Skip to main content
Log in

Gradient-Descent-Based Velocity Observer with a Residual Displacement Term for ILSMC in Contour Following Applications

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

Abstract

Industrial applications such as pick-and-place tasks and CNC machining often involve repetitive motions that may contain periodic disturbances. Due to the periodic nature of these applications, the idea of iterative learning control can be exploited to cope with external periodic disturbances so as to improve system performance. In addition, in order to ensure satisfactory motion accuracy for operation scenarios that may encounter significant disturbance, (e.g., machining and polishing), a robust controller is essential. In particular, iterative learning sliding mode control (ILSMC) is shown to be suitable for control applications that encounter periodic disturbances. However, when the disturbance contains high frequency components, ILSMC may become less effective in compensating for external disturbances if its velocity estimation is not accurate enough. In order to cope with the aforementioned problem, this paper develops a gradient-descent-based velocity observer with a residual displacement compensation term to provide accurate velocity feedback to be used in ILSMC. Both theoretical analysis and experimental results are provided to verify the effectiveness of the proposed approach.

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
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

Abbreviations

x(t):

State vector

\(d_{T} (t)\) :

Periodic disturbance

\(d_{N} (t)\) :

Non-periodic disturbance

L(i):

Learning function for the ith iteration

T(t):

Total accumulated time duration without any measured encoder pulse

Ω :

Magnitude of switching force

References

  1. Chu, C. H., Liu, Y. W., Li, P. C., Huang, L. C., & Luh, Y. P. (2019). Programming by demonstration in augmented reality for the motion planning of a three-axis CNC dispenser. International Journal of Precision Engineering and Manufacturing-Green Technology. https://doi.org/10.1007/s40684-019-00111-7.

    Google Scholar 

  2. Owens, D. H., & Hatonen, J. (2005). Iterative learning control—An optimization paradigm. Annual Reviews in Control, 29, 57–70.

    Article  Google Scholar 

  3. Bristow, D. A., Tharayil, M., & Alleyne, A. G. (2006). A survey of iterative learning control. IEEE Control Systems Magazine, 26, 96–114.

    Article  Google Scholar 

  4. Ahn, H. S., Chen, Y., & Moore, K. L. (2007). Iterative learning control: Brief survey and categorization. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(6), 1099–1121.

    Article  Google Scholar 

  5. Wang, Y., Gao, F., & Doyle, F. J., III. (2009). Survey on iterative learning control, repetitive control, and run-to-run control. Journal of Process Control, 19, 1589–1600.

    Article  Google Scholar 

  6. Arimoto, S., Kawamura, S., & Miyazaki, F. (1984). Bettering operations of robots by learning. Journal of Robotic Systems, 1(2), 123–140.

    Article  Google Scholar 

  7. Tayebi, A. (2004). Adaptive iterative learning control for robot manipulators. Automatica, 40, 1195–1203.

    Article  MathSciNet  MATH  Google Scholar 

  8. Tayebi, A., & Islam, S. (2006). Adaptive iterative learning control for robot manipulators: Experimental results. Control Engineering Practice, 14, 843–851.

    Article  Google Scholar 

  9. Sun, M., Ge, S. S., & Mareels, I. M. Y. (2006). Adaptive repetitive learning control of robotic manipulators without the requirement for initial repositioning. IEEE Transactions on Robotics, 22(3), 563–568.

    Article  Google Scholar 

  10. Chien, C. J., & Tayebi, A. (2008). Further results on adaptive iterative learning control of robot manipulators. Automatica, 44, 830–837.

    Article  MathSciNet  MATH  Google Scholar 

  11. Axehill, J. W., Dressler, I., Gunnarsson, S., Robertsson, A., & Norrlöf, M. (2014). Estimation-based ILC applied to a parallel kinematic robot. Control Engineering Practice, 33, 1–9.

    Article  Google Scholar 

  12. Wang, L., Freeman, C. T., & Rogers, E. (2016). Predictive iterative learning control with experimental validation. Control Engineering Practice, 53, 24–34.

    Article  Google Scholar 

  13. Elci, H., Longman, R.W., Phan, M., Juang, J. N., & Ugoletti, R. (1994). Discrete frequency based learning control for precision Motion Control. In: Proceedings of the 1994 IEEE international conference on systems, man, and cybernetics, San Antonio, TX., USA (pp. 2767–2773).

  14. Tsai, M. S., Lin, M. T., & Yau, H. Y. (2006). Development of command-based iterative learning control algorithm with consideration of friction, disturbance, and noise effects. IEEE Transactions on Control Systems Technology, 14(3), 511–518.

    Article  Google Scholar 

  15. Barton, K. L., & Alleyne, A. G. (2008). A cross-coupled iterative learning control design for precision motion control. IEEE Transactions on Control Systems Technology, 16(6), 1218–1231.

    Article  Google Scholar 

  16. Visioli, A., Ziliani, G., & Legnani, G. (2010). Iterative-learning hybrid force/velocity control for contour tracking. IEEE Transactions on Robotics, 26(2), 388–393.

    Article  Google Scholar 

  17. Yoo, H. W., Ito, S., & Schitter, G. (2016). High speed laser scanning microscopy by iterative learning control of a galvanometer scanner. Control Engineering Practice, 50, 12–21.

    Article  Google Scholar 

  18. Freeman, C. T., Rogers, E., Hughes, A. M., Burridge, J. H., & Meadmore, K. L. (2012). Iterative learning control in health care: Electrical stimulation and robotic-assisted upper-limb stroke rehabilitation. IEEE Control Systems Magazine, 32, 18–43.

    MathSciNet  MATH  Google Scholar 

  19. Lu, J. S., Cheng, M. Y., Su, K. H., & Tsai, M. C. (2018). Wire tension control of an automatic motor winding machine—an iterative learning sliding mode control approach. Robotics and Computer-Integrated Manufacturing, 50, 50–62.

    Article  Google Scholar 

  20. Mainali, K., Panda, S. K., Xu, J. X., & Senjyu, T. (2004). Repetitive position tracking performance enhancement of linear ultrasonic motor with sliding mode-cum-iterative learning control. In Proceedings of the IEEE international conference on mechatronics, ICM’04 (pp. 352–357).

  21. Chen, W., & Chen, Y. Q. (2010). Robust iterative learning control for output tracking via second-order sliding mode technique. In Proceedings of the American control conference (ACC), 2010, Baltimore, MD (pp. 2051–2056).

  22. Slotine, J. J. E., & Li, W. (1991). Applied nonlinear control. New Jersey: Prentice-Hall.

    MATH  Google Scholar 

  23. Utkin, V. I. (1993). Sliding mode control design principles and applications to electric drives. IEEE Transactions on Industrial Electronics, 40(1), 23–36.

    Article  Google Scholar 

  24. Park, S. C., Lee, J. M., & Han, S. I. (2018). Tracking error constrained terminal sliding mode control for ball-screw driven motion systems with state observer. International Journal of Precision Engineering and Manufacturing, 19(3), 359–366.

    Article  Google Scholar 

  25. Liu, J., Peng, Q., Huang, Z., Liu, W., & Li, H. (2018). Enhanced sliding mode control and online estimation of optimal slip ratio for railway vehicle braking systems. International Journal of Precision Engineering and Manufacturing, 19(5), 655–664.

    Article  Google Scholar 

  26. Pham, D. B., & Lee, S. G. (2018). Aggregated hierarchical sliding mode control for a spatial ridable ballbot. International Journal of Precision Engineering and Manufacturing, 19(9), 1291–1302.

    Article  Google Scholar 

  27. Fang, J., Zhang, L., Long, Z., & Wang, M. Y. (2018). Fuzzy adaptive sliding mode control for the precision position of piezo-actuated nano positioning stage. International Journal of Precision Engineering and Manufacturing, 19(10), 1447–1456.

    Article  Google Scholar 

  28. Lu, E., Li, W., Yang, X., & Liu, Y. (2019). Anti-disturbance speed control of low-speed high-torque PMSM based on second-order non-singular terminal sliding mode load observer. ISA Transactions, 88, 142–152.

    Article  Google Scholar 

  29. Sun, N., Yang, T., Fang, Y., Wu, Y., & Chen, H. (2018). Transportation control of double-pendulum cranes with a nonlinear quasi-PID scheme: Design and experiments. IEEE Transaction on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/tsmc.2018.2871627.

    Google Scholar 

  30. Brown, R. H., Schneider, S. C., & Mulligan, M. G. (1992). Analysis of algorithms for velocity estimation from discrete position versus time data. IEEE Transactions on Industrial Electronics, 39(1), 11–19.

    Article  Google Scholar 

  31. Lorenz, R. D., & Patten, K. V. (1998). High resolution velocity estimation for all digital, AC servo drives. In Proceedings of the industry applications society annual meeting (pp. 363–368). Pittsburgh, PA., USA .

  32. Yang, S. M., & Ke, S. J. (2000). Performance evaluation of a velocity observer for accurate velocity estimation of servo motor drives. IEEE Transactions on Industry Applications, 36(1), 98–104.

    Article  Google Scholar 

  33. Wang, L. X. (1999). A course in fuzzy systems (4th ed.). Upper Saddle River: Prentice-Hall.

    Google Scholar 

  34. Bryson, A. E. (1975). Applied optimal control—Optimization, estimation, and control. Washington, DC: Hemisphere.

    Google Scholar 

  35. Chong, E. K. P., & Zak, S. H. (2013). An introduction to optimization (4th ed., pp. 110–126). Hoboken, NJ: Wiley.

    MATH  Google Scholar 

  36. 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(7), 801–813.

    Article  Google Scholar 

  37. Piegl, L. (1991). On NURBS: A survey. IEEE Computer Graphics and Applications, 11(1), 55–71.

    Article  Google Scholar 

  38. Johnson, C. T., & Lorenz, R. D. (1992). Experimental identification of friction and its compensation in precise, position controlled mechanisms. IEEE Transactions on Industry Applications, 28(6), 1392–1398.

    Article  Google Scholar 

  39. Jia, Z. Y., Song, D. N., Ma, J. W., Zhao, X. X., & Zhang, N. (2018). Real-time contour-error estimation methods for three-dimensional free-form parametric curves in contour-following tasks. International Journal of Precision Engineering and Manufacturing, 19(2), 173–182.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Ministry of Science and Technology of the Republic of China, Taiwan, under Grant NSC 102-2221-E-006-204 and MOST 103-2221-E-006-185-MY2.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ming-Yang Cheng.

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

Chang, FT., Cheng, MY. Gradient-Descent-Based Velocity Observer with a Residual Displacement Term for ILSMC in Contour Following Applications. Int. J. Precis. Eng. Manuf. 20, 1691–1703 (2019). https://doi.org/10.1007/s12541-019-00181-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12541-019-00181-2

Keywords

Navigation