fect the performance of the new multi-channel double-crystal monochromator (DCM) system. The con• trol strategy integrates iterative learning control (ILC) and model predictive control (MPC) to reduce ran• dom position errors of the slave motor and eliminate repetitive mechanical installation errors， respectively. The parallelism errors between the master and slave crystals are converted into the repetitive reference mo • tion trajectory of the slave motor， and the MPC is used to improve the slave motor position tracking within a single rotation cycle. Meanwhile， ILC iterations are applied during rotation cycles to eliminate repetitive mechanical installation errors. The proposed control strategy is verified using a single-axis motor motion experimental platform. Experimental results demonstrate that the motor position tracking error reaches 1. 44 ″ -reduction by 99. 64%， 98. 52%， 98. 26%， and 73. 33% as compared to the errors of PID， MPC， DOB+MPC， and ILC+PID combination controller strategies， respectively. The proposed con• 文章编号 1004-924X (2023)09-1335-12 收稿日期： 2022-12-31； 修订日期： 2023-02-01. 基金项目： 国家自然科学基金资助项目(No. 51375349) 第 31 卷 光学 精密工程 trol strategy effectively compensates for crystal parallelism errors and improves parallel alignment accuracy， providing practical application value for the performance enhancement of the new multi-channel DCM system.

As a core component of the X-ray absorption fine structure spectroscopy (XAFS) system, the multi-channel double-crystal monochromator (DCM) can improve the time resolution of the system significantly. In contrast to the conventional single-channel DCM, the multi-channel DCM includes more pairs of crystals that are located separately in the master and slave motor axis with the same driving direction. However, a mismatched parallelism in the pitch direction, which can result from the manual mounting operation between the two separated crystals, directly affects the performance of the flux and the angular stability of the monochromatic beam. This poses a significant challenge to the precision position tracking of this system. In this paper, the mounting errors were translated into repetitive errors in the slave motor when the master motor was rotated at a constant velocity. Therefore, the iterative learning control (ILC) was considered in order to improve the tracking accuracy of the slave motor motion. The zero-magnitude error controller (ZMETC) was used to calculate the learning function to accelerate the convergence of the control inputs, and the convergence conditions of the control signal and error were also given. To validate the effectiveness of the proposed method, comparative experiments were performed on the motor motion platform. Experimental results indicated that the ILC effectively decreased the parallelism errors of the multi-channel DCM under various trajectories by comparing them with feedback controllers and the ZMETC, respectively.