Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

Jingyang Mao; Xiangyu Meng; Derui Ding

doi:10.1109/JAS.2022.105416

Volume 9 Issue 6

Jun. 2022

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2022 > 9(6): 1026-1036

J. Mao, X. Meng, and D. Ding, “Fuzzy set-membership filtering for discrete-time nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1026–1036, Jun. 2022. doi: 10.1109/JAS.2022.105416

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J. Mao, X. Meng, and D. Ding, “Fuzzy set-membership filtering for discrete-time nonlinear systems,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 6, pp. 1026–1036, Jun. 2022. doi: 10.1109/JAS.2022.105416

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Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

doi: 10.1109/JAS.2022.105416

Jingyang Mao^1
,,
Xiangyu Meng^{3
,
,},
Derui Ding^{1, 2
,}

1.
Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
2.
School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC 3122, Australia
3.
Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, 70803 USA

Funds: This work was supported in part by the National Natural Science Foundation of China (61973219, 61933007, 62073158), and the China Scholarship Council (201908310148)

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Author Bio:
Jingyang Mao received the B.Sc. degree in mathematics and applied mathematics from University of Shanghai for Science and Technology, Shanghai, China, in 2015, where he is currently pursuing the Ph.D. degree in control science and engineering.From September 2019 to September 2021, he was a visiting scholar in the Department of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, USA. His research interests include networked control systems as well as protocol-based recursive filtering for nonlinear systems.Dr. Mao is also a very active reviewer for many international journals

Xiangyu Meng received the B.S. degree in information and computing science from Harbin Engineering University, and M.S. degree in control science and engineering from Harbin Institute of Technology, Harbin, China, in 2006 and 2008, respectively. He received the Ph.D. degree in electrical and computer engineering from the University of Alberta, Edmonton, Alberta, Canada, in 2014.From June 2007 to July 2007, and from November 2007 to January 2008, he was a Research Associate with the Department of Mechanical Engineering, University of Hong Kong. From February 2009 to August 2010, he was a Research Award Recipient in the Department of Electrical and Computer Engineering, University of Alberta. From December 2014 to December 2016, he was a Research Fellow with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. From January 2017 to December 2018, he was a Postdoctoral Associate with the Division of Systems Engineering, Boston University, USA. Since 2019, he has been an Assistant Professor with the Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA, USA. His research interests include connected and autonomous vehicles, multi-agent networks, and cyber-physical systems

Derui Ding (Senior Member) received both the B.Sc. degree in Industry Engineering in 2004 and the M.Sc. degree in Detection Technology and Automation Equipment in 2007 from Anhui Polytechnic University, Wuhu, China, and the Ph.D. degree in Control Theory and Control Engineering in 2014 from Donghua University, Shanghai, China.Dr. Ding is currently a Senior Research Fellow with the School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Melbourne, VIC, Australia. From July 2007 to December 2014, he was a teaching assistant and then a lecturer in the Department of Mathematics, Anhui Polytechnic University, Wuhu, China. From June 2012 to September 2012, he was a research assistant in the Department of Mechanical Engineering, the University of Hong Kong, China. From March 2013 to March 2014, he was a visiting scholar in the Department of Information Systems and Computing, Brunel University London, UK. From June 2015 to August 2015, he was a research assistant in the Department of Mathematics, City University of Hong Kong, China. His research interests include nonlinear stochastic control and filtering, as well as multi-agent systems and sensor networks. He has published over 100 papers in refereed international journals.Dr. Ding is a Standing Director of the IEEE PES Intelligent Grid & Emerging Technologies Satellite Committee-China. He received the 2020 Andrew P. Sage Best Transactions Paper Award from the IEEE Systems, Man, and Cybernetics (SMC) Society, and the IET Premium Awards 2018. He is serving as an Associate Editor for Neurocomputing and IET Control Theory & Applications. He also served as a Guest Editor for several issues, including the International Journal of Systems Science, International Journal of General Systems, and Kybernetika
Corresponding author: Xiangyu Meng, e-mail: xmeng5@lsu.edu
Received Date: 2021-10-31
Accepted Date: 2021-12-15

Available Online: 2022-01-08

Abstract

Abstract

In this article, the problem of state estimation is addressed for discrete-time nonlinear systems subject to additive unknown-but-bounded noises by using fuzzy set-membership filtering. First, an improved T-S fuzzy model is introduced to achieve highly accurate approximation via an affine model under each fuzzy rule. Then, compared to traditional prediction-based ones, two types of fuzzy set-membership filters are proposed to effectively improve filtering performance, where the structure of both filters consists of two parts: prediction and filtering. Under the locally Lipschitz continuous condition of membership functions, unknown membership values in the estimation error system can be treated as multiplicative noises with respect to the estimation error. Real-time recursive algorithms are given to find the minimal ellipsoid containing the true state. Finally, the proposed optimization approaches are validated via numerical simulations of a one-dimensional and a three-dimensional discrete-time nonlinear systems.
- Affine model,
- membership functions,
- set-membership filtering,
- stability,
- Takagi-Sugeno fuzzy modeling

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Fuzzy Set-Membership Filtering for Discrete-Time Nonlinear Systems

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