Non-fragile H∞ filtering for Markov jump systems with incomplete transition probabilities and intermittent measurements

This paper studies the non-fragile H
_∞
filtering for Markov jump systems with incomplete transition probabilities and intermittent measurements. The transition probabilities are assumed to be known, uncertain but with accessible bounds and completely unknown. The missing measurement among the plant and the filter is described by a stochastic variable following the Bernoulli random binary distribution. Resorting to Lyapunov theory, sufficient conditions for the designed filter with norm- bounded uncertainties are formed in a set of linear matrix inequalities. Then the closed-loop filtering error system is guaranteed to be stochastically stable with prescribed H
_∞
performance. Finally, simulations show the validity of the proposed method.