中国舰船研究2024，Vol.19Issue(2)：13-20,8.DOI:10.19693/j.issn.1673-3185.03186
基于ASNLS算法的智能浮标浮潜模型参数辨识
Parameter identification of smart float diving model based on ASNLS algorithm
摘要
Abstract
[Objectives]Aiming at the challenge of accurate diving modeling of a smart float,an anti-saturation and noise least squares(ASNLS)algorithm is proposed in this paper to achieve diving multi-parameter identification and depth prediction.[Methods]Firstly,the nonlinear motion characteristics of the smart float actuator were included in the gray box-based diving model to better fit the actual model,and the continuous diving motion equation was transformed into a discrete form to match the real-world discrete data sampling.Subsequently,the aforementioned discrete diving model was constructed into a correlation form to attenuate the influence of data noise.Finally,by adjusting the values of the covariance matrix,the designed diving parameter identification algorithm achieved resistance to data saturation.[Results]Based on the data of the South China Sea deep diving experiment of the smart float in 2021,diving model parameter identifica-tion and depth prediction are carried out.The results demonstrate that the ASNLS algorithm has faster conver-gence speed(31.8%higher than the least squares algorithm)and smaller depth prediction error(average abso-lute percentage errors less than 9%at different depths)than both the traditional least squares algorithm and supports the vector machine algorithm.[Conclusions]Consequently,the ASNLS algorithm can provide ef-fective support for the depth control and prediction of the smart float.关键词
智能浮标/参数辨识/抗数据饱和及测量噪声的最小二乘算法/运动预测/数据饱和Key words
smart float/parameter identification/antisaturation and noise least squares(ASNLS)al-gorithm/motion prediction/data saturation分类
交通工程引用本文复制引用
钟一鸣,于曹阳,曹军军,姚宝恒,连琏..基于ASNLS算法的智能浮标浮潜模型参数辨识[J].中国舰船研究,2024,19(2):13-20,8.基金项目
国家自然科学基金资助项目(51909161,41527901) （51909161,41527901）
上海市自然科学基金资助项目(22ZR1434600) （22ZR1434600）
