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Adaptive Event-triggered Control for Discrete-time Networked Control Systems with Actuator Faults and Nonlinearity

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Abstract

In this paper, the problem on adaptive event-triggered controller design is investigated for a class of discrete-time networked control systems (NCSs), in which parameter uncertainties, actuator faults, and nonlinearity are simultaneously considered. Firstly, in order to reduce the transmission amount, an improved adaptive eventtriggered mechanism (AETM) is proposed and it can dynamically adapt to the controlled NCSs. Secondly, based on the designed AETM, a closed-loop system model is derived with involving network-induced delays. Thirdly, by choosing an augmented Lyapunov-Krasovskii functional (LKF) and using some effective summation inequalities, two less conservative conditions are derived to guarantee the desired stability, in which the co-designs on AETM parameter and controller gain are presented in terms of linear matrix inequalities (LMIs). Finally, two examples with simulations and comparisons are given to illustrate the effectiveness of our proposed methods.

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Authors and Affiliations

  1. School of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, P. R. China

    Luo Zhang, Mou Chen & Tao Li

  2. Institute of Information and Control, Hangzhou Dianzi University, Hangzhou, 310018, P. R. China

    Huijiao Wang

Authors
  1. Luo Zhang
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  2. Mou Chen
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  3. Tao Li
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  4. Huijiao Wang
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Correspondence to Mou Chen.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Editor Jessie (Ju H.) Park. This work was supported in part by the Research Funds for the Central Universities under Grant NG2019002; in part by Jiangsu Province “333” project under Grant BRA2019051 and in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LZ17F030002.

Luo Zhang is currently pursuing a Ph.D. degree in control theory and control engineering from the College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing. His research interests include networked control systems, time delay systems, eventtriggered systems and robust control systems.

Mou Chen is currently a Full Professor with the College of Automation Engineering, NUAA. He was an Academic Visitor with the Department of Aeronautical and Automotive Engineering, Loughborough University, U.K., from 2007 to 2008. From 2008 to 2009, he was a Research Fellow with the Department of Electrical and Computer Engineering, National University of Singapore. He was a Senior Academic Visitor with the School of Electrical and Electronic Engineering, University of Adelaide, Australia, in 2014. His research interests include nonlinear system control, intelligent control, and flight control.

Tao Li received his Ph.D. degree in engineering from Southeast University in 2008 and was a postdoctoral research fellow at School of Instrument Science and Engineering of Southeast University from 2008 to 2011, China. He has been a visiting scholar at Control System Center of The Manchester University from 2016 to 2017, UK. He is currently an associate professor at School of Automation Engineering, Nanjing University of Aeronautics and Astronautics in China. His current research interests include neural networks, time-delay systems, networked control systems, etc.

Huijiao Wang was born in Zhejiang, China, in 1976. She received her B.E. and M.S. degrees in Control Theory and Control Engineering from Hangzhou Dianzi University, Hangzhou, China, in 1997 and 2003, respectively, and a Ph.D. degree in Control Science and Engineering from Zhejiang University, Hangzhou, China, in 2008. In April 2003, she joined Zhejiang Sci-Tech University, where she is currently an Professor. Her research interests include robust control theory, singular systems, timedelay systems, networked control systems and T-S fuzzy systems.

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Zhang, L., Chen, M., Li, T. et al. Adaptive Event-triggered Control for Discrete-time Networked Control Systems with Actuator Faults and Nonlinearity. Int. J. Control Autom. Syst. 18, 2842–2856 (2020). https://doi.org/10.1007/s12555-019-0803-0

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