上海交通大学学报

上海交通大学学报

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用于动力定位船舶运动预测的混合模型算法（网络首发）

  

  1. 1.上海交通大学海洋工程国家重点实验室；2.上海交通大学海南研究院；3.国能远海航运有限公司；4.国家能源集团航运有限公司
  • 基金资助:
    国家自然科学基金（42206192，52031006）； 深远海全天候驻留浮式研究设施研发专项（SSZX22002）

Hybrid Algorithm for Motion Prediction of Dynamically Positioned Ships

  1. （1. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China ; 2. Shanghai Jiao Tong University Hainan Research Institute, Sanya 572024, Hainan, China；3. Guoneng Ocean Shipping Co., Ltd., Beijing 100011, China；4. National Energy Group Shipping Co., Ltd., Beijing 100011, China）

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摘要： 在海洋环境的动力定位场景中，准确预测船舶运动对于提供作业预警信息和运动补偿支持至关重要。针对该场景下船舶运动变化迅速且呈现强非线性的难点，本研究结合物理模型和数据驱动模型的优势，提出了一种船舶运动预测混合模型算法。该混合模型采用物理模块对船舶运动进行初步预测，并通过纠偏模块修正误差。在此基础上，引入控制力预测模块实现物理模块参数的更新以提高预测精度。研究中采用动力定位模型试验数据用于混合模型的训练和测试，并针对预测时长和训练样本数量对模型性能开展深入评估。结果表明，针对纵荡、横荡、首摇运动，混合模型预测未来15 s运动的精度相比长短期记忆(LSTM)网络提高了15%～57%，在运动波峰波谷处的提升最为明显，且在处理分布不均的数据集时具有更强的预测稳定性。综上所述，混合模型具有更高的精度和稳定性，有望为海洋环境中的动力定位作业提供更准确、更稳定的预警信息和运动补偿支持。

关键词: 动力定位, 运动预测, 混合模型, 长短期记忆

Abstract: In marine dynamic positioning scenarios, accurate ship motion prediction is vital for warnings and motion compensation. Addressing the challenges posed by rapid and highly nonlinear ship motion changes in such scenarios, this study propose a hybrid model combining physical and data-driven approaches. The hybrid model employs a physical module to make initial predictions motion and corrects errors through a correction module. Building on this, a control force prediction module is introduced to update the parameters of the physical module, thereby improving prediction accuracy. The study utilizes dynamic positioning model test data for the training and testing of the hybrid model, and conducts an in-depth assessment of model performance with respect to prediction horizon and the number of training samples. Results show that for surge, sway, and yaw, the hybrid model improves 15-second prediction accuracy by 15%~57% over Long ShortTerm Memory (LSTM), with the most significant improvements observed at motion peaks and troughs. Additionally, the hybrid model exhibits stronger prediction stability when dealing with unevenly distributed datasets. In summary, the hybrid model offers higher accuracy and stability, holding promise for providing more accurate and stable warning information and motion compensation support for dynamic positioning operations in marine environments.

Key words: dynamic positioning, motion prediction, hybrid model, Long Short-Term Memory (LSTM)

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