An Unknown Input Multi-Observer Approach for Estimation and Control under Adversarial Attacks
This work addresses the critical problem of secure state estimation and control for cyber-physical systems under adversarial attacks, providing a theoretically guaranteed method for attack isolation and resilient control.
The paper proposes an unknown input multi-observer approach for state estimation, attack isolation, and control of discrete-time linear systems under actuator and sensor false data injection attacks, achieving exponential state estimation despite attacks and enabling stabilization by switching off compromised actuators.
We address the problem of state estimation, attack isolation, and control of discrete-time linear time-invariant systems under (potentially unbounded) actuator and sensor false data injection attacks. Using a bank of unknown input observers, each observer leading to an exponentially stable estimation error (in the attack-free case), we propose an observer-based estimator that provides exponential estimates of the system state in spite of actuator and sensor attacks. Exploiting sensor and actuator redundancy, the estimation scheme is guaranteed to work if a sufficiently small subset of sensors and actuators are under attack. Using the proposed estimator, we provide tools for reconstructing and isolating actuator and sensor attacks; and a control scheme capable of stabilizing the closed-loop dynamics by switching off isolated actuators. Simulation results are presented to illustrate the performance of our tools.