Line-of-sight target tracking control of underactuated autonomous underwater vehicles

This paper studies target tracking control of underactuated autonomous underwater vehicles in the presence of model uncertainties and environmental disturbances. Dynamic surface control, neural networks and adaptive control techniques are employed to develop a target tracking controller for underwater vehicles in three-dimensional space. Then, a Lyapunov-based stability analysis proves that all signals are bounded in the closed-loop control system and tracking errors converge to a neighborhood of the origin. Following advantages are highlighted in this paper: (i) the proposed controller utilizes line-of-sight measurements of range and angle sensors to track a manoeuvring underwater target in three-dimensional space; (ii) computational complexities of the traditional backstepping method are greatly reduced via command filtering by employing dynamic surface control (DSC) technique; (iii) the proposed controller is easily implemented in practice without any prior knowledge of vehicle dynamics, parameters and environmental disturbances. At the end, simulation results demonstrate the tracking performance of the proposed control system for offshore applications.