Decentralized sampled-data H _∞ fuzzy filter for nonlinear large-scale systems

This paper presents a decentralized sampled-data H_∞ fuzzy filter design method for nonlinear large-scale systems which are represented by a Takagi-Sugeno (T-S) fuzzy model. Based on the T-S fuzzy model, the error system between the nonlinear large-scale system and the filter is obtained. The discretization process of the error system is accomplished with the exact discrete-time approach to eliminate the exact-approximate mismatch. By using the discrete-time Lyapunov sense, the sufficient condition of the asymptotic stability for the error system is given and a prescribed level of the H_∞ norm is ensured to guarantee the H_∞ fuzzy filter performance. Finally, numerical examples are given to show the effectiveness of the proposed methods.