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.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
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)
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.
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.
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)
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)
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)
Wang, Y., Xia, Y., Li, H., Zhou, P.: A new integral sliding mode design method for nonlinear stochastic systems. Automatica 90, 304–309 (2018)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Alatas, B.: Chaotic harmony search algorithms. Appl. Math. Comput. 216(9), 2687–2699 (2010)
Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Appl. Math. Comput. 188(2), 1567–1579 (2007)
Khalili, M., Kharrat, R., Salahshoor, K., Sefat, M.H.: Global dynamic harmony search algorithm: Gdhs. Appl. Math. Comput. 228(9), 195–219 (2014)
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)
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)
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)
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)
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)
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)
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)
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)
Mamdani, E.H.: Application of fuzzy algorithms for control of simple dynamic plant. Proc. Inst. Electr. Eng. 121(121), 1585–1588 (1974)
Li, H.X., Gatland, H.B.: Conventional fuzzy control and its enhancement. IEEE Trans. Syst. Man Cybern. B 26(5), 791–796 (1996)
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.
Ye, S.: Fuzzy sliding mode observer with dual SOGI-FLL in sensorless control of PMSM drives. ISA Trans. 85, 161–176 (2019)
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.
Wu, Z., Jiang, B., Karimi, H.R.: A logarithmic descent direction algorithm for the quadratic knapsack problem. Appl. Math. Comput. 369, 1–13 (2019)
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
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
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)
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).
About this article
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
- Dynamic positioning
- Low-level controller
- Permanent magnet synchronous motor
- Fuzzy control
- Chaotic random distribution harmony search