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Cluster Computing

, Volume 22, Supplement 3, pp 5799–5809 | Cite as

Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode

  • Fei WangEmail author
  • Zhi-qiang Chao
  • Lian-bing Huang
  • Hua-ying Li
  • Chuan-qing Zhang
Article

Abstract

Aimed at the nonlinearity and uncertainty of the manipulator system, a RBF (radial basis function) neural network-based fuzzy sliding-mode control method was proposed in this paper, in order to make the manipulator track the given trajectory at an ideal dynamic quality. In this method, the equivalent part of the sliding-mode control is approximated by the RBF neural network, in which no model information is required. Meanwhile, a fuzzy controller is developed to make adaptive adjustment of the sliding-mode control’s switching gains according to the distance between the current motor point and the sliding-mode surface, thus effectively the problem of chattering is solved. This method has, to some extent, improved the performance of response and tracking, and reduced the time of adjustment and chattering of input control. The system stability is verified by Lyapunov’s theorem. The simulation result suggests that the algorithm designed for the three-degree-of-freedom (3DOF) manipulator system is effective.

Keywords

Robot manipulator Trajectory tracking RBF neural network Fuzzy control Sliding mode control Simulation 

References

  1. 1.
    Xu, C.Z.: Research on intelligent backstepping sliding mode control of nonlinear robots. Huaqiao University (2012)Google Scholar
  2. 2.
    Wang, Y.N.: Intelligent Control Engineering of Robot, pp. 7–14. Science Press, Beijing (2004)Google Scholar
  3. 3.
    Ding, X.G.: Research on Robot Control, pp. 7–13. Zhejiang University Press, Hangzhou (2006)Google Scholar
  4. 4.
    Liu, J.K.: Sliding Mode Control Design and Matlab Simulation, pp. 4–16. Tsinghua University Press, Beijing (2012)Google Scholar
  5. 5.
    Yang, Z.Y., Wu, J., Mei, J.P.: Motor-mechanism dynamic model based neural network optimized computed torque control of a high speed parallel manipulator. Mechatronics 17(7), 381–390 (2007)CrossRefGoogle Scholar
  6. 6.
    Hu, S.B., Lu, M.X.: Adaptive double fuzzy sliding mode control for three-links spatial robot. J. Tongji Univ. (Nat Sci.) 40(4), 622–628 (2012)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Emna, Z., Hanene, M., Nabil, D.: Indirect sliding mode neural-network control for holonomic constrained robot manipulator. Int. J. Intell. Syst. Technol. Appl. 9(2), 150–168 (2010)Google Scholar
  8. 8.
    Hu, S.B., Lu, M.X.: Feedback linearization double fuzzy sliding mode control for multi-links robot. Mech. Sci. Technol. Aerosp. Eng. 32(1), 105–115 (2013)Google Scholar
  9. 9.
    Al-khazraji, A., Essounbouli, N., Hamzaoui, A., Nollet, F., Zaytoon, J.: Type-2 fuzzy sliding mode control without reaching phase for nonlinear system. Eng. Appl. Artif. Intell. 24(1), 23–38 (2011)CrossRefGoogle Scholar
  10. 10.
    Guo, L.P., Hung, J.Y., Nelms, R.M.: Comparative evaluation of sliding mode fuzzy controller and PID controller for a boost converter. Electr. Power Syst. Res. 81(1), 99–106 (2011)CrossRefGoogle Scholar
  11. 11.
    Yang, H.L., Jiang, B., Zhang, K.: Direct selfrepairing control for quadrotor helicopter attitude systems. Math. Probl. Eng. 6, 1–11 (2014)Google Scholar
  12. 12.
    Aloui, S., Pages, O., Ei Hajjaji, A., Chaari, A., Yassine, K.: Improved fuzzy sliding mode control for a class of MIMO nonlinear uncertain and perturbed system. Appl. Soft Comput. J. 11(1), 820–826 (2011)CrossRefGoogle Scholar
  13. 13.
    Wai, R.J., Lin, C.M., Hsu, C.F.: Adaptive fuzzy sliding mode control for electrical servo drive. Fuzzy Sets Syst. 143, 295–310 (2004)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zhang, L.Y., Liu, T., Sun, Y.S., Chen, L.: Research on neural network blind equalization algorithm with structure optimized by genetic algorithm. In: 2010 Sixth International Conference on Natural Computation, vol. 8, pp. 1451–1454. (2010)Google Scholar
  15. 15.
    Man, Z.R., Yu, X.H., Eshraghian, K., Panasiwami, M.: A robust adaptive sliding mode tracking control using an RBF neural network for robotic manipulator. In: IEEE International Conference on Neural Networks, vol. 5, pp. 2403–2408. (1995)Google Scholar
  16. 16.
    Da, P.F., Song, W.Z.: Sliding mode control based on fuzzy neural networks for nonlinear systems represented by input-output models. Acta Automatic Sinica 26(1), 136–139 (2000)MathSciNetGoogle Scholar
  17. 17.
    Zhao, H.C., Yu, H.Y., Gu, W.J.: Fuzzy neural network-based sliding mode control for missile’s overload control system. In: 2005 International Conference on Neural Networks and Brain, vol. 3, pp. 1786–1790 (2005)Google Scholar
  18. 18.
    Hu, S.B., Lu, M.X.: Fuzzy sliding mode control for a three-links spatial robot based on RBF neural network. Appl. Mech. Mater. 141(1), 303–307 (2012)Google Scholar
  19. 19.
    Frikha, S., Djemel, M., Derbel, N.: Neural network adaptive control scheme for nonlinear system with Lyapunov approach and sliding mode. Int. J. Intell. Comput. Cybern. 3(3), 495–513 (2010)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Noriaki, S.: Optimal control problem via self-adaptation sliding mode controller with neural network. Electron. Commun. Jpn 94(11), 1043–1049 (2011)Google Scholar
  21. 21.
    Lin, T.C.: Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISO nonlinear system. Commun. Nonlinear Sci. Numer. Simul. 15(12), 4084–4099 (2010)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Wu, X.R.: The control theory research of neural network for the condenser cleaning robot. Hunan university (2011)Google Scholar
  23. 23.
    Hu, S.B., Lu, W.H., Cao, D.M., Xu, H.R.: Fast terminal fuzzy sliding mode control for a three-links spatial robot. Appl. Mech. Mater. 226–228, 840–843 (2012)CrossRefGoogle Scholar
  24. 24.
    Huang, M., Huang, X.H., Tu, X.K., Li, Z.F., Wen, Y.: An online gain tuning proxy-based sliding mode control using neural network for a gait training robotic orthosis. Clust. Comput. 19(4), 1987–2000 (2016)CrossRefGoogle Scholar
  25. 25.
    Chao, Z.Q.: Research on structure optimization and control technology for 6-DOF hydraulic platform. Academy of armored force engineering, (2010)Google Scholar
  26. 26.
    Long, Y., Du, Z.J., Wang, W.D.: RBF neural network with genetic algorithm optimization based sensitivity amplification control for exoskeleton. J. Harbin Inst. Technol. 47(7), 26–30 (2015)MathSciNetGoogle Scholar
  27. 27.
    Van Cuong, P., Wang, Y.N.: Adaptive trajectory tracking neural network control with robust compensator for robot manipulators. Neural Comput. Appl. 27, 525–536 (2016)CrossRefGoogle Scholar
  28. 28.
    Nadeem, F., Alghazzawi, D., Mashat, A., Fakeeh, K., Almalaise, A., Hagras, H.: Modeling and predicting execution time of scientific workflows in the grid using radial basis function neural network. Clust. Comput. 20(3), 2805–2819 (2017)CrossRefGoogle Scholar
  29. 29.
    Zan, P., Yan, G.Z., Huang, B., Yu, L.Z.: Fuzzy wavelet neural network control for pneumatic artificial muscle. J. Syst. Simul. 19(23), 5566–5569 (2007)Google Scholar
  30. 30.
    Huang, D.F., Chen, L.: Wavelet based fuzzy neural network control for free-floating space flexible manipulator to track desired trajectory. Eng. Mech. 29(12), 360–364 (2012)Google Scholar
  31. 31.
    Liu, C., Zhao, S.D.: Application of RBF neural network and sliding mode control for a servo mechanical press. IEEE Int. Conf. Airc. Util. Syst. (AUS) 2016, 346–351 (2016)Google Scholar
  32. 32.
    Liu, J.K.: RBF Neural Network Control for Mechanical Systems (Design, Analysis and Matlab Simulation), pp. 116–120. Tsinghua University Press, Beijing (2014)Google Scholar
  33. 33.
    Wu, B., Xu, W.F., Chen, H.L.: Application of neural networks sliding mode control in tracking control of robot manipulators. Electr. Mach. Control 13(1), 99–104 (2009)Google Scholar
  34. 34.
    Hu, S.B.: Sliding Mode Control for Nonlinear Multi Joint Robot System, pp. 75–77. National Defense Industry Press, Beijing (2015)Google Scholar
  35. 35.
    Takhmar, A., Alghooneh, M., Ali A Moosavian, S.: Chattering eliminated and stable gait planning of biped robots using a fuzzy sliding mode controller. Majlesi J. Electr. Eng. 7(1), 31–35 (2013)Google Scholar
  36. 36.
    Zhang, J.P., Liu, K., Lin, J.F., Ma, X.B., Yu, W.D.: 4-DOF adaptive fuzzy sliding mode control of excavator. J. Mech. Eng. 46(21), 87–92 (2010)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Fei Wang
    • 1
    • 2
    Email author
  • Zhi-qiang Chao
    • 1
  • Lian-bing Huang
    • 3
  • Hua-ying Li
    • 1
  • Chuan-qing Zhang
    • 1
  1. 1.Department of Mechanical EngineeringArmy Academy of Armored ForcesBeijingChina
  2. 2.66336 Unit of PLAGaobeidianChina
  3. 3.Institute of Manned Space System EngineeringBeijingChina

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