$H_{\infty}$ State Estimation for State-Saturated Complex Networks with Markov-Based Nonuniform Sampling

This article investigates the
$H_{\infty}$
state estimation issue for a class of complex networks subject to state saturations. A Markov chain is introduced to model the nonuniform sampling of the measurement output. With aim at estimating the network state, a series of state estimators are constructed in light of the nonuniformly sampled measurements sent by sensors. By employing the Lyapunov stability method, a sufficient condition is presented to ensure that the resulting error system is stochastically stable and the prescribed
$H_{\infty}$
performance index is also satisfied. Subsequently, the gain matrices of the expected estimator are obtained by solving certain matrix inequalities. Finally, a simulation example is given to show the state estimation method's effectiveness.