Distributed Filtering for Discrete-Time T–S Fuzzy Systems With Incomplete Measurements

The distributed filtering problem is addressed in this paper for the discrete-time Takagi–Sugeno (T–S) fuzzy systems with incomplete measurements. The system under consideration includes various network-induced uncertainties, e.g., sensor saturation, quantization error, communication delay, and packet dropouts. Specifically, all these uncertainties are assumed to occur in a stochastic way. In addition, the measurement scheduling issue is also addressed such that only a portion of measurements are broadcasting due to the communication constraints. The main focus is on the design of distributed filters based on the information received locally and from the neighborhood such that the desired estimation performance is guaranteed in terms of the decay rate and disturbance attenuation level. Based on the Lyapunov stability theory, the existence condition for such filters is first proposed and the filter gain parameters are then determined by solving an optimization problem. A simulation example is finally presented to illustrate the effectiveness of the new filtering techniques.