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

作　　者：钟一鸣 于曹阳 曹军军[1] 姚宝恒[1] 连琏[1,2] ZHONG Yiming;YU Caoyang;CAO Junjun;YAO Baoheng;LIAN Lian(School of Oceanography,Shanghai Jiao Tong University,Shanghai 200030,China;State Key Laboratory of Ocean Engineering,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]上海交通大学海洋学院,上海200030 [2]上海交通大学海洋工程全国重点实验室,上海200240

出　　处：《中国舰船研究》2024年第2期13-20,共8页Chinese Journal of Ship Research

基　　金：国家自然科学基金资助项目(51909161,41527901);上海市自然科学基金资助项目(22ZR1434600)。

摘　　要：[目的]针对智能浮标大深度浮潜模型难以精确量化的问题,提出一种抗数据饱和及测量噪声的最小二乘算法(ASNLS),以实现浮潜多参数识别及深度预测。[方法]首先,在智能浮标浮潜运动灰箱模型中引入其执行机构的非线性动作特性以契合实际模型,并将连续型浮潜运动方程转化为离散模式以匹配实际离散的数据采样方式;然后,将离散型运动方程构造为基于相关函数的表达形式,以减弱噪声对参数辨识的影响;最后,通过调整协方差矩阵的取值,实现该浮潜参数辨识算法的抗数据饱和功能。[结果]基于2021年智能浮标在南海的大深度试验数据,开展了浮潜运动模型参数辨识及深度预测,验证结果表明:相较于传统的最小二乘算法及支持向量机算法,ASNLS算法的收敛速度更快(较最小二乘算法提高了31.8%)、深度预测误差更小(不同深度下的平均绝对百分比误差均小于9%)。[结论]ASNLS算法可为智能浮标的深度控制和预报提供有效的浮潜模型支撑。[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 identification and depth prediction are carried out.The results demonstrate that the ASNLS algorithm has faster convergence speed(31.8%higher than the least squares algorithm)and smaller depth prediction error(average absolute 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 effective 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 

分 类 号：U661.33[交通运输工程—船舶及航道工程]