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DEPTH CONTROL OF AN UNDERWATER REMOTELY OPERATED VEHICLE USING NEURAL NETWORK PREDICTIVE CONTROL
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| Zeitschriftentitel: | Jurnal Teknologi |
|---|---|
| Personen und Körperschaften: | , , , , , , |
| In: | Jurnal Teknologi, 74, 2015, 9 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
Penerbit UTM Press
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| Schlagwörter: |
| author_facet |
Mohd Aras, Mohd Shahrieel Abdullah, Shahrum Shah Abdul Rahman, Ahmad Fadzli Nizam Hasim, Norhaslinda Abdul Azis, Fadilah Wee Teck, Lim Mohd Nor, Arfah Syahida Mohd Aras, Mohd Shahrieel Abdullah, Shahrum Shah Abdul Rahman, Ahmad Fadzli Nizam Hasim, Norhaslinda Abdul Azis, Fadilah Wee Teck, Lim Mohd Nor, Arfah Syahida |
|---|---|
| author |
Mohd Aras, Mohd Shahrieel Abdullah, Shahrum Shah Abdul Rahman, Ahmad Fadzli Nizam Hasim, Norhaslinda Abdul Azis, Fadilah Wee Teck, Lim Mohd Nor, Arfah Syahida |
| spellingShingle |
Mohd Aras, Mohd Shahrieel Abdullah, Shahrum Shah Abdul Rahman, Ahmad Fadzli Nizam Hasim, Norhaslinda Abdul Azis, Fadilah Wee Teck, Lim Mohd Nor, Arfah Syahida Jurnal Teknologi DEPTH CONTROL OF AN UNDERWATER REMOTELY OPERATED VEHICLE USING NEURAL NETWORK PREDICTIVE CONTROL General Engineering |
| author_sort |
mohd aras, mohd shahrieel |
| spelling |
Mohd Aras, Mohd Shahrieel Abdullah, Shahrum Shah Abdul Rahman, Ahmad Fadzli Nizam Hasim, Norhaslinda Abdul Azis, Fadilah Wee Teck, Lim Mohd Nor, Arfah Syahida 2180-3722 0127-9696 Penerbit UTM Press General Engineering http://dx.doi.org/10.11113/jt.v74.4811 <jats:p>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. </jats:p> DEPTH CONTROL OF AN UNDERWATER REMOTELY OPERATED VEHICLE USING NEURAL NETWORK PREDICTIVE CONTROL Jurnal Teknologi |
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Penerbit UTM Press, 2015 |
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Jurnal Teknologi |
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| title |
DEPTH CONTROL OF AN UNDERWATER REMOTELY OPERATED VEHICLE USING NEURAL NETWORK PREDICTIVE CONTROL |
| title_unstemmed |
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 |
General Engineering |
| url |
http://dx.doi.org/10.11113/jt.v74.4811 |
| publishDate |
2015 |
| physical |
|
| description |
<jats:p>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. </jats:p> |
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| author | Mohd Aras, Mohd Shahrieel, Abdullah, Shahrum Shah, Abdul Rahman, Ahmad Fadzli Nizam, Hasim, Norhaslinda, Abdul Azis, Fadilah, Wee Teck, Lim, Mohd Nor, Arfah Syahida |
| author_facet | Mohd Aras, Mohd Shahrieel, Abdullah, Shahrum Shah, Abdul Rahman, Ahmad Fadzli Nizam, Hasim, Norhaslinda, Abdul Azis, Fadilah, Wee Teck, Lim, Mohd Nor, Arfah Syahida, Mohd Aras, Mohd Shahrieel, Abdullah, Shahrum Shah, Abdul Rahman, Ahmad Fadzli Nizam, Hasim, Norhaslinda, Abdul Azis, Fadilah, Wee Teck, Lim, Mohd Nor, Arfah Syahida |
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| description | <jats:p>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. </jats:p> |
| doi_str_mv | 10.11113/jt.v74.4811 |
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| spelling | Mohd Aras, Mohd Shahrieel Abdullah, Shahrum Shah Abdul Rahman, Ahmad Fadzli Nizam Hasim, Norhaslinda Abdul Azis, Fadilah Wee Teck, Lim Mohd Nor, Arfah Syahida 2180-3722 0127-9696 Penerbit UTM Press General Engineering http://dx.doi.org/10.11113/jt.v74.4811 <jats:p>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. </jats:p> DEPTH CONTROL OF AN UNDERWATER REMOTELY OPERATED VEHICLE USING NEURAL NETWORK PREDICTIVE CONTROL Jurnal Teknologi |
| spellingShingle | Mohd Aras, Mohd Shahrieel, Abdullah, Shahrum Shah, Abdul Rahman, Ahmad Fadzli Nizam, Hasim, Norhaslinda, Abdul Azis, Fadilah, Wee Teck, Lim, Mohd Nor, Arfah Syahida, Jurnal Teknologi, DEPTH CONTROL OF AN UNDERWATER REMOTELY OPERATED VEHICLE USING NEURAL NETWORK PREDICTIVE CONTROL, General Engineering |
| 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 |
| title_unstemmed | DEPTH CONTROL OF AN UNDERWATER REMOTELY OPERATED VEHICLE USING NEURAL NETWORK PREDICTIVE CONTROL |
| topic | General Engineering |
| url | http://dx.doi.org/10.11113/jt.v74.4811 |