Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

Chuxiong Hu; Ran Zhou; Ze Wang; Yu Zhu; Masayoshi Tomizuka

doi:10.1109/JAS.2022.105689

Volume 9 Issue 7

Jul. 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(7): 1218-1232

C. X. Hu, R. Zhou, Z. Wang, Y. Zhu, and M. Tomizuka, “Real-time iterative compensation framework for precision mechatronic motion control systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1218–1232, Jul. 2022. doi: 10.1109/JAS.2022.105689

Citation:

C. X. Hu, R. Zhou, Z. Wang, Y. Zhu, and M. Tomizuka, “Real-time iterative compensation framework for precision mechatronic motion control systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 7, pp. 1218–1232, Jul. 2022. doi: 10.1109/JAS.2022.105689

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Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

doi: 10.1109/JAS.2022.105689

Chuxiong Hu^{1, 2
,
,},
Ran Zhou^{1, 2
,},
Ze Wang^{1, 2
,},
Yu Zhu^{1, 2
,},
Masayoshi Tomizuka^3
,

1.
State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
2.
Beijing Key Laboratory of Precision/Ultra-Precision Manufacture Equipments and Control, Tsinghua University, Beijing 100084, China
3.
Department of Mechanical Engineering, University of California, Berkeley, CA 94720-1740 USA

Funds: This work was supported in part by the National Nature Science Foundation of China (51922059), in part by the Beijing Natural Science Foundation (JQ19010), and in part by the China Postdoctoral Science Foundation (2021T140371)

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Author Bio:
Chuxiong Hu (Senior Member, IEEE) received the B.S. and Ph.D. degrees in mechatronic control engineering from Zhejiang University in 2005 and 2010, respectively. He is currently an Associate Professor (tenured) at Department of Mechanical Engineering, Tsinghua University. From 2007 to 2008, he was a Visiting Scholar in mechanical engineering at Purdue University, USA. In 2018, he was a Visiting Scholar in mechanical engineering at University of California, USA. His research interests include precision motion control, high-performance multiaxis contouring control and trajectory planning, precision mechatronic systems, intelligent learning and prediction, adaptive robust control, neural networks, iterative learning control, and robot.Prof. Hu was the recipient of the Best Student Paper Finalist at the 2011 American Control Conference, the 2012 Best Mechatronics Paper Award from the ASME Dynamic Systems and Control Division, the 2013 National 100 Excellent Doctoral Dissertations Nomination Award of China, the 2016 Best Paper in Automation Award, and 2018 Best Paper in AI Award from the IEEE Internal Conference on Information and Automation. He is now a Technical Editor for the IEEE/ASME Transactions on Mechatronics

Ran Zhou received the B.S. degree in mechanical engineering in 2020 from Tsinghua University where he is currently working toward the Ph.D. degree in mechanical engineering with the Department of Mechanical Engineering. His research interests include precision motion control, predictive control, and neural network control

Ze Wang (Member, IEEE) received the B.S. degree in automation from the School of Electricity Engineering and Automation, Tianjin University, in 2015, and the Ph.D. degree in mechanical engineering from the Department of Mechanical Engineering, Tsinghua University, in 2020. He is currently a Postdoctoral Fellow with the Department of Mechanical Engineering, Tsinghua University. His research interests include precision motion control, neural network adaptive control, and high-performance multiaxis contouring control.Dr. Wang was the recipient of the Best Student Paper Finalist at the 2017 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, the 2017 Best Student Paper from the IEEE Internal Conference on Information and Automation, and 2019 Top Grade Scholarship for Graduate Students of Tsinghua University

Yu Zhu (Member, IEEE) received the B.S. degree in radio electronics from Beijing Normal University in 1983, and the M.S. degree in computer applications, and the Ph.D. degree in mechanical design and theory from China University of Mining and Technology in 1993 and 2001, respectively.He is currently a Professor with the State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University. He has published more than 180 technical papers. He is also the holder of more than 140 warranted invention patents. His research interests include precision measurement and motion control, ultraprecision mechanical design and manufacturing, two-photon microfabrication, and electronics manufacturing technology and equipment

Masayoshi Tomizuka (Life Fellow, IEEE) received the B.S. and M.S. degrees in mechanical engineering from Keio University, Japan and the Ph. D. degree in mechanical engineering from the Massachusetts Institute of Technology in February 1974. In 1974, he joined the faculty of the Department of Mechanical Engineering at the University of California at Berkeley, where he currently holds the Cheryl and John Neerhout, Jr., Distinguished Professorship Chair. At UC Berkeley, he teaches courses in dynamic systems and controls. His current research interests are optimal and adaptive control, digital control, signal processing, motion control, and control problems related to robotics, machining, manufacturing, information storage devices and vehicles. He served as Program Director of the Dynamic Systems and Control Program of the Civil and Mechanical Systems Division of NSF (2002–2004).He served as Technical Editor of the ASME Journal of Dynamic Systems, Measurement and Control, J-DSMC (1988–93), Editor-in-Chief of the IEEE/ASME Transactions on Mechatronics (1997–99), and Associate Editor of the Journal of the International Federation of Automatic Control, Automatica. He was General Chairman of the 1995 American Control Conference, and served as President of the American Automatic Control Council (1998–99). He is a Life Fellow of the ASME and IEEE and a Fellow of International Federation of Automatic Control (IFAC) and the Society of Manufacturing Engineers. He is the recipient of the Best J-DSMC Best Paper Award (1995, 2010), the DSCD Outstanding Investigator Award (1996), the Charles Russ Richards Memorial Award (ASME, 1997), the Rufus Oldenburger Medal (ASME, 2002), the John R. Ragazzini Award (AACC, 2006), the Richard E. Bellman Control Heritage Award (AACC, 2018), the Honda Medal (ASME, 2019) and the Nathaniel B. Nichols Medal (IFAC, 2020). He is a Member of the National Academy of Engineering
Corresponding author: Chuxiong Hu, e-mail: cxhu@tsinghua.edu.cn
Received Date: 2022-04-13
Accepted Date: 2022-05-05

Available Online: 2022-06-07

Abstract

Abstract

With regard to precision/ultra-precision motion systems, it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances. In this paper, to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control (ILC), a novel real-time iterative compensation (RIC) control framework is proposed for precision motion systems without changing the inner closed-loop controller. Specifically, the RIC method can be divided into two parts, i.e., accurate model prediction and real-time iterative compensation. An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times. In light of predicted errors, a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation. Both the prediction and compensation processes are finished in a real-time motion control sampling period. The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions. Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability, which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.
- Precision motion control,
- prediction model,
- real-time iterative compensation,
- trajectory tracking

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References(41)

References

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Highlights

A novel RIC framework is proposed for precision mechatronic motion control systems
The feedforward trajectory compensation of RIC is developed by accurate predicted tracking errors
Both prediction and compensation processes are merely finished in a real-time sampling period
RIC can not only achieve remarkable tracking accuracy but also possess strong robustness
Compared to current feedforward compensation methods, RIC is simple and efficient to be conducted

Real-Time Iterative Compensation Framework for Precision Mechatronic Motion Control Systems

doi: 10.1109/JAS.2022.105689

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