An Enhanced Fuzzy Control Strategy for Low-Level Thrusters in Marine Dynamic Positioning Systems Based on Chaotic Random Distribution Harmony Search

Abstract

The required control force vector is distributed by the thrusters in marine dynamic positioning system (DPS) to obtain the desired thrust and angle of each thruster. The thrust of the thruster is mapped to the speed of the thruster, and the low-level thrust controller adjusts the speed of the thruster to achieve the vessel’s dynamic position. Based on the previous research, the permanent magnet synchronous motor (PMSM) is selected as the low-level driving motor, and it is combined with the propeller to form the low-level thrusters for the DPS. The fuzzy control strategy is selected for the PMSM controller design, and the proposed chaotic random distribution harmony search (CRDHS) algorithm is used to optimize the fuzzy controller rules’ weights. The proposed CRDHS employs a chaotic map for rule weight adaptation in order to prevent the conventional harmony search to get stuck on local solutions. By adjusting the weights of each fuzzy rule via CRDHS, more consistent control performance is achieved. The required fuzzy output and the fuzzy controller are used for control of the PMSM. Simulation results show that under load disturbance, the fuzzy controller based on CRDHS has better control performance.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

References

  1. 1.

    Hansen, J.F., Wendt, F.: History and state of the art in commercial electric ship propulsion, integrated power systems, and future trends. Proc. IEEE 103(12), 2229–2242 (2015)

    Article  Google Scholar 

  2. 2.

    J. M. Apsley, A. Gonzalez-Villasenor, M. Barnes, A. C. Smith, S. Williamson, J. D. Schud- debeurs, P. J. Norman, C. D. Booth, G. M. Burt, and J. R. Mcdonald, “Propulsion drive models for full electric marine propulsion systems,” IEEE Transactions on Industry Applications, vol. 45, no. 2, pp. 676–684, 2009.

  3. 3.

    J. S. Thongam, M. Tarbouchi, A. F. Okou, D. Bouchard, and R. Beguenane, “Trends in naval ship propulsion drive motor technology,” 2013 IEEE Electrical Power & Energy Conference, pp. 1–5, 2013.

  4. 4.

    Su, Y.X., Zheng, C.H., Duan, B.Y.: Automatic disturbances rejection controller for precise motion control of permanent-magnet synchronous motors. IEEE Trans. Industr. Electron. 52(3), 814–823 (2005)

    Article  Google Scholar 

  5. 5.

    Cai, R., Zheng, R., Liu, M., Li, M.: Robust control of pmsm using geometric model reduction and -synthesis. IEEE Trans. Industr. Electron. 65(1), 498–509 (2018)

    Article  Google Scholar 

  6. 6.

    Kommuri, S.K., Defoort, M., Karimi, H.R., Veluvolu, K.C.: A robust observer-based sensor fault-tolerant control for pmsm in electric vehicles. IEEE Trans. Industr. Electron. 63(12), 7671–7681 (2016)

    Article  Google Scholar 

  7. 7.

    Wang, Y., Xia, Y., Li, H., Zhou, P.: A new integral sliding mode design method for nonlinear stochastic systems. Automatica 90, 304–309 (2018)

    MathSciNet  MATH  Article  Google Scholar 

  8. 8.

    Wang, Y., Feng, Y., Zhang, X., Liang, J.: A new reaching law for antidisturbance sliding-mode control of pmsm speed regulation system. IEEE Trans. Power Electron. 35(4), 4117–4126 (2020)

    Article  Google Scholar 

  9. 9.

    Wang, Q., Yu, H., Wang, M., Qi, X.: An improved sliding mode control using disturbance torque observer for permanent magnet synchronous motor. IEEE Access 7, 36691–36701 (2019)

    Article  Google Scholar 

  10. 10.

    Choi, H.H., Vu, N.T.T., Jung, J.W.: Digital implementation of an adaptive speed regulator for a pmsm. IEEE Trans. Power Electron. 26(1), 3–8 (2010)

    Article  Google Scholar 

  11. 11.

    Wang, Y., Zhou, W., Luo, J., Yan, H., Pu, H., Peng, Y.: Reliable intelligent path following control for a robotic airship against sensor faults. IEEE/ASME Trans. Mechatron. 24(6), 2572–2582 (2019)

    Article  Google Scholar 

  12. 12.

    Wai, R.J., Chang, H.H.: Backstepping wavelet neural network control for indirect field- oriented induction motor drive. IEEE Trans. Neural Networks 15(2), 367–382 (2004)

    Article  Google Scholar 

  13. 13.

    Siami, M., Khaburi, D.A., Rodriguez, J.: Simplified finite control set-model predictive control for matrix converters-fed pmsm drives. IEEE Trans. Power Electron. 33(3), 2438–2446 (2018)

    Article  Google Scholar 

  14. 14.

    Zhou, C., Quach, D.C., Xiong, N., Huang, S.: An improved direct adaptive fuzzy controller of an uncertain pmsm for web-based e-service systems. IEEE Trans. Fuzzy Syst. 23(1), 58–71 (2015)

    Article  Google Scholar 

  15. 15.

    Han, H.C., Hong, M.Y., Yong, K.: Implementation of evolutionary fuzzy pid speed controller for pm synchronous motor. IEEE Trans. Industr. Inf. 11(2), 540–547 (2013)

    Google Scholar 

  16. 16.

    Chaoui, H., Khayamy, M., Aljarboua, A.A.: Adaptive interval type-2 fuzzy logic control for pmsm drives with a modified reference frame. IEEE Trans. Industr. Electron. 64(5), 3786–3797 (2017)

    Article  Google Scholar 

  17. 17.

    Huang, H., Bhuiyan, M.Z.A., Tu, Q., Jiang, C., Xue, J., Ming, P., Li, P.: Fuzzy sliding mode control of servo control system based on variable speeding approach rate. Soft. Comput. 23, 13477–13487 (2019)

    Article  Google Scholar 

  18. 18.

    Cabrera, J.A., Castillo, J.J., Carabias, E., Ortiz, A.: Evolutionary optimization of a motorcycle traction control system based on fuzzy logic. IEEE Trans. Fuzzy Syst. 23(5), 1594–1607 (2015)

    Article  Google Scholar 

  19. 19.

    Xu, J., Zhao, X., Srinivasan, D.: On optimal freeway local ramp metering using fuzzy logic control with particle swarm optimization. IET Intel. Transport Syst. 7(1), 95–104 (2013)

    Article  Google Scholar 

  20. 20.

    Zong, W.G., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simul. Trans. Soc. Model. Simul. Int. 2(2), 60–68 (2001)

    Google Scholar 

  21. 21.

    Alatas, B.: Chaotic harmony search algorithms. Appl. Math. Comput. 216(9), 2687–2699 (2010)

    MATH  Google Scholar 

  22. 22.

    Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188(2), 1567–1579 (2007)

    MathSciNet  MATH  Google Scholar 

  23. 23.

    Khalili, M., Kharrat, R., Salahshoor, K., Sefat, M.H.: Global dynamic harmony search algorithm: Gdhs. Appl. Math. Comput. 228(9), 195–219 (2014)

    MathSciNet  MATH  Google Scholar 

  24. 24.

    Portilla-Flores, E.A., Sánchez-Márquez, A., Flores-Pulido, L., Vega-Alvarado, E., Calva-Yáñez, M.B., Aponte-Rodríguez, J., Niño-Suárez, P.A.: Enhancing the harmony search algorithm performance on constrained numerical optimization. IEEE Access 5, 25759–25780 (2017)

    Article  Google Scholar 

  25. 25.

    Sarkhel, R., Das, N., Saha, A.K., Nasipuri, M.: An improved harmony search algorithm embedded with a novel piecewise opposition based learning algorithm. Eng. Appl. Artif. Intell. 67, 317–330 (2018)

    Article  Google Scholar 

  26. 26.

    Gao, M.L., Li, L.L., Sun, X.M., Luo, D.S.: Face tracking based on differential harmony search. IET Comput. Vision 9(1), 98–109 (2015)

    Article  Google Scholar 

  27. 27.

    Mahto, T., Mukherjee, V.: Fractional order fuzzy pid controller for wind energy based hybrid power system using quasi-oppositional harmony search algorithm. IET Gener. Trans. Distrib. 11(13), 3299–3309 (2017)

    Article  Google Scholar 

  28. 28.

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

    Article  Google Scholar 

  29. 29.

    Wu, D., Ren, F., Qiao, L., Zhang, W.: Active disturbance rejection controller design for dynamically positioned vessels based on adaptive hybrid biogeography-based optimization and differential evolution. ISA Trans. 78, 56–65 (2018)

    Article  Google Scholar 

  30. 30.

    Wu, D., Ren, F., Zhang, W.: An energy optimal thrust allocation method for the marine dynamic positioning system based on adaptive hybrid artificial bee colony algorithm. Ocean Eng. 118, 216–222 (2016)

    Article  Google Scholar 

  31. 31.

    Wu, D., Liu, X., Ren, F., Yin, Z.: An improved thrust allocation method for marine dynamic positioning system. Naval Eng. J. 129(3), 89–98 (2017)

    Google Scholar 

  32. 32.

    Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. Proc. Inst. Electr. Eng. 121(121), 1585–1588 (1974)

    Article  Google Scholar 

  33. 33.

    Li, H.X., Gatland, H.B.: Conventional fuzzy control and its enhancement. IEEE Trans. Syst. Man Cybern. B 26(5), 791–796 (1996)

    Article  Google Scholar 

  34. 34.

    A. N. Seghir, T. Henni, and M. Azira, “Fuzzy and adaptive fuzzy pi controller based vector control for permanent magnet synchronous motor,” 2013 10th IEEE International Conference on Networking, Sensing and Control, pp. 491–496, 2013.

  35. 35.

    Ye, S.: Fuzzy sliding mode observer with dual SOGI-FLL in sensorless control of PMSM drives. ISA Trans. 85, 161–176 (2019)

    Article  Google Scholar 

  36. 36.

    Z. Wu, H. R. Karimi, and C. Dang, “A deterministic annealing neural network algorithm for the minimum concave cost transportation problem,” IEEE Transactions on Neural Networks and Learning Systems, DOI: https://doi.org/10.1109/TNNLS.2019.2955137.

  37. 37.

    Wu, Z., Jiang, B., Karimi, H.R.: A logarithmic descent direction algorithm for the quadratic knapsack problem. Appl. Math. Comput. 369, 1–13 (2019)

    MathSciNet  MATH  Google Scholar 

  38. 38.

    Wang, N., Shun-Feng, Su.: Finite-time unknown observer based interactive trajectory tracking control of asymmetric underactuated surface vehicles. IEEE Trans. Control Syst. Technol. (2019). https://doi.org/10.1109/TCST.2019.2955657

    Article  Google Scholar 

  39. 39.

    N. Wang., Y. Gao., H. Zhao., C. Ki Ahn, Reinforcement learning-based optimal tracking control of an unknown unmanned surface vehicle, In: IEEE Transactions on Neural Networks and Learning Systems, DOI: https://doi.org/10.1109/TNNLS.2020.3009214

  40. 40.

    Wang, N., He, H.: Dynamics-level finite-time fuzzy monocular visual servo of an unmanned surface vehicle. IEEE Trans. Industr. Electron. 67(11), 9648–9658 (2020)

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the anonymous reviewer for his/her valuable suggestions and comments which help improve the quality of the paper. The authors would also like to thank the financial support from the National Natural Science Foundation of China (51809113, 51249006), the Fujian Provincial Science and Technology Department (2019H0019), the Fujian Province Natural Science Foundation (2018J01494), the Fujian Education Department (FBJG20180056, JT180266) and the Program for New Century Excellent Talents in Fujian University (KB16078).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Defeng Wu.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wu, D., Liao, Y., Hu, C. et al. An Enhanced Fuzzy Control Strategy for Low-Level Thrusters in Marine Dynamic Positioning Systems Based on Chaotic Random Distribution Harmony Search. Int. J. Fuzzy Syst. (2020). https://doi.org/10.1007/s40815-020-00989-5

Download citation

Keywords

  • Dynamic positioning
  • Low-level controller
  • Permanent magnet synchronous motor
  • Fuzzy control
  • Chaotic random distribution harmony search