with Recessed Moon Pool via Deterministic and Bayesian Hankel-DMDc
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Abstract (arXiv)
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.
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References · 11
- 2017Deep learning for universal linear embeddings of nonlinear dynamicscited by 491
- 2021Modern Koopman Theory for Dynamical Systemscited by 269
- 2017Learning Koopman Invariant Subspaces for Dynamic Mode Decompositioncited by 156
- 2017Linearly-Recurrent Autoencoder Networks for Learning Dynamicscited by 142
- 2014Dynamic mode decomposition with controlcited by 128
- 2014Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decompositioncited by 49
- 2016Generalizing Koopman Theory to allow for inputs and controlcited by 49
- 2021Bagging, optimized dynamic mode decomposition (BOP-DMD) for robust, stable forecasting with spatial and temporal uncertainty-quantificationcited by 30
- 2024Predicting Ship Responses in Different Seaways using a Generalizable Force Correcting Machine Learning Methodcited by 4
- 2024Analysis, forecasting and system identification of a floating offshore wind turbine using dynamic mode decompositioncited by 3
- 2025Model-free system identification of surface ships in waves via Hankel dynamic mode decomposition with controlcited by 2
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