AL-TAKIE, Adnan A.G. (1982). Kalman filtering techniques applied to the dynamic ship positioning problem. Doctoral, Sheffield City Polytechnic. [Thesis]
Documents
7118:13567
Archive (ZIP) (Ethos)
Al-takie_237035.zip - Accepted Version
Available under License All rights reserved.
Al-takie_237035.zip - Accepted Version
Available under License All rights reserved.
Download (4MB)
7118:437845
PDF (Version of Record)
10694143.pdf - Accepted Version
Available under License All rights reserved.
10694143.pdf - Accepted Version
Available under License All rights reserved.
Download (5MB) | Preview
Abstract
The dynamic ship positioning problem using Kalman filtering techniques
is considered. The main components of the system are discussed. The
ship dynamics, based on a linearised model, are represented by state
equations. These equations involve low and high frequency subsystems.
A simplified design procedure for the implementation of a Kalman filter
is described based on the linearised equations of motion. The Kalman
filter involves a model of the system and is therefore particularly
appropriate for separating the low and high frequency motions of the
vessel. The filtering problem is one of estimating the low-frequency
motions of the vessel so that control can be applied. An optimal
feedback control system simulation based on optimal stochastic control
theory is used. The optimal control performance criterion weighting
matrices Q, R were pre-selected and the optimal feedback gain matrix
was computed. This control scheme involves the low-frequency part
of the ship model. The Kalman filter has been simulated on a digital
computer for different modelled operating conditions. The computer
simulation results showing the behaviour and responses of the Kalman
filter applied to the dynamic ship positioning problem were
investigated. The system dynamics vary as the weather conditions vary
and can be classified from a calm sea condition (Beaufort number 5) to
the worst condition (Beaufort number 9). Different tests involving
systems modelling and filter mismatching have been carried out.
Another field in which the robustness of a Kalman filter has been
assessed involved a test in which the system observation noise
covariance was increased keeping the filter with the usual noise
information. Saving in both computation and computer storage
requirement were achieved using a form of semi-constant filter gain and
reduced-order Kalman filter respectively.
System non-linearities have been considered and a non-linear control
algorithm was proposed and implemented using an extended Kalman filter.
These non-linearities involve the thruster dynamics and the associated
low-frequency part of the system model.
All data that have been used within this work for system implementation
were obtained from two different models ("Wimpey Sealab" and "Star
Hercules" vessels). Our system has been employed by GEC Electrical
Projects Limited, Rugby, for a new vessel ("Star Hercules") and this
has been commissioned and is currently operating successfully off
Brazil.
More Information
Statistics
Downloads
Downloads per month over past year
Share
Actions (login required)
![]() |
View Item |