基于改进LSSVM的船舶操纵运动模型在线参数辨识方法
详细信息 查看官网全文
- 英文论文题名：Online Parameter Identification Method for Ship Maneuvering Model Based on Improved LSSVM
- 论文作者：谢朔 ; 初秀民 ; 柳晨光 ; 吴青
- 英文论文作者：Xie Shuo ; Chu Xiumin ; Liu Chenguang ; Wu Qing ; Engineering Research Center for Transportation Safe ; Ministry of Education ; Wuhan University of Technology ; School of Energy and Power Engineering ; Wuhan University of Technology
- 年：2016
- 作者机构：国家水运安全工程技术研究中心;武汉理工大学能源与动力工程学院;武汉理工大学物流工程学院;
- 论文关键词：船舶操纵性 ; 参数辨识 ; 最小二乘支持向量机 ; 多新息方法2
- 英文论文关键词：Ship Maneuvering ; Parameter Identification ; Least Squares Support Vector Machine ; Multi-Innovation
- 会议召开时间：2016-11-16
- 会议录名称：第十一届中国智能交通年会大会论文集
- 语种：中文
- 分类号：U661.33
- 学会代码：ZJTX
- 会议名称：第十一届中国智能交通年会
- 会议地点：中国重庆
- 主办单位：中国智能交通协会
- 学会名称：中国智能交通协会
- 页数：11
- 文件大小：1210k
- 原文格式：D
- 会议级别：全国
摘要
为实现船舶操纵性的在线预报及自适应运动控制,针对Nomoto二阶非线性运动模型参数辨识问题,将最小二乘支持向量机(least squares support vector machines,LSSVM)与多新息方法相结合,提出一种新的多新息在线LSSVM辨识建模方法。实验结果表明,使用所提出的算法辨识的模型进行预报的拟合误差可达到4.76%以下,能准确拟合船舶操纵运动模型。
In order to achieve online prediction of ship maneuverability and adaptive motion control,least squares support vector machine is combined with a multi-innovation algorithm to put forward a new online identification modeling method based on multi-innovation least squares support vector machine for second order nonlinear nomoto motion model parameters identification.The experimental results show that the fitting error of prediction by using the identified model can reach under 4.76%,and accurately fit ship maneuvering motion model.
In order to achieve online prediction of ship maneuverability and adaptive motion control,least squares support vector machine is combined with a multi-innovation algorithm to put forward a new online identification modeling method based on multi-innovation least squares support vector machine for second order nonlinear nomoto motion model parameters identification.The experimental results show that the fitting error of prediction by using the identified model can reach under 4.76%,and accurately fit ship maneuvering motion model.
引文
[1]船舶操纵性与耐波性[M].北京:交通人民出版社,1988.
[2]乐美龙.船舶操纵性预报与港航操纵运动仿真[M].上海:上海交通大学出版社,2004.
[3]Hu J,Bu R,Yin J,et al.Adaptive control for ship steering based on clonal selection model identification[C]//Control and Decision Conference,2009.CCDC'09.Chinese.IEEE,2009:2863-2868.
[4]Sonnenburg C R,Woolsey C A.Modeling,identification,and control of an unmanned surface vehicle[J].Journal of Field Robotics,2013,30(3):371-398.
[5]Van Gestel T,De Brabanter J,De Moor B,et al.Least squares support vector machines[M].Singapore:World Scientific,2002.
[6]Liu C,Zheng H,Negenborn R R,et al.Trajectory Tracking Control for Underactuated Surface Vessels Based on Nonlinear Model Predictive Control[M]//Computational Logistics.Springer International Publishing,2015:166-180.
[7]王雪刚,邹早建,任如意,等.基于支持向量机的四自由度船舶操纵运动黑箱建模[J].中国造船,2014,55(3):147-155.
[8]LUO W L,ZOU Z J.Parametric identification of ship maneuvering models by using support vector machines[J].Journal of Ship Research,2009,53(1):19-30.
[9]ZHANG X G,ZOU Z J.Black-box modeling of nonlinear hydrodynamic model for ship manoeuvring based on support-vector-regression[C]//International Conference on Marine Simulation and Ship Manoeuvrability 2012(MARSIM2012),2012 Singapore.
[10]徐锋,邹早建,徐小卡,等.基于支持向量机的船舶操纵运动黑箱建模[J].北京航空航天大学学报,2013,11:025.
[11]罗伟林.基于支持向量机方法的船舶操纵运动建模研究[D].上海:上海交通大学船舶与海洋工程系,2009.
[12]张心光,邹早建.基于支持向量回归机的船舶操纵响应模型辨识[J].上海交通大学学报,2011,45(4):501-504.
[13]惠小锁,张显库.船舶4自由度响应型数学模型的研究[J].大连海事大学学报:自然科学版,2013(2):1-4.
[14]Van G T,De B J,De M B,et al.Least squares support vector machines[M].Singapore:World Scientific,2002.
[15]宋召青,郑苏,李志成.最小二乘支持向量机的梯度递推法[C]//2009全国仿真技术学术会议,2009.
[16]DING F.System identification theory and methods[M].Beijing:China Electric Power Press,2012.
[2]乐美龙.船舶操纵性预报与港航操纵运动仿真[M].上海:上海交通大学出版社,2004.
[3]Hu J,Bu R,Yin J,et al.Adaptive control for ship steering based on clonal selection model identification[C]//Control and Decision Conference,2009.CCDC'09.Chinese.IEEE,2009:2863-2868.
[4]Sonnenburg C R,Woolsey C A.Modeling,identification,and control of an unmanned surface vehicle[J].Journal of Field Robotics,2013,30(3):371-398.
[5]Van Gestel T,De Brabanter J,De Moor B,et al.Least squares support vector machines[M].Singapore:World Scientific,2002.
[6]Liu C,Zheng H,Negenborn R R,et al.Trajectory Tracking Control for Underactuated Surface Vessels Based on Nonlinear Model Predictive Control[M]//Computational Logistics.Springer International Publishing,2015:166-180.
[7]王雪刚,邹早建,任如意,等.基于支持向量机的四自由度船舶操纵运动黑箱建模[J].中国造船,2014,55(3):147-155.
[8]LUO W L,ZOU Z J.Parametric identification of ship maneuvering models by using support vector machines[J].Journal of Ship Research,2009,53(1):19-30.
[9]ZHANG X G,ZOU Z J.Black-box modeling of nonlinear hydrodynamic model for ship manoeuvring based on support-vector-regression[C]//International Conference on Marine Simulation and Ship Manoeuvrability 2012(MARSIM2012),2012 Singapore.
[10]徐锋,邹早建,徐小卡,等.基于支持向量机的船舶操纵运动黑箱建模[J].北京航空航天大学学报,2013,11:025.
[11]罗伟林.基于支持向量机方法的船舶操纵运动建模研究[D].上海:上海交通大学船舶与海洋工程系,2009.
[12]张心光,邹早建.基于支持向量回归机的船舶操纵响应模型辨识[J].上海交通大学学报,2011,45(4):501-504.
[13]惠小锁,张显库.船舶4自由度响应型数学模型的研究[J].大连海事大学学报:自然科学版,2013(2):1-4.
[14]Van G T,De B J,De M B,et al.Least squares support vector machines[M].Singapore:World Scientific,2002.
[15]宋召青,郑苏,李志成.最小二乘支持向量机的梯度递推法[C]//2009全国仿真技术学术会议,2009.
[16]DING F.System identification theory and methods[M].Beijing:China Electric Power Press,2012.