Event-triggered filter design for nonlinear cyber-physical systems subject to deception attacks

Area of Science:

• Control Systems Engineering
• Cybersecurity
• Artificial Intelligence

Background:

• Conventional event-triggered mechanisms (ETMs) can trigger spurious data releases due to rapid sampling data changes from external disturbances.
• Cyber-physical systems are vulnerable to cyber-attacks, compromising system security and reliability.
• Fuzzy-model-based approaches are increasingly used in complex control systems.

Purpose of the Study:

• To design a novel event-triggered filter for fuzzy-model-based cyber-physical systems.
• To address the issue of spurious event triggers in conventional ETMs.
• To enhance system resilience and security against cyber-attacks.

Main Methods:

• A new event-triggered mechanism (ETM) is proposed to avoid spurious data releases.
• A resilient filter is designed to ensure system security under cyber-attacks.
• Sufficient conditions for asymptotic stability of the filtering error system are established.

Main Results:

• The proposed ETM effectively prevents spurious event triggers caused by data fluctuations.
• The designed resilient filter ensures the security of the cyber-physical system.
• The filtering error system is proven to be asymptotically stable under the proposed design.

Conclusions:

• The developed event-triggered filter and mechanism offer a robust solution for secure and efficient fuzzy-model-based cyber-physical systems.
• The proposed approach enhances system reliability by mitigating spurious triggers and defending against cyber-attacks.
• Numerical simulations confirm the effectiveness of the proposed design in achieving stability and security.