Sampled-data state estimation for complex dynamical networks with time-varying delay and stochastic sampling

The paper investigates state estimation for complex dynamical networks with time-varying delay and stochastic sampling. Only two different sampling periods are considered which occurrence probabilities are given constants and satisfy Bernoulli distribution. By applying an input-delay approach, the probabilistic sampling state estimator is transformed into a continuous time-delay system with stochastic parameters in the system matrices, where the purpose is to design a state estimator to estimate the network states through available output measurements. Delay-dependent asymptotically stability condition is established for the system of the estimation error, which can be readily solved by using the LMI toolbox in MATIAB, the solvability of derived conditions depends on not only the size of the delay and the sampling period, but also the probability of taking values of the sampling period. Finally, a numerical example is provided to demonstrate the effectiveness of the obtained theoretical results. (C) 2014 Elsevier B.V. All rights reserved.