Encyclopedia of the Sciences of Learning

2012 Edition
| Editors: Norbert M. Seel

Iterative Learning Control

Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-1428-6_65

Synonyms

Definition

Iterative Learning Control (ILC) is a relatively recent, but well-established, area of study in control theory. Various definitions of ILC have been given in the literature. Some of them are quoted here (Ahn et al. 2007 and the references therein):
  • The learning control concept stands for the repeatability of operating a given objective system and the possibility of improving the control input on the basis of previous actual operation data (Arimoto et al. 1984).

  • It is a recursive online control method that relies on less calculation and requires less a priori knowledge about the system dynamics. The idea is to apply a simple algorithm repetitively to an unknown plant, until perfect tracking is achieved (Bien and Huh 1989).

  • Iterative learning control is an approach to improving the transient response performance of the system that operates repetitively over a fixed time interval (Moore 1993).

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References

  1. Ahn, H.-S., Moore, K. L., & Chen, Y. Q. (2007). Iterative learning control: robustness and monotonic convergence in the iteration domain. London: Springer-Verlag, Communications and Control Engineering Series.Google Scholar
  2. Amann, N., Owens, H., & Rogers, E. (1996). Iterative learning control for discrete-time systems with exponential rate of convergence. IEE Proceedings-Control Theory and Applications, 143(2), 217–224.CrossRefGoogle Scholar
  3. Arimoto, S., Kawamura, S., & Miyazaki, F. (1984). Bettering operation of robots by learning. Journal of Robotic Systems, 1(2), 123–140.CrossRefGoogle Scholar
  4. Bien, Z., & Huh, K. M. (1989). Higher-order iterative control algorithm. IEE Proceedings Part D, Control Theory and Applications, 105–112.Google Scholar
  5. Bristow, D. A., Tharayil, M., & Alleyne, A. G. (2006). A survey of iterative learning control: a learning-based method for high-performance tracking control. IEEE Control Systems Magazine, 26(3), 96–114.CrossRefGoogle Scholar
  6. Chen, Y. Q., & Wen, C. (1999). Iterative learning control: convergence, robustness and applications (Lecture Notes series on Control and Information Science, Vol. LNCIS-248). London: Springer.Google Scholar
  7. Markusson, O. (2002). Model and system inversion with applications in nonlinear system identification and control. Ph.D. thesis, Kungliga Tekniska Hogskolan, Sweden.Google Scholar
  8. Moore, K. L. (1993). Iterative learning control for deterministic systems (Advances in Industrial Control). London: Springer.CrossRefGoogle Scholar
  9. Moore, K. L., Chen, Y. Q., Ahn, H.-S. (2006). Iterative learning control: a tutorial and a big picture view. Proceedings of the 2006 IEEE International Conference on Decision and Control (CDC), San Diego, IEEE, pp. 2352–2357.Google Scholar
  10. Phan, M. Q., Longman, R. W., & Moore, K. L. (2000). Unified formulation of linear iterative learning control. AAS/AIAA Space Flight Mechanics Meeting, Clearwater Florida, pp. AAA 00–106.Google Scholar
  11. Verwoerd, M. (2004). Iterative learning control: a critical review. Ph.D. thesis, University of Twente, Netherlands.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringCenter for Self-Organizing and Intelligent Systems (CSOIS) Utah State UniversityLoganUSA
  2. 2.Colorado School of MinesGoldenUSA
  3. 3.Gwangju Institute of Science and TechnologyBuk-guKorea