A Comprehensive Review of Control Methods for Bio-Inspired Autonomous Underwater Vehicles
Abstract
Biomimetic underwater vehicles (BUVs) are an evolving class of autonomous underwater systems that mimic the locomotion mechanisms of aquatic animals to achieve high maneuverability, low acoustic signature, and energy-efficient propulsion. However, their motion control remains challenging due to highly nonlinear dynamics, strong fluid–structure interactions, and tight coupling between propulsion and vehicle motion. The reviewed approaches include classical, robust and adaptive, optimal, fuzzy logic, bio-inspired, and learning–based control techniques. The reviewed methods are compared in terms of robustness, computational complexity, propulsion and actuation characteristics, structural flexibility, validation level, and suitability for different biomimetic architectures. The analysis indicates increasing representation of bio-inspired and learning-based approaches alongside established model-based control methods. The review also identifies several important research gaps, including the limited experimental validation of proposed controllers, insufficient reporting of controller tuning procedures, and the absence of standardized benchmarking methodologies. This review provides practical guidelines for selecting appropriate control strategies according to vehicle architecture, mission requirements, and computational constraints, while outlining promising directions for future research.