Distributed event-triggered state estimation and fault detection of nonlinear stochastic systems

This paper is devoted to investigate the designs of the event-based distributed state estimation and fault detection of the nonlinear stochastic systems over wireless sensor networks (WSNs). The nonlinear stochastic systems as well as the filters corresponding to the multiple sensors are represented by interval type-2 Takagi–Sugeno (T–S) fuzzy models. (1) A new type of fuzzy distributed filters based on event-triggered mechanism is established corresponding to the nodes of the WSN. (2) The overall stability and performance, that is mean-square asymptotic stability in H_∞ sense, of the event-driven fault detection system is analyzed based on Lyapunov stability theory. (3) New techniques are developed to cope with the problem of parametric matrix decoupling for solving the distributed filter gains. (4) Finally, the desired event-based distributed filter matrices are designed subject to the numbers of the fuzzy rules and a series of matrix inequalities. A simulation case is detailed to show the effectiveness of the presented event-based distributed fault detection filtering scheme.