Fuzzy Filtering for Networked Nonlinear Systems with Dynamic Quantization: Quantization-Dependent Event-Triggered Case

This paper investigates the event-triggered robust dissipative filtering problem for discrete-time networked nonlinear systems subject to dynamic quantization and stochastic cyber attacks. The networked nonlinear system under consideration is represented by a Takagi-Sugeno (T-S) fuzzy model with parameter uncertainties. A dynamic quantizer and a novel quantization-dependent event-triggered mechanism are introduced to reduce the communication burden, thereby improving the utilization of the limited network resources. Moreover, a Bernoulli-distributed random variable is adopted to characterize the effects of stochastic cyber attacks. Attention is focused on the design of a filter such that the filtering error system is stochastically stable and satisfies a prescribed dissipative performance level. By applying the matrix inequality decoupling approach, the design conditions for the desired dynamic quantizer and filter are obtained in terms of linear matrix inequalities. Finally, simulation results for the Henon mapping system demonstrate that the proposed design approach guarantees the prescribed dissipative performance of γ=1.5. In addition, the number of triggering events is reduced by 14.13% compared with that of existing quantization-independent methods.