Event-Triggered Distributed Average Tracking with False Data Injection Attacks

This paper presents a study of robust distributed average algorithms focusing on event-triggered mechanisms and false data injection attacks (FDIAs). To mitigate the influence of FDIAs and to reduce communication load in networked control systems, we propose an Event-Triggered Anti-Attack Distributed Average Tracking (ETAA-DAT) algorithm, in which the anti-attack control and the event-triggered control are integrated. Given that the FDIAs are inherently unknown, an extended state observer is introduced to estimate the FDIAs and then compensates for them in control inputs. In the event-triggered mechanism, a fully distributed event-triggering condition is employed and the information exchange between agents occurs only at the moments of the triggered events. Simulation results demonstrate that the proposed ETAA-DAT algorithm is able to achieve accurate average tracking of multiple time-varying reference signals despite the presence of FDIAs, while the communication load can be reduced.