“Dynamic positioning, system identification and control of marine vessels”
Authors: Nour Bargouth, Christer Dalen and David Di Ruscio,Affiliation: University of South-Eastern Norway
Reference: 2022, Vol 43, No 3, pp. 111-117.
Keywords: Dynamic positioning, Kalman filtering, optimal control, model free, linear quadratic, model predictive, control
Abstract: In this paper, various modern model based optimal control methods (as well as one model-free or data-driven) are applied to the dynamical positioning problem of vessels, i.e. we seek to control the surge, sway and yaw motion, using the thrusters and propellers, subject to environmental disturbances, i.e. wind and current. The low-frequency part of Balchen's nonlinear vessel model is selected for these tests.
PDF (3378 Kb)
DOI: 10.4173/mic.2022.3.3References:
[1] Aastrom, K. and Haegglund, T. (1995). PID Controllers: Theory, Design, and Tuning, Instrument Society of America.
[2] Balchen, J.G., Jenssen, N.A., Mathisen, E., and Sælid, S. (1980). A Dynamic Positioning System Based on Kalman Filtering and Optimal Control, Modeling, Identification and Control. 1(3):135--163. doi:10.4173/mic.1980.3.1
[3] Dalen, C., DiRuscio, D., and Nilsen, R. (2015). Model-free optimal anti-slug control of a well-pipeline-riser in the K-Spice/LedaFlow simulator, Modeling, Identification and Control. 36(3):179--188. doi:10.4173/mic.2015.3.5
[4] DiRuscio, D. (2012). Discrete LQ optimal control with integral action: A simple controller on incremental form for MIMO systems, Modeling, Identification and Control. 33(2):35--44. doi:10.4173/mic.2012.2.1
[5] DiRuscio, D. (2013). Model Predictive Control with Integral Action: A simple MPC algorithm, Modeling, Identification and Control. 34(3):119--129. doi:10.4173/mic.2013.3.2
[6] MATLAB. (2020). version 9, 9.0.1718557 (R2020b). The MathWorks Inc., Natick, Massachusetts, USA. Control System Toolbox, Version 10.9. System Identification Toolbox, Version 9.13.
[7] Morari, M., Garcia, C.E., and Prett, D.M. (1988). Model predictive control: Theory and practice, IFAC Proceedings Volumes. 21(4):1--12. IFAC Workshop on Model Based Process Control, Atlanta, GA, USA, 13-14 June. doi:10.1016/B978-0-08-035735-5.50006-1
[8] Saelid, S., JENSSEN, N., and BALCHEN, J. (1983). Design and analysis of a dynamic positioning system based on kalman filtering and optimal control, Automatic Control, IEEE Transactions on. 28:331 -- 339. doi:10.1109/TAC.1983.1103225
[9] Seborg, D., Edgar, T.F., and Mellichamp, D.A. (1989). Process Dynamics and Ciontrol, John Wiley and Sons.
[10] Skogestad, S. (2003). Simple analytic rules for model reduction and PID controller tuning, Journal of Process Control. 13(4):291--309. doi:10.1016/S0959-1524(02)00062-8
[11] Sorensen, A.J., Sagatun, S.I., and Fossen, T.I. (1996). Design of a dynamic positioning system using model-based control, Modeling, Identification and Control. 17(2):135--151. doi:10.4173/mic.1996.2.6
BibTeX:
@article{MIC-2022-3-3,
title={{Dynamic positioning, system identification and control of marine vessels}},
author={Bargouth, Nour and Dalen, Christer and Di Ruscio, David},
journal={Modeling, Identification and Control},
volume={43},
number={3},
pages={111--117},
year={2022},
doi={10.4173/mic.2022.3.3},
publisher={Norwegian Society of Automatic Control}
};