A neural network based heading and position control system of a ship

TL;DR

This paper proposes a neural network-based control system for ship heading and position, addressing nonlinearities and environmental variations where traditional methods fall short, and demonstrates promising simulation results.

Contribution

It introduces an artificial neural network controller for ship navigation, overcoming the limitations of conventional control techniques that require precise mathematical models.

Findings

Neural network controller performs well in simulations.

The system effectively handles nonlinear ship dynamics.

Results show improved control performance over traditional methods.

Abstract

Heading and position control system of ships has remained a challenging control problem. It is a nonlinear multiple input multiple output system. Moreover, the dynamics of the system vary with operating as well as environmental conditions. Conventionally, simple Proportional Integral Derivative controller is used which has well known limitations. Other conventional control techniques have also been investigated but they require an accurate mathematical model of a ship. Unfortunately, accuracy of mathematical models is very difficult to achieve. During the past few decades computational intelligence techniques such as artificial neural networks have been very successful when an accurate mathematical model is not available. Therefore, in this paper, an artificial neural network controller is proposed for heading and position control system. For simulation purposes, a mathematical model with four effective thrusters have been chosen to test the performance of the proposed controller. The final closed loop system has been analyzed and tested through simulation studies. The results are very encouraging.