Hybrid‐driven‐based resilient control for networked T‐S fuzzy systems with time‐delay and cyber‐attacks

Abstract

This work investigates design problem of the hybrid-triggered-based resilient controller for a class of Takagi-Sugeno (T-S) fuzzy systems subject to nondeterministic cyber-attacks, probabilistic time-delay, controller gain variations and actuator failures in which nondeterministic cyber-attacks and hybrid-triggered mechanism are implemented by probabilistic variables gratifying the Bernoulli distributions. To save the network resources, a new kind of hybrid-triggered-based resilient controller design is introduced. By employing Lyapunov technique together with improved Wirtinger's integral inequalities and event-generator function, new delay-dependent sufficient conditions are constructed which assures that the resulting closed-loop system is mean square asymptotically stable and satisfies a prescribed performance. At last, two numerical examples are provided to display the effectiveness and advantage of the constructed method.

Key Vulnerable Nodes Discovery Based on Bayesian Attack Subgraphs and Improved Fuzzy C-Means Clustering

“…The Bayesian theory is a statistical method to deal with uncertainty through observation data. The key of Bayesian theory is to predict possible risks in advance by mathematical methods, and it does not focus on the random attack itself [20]. Many risk analysis methods based on attack graphs combine Bayesian theory to realize risk quantification and prediction analysis [21][22][23].…”