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

doi:10.11772/j.issn.1001-9081.2017082041

计算机应用 ›› 2018, Vol. 38 ›› Issue (3): 900-904.DOI: 10.11772/j.issn.1001-9081.2017082041

• 应用前沿、交叉与综合 • 上一篇下一篇

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

孙功武, 谢基榕, 王俊轩

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

SUN Gongwu, XIE Jirong, WANG Junxuan

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).

摘要/Abstract

摘要： 为提高遗忘因子递推最小二乘（RLS）算法辨识船舶航向运动数学模型参数的快速性和鲁棒性，在分析遗忘因子大小对算法特性影响的基础上，提出一种基于模糊控制的动态遗忘因子RLS算法。该算法从理论模型输出与实际模型输出之间的残差入手来构造评估参数辨识误差大小的评价函数，并将评价函数及其变化率作为模糊控制器的输入，利用模糊控制器结合制定的规则表进行模糊推理并计算遗忘因子的修正量，从而实现遗忘因子的动态调整。仿真结果表明，与恒定遗忘因子RLS算法的对比，该算法能够根据参数辨识误差实时调整遗忘因子的大小，使算法在模型参数平稳时有更高的辨识精度，在模型参数突变时有更快的收敛速度，验证了所提算法的优越性。

关键词: 动态遗忘因子, 递推最小二乘算法, 模糊控制, 船舶航向模型, 参数辨识

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.

Key words: dynamic forgetting factor, Recursive Least Squares (RLS) algorithm, fuzzy control, ship course model, parameter identification

中图分类号:

TP273.2

孙功武, 谢基榕, 王俊轩. 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[J]. 计算机应用, 2018, 38(3): 900-904.

SUN Gongwu, XIE Jirong, WANG Junxuan. Ship course identification model based on recursive least squares algorithm with dynamic forgetting factor[J]. Journal of Computer Applications, 2018, 38(3): 900-904.

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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

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

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