Non-fragile asynchronous state estimation for Markovian switching CVNs with partly accessible mode detection: The discrete-time case

Abstract

This article is devoted to the non-fragile asynchronous state estimation problem for Markovian switching complex-valued networks subject to randomly occurring nonlinearities (RONs) and external stochastic disturbances. Two mutually uncorrelated random variables with known statistical properties are introduced to depict the existing RONs. Considering the case that the information concerning to the system modes cannot be completely acquired by the state estimator to be designed, the asynchronous phenomenon is considered via a hidden Markov model with only partial mode detection probabilities being accessible. By resorting to the intensive stochastic analysis as well as an improved complex-valued reciprocal convex inequality, some mode-dependent criteria are provided which ascertain that the estimation error system is asymptotically mean square stable. In addition, the estimator gains desired can be appropriately designed by resorting to feasible solutions of a set of complex matrix inequalities. One numerical example is also provided to demonstrate effectiveness of the estimation scheme proposed.