Event-triggered fuzzy filtering for nonlinear networked systems with dynamic quantization and stochastic cyber attacks

In this article, the H-infinity filtering issue is considered for discrete-time nonlinear networked systems subject to event-triggered communication scheme, dynamic quantization, and stochastic cyber attacks. The considered nonlinear networked system is described by the Takagi-Sugeno (T-S) fuzzy model. The event-triggered policy and the dynamic quantizer will be considered to realize the effective use of the restricted network bandwidth resources. Moreover, a stochastic variable that satisfies the Bernoulli random binary distribution is employed to characterize the effects of stochastic cyber attacks. This paper focus on the design of full-and reduced-order event-triggered H-infinity filters and the dynamic parameter of the quantizer such that the filtering error system is stochastically stable and satisfies a predefined H-infinity filtering performance. The sufficient design conditions for the event-triggered H-infinity filters and the dynamic parameter of the quantizer are proposed based on linear matrix inequalities (LMIs). Finally, an example based on practical application will be used to verify the effectiveness of the presented design methods. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This article considers the H-infinity filtering issue for discrete-time nonlinear networked systems subject to event-triggered communication scheme, dynamic quantization, and stochastic cyber attacks. The Takagi-Sugeno (T-S) fuzzy model is used to describe the considered nonlinear networked system. The paper focuses on the design of event-triggered H-infinity filters and the dynamic parameter of the quantizer to achieve stochastic stability and predefined H-infinity filtering performance. The design conditions are proposed based on linear matrix inequalities (LMIs), and the effectiveness of the design methods is verified through a practical example.