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Sliding-Mode Control of STENA DRILLMAX Drillship with Environmental Disturbances for Dynamic Positioning

  • C. S. ChinEmail author
  • C. S. Lio
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 582)

Abstract

A dynamic positioning (DP) system for a real drillship, STENA DRILLMAX is developed. Few controllers such as proportional-integral-derivative (PID) controller and sliding mode control (SMC) are compared. The DP system considers external disturbances due to ocean current, wind, and wave using the Pierson-Moskowitz spectrum. The drillship dynamics are described in the horizontal plane. The results show the feasibility of the DP model before actual implementation for STENA DRILLMAX drillship. The PID controller can be performed better than the SMC in heading control but less attractive for velocity regulation. However, the SMC gives a shorter path to reach a targeted position under the external disturbances.

Keywords

Dynamic positioning Proportional-integral-derivative Sliding mode control External environmental disturbances 

Notes

Acknowledgements

The authors are grateful to Newcastle University in Singapore.

References

  1. 1.
    Zheng, M., Zhou, Y., Yang, S., Li, L., Suo, Y.: Design of robust H∞ controller for dynamic positioning ships based on sampled-data control. In: 32nd Youth Academic Annual Conference of Chinese Association of Automation, Anhui, China, pp. 1106–1110 (2017)Google Scholar
  2. 2.
    Fu, M., Sun, J., Wang, D.: Research on thrust allocation of dynamic positioning ship with cycloidal propeller. In: 37th Chinese Control Conference (CCC), Wuhan, China, pp. 620–624 (2018)Google Scholar
  3. 3.
    Zhang, Z., Wu, D. Operator panel design for dynamic positioning simulator. In: 2018 Chinese Automation Congress (CAC), Xi’an, China, pp. 1020–1023 (2018)Google Scholar
  4. 4.
    Chen,Y., Yang, X., Liu, R.: A nonlinear sate estimate for dynamic positioning based on improved particle filter. In: 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, Chongqing, China, pp. 880–884 (2018)Google Scholar
  5. 5.
    Xie, D., Jia, B., Ren, Y.: Control System Design for Dynamic Positioning Ships Using Nonlinear Passive Observer Backstepping, 2018 Chinese Automation Congress (CAC), pp. 4221–4226. Xi’an, China (2018)Google Scholar
  6. 6.
    Chas, C.S., Ferreiro, R.: Introduction to ship dynamic positioning systems. J. Marit. Res. V(1), 79–96. Santander, Spain (2008)Google Scholar
  7. 7.
    Balchen, J.G., Jenssen, N.A., Saelid, S.: A dynamic positioning system based on Kalman filtering and optimal control. Int. J. Model. Ident. Control (IJMIC) 1(3), 135–163 (1980)CrossRefGoogle Scholar
  8. 8.
    Saelid, S., Jenssen, N.A., Balchen, J.G.: Design and analysis of a dynamic positioning system based on Kalman filtering and optimal control. IEEE Trans. Autom. Control 28(3), 331–339 (1983)CrossRefGoogle Scholar
  9. 9.
    Agostinho, A.C., Moratelli Jr., L., Tannuri, E.A., Morishita, H.M.: Sliding mode control applied to offshore dynamic positioning systems. In: Proceedings of the IFAC International Conference on Manoeuvring, Guarujá, Brazil (2009)CrossRefGoogle Scholar
  10. 10.
    Ho, W.H., Chen, S.H., Chou, J.H.: Optimal control of Takagi-Sugeno fuzzy-model-based systems representing dynamic ship positioning systems. Appl. Soft Comput. 13, 3197–3210 (2013)CrossRefGoogle Scholar
  11. 11.
    Yin, Y., Xia, L., Song, L., Ren, Z.: The ship IPMS networked control system modelling and design. Int. J. Model. Ident. Control (IJMIC) 20(3), 234–241 (2013)CrossRefGoogle Scholar
  12. 12.
    Du, J., Yang, Y., Wang, D., Guo, C.: A robust adaptive neural networks controller for maritime dynamic positioning system. Neurocomputing 110, 128–136 (2013)CrossRefGoogle Scholar
  13. 13.
    Fang, M.C., Lee, Z.Y.: Application of neuro-fuzzy algorithm to portable dynamic positioning control system for ships. Int. J. Naval Archit. Ocean Eng. 8, 38–52 (2016)CrossRefGoogle Scholar
  14. 14.
    Wang, Y., Guo, C., Sun, F., Shen, Z., Guo, D.: Dynamic neural-fuzzified adaptive control of ship course with parametric modelling uncertainties. Int. J. Model. Ident. Control (IJMIC) 13(4), 251–258 (2011)CrossRefGoogle Scholar
  15. 15.
    Wang, M., Li, H.S., Qing, M., Rong, B.G.: Intelligent control algorithm for ship dynamic positioning. Arch. Control Sci. 24(LX), 4, 479–497 (2014)Google Scholar
  16. 16.
    Chang, W.J., Ku, C.C., Huang, B.J.: Multi-constrained fuzzy intelligent control for uncertain discrete systems with complex noises: an application to ship steering systems. J. Mar. Eng. Technol. 16(1), 11–21 (2017)CrossRefGoogle Scholar
  17. 17.
    IMCA M 198. Dynamic Positioning Station Keeping Incidents—Incidents Reported for 2007 (2009)Google Scholar
  18. 18.
    Society of Naval Architects and Marine Engineers (SNAME), Principles of Naval Architecture, Vol. III, pp. 41, Section 3—Ship Responses to Regular Waves (1989)Google Scholar
  19. 19.
    Lio, C.S.: Development of a Control System for the Dynamic Positioning of Ships. Dissertation for Bachelor of Engineering in Marine Engineering, School of Marine Science and Technology, Newcastle University (2017)Google Scholar
  20. 20.
    Fossen, T.I.: Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley, UK (2011)CrossRefGoogle Scholar
  21. 21.
    Chin, C.S., Lin, W.P.: Robust genetic algorithm and fuzzy inference mechanism embedded in sliding-mode controller for uncertain underwater robot. IEEE/ASME Trans. Mechatron. 23(2), 655–666 (2018)CrossRefGoogle Scholar
  22. 22.
    Chin, C.S., Lau, M.W.S., Low, E., Seet, G.G.: Software for modelling and simulation of a remotely operated vehicle. Int. J. Simul. Model. 5(3), 114–125 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Faculty of Science, Agriculture, and EngineeringNewcastle University in SingaporeSingaporeSingapore

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