@article{fe450d5f160e49c3a9b1ac51d3b2e8b3,
title = "Design of arbitrary-order robust iterative learning control based on robust control theory",
abstract = "Iterative learning control (ILC) is an effective technique that improves the tracking performance of systems by adjusting the feedforward control signal based on the memory data. The key in ILC is to design learning filters with guaranteed convergence and robustness, which usually involves lots of tuning effort especially in high-order ILC. To facilitate this procedure, this paper proposes a systematics approach to design learning filters for arbitrary-order ILC with guaranteed convergence, robustness and ease of tuning. The filter design problem is transformed into an H∞ optimal control problem for a constructed feedback system. This approach is based on an infinite impulse response (IIR) system and conducted directly in iteration-frequency domain. The proposed algorithm is further advanced to the one that explicitly considers system variations based on μ synthesis. Important characteristics of the proposed approach such as convergence and robustness are explored and demonstrated through both simulations and experiments on a wafer scanning system.",
keywords = "High-precision control, Iterative learning control, Robust control",
author = "Minghui Zheng and Cong Wang and Liting Sun and Masayoshi Tomizuka",
note = "Funding Information: This paper has proposed a systematic approach to design learning filters for arbitrary-order ILC. It is an off-line optimization procedure performed in iteration-frequency domain with guaranteed convergence and ease of tuning. A feedback system is first constructed and the H ∞ optimal control design technique is applied thereafter to obtain the optimal learning filters. This approach is further advanced based on μ synthesis to explicitly take system variations into consideration. Important characteristics such as convergence and robustness are demonstrated and validated through simulations and experiments on a laboratory model of wafer scanning systems. Additionally, one possible approach of designing high-order non-causal ILC using the proposed framework has been discussed. Two follow-up directions will be explored in the future: (1) the proposed framework will be applied to a multi-input-multi-output system and extended to a more generic formulation where an N th-order ILC uses the information from both the preceding iterations and the current iteration, which is an optimization procedure that involves both feedforward and feedback controllers; (2) the proposed framework will be extended to a more generic case that including both the causal and non-causal learning filters comprehensively , which is challenging and significant for the frequency-domain design of high-order ILC. Minghui Zheng is currently a Ph.D. Candidate in the Department of Mechanical Engineering at University of California, Berkeley. She received her Bachelor degree in Engineering Mechanics and Master design in Control Science and Engineering from Beijing University of Aeronautics and Astronautics, Beijing, China. Her research interests include advanced learning and control with applications to high-precision systems and robotics. Dr. Cong Wang is a faculty member in the ECE and MIE departments at New Jersey Institute of Technology. Before joining NJIT in 2015, Dr. Wang was a Lecturer and Research Engineer at University of California, Berkeley. He obtained his PhD degree in Mechanical Engineering from UC Berkeley in 2014, before which he attended Tsinghua University and obtained his master{\textquoteright}s degree in Automotive Engineering and bachelor{\textquoteright}s degree in Mechanical Engineering and Automation in 2010 and 2008 respectively. Dr. Wang{\textquoteright}s research focuses on robotics and control systems with an emphasis on advanced control theories, robotic manufacturing and semiconductor fabrication. Liting Sun received her B.S. degree in 2009 from University of Science and Technology of China (USTC) in Mechanical Engineering. She conducted research as a visiting scholar from 2012 to 2015, and currently is pursuing the Ph.D. degree, both in Mechanical Engineering, University of California at Berkeley. Her research interests include iterative learning control, adaptive control, optimization-based control, multirate control, and vibration rejection. Liting Sun is the 2009 Excellent College Graduate of University of Science and Technology of China, with the undergraduate thesis selected as 2009 Excellent Undergraduate Thesis of USTC. She was a recipient of the Chinese Council Scholarship from years 2012–2014. Masayoshi Tomizuka was born in Tokyo, Japan in 1946. He received his B.S. and M.S. degrees in Mechanical Engineering from Keio University, Tokyo, Japan and his 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), an Associate Editor of the Journal of the International Federation of Automatic Control, Automatica and the European Journal of Control. 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 Fellow of the ASME, the Institute of Electric and Electronics Engineers (IEEE), 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) and the John R. Ragazzini Award (2006). Publisher Copyright: {\textcopyright} 2017 Elsevier Ltd",
year = "2017",
month = nov,
doi = "10.1016/j.mechatronics.2017.08.009",
language = "English (US)",
volume = "47",
pages = "67--76",
journal = "Mechatronics",
issn = "0957-4158",
publisher = "Elsevier Limited",
}