Analysis of roll center compensation method for underwater gliders based on deep learning

Underwater glider (UG) is one of the most promising low-power and long-voyage autonomous ocean observation platforms. During the gliding, the roll regulation unit (RRU) frequently works to regulate the disturbed heading due to the influence of time-varying current, biofouling, and cumulative error of RRU. In this paper, an accurate energy consumption model of RRU (ERRU) is established to quantify the impact of RRU on the overall energy consumption, and the trial data collected by the Petrel-L glider, China, in the South China Sea are used to verify the accuracy of ERRU. To minimize the working frequency of RRU, a new roll center compensation method (RCCM) is proposed based on variational mode decomposition and long short-term memory (VMD-LSTM). This study analyzes six classical prediction methods based on deep learning, and the VMD-LSTM at k = 5 method is the optimal one to predict the deviation of roll center for UGs. The results indicate that the proposed RCCM can obtain accurate and reliable compensation value for the roll center and effectively restrain the working frequency of RRU. Compared with the data before compensation, the ERRU of the Petrel-L glider is reduced by approximately 22%. Furthermore, the RCCM can apply to other similar UGs.