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Robust Fuzzy Adaptive Output Feedback Optimal Tracking Control for Dynamic Positioning of Marine Vessels with Unknown Disturbances and Uncertain Dynamics

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Abstract

In this paper, a robust adaptive output feedback optimal tracking control design based on fuzzy logic systems (FLSs) is proposed for dynamic positioning (DP) of marine vessels with unknown environmental disturbances and uncertain dynamics. Firstly, a fuzzy state observer (FSO) is developed to obtain the unmeasured velocities and approximate the uncertain dynamics of the DP system. Then, the vectorial backstepping technique is adopted to design a feedforward controller. A FLS-based single critic structure is established to approximate the performance index function. Subsequently, an adaptive optimal feedback controller is presented by utilizing adaptive dynamic programming (ADP) method. Furthermore, the unknown environmental disturbances are estimated by a disturbance observer (DO) via the FSO to make the DP system robust. Therefore, the entire DP optimal control scheme is comprised of a feedforward controller, a feedback controller and an estimation of disturbances. The proposed robust adaptive optimal tracking control method can track the desired trajectory, and it is proved that all signals in the closed-loop DP system are uniformly ultimately bounded (UUB). Finally, simulation results are presented to illustrate the validity of the proposed DP control scheme.

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References

  1. Du, J.L., Hu, X., Krstić, M., et al.: Dynamic positioning of ships with unknown parameters and disturbances. Control Eng. Pract. 76, 22–30 (2018)

    Article  Google Scholar 

  2. Sørensen, A.J.: A survey of dynamic positioning control systems. Annu. Rev. Control. 35(1), 123–136 (2011)

    Article  Google Scholar 

  3. Gao, X.Y., Li, T.S., Shan, Q.H., et al.: Online optimal control for dynamic positioning of vessels via time-based adaptive dynamic programming. J. Ambient Intell. Humaniz. Comput. (2019). https://doi.org/10.1007/s12652-019-01522-9

    Article  Google Scholar 

  4. Liu, L., Zhang, W.D., Wang, D., et al.: Event-triggered extended state observers design for dynamic positioning vessels subject to unknown sea loads. Ocean Eng. (2020). https://doi.org/10.1016/j.oceaneng.2020.107242

    Article  Google Scholar 

  5. Fossen, T.I., Grøvlen, A.: Nonlinear output feedback control of dynamically positioned ships using vectorial observer backstepping. IEEE Trans. Control Syst. Technol. 6(1), 121–128 (1998)

    Article  Google Scholar 

  6. Fossen, T.I., Strand, J.P.: Passive nonlinear observer design for ships using Lyapunov methods: full-scale experiments with a supply vessel. Automatica 35(1), 3–16 (1999)

    Article  MathSciNet  Google Scholar 

  7. Do, K.D.: Global robust and adaptive output feedback dynamic positioning of surface ships. J. Mar. Sci. Appl. 10(3), 325–332 (2011)

    Article  Google Scholar 

  8. Du, J.L., Hu, X., Liu, H., et al.: Adaptive robust output feedback control for a marine dynamic positioning system based on a high-gain observer. IEEE Trans. Neural Netw. Learn. Syst. 26(11), 2775–2786 (2015)

    Article  MathSciNet  Google Scholar 

  9. Lin, X.G., Nie, J., Jiao, Y., et al.: Nonlinear adaptive fuzzy output-feedback controller design for dynamic positioning system of ships. Ocean Eng. 158, 186–195 (2018)

    Article  Google Scholar 

  10. Liang, K., Lin, X.G., Chen, Y., et al.: Adaptive sliding mode output feedback control for dynamic positioning ships with input saturation. Ocean Eng. (2020). https://doi.org/10.1016/j.oceaneng.2020.107245

    Article  Google Scholar 

  11. Balchen, J.G., Jenssen, N.A., Sælid, S.: Dynamic positioning of floating vessels based on Kalman filtering and optimal control. In: Proceedings of the 19th IEEE Conference on Decision and Control, New York (1980)

  12. Sælid, 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, 31–339 (1983)

    Article  Google Scholar 

  13. 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(7), 3197–3210 (2013)

    Article  Google Scholar 

  14. Huang, H., Sharma, S., Zhuang, Y., et al.: Dynamic positioning of an uninhabited surface vehicle using state-dependent Riccati equation and pseudospectral method. Ocean Eng. 133, 215–223 (2017)

    Article  Google Scholar 

  15. Jayasiri, A., Nandan, A., Imtiaz, S., et al.: Dynamic positioning of vessels using a UKF-based observer and an NMPC-based controller. IEEE Trans. Autom. Sci. Eng. 14(4), 1778–1785 (2017)

    Article  Google Scholar 

  16. Peng, Z., Liu, L., Wang, J.: Output-feedback flocking control of multiple autonomous surface vehicles based on data-driven adaptive extended state observers. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/TCYB.2020.3009992

    Article  Google Scholar 

  17. Do, K.D.: Practical control of underactuated ships. Ocean Eng. 37(13), 1111–1119 (2010)

    Article  Google Scholar 

  18. Xia, G., Xue, J., Jiao, J.: Dynamic positioning control system with input time-delay using fuzzy approximation approach. Int. J. Fuzzy Syst. 20, 630–639 (2018)

    Article  MathSciNet  Google Scholar 

  19. Zhu, L., Li, T., Yu, R., et al.: Observer-based adaptive fuzzy control for intelligent ship autopilot with input saturation. Int. J. Fuzzy Syst. 22, 1416–1429 (2020)

    Article  Google Scholar 

  20. Chang, C., Chang, W.: Robust fuzzy control with transient and steady-state performance constraints for ship fin stabilizing systems. Int. J. Fuzzy Syst. 21, 518–531 (2019)

    Article  MathSciNet  Google Scholar 

  21. Cheng, Y., Sun, Z., Huang, Y., et al.: Fuzzy categorical deep reinforcement learning of a defensive game for an unmanned surface vessel. Int. J. Fuzzy Syst. 21, 592–606 (2019)

    Article  Google Scholar 

  22. Skjetne, R., Smogeli, Ø., Fossen, T.I.: Modeling, identification and adaptive maneuvering of Cybership II: a complete design with experiments. In: Proceedings of the IFAC Conference Control Applications in Marine Systems, pp. 203–208 (2004)

  23. Hao, L., Zhang, H., Guo, G., et al.: Quantized sliding mode control of unmanned marine vehicles: various thruster faults tolerated with a unified model. IEEE Trans. Syst. Man Cybern.: Syst. (2019). https://doi.org/10.1109/TSMC.2019.2912812

    Article  Google Scholar 

  24. Hao, L., Zhang, H., Li, H., et al.: Sliding mode fault-tolerant control for unmanned marine vehicles with signal quantization and time-delay. Ocean Eng. 215, 107882 (2020)

    Article  Google Scholar 

  25. Liu, L., Wang, D., Peng, Z., et al.: Bounded neural network control for target tracking of underactuated autonomous surface vehicles in the presence of uncertain target dynamics. IEEE Trans. Neural Netw. Learn. Syst. 30(4), 1241–1249 (2018)

    Article  MathSciNet  Google Scholar 

  26. Yu, W., Xu, H., Feng, H.: Robust adaptive fault-tolerant control of dynamic positioning vessel with position reference system faults using backstepping design. Int. J. Robust Nonlinear Control (2018). https://doi.org/10.1002/rnc.3873

    Article  MathSciNet  MATH  Google Scholar 

  27. Yu, W., Xu, H., Han, X.: Fault-tolerant control for dynamic positioning vessel with thruster faults based on the neural modified extended state observer. IEEE Trans. Syst. Man Cybern.: Syst. (2019). https://doi.org/10.1109/TSMC.2019.2956806

    Article  Google Scholar 

  28. Sun, W., Lin, W., Su, S., et al.: Reduced adaptive fuzzy decoupling control for lower limb exoskeleton. IEEE Trans. Cybern. (2020). https://doi.org/10.1109/TCYB.2020.2972582

    Article  Google Scholar 

  29. Sun, W., Wu, Y., Sun, Y., et al.: Command filter-based finite-time adaptive fuzzy control for uncertain nonlinear systems with prescribed performance. IEEE Trans. Fuzzy Syst. 28(12), 3161–3170 (2020)

    Article  Google Scholar 

  30. Bai, W., Li, T., Tong, S.: NN reinforcement learning adaptive control for a class of nonstrict-feedback discrete-time systems. IEEE Trans. Cybern. 50(11), 4573–4584 (2020)

    Article  Google Scholar 

  31. Li, Y., Tong, S., Li, T.: Composite adaptive fuzzy output feedback control design for uncertain nonlinear strict-feedback systems with input saturation. IEEE Trans. Cybern. 45(10), 2299–2308 (2015)

    Article  Google Scholar 

  32. Dierks, T., Jagannathan, S.: Online optimal control of affine nonlinear discrete time systems with unknown internal dynamics by using time-based policy update. IEEE Trans. Neural Netw. Learn. Syst. 23(7), 1118–1129 (2012)

    Article  Google Scholar 

  33. Xiao, G.Y., Zhang, H.G., Luo, Y.: Online optimal control of unknown discrete-time nonlinear systems by using time-based adaptive dynamic programming. Neurocomputing 165, 163–170 (2015)

    Article  Google Scholar 

  34. Zhang, H.G., Cui, L.L., Zhang, X., et al.: Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method. IEEE Trans. Neural Netw. 22(12), 2226–2236 (2011)

    Article  Google Scholar 

  35. Gao, W., Jiang, Y., Jiang, Z., et al.: Output-feedback adaptive optimal control of interconnected systems based on robust adaptive dynamic programming. Automatica 72, 37–45 (2016)

    Article  MathSciNet  Google Scholar 

  36. Zargarzadeh, H., Dierks, T., Jagannthan, S.: Optimal control of nonlinear continuous-time systems in strict-feedback form. IEEE Trans. Neural Netw. Learn. Syst. 26(10), 2535–2549 (2015)

    Article  MathSciNet  Google Scholar 

  37. Sun, K.K., Li, Y.M., Tong, S.C.: Fuzzy adaptive output feedback optimal control design for strict-feedback nonlinear systems. IEEE Trans. Syst. Man Cybern. Syst. 47(1), 33–44 (2017)

    Article  Google Scholar 

  38. Fossen, T.I.: Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley, Chichester (2011)

    Book  Google Scholar 

  39. Wang, L.X.: Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Prentice-Hall, Englewood Cliffs (1994)

    Google Scholar 

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Acknowledgements

We sincerely thank the anonymous reviewers for all the thoughtful and very constructive comments. The insightful suggestions help us to improve the quality of this paper significantly. We would like to thank the editor for coordinating the review of this paper.

Funding

This work is supported in part by the National Natural Science Foundation of China (under Grant Nos. 51939001, 61976033, 61903092); the Science and Technology Innovation Funds of Dalian (under Grant No. 2018J11CY022); the Liaoning Revitalization Talents Program (under Grant Nos. XLYC1908018); the Fundamental Research Funds for the Central Universities (under Grant No. 3132019345); the Doctoral Innovation Project of Dalian Maritime University (under Grant No. BSCXXM002).

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Correspondence to Tieshan Li.

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Gao, X., Li, T., Yuan, L. et al. Robust Fuzzy Adaptive Output Feedback Optimal Tracking Control for Dynamic Positioning of Marine Vessels with Unknown Disturbances and Uncertain Dynamics. Int. J. Fuzzy Syst. 23, 2283–2296 (2021). https://doi.org/10.1007/s40815-021-01101-1

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  • DOI: https://doi.org/10.1007/s40815-021-01101-1

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