Advertisement

Introduction

  • Dong Shen
Chapter

Abstract

This chapter presents the fundamental knowledge of iterative learning control (ILC) and the motivation of this monograph. We start with the introduction of ILC, where the inherent principle and common structure of ILC are detailed. Then, we concentrate on the ILC research progress with passive incomplete information, where an in-depth literature review is provided. The data dropout problem is first elaborated and other incomplete information problems including random iteration-varying lengths and communication asynchronization are then discussed. The structure arrangement of this monograph is also presented.

References

  1. 1.
    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
  2. 2.
    Arimoto, S., Kawamura, S., Miyazaki, F.: Bettering operation of robots by learning. J. Robotic Syst. 1(2), 123–140 (1984)CrossRefGoogle Scholar
  3. 3.
    Casalino, G., Bartolini, G.: A learning procedure for the control of movements of robotic manipulators. In: Proceedings of the IASTED Symposium Robotics and Automation, pp. 108–111 (1984)Google Scholar
  4. 4.
    Craig, J.: Adaptive control of manipulators through repeated trials. In: Proceedings of the American Control Conference, pp. 1566–1573 (1984)Google Scholar
  5. 5.
    Moore, K.L.: Iterative Learning Control Control for Deterministic Systems. Springer, Berlin (1993)Google Scholar
  6. 6.
    Bien, Z., Xu, J.-X.: Iterative Learning Control - Analysis, Design, Integration and Applications. Kluwer Academic Publishers, Dordrecht (1998)CrossRefGoogle Scholar
  7. 7.
    Chen, Y.Q., Wen, C.: Iterative Learning Control: Convergence, Robustness and Applications. Springer, London (1999)CrossRefGoogle Scholar
  8. 8.
    Xu, J.-X., Tan, Y.: Linear and Nonlinear Iterative Learning Control. Springer, New York (2003)zbMATHGoogle Scholar
  9. 9.
    Ahn, H.-S., Moore, K.L., Chen, Y.Q.: Iterative Learning Control: Robustness and Monotonic Convergence for Interval Systems. Springer, Berlin (2007)CrossRefGoogle Scholar
  10. 10.
    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
  11. 11.
    Ahn, H.-S., Chen, Y.Q., Moore, K.L.: Iterative learning control: survey and categorization from 1998 to 2004. IEEE Trans. Syst. Man Cybern. Part C 37(6), 1099–1121 (2007)CrossRefGoogle Scholar
  12. 12.
    Wang, Y., Gao, F., Doyle III, F.J.: Survey on iterative learning control, repetitive control and run-to-run control. J. Process Control 19(10), 1589–1600 (2009)CrossRefGoogle Scholar
  13. 13.
    Moore, K.L., Xu, J.-X.(Guest eds.): Special issue on iterative learning control. Int. J. Control 73(10), 819–999 (2000)Google Scholar
  14. 14.
    Special issue on iterative learning control. Asian J. Control 4(1), 1–118 (2002)Google Scholar
  15. 15.
    Ahn, H.-S., Moore, K.L.(Guest eds.): Special issue on iterative learning control. Asian J. Control 13(1), 1–212 (2011)Google Scholar
  16. 16.
    Freeman, C.T., Tan, Y.(Guest eds): Special issue on iterative learning control and repetitive control. Int. J. Control 84(7), 1193–1294 (2011)Google Scholar
  17. 17.
    Tayebi, A., Abdul, S., Zaremba, M.B., Ye, Y.: Robust iterative learning control design: application to a robot manipulator. IEEE/ASME Trans. Mechatron 13(5), 608–613 (2008)CrossRefGoogle Scholar
  18. 18.
    Freeman, C., Lewin, P., Rogers, E., Ratcliffe, J.: Iterative learning control applied to a gantry robot and conveyor system. Trans. Inst. Meas. Control 32(3), 251–264 (2010)CrossRefGoogle Scholar
  19. 19.
    Inaba, K.: Iterative learning control for industrial robots with end effector sensing. Ph.D. dissertation, University of California, Berkeley (2008)Google Scholar
  20. 20.
    Hoelzle, D.J., Alleyne, A.G., Johnson, A.J.W.: Iterative Learning Control for Robotic Deposition Using Machine Vision. In: Proceedings of the American Control Conference, pp. 4541–4547 (2008)Google Scholar
  21. 21.
    Chen, Y.Q., Moore, K.L., Yu, J., Zhang, T.: Iterative learning control and repetitive control in hard disk drive industry–a tutorial. Int. J. Adap. Control Signal Process. 22(4), 325–343 (2008)CrossRefGoogle Scholar
  22. 22.
    Wu, S.-C., Tomizuka, M.: An iterative learning control design for self-servoWriting in hard disk drives. Mechatronics 20(1), 53–58 (2010)CrossRefGoogle Scholar
  23. 23.
    Liu, T., Gao, F.: IMC-based iterative learning control for batch processes with time delay variation. J. Process Control 20(2), 173–180 (2010)CrossRefGoogle Scholar
  24. 24.
    Liu, T., Gao, F.: Robust two-dimensional iterative learning control for batch processes with state delay and time-varying uncertainties. Chem. Eng. Sci. 65(23), 6134–6144 (2010)CrossRefGoogle Scholar
  25. 25.
    Lin, H., Antsaklis, P.J.: Stability and persistent disturbance attenuation properties for networked control systems: switched system approach. Int. J. Control 78(18), 1447–1458 (2005)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Sinopoli, B., Schenato, L., Franceschetti, M., Poolla, K., Jordan, M.I., Sastry, S.S.: Kalman filtering with intermittent observations. IEEE Trans. Autom. Control 49(9), 1453–1464 (2004)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Shi, Y., Yu, B.: Output feedback stabilization of networked control systems with random delays modeled by Markov chains. IEEE Trans. Autom. Control 54(7), 1668–1674 (2009)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Ahn, H.S., Chen, Y.Q., Moore, K.L.: Intermittent iterative learning control. In: Proceedings of the 2006 IEEE International Symposium on Intelligent Control, pp. 832–837 (2006)Google Scholar
  29. 29.
    Ahn, H.S., Moore, K.L., Chen, Y.Q.: Discrete-time intermittent iterative learning controller with independent data dropouts. In: Proceedings of the 2008 IFAC World Congress, pp. 12442–12447 (2008)Google Scholar
  30. 30.
    Ahn, H.S., Moore, K.L., Chen, Y.Q.: Stability of discrete-time iterative learning control with random data dropouts and delayed controlled signals in networked control systems. In: Proceedings the IEEE International Conference Control Automation, Robotics, and Vision, pp. 757–762 (2008)Google Scholar
  31. 31.
    Bu, X., Hou, Z.-S., Yu, F.: Stability of first and high order iterative learning control with data dropouts. Int. J. Control Autom. Syst. 9(5), 843–849 (2011)CrossRefGoogle Scholar
  32. 32.
    Bu, X., Yu, F., Hou, Z.-S., Wang, F.: Iterative learning control for a class of nonlinear systems with random packet losses. Nonlinear Anal. Real World Appl. 14(1), 567–580 (2013)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Bu, X., Hou, Z.-S., Yu, F., Wang, F.: H-\(\infty \) iterative learning controller design for a class of discrete-time systems with data dropouts. Int. J. Syst. Sci. 45(9), 1902–1912 (2014)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Bu, X., Hou, Z.-S., Jin, S., Chi, R.: An iterative learning control design approach for networked control systems with data dropouts. Int. J. Robust Nonlinear Control 26, 91–109 (2016)MathSciNetCrossRefGoogle Scholar
  35. 35.
    Huang, L.-X., Fang, Y.: Convergence analysis of wireless remote iterative learning control systems with dropout compensation. Math. Probl. Eng. 2013, 609284 (2013)MathSciNetGoogle Scholar
  36. 36.
    Liu, J., Ruan, X.: Networked iterative learning control approach for nonlinear systems with random communication delay. Int. J. Syst. Sci. 47(16), 3960–3969 (2016)MathSciNetCrossRefGoogle Scholar
  37. 37.
    Liu, C., Xu, J.-X., Wu, J.: Iterative learning control for remote control systems with communication delay and data dropout. Math. Probl. Eng. 2012, 705474 (2012)MathSciNetzbMATHGoogle Scholar
  38. 38.
    Shen, D., Wang, Y.: ILC for networked discrete systems with random data dropouts: a switched system approach. In: Proceedings of the 33rd Chinese Control Conference, pp. 8670–8677 (2014)Google Scholar
  39. 39.
    Shen, D., Zhang, C., Xu, Y.: Two compensation schemes of iterative learning control for networked control systems with random data dropouts. Inf. Sci. 381, 352–370 (2017)CrossRefGoogle Scholar
  40. 40.
    Shen, D., Zhang, C., Xu, Y.: Intermittent and successive ILC for stochastic nonlinear systems with random data dropouts. Asian J. Control (2018).  https://doi.org/10.1002/asjc.1480
  41. 41.
    Shen, D., Xu, J.-X.: A novel Markov chain based ILC analysis for linear stochastic systems under general data dropouts environments. IEEE Trans. Autom. Control 62(11), 5850–5857 (2017)MathSciNetCrossRefGoogle Scholar
  42. 42.
    Pan, Y.-J., Marquez, H.J., Chen, T., Sheng, L.: Effects of network communications on a class of learning controlled non-linear systems. Int. J. Syst. Sci. 40(7), 757–767 (2009)MathSciNetCrossRefGoogle Scholar
  43. 43.
    Shen, D., Wang, Y.: Iterative learning control for networked stochastic systems with random packet losses. Int. J. Control 88(5), 959–968 (2015)MathSciNetzbMATHGoogle Scholar
  44. 44.
    Shen, D., Wang, Y.: ILC for networked nonlinear systems with unknown control direction through random Lossy channel. Syst. Control Lett. 77, 30–39 (2015)MathSciNetCrossRefGoogle Scholar
  45. 45.
    Saab, S.S.: A discrete-time stochastic learning control algorithm. IEEE Trans. Autom. Control 46(6), 877–887 (2001)MathSciNetCrossRefGoogle Scholar
  46. 46.
    Hassibi, A., Boyd, S.P., How, J.P.: Control of asynchronous dynamical systems with rate constraints on events. In: Proceedings the 38th IEEE Conference on Decision and Control, pp. 1345–1351 (1999)Google Scholar
  47. 47.
    Shen, D., Chen, H.-F.: Iterative learning control for large scale nonlinear systems with observation noise. Automatica 48, 577–582 (2012)MathSciNetCrossRefGoogle Scholar
  48. 48.
    Li, X.D., Chow, T.W.S., Ho, J.K.L.: 2D system theory based iterative learning control for linear continuous systems with time delays. IEEE Trans. Circuits Syst. 52(7), 1421–1430 (2005)MathSciNetCrossRefGoogle Scholar
  49. 49.
    Meng, D., Jia, Y., Du, J., Yu, F.: Robust iterative learning control design for uncertain time-delay systems based on a performance index. IET Control Theory Appl. 4(5), 759–772 (2010)MathSciNetCrossRefGoogle Scholar
  50. 50.
    Shen, D., Mu, Y., Xiong, G.: Iterative learning control for non-linear systems with deadzone input and time delay in presence of measurement noise. IET Control Theory Appl. 5(12), 1418–1425 (2011)MathSciNetCrossRefGoogle Scholar
  51. 51.
    Seel, T., Schauer, T., Raisch, J.: Iterative learning control for variable pass length systems. In: Proceedings of the 18th IFAC world congress, pp. 4880–4885 (2011)CrossRefGoogle Scholar
  52. 52.
    Seel, T., Werner, C., Schauer, T.: The adaptive drop foot stimulator - multivariable learning control of foot pitch and roll motion in paretic gait. Med. Eng. Phys. 38(11), 1205–1213 (2016)CrossRefGoogle Scholar
  53. 53.
    Seel, T., Werner, C., Raisch, J., Schauer, T.: Iterative learning control of a drop foot neuroprosthesis - generating physiological foot motion in paretic gait by automatic feedback control. Control Eng. Pract. 48, 87–97 (2016)CrossRefGoogle Scholar
  54. 54.
    Seel, T., Schauer, T., Raisch, J.: Monotonic convergence of iterative learning control systems with variable pass length. Int. J. Control 90(3), 393–406 (2017)MathSciNetCrossRefGoogle Scholar
  55. 55.
    Li, X., Xu, J.-X., Huang, D.: An iterative learning control approach for linear systems with randomly varying trial lengths. IEEE Trans. Autom. Control 59(7), 1954–1960 (2014)MathSciNetCrossRefGoogle Scholar
  56. 56.
    Li, X., Xu, J.-X., Huang, D.: Iterative learning control for nonlinear dynamic systems with randomly varying trial lengths. Int. J. Adap. Control Signal Process. 29(11), 1341–1353 (2015)MathSciNetCrossRefGoogle Scholar
  57. 57.
    Li, X., Xu, J.-X.: Lifted system framework for learning control with different trial lengths. Int. J. Autom. Comput. 12(3), 273–280 (2015)MathSciNetCrossRefGoogle Scholar
  58. 58.
    Shen, D., Zhang, W., Wang, Y., Chien, C.-J.: On almost sure and mean square convergence of p-type ILC under randomly varying iteration lengths. Automatica 63, 359–365 (2016)MathSciNetCrossRefGoogle Scholar
  59. 59.
    Shen, D., Zhang, W., Xu, J.-X.: Iterative learning control for discrete nonlinear systems with randomly iteration varying lengths. Syst. Control Lett. 96, 81–87 (2016)MathSciNetCrossRefGoogle Scholar
  60. 60.
    Li, X., Shen, D.: Two novel iterative learning control schemes for systems with randomly varying trial lengths. Syst. Control Lett. 107, 9–16 (2017)MathSciNetCrossRefGoogle Scholar
  61. 61.
    Shi, J., He, X., Zhou, D.: Iterative learning control for nonlinear stochastic systems with variable pass length. J. the Frankl. Inst. 353, 4016–4038 (2016)MathSciNetCrossRefGoogle Scholar
  62. 62.
    Wei, Y.-S., Li, X.-D.: Varying trail lengths-based iterative learning control for linear discrete-time systems with vector relative degree. Int. J. Syst. Sci. 48(10), 2146–2156 (2017)MathSciNetCrossRefGoogle Scholar
  63. 63.
    Liu, S., Debbouche, A., Wang, J.: On the iterative learning control for stochastic impulsive differential equations with randomly varying trial lengths. J. Comput. Appl. Math. 312, 47–57 (2017)MathSciNetCrossRefGoogle Scholar
  64. 64.
    Liu, S., Wang, J.: Fractional order iterative learning control with randomly varying trial lengths. J. the Frankl. Inst. 354, 967–992 (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.College of Information Science and TechnologyBeijing University of Chemical TechnologyBeijingChina

Personalised recommendations