用数据驱动方法精准建模带凹陷月池的浮式船体动力学，可预测未知海况响应。
System Identification of a Moored ASV with Recessed Moon Pool via Deterministic and Bayesian Hankel-DMDc
- 基于哈肯动态模态分解与控制（HDMDc）构建低阶模型，融合实验数据识别系统动态。
- 在规则与不规则波浪下验证，模型对未见海况的响应预测误差低，准确率高。
- 首次实现数据驱动模型跨海况泛化，适合海上结构物建模与控制研究者参考。
本研究针对带凹陷月池的小型自主水面艇（ASV）在系泊状态下的系统辨识问题，采用哈肯动态模态分解与控制（HDMDc）及其贝叶斯扩展（BHDMDc）方法。实验在中船重工海洋研究所（CNR-INM）拖曳水池中进行，针对Codevintec CK-14e ASV，在规则与不规则迎浪条件下采集船舶运动与系缆载荷数据。由于月池内液体晃荡引发非线性响应，建模难度大。基于实测数据构建了数据驱动的降阶模型（ROM）。HDMDc框架实现了对船舶动态的高精度确定性预测，而贝叶斯形式则通过考虑超参数选择的不确定性，提供了响应不确定性的量化表征。与实验数据对比验证表明，两种方法均可准确预测未见过的规则与不规则波浪激励下的船舶响应。结果证明，基于HDMDc的降阶模型是系统辨识的有效数据驱动替代方案，首次展示了其在不同于训练集的海况下的泛化能力，能以高精度复现船舶动态。
原文摘要 · Abstract (English)
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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