上海交通大学学报

上海交通大学学报

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基于GRU-MPC的双全回转推进拖轮轨迹跟踪控制（网络首发）

  

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

Trajectory tracking control of ASD tugs based on GRU-MPC

  1. （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）

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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。此控制器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。由于优化求解复杂度提升，当预测步长增加时，控制精度的提升同时导致求解时间增加。本研究为拖轮的精确轨迹跟踪控制提供新的思路，也为类似的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: For the trajectory tracking control problem of azimuth stern drive (ASD) tug, it is proposed to construct a three-degree-of-freedom motion data driving model of the tug through gated recurrent unit (GRU) neural network, and construct a model predictive control (MPC) trajectory tracking controller based on the GRU model, to overcome the restriction of the traditional control method 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, and the effectiveness of the proposed scheme is verified by simulation experiments. The model accuracy is good under noise interference. By comparing the control performance under different prediction step sizes, the influence of prediction step size on the control effect and solution time is explored. Due to the increase in the complexity of the optimization solution, when the prediction step size increases, the increase in the control accuracy leads to an increase in the solution time at the same time. This study provides new ideas for the precise trajectory tracking control of tugs, and also provides valuable references for similar nonlinear system control studies.

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

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