Robust state estimation for Markov jump genetic regulatory networks based on passivity theory |
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| Authors: | Li Lu Bing He Chuntao Man Shun Wang |
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| Institution: | 1. College of Electrical Engineering, Shanghai Dianji University, Shanghai, China;2. Maintenance company, State Grid Shanghai Municipal Electric Power Company, Shanghai, China;3. Department of Automation, Harbin University of Science and Technology, Harbin, China;4. Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin, China |
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| Abstract: | In this article, the robust state estimation problem for Markov jump genetic regulatory networks (GRNs) based on passivity theory is investigated. Moreover, the effect of time‐varying delays is taken into account. The focus is on designing a linear state estimator to estimate the concentrations of the mRNAs and the proteins of the GRNs, such that the dynamics of the state estimation error can be stochastically stable while achieving the prescribed passivity performance. By applying the Lyapunov–Krasovskii functional method, delay‐dependent criteria are established to ensure the existence of the mode‐dependent estimator in the form of linear matrix inequalities. Based on the obtained results, the parameters of the desired estimator gains can be further calculated. Finally, a numerical example is given to illustrate the effectiveness of our proposed methods. © 2015 Wiley Periodicals, Inc. Complexity 21: 214–223, 2016 |
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| Keywords: | Markov jump genetic regulatory networks passivity theory state estimation |
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