Parameter Identification of an Unmanned Surface Vessel Nomoto Model Based on an Improved Extended Kalman Filter

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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Bibliographic Details
Main Authors: Sihang Lu, Baolin Wang, Zaopeng Dong, Zhihao Hu, Yilun Ding, Wangsheng Liu
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Applied Sciences
Subjects:
unmanned surface vessel (USV)
second-order nonlinear Nomoto model
extended Kalman filter (EKF)
locally weighted regression (LWR)
parameter identification
Online Access:https://www.mdpi.com/2076-3417/15/1/161
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