Robust estimation for discrete time‐delay Markov jump systems with sensor non‐linearity and missing measurements

Abstract

This study addresses the ℋ∞ filtering design issue for a class of time-delay Markov jump system with non-linear characteristics. A stochastic system with sensor saturation and intermittent measurements is considered in the authors study. Random noise depending on state and external-disturbance are also taken into account. A decomposition approach and a bernoulli process are utilised to model the characteristic of sensor saturation and missing measurements, respectively. By transforming the filtering error system into an input–output form, sufficient conditions for the stochastic stability of the system with a prescribed ℋ∞ level are presented with the help of Scaled Small Gain theorem developed for stochastic systems. Based on the proposed conditions, the rubost filter design approach is proposed. A numerical example is finally provided to demonstrate effectiveness of the proposed approahc.