PREDICTION OF ENVIRONMENTAL FORCES AND PERFORMANCE ANALYSIS OF DYNAMIC POSITIONING SYSTEM - NEURAL NETWORK APPROACH

Abstract

The dynamic positioning (DP) system is a control system which is vital for station keeping, autopilot, and other navigation functions. The safe and efficient operation of DP system depends upon the control system. Therefore, robustness of the DP control system is a fundamental basic requirement for offshore marine activities. The external disturbances that influence the performance and robustness of the control system are due to environmental loads from wind, wave and currents. Till date, these disturbances are treated as constant disturbances. However, these loads are not constant and are dynamic in nature. In this thesis, we use various state-of-the-art neural networks to predicted wind, wave and current characteristics using measured historical data’s and then calculate the loads induced by them on the marine structures. These environmental load information is then used to study the performance of a DP control system. For this purpose, a robust DP control system is build using H∞ control. The closed loop performance and stability of the DP system is then analyzed by simulation studies with and without the knowledge of environmental loads. From the simulation results, it has been proved that the closed-loop performance and asymptotic internal stability of the DP-control system has improved.