Advertisement

Neural-Network-Based Modular Dynamic Surface Control for Surge Speed Tracking of an Unmanned Surface Vehicle Driven by a DC Motor

  • Chengcheng Meng
  • Lu Liu
  • Zhouhua PengEmail author
  • Dan WangEmail author
  • Haoliang Wang
  • Gang Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)

Abstract

In this paper, a surge speed tracking problem for an unmanned surface vehicle (USV) driven by a direct current (DC) motor is investigated. The surge velocity tracking controller is developed based on a modular neural dynamic surface control (MNDSC) design method. Specifically, two neural-network-based identifiers are designed to identify the uncertainties existing in surge dynamics, propeller and DC motor. Then, a robust adaptive surge speed controller based on a dynamic surface control design method is designed by using the recovered unknown information from identifiers. The proposed surge velocity tracking controller is able to track any time-varying bounded velocity profiles. The stability of the closed-loop system is established based on cascade theory and input-to-state stability (ISS) theory. The effectiveness of the proposed surge speed controller for the USV is illustrated via simulations.

Keywords

Surge speed tracking Neural networks Dynamic surface control DC motor Modular design 

References

  1. 1.
    Ramos, P.A., Neves, M.V., Pereira, F.L.: Mapping and initial dilution estimation of an ocean outfall plume using an autonomous underwater vehicle. Cont. Shelf Res. 27(5), 583–593 (2007)Google Scholar
  2. 2.
    Bayat, B., Crasta, N., Crespi, A., Pascoal, A.M., Ijspeert, A.: Environmental monitoring using autonomous vehicles: a survey of recent searching techniques. Curr. Opin. Biotechnol. 45, 76–84 (2017)Google Scholar
  3. 3.
    Jin, K., Wang, H., Yi, H., Liu, J., Wang, J.: Key technologies and intelligence evolution of maritime UV. Chin. J. Ship Res. 13(6), 1–8 (2018)Google Scholar
  4. 4.
    Li, F., Yi, H.: Application of USV maritime safety supervision. Chin. J. Ship Res. 13(6), 27–33 (2018)Google Scholar
  5. 5.
    Peng, Z., Wang, D., Chen, Z., Hu, X., Lan, W.: Adaptive dynamic surface control for formations of autonomous surface vehicles with uncertain dynamics. IEEE Trans. Control Syst. Technol. 21(2), 513–520 (2013)Google Scholar
  6. 6.
    Peng, Z., Wang, D., Zhang, H., Sun, G.: Distributed neural network control for adaptive synchronization of uncertain dynamical multiagent systems. IEEE Trans. Neural Netw. Learn. Syst. 25(8), 1508–1519 (2014)Google Scholar
  7. 7.
    Peng, Z., Wang, D., Shi, Y., Wang, H., Wang, W.: Containment control of networked autonomous underwater vehicles with model uncertainty and ocean disturbances guided by multiple leaders. Inf. Sci. 316(20), 163–179 (2015)Google Scholar
  8. 8.
    Peng, Z., Wang, D., Wang, W., Liu, L.: Neural adaptive steering of an unmanned surface vehicle with measurement noises. Neurocomputing 186, 228–234 (2016)Google Scholar
  9. 9.
    Liu, Z., Zhang, Y., Yu, Y., Yuan, C.: Unmanned surface vehicles: an overview of developments and challenges. Annu. Rev. Control 41, 71–93 (2016)Google Scholar
  10. 10.
    Peng, Z., Wang, J., Wang, D.: Containment maneuvering of marine surface vehicles with multiple parameterized paths via spatial-temporal decoupling. IEEE/ASME Trans. Mechatron. 22(2), 1026–1036 (2017)Google Scholar
  11. 11.
    Peng, Z., Wang, J., Wang, D.: Distributed containment maneuvering of multiple marine vessels via neurodynamics-based output feedback. IEEE Trans. Ind. Electron. 64(5), 3831–3839 (2017)Google Scholar
  12. 12.
    Peng, Z., Wang, D., Wang, J.: Predictor-based neural dynamic surface control for uncertain nonlinear systems in strict-feedback form. IEEE Trans. Neural Netw. Learn. Syst. 28, 2156–2167 (2017)Google Scholar
  13. 13.
    Peng, Z., Wang, J.: Output-feedback path-following control of autonomous underwater vehicles based on an extended state observer and projection neural networks. IEEE Trans. Syst. Man Cybern. Syst. 48(4), 535–544 (2018)Google Scholar
  14. 14.
    Peng, Z., Wang, J., Wang, D.: Distributed maneuvering of autonomous surface vehicles based on neurodynamic optimization and fuzzy approximation. IEEE Trans. Control Syst. Technol. 26(3), 1083–1090 (2018)Google Scholar
  15. 15.
    Peng, Z., Wang, J., Han, Q.: Path-following control of autonomous underwater vehicles subject to velocity and input constraints via neurodynamic optimization. IEEE Trans. Ind. Electron. 1 (2018)Google Scholar
  16. 16.
    Peng, Z., Wang, J., Wang, J.: Constrained control of autonomous underwater vehicles based on command optimization and disturbance estimation. IEEE Trans. Ind. Electron. 66(5), 3627–3635 (2019)Google Scholar
  17. 17.
    Kragelund, S., Dobrokhodov, V., Monarrez, A., Hurban, M., Khol, C.: Adaptive speed control for autonomous surface vessels. In: MTS/IEEE Oceans - San Diego, pp. 1–10 (2013)Google Scholar
  18. 18.
    Svendsen, C.H., Hoick, N.O., Galeazzi, R., Blanke, M.: L1 adaptive manoeuvring control of unmanned high-speed water craft. IFAC Proc. Vol. 45(27), 144–151 (2012)Google Scholar
  19. 19.
    Zhao, Z., Yang, Y., Zhou, J., Li, L., Yang, Q.: Adaptive fault-tolerant PI tracking control for ship propulsion system. ISA Trans. 80, 279–285 (2018)Google Scholar
  20. 20.
    Fossen, T.I., Blanke, M.: Nonlinear output feedback control of underwater vehicle propellers using feedback form estimated axial flow velocity. IEEE J. Oceanic Eng. 25(2), 241–255 (2000)Google Scholar
  21. 21.
    Wang, D., Huang, J.: Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Trans. Neural Netw. 16(1), 195–202 (2005)Google Scholar
  22. 22.
    Emhemed, A.A.A., Mamat, R.B.: Modelling and simulation for industrial DC motor using intelligent control. Procedia Eng. 41, 420–425 (2012)Google Scholar
  23. 23.
    Barnitsas, M.M., Ray, D., Kinley, P.: KT, KQ and efficiency curves for the Wageningen B-series propellers (2012)Google Scholar
  24. 24.
    Fossen, T.I.: Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley, Hoboken (2011)Google Scholar
  25. 25.
    Peng, Z., Wang, D., Wang, J.: Cooperative dynamic positioning of multiple marine offshore vessels: a modular design. IEEE/ASME Trans. Mechatron. 21(3), 1210–1221 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Marine Electrical EngineeringDalian Maritime UniversityDalianPeople’s Republic of China
  2. 2.School of Information EngineeringJiangxi University of Science and TechnologyGanzhouPeople’s Republic of China

Personalised recommendations