中国舰船研究2025，Vol.20Issue(1)：38-46,9.DOI:10.19693/j.issn.1673-3185.03816
基于改进扩展卡尔曼滤波算法的无人艇MMG模型参数辨识
Parameter identification of unmanned surface vehicle MMG model based on an improved extended Kalman filter
摘要
Abstract
[Objectives]To construct an accurate MMG(mathematical model group)model for a water-jet propulsion unmanned surface vehicle,the traditional extended Kalman filter algorithm and improved extended Kalman filter algorithm are combined with the real-world boat data for parameter identification.[Methods]First,based on the traditional EKF algorithm,in order to fully utilize the valuable information hidden in the historical data,an improved EKF algorithm integrating multi-innovation theory and dynamic for-getting factor is proposed.Then,using the real-world unmanned surface vehicle data,the unknown parameters in the MMG model are identified.Finally,the identified parameter values are substituted into the established MMG model,and the rudder angle and main engine speed consistent with the real boat data are input.The heading angle,longitudinal velocity,transverse velocity,heading angle rate and position information data are obtained through simulation,and the comparative analysis is carried out.[Results]The results indicate that compared with the traditional EKF algorithm,the root mean squared error index and the symmetric mean abso-lute percentage error index of the improved EKF algorithm are closer to 0.Specifically,the root mean squared error index is reduced by up to 20.02%at the highest,and the symmetric mean absolute percentage error index is reduced by 26.84%at the highest.[Conclusions]The simulation results demonstrate that the improved extended Kalman filter algorithm has higher identification accuracy,verifying the accuracy of the MMG mod-el established by the algorithm.关键词
无人艇/参数辨识/MMG模型/扩展卡尔曼滤波/多新息理论/动态遗忘因子Key words
unmanned vehicles/parameter identification/MMG model/extended Kalman filters/multi-innovation theory/dynamic forgetting factor分类
交通工程引用本文复制引用
孙蓬勃,董早鹏,刘伟,盛金亮,李志豪..基于改进扩展卡尔曼滤波算法的无人艇MMG模型参数辨识[J].中国舰船研究,2025,20(1):38-46,9.基金项目
国家自然科学基金资助项目(51709214) （51709214）
