Event-based H_∞ fault estimation for networked time-varying systems with randomly occurring nonlinearities and (x, v)-dependent noises

In this paper, the problem of finite-horizon H_∞ fault estimation is investigated for a class of networked time-varying stochastic systems with randomly occurring nonlinearities and state- and disturbance-dependent noises (also called (x, v)-dependent noises). An event-triggered scheme is proposed to reduce data transmission burden where the current measurement is transmitted only when the certain condition is satisfied. The aim of the addressed problem is to design a fault estimator, in the presence of randomly occurring nonlinearities and (x, v)-dependent noises, such that faults can be estimated through measurement outputs. By employing the stochastic analysis method, the sufficient conditions are derived to guarantee that the error dynamics of estimations satisfies a prescribed H_∞ performance constraint. Moreover, the parameters of fault estimator can be calculated via the recursive linear matrix inequality (RLMI) approach. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed method.