Fault Detection Filter Design for Nonlinear Singular Systems With Markovian Jump Parameters

The fault detection problem for a class of Markovian jump singular systems, subject to repeated scalar nonlinearities, is investigated. Sufficient conditions are obtained for the existence of a robust fault detection filter, designed to guarantee that the residual system is stochastically stale while achieving the desired performance. Furthermore, the cone complementarity linearization approach is utilized to transform the original nonconvex feasibility issue into a sequential minimization issue, in terms of linear matrix inequalities, which are calculated with standard optimization software. Thus, it is possible to construct an ideal fault detection filter, if the earlier conditions have feasible solutions. Finally, a numerical example is given to illustrate the effectiveness of this approach.