State Estimation for Coupled Output Complex Dynamical Networks with Stochastic Sampled-Data

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In this paper, the problem of the state estimation on complex dynamical networks coupled with stochastically sampling is studied. By using the input delay approach, the sampled-data system is transformed into a time-continuous system where the data at sampling periods have two different time-varying delays randomly happening between two intervals which have different known lower and upper bounds. The sampling points occur with two known probabilities and satisfy Bernoulli distribution. There are two different types of stochastically-sampled-data considered in this complex dynamical network. One of the stochastically-sampled-data is used to data exchange over the dynamical network, the other is used to construct the Luenburger state estimator as feedback data. We derive the state estimator for the complex dynamical network with new models which combined with two types of above stochastic-sampled-data, and delay-dependent asymptotical stabilities of the estimation error has been solved by constructing a Lyapunov–Krasovskii functional and the linear matrix inequality toolbox in MATLAB. Finally, a numerical example has been illustrated to demonstrate the effectiveness of the proposed results.