Nonlinear Modeling and Identification of vessel based on constrained quadratic programming method

In this research work, a vessel is used as a case study to investigate modeling and system identification. The discrete nonlinear 3-DOF state-space model with surge speed, lateral speed and yaw angle as state variables is established. A constrained quadratic programming method is proposed in this paper to identify parameters of nonlinear model, the steady-state data of vessel motions are the constraints and the transient-state data of vessel motions are used as training data to solve quadratic programming problem. The simulation results demonstrate the effectiveness of the method presented in this paper.

[1]  C. Guedes Soares,et al.  Parameter Identification of Ship Maneuvering Model Based on Support Vector Machines and Particle Swarm Optimization , 2016 .

[2]  Xinyu Li,et al.  Measures to diminish the parameter drift in the modeling of ship manoeuvring using system identification , 2017 .

[3]  Zhixiang Liu,et al.  Unmanned surface vehicles: An overview of developments and challenges , 2016, Annu. Rev. Control..

[4]  Naoya Umeda,et al.  Estimating maneuvering coefficients using system identification methods with experimental, system-based, and CFD free-running trial data , 2012 .

[5]  Michael J. Grimble,et al.  Ship forward speed loss minimization using nonlinear course keeping and roll motion controllers , 2016 .

[6]  Roger Skjetne,et al.  Modeling, identification, and adaptive maneuvering of CyberShip II: A complete design with experiments , 2004 .

[7]  Jianda Han,et al.  Quasi-LPV modeling and identification for a water-jet propulsion USV: An experimental study , 2014, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014).

[8]  D J Stilwell,et al.  Control-Oriented Planar Motion Modeling of Unmanned Surface Vehicles , 2010, OCEANS 2010 MTS/IEEE SEATTLE.

[9]  Philip A. Wilson,et al.  Optimal trim control of a high-speed craft by trim tabs/interceptors. Part I: pitch and surge coupled dynamic modelling using sea trial data , 2017 .

[10]  K.R. Muske,et al.  Identification of a control oriented nonlinear dynamic USV model , 2008, 2008 American Control Conference.

[11]  Hao Wang,et al.  Predictor-based LOS guidance law for path following of underactuated marine surface vehicles with sideslip compensation , 2016 .

[12]  C. Kravaris,et al.  Time-discretization of nonlinear control systems via Taylor methods , 1999 .

[13]  R. Negenborn,et al.  Trajectory tracking of autonomous vessels using model predictive control , 2014 .

[14]  Elías Revestido Herrero,et al.  Two-step identification of non-linear manoeuvring models of marine vessels , 2012 .

[15]  Jianda Han,et al.  Nonlinear Modeling for a Water-Jet Propulsion USV: An Experimental Study , 2017, IEEE Transactions on Industrial Electronics.

[16]  C. Guedes Soares,et al.  Identification of ship manoeuvring motion based on nu-support vector machine , 2019, Ocean Engineering.

[17]  Xianku Zhang,et al.  Multi-innovation auto-constructed least squares identification for 4 DOF ship manoeuvring modelling with full-scale trial data. , 2015, ISA transactions.

[18]  Philippe Sergent,et al.  Simulation of ship maneuvering in a confined waterway using a nonlinear model based on optimization techniques , 2017 .

[19]  Kensaku Nomoto,et al.  On the steering qualities of ships , 1956 .

[20]  Zou Zao-jian,et al.  Parametric estimation of ship maneuvering motion with integral sample structure for identification , 2015 .

[21]  Gyeong Joong Lee,et al.  Estimation of the Roll Hydrodynamic Moment Model of a Ship by Using the System Identification Method and the Free Running Model Test , 2007, IEEE Journal of Oceanic Engineering.

[22]  Leigh McCue,et al.  Handbook of Marine Craft Hydrodynamics and Motion Control [Bookshelf] , 2016, IEEE Control Systems.