Secure Estimation for Cyber-Physical Systems via Sliding Mode.

Area of Science:

• Cyber-Physical Systems
• Control Theory
• Network Security

Background:

• Cyber-physical systems (CPSs) face significant vulnerabilities to sophisticated cyber attacks targeting sensors.
• Attacks can arbitrarily modify sensor data, compromising system integrity and state estimation.
• Existing methods often struggle to address both unknown inputs and malicious sensor attacks simultaneously.

Purpose of the Study:

• To develop a robust and secure state reconstruction method for CPSs under sensor attacks and unknown inputs.
• To design an observer that guarantees sparse estimation of system states.
• To enhance computational efficiency through an event-triggered mechanism.

Main Methods:

• Utilized a linear discrete-time state-space model and a sparse vector to represent attacks.
• Developed a novel sliding-mode observer with a projection operator for secure state estimation.
• Integrated an event-triggered scheme for resource optimization.
• Analyzed the effectiveness of the projection operator and sliding-mode observer.

Main Results:

• A projected sliding-mode observer-based estimation algorithm was developed for secure state reconstruction.
• The algorithm effectively estimates system states from sensor measurements corrupted by malicious attacks.
• The projection operator ensures sparsity of the estimations.
• Convergence of the proposed secure estimation algorithm was mathematically proven.

Conclusions:

• The proposed algorithm provides a secure and effective solution for state reconstruction in CPSs facing sensor attacks.
• The integration of a projection operator and sliding-mode observer ensures robust and sparse state estimation.
• The event-triggered scheme optimizes computational resource usage without compromising security or accuracy.