Non-fragile guaranteed cost control for networked nonlinear Markov jump systems under multiple cyber-attacks |
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| Institution: | 1. School of Engineering, QuFu Normal University, Rizhao 276800, China;2. Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong;1. School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China;2. Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing, 210044, China;3. Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot, Nanjing, 210044, China;1. Department of Electrical Engineering, Shiraz University of Technology, Shiraz 71557-13876, Iran;2. Department of Electrical Engineering, University of Zanjan, Zanjan 45371-38791, Iran;3. Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, Douliou, Yunlin 640301, Taiwan;1. State Key Laboratory of Automotive Simulation and Control, Jilin University, Renmin Street No.5988, Changchun, China;2. Intelligent Connected Vehicle Development Institute, China Faw Group Co., Ltd, China;1. School of Mathematics, Harbin Institute of Technology, Harbin, 150001, China;2. School of Computer and Control Engineering, Yantai University, Yantai, Shandong 264005, China |
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| Abstract: | This paper is concerned with the problem of non-fragile guaranteed cost control (GCC) for networked nonlinear Markov jump systems subject to multiple cyber-attacks, which are characterized by Takagi–Sugeno (T–S) fuzzy model with time-varying delay. Specifically, a variety of cyber-attacks, including deception attacks and Denial-of-Service (DoS) attacks, are considered, which occur in the forward and feedback communication links, respectively. To achieve stochastic stability under guaranteed cost function (GCF), the paper proposes a Lyapunov–Krasovskii (L–K) function approach. The approach derives sufficient conditions for stochastic stability, and obtains non-fragile controller gains and the uniform upper bound of the GCF using linear matrix inequalities (LMIs) technique. Finally, the effectiveness of the proposed algorithm is evaluated by simulation experiment. |
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