Event‐Triggered Distributed Resilient State Estimator for Nonlinear Cyber‐Physical Systems Under Hybrid Cyberattacks

ABSTRACT This article investigates the problem of event‐triggered distributed resilient state estimator (ET‐DRSE) for nonlinear cyber‐physical systems (NCPSs) under hybrid cyberattacks. A stochastic model for hybrid cyberattacks is constructed by considering both false data injection (FDI) and Denial‐of‐Service (DoS) attacks. By proposing an event‐triggered mechanism, the network burden is reduced by decreasing the transmission rate of measurement information. To improve the flexibility, a detector‐based distributed resilient state estimator framework is derived. Coupled Riccati‐like difference equations (CRDEs) are proposed to obtain an upper bound (UB) of the estimation error covariance (EEC) of every subsystem. An estimation gain is designed to minimize the UB upon the error covariance of the resilient estimator by recursively solving the CRDEs. It is proved that the estimation error is probabilistically bounded through the application of random analytic theory. Finally, the performance of the proposed ET‐DRSE is verified through a numerical example and a chemical process example.