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Computationally efficient MPC for path following of underactuated marine vessels using projection neural network

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Abstract

A practical model predictive control (MPC) for path following of underactuated marine vessels, which is a representative marine application, is presented in this paper. Taking advantage of the capability of dealing with multivariable system and input saturation, the MPC method is used to transform the underactuated control problem into the optimization problem with incorporation of input (rudder) constraints. Considering the implementation obstacle of solving optimization problem formulated by the MPC method efficiently, the projection neural network, which is known as parallel computational capability, is employed here to improve the computational efficiency. The full information of ship motion is normally difficult to obtain directly due to the lack of enough measurements; therefore, the state observer is also included. A simple linear model represented the main dynamics of path following of underactuated marine vessels is conceived as predictive (control design) model; meanwhile, in order to demonstrate the effectiveness of proposed control design, all the comparative studies are conducted on a nonlinear high-fidelity simulation model. The simulation results validate that the proposed control design is effective and efficient.

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References

  1. Ashrafiuon H, Muske KR, McNich LC (2010) Review of nonlinear tracking and set-point control approaches for autonomous underactuated marine vehicles. In: American control conference, Baltimore, MD, USA, pp 5203–5211

  2. Perez T (2005) Ship motion control: course keeping and roll stabilization using rudder and fins. Springer, Berlin

    Google Scholar 

  3. Fossen TI (2011) Handbook of marine craft hydrodynamics and motion control. Wiley, New York

    Book  Google Scholar 

  4. Ren RY, Zou ZJ, Wang YD, Wang XG (2018) Adaptive Nomoto model used in the path following problem of ships. J Mar Sci Technol 23(14):1–11

    Google Scholar 

  5. Zhang J, Sun TR, Liu ZL (2017) Robust model predictive control for path-following of underactuated surface vessels with roll constraints. Ocean Eng 143:125–132

    Article  Google Scholar 

  6. Maghenem M, Belleter DJW, Paliotta C, Pettersen KY (2017) Observer based path following for underactuated marine vessels in the presence of ocean currents: a local approach. 2017IFAC 50(1):13654–13661

    MATH  Google Scholar 

  7. Jiang ZP (2002) Global tracking control of underactuated ships by Lyapunov’s direct method. Automatica 38(2):301–309

    Article  MATH  Google Scholar 

  8. Do KD, Pan J (2006) Underactuated ships follow smooth paths with integral actions and without velocity measurements for feedback: theory and experiments. IEEE Trans Control Syst Technol 14(2):308–322

    Article  Google Scholar 

  9. Do KD (2016) Global robust adaptive path-tracking control of underactuated ships under stochastic disturbances. Ocean Eng 111(1):267–278

    Article  Google Scholar 

  10. Lefeber E, Pettersen KY, Nijmeijer H (2003) Tracking control of underactuated ship. IEEE Trans Control Syst Technol 11(1):52–61

    Article  Google Scholar 

  11. Lee TC, Jiang ZP (2004) New cascade approach for global k-exponential tracking of underactuated ships. IEEE Trans Autom Control 49(12):2297–2303

    Article  MathSciNet  MATH  Google Scholar 

  12. Yan Z, Wang J (2012) Model predictive control for tracking of underactuated vessels based on recurrent neural networks. IEEE J Ocean Eng 37(4):717–726

    Article  Google Scholar 

  13. Li Z, Sun J, Oh S (2009) Design, analysis and experimental validation of a robust nonlinear path following controller for marine surface vessels. Automatica 45(7):1649–1658

    Article  MathSciNet  MATH  Google Scholar 

  14. Liu C, Chen CLP, Zou ZJ, Li TS (2017) Adaptive NN-DSC control design for path following of underactuated surface vessels with input saturation. Neurocomputing 267:466–474

    Article  Google Scholar 

  15. Liu L, Wang D, Peng ZH (2017) ESO-based line-of-sight guidance law for path following of underactuated marine surface vehicles with exact sideslip compensation. IEEE J Ocean Eng 42(2):477–487

    Article  Google Scholar 

  16. Fossen TI, Pettersen KY, Galeazzi R (2015) Line-of-Sight path following for Dubins paths with adaptive sideslip compensation of drift forces. IEEE Trans Control Syst Technol 23(2):820–827

    Article  Google Scholar 

  17. Abdurahman B, Savvaris A, Tsourdos A (2017) A switching LOS guidance with relative kinematics for path-following of underactuated underwater vehicles. 2017IFAC 50(1):2290–2295

    Google Scholar 

  18. Oh SR, Sun J (2010) Path following of underactuated marine surface vessels using line-of-sight based model predictive control. Ocean Eng 37:289–295

    Article  Google Scholar 

  19. Kanev S, Verhaegen M (2006) Robustly asymptotically stable finite-horizon MPC. Automatica 42(12):2189–2194

    Article  MathSciNet  MATH  Google Scholar 

  20. Qin SJ, Badgwell TA (2003) A survey of industrial model predictive control technology. Control Eng Pract 11(7):733–764

    Article  Google Scholar 

  21. Mayne DQ (2014) Model predictive control: recent developments and future promise. Automatica 50(12):2967–2986

    Article  MathSciNet  MATH  Google Scholar 

  22. Xiao HZ, Chen CLP (2019) Incremental updating multi-robot formation using nonlinear model predictive control method with general projection neural network. IEEE Trans Ind Electron 66(6):4502–4512

    Article  Google Scholar 

  23. Xia YS, Leung H, Wang J (2002) A projection neural network and its application to constrained optimization problems. IEEE Trans Circuits Syst Fundam Theory Appl 49(4):447–458

    Article  MathSciNet  MATH  Google Scholar 

  24. Xiao HZ, Li ZJ, Chen CLP (2016) Formation control of leader-follower mobile robots systems using model predictive control based on neural-dynamics optimization. IEEE Trans Ind Electron 63(9):5752–5762

    Article  Google Scholar 

  25. Li ZJ, Xiao HZ, Yang CG, Zhao YW (2015) Model predictive control of nonholonomic chained systems using general projection neural networks optimization. IEEE Trans Syst Man Cybernet Syst 45(10):1313–1321

    Article  Google Scholar 

  26. Xia YS, Wang J (2016) A Bi-projection neural network for solving constrained quadratic optimization problems. IEEE Trans Neural Netw Learn Syst 27(2):214–224

    Article  MathSciNet  Google Scholar 

  27. Yan Z, Fan JC, Wang J (2017) A collective neurodynamic approach to constrained global optimization. IEEE Trans Neural Netw Learn Syst 28(5):1206–1215

    Article  Google Scholar 

  28. Hagan MT, Demuth HB, Beale M et al (2002) Neural network design. China Machine Press, Beijing

    Google Scholar 

  29. Tank DW, Hopfield JJ (1986) Simple neural network optimization network: an A\D converter, signal decision circuit, and a linear programming circuit. IEEE Trans Circuit Syst 33(5):533–541

    Article  Google Scholar 

  30. Hopfield JJ, Tank DW (1986) Computing with neural circuits: a model. Science 233:625–633

    Article  Google Scholar 

  31. Kennedy MP, Chua LO (1988) Neural networks for nonlinear programming. IEEE Trans Circuits Syst CAS 35(5):554–562

    Article  MathSciNet  Google Scholar 

  32. Rodriguez-Vazquez A, Dominguer-Castro R, Rueda A et al (1990) Nonlinear switched: capacitor ‘neural’ networks for optimization problems. IEEE Trans Circuits Syst 37(3):384–398

    Article  MathSciNet  Google Scholar 

  33. Wu X, Xia Y, Li J, Chen WK (1996) A high-performance neural network for solving linear and quadratic programming problems. IEEE Trans Neural Netw 7(3):643–651

    Article  Google Scholar 

  34. Xia Y, Wang J (2004) A recurrent neural network for nonlinear convex optimization subject to nonlinear inequality constraints. IEEE Trans Circuits Syst 51(7):1385–1394

    Article  MathSciNet  MATH  Google Scholar 

  35. Seong CY (2001) Neural dynamic optimization for control systems-part I: background. IEEE Trans Syst Man Cybern 31(4):482–489

    Article  Google Scholar 

  36. Oshaba AS, Ali ES, Abd Elazim SM (2017) PI controller design for MPPT of photovoltaic system supplying SRM via BAT search algorithm. Neural Comput Appl 28(4):651–667

    Article  Google Scholar 

  37. Abd-Elazim SM, Ali ES (2018) Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm. Neural Comput Appl 30(2):607–616

    Article  Google Scholar 

  38. Luenberger DG (1972) An introduction to observers. IEEE Trans Autom Control 16(6):596–602

    Article  MathSciNet  Google Scholar 

  39. Ioannou PA, Sun J (2012) Robust adaptive controller. Dover Publications, New York

    Google Scholar 

  40. Rana MS, Pota HR, Petersen IR (2013) High-speed AFM image scanning using observer-based MPC-notch control. IEEE Trans Nanotechnol 12(2):246–254

    Article  Google Scholar 

  41. Wang LP (2009) Model predictive control system design and implementation using MATLAB. Springer, Berlin

    Google Scholar 

  42. Sun JV, Kolmanovsky I, Ghaemi R, Chen SH (2007) A stable block model predictive control with variable implementation horizon. Automatica 43(11):1945–1953

    Article  MathSciNet  MATH  Google Scholar 

  43. Breivik M (2003) Nonlinear maneuvering control of underactuated ships. Master Thesis, Norwegian University of Science and Technology, Norway

  44. Li Z, Sun J (2012) Disturbance compensating model predictive control with application to ship heading control. IEEE Trans Control Syst Technol 20(1):257–265

    Google Scholar 

  45. Liu C, Sun J, Zou ZJ (2015) Integrated line of sight and model predictive control for path following and roll motion control using rudder. J Ship Res 59(2):99–112

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (Nos. 61374114, 61751202, 61751205, 51779026), the Natural Science Foundation of Liaoning (20170580081), the Fundamental Research Funds for the Central Universities under Grants 3132017114 and 3132018251, and the Postdoctoral innovation talent support plan (BX201700041).

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Authors and Affiliations

  1. College of Navigation, Dalian Maritime University, Dalian, China

    Cheng Liu & Cheng Li

  2. College of Marine Engineering, Dalian Maritime University, Dalian, China

    Wenhua Li

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  1. Cheng Liu
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  2. Cheng Li
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  3. Wenhua Li
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Correspondence to Cheng Liu.

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Liu, C., Li, C. & Li, W. Computationally efficient MPC for path following of underactuated marine vessels using projection neural network. Neural Comput & Applic 32, 7455–7464 (2020). https://doi.org/10.1007/s00521-019-04273-y

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  • DOI: https://doi.org/10.1007/s00521-019-04273-y

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