文献类型：期刊文献

英文题名：Real-time parameter identification of ship maneuvering response model based on nonlinear Gaussian Filter

作者：Wang, Sisi[1,2];Wang, Lijun[1];Im, Namkyun[2];Zhang, Weidong[1];Li, Xijin[1]

机构：[1]Guangdong Ocean Univ, Maritime Coll, Zhanjiang 524088, Guangdong, Peoples R China;[2]Mokpo Maritime Univ, Div Nav Sci, Mokpo 58628, South Korea

年份：2022

卷号：247

外文期刊名：OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000783632100002)、、EI(收录号：20220811684495)、Scopus(收录号：2-s2.0-85124825474)、WOS

基金：Acknowledgement This work was partially supported by National Science Foundation of China (No.52171346) , Scientific Research Start-up Funds of Guangdong Ocean University under Grants E15031 and R17012; Characteristic Innovation Projects of Guangdong Province under Grants 2017KTSCX088, 2017KTSCX092 and 2019KTSCX230.

语种：英文

外文关键词：Nonlinear Gaussian filter; Real -time parameter identification; Nomoto model; Unscented Kalman filter; Cubature Kalman filter

外文摘要：In order to solve the problem of parameter identification of nonlinear ship motion model in ship autonomous navigation control, a real-time parameter identification method based on nonlinear Gaussian filtering algorithm and nonlinear ship response model is proposed. It is proved theoretically that the influence of parameter drift on parameter identification can be reduced by increasing the number of observers and filters, and the system identification accuracy can be improved. The validity of the proposed method is verified by parameter identification experiments based on Zig-zag motion simulation data of Mariner standard ship model. Simulation results show that compared with EKF, the nonlinear Gaussian filter algorithm can effectively improve the parameter identification accuracy and reduce the computational complexity. The application of parallel structure is helpful to improve the identification accuracy and convergence rate of nonlinear Gaussian filter algorithm.