为了解决动力定位（dynamic positioning，DP）船在复杂海况下的波浪运动滤波与控制问题，根据DP船耦合运动特点和波浪运动模型，提出一种基于Kalman技术的船舶三自由度解耦滤波方法，构建一种基于DP船状态观测方程的船舶动力定位仿真系统，并提出适用于DP船状态观测方程中的传感器噪声协方差矩阵和测量协方差矩阵参数的选择方法。船舶动力定位仿真结果表明，与传统非线性被动式滤波方法相比，本文方法对船舶一阶波浪运动的滤波效果更好，航向滤除了98%以上的一阶波浪运动，北东方向滤除了99%以上的一阶波浪运动，对传感器测量白噪声信号也有较好的抑制作用。对于船舶及其他海洋航行器运动控制技术的研究具有理论意义和工程价值。
To solve the problem of wave motion filtering and efficient motion control of dynamic positioning ships in complex sea conditions. According to the DP ships' three degrees of freedom coupling movement characteristic and wave motion, this paper proposes a method of decoupling ships' three degrees of freedom based on Kalman filtering which constructs a DP simulation system based on DP ships' three degrees of freedom observation equations and raises a selection method which applies to sensor noise covariance matrix and parameters of covariance measurement matrix in DP ships' observation equations. The simulation results of dynamic positioning show that: compared with conventional nonlinear passive filtering method, the filtering effect of this method is better for the first-order wave motion of ships. The heading filter out more than 98% of the first order wave motion, and the north and east direction filter out more than 99% of the first order wave motion, which also has a good inhibitory effect on the white noise signal measured by the sensor. It has a certain guiding significance and strong engineering value for the research of navigation, motion control, and thrust allocation methods of ships and marine vessels.
2022,44(17): 40-45 收稿日期：2021-11-30
DOI：10.3404/j.issn.1672-7649.2022.17.008
分类号：U664.82
基金项目：国家重点研发计划资助项目（2018YFC1406000）；中海油研究总院项目（LSZX-2020-HN-05-04）
作者简介：李新飞(1980-)，男，博士后，工程师，研究方向为船舶运动控制与仿真技术
参考文献：
[1] SELIMOVIC D, LERGA J, PRPIC-ORSIC J, et al. Improving the performance of dynamic ship positioning systems: a review of filtering and estimation techniques[J]. Journal of Marine Science and Engineering, 2020, 8(4): 28
[2] 骆寒冰, 佟海兵, 谢芃, 等. 动力定位船舶循迹控制海上实测与数值模拟研究[J]. 中国造船, 2020, 61(1): 18–28
LUO H B, TONG H B, XIE F, et al. Marine measurement and numerical simulation of dynamic positioning ship tracking control[J]. Ship Building of China, 2020, 61(1): 18–28
[3] 陈施奇. Kalman滤波器在船舶动力定位系统中的应用[J]. 舰船科学技术, 2019, 41(14): 73–75
CHEN S Q. Application of Kalman filter in ship dynamic positioning system[J]. Ship Science and Technology, 2019, 41(14): 73–75
[4] FOSSEN T I, PEREZ T. Kalman filtering for positioning and heading control of ships and offshore rigs estimating the effects of waves, wind, and current[J]. IEEE Control Systems Magazine, 2009, 29(6): 32–46
[5] HASSANI V, PASCOAL A M, SORENSEN A J, et al. A novel methodology for adaptive wave filtering of marine vessels: theory and experiments[C]//Proceedings of the 52nd IEEE Annual Conference on Decision and Control (CDC). Florence, Italy, 2013: 6162–6167.
[6] 李杨, 徐雪峰, 赵光. 基于扩展状态观测器的海浪滤波算法及仿真[J]. 中国舰船研究, 2020, 15(S1): 1–5+33
LI Y, XU X F, ZHAO G. Wave filtering algorithm and aimulation based on extended state observer[J]. Chinese Journal of Ship Research, 2020, 15(S1): 1–5+33
[7] 邓英杰, 张显库, 张国庆. 水面舰船动力定位系统ESO输入饱和控制[J]. 系统工程与电子技术, 2019, 41(5): 1110–1117
DENG Y J, ZHANG X K, ZHANG G Q. ESO input saturation control for dynamic positioning system of surface ship[J]. Journal of Systems Engineering and Electronics, 2019, 41(5): 1110–1117
[8] 李君. 非线性观测器在船舶动力定位系统DP的应用[J]. 舰船科学技术, 2021, 43(16): 208–210
LI J. Application of nonlinear observer in ship dynamic positioning system[J]. Ship Science and Technology, 2021, 43(16): 208–210
[9] 蒋帆, 徐海祥, 冯辉, 等. 基于模型预测扩展卡尔曼滤波的动力定位状态估计[J]. 船舶工程, 2019, 41(7): 86–91+132
JIANG F, XU H X, FENG H, et al. Dynamic positioning state estimation based on model predictive extended Kalman filter[J]. Ship Engineering, 2019, 41(7): 86–91+132
[10] BENETAZZO F, IPPOLITI G, LONGHI S, et al. Advanced control for fault-tolerant dynamic positioning of an offshore supply vessel[J]. Ocean Engineering, 2015, 106: 472–484
[11] 朱楠, 方伟. 卡尔曼滤波在组合导航中的仿真与分析[J]. 智能计算机与应用, 2020, 10(6): 300–302+309
[12] 边信黔, 付明玉, 王元慧. 船舶动力定位[M]. 北京: 科学出版社, 2011: 118–120.
