Intelligent Fuzzy Kinetic Control for an Under-Actuated Autonomous Surface Vehicle via Stochastic Gradient Descent

  • Yue Jiang
  • Lu Liu
  • Zhouhua PengEmail author
  • Dan WangEmail author
  • Nan Gu
  • Shengnan Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11555)


In this paper, an intelligent fuzzy kinetic control scheme based on online identification is developed for an under-actuated autonomous surface vehicle in the presence of unknown uncertainties and disturbances from ocean environment. An adaptive fuzzy system is used to approximate the unknown dynamics in real time by using recorded input and output data of the vessel. To improve the learning performance, the parameters of the fuzzy system are updated based on a stochastic gradient descent approach and a predictor design. With the estimated dynamics from the fuzzy system, a robust kinetic controller is designed without any off-line learning. The proposed intelligent fuzzy control method can be applied at the kinetic level of various control scenarios, such as target tracking, path following and trajectory tracking.


Intelligent fuzzy kinetic control Stochastic gradient descent Under-actuated autonomous surface vehicle Unknown dynamics 


  1. 1.
    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. 18(9), 2156–2167 (2017)Google Scholar
  2. 2.
    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
  3. 3.
    Peng, Z., Wang, J., Wang, D.: Distributed containment maneuvering of multiple marine vessels via neurodynamics-based output feedback. IEEE Trans. Control Syst. Technol. 64(5), 3831–3839 (2017)Google Scholar
  4. 4.
    Cui, R., Ge, S.S., How, B.V.E., Choo, Y.S.: Leader-follower formation control of underactuated autonomous underwater vehicles. Ocean Eng. 37(7), 1491–1502 (2010)Google Scholar
  5. 5.
    Xiang, X., Yu, C., Zhang, Q.: Robust fuzzy 3D path following for autonomous underwater vehicle subject to uncertainties. Comput. Oper. Res. 84, 165–177 (2017)Google Scholar
  6. 6.
    Shi, Y., Shen, C., Buckham, B.: Integrated path planning and tracking control of an AUV: a unified receding horizon optimization approach. IEEE/ASME Trans. Mechatron. 22(3), 1163–1173 (2017)Google Scholar
  7. 7.
    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
  8. 8.
    Li, F., Yi, H.: Application of USV to maritime safety supervision. Chin. J. Ship Res. 13(6), 27–33 (2018)Google Scholar
  9. 9.
    Zhao, R., Xu, J., Xiang, X., Xu, G.: A review of path planning and cooperative control for MAUV systems. Chin. J. Ship Res. 13(6), 58–65 (2018)Google Scholar
  10. 10.
    Peng, Z., Wang, J.: Output-feedback path-following control of autonomous underwater vehicles based on an extended state observer and projection neural network. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 48(4), 535–544 (2018)Google Scholar
  11. 11.
    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
  12. 12.
    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. (2019). Scholar
  13. 13.
    Liu, L., Wang, D., Peng, Z.: State recovery and disturbance estimation of unmanned surface vehicles based on nonlinear extended state observers. Ocean Eng. 171, 625–632 (2018). Scholar
  14. 14.
    Yang, Y., Zhou, C., Ren, J.: Model reference adaptive robust fuzzy control for ship steering autopilot with uncertain nonlinear systems. Appl. Soft Comput. 3(4), 305–316 (2003)Google Scholar
  15. 15.
    Li, Y., Tong, S.: Adaptive fuzzy output-feedback stabilization control for a class of switched nonstrict-feedback nonlinear systems. IEEE Trans. Cybern. 47(7), 1007–1016 (2017)Google Scholar
  16. 16.
    Hou, X., Zou, A., Tan, M.: Adaptive control of an electrically driven nonholonomic mobile robot via backstepping and fuzzy approach. IEEE Trans. Control Syst. Technol. 17(4), 803–815 (2009)Google Scholar
  17. 17.
    Bottou, L.: Stochastic gradient learning in neural networks. Proc. Neuronîmes 91(8), 12 (1991)Google Scholar
  18. 18.
    Yang, X., Zheng, X., Gao, H.: SGD-based adaptive NN control design for uncertain nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 99, 1–13 (2018). Scholar
  19. 19.
    Hardt, M., Recht, B., Singer, Y.: Train faster, generalize better: stability of stochastic gradient descent. In: Proceedings of 33rd International Conference on Extreme Learning Machines, pp. 1225–1234 (2016)Google Scholar
  20. 20.
    Wang, L.: Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Prentice Hall, Upper Saddle River (1994)Google Scholar
  21. 21.
    Poggio, T., Voinea, S., Rosasco, L.: Online learning, stability, and stochastic gradient descent. arXiv:1105.4701 (2011)
  22. 22.
    Fossen, T.: Handbook of Marine Craft Hydrodynamics and Motion Control. Wiley, Hoboken (2011)Google Scholar
  23. 23.
    Guo, B., Zhao, Z.: On convergence of tracking differentiator. Int. J. Control 84(4), 693–701 (2011)Google Scholar
  24. 24.
    Krstic, M., Kokotovic, P., Kanellakopoulos, I.: Nonlinear and Adaptive Control Design. Wiley, Hoboken (1995)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

Personalised recommendations