Articles uploaded in MyJurnal |
|
|
View Article |
Depth control of an underwater remotely operated vehicle using neural network predictive control
Mohd Shahrieel Mohd Aras1, Shahrum Shah Abdullah2, Ahmad Fadzli Nizam Abdul Rahman3, Norhaslinda Hasim4, Fadilah Abdul Azis5, Lim, Wee Teck6, Arfah Syahida Mohd Nor7.
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.
Affiliation:
- Universiti Teknikal Malaysia Melaka, Malaysia
- Universiti Teknologi Malaysia International Campus, Malaysia
- Universiti Teknikal Malaysia Melaka, Malaysia
- Universiti Teknikal Malaysia Melaka, Malaysia
- Universiti Teknikal Malaysia Melaka, Malaysia
- Universiti Teknikal Malaysia Melaka, Malaysia
- Universiti Teknikal Malaysia Melaka, Malaysia
Toggle translation
Download this article (This article has been downloaded 138 time(s))
|
|
Indexation |
Indexed by |
MyJurnal (2021) |
H-Index
|
6 |
Immediacy Index
|
0.000 |
Rank |
0 |
Indexed by |
Scopus 2020 |
Impact Factor
|
CiteScore (1.4) |
Rank |
Q3 (Engineering (all)) |
Additional Information |
SJR (0.191) |
|
|
|
|
|