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Iterative Learning Control/Evidence
Method evidence record

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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Iterative Learning Control
Taxonomic method record · ml-model / control-theory
  • 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
  • Moore, K. L. (1993). Iterative learning control for trajectory tracking. Advances in Industrial Control, Springer-Verlag. · URL
  • Bien, Z., & Xu, J. X. (2007). Iterative Learning Control: Analysis, Design, Integration and Applications. Kluwer Academic Publishers. · URL
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketAdaptive Controlmachine-suggested · Relational suggestion, not evidence.Same method familyFeedback Linearizationmachine-suggested · Relational suggestion, not evidence.Same method familyModel Predictive Controlmachine-suggested · Relational suggestion, not evidence.Same method familySliding Mode Controlmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

3 recorded citations, copied from the method source record.

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