On Joint Reconstruction of State and Input-Output Injection Attacks for Nonlinear Systems

TL;DR

This paper proposes a robust observer-based method for jointly reconstructing states and identifying attack signals in nonlinear systems with compromised sensors and actuators, ensuring accurate estimation despite attacks.

Contribution

It introduces a novel unknown input observer bank approach for nonlinear systems that can asymptotically estimate states and attack signals while isolating compromised sensors and actuators.

Findings

The method accurately estimates states and attack signals under small attack conditions.

The approach effectively isolates compromised sensors and actuators.

Numerical examples demonstrate the method's robustness and effectiveness.

Abstract

We address the problem of robust state reconstruction for discrete-time nonlinear systems when the actuators and sensors are injected with (potentially unbounded) attack signals. Exploiting redundancy in sensors and actuators and using a bank of unknown input observers (UIOs), we propose an observer-based estimator capable of providing asymptotic estimates of the system state and attack signals under the condition that the numbers of sensors and actuators under attack are sufficiently small. Using the proposed estimator, we provide methods for isolating the compromised actuators and sensors. Numerical examples are provided to demonstrate the effectiveness of our methods.