Finite-time distributed H∞ filtering for Takagi-Sugeno fuzzy system with uncertain probability sensor saturation under switching network topology: Non-PDC approach

This paper mainly investigates the stochastic finite-time distributed H∞ filtering problem for more general Takagi-Sugeno fuzzy systems (TSFSs) with immeasurable premise variables over wireless sensor networks (WSNs) with switching topology. The practical factors including sensor saturation and measurement missing, which are modeled by mutually independent Bernoulli processes with uncertain probability, are taken into account. Utilizing non-parallel-distributed-compensation (non-PDC) scheme, a switching-type distributed filter based on estimated premise variables is designed to realize the sharing of filtering information and measurement information. A distributed robust filtering method is proposed to analyze the distributed filtering error system (DFES) containing unknown premise variables. Then by constructing a model-dependent fuzzy Lyapunov function, new less conservative sufficient conditions in term of LMIs are obtained to ensure the DFES stochastic finite-time bounded and achieving a modified H∞ performance index under bounded disturbance. By solving a convex optimization problem, the filter gain parameters and average dwell time of topology switching signal are determined. Finally, a tunnel diode circuit in sensor networks is considered to verify the theoretical findings.