针对船桨子系统变系数、非线性特性导致的航速控制难题，本文提出一种基于自适应神经网络的舰船航速自动控制方法，旨在提升航速控制精度。首先，分析螺旋桨推力、阻力与航速的关系；其次，采用自适应循环神经网络，设计一阶严格反馈控制系统，依据航速跟踪误差确定控制率，并根据航行环境和船舶状态动态调整控制参数，实现航速精确控制。实验结果表明，该方法能够精准控制舰船航速，使航行轨迹最大化接近期望轨迹，航迹角偏移接近于0，验证了其在航速控制中的高精度和稳定性。
This study proposes a ship speed automatic control method based on adaptive neural network to address the challenges of speed control caused by the variable coefficients and nonlinear characteristics of the ship propeller subsystem, aiming to improve the accuracy of speed control. Firstly, analyze the relationship between propeller thrust, drag, and speed; Secondly, an adaptive recurrent neural network is used to design a first-order strict feedback control system. The control rate is determined based on the speed tracking error, and the control parameters are dynamically adjusted according to the navigation environment and ship status to achieve precise speed control. The experimental results show that this method can accurately control the ship's speed, maximize the navigation trajectory to approach the expected trajectory, and achieve a trajectory angle offset close to 0, verifying its high accuracy and stability in speed control.
2025,47(14): 155-158 收稿日期：2024-9-14
DOI：10.3404/j.issn.1672-7649.2025.14.023
分类号：U664.7
作者简介：王珂(1990-),女,硕士,讲师,研究方向为大数据与网络安全
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