带凹入式月池的系泊自主水面艇的确定性及贝叶斯Hankel-DMDc系统辨识
System Identification of a Moored ASV with Recessed Moon Pool via Deterministic and Bayesian Hankel-DMDc
AI总结：
本文针对带凹入式月池的系泊自主水面艇，提出基于HDMDc及其贝叶斯扩展的数据驱动降阶建模方法，首次验证了其在不同海况下的泛化能力与高精度预测性能。
摘要：
本研究利用带控制的Hankel动态模式分解（HDMDc）及其贝叶斯扩展（BHDMDc），解决小型自主水面艇（ASV）在系泊条件下的系统辨识问题。实验在CNR-INM拖曳水池中，对一艘Codevintec CK-14e自主水面艇在不规则和规则迎浪波浪条件下进行。所研究的自主水面艇具有凹入式月池，该月池因晃荡而引发非线性响应，从而增加了建模难度。基于船舶运动和系泊载荷的测量数据，构建了数据驱动的降阶模型。HDMDc框架对船舶动力学提供了准确的确定性预测，而贝叶斯公式通过考虑超参数选择中的变异性，实现了对模型响应的不确定性感知表征。与实验数据的验证表明，HDMDc和BHDMDc均能预测船舶对未见过的规则和不规则波浪激励的响应。总之，本研究表明，基于HDMDc的降阶模型是系统辨识的一种可行的数据驱动替代方案，首次证明了它们在与训练集不同的海况下的泛化能力，在复现船舶动力学方面达到了高精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
