Abstract
RRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements. This paper proposes a solution of this stabilization problem that is based on an ANN (Artificial Neural Network) controller. The controller has been trained using supervised learning. The simulation studies have been carried out using MATLAB and a non-linear model of a container ship. It has been demonstrated that the proposed controller regulates heading and also controls roll angle very successfully.
Highlights
RRS (Rudder Roll Stabilization) of Ships is a difficult problem because of its associated non-linear dynamics, coupling effects and complex control requirements
The feasibility of using ANNs for a ship steering control system was first studied by Endo et al [1] and Pugh [2]
This paper has presented a NN based controller for RRS for a simulated container ship
Summary
This paper presents a neural network controller which controls the course as well as the roll response of a ship simultaneously. In other words, it behaves as a RRS. Large roll motions affect the comfort and efficiency of crew members and the accuracy of electrical mechanisms and the accuracy of control for the ship course [20]. Since every ship has a steering system, the employment of an RRS to simultaneously control the roll and heading motions is a cost effective approach and improves the ship’s capabilities to perform assigned tasks in rough weather conditions. The RRS has approximately the same effectiveness as can be achieved with the fin stabilizers [22]
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