Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine
To predict the manoeuvrability of unmanned marine vehicle and improve its manoeuvrability, the parameters of the manoeuvring model of unmanned marine vehicle need to be obtained. Aiming at the inconvenience of obtaining model parameters under the traditional experimental method, this article studies...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2019-01-01
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| Series: | International Journal of Advanced Robotic Systems |
| Online Access: | https://doi.org/10.1177/1729881418825095 |
| _version_ | 1818240389080940544 |
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| author | Zaopeng Dong Xin Yang Mao Zheng Lifei Song Yunsheng Mao |
| author_facet | Zaopeng Dong Xin Yang Mao Zheng Lifei Song Yunsheng Mao |
| author_sort | Zaopeng Dong |
| collection | DOAJ |
| description | To predict the manoeuvrability of unmanned marine vehicle and improve its manoeuvrability, the parameters of the manoeuvring model of unmanned marine vehicle need to be obtained. Aiming at the inconvenience of obtaining model parameters under the traditional experimental method, this article studies the parameter identification of unmanned marine vehicle’s manoeuvring model based on extended Kalman filter and support vector machine. Firstly, the second-order nonlinear manoeuvring response model of unmanned marine vehicle is discretized by the difference method, and the corresponding data are collected by the manoeuvring motion simulation of the response model. Secondly, the discrete response model is transformed into an augmented state vector based on extended Kalman filter, and the optimal estimation of the state vector is calculated to identify the parameters. And then, the discrete response model is transformed into a support vector machine-based regression model, the collected data are processed and a set of support vectors are obtained to further identify the parameters of the response model. Finally, by comparing the simulation experiments’ results from the original model and the identification model, the recognition results-based extended Kalman filter and support vector machine are analysed and some research results are obtained. The results of this article will provide a powerful reference for the design of unmanned marine vehicle’s motion control algorithm. |
| first_indexed | 2024-12-12T13:12:40Z |
| format | Article |
| id | doaj.art-f38e82a6e90844449b87e00f8bceced4 |
| institution | Directory Open Access Journal |
| issn | 1729-8814 |
| language | English |
| last_indexed | 2024-12-12T13:12:40Z |
| publishDate | 2019-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | International Journal of Advanced Robotic Systems |
| spelling | doaj.art-f38e82a6e90844449b87e00f8bceced42022-12-22T00:23:30ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142019-01-011610.1177/1729881418825095Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machineZaopeng Dong0Xin Yang1Mao Zheng2Lifei Song3Yunsheng Mao4 School of Transportation, Wuhan University of Technology, Wuhan, Hubei, China School of Transportation, Wuhan University of Technology, Wuhan, Hubei, China National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan, Hubei, China School of Transportation, Wuhan University of Technology, Wuhan, Hubei, China School of Transportation, Wuhan University of Technology, Wuhan, Hubei, ChinaTo predict the manoeuvrability of unmanned marine vehicle and improve its manoeuvrability, the parameters of the manoeuvring model of unmanned marine vehicle need to be obtained. Aiming at the inconvenience of obtaining model parameters under the traditional experimental method, this article studies the parameter identification of unmanned marine vehicle’s manoeuvring model based on extended Kalman filter and support vector machine. Firstly, the second-order nonlinear manoeuvring response model of unmanned marine vehicle is discretized by the difference method, and the corresponding data are collected by the manoeuvring motion simulation of the response model. Secondly, the discrete response model is transformed into an augmented state vector based on extended Kalman filter, and the optimal estimation of the state vector is calculated to identify the parameters. And then, the discrete response model is transformed into a support vector machine-based regression model, the collected data are processed and a set of support vectors are obtained to further identify the parameters of the response model. Finally, by comparing the simulation experiments’ results from the original model and the identification model, the recognition results-based extended Kalman filter and support vector machine are analysed and some research results are obtained. The results of this article will provide a powerful reference for the design of unmanned marine vehicle’s motion control algorithm.https://doi.org/10.1177/1729881418825095 |
| spellingShingle | Zaopeng Dong Xin Yang Mao Zheng Lifei Song Yunsheng Mao Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine International Journal of Advanced Robotic Systems |
| title | Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine |
| title_full | Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine |
| title_fullStr | Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine |
| title_full_unstemmed | Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine |
| title_short | Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine |
| title_sort | parameter identification of unmanned marine vehicle manoeuvring model based on extended kalman filter and support vector machine |
| url | https://doi.org/10.1177/1729881418825095 |
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