Fault Detection Filtering for Nonhomogeneous Markovian Jump Systems via a Fuzzy Approach

Abstract

This paper investigates the problem of fault detection filter design for nonhomogeneous Markovian jump systems by a Takagi-Sugeno fuzzy approach. Attention is focused on the construction of a fault detection filter to ensure the estimation error dynamic to be stochastically stable and the prescribed performance requirement can be satisfied. The designed fuzzy model-based fault detection filter can guarantee the sensitivity of the residual signal to faults and the robustness of the external disturbances. By using the cone complementarity linearization algorithm, the existence conditions for the design of fault detection filters are provided. Meanwhile, the error between the residual signal and the fault signal is made as small as possible. Finally, a practical application are given to illustrate the effectiveness of the proposed technique.