Adaptive neural dissipative control for markovian jump cyber-Physical systems against sensor and actuator attacks
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 d...
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| Published in | Journal of the Franklin Institute Vol. 360; no. 12; pp. 7676 - 7698 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.08.2023
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| Online Access | Get full text |
| ISSN | 0016-0032 1879-2693 |
| DOI | 10.1016/j.jfranklin.2023.06.009 |
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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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| AbstractList | 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. |
| Author | Chen, Zixian Kalidass, Mathiyalagan Xing, Bin Zhao, Ning Zhang, Huiyan |
| Author_xml | – sequence: 1 givenname: Huiyan orcidid: 0000-0003-3406-8954 surname: Zhang fullname: Zhang, Huiyan email: huiyanzhang@ctbu.edu.cn organization: National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing 400067, China – sequence: 2 givenname: Zixian surname: Chen fullname: Chen, Zixian email: zixianchen133@gmail.com organization: School of Mechanical Engineering, Chongqing Technology and Business University, Chongqing, China – sequence: 3 givenname: Ning surname: Zhao fullname: Zhao, Ning email: zhaoning_hrbeu@163.com organization: College of Control Science and Engineering, Bohai University, Jinzhou 121013, China – sequence: 4 givenname: Bin surname: Xing fullname: Xing, Bin email: xingbin@htyw.casic.com organization: Chongqing Innovation Center of Industrial Big-Data Co., Ltd., Chongqing, 400707, China – sequence: 5 givenname: Mathiyalagan orcidid: 0000-0003-2323-3328 surname: Kalidass fullname: Kalidass, Mathiyalagan email: kmathimath@buc.edu.in organization: Department of Mathematics, Bharathiar University, Coimbatore, Tamilnadu 641046, India |
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