Quantized Filtering of Markovian Jump LPV Systems

Abstract

This chapter studies the quantized $\mathcal {H}_{\infty }$ filtering problem for discrete time Markovian jump linear parameter-varying (LPV) systems subject to intermittent measurements. A logarithmic mode-independent quantizer is employed to quantize the measured output of the underlying plant and a Bernoulli distributed stochastic variable is utilized to model the data missing phenomena. By using the parameter dependent Lyapunov functional method, a sufficient parameterized linear matrix inequality (PLMI) type condition is proposed for the filtering error system. The basic functions and gridding technique are utilized to solve the corresponding parameterized convex problem. Moreover, the explicit expressions of the desired filter parameters are also established.