Design of a robust adaptive sliding mode control using recurrent fuzzy wavelet functional link neural networks for industrial robot manipulator with dead zone

  • Nguyen Xuan Quynh
  • Wang Yao Nan
  • Vu Thi YenEmail author
Original Research Paper


This paper addresses the problem of trajectory tracking control for industrial robot manipulators (IRMs) in the presence of external disturbances and uncertain dynamics. A novel robust adaptive recurrent fuzzy wavelet functional link neural network (RFWFLNN) controller based on dead-zone compensator is proposed in order to improve the position tracking performance. To handle the unknown dynamics of the IRMs, the robust adaptive RFWFLNNs are applied to approximate the unknown dynamics. The online learning laws and estimation of the dead zone are determined by using Lyapunov stability theory and the approximation theory. In addition, the robust SMC is applied to eliminate the estimation errors and disturbances of the IRM control system. Therefore, the RFWFLNN controller for IRMs can guarantee not only the robustness and stability but also the position tracking performance. Some simulations and experiments performed on three-link IRMs are provided to prove the robustness and effectiveness of the RFWFLNNs. The superiority of the RFWFLNN controller is also demonstrated based on comparisons with fuzzy wavelet neural networks and PID controllers.


Recurrent fuzzy wavelet function link neural networks Fuzzy neural networks Adaptive robust control Industrial robot manipulators 



  1. 1.
    Carelli R, Kelly R (1991) An adaptive impedance/force controller for robot manipulators. IEEE Trans Autom Control 36(8):967–971MathSciNetCrossRefGoogle Scholar
  2. 2.
    Yang Y, Feng G, Ren J (2004) A combined backstepping and small-gain approach to robust adaptive fuzzy control for strict—feedback nonlinear systems. IEEE Trans Syst 34(3):406–420Google Scholar
  3. 3.
    Wai RJ, Yang ZW (2008) Adaptive fuzzy neural network control design via a T–S fuzzy model for a robot manipulator including actuator dynamics. IEEE Trans Syst Man Cybern 38(5):1326–1346CrossRefGoogle Scholar
  4. 4.
    Islam S, Liu PX (2011) Robust adaptive fuzzy output feedback control system for robot manipulators. IEEE/ASME Trans Mechatron 16(2):288–296CrossRefGoogle Scholar
  5. 5.
    He W, Amoateng DO, Yang C, Gong DI (2017) Adaptive neural network control of a robotic manipulator with unknown backlash – like hysteresis. IET Control Theory Appl 11(4):567–575MathSciNetCrossRefGoogle Scholar
  6. 6.
    Shojaei K (2017) Neural adaptive output feedback formation control of type (m, s) wheeled mobile robots. IET Control Theory Appl 11(4):504–515MathSciNetCrossRefGoogle Scholar
  7. 7.
    Sun C, He W, Hong J (2017) Neural network control of a flexible robotic manipulator using the lumped spring—mass model. IEEE Trans Syst Man Cybern 47(8):1863–1874CrossRefGoogle Scholar
  8. 8.
    Li Z, Xia Y, Wang D, Zhai DH, Su CY, Zhao X (2016) Neural network—based control of networked trilateral teleoperation with geometrically unknown constraints. IEEE Trans Cybern 46(5):1051–1064CrossRefGoogle Scholar
  9. 9.
    He W, Dong Y, Sun C (2016) Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Trans Syst Man Cybern: Syst 46(3):334–344CrossRefGoogle Scholar
  10. 10.
    Nikdel N, Nildel P, Badamchizadeh MA, Hassanzadeh I (2014) Using neural network model predictive control for controlling shape memory alloy based manipulator. IEEE Trans Ind Electron 61(3):1394–1401CrossRefGoogle Scholar
  11. 11.
    Dierks T, Jagannathan S (2010) neural network output feedback control of robot formations. IEEE Trans Syst Man Cybern- Part B: Cybern 40(2):383–399CrossRefGoogle Scholar
  12. 12.
    Patra JC, Pal RN, Chatterji BN, Panda G (1999) Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Trans Syst Man Cybern 29(2):254–262CrossRefGoogle Scholar
  13. 13.
    Patra JC, Kot AC (2002) Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks. IEEE Trans Syst Man Cybern 32(4):505–511CrossRefGoogle Scholar
  14. 14.
    Wai RJ, Muthusamy R (2014) Design of fuzzy-neural-network-inherited backstepping control for robot manipulator including actuator dynamics. IEEE Trans Fuzzy Syst 22(4):709–722CrossRefGoogle Scholar
  15. 15.
    Wai RJ, Muthusamy R (2013) Fuzzy-neural-network inherited sliding-mode control for robot manipulator including actuator dynamics. IEEE Trans Neural Netw Learn Syst 24(2):274–287CrossRefGoogle Scholar
  16. 16.
    Han SI, Lee JM (2014) Fuzzy echo state neural networks and funnel dynamic surface control for prescribed performance of a nonlinear dynamic system. IEEE Trans Ind Electron 61(2):1099–1112CrossRefGoogle Scholar
  17. 17.
    Han SI, Lee JM (2014) Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems. ISA Trans 53:33–43CrossRefGoogle Scholar
  18. 18.
    Agand P, Shoorehdeli MA, Sedigh AK (2017) Adaptive recurrent neural network with Lyapunov stability learning rules for robot dynamic terms identification. Eng Appl Artif Intell 65:1–11CrossRefGoogle Scholar
  19. 19.
    El-Nagar AM (2018) Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network—a novel structure”. ISA Trans 72:205–217CrossRefGoogle Scholar
  20. 20.
    Wai RJ (2003) Robust control for nonlinear motor-mechanism coupling system using wavelet neural network. IEEE Tran Syst Man Cybern 33(3):489–497CrossRefGoogle Scholar
  21. 21.
    Wei S, Wang Y, Zuo Y (2012) wavelet neural networks robust control of farm transmission line deicing robot manipulators. Comput Stand Interfaces 34(3):327–333CrossRefGoogle Scholar
  22. 22.
    Kahkeshi MS, Sheikholeslam F, Zekri M (2013) Design of adaptive fuzzy wavelet neural sliding mode controller for uncertain nonlinear systems. ISA Trans 52(3):342–350CrossRefGoogle Scholar
  23. 23.
    Hou R, Wang L, Gao Q, Hou Y, Wang C (2017) Indirect adaptive fuzzy wavelet neural network self – recurrent consequent part for AC servo system. ISA Trans 70:298–307CrossRefGoogle Scholar
  24. 24.
    Solgi Y, Ganjefar S (2018) Variable structure fuzzy wavelet neural network controller for complex nonlinear systems. Appl Soft Comput 64:674–685CrossRefGoogle Scholar
  25. 25.
    Vu TY, Wang YN, Pham VC, Nguyen XQ, Vu HT (2017) Robust adaptive sliding mode control for industrial robot manipulator using fuzzy wavelet neural network. Int J Control Autom Syst 15(6):2930–2941CrossRefGoogle Scholar
  26. 26.
    Tsai CH, Chuang HT (2004) Deadzone compensation based on constrained RBF neural network. J Frank Inst 341:361–374CrossRefGoogle Scholar
  27. 27.
    Selmic R, Lewis FL (2000) Deadzone compensation in motion control systems using neural networks. IEEE Trans Autom Control 45(4):602–613MathSciNetCrossRefGoogle Scholar
  28. 28.
    Lewis FL, Tim K, Wang LZ, Li ZX (1999) Deadzone compensation in motion control systems using adaptive fuzzy control system. IEEE Trans Control Syst Technol 7(6):731–742CrossRefGoogle Scholar
  29. 29.
    Slotine JJE, Li W (1991) Applied nonlinear control. Prentice-Hall, HobokenzbMATHGoogle Scholar
  30. 30.
    Abiyev RH, Kaynak O (2008) Fuzzy wavelet neural networks for identification and control of dynamic plants—a novel structure ad a comparative study. IEEE Trans Ind Electron 55(8):3133–3140CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Nguyen Xuan Quynh
    • 1
    • 2
  • Wang Yao Nan
    • 1
  • Vu Thi Yen
    • 1
    • 2
    Email author
  1. 1.College of Electrical and Information EngineeringHunan UniversityChangshaChina
  2. 2.Faculty of Electrical Engineering TechnologyHanoi University of IndustryHanoiVietnam

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