• 应用前沿、交叉与综合 •

### 基于动态遗忘因子递推最小二乘算法的船舶航向模型辨识

1. 中国船舶科学研究中心 深海载人装备国家重点实验室, 江苏 无锡 214082
• 收稿日期:2017-08-21 修回日期:2017-10-21 出版日期:2018-03-10 发布日期:2018-03-07
• 通讯作者: 孙功武
• 作者简介:孙功武(1990-),男,安徽合肥人,工程师,硕士,主要研究方向:舰船智能控制、系统辨识;谢基榕(1977-),男,江苏无锡人,研究员,博士,主要研究方向:舰船智能控制、一体化信息系统;王俊轩(1986-),男,江苏无锡人,高级工程师,硕士,主要研究方向:舰船智能控制、一体化信息系统。
• 基金资助:
江苏省自然科学基金资助项目（BK20170217）。

### Ship course identification model based on recursive least squares algorithm with dynamic forgetting factor

1. State Key Laboratory of Deep-sea Manned Vehicles, China Ship Scientific Research Center, Wuxi Jiangsu 214082, China
• Received:2017-08-21 Revised:2017-10-21 Online:2018-03-10 Published:2018-03-07
• Supported by:
This work is partially supported by the Natural Science Foundation of Jiangsu Province (BK20170217).

Abstract: To improve the speed and robustness of Recursive Least Squares (RLS) algorithm with forgetting factor in the parameter identification of ship course motion mathematical model, an RLS algorithm with dynamic forgetting factor based on fuzzy control was proposed. Firstly, the residual between the theoretical model output and actual model output was calculated. Secondly, an evaluation function was constructed on the basis of the residual, to assess the parameter identification error. Then, a fuzzy controller with evaluation function and its change rate as two inputs was adopted to realize the dynamic adjustment of the forgetting factor. Combined with designed fuzzy control rule table, the modification of the forgetting factor was obtained by the fuzzy controller at last. Simulation results show that the forgetting factor can be adjusted according to the parameter identification error in the presented algorithm, which achieves higher precision and faster parameter identification than RLS algorithm with constant forgetting factor.