Machine learningAdaptive Control

Iterative Learning Control

Iterative Learning Control (ILC) is a control method for systems that perform the same task repeatedly (trajectory tracking over a fixed time interval). The key idea is to use error information from previous trials to update the input for the next trial, progressively improving tracking accuracy. Pioneered by Arimoto et al. in 1984, ILC is ideal for robotic manufacturing, semiconductor processing, and any application where the same motion must be repeated many times with high precision.

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Sources

  1. Arimoto, S., Kawamura, S., & Miyazaki, F. (1984). Bettering operation of robots by learning. Journal of Robotic Systems, 1(2), 123-140. DOI: 10.1002/rob.4620010203
  2. Moore, K. L. (1993). Iterative learning control for trajectory tracking. Advances in Industrial Control, Springer-Verlag. DOI: 10.1007/BFb0040046
  3. Bien, Z., & Xu, J. X. (2007). Iterative Learning Control: Analysis, Design, Integration and Applications. Kluwer Academic Publishers. link

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Referenced by

ScholarGateIterative Learning Control (Iterative Learning Control). Retrieved 2026-06-04 from https://scholargate.app/en/control-theory/iterative-learning-control