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Error-driven-based adaptive nonlinear feedback control of course-keeping for ships

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Abstract

This paper presents a robust adaptive nonlinear feedback algorithm based on error-driven function for ships course-keeping which are subjected to the unknown time-varying disturbances, uncertain ship model parameters and control saturation. Nonlinear feedback and adaptive techniques are used to design the control law. The error-driven function is designed to avoid the input saturation and adjust the control gain. The designed adaptive law not only achieves the self-regulation of course-keeping control system but also adjusts parameters adaptively. In addition, the Lyapunov direct method is utilized to analyze the stability of course-keeping system. Theoretical analysis indicates that the designed control law can achieve the ship course-keeping while ensuring that all signals are bound. Finally, the effectiveness of the developed control strategy is demonstrated by simulations and comparative results.

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References

  1. Zhang XK (2012) Ship motion concise robust control. Science Press, Beijing, pp 1–15

    Google Scholar 

  2. Skjetne R, Smogeli O, Fossen TI (2004) Modeling, identification and adaptive maneuvering of Cybership II: a complete design with experiments. Proc IFAC Conf Control Appl Marine Syst 2004:203–208

    Google Scholar 

  3. Ge SS, He W, How BVE, Choo YS (2010) Boundary control of a coupled nonlinear flexible marine ris. IEEE Trans Control Syst Technol 18(5):1080–1091

    Article  Google Scholar 

  4. Do KD, Jiang ZP, Pan J (2003) Robust global stabilization of underactuated ships on a linear course: state and output feedback. Int J Control 76(1):1–17

    Article  MathSciNet  Google Scholar 

  5. Zhang ST, Guang R (2005) Fuzzy adaptive control for ship steering autopilot based on backstepping technique. J Traffic Trans Eng 5(4):72–76

    Google Scholar 

  6. Le M-D et al (2003) A new and effective fuzzy PID autopilot for ships. Inst Electr Electron Engineers (IEEE) 3:1411–1415

    Google Scholar 

  7. Le M-D et al (2004) Study on a new and effective fuzzy PID ship autopilot. Artif Life Robot 8:197–220

    Article  Google Scholar 

  8. Feng YX, Zhang XK (2018) Ship course keeping control based on nonlinear decoration fuzzy PID. Ship Eng 40(1):202–205

    Google Scholar 

  9. Dlabač T et al (2019) PSO-BASED PID controller design for ship course-keeping autopilot. Brodogradnja 70(4):1–15

    Article  Google Scholar 

  10. Li G, Guo C, Li Y, Deng W (2015) Fractional-order PID Controller of USV course-keeping using hybrid GA-PSO algorithm 2015. In: 8th International Symposium on Computational Intelligence and Design (ISCID), p 506–509

  11. Ri-Fan J, Xian-Ku Z (2018) Grey prediction–based concise robust control of ship course-keeping. J Dalian Polytechnic Univ 37(1):63–66

    Google Scholar 

  12. Liu H, Ma N, Gu XC (2018) Ship course following and course keeping in restricted waters based on model predictive control. TransNav Int J Mar Navig Saf Sea Transp 12:305–312

    Article  Google Scholar 

  13. Xia GQ, Luan TT (2015) Study of ship heading control using RBF neural network. Int J Control Autom 8(10):227–236

    Article  Google Scholar 

  14. Bai YM, Cao YC, Li TS (2019) Optimized backstepping design for ship course following control based on actor-critic architecture with input saturation. IEEE Access 7:73516–73528

    Article  Google Scholar 

  15. Zhang XK, Zhang Q, Ren HX, Yang GP (2018) Linear reduction of backstepping algorithm based on nonlinear decoration for ship course-keeping control system. Ocean Eng 147:1–8

    Article  Google Scholar 

  16. Zhang Q, Jiang N, Hu YC, Pan DW (2017) Design of course-keeping controller for a ship based on backstepping and neural networks. Int J e-Navig Maritime Economy 7:34–41

    Article  Google Scholar 

  17. Zhang Q, Zhang XK (2017) Nonlinear improved concise backstepping control of course keeping for ships. IEEE Access 70(06):1–8

    Google Scholar 

  18. Songlin Hu, Yue D, Xie X, Ma Y, Yin X (2018) Stabilization of neural-network-based control systems via event-triggered control with nonperiodic sampled data. IEEE Trans Neural Netw Learn Syst 29(3):573–585

    Article  MathSciNet  Google Scholar 

  19. Swaroop D, Hedrick JK, Yip PP, Gerdes JC (2000) Dynamic surface control for a class of nonlinear systems. IEEETrans Autom Control 45(10):1893–1899

    Article  MathSciNet  Google Scholar 

  20. Zhang Q, Hu YC, Wang AQ et al (2017) Nonlinear adaptive control algorithm based on dynamic surface control and neural networks for ship course-keeping controller. J Appl Sci Eng 20(2):157–164

    Google Scholar 

  21. Zhang YX, Su DM, Xu HJ, Yong L (2017) Ship course-keeping control and design. Ship Sci Technol 39(10):64–66

    Google Scholar 

  22. Liu Z, Gu W, Gao D (2016) Ship course keeping using eigenvalue decomposition adaptive sliding mode control. Techno-Ocean Kobe 2016:687–691

    Google Scholar 

  23. Hu SL, Yue D, Yin XX, Xie XP, Ma Y (2016) Adaptive event-triggered control for nonlinear discrete-time systems. Int J Robust Nonlinear Control 26(18):4104–4125

    Article  MathSciNet  Google Scholar 

  24. Zhang ZH, Zhang XK, Zhang GQ (2018) ANFIS-based course-keeping control for ships using nonlinear feedback technique. J Mar Sci Technol 2018:1–8

    Google Scholar 

  25. Zhang XK, Zhang GQ (2016) Design of ship course-keeping autopilot using a sine function based nonlinear feedback technique. J Navig 69(02):246–256

    Article  Google Scholar 

  26. Ma C, Zhang XK (2017) Ship course keeping control based on nonlinear feedback of exponential function. Ship Eng 39:162–165

    Google Scholar 

  27. Hu S, Juang J (2011) Robust nonlinear ship course-keeping control under the influence of high wind and large wave disturbances. In: 2011 8th Asian Control Conference (ASCC), Kaohsiung, pp 393–398

  28. Yang PH, Hu SS, Su CM, Juang JY (2000) Nonlinear H ∞ control theory applied to ship course-keeping problem. Chin Autom Control 2000:688–693

    Google Scholar 

  29. Zwierzewicz Z (2014) On the ship course-keeping control system design by using robust and adaptive control. In: 2014 19th International conference on methods and models in automation and robotics (MMAR), Miedzyzdroje, pp 189–194

  30. Liu ZQ (2017) Ship adaptive course keeping control with nonlinear disturbance observer. IEEE Access 5:17567–17575

    Article  Google Scholar 

  31. Sun XF, Chen WS (2016) Global generalized exponential/finite-time control for course keeping of ships. Int J Control 89(6):1169–1179

    Article  MathSciNet  Google Scholar 

  32. Jia XL, Yang YS (1997) Mathematical model of ship motion. Dalian Maritime University Press, China

    Google Scholar 

  33. Fossen TI (2011) Handbook of marine craft hydrodynamics and motion control. Wiley, Hoboken

    Book  Google Scholar 

  34. Zhang XK, Jin YC (2013) Control system modeling and digital simulation. Dalian Maritime University Press, Dalian

    Google Scholar 

  35. Li JF, Li TS (2013) Direct adaptive neural network tracking control with input saturation. J Appl Sci-Electron Inf Eng 31(3):294–302

    Google Scholar 

  36. Kallstrom CG (1982) Identification and adaptive control applied to ship steering. Ph. D. Thesis. Lund Institute of Technology, Lund, Sweden

  37. Wang HD, Wang QR, Yan L, Wu XZ (2014) A new robust adaptive backstepping for ship course-keeping control. Appl Mech Mater 536–537:793–979

    Article  Google Scholar 

  38. Zhang Q, Zhang XK, Im NK (2017) Ship nonlinear-feedback course keeping algorithm based on MMG model driven by bipolar sigmoid function for berthing. Int J Naval Archit Ocean Eng 9(9):525–536

    Article  Google Scholar 

  39. Zhang GQ, Zhang XK, Zheng YF (2015) Adaptive neural path-following control for under actuatedships in fields of marine practice. Ocean Eng 104:558–567

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the National Natural Science Foundation of China (61873071, 51911540478, G61773015), key research and development plan of Shandong province (2018GGX105014, 2019JZZY020712), Shandong Jiaotong University PhD Startup Foundation of Scientific Research and Shandong Jiaotong University “Climbing” Research Innovation Team Program (SDJTUC1802).

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Authors and Affiliations

  1. Navigation College, Shandong Jiaotong University, Weihai, 264209, Shandong, China

    Qiang Zhang, Meijuan Zhang & Yancai Hu

  2. School of Maritime, Zhejiang Ocean University, Zhoushan, 316022, Zhejiang, China

    Guibing Zhu

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  1. Qiang Zhang
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  2. Meijuan Zhang
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  3. Yancai Hu
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  4. Guibing Zhu
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Correspondence to Yancai Hu.

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Zhang, Q., Zhang, M., Hu, Y. et al. Error-driven-based adaptive nonlinear feedback control of course-keeping for ships. J Mar Sci Technol 26, 357–367 (2021). https://doi.org/10.1007/s00773-020-00741-6

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  • DOI: https://doi.org/10.1007/s00773-020-00741-6

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