基于确定性与贝叶斯Hankel-DMDc的带凹槽月池系泊ASV系统辨识研究 (System Identification of a Moored ASV with Recessed Moon Pool via Deterministic and Bayesian Hankel-DMDc)

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.

翻译：本研究采用带控制的Hankel动态模态分解（HDMDc）及其贝叶斯扩展（BHDMDc）方法，针对系泊状态下的小型自主水面艇（ASV）进行系统辨识。实验在CNR-INM拖曳水池中采用Codevintec CK-14e型ASV进行，涵盖不规则与规则迎浪海况。所研究的ASV具有凹槽式月池结构，其内部流体晃荡会引发非线性响应，从而增加了建模难度。基于船舶运动与系泊载荷的测量数据，建立了数据驱动的降阶模型。HDMDc框架提供了船舶动力学的精确确定性预测，而贝叶斯公式通过考虑超参数选择的变异性，实现了对模型响应的不确定性感知表征。通过实验数据验证表明，HDMDc与BHDMDc均能有效预测船舶对未见过规则/不规则波浪激励的响应。研究最终证明，基于HDMDc的降阶模型可作为系统辨识的可行数据驱动替代方案，首次展示了其在训练集外海况条件下的泛化能力，并在复现船舶动力学特性方面达到了高精度。