Decentralized sampled-data H_∞ fuzzy filtering with exponential time-varying gains for nonlinear interconnected systems

This study proposes a technique to design a decentralized sampled-data H_∞ fuzzy filter for nonlinear interconnected systems. Unlike previous studies, a novel sampled-data fuzzy filter design includes exponential time-varying gains, which leads to enlargement of the sampling period and improvement in the decay rate performance of the filter. The filter design technique includes both measurable and immeasurable premise variable cases. In the latter case, the H_∞ performance criterion is newly defined to minimize the effect of immeasurability on filtering performance. This study also focuses on deriving less conservative design conditions of the filter. First, a novel two-sided looped Lyapunov–Krasovskii functional (LKF) is constructed to make full use of the actual sampling pattern. Then, by utilizing an extended reciprocally convex matrix inequality along with Writinger-based integral inequality, less conservative design conditions are derived as linear matrix inequalities (LMIs)-based optimal problem. Finally, the effectiveness and superior performance of the proposed filtering design techniques are demonstrated through two simulation examples.