Motion control of underwater vehicle based on least disturbance BP algorithm
Up to now, some technology of neural networks are developed to solve the non-linearity of researched objects and to implement the adaptive control in many engineering fields, and some good results were achieved. Though it puts some questions over to design application structure with neural networks,...
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Published in | Journal of marine science and application Vol. 1; no. 1; pp. 16 - 20 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
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Heidelberg
Springer Nature B.V
01.06.2002
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Abstract | Up to now, some technology of neural networks are developed to solve the non-linearity of researched objects and to implement the adaptive control in many engineering fields, and some good results were achieved. Though it puts some questions over to design application structure with neural networks, it is really unknowable about the study mechanism of those. But, the importance of study ratio is widely realized by many scientists now, and some methods on the modification of that are provided. The main subject is how to improve the stability and how to increase the convergent rate of networks by defining a good form of the study ratio. Here a new algorithm named LDBP (least disturbance BP algorithm) is proposed to calculate the ratio online according to the output errors, the weights of network and the input values. The algorithm is applied to the control of an autonomous underwater vehicle designed by HEU. The experimental results show that the algorithm has good performance and the controller designed based on it is fine. |
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AbstractList | Up to now, some technology of neural networks are developed to solve the non-linearity of researched objects and to implement the adaptive control in many engineering fields, and some good results were achieved. Though it puts some questions over to design application structure with neural networks, it is really unknowable about the study mechanism of those. But, the importance of study ratio is widely realized by many scientists now, and some methods on the modification of that are provided. The main subject is how to improve the stability and how to increase the convergent rate of networks by defining a good form of the study ratio. Here a new algorithm named LDBP (least disturbance BP algorithm) is proposed to calculate the ratio online according to the output errors, the weights of network and the input values. The algorithm is applied to the control of an autonomous underwater vehicle designed by HEU. The experimental results show that the algorithm has good performance and the controller designed based on it is fine. Up to now, some technology of neural networks are developed to solve the non-linearity of researched objects and to implement the adaptive control in many engineering fields, and some good results were achieved. Though it puts some questions over to design application structure with neural networks, it is really unknowable about the study mechanism of those. But, the importance of study ratio is widely realized by many scientists now, and some methods on the modification of that are provided. The main subject is how to improve the stability and how to increase the convergent rate of networks by defining a good form of the study ratio. Here a new algorithm named LDBF (least disturbance BP algorithm) is proposed to calculate the ratio online according to the output errors, the weights of network and the input values. The algorithm is applied to the control of an autonomous underwater vehicle designed by HEU. The experimental results show that the algorithm has good performance and the controller designed based on it is fine. |
Author | Liu, Jian-cheng Liu, Xue-min Xu, Yu-ru |
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Cites_doi | 10.1109/48.107144 10.2534/jjasnaoe1968.1989.166_503 10.1109/72.788652 10.1109/72.655026 10.1109/72.655044 10.1109/72.809098 |
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Copyright | Harbin Engineering University 2002. |
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References | Q Song (BF02921411_CR8) 1999; 10 BF02921411_CR5 Ö G Ali (BF02921411_CR7) 1999; 10 J R Noriega (BF02921411_CR6) 1998; 9 J Yuh (BF02921411_CR4) 1994; 15 C H Hou (BF02921411_CR9) 1998; 9 T Fujii (BF02921411_CR3) 1989; 166 Nai-yao Zhang (BF02921411_CR1) 1998 Liang Peng (BF02921411_CR2) 1995; 13 |
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SubjectTerms | Adaptive control Algorithms Autonomous underwater vehicles Control systems design Marine Motion control Neural networks Underwater vehicles |
Title | Motion control of underwater vehicle based on least disturbance BP algorithm |
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