## 1 Introduction
[23] proposed a novel roll center compensation method (RCCM) utilizing variational mode decomposition and long short-term memory (VMD-LSTM) to accurately predict and minimize the roll regulation unit's (RRU's) energy consumption. [PERSON] et al. [10] introduced a hybrid model that integrates single prediction models with optimal weights, utilizing a simulated annealing-optimized Frank-Wolfe method for weight derivation, in order to accurately forecast the surfacing positioning point (SPP) of UGs at various time scales. Building upon these advancements, other studies have explored various deep learning architectures and hybrid models to further enhance the predictive accuracy and operational efficiency of underwater gliders in diverse and dynamic marine environments. [PERSON] et al. [24] proposed two prediction tools using deep learning, LSTM, and Transformer, to perform real-time in-situ prediction of ocean currents at any location. [PERSON] et al. [25] proposed a novel navigation method for AUV with hybrid recurrent neural networks, which could also satisfy the real-time requirement. [PERSON] et al. [26] proposed a VMD-BiGRU model for short-term buoy motion prediction, effectively enhancing accuracy by decomposing signals and capturing motion patterns across frequency scales. [PERSON] et al. [27] proposed an SSA-optimized Informer model with CEEMDAN to forecast long-sequence ship motion attitude, enhancing prediction performance across varied sea conditions. [PERSON] et al. [28] proposed a joint multi-model machine learning method based on confidence to improve ship stability prediction accuracy, reducing input features and enhancing robustness. In order to further elaborate the literature survey, we show the main contributions of past studies in Table 1.  
Despite significant advancements in predictive modeling for control parameters within the domain of UG research, the majority of studies have been validated through simulations, reflecting an absence of extensive empirical data. Consequently, there remains a substantial opportunity for enhancing the predictive accuracy of these models, particularly through the integration of more diverse and extensive datasets [29; 30; 31]. UGs are driven by buoyancy and have weak maneuverability when working within a strong ocean current [32; 33]. Furthermore, UGs cannot be positioned underwater because of their lack of acoustic devices or inertial sensors, which introduces uncertainty in the control parameters within a single profile and exhibits strong nonlinear characteristics [34]. Therefore, the development of a novel parameter prediction method is crucial for the local or global control of UGs. Concurrently, the prediction of key UG control parameters, particularly the rudder angle, aids in the dissemination of control decisions and heading maintenance during task execution. To sum up, due to the large time-space span during the observation mission, UG control parameters, particularly the rudder angle, are challenging to predict accurately utilizing traditional deep neural network methods, which exhibit a deficiency in this regard. Considering the vast and variable dataset of UGs, deep learning (DL) [42; 43] is applied to establish the method that can predict the new parameter of the rudder angle in the next profile to guide the adjustment for heading correction in advance. The integration of DWT [44] and FFT-Attention mechanisms [45] into DL models has shown promise in capturing both high-frequency details and low-frequency trends in UG data. Such hybrid models can significantly enhance the robustness and accuracy of predictions by leveraging the strengths of both time- and frequency-domain analyses. Furthermore, the use of recursive neural networks (RNNs) [46], such as long short-term memory (LSTM) [47] and gated recurrent unit (GRU) [48] networks, has proven effective in capturing temporal dependencies in