Asynchronous \(\mathcal{H}_{\infty}\) estimation for two-dimensional nonhomogeneous Markovian jump systems with randomly occurring nonlocal sensor nonlinearities (Q1664945)

Summary: This paper is devoted to the problem of asynchronous \(\mathcal{H}_{\infty}\) estimation for a class of two-dimensional (2D) nonhomogeneous Markovian jump systems with nonlocal sensor nonlinearity, where the nonlocal measurement nonlinearity is governed by a stochastic variable satisfying the Bernoulli distribution. The asynchronous estimation means that the switching of candidate filters may have a lag to the switching of system modes, and the varying character of transition probabilities is considered to reside in a convex polytope. The jumping process of the error system is modeled as a two-component Markov chain with extended varying transition probabilities. A stochastic parameter-dependent approach is provided for the design of \(\mathcal{H}_{\infty}\) filter such that, for randomly occurring nonlocal sensor nonlinearity, the corresponding error system is mean-square asymptotically stable and has a prescribed \(\mathcal{H}_{\infty}\) performance index. Finally, a numerical example is used to illustrate the effectiveness of the developed estimation method.