TY - GEN
T1 - Adaptive Learning Control with Extended Bandwidth for Synchronization Motion Stages
AU - Sun, Pengyu
AU - Song, Fazhi
AU - Chen, Shuaiqi
AU - Liu, Yang
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - This paper proposes a novel method to improve synchronization precision and reduce the influence of stochastic noises in lithography motion stages. Specifically, the multiple-channel adaption-learning-function synchronization iterative learning control (MASILC) method we give uses a multiple-channel approach based on A-Type of ILC, with adaptive parameters to address the divergence situation caused by stochastic noises in different channels. We verify the superiority of the MASILC method and analyze its stability and convergence performance in theory. Furthermore, we introduce an effective adaptive method inspired by the stochastic acceleration optimization method. Simulation comparisons with analogous approaches demonstrate the effectiveness and superiority of the proposed method in various circumstances.
AB - This paper proposes a novel method to improve synchronization precision and reduce the influence of stochastic noises in lithography motion stages. Specifically, the multiple-channel adaption-learning-function synchronization iterative learning control (MASILC) method we give uses a multiple-channel approach based on A-Type of ILC, with adaptive parameters to address the divergence situation caused by stochastic noises in different channels. We verify the superiority of the MASILC method and analyze its stability and convergence performance in theory. Furthermore, we introduce an effective adaptive method inspired by the stochastic acceleration optimization method. Simulation comparisons with analogous approaches demonstrate the effectiveness and superiority of the proposed method in various circumstances.
KW - adaptive parameters
KW - iterative learning control (ILC)
KW - lithography motion systems
KW - multiple-channel method
KW - synchronization control
UR - https://www.scopus.com/pages/publications/85166012155
U2 - 10.1109/DDCLS58216.2023.10166687
DO - 10.1109/DDCLS58216.2023.10166687
M3 - 会议稿件
AN - SCOPUS:85166012155
T3 - Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
SP - 1793
EP - 1798
BT - Proceedings of 2023 IEEE 12th Data Driven Control and Learning Systems Conference, DDCLS 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2023
Y2 - 12 May 2023 through 14 May 2023
ER -