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

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