Safety Control for Cyber–Physical Systems Under False Data Injection Attacks

Cyber–physical systems (CPSs) are increasingly susceptible to cyber threats, especially false data injection (FDI) attacks, which can compromise their stability and safety. Ensuring system safety while mitigating such attacks is a critical challenge. In the paper, we address the safety control issue for CPSs by designing a control strategy that considers both false data injection attacks and physical safety constraints. A baseline controller is first designed to guarantee system stability when there are no attacks and when the safety constraints are satisfied. To address FDI attacks, we propose a neural network-based estimator to detect and estimate the magnitude of such attacks. The attack estimate is then incorporated into the controller to dynamically adjust control actions, ensuring that the system remains stable and resilient to malicious interference. Furthermore, we introduce a safety control algorithm based on control barrier functions to enforce safety constraints, where the attack estimate is integrated to handle unknown attacks. Finally, the effectiveness of the proposed scheme is validated by simulation results, demonstrating that the combined control strategy outperforms traditional methods in both attack mitigation and safety enforcement.