Depth control of an 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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| Format: | Article |
| Language: | en |
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2015
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| Online Access: | http://eprints.utem.edu.my/id/eprint/14663/1/4811-13498-1-SM.pdf http://eprints.utem.edu.my/id/eprint/14663/ |
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| _version_ | 1832716965569888256 |
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| author | Mohd Aras, Mohd Shahrieel Abdullah, Shahrum Shah Abdul Rahman, Ahmad Fadzli Nizam Hasim, Norhaslinda Abdul Azis, Fadilah Lim Wee, Teck Mohd Nor, Arfah Syahida |
| author_facet | Mohd Aras, Mohd Shahrieel Abdullah, Shahrum Shah Abdul Rahman, Ahmad Fadzli Nizam Hasim, Norhaslinda Abdul Azis, Fadilah Lim Wee, Teck Mohd Nor, Arfah Syahida |
| author_sort | Mohd Aras, Mohd Shahrieel |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. |
| format | Article |
| id | my.utem.eprints-14663 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2015 |
| record_format | eprints |
| spelling | my.utem.eprints-146632015-07-06T07:48:11Z http://eprints.utem.edu.my/id/eprint/14663/ Depth control of an underwater remotely operated vehicle using neural network predictive control Mohd Aras, Mohd Shahrieel Abdullah, Shahrum Shah Abdul Rahman, Ahmad Fadzli Nizam Hasim, Norhaslinda Abdul Azis, Fadilah Lim Wee, Teck Mohd Nor, Arfah Syahida TC Hydraulic engineering. Ocean engineering 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. 2015-06-15 Article PeerReviewed application/pdf en http://eprints.utem.edu.my/id/eprint/14663/1/4811-13498-1-SM.pdf Mohd Aras, Mohd Shahrieel and Abdullah, Shahrum Shah and Abdul Rahman, Ahmad Fadzli Nizam and Hasim, Norhaslinda and Abdul Azis, Fadilah and Lim Wee, Teck and Mohd Nor, Arfah Syahida (2015) Depth control of an underwater remotely operated vehicle using neural network predictive control. Jurnal Teknologi, UTM, 74 (9). pp. 85-93. ISSN 0127-9696 |
| spellingShingle | TC Hydraulic engineering. Ocean engineering Mohd Aras, Mohd Shahrieel Abdullah, Shahrum Shah Abdul Rahman, Ahmad Fadzli Nizam Hasim, Norhaslinda Abdul Azis, Fadilah Lim Wee, Teck Mohd Nor, Arfah Syahida Depth control of an underwater remotely operated vehicle using neural network predictive control |
| title | Depth control of an underwater remotely operated vehicle using neural network predictive control |
| title_full | Depth control of an underwater remotely operated vehicle using neural network predictive control |
| title_fullStr | Depth control of an underwater remotely operated vehicle using neural network predictive control |
| title_full_unstemmed | Depth control of an underwater remotely operated vehicle using neural network predictive control |
| title_short | Depth control of an underwater remotely operated vehicle using neural network predictive control |
| title_sort | depth control of an underwater remotely operated vehicle using neural network predictive control |
| topic | TC Hydraulic engineering. Ocean engineering |
| url | http://eprints.utem.edu.my/id/eprint/14663/1/4811-13498-1-SM.pdf http://eprints.utem.edu.my/id/eprint/14663/ |
| url_provider | http://eprints.utem.edu.my/ |