Quantized H∞ filtering for Markovian jump LPV systems with intermittent measurements

This paper investigates the problem of quantized H∞ filtering for a class of discrete-time linear parameter-varying systems with Markovian switching under data missing. The measured output of the plant is quantized by a logarithmic mode-independent quantizer. The data missing phenomenon is modeled by a stochastic variable. The purpose of the problem addressed is to design a full-order H∞ filter such that the filtering error dynamics is stochastically stable and the prescribed noise attenuation level in the H∞ sense can be achieved. Sufficient conditions are derived for the existence of such filters in terms of parameterized linear matrix inequalities. Then the corresponding filter synthesis problem is transformed into a convex optimization problem that can be efficiently solved by using standard software packages. A simulation example is utilized to demonstrate the usefulness of the developed theoretical results