Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

Jinhua She; Kou Miyamoto; Qing-Long Han; Min Wu; Hiroshi Hashimoto; Qing-Guo Wang

doi:10.1109/JAS.2022.105929

Volume 10 Issue 4

Apr. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(4): 957-968

J. She, K. Miyamoto, Q.-L. Han, M. Wu, H. Hashimoto, and Q.-G. Wang, “Generalized-extended-state-observer and equivalent-input-disturbance methods for active disturbance rejection: Deep observation and comparison,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 957–968, Apr. 2023. doi: 10.1109/JAS.2022.105929

Citation:

J. She, K. Miyamoto, Q.-L. Han, M. Wu, H. Hashimoto, and Q.-G. Wang, “Generalized-extended-state-observer and equivalent-input-disturbance methods for active disturbance rejection: Deep observation and comparison,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 957–968, Apr. 2023. doi: 10.1109/JAS.2022.105929

Citation:

J. She, K. Miyamoto, Q.-L. Han, M. Wu, H. Hashimoto, and Q.-G. Wang, “Generalized-extended-state-observer and equivalent-input-disturbance methods for active disturbance rejection: Deep observation and comparison,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 957–968, Apr. 2023. doi: 10.1109/JAS.2022.105929

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

doi: 10.1109/JAS.2022.105929

1.
School of Engineering, Tokyo University of Technology, Hachioji, Tokyo 192-0982, Japan
2.
Institute of Technology, Shimizu Corporation, Koto, Tokyo 135-0044, Japan
3.
School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
4.
School of Automation, China University of Geosciences, Wuhan 430074, the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, and the Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China
5.
School of Industrial Technology, Advanced Institute of Industrial Technology, Tokyo 140-0011, Japan
6.
Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai 519087; Guangdong Key Lab of AI and Multi-Modal Data Processing, Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science; and BNU-HKBU United International College, Zhuhai 519087, China

Funds: This work was supported in part by the JSPS (Japan Society for the Promotion of Science) KAKENHI (20H04566, 22H03998), the National Natural Science Foundation of China (61873348), the Natural Science Foundation of Hubei Province, China (2020CFA031), and Wuhan Applied Foundational Frontier Project (2020010601012175)

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Author Bio:
Jinhua She (Fellow, IEEE) received the B.S. degree from Central South University in 1983, and the M.S. and Ph.D. degrees from Tokyo Institute of Technology, Japan, in 1990 and 1993, respectively, all in engineering. In 1993, he joined the School of Engineering, Tokyo University of Technology, where he is currently a Professor. His research interests include the application of control theory, repetitive control, process control, m-learning, and assistive robotics. Dr. She is a Member of the Chinese Association of Automation (CAA), the Society of Instrument and Control Engineers (SICE), Institute of Electrical Engineers of Japan (IEEJ), the Japan Society of Mechanical Engineers (JSME), Architectural Institute of Japan (AIJ), and the Asian Control Association (ACA). He was the Recipient of the International Federation of Automatic Control (IFAC) Control Engineering Practice Paper Prize in 1999 (jointly with M. Wu and M. Nakano)

Kou Miyamoto (Member, IEEE) received the B.S. degree in engineering from University of Tsukuba, Japan in 2014, and the M.S. and Ph.D. degrees in engineering from Tokyo Institute of Technology in 2016 and 2019, respectively. In 2019, he joined the Institute of Technology in Shimizu Corporation, where he is currently a Researcher. His research interests include active control for civil structures, structural control, and robust control for buildings. Dr. Miyamoto is a Member of Japan Society of Mechanical Engineers (JSME) and Architectural Institute Japan (AIJ)

Qing-Long Han (Fellow, IEEE) received the B.Sc. degree in mathematics from Shandong Normal University in 1983, and the M.Sc. and Ph.D. degrees in control engineering from East China University of Science and Technology, in 1992 and 1997, respectively. He is Pro Vice-Chancellor (Research Quality) and a Distinguished Professor at Swinburne University of Technology, Australia. He held various academic and management positions at Griffith University and Central Queensland University, Australia. His research interests include networked control systems, multi-agent systems, time-delay systems, smart grids, unmanned surface vehicles, and neural networks.Professor Han was awarded The 2021 Norbert Wiener Award (the Highest Award in systems science and engineering, and cybernetics) and The 2021 M. A. Sargent Medal (the Highest Award of the Electrical College Board of Engineers Australia). He was the recipient of The 2022 IEEE Systems, Man, and Cybernetics Society Andrew P. Sage Best Transactions Paper Award, The 2021 IEEE/CAA Journal of Automatica Sinica Norbert Wiener Review Award, The 2020 IEEE Systems, Man, and Cybernetics Society Andrew P. Sage Best Transactions Paper Award, The 2020 IEEE Transactions on Industrial Informatics Outstanding Paper Award, and The 2019 IEEE Systems, Man, and Cybernetics Society Andrew P. Sage Best Transactions Paper Award.Professor Han is a Member of the Academia Europaea (The Academy of Europe). He is a Fellow of The International Federation of Automatic Control (IFAC) and a Fellow of The Institution of Engineers Australia (IEAust). He is a Highly Cited Researcher in both Engineering and Computer Science (Clarivate). He has served as an AdCom Member of IEEE Industrial Electronics Society (IES), a Member of IEEE IES Fellows Committee, and Chair of IEEE IES Technical Committee on Networked Control Systems. Currently, he is Editor-in-Chief of IEEE/CAA Journal of Automatica Sinica, Co-Editor-in-Chief of IEEE Transactions on Industrial Informatics, and Co-Editor of Australian Journal of Electrical and Electronic Engineering

Min Wu (Fellow, IEEE) received the B.S. and M.S. degrees from Central South University in 1983 and 1986, respectively, and the Ph.D. degree from Tokyo Institute of Technology, Japan, in 1999, all in engineering. He was a Faculty Member of the School of Information Science and Engineering, Central South University, from 1986 to 2014, and became a Professor in 1994. In 2014, he joined the China University of Geosciences, where he is currently a Professor of Control Science and Engineering in the School of Automation. He was a Visiting Scholar with the Department of Electrical Engineering, Tohoku University, Japan, from 1989 to 1990, and a Visiting Research Scholar with the Department of Control and Systems Engineering, Tokyo Institute of Technology, from 1996 to 1999. He was a Visiting Professor with the School of Mechanical, Materials, Manufacturing Engineering and Management, University of Nottingham, U.K., from 2001 to 2002. His current research interests include robust control, process control, and intelligent systems. Dr. Wu is a Fellow of the Chinese Association of Automation (CAA). He received the International Federation of Automatic Control (IFAC) Control Engineering Practice Prize Paper Award in 1999 (together with M. Nakano and J. She)

Hiroshi Hashimoto (Member, IEEE) received the Ph.D. degree in science engineering from Waseda University, Japan, in 1990. He is currently a Professor at the School of Industrial Technology, Advanced Institute of Industrial Technology, Japan, where he researches intelligent robots, cybernetic interfaces, vision systems, welfare technology, and e-learning. His current research interests include mechatronics and the application of control theory. Dr. Hashimoto is a Member of the Society of Instrument and Control Engineers (SICE) and the Institute of Electrical Engineers of Japan (IEEJ)

Qing-Guo Wang received the Ph.D. degree in 1987 with highest honor from Zhejiang University. He is a Member of Academy of Science of South Africa. He is currently a Chair Professor with BNU-HKBU United International College; a Professor with BNU-UIC Institute of Artificial Intelligence and Future Networks; and a Professor with Beijing Normal University. He was AvH Research Fellowship of Germany from 1990 to 1992. From 1992 to 2015, he was with Department of Electrical and Computer Engineering of the National University of Singapore, where he became a Full Professor in 2004. He was a Distinguished Professor with Institute for Intelligent Systems, University of Johannesburg, South Africa, 2015–2020. His research lies in the field of automation/AI with focuses on modeling, estimation, prediction, control, and optimization. He has published 360+ technical papers in international journals and seven research monographs. He received nearly 20000 citations with h-index of 77. He was presented with the award of the most cited article of the journal “Automatica” in 2006–2010 and was in the Thomson Reuters list of the highly cited researchers 2013 in Engineering. He received the prize of the most influential paper of the 30 years of the journal “Control Theory and Applications” in 2014. He was on Stanford University list of World’s Top 2% Scientists 2020 (both career and year). He is currently the deputy Editor-in-Chief of the ISA Transactions (USA)
Corresponding author: Jinhua She, e-mail: she@stf.teu.ac.jp
Received Date: 2022-04-17
Revised Date: 2022-06-07
Accepted Date: 2022-06-21

Available Online: 2022-08-18

Abstract

Abstract

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state responses. This paper presents a deep observation on and a comparison between two of those methods: the generalized extended-state observer (GESO) and the equivalent input disturbance (EID) from assumptions, system configurations, stability conditions, system design, disturbance-rejection performance, and extensibility. A time-domain index is introduced to assess the disturbance-rejection performance. A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method. A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

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The generalized extended-state observer (GESO) and the equivalent input disturbance (EID) are compared
A detailed observation of disturbance-suppression mechanisms is carried out
A time-domain index is introduced to assess the disturbance-rejection performance

Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

doi: 10.1109/JAS.2022.105929

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通讯作者: 陈斌, bchen63@163.com

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