Hostname: page-component-78c5997874-8bhkd Total loading time: 0 Render date: 2024-11-08T08:36:23.577Z Has data issue: false hasContentIssue false
  • English
  • Français

Asymptotic Regulation of Dynamically Positioned Vessels with Unknown Dynamics and External Disturbances

Published online by Cambridge University Press:  18 June 2019

Xin Hu Open the ORCID record for Xin Hu [Opens in a new window] ,
Jialu Du Open the ORCID record for Jialu Du [Opens in a new window] ,
Jian Li  and
Yuqing Sun
Xin Hu
Affiliation:
(School of Marine Electrical Engineering, Dalian Maritime University, Dalian, Liaoning, 116026, China)
Jialu Du*
Affiliation:
(School of Marine Electrical Engineering, Dalian Maritime University, Dalian, Liaoning, 116026, China)
Jian Li
Affiliation:
(School of Marine Electrical Engineering, Dalian Maritime University, Dalian, Liaoning, 116026, China)
Yuqing Sun
Affiliation:
(School of Marine Engineering, Dalian Maritime University, Dalian, Liaoning, 116026, China)
*
E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

A robust adaptive nonlinear asymptotic regulating control law is designed for dynamically positioned vessels exposed to unknown time-varying external disturbances incorporating Fuzzy Logic Systems (FLSs), projection operators, and the “robustifying” term into the vectorial backstepping technique. The FLSs approximate the vessel unknown dynamics and the update laws based on the online projection operators update the fuzzy weight vectors. The robustifying term handles the external disturbances and the fuzzy approximation errors. The designed Dynamic Positioning (DP) control law achieves asymptotic regulation of the vessel's position and heading and makes the other signals in the DP closed-loop control system of vessels be uniformly ultimately bounded. Simulations based on the Marine System Simulator toolbox validate the designed DP control law.

Keywords

Dynamic positioningUncertaintiesVectorial backsteppingAsymptotic regulation
Type
Research Article
Information
The Journal of Navigation , Volume 73 , Issue 2 , March 2020 , pp. 253 - 266
Copyright
Copyright © The Royal Institute of Navigation 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bertin, D., Bittanti, S., Meron, i S. and Savaresi, S. M. (2000). Dynamic positioning of a single-thruster vessel by feedback linearization. Proceedings of the IFAC conference on manoeuvring and control of marine craft, Aalborg, Denmark, 275–280.CrossRefGoogle Scholar
Chang, W. J., Liang, H. J.and Ku, C. C. (2010). Fuzzy controller design subject to actuator saturation for dynamic ship positioning systems with multiplicative noises. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 224(6), 725–736.Google Scholar
Chang, W. J., Chen, G. J.and Yeh, Y. L. (2002). Fuzzy control of dynamic positioning systems for ships. Journal of Marine Science and Technology, 10(1), 47–53.Google Scholar
Daniel, J. J. S. (1984). Dynamic positioning systems. The Journal of Navigation, 37(2), 264–270.CrossRefGoogle Scholar
Do, K. D. (2011). Global robust and adaptive output feedback dynamic positioning of surface ships. Journal of Marine Science and Application, 10(3), 325–332.CrossRefGoogle Scholar
Du, J. L., Hu, X., Krstić, M.and Sun, Y. Q. (2016). Robust dynamic positioning of ships with disturbances under input saturation. Automatica, 73, 207–214.CrossRefGoogle Scholar
Fossen, T. I. (2011). Handbook of marine craft hydrodynamics and motion control. John Wiley & Sons, Inc., Chichester, UK.CrossRefGoogle Scholar
Fossen, T. I. and Strand, J. P. (1999). Passive nonlinear observer design for ships using Lyapunov methods: Full-scale experiments with a supply vessel. Automatica, 35(1), 3–16.CrossRefGoogle Scholar
Gang, T. (1997). A simple alternative to the Barbalat lemma. IEEE Transactions on Automatic Control, 42(5), 698–698.Google Scholar
Hassani, V., Sørensen, A. J., Pascoal, A. M. and Aguiar, A. P. (2012). Multiple model adaptive wave filtering for dynamic positioning of marine vessels. Proceedings of the 2012 American Control Conference, Montreal, QC, Canada, 6222–6228.CrossRefGoogle Scholar
Hassani, V., Sorensen, A. J., Pascoal, A. M.and Athans, M. (2017). Robust dynamic positioning of offshore vessels using mixed-μ synthesis modeling, design, and practice. Ocean Engineering, 129, 389–400.CrossRefGoogle Scholar
Hu, X., Du, J. L. and Shi, J. W. (2015). Adaptive fuzzy controller design for dynamic positioning system of vessels. Applied Ocean Research, 53, 46–53.CrossRefGoogle Scholar
Hu, X., Du, J. L. and Sun, Y. Q. (2017). Robust adaptive control for dynamic positioning of ships. IEEE Journal of Oceanic Engineering, 42(1), 826–835.CrossRefGoogle Scholar
Krstić, M., Kanellakopoulos, I.and Kokotović, P. (1995). Nonlinear and adaptive control design. New York, USA: John Wiley & Sons, Inc.Google Scholar
Kwan, C. M., Dawson, D. M.and Lewis, F. L. (1995). Robust adaptive control of robots using neural network: global tracking stability. Proceedings of the 34th Conference on Decision & Control, New Orleans, LA, December, 1846–1851.CrossRefGoogle Scholar
Li, Y. M. and Tong, S. C. (2016). Hybrid adaptive fuzzy control for uncertain MIMO nonlinear systems with unknown dead-zones. Information Sciences, 328(5), 97–114.CrossRefGoogle Scholar
Loria, A., Fossen, T. I. and Panteley, E. (2000). A separation principle for dynamic positioning of ships: Theoretical and experimental results. IEEE Transactions on Control Systems Technology, 8(2), 332–343.CrossRefGoogle Scholar
Nguyen, T. D., Sorensen, A. J.and Quek, S. T. (2007). Design of hybrid controller for dynamic positioning from calm to extreme sea conditions. Automatica, 43, 768–785.CrossRefGoogle Scholar
Tannuri, E. A., Agostinho, A. C., Morishita, H. M. and Moratelli, L. Jr (2010). Dynamic positioning systems: An experimental analysis of sliding mode control. Control Engineering Practice, 18(3), 1121–1132.CrossRefGoogle Scholar
Veksler, A., Johansen, T. A., Borrelli, F.and Realfsen, B. (2016). Dynamic positioning with model predictive control. IEEE Transactions on Control Systems Technology, 24(1), 1340–1353.CrossRefGoogle Scholar
Wang, L. X. (1997). A course in fuzzy systems and control. Prentice-Hall, Inc. Upper Saddle River, NJ, USA.Google Scholar
Xiang, X. B., Yu, C. Y.and Zhang, Q. (2017). Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties. Computers and Operations Research, 84, 165–177.CrossRefGoogle Scholar
Yu, C. Y., Xiang, X. B., Zhang, Q.and Xu, G. H. (2018). Adaptive fuzzy trajectory tracking control of an under-actuated autonomous underwater vehicle subject to actuator saturation. International Journal of Fuzzy Systems, 20(1), 269–279.CrossRefGoogle Scholar