Security Dissipative Fuzzy Filtering for Networked Nonlinear Systems With Stochastic Cyber-Attacks

This paper addresses the security dissipative filtering problem for discrete-time networked nonlinear systems under stochastic cyber-attacks. The Takagi-Sugeno (T-S) fuzzy model methodology is adopted to describe the discussed nonlinear plant. Both the dynamic quantization and the event-triggered schemes are adopted such that the reasonable utilization regarding the restricted communication and computation resources can be achieved. Additionally, the effects of stochastic cyber-attacks depicted by Bernoulli distribution are also considered. The objective of this paper is to design the dynamic quantizer and full- and reduced-order filters such that the stochastic stability and the prescribed dissipative filtering performance index of the filtering error system are satisfied. The co-design conditions for the desired dynamic quantizer and the filters are formulated in terms of linear matrix inequalities by applying the matrix inequality decoupling strategy. Conclusively, the effectiveness and the superiority of the presented filtering scheme will be demonstrated via two practical examples.