Sampled-data state estimation for genetic regulatory networks with time-varying delays

This study examines the sampled-data state estimation problem for genetic regulatory networks (GRNs) with time-varying delays. Instead of the continuous measurements, the sampled measurements are used to estimate the true concentration of mRNAs and proteins of the GRNs. By changing the sampling period into a bounded time-varying delay, the error dynamics of the considered GRN is derived in terms of a dynamical system with time-varying delays. Sufficient conditions are derived such that the augmented system governing the error dynamics is globally asymptotically stable. The design of the desired state estimator is proposed by constructing a suitable Lyapunov–Krasovskii functional (LKF), and the design procedure can be easily achieved by solving a set of linear matrix inequalities (LMIs). Finally, the proposed method is validated through the numerical simulation which shows the effectiveness the our results.