A Detailed Review on Intelligent and Robust Control Strategies for Autonomous Underwater Vehicles with Emphasis on Navigation, Path Tracking, and Stability Enhancement - Linkan Priyadarsini; Shubhasri Kundu; Manoj Kumar Maharana; Bibhu Prasad Ganthia (International Journal of Electronics Automation, 2025)
Autonomous Underwater Vehicles (AUVs) have gained significant attention due to their applications in ocean exploration, underwater surveillance, environmental monitoring, and offshore industries. The control of AUVs presents various challenges due to the highly dynamic and uncertain underwater environment, nonlinear hydrodynamics, and external disturbances. This review paper explores various control strategies employed for AUVs, including classical control methods such as Proportional-Integral-Derivative (PID) controllers, modern techniques like Model Predictive Control (MPC), and intelligent control approaches utilizing Artificial Intelligence (AI) and Reinforcement Learning (RL). The study compares these techniques in terms of stability, robustness, adaptability, and energy efficiency. Additionally, hybrid control frameworks that integrate multiple strategies for enhanced performance are discussed. The paper aims to provide insights into the latest advancements in AUV control and future research directions for improving their autonomy and operational efficiency.
Keywords: Adaptive control; Artificial Intelligence (AI); Autonomous Underwater Vehicles (AUVs); Hybrid Control; Model predictive control (MPC); Nonlinear Hydrodynamics; Ocean Exploration; Proportional- Integral-Derivative (PID) Control; Reinforcement learning (RL); Underwater Robotics; control strategies