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Output-based Adaptive Iterative Learning Control of Uncertain Linear Systems Applied to a Wafer Stage

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  • Control Theory and Applications
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Abstract

Iterative learning control (ILC) is an efficient technique applicable to improving the tracking performance of systems that have a repetitive nature. In this paper, point-to-point movements with time-iteration-varying disturbances are considered instead. A new output-based adaptive ILC scheme consisting of an adaptive second-order ILC and an iterative learning estimation of time-iteration-varying disturbances is proposed for a class of linear systems with unknown parameters. The proposed algorithm is used to improve trajectory tracking performance without requiring a plant model or a sensitivity function, and without assuming the initial condition to be zero. In order to verify the proposed algorithm, it is applied to a wafer stage, and the obtained tracking performance is compared with that obtained using a traditional second-order ILC algorithm; better results are obtained using the proposed method.

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References

  1. D. Bruijnen and N. V. Dijk, “Combined input shaping and feedforward control for flexible motion systems,” Proc. of American Control Conference (ACC), June 2012.

  2. S. Van der Meulen, R. L. Tousain, and O. H. Bosgra, “Fixed structure feedforward controller design exploiting iterative trials: Applied to a wafer stage and a desktop printer,” Journal of Dynamic Systems, Measurement, and Control, vol. 130, no. 5, p. 051006, 2008.

    Article  Google Scholar 

  3. S. Arimoto, S. Kawamura, and F. Miyazaki, “Bettering operation of robots by learning,” Journal of Field Robotics, vol. 1, no. 2, pp. 123–140, 1984.

    Google Scholar 

  4. H. Anwaar, Y. Y. Xin, and S. Ijaz, “A comprehensive survey on recent developments in iterative learning control algorithms and applications,” Proc. of 29th Chinese Control Decision Conference (CCDC), pp. 3282–3289, 2017.

  5. X. Jin and J. X. Xu, “Iterative learning control for output-constrained systems with both parametric and nonparametric uncertainties,” Automatica, vol. 49, no. 8, pp. 2508–2516, 2013.

    Article  MathSciNet  Google Scholar 

  6. J. X. Xu, “A survey on iterative learning control for nonlinear systems,” International Journal of Control, vol. 84, no. 7, pp. 1275–1294, 2011.

    Article  MathSciNet  Google Scholar 

  7. F. Memon and C. Shao, “An optimal approach to online tuning method for PID type iterative learning control,” International Journal of Control, Automation, and Systems, vol. 18, no. 8, pp. 1926–1935, August 2020.

    Article  Google Scholar 

  8. L. Zhang, W. Chen, J. Liu, and C. Wen, “A robust adaptive iterative learning control for trajectory tracking of permanentmagnet spherical actuator,” IEEE Transactions on Industrial Electronics, vol. 63, no. 1, pp. 291–301, 2015.

    Article  Google Scholar 

  9. A. Tayebi, S. Abdul, M. B. Zaremba, and Y. Ye, “Robust iterative learning control design: Application to a robot manipulator,” IEEE/ASME Transactions on Mechatronics, vol. 13, no. 5, pp. 608–613, 2008.

    Article  Google Scholar 

  10. Q. Yu, Z. Hou, and J. Xu, “D-type ILC based dynamic modeling and norm optimal ILC for high-speed trains,” IEEE Transactions on Control Systems Technology, vol. 26, no. 2, pp. 652–663, 2018.

    Article  Google Scholar 

  11. R. de Rozario, T. Oomen, and M. Steinbuch, “Iterative learning control and feedforward for LPV systems: Applied to a position-dependent motion system,” Proc. of American Control Conference, pp. 3518–3523, May 2017.

  12. M. Butcher and A. Karimi, “Linear parameter-varying iterative learning control with application to a linear motor system,” IEEE/ASME Transactions on Mechatronics, vol. 15, no. 3, pp. 412–420, June 2010.

    Article  Google Scholar 

  13. B. Chu, C. T. Freeman, and D. H. Owens, “A novel design framework for point-to-point ILC using successive projection,” IEEE Transactions on Control Systems Technology, vol. 23, no. 3, pp. 1156–1163, May 2015.

    Article  Google Scholar 

  14. J. Park, P. H. Chang, H. S. Park, and E. Lee, “Design of learning input shaping technique for residual vibration suppression in an industrial robot,” IEEE/ASME Transactions on Mechatronics, vol. 11, no. 1, pp. 55–65, Feb. 2006.

    Article  Google Scholar 

  15. B. E. Helfrich, C. Lee, D. A. Bristow, X. H. Xiao, J. Dong, A. G. Alleyne, S. M. Salapaka, and P. M. Ferreira, “Combined H∞-feedback control and iterative learning control design with application to nanopositioning systems,” IEEE Transactions on Control Systems Technology, vol. 18, no. 2, pp. 336–351, March 2010.

    Article  Google Scholar 

  16. J. de Best, L. Liu, M. J. G. Molengraft, and M. Steinbuch, “Second-order iterative learning control for scaled setpoints,” IEEE Transactions on Control Systems Technology, vol. 23, no. 2, pp. 805–812, March 2015.

    Article  Google Scholar 

  17. I. Rotariu, M. Steinbuch, and R. Ellenbroek, “Adaptive iterative learning control for high precision motion systems,” IEEE Transactions on Control Systems Technology, vol. 16, no. 5, pp. 1075–1082, September 2008.

    Article  Google Scholar 

  18. D. Shen and J.-X. Xu, “Adaptive learning control for nonlinear systems with randomly varying iteration lengths,” IEEE Transactions on Neural Networks and Learning Systems, pp. 1–14, July 2018.

  19. S. T. Jin, Z. Hou, R. Chi, and Y. Li, “Discrete-time adaptive iterative learning control for permanent magnet linear motor,” Proc. of IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS), 2011.

  20. Y. Chen, J. X. Xu, and C. Wen, “High-order terminal iterative learning control scheme,” Proc. of IEEE Conference on Decision Control, 1997.

  21. Y. T. Kim, H. Lee, H. S. Noh, and Z. Z. Bien, “Robust higher-order iterative learning control for a class of nonlinear discrete-time systems,” Proc. of IEEE International Conference on Systems, Man and Cybernetics, vol. 3, pp. 2219–2224, October 2003.

    Google Scholar 

  22. J. Hatonen, D. H. Owens, and K. Feng, “Basis functions and parameter optimisation in high-order iterative learning control,” Automatica, vol. 42, no. 2, pp. 287–294, May 2006.

    Article  MathSciNet  Google Scholar 

  23. S. Gunnarsson and M. Norrlöf, “On the disturbance properties of high order iterative learning control algorithms,” Automatica, vol. 42, no. 11, pp. 2031–2034, June 2006.

    Article  MathSciNet  Google Scholar 

  24. R. Chi, B. Huang, Z. Hou, and S. Jin, “Data-driven high-order terminal iterative learning control with a faster convergence speed,” International Journal of Robust and Nonlinear Control, vol. 28, no. 1 pp. 103–119, January 2018.

    Article  MathSciNet  Google Scholar 

  25. F. Song, L. Yang, J. X. Xu, X. Yang, and Z. Qiao, “Data-driven iterative feedforward tuning for a wafer stage: A high-order approach based on instrumental variables,” IEEE Transactions on Industrial Electronics, vol. 66, no. 4, pp. 3106–3116, 2018.

    Article  Google Scholar 

  26. J. S. Freudenberg, C. V. Hollot, R. H. Middleton, and V. Toochinda. “Fundamental design limitations of the general control configuration,” IEEE Transactions on Automatic Control, vol. 48, no. 8, pp. 1355–1370, August 2003.

    Article  MathSciNet  Google Scholar 

  27. S. Mishra and M. Tomizuka, “Segmented iterative learning control for precision positioning of waferstages,” IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2007.

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Authors and Affiliations

  1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, No.88 Yingkou road, Erdao district, Changchun, China

    Mingsheng Cao, Yumeng Bo & Huibin Gao

  2. University of Chinese Academy of Sciences, Beijing, China

    Mingsheng Cao

Authors
  1. Mingsheng Cao
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  2. Yumeng Bo
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  3. Huibin Gao
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Correspondence to Mingsheng Cao.

Additional information

Mingsheng Cao received his B.S. degree in Changchun University of Science and Technology in 2013. He is currently pursuing a Ph.D. degree in mechanical engineering in Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences. His research interests include iterative learning control, sliding-model control and robust control.

Yumeng Bo received her B.S. degree in University of Science and Technology of China in 2017. She is currently pursuing an M.S. degree in mechanical engineering in Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences. Her research interests include iterative learning control, adaptive control and precision motion control.

Huibin Gao received his B.S. degree in Jilin University in 1985. He received an M.S. degree in Jilin University in 1990. He is currently a researcher in Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences. His research interests include iterative learning control, photoelectric tracking and precision motion control.

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Cao, M., Bo, Y. & Gao, H. Output-based Adaptive Iterative Learning Control of Uncertain Linear Systems Applied to a Wafer Stage. Int. J. Control Autom. Syst. 20, 741–749 (2022). https://doi.org/10.1007/s12555-020-0564-9

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  • DOI: https://doi.org/10.1007/s12555-020-0564-9

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