基于加权遗忘多新息RLS的无人艇响应模型在线参数辨识

张海胜, 董早鹏, 杨莲, 张铮淇, 齐诗杰, 李家康

doi:10.16411/j.cnki.issn1006-7736.2023.02.002

大连海事大学学报 >

2023 , Vol. 49 >Issue 2: 15 - 22

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2023.02.002

船舶与海洋工程

基于加权遗忘多新息RLS的无人艇响应模型在线参数辨识

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(1. 武汉理工大学船海与能源动力工程学院，武汉 430063；2. 中国船舶集团有限公司综合技术经济研究院，北京 100081)

网络出版日期: 2023-03-06

基金资助

国家自然科学基金资质项目(51709214)

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Online parameter identification of unmanned vehicle response model based on weighted forgetting multiple innovation RLS

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(1. School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China; 2. China Institute of Marine Technology＆Economy, Beijing 100081, China)

张海胜（2000 — ），男，研究生，研究方向：无人艇操纵性参数辨识。

Online published: 2023-03-06

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摘要

为实现无人艇运动数学模型的精确在线建模，针对遗忘多新息递推最小二乘算法精度较差，并且不能充分利用多组数据误差之间的差异进行参数递推的问题，提出加权形式的遗忘多新息递推最小二乘算法，并设计一种基于数据递推误差确定权值的加权策略。基于一艘喷推艇的实艇数据和无人艇操纵运动二阶非线性响应模型对本文算法进行验证，结果表明，较传统遗忘多新息递推最小二乘算法，本文算法的艏向角均方根误差指标可降低6°左右，决定系数指标最高可达0.9927，证明本文算法具有较强的在线参数辨识建模能力。

关键词： 无人艇; 响应模型; 在线参数辨识; 递推最小二乘; 多新息; 遗忘因子

本文引用格式

张海胜, 董早鹏, 杨莲, 张铮淇, 齐诗杰, 李家康 . 基于加权遗忘多新息RLS的无人艇响应模型在线参数辨识[J]. 大连海事大学学报, 2023 , 49(2) : 15 -22 . DOI: 10.16411/j.cnki.issn1006-7736.2023.02.002

Abstract

In order to achieve accurate online modeling of the unmanned surface vehicle maneuvering motion, a new weighted strategy has been designed. This strategy can adjust the weight of the corresponding data in the parameter recursion in real-time according to the difference between the data. The proposed algorithm is validated based on experimental data from a water-jet-propelled unmanned surface vehicle. The bow angle data is predicted using the model obtained from the improved algorithm, its square root error can be reduced by about 6 °, and the maximum decision coefficient can reach 0.9927. The results show that the algorithm has a strong online parameter identification modeling ability.

Key words： unmanned surface vehicle; response model; online parameter identification; recursive least squares; multi-innovation; forgetting factor

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