基于RLSM的海洋无人航行器操纵性参数辨识

龚涛; 董早鹏

doi:10.16411/j.cnki.issn1006-7736.2019.02.007

大连海事大学学报 >

2019 , Vol. 45 >Issue 2: 50 - 57

DOI: https://doi.org/10.16411/j.cnki.issn1006-7736.2019.02.007

船舶与海洋工程

基于RLSM的海洋无人航行器操纵性参数辨识

龚涛 ,
董早鹏

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(1. 中国舰船研究设计中心, 武汉 430064;2. 武汉理工大学交通学院, 武汉 430063)

龚涛（1975 — ），男，高级工程师，E-mail：gongtaochuanbo@163.com.

收稿日期: 2019-01-24

修回日期: 2019-03-04

网络出版日期: 2019-03-05

基金资助

国家自然科学基金资助项目（51709214；51809203）；中国博士后科学基金资助项目(2018M642941).

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Maneuvering parameters identification of unmanned marine vehicle based on RLSM

GONG Tao ,
DONG Zao-peng

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（1.China Ship Development and Design Center, Wuhan 430064, China;2.School of Transportation,Wuhan University of Technology,Wuhan 430063, China )

Received date: 2019-01-24

Revised date: 2019-03-04

Online published: 2019-03-05

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摘要

针对传统海洋无人航行器(unmanned marine vehicle, UMV)操纵性参数辨识过程中，采用经典最小二乘法的辨识精度对基础数据量的依赖性较高、辨识误差较大的问题，提出一种改进的递推式最小二乘法(recursive least squares method, RLSM)用于UMV的操纵性参数辨识.首先，推导了UMV的操纵性响应模型，基于四阶龙格库塔法进行相应的数值仿真数据采集；然后，对所建立的辨识模型进行离散化处理，简化成标准的递推式最小二乘法模式，以便于进行参数辨识设计；最后，根据辨识结果进行5°、10°、20°、30°正弦和Z形的半物理仿真实验，结果验证了所提出RLSM辨识算法的有效性、可靠性和优越性.

关键词： 海洋无人航行器（UMV）; 船舶操纵性; 参数辨识; 最小二乘法（LSM）; 递推式最小二乘法（RLSM）

本文引用格式

龚涛 , 董早鹏 . 基于RLSM的海洋无人航行器操纵性参数辨识[J]. 大连海事大学学报, 2019 , 45(2) : 50 -57 . DOI: 10.16411/j.cnki.issn1006-7736.2019.02.007

Abstract

As the high dependence of identification accuracy on the amount of basic data and the problem of large identification error using the classical least squares method in the process of maneuverability parameter identification of unmanned marine vehicle (UMV)，an improved recursive least squares method (RLSM) was proposed to identify the operational parameters of UMV. Firstly, the maneuverability response model of UMV was deduced, and the corresponding numerical simulation data acquisition was carried out based on the fourth-order Runge-Kutta method. Secondly, the established identification model was discretized and simplified to the standard recursive least squares model for parameter identification design. Finally, the semi-physical simulation experiments of 5°, 10°, 20°, 30° sine and Z-shaped were carried out according to the identification results, which verifies the validity, reliability and superiority of the proposed RLSM identification algorithm.

Key words： unmanned marine vehicle (UMV); ship maneuvering; parameter identification; least square method (LSM); recursive least squares method (RLSM)

参考文献

[1]WANG N, LV S L, ZHANG W D, et al.Finite- time observer based accurate tracking control of a marine vehicle with complex unknowns[J].Ocean Engineering, 2017, 145(15):406-415
[2]DONG Z P, WAN L, LIU T, et al.Horizontal-plane trajectory-tracking control of an underactuated unmanned marine vehicle in the presence of ocean currents[J].International Journal of Advanced Robotic Systems, 2016, 13(3):1-14
[3] WANG Y Y, JIANG S R, CHEN B, et al.Trajectory tracking control of underwater vehicle-manipulator system using discrete time delay estimation.[J]. IEEE Access, 2017, 5(无):7435 -7443
[4]丁彦侃, 俞孟蕻.并行扩展卡尔曼滤波的船舶模型参数辨识研究[J].船舶工程, 2015, 37(1):72-74
[5]JUAN P J A, DECIO C D, JULIO C A..Experimental model identification of open-frame underwater vehicles [J].Ocean Engineering, 2013, 60(无):81-94
[6]薛彩霞, 俞孟蕻, 丁彦侃.小波阈值消噪在耙吸挖泥船参数辨识中的应用[J].船舶工程, 2015, 37(10):72-75
[7]STEPHEN C.M,LOUIS LW. Experimental identification of six-degree-of-freedom coupled dynamic plant models for underwater robot vehicles[J].IEEE Journal of Oceanic Engineering, 2014, 39(4):662-671
[8]MOHAMMAD T S, POURIA S, MOSTAFA Z.Extended and unscented kalman filters for parameter estimation of an autonomous underwater vehicle[J].Ocean Engineering, 2014, 91(无):329-339
[9]MOHAMMAD T S, HAMIDREZA M D, ALIREZA F, et al.Identification of an autonomous underwater vehicle hydrodynamic model using the extended,cubature,and transformed unscented kalman filter[J].IEEE Journal of Oceanic Engineering, 2018, 43(2):457-467
[10]XIAN R H, ZAO J Z.SVR-based identification of nonlinear roll motion equation for FPSOs in regular waves[J].Ocean Engineering, 2015, 109(无):531-538
[11]朱大奇, 袁义丽, 邓志刚.水下机器人参数辨识的量子粒子群算法[J].控制工程, 2015, 22(3):531-537
[12]ELIAS R H, FRANCISCO J V, JOSE M R R.Improving parameter estimation efficiency of a non linear manoeuvring model of an underwater vehicle based on model basin data[J]. Applied Ocean Research, 2018, 76(无):125-138
[13]谢朔, 初秀民, 柳晨光, 等.基于改进LSSVM的船舶操纵运动模型在线参数辨识方法[J].中国造船, 2018, 59(2):178-189
[14]谢朔, 初秀民, 柳晨光, 等.基于多新息最小二乘法的船舶操纵响应模型参数辨识[J].中国航海, 2017, 40(1):73-78
[15]谢朔, 陈德山, 初秀民, 等.改进多新息卡尔曼滤波法辨识船舶响应模型[J].哈尔滨工程大学学报, 2018, 39(2):282-289
[16]朱红坤, 郭蕴华, 牟军敏, 等.基于多传感器递推总体最小二乘融合的水下机器人动力学模型参数辨识[J].船舶力学, 2017, 21(10):1263-1269
[17]OSAMA H, SREENATHA G A, HYUNGBO S, et al.Model-based adaptive control system for autonomous underwater vehicles [J]. , 2016, 127: 58-69.[J].Ocean Engineering, 2016, 127(无):58-69
[18]牛文栋, 王延辉, 杨艳鹏, 等.混合驱动水下滑翔机水动力参数辨识[J].力学学报, 2016, 48(4):813-822
[19]秦余钢, 秦余钢, 马勇, 等.基于改进最小二乘算法的船舶操纵性参数辨识[J].吉林大学学报工学版, 2016, 46(3):897-903
[20]霍聪, 董文才.基于拓展Kalman滤波的船舶自由横摇参数辨识[J].武汉理工大学学报交通科学与工程版, 2016, 40(2):214-218/226
[21]张心光, 邹早建, 王岩松.基于支持向量回归机和粒子群算法的船舶操纵运动模型辨识[J].船舶力学, 2016, 20(11):1427-1432
[22]宁方鑫, 熊勇, 牟军敏, 等.基于AIS数据的船舶操纵性指数辨识研究[J].系统仿真学报, 2017, 29(2):402-408
[23]BENEDETTO A, RICCARDO C, LUCA P, et al.Identification of the main hydrodynamic parameters of Typhoon AUV from a reduced experimental dataset[J].Ocean Engineering, 2018, 147(无):77-88
[24]盛振邦刘应中.船舶原理. 下册[M]. 上海交大出版社, 2005.
[25]ZHANG X G, ZOU Z J.Application of wavelet denoising in the modeling of ship manoeuvring motion[J].Journal of Ship Mechanics, 2011, 15(6):616-622

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