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Robust event‐triggered model predictive control for cyber‐physical systems under denial‐of‐service attacks
Author(s) -
Sun YuanCheng,
Yang GuangHong
Publication year - 2019
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.4654
Subject(s) - denial of service attack , computer science , model predictive control , network packet , cyber physical system , networked control system , stability (learning theory) , control theory (sociology) , constraint (computer aided design) , scheduling (production processes) , control (management) , mathematical optimization , engineering , computer security , the internet , mathematics , operating system , mechanical engineering , artificial intelligence , machine learning , world wide web
Summary This paper investigates the resilient control problem for constrained continuous‐time cyber‐physical systems subject to bounded disturbances and denial‐of‐service (DoS) attacks. A sampled‐data robust model predictive control law with a packet‐based transmission scheduling is taken advantage to compensate for the loss of the control data during the intermittent DoS intervals, and an event‐triggered control strategy is designed to save communication and computation resources. The robust constraint satisfaction and the stability of the closed‐loop system under DoS attacks are proved. In contrast to the existing studies that guarantee the system under DoS attacks is input‐to‐state stable, the predicted input error caused by the system constraints can be dealt with by the input‐to‐state practical stability framework. Finally, a simulation example is performed to verify the feasibility and efficiency of the proposed strategy.