Angle of Attack Enhanced Trajectory Prediction for Underwater Gliders Based on Fuzzy-Fusion of Inference and Dynamics

Accurate trajectory estimation for underwater gliders remains a significant challenge due to the limited observability of horizontal displacement during submerged operations. This work presents a novel hybrid framework that enhances horizontal trajectory prediction by integrating reinforcement learning (RL)-based inference with the dynamic response of a motion model via fuzzy logic fusion. A proximal policy optimization agent is trained to generate an angle of attack (AoA) sequence that minimizes terminal displacement error, providing task-optimal guidance based on a kinematics driven transition model. To address the absence of physical constraints and the lack of detailed dynamic modeling in the RL process, a fuzzy fusion strategy is introduced. This strategy adaptively blends the RL inferred and dynamics derived AoA according to discrepancies in motion state predictions. Experimental results from indoor trials demonstrate that the proposed method achieves endpoint localization accuracy within 0.3 m. Meanwhile, the fused AoA maintains strong consistency with the measured AoA sequence, with a Kullback–Leibler divergence below 0.06. The proposed framework provides an adaptive and computationally efficient solution for glider localization under partial observability, highlighting the potential of fuzzy system integration in advancing data-driven autonomy for marine platforms.