船舶在海上航行时会受到各种复杂环境因素的影响，而这些因素会导致船舶出现耦合效应，进而影响其操纵性能和航行安全性。针对传统船舶运动系统解耦方法响应时间较长、稳定性较差的问题，研究采用最小二乘法进行船舶运动系统参数的辨识，引入遗忘因子对最小二乘法进行改进，并将粒子群优化算法和模拟退火算法结合，根据辨识的参数进行船舶运动系统的解耦，设计出一种混合算法。结果显示，在平均检测精度和推理速度计算中，设计算法与其他3种方法相比，平均检测精度分别提升了19.9%、15.7%、11.5%，推理速度分别提升了82.3、67.4、19.3 fps，证明了其精度和计算效率较高。研究提出算法在解耦合过程中展现了较好性能，为实现高效稳定的船舶运动控制提供了新技术途径。
Ships are subject to various complex environmental factors during maritime navigation, which can lead to coupling effects and subsequently affect their maneuverability and navigation safety. In response to the problems of long response time and poor stability in traditional decoupling methods for ship motion systems, this study adopts the least squares method for parameter identification of ship motion systems. The forgetting factor is introduced to improve the least squares method, and the particle swarm optimization algorithm and simulated annealing algorithm are combined to decouple the ship motion system based on the identified parameters. A hybrid algorithm is designed. The results showed that compared with the other three methods, the designed algorithm improved the average detection accuracy by 19.9%, 15.7% and 11.5%, the inference speed by 82.3, 67.4, and 19.3 fps, respectively, in the calculation of average detection accuracy and inference speed, proving its high accuracy and computational efficiency. The proposed algorithm has demonstrated good performance in the decoupling process, providing a new technological approach for achieving efficient and stable ship motion control.
2025,47(10): 166-170 收稿日期：2024-9-2
DOI：10.3404/j.issn.1672-7649.2025.10.028
分类号：U661
作者简介：顾钦平（1984-），男，硕士，工程师，研究方向为计算机网络安全及应用
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