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

doi:10.3901/JME.260692

Abstract

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

CLC Number:

TH137

CHEN Xiafei, ZENG Hai, WANG Yingtao, JIANG Xue, LIU Xiaoping, ZHANG Lijie. An Online Adaptive Model Predictive Control Strategy for Wave Compensation Platforms Based on Ship Motion Prediction[J]. Journal of Mechanical Engineering, 2026, 62(13): 144-154.

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URL: http://www.cjmenet.com.cn/EN/10.3901/JME.260692

http://www.cjmenet.com.cn/EN/Y2026/V62/I13/144

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