Skip to main content
Log in

Precision Trajectory Tracking on XY Motion Stage Using Robust Interval Type-2 Fuzzy PI Sliding Mode Control Method

  • Regular Paper
  • Published:
International Journal of Precision Engineering and Manufacturing Aims and scope Submit manuscript

Abstract

Precision contour tracking is one of the most important factors used to determine product quality in a machining tool. An interval type-2 fuzzy proportional–integral (PI) sliding mode control (IT2FPISMC) system is proposed herein to control the mover position of the two-axis motion stage with optical encoder sensors for trajectory feedback. A type-2 fuzzy method that can handle rule uncertainties is developed to approach the unknown nonlinear systems. The PI term is used to approximate the discontinuous control signal and mitigate the chattering phenomenon in the presence of unmodeled system dynamics and external disturbances. The adaptive control laws are derived based on the Lyapunov theorem, such that the closed-loop stability is guaranteed, and the output tracking errors of the system asymptotically converge to zero. A non-uniform rational B-spline interpolator with high accuracy is adopted in the biaxial linear stage. Moreover, typical circular, bowknot, heart, and star reference contours are tested. The results on the average tracking error and the tracking error standard deviation are experimented and compared to illustrate the performance of our proposed method. The tracking performance obtained from the IT2FPISMC method is better than that of the conventional method. Furthermore, the proposed method can achieve robustness for tracking different reference contours in industrial applications.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Slotine, J. E., & Li, W. (1991). Applied nonlinear control. Englewood Cliffs, NJ: Prentice Hall.

    MATH  Google Scholar 

  2. Wu, J., Xiong, Z., & Ding, H. (2015). Integral design of contour error model and control for biaxial system. International Journal of Machine Tools and Manufacture,89, 159–169.

    Article  Google Scholar 

  3. Lin, F. J., Shieh, H. J., Shieh, P. H., & Shen, P. H. (2006). An adaptive recurrent-neural-network motion controller for X-Y Table in CNC machine. IEEE Transaction on system Man and Cybernetics,36(2), 286–299.

    Google Scholar 

  4. Lin, F. J., Chou, P. H., & Kung, Y. S. (2008). Robust fuzzy neural network controller with nonlinear disturbance observer for two-axis motion control system. IET Control Theory and Applications,2(2), 151–167.

    Article  Google Scholar 

  5. El-Sousy, F. F. M. (2016). Intelligent mixed H2/H∞ adaptive tracking control system design using self-organizing recurrent fuzzy-wavelet-neural-network for uncertain two-axis motion control system. Applied Soft Computing,41, 22–50.

    Article  Google Scholar 

  6. Lin, F. J., & Shen, P. H. (2006). Robust fuzzy neural network sliding-mode control for two-axis motion control system. IEEE Transactions on Industrial Electronics,53(4), 1209–1225.

    Article  Google Scholar 

  7. Lin, F. J., Shieh, P. H., & Shen, P. H. (2006). Robust recurrent-neural-network sliding-mode control for the X-Y table of a CNC machine. IEE Proceedings—Control Theory and Applications,153(1), 111–123.

    Article  Google Scholar 

  8. Piegl, L., & Tiller, W. (1997). The NURBS books (2nd ed.). Berlin: Springer.

    Book  Google Scholar 

  9. Lee, A. C., Lin, M. T., Pana, Y. R., & Lin, W. Y. (2011). The feedrate scheduling of NURBS interpolator for CNC machine tools. Computer-Aided Design,43, 612–628.

    Article  Google Scholar 

  10. Yau, H. T., Lin, M. T., & Tsai, M. S. (2006). Real-time NURBS interpolation using FPGA for high speed motion control. Computer-Aided Design,38, 1123–1133.

    Article  Google Scholar 

  11. Cheng, M. Y., Tsai, M. C., & Kuo, J. C. (2002). Real-time NURBS command generators for CNC servo controllers. International Journal of Machine Tools and Manufacture,42, 801–881.

    Article  Google Scholar 

  12. Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning-1. Information Sciences,8, 199–249.

    Article  MathSciNet  Google Scholar 

  13. Mendel, J. M., John, R. I., & Feilong, L. (2006). Interval type-2 fuzzy logic systems made simple. IEEE Transactions on Fuzzy Systems,14(6), 808–821.

    Article  Google Scholar 

  14. Mendel, J. M. (2014). General type-2 fuzzy logic systems made simple: A tutorial. IEEE Transactions on Fuzzy Systems,22(5), 1162–1182.

    Article  Google Scholar 

  15. Liang, Q., & Mendel, J. M. (2000). Interval type-2 fuzzy logic systems: Theory and design. IEEE Transactions on Fuzzy Systems,8(5), 535–550.

    Article  Google Scholar 

  16. Lin, P. Z., Lin, C. M., Hsu, C. F., & Lee, T. T. (2005). Type-2 fuzzy controller design using a sliding-mode approach for application to DC-DC converters. IEE Proceedings—Electric Power Applications,152(6), 1482–1488.

    Article  Google Scholar 

  17. Wu, D., & Tan, W. W. (2010). Interval type-2 fuzzy PI controllers: Why they are more robust. In Proceedings of 2010 IEEE international conference on granular computing (pp. 802–807).

  18. Nie, M., & Tan, W. W. (2012). Modeling capability of type-1 fuzzy set and interval type-2 fuzzy set. In Proceedings of 2012 IEEE international congress on fuzzy system (pp. 1–8).

  19. Navarro, G., Umberger, D. K., & Manic, M. (2017). VD-IT2, virtual disk cloning on disk arrays using a type-2 fuzzy controller. IEEE Transactions on Fuzzy Systems,25(6), 1752–1764.

    Article  Google Scholar 

  20. Utkin, V., Guldner, J., & Shi, J. (2009). Sliding mode control in electro-mechanical systems (2nd ed.). Boca Raton: CRC Press.

    Book  Google Scholar 

  21. Ho, H. F., Wong, Y. K., & Rad, A. B. (2009). Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems. Simulation Modeling Practice and Theory,17, 1199–1210.

    Article  Google Scholar 

  22. Lin, T. C. (2010). Based on interval type-2 fuzzy-neural network direct adaptive sliding mode control for SISI nonlinear systems. Communications in Nonlinear Science and Numerical Simulation,15, 4084–4099.

    Article  MathSciNet  Google Scholar 

  23. Ghaemi, M., & Akbarzader-T, M. R. (2014). Indirect adaptive interval type-2 fuzzy PI sliding mode control for a class of uncertain nonlinear systems. Iranian Journal of Fuzzy Systems,11(5), 1–21.

    MathSciNet  Google Scholar 

  24. Ghaemi, M., Akbarzadeh M.-R. T., & Mohsen Jalaeian, F. (2012). Adaptive interval type-2 fuzzy PI sliding mode control with optimization of membership functions using genetic algorithm. In 2012 2nd international eConference on computer and knowledge engineering (ICCKE), October 18–19 (pp. 123–128).

  25. Sun, N., Yang, T., Fang, Y., Wu, Y., & Chen, H. (2019). Transportation control of double-pendulum cranes with a nonlinear quasi-PID scheme: Design and experiments. IEEE Transactions on Systems, Man, and Cybernetics: Systems,49(7), 1408–1418.

    Article  Google Scholar 

  26. Yang, T., Sun, N., Chen, H., & Fang, Y. (2019). Neural network-based adaptive antiswing control of an underactuated ship-mounted crane with roll motions and input dead zones.IEEE Transactions on Neural Networks and Learning Systems (pp. 1–14) (early access).

Download references

Acknowledgements

The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for a financial support of this research (Contract No: MOST 105-2622-E-224-010-CC3, MOST 106-2731-M-224-001, MOST 107-2221-E-224-040, MOST 107-2731-M-224-001).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei-Lung Mao.

Ethics declarations

Conflict of interest

Wei-Lung Mao, and Ding-Yu Shiu have received research grants from Ministry of Science and Technology of the Republic of China, Taiwan. Wei-Lung Mao declares that he has no conflict of interest. Ding-Yu Shiu declares that he has no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mao, WL., Shiu, DY. Precision Trajectory Tracking on XY Motion Stage Using Robust Interval Type-2 Fuzzy PI Sliding Mode Control Method. Int. J. Precis. Eng. Manuf. 21, 797–818 (2020). https://doi.org/10.1007/s12541-019-00267-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-019-00267-x

Keywords

Navigation