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

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

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...

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Bibliographic Details
Main Authors: Mohd Aras, Mohd Shahrieel, Abdullah, Shahrum Shah, Abdul Rahman, Ahmad Fadzli Nizam, Abd Azis, Fadilah, Hasim, Norhaslinda, Lim , Wee Teck, Mohd Nor, Arfah Syahida
Format: Conference or Workshop Item
Language:English
Published: 2014
Subjects:
TC Hydraulic engineering. Ocean engineering
Online Access:http://eprints.utem.edu.my/id/eprint/14061/1/02012015162428-0001.pdf
http://eprints.utem.edu.my/id/eprint/14061/
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Institution: Universiti Teknikal Malaysia Melaka
Language: English
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Summary: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.

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