## 1 Introduction
Disturbance estimation and friction compensation can effectively suppress the influence of external disturbances but cannot mitigate the motion perturbation induced by model uncertainty and external disturbance variation. For repeatedly performed positional command paths, if repeated external disturbances influence the motion of a feed drive axis, iterative learning control can effectively suppress the influence of external disturbances and provide better positional motion results to the feed drive axis. Therefore, iterative learning control was applied to design iterative learning contour control (ILCC) to improve the contour motion accuracy of feed drive axes. [PERSON] et al. [14] adopted a hidden Markov model to spatially map the command path and actual trajectory, and proposed a hybrid evaluation function to estimate the contour error of the tool position and orientation. Furthermore, they used spatial iterative learning control to compensate for the contour errors and generate numerical control commands to improve the contour accuracy of five-axis CNC machine tools. [PERSON] et al. [15] transformed the planar motion in a Cartesian coordinate frame into a polar coordinate frame, defined the polar-radius error, and designed a real-time iterative compensation method to reduce the polar-radius error-equivalent contour error for planar motion control systems. Based on a third-order contour error prediction model, [PERSON] et al. [16] proposed an online iterative trajectory pre-compensation control design to improve the contour motion performance of multi-axis motion systems. Considering that motion control systems are usually influenced by factors such as model parameter variations, external disturbance, and non-linear friction, [PERSON] and [PERSON] [17] adopted the asymmetric Gaussian-like function to design an adaptive learning law and then suggested contour error control using the iterative learning method. [PERSON] et al. [18] considered the properties of motion trajectory and time-varying random disturbance to design a time-varying weighting matrix and then proposed a norm-optimal cross-coupling iterative learning control using a time-varying control method. [PERSON] et al. [19] transformed the dynamic model of the motion control system from time domain to space domain and then proposed the spatial iterative learning control. [PERSON] et al. [20] proposed a double-iterative learning and cross-coupling control design to improve the individual axis and contour tracking performance of biaxial motion systems. [PERSON] et al. [21] proposed an ILCC design that modified numerical control programs by an iterative approach to improve the contour motion performance of the feed drive axes of a CNC machine tool. [PERSON] et al. [22] referred to contour errors and proposed an embedded ILCC that can diminish the contour errors of a CNC machine tool during feed drive motions by iteratively modifying the reference trajectory of each feed drive axis. Due to the complexity of contour error models and the requirement of a large amount of collected data and processing time while using the offline iterative learning control, [PERSON] and [PERSON] [23] developed an online iterative learning control that referred to equivalent contour errors as the control target. [PERSON] et al. [24] adopted the concept of position domain control and then designed the cross-coupled iterative learning control to improve the synchronization and performance of non-linear contour tracking motions. [PERSON] et al. [25] integrated the proportional-derivative control and disturbance observer to develop an ILCC that can simultaneously improve contour motion accuracy and degrade the influence of external disturbance on the feed drive axes of a CNC machine tool. Based on the Newton extremum-seeking algorithm, [PERSON] et al. [26] proposed an ILCC design to project contour errors to each motion axis and then adjust the axial position reference commands using an iterative learning approach.