Browse By Repository:

 
 
 
   

Depth control of an unmanned underwater remotely operated vehicle using neural network predictive control

Mohd Aras, Mohd Shahrieel and Abdullah, Shahrum Shah and Abdul Rahman, Ahmad Fadzli Nizam and Abd Azis, Fadilah and Hasim, Norhaslinda and Lim , Wee Teck and Mohd Nor, Arfah Syahida (2014) Depth control of an unmanned underwater remotely operated vehicle using neural network predictive control. In: The 5th International Conference on Underwater System Technology : Theory and Application (USYS'14), 3-4 December 2014, Bayview Hotel Melaka.

[img]
Preview
PDF
02012015162428-0001.pdf - Published Version

Download (229Kb)

Abstract

This paper investigates the depth control of an unmanned underwater remotely operated vehicle (ROV) using neural network predictive control (NNPC). The NNPC is applied to control the depth of the ROV to improve the performances of system response in terms of overshoot. To assess the viability of the method, the system was simulated using MATLAB/Simulink by neural network predictive control toolbox. In this paper also investigates the number of data samples (1000, 5000 and 10,000) to train neural network. The simulation reveals that the NNPC has the better performance in terms of its response, but the execution time will be increased. The comparison between other controller such as conventional PI controller, Linear Quadratic Regulation (LQR) and fuzzy logic controller also covered in this paper where the main advantage of NNPC is the fastest system response on depth control.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: depth control, unmanned underwater remotely operated vehicle, neural network predictive control
Subjects: T Technology > TC Hydraulic engineering. Ocean engineering
Divisions: Faculty of Electrical Engineering > Department of Mechatronics Engineering
Depositing User: Dr Mohd Shahrieel Mohd Aras
Date Deposited: 26 Jan 2015 03:02
Last Modified: 28 May 2015 04:36
URI: http://eprints.utem.edu.my/id/eprint/14061

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year