基于GRU-MPC的双全回转推进拖轮轨迹跟踪控制

doi:10.16183/j.cnki.jsjtu.2024.146

上海交通大学学报 ›› 2026, Vol. 60 ›› Issue (3): 418-426.doi: 10.16183/j.cnki.jsjtu.2024.146

基于GRU-MPC的双全回转推进拖轮轨迹跟踪控制

李诗杰¹^,²^,³, 刘泰序³, 刘佳仑¹^,⁴^,⁵(), 董智霖³, 徐诚祺³

¹ 武汉理工大学水路交通控制全国重点实验室, 武汉 430063
² 上海交通大学海洋工程全国重点实验室, 上海 200240
³ 武汉理工大学交通与物流工程学院, 武汉 430063
⁴ 武汉理工大学智能交通系统研究中心, 武汉 430063
⁵ 国家水运安全工程技术研究中心, 武汉 430063

收稿日期:2024-04-28 修回日期:2024-05-20 接受日期:2024-06-17 出版日期:2026-03-28 发布日期:2026-03-30
通讯作者: 刘佳仑,研究员,博士生导师;E-mail:jialunliu@whut.edu.cn.
作者简介:李诗杰(1988—),副教授,从事船舶智能航行运动控制研究.
基金资助:
国家重点研发计划(2022YFE0125200);海洋工程全国重点实验室(上海交通大学)开放基金(GKZD010089)

Trajectory Tracking Control of Azimuth Stern Drive Tugs Based on GRU-MPC

LI Shijie¹^,²^,³, LIU Taixu³, LIU Jialun¹^,⁴^,⁵(), DONG Zhilin³, XU Chengqi³

¹ State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China
² State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
³ School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
⁴ Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, China
⁵ National Engineering Research Center for Water Transport Safety, Wuhan 430063, China

Received:2024-04-28 Revised:2024-05-20 Accepted:2024-06-17 Online:2026-03-28 Published:2026-03-30

摘要/Abstract

摘要：

针对双全回转尾推进拖轮轨迹跟踪控制问题,提出通过门控循环单元(GRU)神经网络构建拖轮三自由度运动数据驱动模型,并基于GRU模型构建模型预测控制(MPC)轨迹跟踪控制器,克服传统控制方法对精确系统机理模型限制.在不改变拖轮推进器转速前提下,通过调节左右舵角对拖轮速度与航向进行调控,并通过仿真实验验证所提出方案的有效性.在噪声干扰下,模型精度良好.通过对比不同预测步长下的控制性能,探讨预测步长对控制效果及求解时间的影响.当预测步长增加时,控制精度得到提升,但由于优化求解复杂度提升,求解时间增加.本研究为拖轮的精确轨迹跟踪控制提供新思路,也为类似非线性系统控制研究提供有价值的参考.

关键词: 全回转尾推进型拖轮控制, 模型预测控制, 门控循环单元神经网络, 轨迹跟踪控制, 数据驱动模型

Abstract:

To address the trajectory tracking control problem of azimuth stern drive tug, a three-degree-of-freedom motion data-driven model of the tug is developed by using gated recurrent unit (GRU) neural network, and a model predictive control (MPC) trajectory tracking controller is designed based on the GRU model to overcome the limitations of the traditional control methods that rely on the precise system mechanism model. This controller regulates the tug speed and heading by adjusting the left and right rudder angles without changing the tug propeller speed. Simulation experiments are conducted to validate the effectiveness of the proposed scheme, showing that the model achieves satisfactory accuracy even under noise interference. Furthermore, by comparing the control performance under different prediction step sizes, the influences of these parameters on the control effect and solution time are explored. Due to the increase in the complexity of the optimization solution, when the prediction horizon increases, the control accuracy improves, but the solution time also rises. This study provides new ideas for the precise trajectory tracking control of tugs, and offers valuable references for the control of similar nonlinear systems.

Key words: azimuth stern drive tug control, model predictive control (MPC), gated recurrent unit (GRU) neural network, trajectory tracking control, data-driven model

中图分类号:

U664.82

李诗杰, 刘泰序, 刘佳仑, 董智霖, 徐诚祺. 基于GRU-MPC的双全回转推进拖轮轨迹跟踪控制[J]. 上海交通大学学报, 2026, 60(3): 418-426.

LI Shijie, LIU Taixu, LIU Jialun, DONG Zhilin, XU Chengqi. Trajectory Tracking Control of Azimuth Stern Drive Tugs Based on GRU-MPC[J]. Journal of Shanghai Jiao Tong University, 2026, 60(3): 418-426.

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参考文献 16

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参数	取值
质量/kg	187.6
排水, Δ/m³	0.187 6
型深,D/m	0.248 5
船宽,B/m	0.695 2
船长,L/m	2.152
m₁₁/kg	216.472 7
m₂₂/kg	183.490 6
m₃₃/kg	0.063 2
d₁₁/(kg·s^-1)	44.187 8
d₂₂/(kg·s^-1)	152.938 0
d₃₃/(kg·s^-1)	0.262 9

参数	e_RMSE	R²
u	0.000 5 m/s	0.956 0
v	0.008 2 m/s	0.940 2
r	0.252 5 (°)/s	0.928 4

N_p	e_RMSE/m	平均求解时间/s
1	9.49	0.03
3	3.79	0.32
5	3.51	1.15

基于GRU-MPC的双全回转推进拖轮轨迹跟踪控制

Trajectory Tracking Control of Azimuth Stern Drive Tugs Based on GRU-MPC

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