船舶动力学模型对于船舶的智能导航和控制器设计至关重要，基于高斯过程的非参数回归被用于船舶动力学模型辨识。它可以捕捉船舶运动中的强非线性和运动耦合，并处理不确定性和噪声的影响，辨识得到的模型能够在传感器信号丢失的情况下，提供未来一段时间内船舶加速度、速度和位置信息。KVLCC2船舶的试验数据用于验证所提方法的有效性，结果表明，高斯过程回归可以准确预测船舶状态，1000步的位置预测误差为0.599 m。
Ship dynamics model is crucial for intelligent navigation and design of the ship’s controller, nonparametric regression based on Gaussian process regression is used for ship dynamics model identification. it can capture the strong nonlinearity and motion cou-pling in ship motion, and deal with the presence of uncertainty and noise. The identified model can provide ship acceleration, speed and position information for a period of time in the future when the sensor signal is lost. The experimental data of KVLCC2 ship are used to verify the validity of the proposed method，the results show that Gaussian process regression can provide accurate predictions of ship’s state, the position prediction error of 1000 steps is 0.599 m.
2022,44(19): 1-5 收稿日期：2021-08-12
DOI：10.3404/j.issn.1672-7649.2022.19.001
分类号：U675.91
作者简介：陈刚(1992-)，男，博士，研究方向为系统辨识及控制
参考文献：
[1] ABKOWITZ M A. Measurements of hydrodynamic characteristics from ship maneuvering trials by system identification[C]// Transactions SNAME 88, 1980: 283?318.
[2] NORRBIN N H. Ship Manoeuvring with Application to Shipborne Predictors and Real-Time Simulators[J]. Journal of Mechanical Engineering Science, 1972, 14(1): 91?107.
[3] HOLZHUTER T. Robust identification scheme in an adaptive track-controller for ships[C]// IFAC Proceedings Volumes, 1990, 23: 461?466.
[4] YOON H K, RHEE K P. Identification of hydrodynamic coefficients in ship maneuvering equations of motion by Estimation-Before-Modeling technique[J]. Ocean Engineering, 2003, 30(18): 2379?2404.
[5] LUO W L, MOREIRA L, SOARES C G. Manoeuvring simulation of catamaran by using implicit models based on support vector machines[J]. Ocean Engineering, 2014, 82(15): 150?159.
[6] ZHU M, HAHN A, WEN Y Q. Identification-based simplified model of large container ships using support vector machines and artificial bee colony algorithm[J]. Applied Ocean Research, 2017, 68(1): 249–261
[7] LUO W L, LI X. Measures to diminish the parameter drift in the modeling of ship manoeuvring using system identification[J]. Applied Ocean Research, 2017, 67(1): 9–20
[8] XU Haitong, HASSANI V, SOARES C G. Parameters Estimation of Nonlinear Manoeuvring Model for Marine Surface Ship Based on PMM Tests[C]. ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering, 2018, 11: 19?23.
[9] XU Haitong, SOARES C G. Experimental investigation of shallow water effect on vessel steering model using system identification method[J]. Ocean Engineering, 2017, 199: 106940.
[10] XU Haitong, HASSANI V, SOARES C G. Uncertainty analysis of the hydrodynamic coefficients estimation of a nonlinear manoeuvring model based on planar motion mechanism tests[J]. Ocean Engineering, 2019, 173: 450?459.
[11] XU H, HINOSTROZA M, HASSANI V, et al. Real-time parameter estimation of nonlinear vessel steering model using support vector machine[J]. Journal of Offshore Mechanics and Arctic Engineering, 2019, 141(6): 1?9.
[12] MU D, WANG G, FAN Y, et al. Modeling and Identification for Vector Propulsion of an Unmanned Surface Vehicle: Three Degrees of Freedom Model and Response Model[J]. Sensors, 2018, 18(6): 1889.
[13] SUTULO S, SOARES C G. An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests[J]. Ocean Engineering, 2014, 15(79): 10?25.
[14] WANG N, ER M J, HAN M. Large tanker motion model identification using generalized ellipsoidal basis function-based fuzzy neural networks[J]. IEEE Transactions on Cybernetics, 2015, 45(12): 2732?2743.
[15] WANG Z, XU H, XIA L, et al. Kernel-based support vector regression for nonparametric modeling of ship maneuvering motion[J]. Ocean Engineering, 2014, 216: 10?25.
[16] W, REN J, LI T. Modified genetic optimization-based locally weighted learning identification modeling of ship maneuvering with full scale trial [J]. Future Generation Computer Systems, 2019, 93: 1036?1045.
[17] MORENO-SALINAS D, MORENO R, PEREIRA A, et al. Modelling of a surface marine vehicle with kernel ridge regression confidence machine[J]. Applied Soft Computing Journal, 2019, 76(1): 237?250.
[18] MORENO R, MORENO-SALINAS D, Aranda J. Black-box marine vehicle identification with regression techniques for random manoeuvres[J]. Electronics, 2019, 8(5): 492?492.
[19] MORENO R, BESADA-PORTAS E, ARANDA J, Symbolic regression for marine vehicles identification[J]. IFAC-Papers Online, 2015, 48(16): 210?216.
[20] CARRON A, ARCARI E, WERMELINGER M, et al. Data-driven model predictive control for trajectory tracking with a robotic arm [J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3578?3765.
[21] KABZAN J, HEWING L, LINIGER A. Learning-based model predictive control for autonomous racing [J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3363?3370.
[22] RAMIRE W A, LEONG Z Q, HUNG N, et al. Non-parametric dynamic system identification of ships using multi-output Gaussian Processes[J]. Ocean Engineering, 2018, 166: 26?36.
[23] XUE Y, LIU Y, JI C, et al. System identification of ship dynamic model based on Gaussian process regression with input noise [J]. Ocean Engineering, 2020, 216: 107862.
[24] FOSSEN T I. Handbook of marine craft hydrodynamics and motion control[M]. 2011: 134?135.
[25] RASMUSSEN C E, WILLIAMS K I. Gaussian processes for machine learning[M]. 2006: 8?31.
