Quantized Asynchronous Filtering for Fuzzy Semi-Markov Switching Systems With Multiple Stochastic Delays Under Deception Attacks

This article addresses the quantized asynchronous filtering issue for semi-Markov switching systems described by the Takagi–Sugeno fuzzy model with multiple stochastic delays under deception attacks. First, considering communication congestion and the randomly changing environment, multiple stochastic time-varying delays are introduced via the Bernoulli distribution. Second, a mode-dependent quantizer is proposed to save the limited network bandwidth. Third, to enhance network security, the measurement output signal is assumed to suffer from deception attacks during the transmission process. Subsequently, under the influences of the above-mentioned three factors, a novel filtering error model is well constructed. Further, by applying the Lyapunov–Krasovskii stability theory and the stochastic analysis techniques, a delay-dependent stochastic stability criterion with less conservatism is given to ensure that the filtering error system is stochastically stable with a prescribed
$\mathcal {H}_{\infty }$
performance, where the feasible asynchronous filter gains and dynamic parameters can be obtained in a unified framework. Ultimately, two examples are employed to demonstrate the availability of the designed filter method.