Improved asynchronous fault detection filter design for singular fuzzy Markovian jump systems against multi-cyber-attacks

Abstract

This paper delves into the design of an asynchronous Fault Detection Filter (FDF) for Singular Fuzzy Markovian Jump Systems (SFMJSs), particularly under the influence of multi-cyber-attacks, leveraging an enhanced adaptive event-triggered mechanism (AETM). Initially, a Hidden Markovian Model (HMM) is employed to characterize the asynchronous interactions between SFMJSs and FDF. Subsequently, a mode-dependent AETM alongside a dynamic quantization protocol is synergistically utilized to optimize communication bandwidth and augment data transmission efficiency. Additionally, the study introduces an expansive range of multi-cyber-attacks, encompassing deception, replay, and Denial-of-Service (DoS) attacks, to mirror inherent vulnerabilities in wireless signal transmission. Furthermore, the H∞ performance of the FDF error system is assessed within a comprehensive framework using Linear Matrix Inequalities (LMIs), guided by a generalized performance index. This research establishes innovative admissibility criteria and filter conditions, aimed at guaranteeing the feasibility of desired FDF gains and ensuring the exponential admissibility of the FDF error system, underpinned by a generalized performance index. This manuscript concludes with two illustrative examples to demonstrate the efficacy of the proposed methodologies.