Diving dynamics identification and motion prediction for marine crafts using field data

IF 13 1区 工程技术 Q1 ENGINEERING, MARINE
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引用次数: 0

Abstract

Ensuring accurate parameter identification and diving motion prediction of marine crafts is essential for safe navigation, optimized operational efficiency, and the advancement of marine exploration. Addressing this, this paper proposes an instrumental variable-based least squares (IVLS) algorithm. Firstly, aiming to balance complexity with accuracy, a decoupled diving model is constructed, incorporating nonlinear actuator characteristics, inertia coefficients, and damping coefficients. Secondly, a discrete parameter identification matrix is designed based on this dedicated model, and then a IVLS algorithm is innovatively derived to reject measurement noise. Furthermore, the stability of the proposed algorithm is validated from a probabilistic point of view, providing a solid theoretical foundation. Finally, performance evaluation is conducted using four depth control datasets obtained from a piston-driven profiling float in Qiandao Lake, with desired depths of 30 m, 40 m, 50 m, and 60 m. Based on the diving dynamics identification results, the IVLS algorithm consistently shows superior performance when compared to recursive weighted least squares algorithm and least squares support vector machine algorithm across all depths, as evidenced by lower average absolute error (AVGAE), root mean square error (RMSE), and maximum absolute error values and higher determination coefficient (R2). Specifically, for desired depth of 60 m, the IVLS algorithm achieved an AVGAE of 0.553 m and RMSE of 0.655 m, significantly outperforming LS-SVM with AVGAE and RMSE values of 8.782 m and 11.117 m, respectively. Moreover, the IVLS algorithm demonstrates a remarkable generalization capability with R2 values consistently above 0.95, indicating its robustness in handling varied diving dynamics.

利用现场数据识别和预测海上船只的潜水动态
确保准确的参数识别和潜水艇运动预测对安全航行、优化运行效率和推进海洋勘探至关重要。为此,本文提出了一种基于工具变量的最小二乘法(IVLS)算法。首先,为了兼顾复杂性和准确性,本文构建了一个解耦潜水模型,其中包含非线性执行器特性、惯性系数和阻尼系数。其次,基于该专用模型设计了离散参数识别矩阵,然后创新性地推导出一种 IVLS 算法,以剔除测量噪声。此外,还从概率角度验证了所提算法的稳定性,为其提供了坚实的理论基础。最后,利用从千岛湖活塞驱动剖面浮筒上获取的四个深度控制数据集进行了性能评估,期望深度分别为 30 米、40 米、50 米和 60 米。根据潜水动力学识别结果,与递归加权最小二乘法算法和最小二乘支持向量机算法相比,IVLS 算法在所有深度上都表现出更优越的性能,具体表现为更低的平均绝对误差(AVGAE)、均方根误差(RMSE)和最大绝对误差值以及更高的判定系数(R2)。具体而言,对于 60 米的期望深度,IVLS 算法的平均绝对误差(AVGAE)为 0.553 米,均方根误差(RMSE)为 0.655 米,明显优于平均绝对误差(AVGAE)和均方根误差(RMSE)分别为 8.782 米和 11.117 米的 LS-SVM。此外,IVLS 算法还具有出色的泛化能力,R2 值始终高于 0.95,这表明该算法在处理各种潜水动态时具有很强的鲁棒性。
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来源期刊
CiteScore
11.50
自引率
19.70%
发文量
224
审稿时长
29 days
期刊介绍: The Journal of Ocean Engineering and Science (JOES) serves as a platform for disseminating original research and advancements in the realm of ocean engineering and science. JOES encourages the submission of papers covering various aspects of ocean engineering and science.
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