Robust state estimation for delayed genetic regulatory networks using sampled-data

摘要

In this paper, we investigate the robust state estimation problem for a class of genetic regulatory networks (GRNs) subject to time-varying delays and parameter uncertainties via utilizing sampled-data method. By substituting the continuous measurements, we use the sampled measurements to estimate the concentrations of mRNAs and proteins. Based on the extended Wirtinger inequality, a new discontinuous Lyapunov functional is constructed. Via using Jensen inequality and the Lower bounds theorem, a sufficient criterion is derived, which guarantees the globally robustly asymptotic stability of the augmented system. Further, the required state estimators can be designed by solving a set of linear matrix inequalities (LMIs). Finally, two numerical examples are provided to demonstrate the effectiveness and less conservatism of the obtained results.