Fast-Update Iterative Learning Control for Performance Enhancement with Application to Motion Systems

Yulin Wang, Tesheng Hsiao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Iterative learning control (ILC) algorithms are typically used to improve the performance of repetitive processes. Numerous successful applications of ILC, such as computer numerical control (CNC) machining processes, robot manipulation, and lithography processes, have been reported. However, ILC often exhibits less than satisfactory performance since the control unit operates at a limited sampling rate in consideration of cost. In this study, the multiloop, multirate structure of servo motor control systems is taken advantage of and a fast-update multirate ILC (FILC) scheme is proposed for high-accuracy trajectory tracking, where compensation is updated at a rate faster than that of the original position loop without the need for redesigning the feedback controller. The key difficulty related to the design of the FILC lies in the extremely complex and time-varying dynamics inherent in multirate systems. To address this problem, a novel equivalent single-rate parametric model description of the multirate system is derived, which enables the use of the efficient norm optimal ILC algorithm. Consequently, a computationally efficient FILC is obtained to improve the performance. Subsequently, the proposed FILC is applied to the position control of the CNC motion stage. Simulation and experimental results are used to verify the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)79458-79468
Number of pages11
JournalIEEE Access
StatePublished - 2022


  • Iterative learning control
  • linear periodic time varying
  • multirate system
  • norm optimal
  • precise motion control


Dive into the research topics of 'Fast-Update Iterative Learning Control for Performance Enhancement with Application to Motion Systems'. Together they form a unique fingerprint.

Cite this