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An optimal thrust allocation algorithm with bivariate thrust efficiency function considering hydrodynamic interactions

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Abstract

Thrust allocation is of great importance for the application of Dynamic Positioning System (DPS). For dynamically positioned vessels, the thrust allocation is formulated as a nonlinear optimization problem, where the demanded forces and moments are distributed among the available thrusters. Both hydrodynamic interaction effects and physical limitations of thrusters affect the thrust generation. Therefore, the thrust allocation algorithm can be improved if these effects are considered. We propose a bivariate thrust efficiency function, dealing with both the forward thruster angle and the rear thruster angle, to describe the thrust loss. The thrust efficiency function is obtained from the model tests and approximated by the Radial Basis Function (RBF) neural network. The consequent thrust allocation problem is solved by the Sequential Quadratic Programming (SQP) algorithm with slack variables. The numerical simulations demonstrate a maximum power reduction of 16.03% compared with the forbidden zone algorithm. The proposed algorithm can also enhance system stability, highlighting the advantages of taking bivariate thrust efficiency function into account.

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References

  1. Li B (2013) Study on Environmental Loads Feed-forward Dynamic Positioning System, Master degree thesis. Shanghai Jiao Tong University, Shanghai

  2. Zhao ZG, Yang JM, Wang L, Cheng JY (2002) The development and research method of dynamic positioning system. Ocean Eng 20(1):91–97

    Google Scholar 

  3. Tannuri EA, Morishita HM (2006) Experimental and numerical evaluation of a typical dynamic positioning system. Appl Ocean Res 28(2):133–146

    Article  Google Scholar 

  4. Johansen TA, Fossen TI, Berge SP (2004) Constrained nonlinear control allocation with singularity avoidance using sequential quadratic programming. IEEE Trans Control Syst Technol 12(1):211–216

    Article  Google Scholar 

  5. Nocedal J, Wright S (2006) Numerical optimization, Chapter 7 (p 164) and 18 (p 529). Springer, Berlin

    Google Scholar 

  6. Fossen TI (2011) Handbook of marine craft hydrodynamics and motion control, Chapters 12 and 13. Wiley

    Book  Google Scholar 

  7. Johansen TA, Fossen TI (2013) Control allocation—a survey. Automatica 49(5):1087–1103

    Article  MathSciNet  Google Scholar 

  8. Wu DF, Ren FK, Zhang WD (2016) An energy optimal thrust allocation method for the marine dynamic positioning system based on adaptive hybrid artificial bee colony algorithm. Ocean Eng 118:216–226

    Article  Google Scholar 

  9. Gao DJ, Wang XY, Wang TZ, Wang YD, Xu XB (2019) Optimal thrust allocation strategy of electric propulsion ship based on improved non-dominated sorting genetic algorithm II. IEEE Access 7:135247–135255

    Article  Google Scholar 

  10. Yadav P, Kumar R, Panda SK, Chang CS (2012) Energy-efficient thrust allocation for semi-submersible oil rig platforms using improved harmony search algorithm. IEEE Trans Industr Inf 8(4):913–924

    Article  Google Scholar 

  11. Skjong S, Pedersen E (2017) Nonangular MPC-based thrust allocation algorithm for marine vessels—a study of optimal thruster commands. IEEE Transact Transp Electrif 3(3):792–807

    Article  Google Scholar 

  12. Liu FR, Tang SQ, Chen CP (2014) Dynamic thrust allocation of dynamic positioning vessel based on model predictive control. Adv Mater Res 1049:996–999

    Article  Google Scholar 

  13. Veksler A, Johansen TA, Borrelli F, Realfsen B (2016) Dynamic positioning with model predictive control. IEEE Transact Control Syst Technol 24(4):1340–1353

    Article  Google Scholar 

  14. Ye BY, Xiong JB, Wang QR, Luo Y (2020) Design and implementation of pseudo-inverse thrust allocation algorithm for ship dynamic positioning. IEEE Access 8:16830–16837

    Article  Google Scholar 

  15. Kim SW, Kim MH (2018) Fuel-optimal thrust-allocation algorithm using penalty optimization programing for dynamic-positioning-controlled offshore platforms. Energies 11(8):2128

    Article  Google Scholar 

  16. Zhao L, Roh M-I (2015) A thrust allocation method for efficient dynamic positioning of a semisubmersible drilling rig based on the hybrid optimization algorithm. Math Probl Eng 2015:1–12

    Google Scholar 

  17. Serraris J-J (2009) Time domain analysis for DP simulations. In: ASME 2009 28th international conference on ocean, offshore and arctic engineering. pp 595–605

  18. Cozijn JL, Hallmann R (2013) Thruster-interaction effects on a DP semi-submersible and a drill ship: measurement and analysis of the thruster wake flow. In: ASME 2013 32nd international conference on ocean, offshore and arctic engineering

  19. Wang L, Wang L, Yang SZ (2009) Analysis of dynamic positioning system performance for a semi-submersible platform. In: Proceedings of the international offshore and polar engineering conference. pp 192–198

  20. Yadav P, Kumar R, Panda SK, Chang CS (2014) Optimal thrust allocation for semisubmersible oil rig platforms using improved harmony search algorithm. IEEE J Ocean Eng 39(3):526–539

    Article  Google Scholar 

  21. Li B, Wang L (2014) Thrust allocation with dynamic forbidden sectors in dynamic positioning system. J Ship Mech 18(9):1024–1034

    Google Scholar 

  22. Xu SW, Wang XF, Wang L, Li B (2016) A dynamic forbidden sector skipping strategy in thrust allocation for marine vessels. Int J Offshore Polar Eng 26(2):175–182

    Article  MathSciNet  Google Scholar 

  23. Arditti F, Tannuri EA (2012) Experimental analysis of a thrust allocation algorithm for DP systems considering the interference between thrusters and thruster-hull. IFAC Proc Vol 45(27):43–48

    Article  Google Scholar 

  24. Arditti F, Cozijn JL, Van Daalen EFG (2014) An advanced thrust allocation algorithm for DP applications, taking into account interaction effects and physical limitations. In: ASME 2014 33rd international conference on ocean, offshore and arctic engineering. American Society of Mechanical Engineers Digital Collection

  25. Arditti F, Souza FL, Martins TC, Tannuri EA (2015) Thrust allocation algorithm with efficiency function dependent on the azimuth angle of the actuators. Ocean Eng 105:206–216

    Article  Google Scholar 

  26. Li GY, Yang LJ (2018) Neural·Fuzzy·Predictive Control and MATLAB Realization, Chapter 1, 4th edn. Publishing House of Electronics Industry, Beijing

    Google Scholar 

  27. Zhao M, Liu ZB, Ren BS, Liu HH (2010) Improved ANN algorithm based on the change of search direction. In: 2010 international conference on computational and information sciences. pp 529–532

  28. Arditti F, Cozijn H, Daalen EFGV, Tannuri EA (2018) Dynamic Positioning simulations of a Thrust Allocation Algorithm considering Hydrodynamic interactions. IFAC-PapersOnLine 51(29):122–127

    Article  Google Scholar 

  29. Nocedal J, Wright S (2006) Numerical optimization, Chapter 18.3. Springer, Berlin

    Google Scholar 

  30. Wang L, Yang J, He H, Xu S, Su T-C (2016) Numerical and experimental study on the influence of the set point on the operation of a thruster-assisted position mooring system. Int J Offshore Polar Eng 26(4):423–432

    Article  Google Scholar 

  31. He H, Wang L, Zhu Y, Xu S (2020) Numerical and experimental study on the docking of a dynamically positioned barge in float-over installation. Ships Offshore Struct. https://doi.org/10.1080/17445302.2020.1737452

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the financial support from the Ministry of Industry and Information Technology [Mooring position technology: floating support platform engineering (II)] and the National Natural Science Foundation of China (Grant 51979167).

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Authors and Affiliations

  1. State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, China

    Ziying Tang, Huacheng He, Lei Wang & Xuefeng Wang

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  1. Ziying Tang
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  2. Huacheng He
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  3. Lei Wang
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  4. Xuefeng Wang
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Correspondence to Lei Wang.

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Tang, Z., He, H., Wang, L. et al. An optimal thrust allocation algorithm with bivariate thrust efficiency function considering hydrodynamic interactions. J Mar Sci Technol 27, 52–66 (2022). https://doi.org/10.1007/s00773-021-00814-0

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