Modelling Using Neural Networks and Dynamic Position Control for Unmanned Underwater Vehicles

Abstract

Underwater construction, maintenance, and mapping use autonomous underwater vehicles (AUVs) for path planning, path following, and target tracking operations. However, dynamic position management and localization of AUVs are critical issues. Correct localization and dynamic position management to prevent drifts can be used to acquire information on energy efficiency, another crucial topic. In this paper, AUV dynamic modeling using experimental data and position control is studied. The experiments were implemented on a Delphin2 scaled AUV model belonging to the Engineering and Environment Faculty, University of Southampton, UK. Hover and flight style motions according to the different speeds of Delphin2 were implemented in the test tank. Nonlinear coupled mathematical models were studied using shallow neural networks. The models are formed into depth-pitch and heading motion black-box models using the shallow neural network (SNN) algorithm. Proportional integral derivative control of heading motions and depth-pitch motion simulation studies were applied to the SNN model.