Event-based asynchronous and resilient filtering for Markov jump singularly perturbed systems against deception...

Area of Science:

• Control Systems Engineering
• Networked Systems Security
• Stochastic Systems Analysis

Background:

• Markov jump singularly perturbed systems (MJSPSs) are susceptible to stochastic deception attacks.
• Existing filtering methods may not adequately address asynchronous data and deception vulnerabilities.

Purpose of the Study:

• To design a dynamic event-based asynchronous dissipative filter for MJSPSs under stochastic deception attacks.
• To develop a novel transmission protocol minimizing data transmission while ensuring filter performance.
• To provide criteria for stochastic stability and guaranteed dissipative performance.

Main Methods:

• A dynamic event-based transmission protocol is introduced.
• Deception attacks are modeled using Bernoulli distribution.
• An asynchronous filter is designed using linear matrix inequality (LMI) techniques.
• Joint design of the transmission protocol and filter is proposed.

Main Results:

• Efficient criteria for stochastic stability of the filtering error system are derived.
• Predetermined dissipative performance is achieved.
• The proposed method effectively reduces data transmission.
• Numerical and RC circuit simulations validate the approach.

Conclusions:

• The developed dynamic event-based asynchronous dissipative filter enhances resilience against stochastic deception attacks in MJSPSs.
• The proposed method offers an effective way to jointly design the event-triggered protocol and the asynchronous filter.
• The approach demonstrates practical applicability and significant benefits.