Resilient energy-to-peak filtering for linear parameter-varying systems under random access protocol

Abstract

In this paper, we consider the energy-to-peak filtering issue for a class of linear parameter-varying (LPV) systems with time delays subject to certain communication regulation under which only one sensor is allowed to transmit its measurement data at each transmission instant. The data communication is regulated by the random access protocol (RAP) for the purpose of avoiding data collisions. The main purpose of this paper is to design an LPV filter such that the resultant filtering error system is asymptotically stable and also satisfies the prescribed $l_2$-$l_{\mathrm{\infty }}$ performance in the mean square. Taking into account both the LPV nature and the possible gain perturbations, a parameter-dependent resilient filter is constructed according to the plant dynamics and scheduling behaviour of the RAP. The desired filter gain matrices are obtained by solving a set of linear matrix inequalities. Finally, a simulation example is given to validate the effectiveness and correctness of the filter design scheme.

Keywords:

• Liner parameter-varying systems
• energy-to-peak filtering
• resilient filter
• random access protocol
• parameter-dependent filter

Disclosure Statement

No potential conflict of interest was reported by the authors.

Data availability statement

Data sharing is not applicable to this paper as no new data were created or analysed in this study.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China [grant numbers 12171124 and 61703242], the Talent Training Project of Reform and Development Foundation for Local Universities from Central Government of China (Youth Talent Project), the Shandong Provincial Natural Science Foundation of China [grant number ZR2020MF071], the Anhui Provincial Natural Science Foundation [grant number 2108085MA07], and the AHPU Youth Top-Notch Talent Support Program [grant number 2018BJRC009].

Notes on contributors

Haoyang Yu

Haoyang Yu received the B.Eng. degree in electrical engineering and automation from the Shandong University of Science and Technology, Qingdao, China, in 2020, where he is currently pursuing the M.S, degree with the College of Electrical Engineering and Automation. His current research interest is the filtering for networked systems.

Jun Hu

Jun Hu received the B.Sc. degree in information and computation science and the M.Sc. degree in applied mathematics from the Harbin University of Science and Technology, Harbin, China, in 2006 and 2009, respectively, and the Ph.D. degree in control science and engineering from the Harbin Institute of Technology, Harbin, in 2013. From 2010 to 2012, he was a visiting Ph.D. student with the Department of Information Systems and Computing, Brunel University London, Uxbridge, U.K. From 2014 to 2016, he was an Alexander von Humboldt Research Fellow with the University of Kaiserslautern, Kaiserslautern, Germany. From 2018 to 2021, he was a Research Fellow with the University of South Wales, Cardiff, U.K. He is a Professor and Ph.D. supervisor in the Department of Mathematics, Harbin University of Science and Technology, Harbin 150080, China. His research interests include nonlinear control, filtering and fault estimation, time-varying systems and complex networks. He has published more than 80 papers in refereed International Journals. Dr. Hu serves as a Reviewer for Mathematical Reviews, an Editor for Neurocomputing, Journal of Intelligent and Fuzzy Systems, Neural Processing Letters, and Systems Science and Control Engineering, and a Guest Editor for the International Journal of General Systems and Information Fusion.

Baoye Song

Baoye Song received the B.S. degree in automation in 2005, the M.S. degree in control theory and control engineering in 2008 both from Qingdao University of Science and Technology, Qingdao, China, and the Ph.D. degree in control theory and control engineering in 2011 from Shandong University, Jinan, China. Dr. Song is currently an associate professor with Shandong University of Science and Technology. His research interests include nonlinear filtering, intelligent optimization algorithm, robotics and fault diagnosis.

Hongjian Liu

Hongjian Liu (Member, IEEE) received his B.Sc. degree in applied mathematics in 2003 from Anhui University, Hefei, China, and the M.Sc. degree in detection technology and automation equipment in 2009 from Anhui Polytechnic University, Wuhu, China, and the Ph.D. degree in control science and engineering in 2018 from Donghua University, Shanghai, China. In 2016, he was a Research Assistant with the Department of Mathematics, Texas A&M University at Qatar, Doha, Qatar, for two months. From March 2017 to March 2018, he was a Visiting Scholar in the Department of Information Systems and Computing, Brunel University London, UK. He is currently a Professor in the School of Mathematics and Physics, Anhui Polytechnic University, Wuhu, China. Dr. Liu's current research interests include filtering theory, memristive neural networks, and network communication systems. He is a very active reviewer for many international journals.

Xiaojian Yi

Xiaojian Yi was born in 1987. He received the B.S. degree in control technology in 2010 from the North University of China, Taiyuan, China, and the M.S. degree in 2012 and Ph.D. degree in 2016 both in reliability engineering from Beijing Institute of Technology, Beijing, China. During 2015-2016, he was a jointly trained PhD student in the University of Ottawa, Canada, to study robot reliability and maintenance. From 2016 to 2020, he was an Associate Professor with the China North Vehicle Research Institute. He is currently an Associate Professor with the Beijing Institute of Technology, Beijing, China. He is the author of two books and more than 100 articles, and is also the holder of 8 patents. His research interests include system reliability analysis, intelligent control, fault diagnosis and health management.