Non-fragile l₂-l_∞ state estimation for time-delayed artificial neural networks: an adaptive event-triggered approach

Abstract

In this paper, the state estimation problem is investigated for a kind of time-delayed artificial neural networks subject to gain perturbations under the adaptive event-triggering scheme. To avoid wasting resources, the event-triggering scheme is adopted during the data transmission process from the sensors to the estimator where the triggering threshold can be dynamically adjusted. By means of the Lyapunov stability theory, sufficient conditions are provided to ensure that the estimation error dynamics achieves both the asymptotical stability and the $l_2$-$l_{\mathrm{\infty }}$ performance. The desired non-fragile estimator gain is parameterised by solving certain matrix inequalities. At last, the usefulness of the proposed event-based non-fragile state estimator is shown via a numerical simulation example.

Keywords:

• Artificial neural networks
• adaptive event-triggering scheme
• non-fragile state estimation
• $l_2$-$l_{\mathrm{\infty }}$ performance

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing is not applicable to this article 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 62003213 and 61903253].

Notes on contributors

Licheng Wang

Licheng Wang received the B.Sc. degree in automation in 2011 from Weifang University, Weifang, China, and the M.Sc. degree and the Ph.D. degree in control science and engineering from University of Shanghai for Science and Technology, Shanghai, China, in 2014 and 2019, respectively. Since Nov. 2016-Nov. 2018, he has been a visiting Ph.D. student in the Department of Electronic and Computer Engineering at Brunel University London in the UK. He is currently a Post-Doctoral Research Fellow with the Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China. His research interests include nonlinear stochastic control and filtering, as well as complex networks and sensor networks. Dr. Wang is currently a reviewer for some international journals.

Shuai Liu

Shuai Liu received the Ph.D. degree in system analysis and integration from the University of Shanghai for Science and Technology, Shanghai, China, in 2018. From 2017 to 2018, she was a visiting Ph.D. student with the Department of Computer Science, Brunel University London, Uxbridge, U.K. She is currently an Associate Professor with the College of Science, University of Shanghai for Science and Technology. She has published over thirty papers in refereed international journals. Her current research interests include sensor networks, set-membership filtering, model predictive control, as well as Kalman filtering. Dr. Liu is a recipient of the National Postdoctoral Innovative Talent Scholarship of China. She is currently a reviewer for some international journals.

Yuhan Zhang

Yuhan Zhang received the B.Eng. degree in electronic information science and technology from the Shandong University of Science and Technology, Qingdao, China, in 2016, and the M.Sc. degree in marketing from the University of Nottingham, Nottingham, UK, in 2017. She is currently pursuing the Ph.D. degree in control science and engineering from Shandong University of Science and Technology, Qingdao, China. Her current research interests include the control and filtering of networked systems, reinforcement learning, and neural networks. She is a very active reviewer for many international journals.

Derui Ding

Derui Ding 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, Hong Kong. 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, Hong Kong. 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 Senior Member of the Institute of Electrical and Electronic Engineers and a Standing Director of the IEEE PES Intelligent Grid & Emerging Technologies Satellite Committee-China. He received the 2021 Nobert Wiener Review Award from IEEE/CAA Journal of Automatica Sinica, the 2020 Andrew P. Sage Best Transactions Paper Award from the IEEE Systems, Man, and Cybernetics (SMC) Society, and the 2018 IET Premium Award. He is serving as an Associate Editor for Neurocomputing and IET Control Theory & Applications, a member of Early Career Advisory Board for IEEE/CAA Journal of Automatica Sinica, and also served as a Guest Editor for several issues, including the International Journal of Systems Science, International Journal of General Systems, and Kybernetika.

Xiaojian Yi

Xiaojian Yi received the B.S. degree in control technology from the North University of China, Taiyuan, China, in 2010, and the M.S. and Ph.D. degrees in reliability engineering from the Beijing Institute of Technology, Beijing, China, in 2012 and 2016, respectively. During 2015 and 2016, he was a jointly trained Ph.D. student with the University of Ottawa, Ottawa, ON, 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. He is the author of two books and more than 100 articles, and is also the holder of eight patents. His research interests include system.