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The article discusses the issue of designing a dynamic ship positioning system making use of the adaptive vectorial backstepping method and RBF type artificial neural networks. In the article, the backstepping controller is used to determine control laws and neural network weight adaptation laws. The artificial neural network is applied at each time instant to approximate nonlinear functions containing parametric uncertainties. The proposed control system does not require precise knowledge of the model of ship dynamics and external disturbances, it also eliminates the problem of analytical determination of the regression matrix when designing the control law with the aid of the adaptive backstepping procedure.

Dynamically positioned vessels, used by offshore industry, use not only satellite navigation but also different positioning systems, often referred to as reference' systems. Most of them use multiple technical devices located outside the vessel which creates some problems with their accessibility and performance. In this paper, a basic concept of reference system independent from any external device is presented, basing on hydroacoustics and Simultaneous Localization and Mapping (SLAM) method. Theoretical analysis of its operability is also performed.

The paper examines the problem of dynamic ship positioning with the use of Kalman Filter- and Particle Filter-based sensor fusion algorithms. The proposed approach enables to estimate accurately the ship's state vector by fusing the vessel's position and heading measurements coming from on-board sensors together with distance measurements coming from sensors located at the coast (e.g. radar). The estimated state vector is used in turn, in a control loop, to regulate the horizontal position and heading of the vessel. The performance of dynamic positioning of the ship based on Kalman and Particle Filtering is evaluated through simulation experiments. [ABSTRACT FROM AUTHOR]