• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2026, Vol. 62 ›› Issue (13): 144-154.doi: 10.3901/JME.260692

• 机械动力学 • 上一篇    下一篇

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基于舰船运动预报的海浪补偿平台在线自适应模型预测控制策略

陈夏非1, 曾海1, 王瑛涛1, 姜雪1, 刘小平1, 张立杰1,2   

  1. 1. 燕山大学河北省重型机械流体动力传输与控制重点实验室 秦皇岛 066004;
    2. 燕山大学河北省并联机器人与机电系统实验室 秦皇岛 066004
  • 收稿日期:2025-06-28 修回日期:2025-12-18 发布日期:2026-08-28
  • 作者简介:陈夏非,男,1994年出生,博士研究生。研究方向为并联机器人运动规划、运动控制、虚拟现实技术;E-mail:chenxiafi@163.com;张立杰(通信作者),男,1969年出生,教授,博士研究生导师。主要研究方向为机构学、机械结构力学性能分析及结构优化设计、电液控制系统、流体机械优化设计;E-mail:zhangljys@126.com
  • 基金资助:
    国家自然科学基金资助项目(51875499)。

An Online Adaptive Model Predictive Control Strategy for Wave Compensation Platforms Based on Ship Motion Prediction

CHEN Xiafei1, ZENG Hai1, WANG Yingtao1, JIANG Xue1, LIU Xiaoping1, ZHANG Lijie1,2   

  1. 1. Hebei Key Laboratory of Heavy Machinery Fluid Power Transmission and Control, Yanshan University, Qinhuangdao 066004;
    2. Parallel Robot and Mechatronic System of Laboratory of Hebei Provincie, Yanshan University, Qinhuangdao 066004
  • Received:2025-06-28 Revised:2025-12-18 Published:2026-08-28

摘要: 电液伺服Stewart平台能够抵消船舶或海上设备在波浪作用下的运动,为海上作业提供稳定的工作环境。然而,海况的强动态性、Stewart平台各支腿之间的强耦合性以及电液伺服系统参数的非线性,使精确的波浪补偿控制极具挑战。为此,本文提出了一种基于舰船运动预报的在线自适应模型预测控制策略。首先,建立融合一维与二维舰船运动特征的卷积神经网络-长短期记忆网络(One-dimensional-two-dimensional convolutional neural network-long short-term memory,1D-2D-CNN-LSTM)舰船位姿预测模型;然后,构建基于递推最小二乘算法的液压驱动单元自适应模型预测控制(Adaptive model predictive control,AMPC)策略;最后,将舰船位姿预测与自适应模型预测控制相结合,形成海浪补偿预测控制框架。实验结果表明:在 4 级海况随机激励下,与传统模型预测控制(Model predictive control,MPC)波浪补偿算法相比,所提控制策略在六自由度上的平均绝对误差降低了 56%~81%,均方根误差降低了 55%~80%。

关键词: 海浪补偿, 自适应模型预测控制, 舰船运动预测, 电液伺服, Stewart平台

Abstract: The motion of ships or offshore equipment induced by waves is counteracted by the electro-hydraulic servo Stewart platform, thereby providing a stable working environment for offshore operations. However, precise wave compensation control is made extremely challenging by the highly dynamic nature of sea states, the strong coupling among the platform’s legs, and the nonlinear parameters of the electro-hydraulic servo system. To address these issues, an online adaptive model predictive control (AMPC) strategy is proposed based on ship motion prediction. First, a ship pose prediction model is developed using a one-dimensional and two-dimensional feature-fusion convolutional neural network–long short-term memory network (1D-2D-CNN-LSTM). An adaptive model predictive control framework for the hydraulic actuation units is constructed based on a recursive least-squares algorithm. Finally, the ship pose prediction model is integrated with the AMPC approach to form a predictive wave compensation control framework. Experimental results are shown to indicate that under random wave excitation at sea state level 4, the proposed control strategy reduces the mean absolute error by 56%–81% and the root mean square error by 55%–80% across all six degrees of freedom, compared with the conventional model predictive control (MPC) method.

Key words: wave compensation, adaptive model predictive control, ship motion prediction, electro-hydraulic servo, Stewart platform

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