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Iterative Learning Control

  • Krzysztof PatanEmail author
Chapter
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 197)

Abstract

The chapter presents original research results in the area of nonlinear iterative-learning control. We propose a novel ILC scheme developed using neural networks. The following two cases are described: dynamic and static learning controllers and in both cases the controller is designed in such a way as to minimize the tracking error. This task is accomplished by an appropriate training of the neural controller after each repetition of the control system. Additionally, the chapter contains both the stability and convergence analysis of the proposed nonlinear ILC. The portrayed control strategies are tested on the examples of a pneumatic servomechanism and a magnetic suspension system.

References

  1. 1.
    Arimoto, S., Kawamura, S., Miyazaki, F.: Bettering operation of robots by learning. J. Robot. Syst. 1(2), 123–140 (1984)CrossRefGoogle Scholar
  2. 2.
    Bristow, D.A., Tharayil, M., Alleyne, A.G.: A survey of iterative learning control: a learning-based method for high-performance tracking control. IEEE Control Syst. Mag. 26(3), 96–114 (2006)CrossRefGoogle Scholar
  3. 3.
    Chen, Y., Wen, C.: Iterative Learning Control. Convergence, Robustness, Applications. Lecture Notes in Control and Information Sciences, vol. 248. Springer, London (1999)CrossRefGoogle Scholar
  4. 4.
    Chi, R., Hou, Z.: A new neural network-based adaptive ILC for nonlinear discrete-time systems with dead zone scheme. J. Syst. Sci. Complex. 22, 435–445 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Chow, T.W.S., Li, X.D., Fang, Y.: A real-time learning control approach for nonlinear continuous-time system using recurrent neural networks. IEEE Trans. Ind. Electron. 47, 478–486 (2000)CrossRefGoogle Scholar
  6. 6.
    Freeman, C., Hughes, A.M., Burridge, J., Chappell, P., Lewin, P., Rogers, E.: Iterative learning control of FES applied to the upper extremity for rehabilitation. Control Eng. Pract. 17(3), 368–381 (2009)CrossRefGoogle Scholar
  7. 7.
    Freeman, C.T., Rogers, E., Hughes, A., Burridge, J.H., Meadmore, K.L.: Iterative learning control in health care: electrical stimulation and robotic-assisted upper-limb stroke rehabilitation. IEEE Control Syst. 32(1), 18–43 (2012)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gao, F., Yang, Y., Shao, C.: Robust iterative learning control with applications to injection molding process. Chem. Eng. Sci. 56(24), 7025–7034 (2001)CrossRefGoogle Scholar
  9. 9.
    Havlicsek, H., Alleyne, A.: Nonlinear control of an electrohydraulic injection molding machine via iterative adaptive learning. IEEE/ASME Trans. Mechatron. 4(3), 312–323 (1999)CrossRefGoogle Scholar
  10. 10.
    Haykin, S.: Neural Networks. A Comprehensive Foundation, 2nd edn. Prentice-Hall, New Jersey (1999)zbMATHGoogle Scholar
  11. 11.
    Kim, D.I., Kim, S.: An iterative learning control method with application for CNC machine tools. IEEE Trans. Ind. Appl. 32(1), 66–72 (1996)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Kowalów, D., Patan, M.: Optimal sensor selection for model identification in iterative learning control of spatio-temporal systems. In: 21st IEEE Conference on Methods and Models in Automation and Robotics (MMAR), pp. 70–75 (2016)Google Scholar
  13. 13.
    Lee, J.H., Lee, K.S.: Iterative learning control applied to batch processes: an overview. Control Eng. Pract. 15(10), 1306–1318 (2007)CrossRefGoogle Scholar
  14. 14.
    Mezghani, M., Roux, G., Cabassud, M., Le Lann, M.V., Dahhou, B., Casamatta, G.: Application of iterative learning control to an exothermic semibatch chemical reactor. IEEE Trans. Control Syst. Technol. 10(6), 822–834 (2002)CrossRefGoogle Scholar
  15. 15.
    Moore, K.L.: Iterative Learning Control for Deterministic Systems. Advances in Industrial Control. Springer, London (1993)CrossRefGoogle Scholar
  16. 16.
    Nelles, O.: Nonlinear System Identification. From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin (2001)zbMATHGoogle Scholar
  17. 17.
    Owens, D.H., Daley, S.: Iterative learning control - monotonicity and optimization. Int. J. Appl. Math. Comput. Sci. 18, 279–293 (2008)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Paszke, W., Rogers, E., Gałkowski, K., Cai, Z.: Robust finite frequency range iterative learning control design and experimental verification. Control Eng. Pract. 21(10), 1310–1320 (2013)CrossRefGoogle Scholar
  19. 19.
    Patan, K.: Neural network based model predictive control: fault tolerance and stability. IEEE Trans. Syst. Control Technol. 23(3), 1147–1155 (2015)CrossRefGoogle Scholar
  20. 20.
    Patan, K., Patan, M.: Design and convergence of iterative learning control based on neural networks. In: Proceedings of the European Control Conference, ECC 2018, Limassol, Cyprus (2018)Google Scholar
  21. 21.
    Patan, K., Patan, M., Kowalów, D.: Neural networks in design of iterative learning control for nonlinear systems. In: IFAC Papers On-line, 20th IFAC World Congress, Toulouse, France, vol. 50, pp. 13402–13407 (2017).  https://doi.org/10.1016/j.ifacol.2017.08.2277
  22. 22.
    Quinn, S.L., Harris, T.J., Bacon, D.W.: Accounting for uncertainty in control-relevant statistics. J. Process Control 15, 675–690 (2005)CrossRefGoogle Scholar
  23. 23.
    Rogers, E., Galkowski, K., Owens, D.H.: Control Systems Theory and Applications for Linear Repetitive Processes, vol. 349. Springer, Berlin (2007)zbMATHGoogle Scholar
  24. 24.
    Seel, T., Schauer, T., Raisch, J.: Monotonic convergence of iterative learning control systems with variable pass length. Int. J. Control 90, 393–406 (2017)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Shen, D., Zhang, W., Xu, J.: Iterative learning control for discrete nonlinear systems with randomly iteration varying lengths. Syst. Control Lett. 96, 81–87 (2016)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Tayebi, A., Abdul, S., Zaremba, M., Ye, Y.: Robust iterative learning control design: application to a robot manipulator. IEEE/ASME Trans. Mechatron. 13(5), 608–613 (2008)CrossRefGoogle Scholar
  27. 27.
    Uchiyama, M.: Formulation of high-speed motion pattern of a mechanical arm by trial. Trans. SICE (Soc. Instrum. Contr. Eng.) 14(6), 706–712 (1978)Google Scholar
  28. 28.
    Xiong, W., Ho, D.W.C., Yu, X.: Saturated finite interval iterative learning for tracking of dynamic systems with hnn-structural output. IEEE Trans. Neural Netw. Learn. Syst. 27, 1578–1584 (2016)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Xu, J.X., Tan, Y.: Linear and Nonlinear Iterative Learning Control for Deterministic Systems. Lecture Notes in Control and Information Sciences, vol. 291. Springer, Berlin (2003)Google Scholar
  30. 30.
    Yang, D.R., Lee, K.S., Ahn, H.J., Lee, J.H.: Experimental application of a quadratic optimal iterative learning control method for control of wafer temperature uniformity in rapid thermal processing. IEEE Trans. Semicond. Manuf. 16(1), 36–44 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Institute of Control and Computation EngineeringUniversity of Zielona GóraZielona GóraPoland

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