Adaptive neural dissipative control for markovian jump cyber-Physical systems against sensor and actuator attacks |
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| Institution: | 1. National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China;2. Chongqing Innovation Center of Industrial Big-Data Co., Ltd., Chongqing, 400707, China;3. School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, China;4. College of Control Science and Engineering, Bohai University, Jinzhou 121013, China;5. Department of Mathematics, Bharathiar University, Coimbatore, Tamilnadu 641046, India;1. Department of Applied Mathematics, Bharathiar University, Coimbatore 641046, India;2. Department of Mathematical Sciences, Shibaura Institute of Technology, Saitama 337-8570, Japan;1. College of Science, Hohai University, Nanjing 210098, PR China;2. School of Mathematics and Informational Science, Yantai University, Yantai 264005, PR China;3. School of Mathematics, Southeast University, Nanjing 210096, China;4. Yonsei Frontier Lab, Yonsei University, Seoul 03722, South Korea;5. School of Automation and Electrical Engineering, Linyi University, Linyi 276005, China;1. The Department of College of Information Science, Engineering Northeastern University, Shenyang 110819, China;2. The School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, P.R. China;1. School of Cyber Science and Engineering, Sichuan University, Chengdu 610065, China;2. School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China;3. School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China;4. State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation, Southwest Petroleum University, Chengdu 610050, China |
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| Abstract: | This work addresses the issue of adaptive neural dissipative control for Markovian Jump Cyber-Physical Systems subject to output-dependent sensor and state-dependent actuator attacks. Attackers can inject false information into feedforward and feedback signals to degrade system performance or even destabilize the system. To identify and approximate attack signals, neural network technique is employed. Attacks are successfully withstood by constructing the estimated signals of these approximate functions. New adaptive state and output feedback controllers are being developed in the meantime. Then, by adapting Lyapunov function technique, sufficient conditions are provided to achieve the stochastic stability of the considered system with extended dissipation. Last, two practical examples are applied to elucidate the effectiveness of the devised adaptive neural control approaches. |
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