Precision Synchronous Control of Multiple Motion Systems: A Tube-Based MPC Approach

Shuaiqi Chen; Fazhi Song; Yue Dong; Ning Cui; Yang Liu; Xinkai Chen

IEEE/CAA Journal of Automatica Sinica

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S. Chen, F. Song, Y. Dong, N. Cui, Y. Liu, and X. Chen, “Precision synchronous control of multiple motion systems: A tube-based MPC approach,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 5, pp. 1–15, May 2025.

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S. Chen, F. Song, Y. Dong, N. Cui, Y. Liu, and X. Chen, “Precision synchronous control of multiple motion systems: A tube-based MPC approach,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 5, pp. 1–15, May 2025.

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Precision Synchronous Control of Multiple Motion Systems: A Tube-Based MPC Approach

Funds: This work was supported by National Natural Science Foundation of China (52375530, 52075132), Natural Science Foundation of Heilongjiang Province (YQ2022E025), State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment (Guangdong University of Technology) (JMDZ202312), Fundamental Research Funds for the Central Universities (HIT.OCEF.2024034), and Space Drive and Manipulation Mechanism Laboratory of BICE and National Key Laboratory of Space Intelligent Control (BICE-SDMM-2024-01)

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Author Bio:
Shuaiqi Chen received the B.E. degree in electrical engineering and automation from the School of Electronic and Information Engineering, Changchun University of Science and Technology in 2016, and the M.S. degree in control engineering from the School of Mechatronics and Electronic Engineering, Changchun University of Technology in 2020. He is currently a Ph.D. candidate in control science and engineering at the School of Aeronautics, Harbin Institute of Technology. His research focuses on model predictive control and its application to high-precision motion systems

Fazhi Song (Member, IEEE) received the Ph.D. degree in control science and engineering from Harbin Institute of Technology in 2019. From March 2017 to March 2018, he was a joint-training Ph.D. student with National University of Singapore, Singapore. He is currently an Associate Professor with Harbin Institute of Technology. His research interests include machine learning, system identification and motion control with applications in high-precision mechanical servo systems

Yue Dong (Member, IEEE) received the B.S. degree in automation from the Harbin Engineering University in 2011, the M.S. and Ph.D. degrees in control science and engineering from the Harbin Institute of Technology in 2013 and 2021, respectively. From 2015 to 2016, he was an exchange student with Queen’s University Belfast, UK. He is currently an Research Associate of instrument science and technology with the Harbin Institute of Technology. His research interests include trajectory planning and residual vibration suppression for ultra-precision positioning devices, design and application of advanced motors, vibration signal detection and data fusion

Ning Cui received the B.E. degree in automation from the School of Mechanical and Electrical Engineering, Northeast Forestry University in 2006, and the M.S. degree in microelectronics and solid-state electronics from the School of Aeronautics, Harbin Institute of Technology in 2009. He is currently a Ph.D. candidate in instrumentation science and technology at the School of Instrument Science and Engineering, Harbin Institute of Technology. His research focuses on motion control with applications in high-precision mechanical servo systems

Yang Liu (Member, IEEE) received the B.S. degree in automation, the M.S. and the Ph.D. degrees in control science and engineering from Harbin Institute of Technology in 2004, 2006, 2011, respectively. He was a Research Fellow in Queen’s University Belfast, UK from 2014 to 2015. Now, he is a Professor with Harbin Institute of Technology. His current research interests include system identification and precision motion control

Xinkai Chen (Fellow, IEEE) received the Ph.D. degree in engineering from Nagoya University, Japan in 1999. He is currently a Professor in the Department of Electronic Information Systems, Shibaura Institute of Technology, Japan. His research interests include adaptive control, smart materials, hysteresis, sliding mode control, machine vision, and observer. Dr. Chen has served as Senior Editor of IEEE/ASME Transactions on Mechatronics, and Associate Editor of several journals, including IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Industrial Electronics, etc. He has also severed for international conferences as organizing committee members including General Chairs, Program Chairs, General Co-Chairs, Program Co-Chairs, etc. He is a Member of The Engineering Academy of Japan
Corresponding author: Fazhi Song, e-mail: fazsong@hit.edu.cn; Yang Liu, e-mail: hitlg@hit.edu.cn
Received Date: 2024-09-29
Revised Date: 2024-11-18
Accepted Date: 2025-01-22

Available Online: 2025-03-27

Abstract

Abstract

Lithography machines operate in scanning mode for the fabrication of large-scale integrated circuits (ICs), requiring high-precision synchronous motion between the reticle and wafer stages. Disturbances generated by each stage during high-acceleration movements are transmitted through the base frame, resulting in degradation of synchronization performance. To address this challenge, this paper proposes a tube-based model predictive control (tube-MPC) approach for synchronization in lithography machines. First, the proposed modeling method accurately characterizes the coupling disturbances and synchronization dynamics. Subsequently, a tube-MPC approach is developed to ensure that the states of the nominal system are constrained within the terminal constraint set. To reduce the complexity of online computations, an approach is employed to transform online optimization problems into offline problems by creating an online lookup table. This enables the determination of optimal control inputs via a simplified online optimization algorithm. The robustness and trajectory tracking performance of the proposed approach are verified through simulation experiments, demonstrating its effectiveness in enhancing the synchronization performance of multiple motion systems.
- Coupled disturbance,
- model predictive control,
- multiple systems,
- precision motion control,
- robust control,
- wafer scanner

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References(36)

References

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