A statistical modeling and tracking control approach to marine vehicle

For actualization of ship tracking control along a desired path with constant velocity, a statistical modeling method is proposed to represent the tracking dynamic behavior of ship. Firstly, a SISO RBF-ARX model is built for characterizing ship motion between heading angle deviation and rudder angle of ship. To represent ship motion's nonlinearity, rolling angle is used as the RBF-ARX model index to make the model parameters vary with ship moving state. The RBF-ARX model is identified off-line by using previously observed real data. Then, a state space model combined with the relationship between heading angle deviation and position tracking error of ship is proposed to represent ship tracking motion behavior. On the basis of the identified ship's state-space type tracking motion model, a predictive controller is designed to steer ship moving forward with constant velocity along a predefined reference path. The effectiveness of the modeling and control methods proposed in this paper is demonstrated by the tracking control simulation in which the modeling data is observed from a real experimental ship.

[1]  K. Ohtsu.  Recent development on analysis and control of ship's motions , 1999, Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328).

[2]  Kristin Ytterstad Pettersen,et al.  Tracking control of an underactuated ship , 2003, IEEE Trans. Control. Syst. Technol..

[3]  Genshiro Kitagawa,et al.  Statistical Analysis of the AR Type Ship’s Autopilot System , 1984 .

[4]  Jun Wu,et al.  Nonlinear system modeling and predictive control using the RBF nets-based quasi-linear ARX model☆ , 2009 .

[5]  Kazushi Nakano,et al.  RBF-ARX model based nonlinear system modeling and predictive control with application to a NOx decomposition process , 2004 .

[6]  H. Nijmeijer,et al.  Underactuated ship tracking control: Theory and experiments , 2001 .

[7]  Chen Guo,et al.  Nonlinear adaptive ship course tracking control based on backstepping and Nussbaum gain , 2004, Proceedings of the 2004 American Control Conference.

[8]  Kazushi Nakano,et al.  Nonlinear Predictive Control Using Neural Nets-Based Local Linearization ARX Model—Stability and Industrial Application , 2007, IEEE Transactions on Control Systems Technology.

[9]  Zhong-Ping Jiang,et al.  Universal controllers for stabilization and tracking of underactuated ships , 2002, Syst. Control. Lett..

[10]  Kohei Ohtsu,et al.  Study on Optimum Tracking Control with Linearized Model for Vessel , 2007 .

[11]  K. D. Do,et al.  Global tracking control of underactuated ships with nonzero off-diagonal terms in their system matrices , 2005, Autom..

[12]  Yukihiro Toyoda,et al.  A parameter optimization method for radial basis function type models , 2003, IEEE Trans. Neural Networks.

[13]  Thor I. Fossen,et al.  Path following control system for a tanker ship model , 2007 .

[14]  大津 皓平.  Statistical analysis and design of a rudder roll stabilization system , 1997 .

[15]  Genshiro Kitagawa,et al.  Batch‐adaptive ship's autopilots* , 2000 .

[16]  Toshio Iseki,et al.  Bayesian estimation of directional wave spectra based on ship motions , 1998 .

[17]  Genshiro Kitagawa,et al.  A new ship's auto pilot design through a stochastic model, , 1979, Autom..

[18]  Zhong-Ping Jiang,et al.  Global tracking control of underactuated ships by Lyapunov's direct method , 2002, Autom..