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A Sampled-data Approach to Robust H State Estimation for Genetic Regulatory Networks with Random Delays

  • Weilu Chen
  • Dongyan Chen
  • Jun Hu
  • Jinling Liang
  • Abdullah M. Dobaie
Regular Paper Control Theory and Applications

Abstract

This paper is concerned with the robust H state estimation problem for a class of uncertain genetic regulatory networks (GRNs) with random delays and external disturbances by using sample-data method. An important feature of this paper is that the time-varying delays are assumed to be random and their probability distributions are known a priori. By substituting the continuous measurements, the sampled measurements are used to estimate the concentrations of mRNAs and proteins. On the basis of the extended Wirtinger inequality, a discontinuous Lyapunov functional is introduced. Then, some sufficient conditions are derived in terms of a set of linear matrix inequalities (LMIs), which ensure that the error system is globally asymptotically stable in the meansquare sense and satisfies H performance. Further, the explicit expression of the required estimator gain matrices is proposed. Finally, a numerical example is used to illustrate the effectiveness and feasibility of the obtained estimation method.

Keywords

Discontinuous Lyapunov functional genetic regulatory networks random delays sampled-data approach state estimation 

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Copyright information

© Institute of Control, Robotics and Systems and The Korean Institute of Electrical Engineers and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Applied MathematicsHarbin University of Science and TechnologyHarbinChina
  2. 2.School of MathematicsSoutheast UniversityNanjingChina
  3. 3.Department of Electrical and Computer Engineering, Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia

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