A Survey of Control Approaches for Unmanned Underwater Vehicles

This paper presents a comprehensive survey of control approaches for Unmanned Underwater Vehicles (UUVs). UUVs have gained significant attention in various applications such as ocean exploration, environmental monitoring, underwater inspections, and military operations. The control systems of UUVs play a crucial role in ensuring their safe and efficient operation in challenging underwater environments. In this survey, we review the state-of-the-art control techniques employed in UUVs, categorizing them based on their control strategies, including classical control, adaptive control, robust control, and intelligent control. The survey encompasses both traditional control methods and emerging approaches, such as bio-inspired and swarm-based control, machine learning, and deep reinforcement learning. We provide an overview of the key principles, advantages, and limitations of each control approach, highlighting their applicability and performance in different underwater scenarios. Furthermore, we discuss the challenges and open research directions in UUV control, including localization and mapping, path planning, obstacle avoidance, coordination of multi-vehicle systems, energy management, and fault tolerance. The survey aims to assist researchers, engineers, and practitioners in understanding the diverse control approaches available for UUVs and to provide insights for the development of more advanced and effective control systems. Overall, this paper contributes to the advancement of UUV control technology and promotes the realization of autonomous and intelligent underwater vehicles for various underwater applications