Set-Membership Filtering Approach to Dynamic Event-Triggered Fault Estimation for a Class of Nonlinear Time-Varying Complex Networks

The present study addresses the problem of fault estimation for a specific class of nonlinear time-varying complex networks, utilizing an unknown-input-observer approach within the framework of dynamic event-triggered mechanism (DETM). In order to optimize communication resource utilization, the DETM is employed to determine whether the current measurement data should be transmitted to the estimator or not. To guarantee a satisfactory estimation performance for the fault signal, an unknown-input-observer-based estimator is constructed to decouple the estimation error dynamics from the influence of fault signals. The aim of this paper is to find the suitable estimator parameters under the effects of DETM such that both the state estimates and fault estimates are confined within two sets of closed ellipsoid domains. The techniques of recursive matrix inequality are applied to derive sufficient conditions for the existence of the desired estimator, ensuring that the specified performance requirements are met under certain conditions. Then, the estimator gains are derived by minimizing the ellipsoid domain in the sense of trace and a recursive estimator parameter design algorithm is then provided. Finally, a numerical example is conducted to demonstrate the effectiveness of the designed estimator.