Continuous-time system identification of a ship on a river

Model-based control strategies require accurate modeling of a system. Physical modeling leads to differential equations where the parameters can then be estimated from experimental data. In this paper, we present the continuous-time identification of the ship dynamics based on real data collected in open loop. In particular, the models for the drift and yaw dynamics are estimated for one ship. The obtained models show good results when tested with validation data and could be used, for example, for autopilot control strategies.

[1]  P. Young,et al.  Refined instrumental variable methods of recursive time-series analysis Part II. Multivariable systems , 1979 .

[2]  Mogens Blanke,et al.  Multivariable identification of ship steering and roll motions , 1997 .

[3]  B. Wahlberg.  System identification using Laguerre models , 1991 .

[4]  H. Unbehauen,et al.  Identification of continuous-time systems , 1991 .

[5]  Hugues Garnier,et al.  Continuous-time model identification from sampled data: Implementation issues and performance evaluation , 2003 .

[6]  Liuping Wang,et al.  Identification of Continuous-time Models from Sampled Data , 2008 .

[7]  P. Young,et al.  Refined instrumental variable methods of recursive time-series analysis Part I. Single input, single output systems , 1979 .

[8]  Juan I. Yuz,et al.  Continuous-time system identification of the steering dynamics of a ship on a river , 2014, Int. J. Control.

[9]  Bo Wahlberg,et al.  Parametric Signal Modelling using Laguerre Filters , 1993 .

[10]  Karl Johan Åström,et al.  Identification of ship steering dynamics , 1976, Autom..

[11]  John F. MacGregor,et al.  Multi-output process identification , 1997 .

[12]  Hugues Garnier,et al.  Refined instrumental variable methods for identification of LPV Box-Jenkins models , 2010, Autom..

[13]  Håkan Hjalmarsson,et al.  Modelling of Random Processes using Orthonormal Bases , 1996 .

[14]  Roger Skjetne,et al.  A Nonlinear Ship Manoeuvering Model: Identification and adaptive control with experiments for a model ship , 2004 .

[15]  Alexander Lutz.  Kollisionserkennung und -vermeidung auf Binnenwasserstraßen , 2011 .

[16]  Taketoshi Kawabe,et al.  Identification of a toroidal continuously variable transmission using continuous-time system identification methods , 2006 .

[17]  S. Nomoto,et al.  Experimental investigation of the relationship between IP network performances and speech quality of VoIP , 2003, 10th International Conference on Telecommunications, 2003. ICT 2003..

[18]  Lennart Ljung,et al.  Experiments with Identification of Continuous Time Models , 2009 .

[19]  Ernst Dieter Gilles,et al.  Disturbance Estimation for Feedforward Control of Inland Vessels , 2010 .

[20]  Mogens Blanke,et al.  Optimised experiment design for identification of marine systems , 1999 .

[21]  H. Garnier,et al.  A PMF-based subspace method for continuous-time model identification. Application to a multivariable winding process , 2001 .

[22]  Hugues Garnier,et al.  Direct identification of continuous-time linear parameter-varying input/output models , 2011 .

[23]  Thor I. Fossen,et al.  Guidance and control of ocean vehicles , 1994 .

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

[25]  J. R. Trapero,et al.  Recursive Estimation and Time-Series Analysis. An Introduction for the Student and Practitioner, Second edition, Peter C. Young. Springer (2011), 504 pp., Hardcover, $119.00, ISBN: 978-3-642-21980-1 , 2015 .

[26]  E. D. Gilles,et al.  Drift dynamics modeling for automatic track-keeping of inland vessels , 2003 .