Frequency-Domain Modeling-Free Iterative Learning Control for Point-To-Point Motion

The data-driven autonomous feedforward (FF) control design technique known as frequency-domain modelling-free iterative learning control (MFILC) has gained attention for its ability to achieve fine motion control performance without excessive labor. However, the existing frequency-domain MFILC methods that rely on empirical transfer function estimation are not suitable for point-to-point (PTP) motion due to the leakage error that occurs in frequency response function (FRF) estimation. To address this issue, this study proposes an improved frequency-domain MFILC method that employs the differential filtering-based empirical transfer function estimation for FRF estimation. This enhancement enables the proposed method to learn the FF compensation in both reciprocating and PTP motion. Simulations were conducted to evaluate the effectiveness of the proposed method in achieving fast and precise motion control of a galvano scanner.