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Event-triggered state estimation for T-S fuzzy affine systems based on piecewise Lyapunov-Krasovskii functionals

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Abstract

This paper investigates the problem of event-triggered H∞ state estimation for Takagi-Sugeno (T-S) fuzzy affine systems. The objective is to design an event-triggered scheme and an observer such that the resulting estimation error system is asymptotically stable with a prescribed H∞ performance and at the same time unnecessary output measurement transmission can be reduced. First, an event-triggered scheme is proposed to determine whether the sampled measurements should be transmitted or not. The output measurements, which trigger the condition, are supposed to suffer a network-induced time-varying and bounded delay before arriving at the observer. Then, by adopting the input delay method, the estimation error system can be reformulated as a piecewise delay system. Based on the piecewise Lyapunov-Krasovskii functional and the Finsler’s lemma, the event-triggered H∞ observer design method is developed. Moreover, an algorithm is proposed to co-design the observer gains and the eventtriggering parameters to guarantee that the estimation error system is asymptotically stable with a given disturbance attenuation level and the signal transmission rate is reduced as much as possible. Simulation studies are given to show the effectiveness of the proposed method.

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Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, City University of Hong Kong, Kowloon, Hong Kong, China

    Meng Wang & Gang Feng

  2. State Key Laboratory of Robotics and Systems & Research Institute of Intelligent Control and Systems, Harbin Institute of Technology, Harbin, Heilongjiang, 150080, China

    Jianbin Qiu

Authors
  1. Meng Wang
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  2. Jianbin Qiu
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  3. Gang Feng
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Corresponding author

Correspondence to Gang Feng.

Additional information

This work was supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China (No. CityU-11211818), the Self-Planned Task of State Key Laboratory of Robotics and Systems of Harbin Institute of Technology (No. SKLRS201801A03) and the National Natural Science Foundation of China (No. 61873311).

Meng WANG received the B.Eng. degree in Automation from Northeastern University at Qinhuangdao, Qinhuangdao, China, in 2011, and the M.Eng. degree in Control Science and Engineering from the Harbin Institute of Technology, Harbin, China, in 2013. He is currently pursuing the Ph.D. degree in Mechanical and Biomedical Engineering from the City University of Hong Kong, Kowloon, Hong Kong. His research interests include robust control and filtering, fuzzy systems and control, and their engineering applications.

Jianbin QIU received the B.Eng. and Ph.D. degrees in Mechanical and Electrical Engineering from the University of Science and Technology of China (USTC), Hefei, China, in 2004 and 2009, respectively. He also received the Ph.D. degree in Mechatronics Engineering from the City University of Hong Kong, Kowloon, Hong Kong in 2009. He has been with the School of Astronautics, Harbin Institute of Technology since 2009, where is currently a Full Professor. His current research interests include intelligent and hybrid control systems, signal processing, and robotics. Prof. Qiu is a Senior Member of IEEE and serves as the chairman of the IEEE Industrial Electronics Society Harbin Chapter, China. He is an Associate Editor of IEEE Transactions on Cybernetics.

Gang FENG received the Ph.D. degree in Electrical Engineering from the University of Melbourne, Australia. He has been with City University of Hong Kong since 2000 after serving as lecturer/senior lecturer at School of Electrical Engineering, University of New South Wales, Australia, 1992–1999. He is now Chair Professor of Mechatronic Engineering. He has been awarded an Alexander von Humboldt Fellowship, the IEEE Transactions on Fuzzy Systems Outstanding Paper Award, and Changjiang chair professorship from Education Ministry of China. He is listed as a SCI highly cited researcher by Clarivate Analytics. His current research interests include multi-agent systems and control, intelligent systems and control, and networked systems and control. Prof. Feng is an IEEE Fellow, an associate editor of Journal of Systems Science and Complexity, and was an associate editor of IEEE Transactions on Automatic Control, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Systems, Man & Cybernetics–Part C, Mechatronics, and Journal of Control Theory and Applications.

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Wang, M., Qiu, J. & Feng, G. Event-triggered state estimation for T-S fuzzy affine systems based on piecewise Lyapunov-Krasovskii functionals. Control Theory Technol. 17, 99–111 (2019). https://doi.org/10.1007/s11768-019-8189-3

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  • DOI: https://doi.org/10.1007/s11768-019-8189-3

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