Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman Filter
The accurate nonlinear modeling of an unmanned surface vessel (USV) is essential for advanced control and operational performance. This paper combines the locally weighted regression (LWR) algorithm and the extended Kalman filter (EKF) for parameter identification using state data from full-scale ve...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/161 |
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| author | Sihang Lu Baolin Wang Zaopeng Dong Zhihao Hu Yilun Ding Wangsheng Liu |
| author_facet | Sihang Lu Baolin Wang Zaopeng Dong Zhihao Hu Yilun Ding Wangsheng Liu |
| author_sort | Sihang Lu |
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| description | The accurate nonlinear modeling of an unmanned surface vessel (USV) is essential for advanced control and operational performance. This paper combines the locally weighted regression (LWR) algorithm and the extended Kalman filter (EKF) for parameter identification using state data from full-scale vessel experiments. To mitigate the effects of disturbances and abrupt changes in the full-scale vessel data, LWR filtering is applied for data smoothing before parameter identification. The EKF is then used to estimate the unknown parameters in the second-order nonlinear Nomoto model of the USV. These parameters are incorporated into the Nomoto model, and simulations are conducted by inputting the same rudder inputs as in the experimental data. The predicted heading angle and yaw rate are compared with experimental results, showing that the mean absolute error (MAE) for the heading angle is within 10° and the MAE for the yaw rate is within 1.5°/s. Additionally, the coefficient of determination (R<sup>2</sup>) values for both predictions are above 0.93. The simulation results demonstrate that the combination of LWR filtering and EKF effectively identifies parameters and models the nonlinear response of the USV, achieving high accuracy in the established second-order model. |
| format | Article |
| id | doaj-art-68743c2871fc44a4839e8e5ba1d193c2 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-68743c2871fc44a4839e8e5ba1d193c22025-01-10T13:14:39ZengMDPI AGApplied Sciences2076-34172024-12-0115116110.3390/app15010161Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman FilterSihang Lu0Baolin Wang1Zaopeng Dong2Zhihao Hu3Yilun Ding4Wangsheng Liu5Key Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan University of Technology, Wuhan 430063, ChinaKey Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan University of Technology, Wuhan 430063, ChinaKey Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan University of Technology, Wuhan 430063, ChinaKey Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan University of Technology, Wuhan 430063, ChinaKey Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan University of Technology, Wuhan 430063, ChinaKey Laboratory of High Performance Ship Technology (Wuhan University of Technology), Ministry of Education, Wuhan University of Technology, Wuhan 430063, ChinaThe accurate nonlinear modeling of an unmanned surface vessel (USV) is essential for advanced control and operational performance. This paper combines the locally weighted regression (LWR) algorithm and the extended Kalman filter (EKF) for parameter identification using state data from full-scale vessel experiments. To mitigate the effects of disturbances and abrupt changes in the full-scale vessel data, LWR filtering is applied for data smoothing before parameter identification. The EKF is then used to estimate the unknown parameters in the second-order nonlinear Nomoto model of the USV. These parameters are incorporated into the Nomoto model, and simulations are conducted by inputting the same rudder inputs as in the experimental data. The predicted heading angle and yaw rate are compared with experimental results, showing that the mean absolute error (MAE) for the heading angle is within 10° and the MAE for the yaw rate is within 1.5°/s. Additionally, the coefficient of determination (R<sup>2</sup>) values for both predictions are above 0.93. The simulation results demonstrate that the combination of LWR filtering and EKF effectively identifies parameters and models the nonlinear response of the USV, achieving high accuracy in the established second-order model.https://www.mdpi.com/2076-3417/15/1/161unmanned surface vessel (USV)second-order nonlinear Nomoto modelextended Kalman filter (EKF)locally weighted regression (LWR)parameter identification |
| spellingShingle | Sihang Lu Baolin Wang Zaopeng Dong Zhihao Hu Yilun Ding Wangsheng Liu Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman Filter Applied Sciences unmanned surface vessel (USV) second-order nonlinear Nomoto model extended Kalman filter (EKF) locally weighted regression (LWR) parameter identification |
| title | Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman Filter |
| title_full | Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman Filter |
| title_fullStr | Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman Filter |
| title_full_unstemmed | Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman Filter |
| title_short | Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman Filter |
| title_sort | parameter identification of an unmanned surface vessel nomoto model based on an improved extended kalman filter |
| topic | unmanned surface vessel (USV) second-order nonlinear Nomoto model extended Kalman filter (EKF) locally weighted regression (LWR) parameter identification |
| url | https://www.mdpi.com/2076-3417/15/1/161 |
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