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- W4288391409 abstract "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." @default.
- W4288391409 created "2022-07-29" @default.
- W4288391409 creator A5044422357 @default.
- W4288391409 creator A5047812717 @default.
- W4288391409 date "2022-01-01" @default.
- W4288391409 modified "2023-09-26" @default.
- W4288391409 title "Fast-Update Iterative Learning Control for Performance Enhancement With Application to Motion Systems" @default.
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- W4288391409 doi "https://doi.org/10.1109/access.2022.3194532" @default.
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