Predicting heave and pitch motions of an FPSO using meta-learning

Abstract

Real-time motion prediction is helpful in guaranteeing the operation stability of a Floating Production Storage Offloading (FPSO) unit. Recurrent neural networks (RNNs) are becoming feasible alternatives to numerical simulations for motion prediction as artificial intelligence develops rapidly. In this study, model-agnostic meta-learning (MAML) is combined with RNNs to deterministically predict the heave and pitch motions of a ship-shaped FPSO. This approach is motivated by the fact that MAML improves training efficiency without losing accuracy. The data came from a scaled model test conducted at Shanghai Jiao Tong University’s deepwater wave basin. Before introducing MAML, we verified that long short-term memory (LSTM) and gated recurrent unit (GRU) could accurately predict the heave and pitch of about 7.68 s into the future. With fewer learnable parameters than LSTM, GRU demonstrates slightly better accuracy. Therefore, this study focuses particularly on the combination of GRU and MAML. The parameters of MAML, including order of derivative, step size, number of adaption gradient updates, and batch size of the tasks, are evaluated systemically in terms of accuracy and training efficiency. With the assistance of MAML, GRU’s training efficiency for heave and pitch has significantly improved, increasing by approximately 65% and 55%, respectively. Meanwhile, the prediction error for both has decreased by about 10%. Notably, MAML’s performance is minimally affected by variations in incoming wave direction and sea state, as well as the randomness and temporal variability of the motion. MAML is a powerful tool that enables RNNs to achieve real-time prediction of FPSO motion.