Trajectory tracking control of autonomous underwater vehicles: a review from classical methods to AI-based approaches

Autonomous Underwater Vehicles (AUVs) are essential for applications such as seabed mapping, environmental monitoring, offshore infrastructure inspection, and search-and-rescue operations. However, achieving accurate trajectory tracking remains a fundamental challenge due to nonlinear and strongly coupled six-degree-of-freedom dynamics, hydrodynamic parameter uncertainties, environmental disturbances, and limitations introduced by sensor noise, actuator faults, and imperfect modeling of added mass, damping, and Coriolis–centripetal forces.Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, this review systematically classifies trajectory-tracking control strategies for AUVs into three main categories. (i) Classical controllers, including PID and Sliding Mode Control (SMC), are analyzed for their simplicity, robustness, and ease of implementation. (ii) Intelligent controllers, such as Fuzzy Logic Control (FLC), Reinforcement Learning (RL), and Physics-Informed Neural Networks (PINNs), are reviewed for their adaptability, learning capabilities, and effectiveness in handling nonlinearities and time-varying disturbances, while addressing challenges related to data availability and computational cost. (iii) Hybrid approaches, including Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Physics-Informed Reinforcement Learning (PI-RL), Fault-Tolerant Control (FTC), and Sim-to-Real Transfer (SRT) techniques, are examined for their ability to integrate model-based reliability with data-driven adaptability and improve resilience under uncertainties, noise, and faults.Classical controllers provide structural simplicity and robustness but suffer reduced accuracy in highly nonlinear and noisy environments. Intelligent methods like RL and PINNs improve adaptability and reduce tracking errors but demand extensive data and computational resources. Hybrid approaches, particularly ANFIS and PI-RL, achieve high tracking accuracy and maintain robust performance under various uncertainties. Sim-to-Real Transfer (SRT) techniques further enhance real-world deployment. A meta-analysis of 120 peer-reviewed studies quantifies performance trends in terms of root-mean-square error (RMSE), settling time, robustness, and computational cost. The review highlights future research opportunities in domain randomization, adaptive fault-tolerant control, and physics-guided hybrid learning to enable reliable real-world AUV operations.