A Survey of Control Technologies for Autonomous Underwater Vehicles

Autonomous Underwater Vehicles (AUVs) require robust control strategies to ensure stable trajectory tracking in dynamic and uncertain environments. This paper presents a systematic literature review of recent advancements in AUV control methodologies. A total of 52 peer-reviewed papers from 2023 to 2024 were analyzed, categorized by control techniques, including Backstepping, Sliding Mode Control, Proportional-Integral-Derivative (PID) controllers, Reinforcement Learning (RL), Neural Networks (NN), and Model Predictive Control (MPC). Results indicate that MPC is the most commonly applied method, followed by Backstepping and hybrid approaches integrating PID and RL. The Lyapunov function was widely used for stability analysis. However, most studies relied on simulations rather than real-world implementations. Future research should emphasize experimental validation on physical AUVs to bridge the gap between theoretical advancements and practical applications.