<p>This study addresses the resilient event-triggered fuzzy control of nonlinear cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. When event-triggered fuzzy control is employed, a mismatch between the premise variables of the fuzzy controller and the Takagi-Sugeno (TS) fuzzy model is induced by the event-based sampling and the data loss caused by DoS attacks. To properly deal with this issue, we propose a dynamic event-triggering mechanism that can compensate for the parameter mismatch and reduce the usage of communication resources. We also estimate the region of admissible initial conditions where convergence of the closed-loop trajectories is guaranteed even in the presence of DoS cyberattacks. The relation between the size of this region and the average duration of the DoS attack is also established. Furthermore, an optimization problem is provided to maximize resilience to DoS cyberattacks, minimize the number of transmissions, and maximize the region of attraction estimation. The proposed methodology is validated via two numerical examples. </p>

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Resilient Dynamic Event-Triggered Fuzzy Control Against DoS Attacks on Cyber-Physical Systems

  • Pedro Henrique S. Coutinho,
  • Iury Bessa,
  • Reinaldo M. Palhares

摘要

This study addresses the resilient event-triggered fuzzy control of nonlinear cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. When event-triggered fuzzy control is employed, a mismatch between the premise variables of the fuzzy controller and the Takagi-Sugeno (TS) fuzzy model is induced by the event-based sampling and the data loss caused by DoS attacks. To properly deal with this issue, we propose a dynamic event-triggering mechanism that can compensate for the parameter mismatch and reduce the usage of communication resources. We also estimate the region of admissible initial conditions where convergence of the closed-loop trajectories is guaranteed even in the presence of DoS cyberattacks. The relation between the size of this region and the average duration of the DoS attack is also established. Furthermore, an optimization problem is provided to maximize resilience to DoS cyberattacks, minimize the number of transmissions, and maximize the region of attraction estimation. The proposed methodology is validated via two numerical examples.