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International Journal of Fuzzy Systems

, Volume 21, Issue 3, pp 793–808 | Cite as

Development of an Adaptive Fuzzy Sliding Mode Trajectory Control Strategy for Two-axis PMSM-Driven Stage Application

  • Wei-Lung MaoEmail author
  • Guan-You Liu
Article
  • 60 Downloads

Abstract

Precision trajectory control in modern machining processes is an important issue for two-axis contour tracking applications. In this paper, an adaptive fuzzy sliding mode control (AFSMC) is designed and proposed for effective and stable control of the industrial X–Y-axis motion stage. The aim of the control strategy is to apply fuzzy systems to approximate unknown nonlinear functions and to use soft fuzzy switching to approximate a discontinuous control signal such that it can alleviate the chattering phenomenon in the presence of unmodeled system dynamics and external disturbances. Based on our AFSMC method, the associated robust performance can be conducted effectively for different trajectory tracking. To ensure parameter boundaries, projection algorithm is utilized for the adaptive control law. The AFSMC adaptation scheme adjusts the fuzzy parameter vectors based on the Lyapunov theorem approach, so that the asymptotic stability of the developed motion system can be guaranteed. The XY linear table system is experimentally investigated with four typical contours, namely star, circular, four-leaf, and window reference contours. Simulation and experimental results indicate that the proposed AFSMC achieves improved tracking capability and reveal that the AFSMC outperforms other comparison schemes with regard to model uncertainties and cross-coupling interference.

Keywords

Permanent magnet synchronous motor (PMSM) Adaptive fuzzy sliding mode control (AFSMC) Precision motion control Trajectory control 

Notes

Acknowledgments

The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract No. MOST 105-2622-E-224 -010 –CC3, and MOST 107-2221-E-224-040-.

References

  1. 1.
    Slotine, J.J.E., Li, W.P.: Applied Nonlinear Control. Prentice-Hall, Englewood Cliffs (1991)zbMATHGoogle Scholar
  2. 2.
    Wang, L.X.: Adaptive Fuzzy Systems and Control: Design and Stability. Prentice-Hall, Englewood Cliffs (1994)Google Scholar
  3. 3.
    Wang, J., Rad, A.B., Chan, P.T.: Indirect adaptive fuzzy sliding mode control: part I: fuzzy switching. Fuzzy Sets Syst. 12(1), 21–30 (2001)zbMATHGoogle Scholar
  4. 4.
    Chan, P.T., Rad, A.B., Wang, J.: Indirect adaptive fuzzy sliding mode control: part II: parameter projection and supervisory control. Fuzzy Sets Syst. 12(1), 31–43 (2001)CrossRefzbMATHGoogle Scholar
  5. 5.
    Wang, L.X.: Stable adaptive fuzzy control of nonlinear systems. IEEE Trans. Fuzzy Syst. 2(2), 146–155 (1993)CrossRefGoogle Scholar
  6. 6.
    Fujimoto, H., Takemura, T.: High-precision control of ball-screw-driven stage based on repetitive control using n-times learning filter. IEEE Trans. Ind. Electron. 61(7), 3694–3703 (2014)CrossRefGoogle Scholar
  7. 7.
    Kung, Y.S., Than, H., Chung, T.Y.: FPGA-realization of a self-tuning PID Controller for X-Y table with RBF neural network identification. Microsyst. Technol. 24(1), 243–253 (2016)CrossRefGoogle Scholar
  8. 8.
    Liu, Z.Z., Luo, F.L., Rahman, M.A.: Robust and precision motion control system of linear-motor direct drive for high-speed X-Y table positioning mechanism. IEEE Trans. Ind. Electron. 52(5), 1357–1363 (2005)CrossRefGoogle Scholar
  9. 9.
    Wu, J., Xiong, Z., Ding, H.: Integral design of contour error model and control for biaxial system. Int. J. Mach. Tools Manuf 89, 159–169 (2015)CrossRefGoogle Scholar
  10. 10.
    Lin, F.J., Shieh, H.J., Shieh, P.H., Shen, P.H.: An adaptive recurrent-neural-network motion controller for X-Y table in CNC machine. IEEE Trans. Syst. Man Cybern. 36(2), 286–299 (2006)Google Scholar
  11. 11.
    Lin, F.J., Chou, P.H., Kung, Y.S.: Robust fuzzy neural network controller with nonlinear disturbance observer for two-axis motion control system. IET Control Theory Appl. 2(2), 151–167 (2008)CrossRefGoogle Scholar
  12. 12.
    El-Sousy, F.F.M.: Intelligent mixed H2/H adaptive tracking control system design using self-organizing recurrent fuzzy-wavelet-neural-network for uncertain two-axis motion control system. Appl. Soft Comput. 41, 22–50 (2016)CrossRefGoogle Scholar
  13. 13.
    Lin, F.J., Shieh, P.H., Shen, P.H.: Robust recurrent-neural-network sliding-mode control for the X-Y table of a CNC machine. IET Control Theory Appl. 153(1), 111–123 (2006)CrossRefGoogle Scholar
  14. 14.
    Xi, X.C., Zhao, W.S., Poo, A.N.: Improving CNC contouring accuracy by robust digital integral sliding mode control. Int. J. Mach. Tools Manuf 88, 51–61 (2015)CrossRefGoogle Scholar
  15. 15.
    Han, S.I., Lee, J.M.: Adaptive dynamic surface control with sliding mode control and RWNN for robust position of a linear motion state. Mechantronics 22(2), 222–238 (2012)CrossRefGoogle Scholar
  16. 16.
    Xu, Q., Li, Y.: Dynamic modeling and sliding mode control of an XY micropositioning stage. In: The 9th International Symposium on Robot Control (SYROCO’09), pp. 781–786 (2009)Google Scholar
  17. 17.
    Lin, F.J., Chou, P.H., Kung, Y.S.: Robust fuzzy neural network controller with nonlinear disturbance observer for two-axis motion control system. IET Control Theory Appl. 2(2), 151–167 (2008)CrossRefGoogle Scholar
  18. 18.
    Ho, H.F., Wong, Y.K., Rad, A.B.: Adaptive fuzzy sliding mode control design: Lyapunov approach. In: Proceeding of the 5th Asia Control Conference, pp. 1502–1507 (2004)Google Scholar
  19. 19.
    Ho, H.F., Wong, Y.K., Rad, A.B.: Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems. Simul. Model. Pract. Theory 17(7), 1199–1210 (2009)CrossRefGoogle Scholar
  20. 20.
    Zhao, P., Shi, Y., Huang, J.: Proportional-integral based fuzzy sliding mode control of the milling head. Control Eng. Pract. 53, 1–13 (2016)CrossRefGoogle Scholar
  21. 21.
    Hua, J., An, L.X., Li, Y.M.: Bonic fuzzy sliding mode control and robustness analysis. Appl. Math. Model. 39, 4482–4493 (2015)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Kahkeshi, M.S., Sheikholelam, F., Zekri, M.: Design of adaptive fuzzy wavelet neural sliding mode controller for uncertain nonlinear systems. ISA Trans. 52(3), 342–350 (2013)CrossRefGoogle Scholar
  23. 23.
    Wang, Z., Hu, C., Shu, Y., He, S., Yang, K., Zhang, M.: Neural network learning adaptive robust control of an industrial linear motor driven stage with disturbance rejection ability. IEEE Trans. Ind. Inform. 13(5), 2172–2183 (2017)CrossRefGoogle Scholar
  24. 24.
    Chen, Z., Yao, B., Wang, Q.: Adaptive robust precision motion control of linear motors with integrated compensation of nonlinearities and bearing flexible modes. IEEE Trans. Ind. Inform. 11(5), 1179–1189 (2015)CrossRefGoogle Scholar
  25. 25.
    EI-Sousy, F.F.M., Abuhasel, K.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), 7640–7786 (2018)Google Scholar
  26. 26.
    Wang, N., Su, S.F., Yin, J., Zheng, Z., Er, M.J.: Global asymptotic model-free trajectory-independent tracking control of an uncertain marine vehicle: an adaptive universe-based fuzzy control approach. IEEE Trans. Fuzzy Syst. 26(3), 1613–1625 (2018)CrossRefGoogle Scholar
  27. 27.
    Wang, N., Lin, S., Zhang, W., Liu, Z., Er, M.J.: Finite-time observer based accurate tracking control of a marine vehicle with complex unknowns. Ocean Eng. 145, 406–415 (2017)CrossRefGoogle Scholar
  28. 28.
    Wang, N., Sun, J.C., Han, M., Zheng, Z., Er, M.J.: Adaptive approximation-based regulation control for a class of uncertain nonlinear systems without feedback linearizability. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3747–3760 (2018)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Wang, N., Sun, J.C., Er, M.J.: Tracking-error-based universal adaptive fuzzy control of output tracking of nonlinear systems with completely unknown dynamics. IEEE Trans. Fuzzy Syst. 26(2), 869–883 (2018)CrossRefGoogle Scholar
  30. 30.
    Wang, N., Su, S.F., Han, M., Chen, W.H.: Backpropagating constraints based trajectory tracking control of a quadrotor with constrained actuator dynamics and complex unknowns. IEEE Trans. Syst. Man Cybern. Syst. Early access (2018)Google Scholar

Copyright information

© Taiwan Fuzzy Systems Association 2019

Authors and Affiliations

  1. 1.Graduate School of Engineering Science and Technology and Department of Electrical EngineeringNational Yunlin University of Science and TechnologyDouliouTaiwan, ROC

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