Event-triggered control of interconnected nonlinear systems subjected to cyber-attacks and time-varying coupling |
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| Institution: | 1. Department of Electrical Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran;2. Department of Mechanical and Industrial Engineering, Norwegian University of Science and Technology, Trondheim, Norway;1. School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China;2. College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China;1. Department of Multidisciplinary Engineering, Texas A&M University, 6200 Tres Lagos Blvd, Higher Education Center at McAllen, McAllen, 78504, Texas, USA;2. Department of Robotics and Advanced Manufacturing, Center for Research and Advanced Studies (CINVESTAV-IPN), Av. Industria Metalurgica, Ramos Arizpe, 25900, Coahuila, Mexico;1. College of Engineering University of Hail Po.Box 2440, Hail, Kingdom of Saudi Arabia;2. National School of Engineering of Sfax, University of Sfax, Lab-STA, LR11ES50, 3038, Sfax, Tunisia;3. Modeling, Information, and Systems Laboratory, University of Picardie Jules Verne, UFR of Sciences, 33 Rue St Leu Amiens 80000, France;1. School of Mathematics and Statistics & FJKLMAA, Fujian Normal University, Fuzhou 350117, PR China;2. School of Big Data, Fuzhou University of International Studies and Trade, Fuzhou 350202, PR China |
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| Abstract: | This paper addresses the problem of efficient control of nonlinear distributed networked control systems in the presence of stochastic deception attacks and time-varying coupling strength. A strategy combining model-based and event-triggered control to reduce the number of transmissions over a network thereby, saving network resources is proposed. In this strategy, a plant model at the controller end is used to estimate the state of each subsystem. Further, the control law between the two adjacent triggering instants changes in accordance with dynamics of the plant model. The nonlinearities present in each subsystem are approximated via neural network. The neural network weights and feedback signal are updated only when the event-triggering condition at the sensor end is violated. Also, a lower bound on the inter-event time is computed to avoid the occurrence of Zeno phenomena. Finally, the efficacy of the proposed methodology are verified through simulation examples. |
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