A novel Robust Adaptive Control Using RFWNNs and Backstepping for Industrial Robot Manipulators with Dead-Zone

Abstract

This paper proposes a novel robust adaptive-backstepping-recurrent-fuzzy-wavelet-neural-networks controller (ABRFWNNs) based on dead zone compensator for Industrial Robot Manipulators (IRMs) in order to improve high correctness of the position tracking control with the presence of the unknown dynamics, and disturbances. To deal on the unknown dynamics of the robot system problems, the proposed controller used recurrent-fuzzy-wavelet-neural-networks (RFWNNs) to approximate the unknown dynamics. The online adaptive control training laws and estimation of the dead-zone are determined by Lyapunov stability theory and the approximation theory. In this method, the robust sliding-mode-control (SMC) is constructed to optimize parameter vectors, solve the approximation error and higher order terms. Therefore, the stability, robustness, and desired tracking performance of ABRFWNNs for IRMs are guaranteed. The simulations and experiments performed on three-link IRMs are provided in comparison with fuzzy-wavelet-neural-networks (FWNNs) and proportional-integral-derivative (PID) to demonstrate the robustness and effectiveness of the ARBFWNNs.

This is a preview of subscription content, log in to check access.

References

  1. 1.

    Wang, L.X.: Stable Adaptive Fuzzy Control of Nonlinear Systems. IEEE Trans. Fuzzy Syst. 1(2), 46–155 (1993)

    Article  Google Scholar 

  2. 2.

    Tong, S., Chen, B., Wang, Y.: Fuzzy adaptive output feedback control for MIMO nonlinear systems. Fuzzy Sets Syst. 156, 285–299 (2005)

    MathSciNet  Article  Google Scholar 

  3. 3.

    Tong, S., Li, H.X.: Fuzzy Adaptive Sliding – Mode Control for MIMO Nonlinear Systems. IEEE Trans. Fuzzy Syst. 11(3), 354–360 (2003)

    Article  Google Scholar 

  4. 4.

    Pan, W., Lyu, M., Hwang, K.S., Ju, M.Y., Shi, H.B.: A neuro Fuzzy Visual Servoing Controller for an Articulated Manipulator. IEEE Access. 6, 3346–3357 (2018)

    Article  Google Scholar 

  5. 5.

    Yesim, O., Okyay, K.: Control of a direct drive robot using fuzzy spiking neural networks with variable structure systems-based learning algorithm. Neurocomputing. 149, 690–699 (2015)

    Article  Google Scholar 

  6. 6.

    Sabahi, K., Ghaemi, S., Liu, J., Badamchizadeh, M.A.: Indirect predictive type-2 fuzzy neural network controller for a class of nonlinear input - delay systems. ISA Trans. 71, 185–195 (2017)

    Article  Google Scholar 

  7. 7.

    Wai, R.J., Chen, P.C.: Robust Neural-Fuzzy-Network Control for Robot Manipulator Including Actuator Dynamics. IEEE Trans. Ind. Electron. 53(4), 1328–1349 (2006)

    Article  Google Scholar 

  8. 8.

    Wai, R.J., Lin, Y.W.: Adaptive Moving-Target Tracking Control of a Vision-Based Mobile Robot via a Dynamic Petri Recurrent Fuzzy Neural Network. IEEE Trans. Fuzzy Syst. 21(4), 688–701 (2013)

    Article  Google Scholar 

  9. 9.

    Wang, Y.C., Chien, C.J., Teng, C.C.: Direct Adaptive Iterative Learning Control of Nonlinear Systems Using an Output-Recurrent Fuzzy Neural Network. IEEE Transaction on systems, Man, and cybernetics – part b: Cybernetics. 34(3), 1348–1359 (2004)

    Article  Google Scholar 

  10. 10.

    Lin, F.J., Shieh, P.H.: Recurrent RBFN-Based Fuzzy Neural Network Control for X-Y-Θ Motion Control Stage Using Linear Ultrasonic Motors. IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 53(12), 2450–2464 (2006)

    Article  Google Scholar 

  11. 11.

    Lin, F.J., Sun, I.F., Yang, K.J., Chang, J.K.: Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive. IEEE Trans. Fuzzy Syst. 24(1), 153–167 (2016)

    Article  Google Scholar 

  12. 12.

    Lin, F.J., Huang, P.K., Chou, W.D.: Recurrent-Fuzzy-Neural-Network-Controlled Linear Induction Motor Servo Drive Using Genetic Algorithms. IEEE Trans. Ind. Electron. 54(3), 1449–1461 (2007)

    Article  Google Scholar 

  13. 13.

    Dehghan, S.A.M., Danesh, M., Sheikholeslam, F., Zekri, M.: Adaptive force–environment estimator for manipulators based on adaptive wavelet neural network. Appl. Soft Comput. 28, 527–540 (2015)

    Article  Google Scholar 

  14. 14.

    Wei, S., Wang, Y., Zuo, Y.: Wavelet neural networks robust control of farm transmission line deicing robot manipulators. Computer Standards & Interfaces. 34, 327–333 (2012)

    Article  Google Scholar 

  15. 15.

    Khan, M.M., Mendes, A., Zhang, P., Chalup, S.K.: Evolving multi-dimensional wavelet neural networks for classification using Cartesian Genetic Programming. Neurocomputing. 247, 39–58 (2017)

    Article  Google Scholar 

  16. 16.

    Fayez, F.M.E.S., Khaled, A.A.: Adaptive Nonlinear Disturbance Observer Using a Double-Loop Self-Organizing Recurrent Wavelet Neural Network for a Two-Axis Motion Control System. IEEE Trans. Ind. Appl. 54(1), 764–786 (2018)

    Article  Google Scholar 

  17. 17.

    Lin, F.J., Hung, Y.C., Ruan, K.C.: An Intelligent Second-Order Sliding-Mode Control for an Electric Power Steering System Using a Wavelet Fuzzy Neural Network. IEEE Trans. Fuzzy Syst. 22(6), 1598–1611 (2014)

    Article  Google Scholar 

  18. 18.

    Wu, X., Wang, Y.N., Dang, X.J.: Robust adaptive sliding-mode control of condenser-cleaning mobile manipulator using fuzzy wavelet neural network. Fuzzy Sets Syst. 235, 62–82 (2014)

    MathSciNet  Article  Google Scholar 

  19. 19.

    Rahib, H.A., Okyay, K.: Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study. IEEE Trans. Ind. Electron. 55(8), 3133–3140 (2008)

    Article  Google Scholar 

  20. 20.

    Tsai, C.H., Chuang, H.T.: Deadzone compensation based on constrained RBF neural network. Journal of the Franklin Institute. 341, 361–374 (2004)

    Article  Google Scholar 

  21. 21.

    He, W., Dong, Y., Sun, C.: Adaptive neural network control of unknown nonlinear affine systems with input deadzone and output constraint. ISA Trans. 58, 96–104 (2014)

    Article  Google Scholar 

  22. 22.

    Han, S.I., Lee, J.: Finite-time sliding surface constrained control for a robot manipulator with an unknown deadzone and disturbance. ISA Trans. 65, 307–318 (2016)

    Article  Google Scholar 

  23. 23.

    Selmic, R., Lewis, F.L.: Deadzone compensation in motion control systems using neural networks. IEEE Trans. Autom. Control. 45(4), 602–613 (2000)

    MathSciNet  Article  Google Scholar 

  24. 24.

    Abiyev, R.H., Kaynak, O.: Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study. IEEE Trans. Industrial Electronics. 55(8), 3133–3140 (2008)

    Article  Google Scholar 

  25. 25.

    Lewis, F.L., Tim, K., Wang, L.Z., Li, Z.X.: Deadzone compensation in motion control systems using adaptive fuzzy control system. IEEE Trans. Control Syst. Technol. 7(6), 731–742 (1999)

    Article  Google Scholar 

  26. 26.

    Slotine, J.J.E., Li, W.: Applied Nonlinear Control. Prentice – Hall, Hoboken (1991)

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant nos. 61175075) National Hightech Research and Development Projects (Grant nos. 2012AA112312, Grant nos. 2012AA11004). The authors would like to thank the editor and the reviewers for their invaluable suggestions, which greatly improved the quality for this paper dramatically.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Vu Thi Yen.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Quynh, N.X., Nan, W.Y. & Yen, V.T. A novel Robust Adaptive Control Using RFWNNs and Backstepping for Industrial Robot Manipulators with Dead-Zone. J Intell Robot Syst 98, 679–692 (2020). https://doi.org/10.1007/s10846-019-01089-9

Download citation

Keywords

  • Industrial robot
  • Unknown dead-zone
  • Recurrent wavelet fuzzy neural networks
  • Adaptive control