A Composite Linear Active Disturbance Rejection Control-Sliding Mode Control Strategy with Nominal Model Compensation for Precision Motion Tracking in Semiconductor Die Attach Machines

In this paper, the concept of symmetry is utilized to design the composite controller for the die attach machine’s motion platform—that is, the construction and the solution of the nominal model-based composite controller design approach are symmetrical. With escalating demands for ultra-high-speed operations and microscale positioning accuracy (<5 μm) in semiconductor manufacturing, motion platforms face critical challenges, including high-speed instability, positioning jitter, and insufficient disturbance rejection. To address these limitations, a composite control strategy integrating nominal model-based linear active disturbance rejection control (NMLADRC) with sliding mode control (SMC) is developed. The synergistic interaction ensures the concurrent realization of robust tracking accuracy and rapid transient convergence. Simulation results demonstrate significant improvements over conventional PI control, LADRC, and NMLADRC. The phase lag is reduced by 50.04%, 36.34%, and 23.07%, respectively, while positioning time within ±5 μm accuracy threshold is shortened by 44.00%, 56.31%, and 31.51% when tracking the executed motion profile. The composite controller substantially enhances motion control precision, strengthens disturbance rejection capability, and improves system stability during high-speed operations. These advancements highlight the method’s strong practical applicability in precision motion control systems requiring both rapid response and microscale positioning accuracy.

Authors

Institutions

Publication Details

Journal
Symmetry
Published
2025-04-23
DOI
https://doi.org/10.3390/sym17050636
Citations
1
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
1.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Composite Linear Active Disturbance Rejection Control-Sliding Mode Control Strategy with Nominal Model Compensation for Precision Motion Tracking in Semiconductor Die Attach Machines

Huairong Chen, Xiang Zhang, Wen Li, Yonghong Zhang et al.
1 citations
Symmetry
Iterative Learning Control Systems
1.21
article

A Composite Linear Active Disturbance Rejection Control-Sliding Mode Control Strategy with Nominal Model Compensation for Precision Motion Tracking in Semiconductor Die Attach Machines

Huairong Chen, Xiang Zhang, Wen Li, Yonghong Zhang, Weiming Liang
article en
1 citations

Abstract

In this paper, the concept of symmetry is utilized to design the composite controller for the die attach machine’s motion platform—that is, the construction and the solution of the nominal model-based composite controller design approach are symmetrical. With escalating demands for ultra-high-speed operations and microscale positioning accuracy (<5 μm) in semiconductor manufacturing, motion platforms face critical challenges, including high-speed instability, positioning jitter, and insufficient disturbance rejection. To address these limitations, a composite control strategy integrating nominal model-based linear active disturbance rejection control (NMLADRC) with sliding mode control (SMC) is developed. The synergistic interaction ensures the concurrent realization of robust tracking accuracy and rapid transient convergence. Simulation results demonstrate significant improvements over conventional PI control, LADRC, and NMLADRC. The phase lag is reduced by 50.04%, 36.34%, and 23.07%, respectively, while positioning time within ±5 μm accuracy threshold is shortened by 44.00%, 56.31%, and 31.51% when tracking the executed motion profile. The composite controller substantially enhances motion control precision, strengthens disturbance rejection capability, and improves system stability during high-speed operations. These advancements highlight the method’s strong practical applicability in precision motion control systems requiring both rapid response and microscale positioning accuracy.

SymmetryVol. 17(5)
Nanjing University of Information Science and Technology (CN)
Openalex Percentile: Top 22%
Iterative Learning Control Systems
1.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.