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Extended Dissipative Synchronization of Reaction–Diffusion Genetic Regulatory Networks Based on Sampled-data Control

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Abstract

This work focuses on the extended dissipative synchronization control issue for reaction-diffusion genetic regulatory networks. The purpose of this paper is to design the sampled-data controllers that can meet the transmission requirement of networks and decrease the measure of the transmitted data signal. Furthermore, in order to take full advantage of actual sampling information, a Lyapunov–Krasovskii functional containing some sampled-instant-dependent terms is constructed. Then, sufficient conditions are derived, which guarantee that the error system satisfying extended dissipative performance index and the states of the system converge to zero asymptotically. Meanwhile, the desired sampled-data control gains are obtained by solving the convex optimization problem. Finally, a simulation example is provided to verify the established results.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Anhui University of Technology, Ma’anshan, 243002, China

    Yuqing Qin, Feng Li, Jing Wang & Hao Shen

  2. Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, Ma’anshan, 243002, China

    Feng Li

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  1. Yuqing Qin
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  2. Feng Li
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  3. Jing Wang
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  4. Hao Shen
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Correspondence to Feng Li or Hao Shen.

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This work is supported by National Natural Science Foundation of China under Grant 61973199, 61873002, 61703004, by the Anhui Provincial Natural Science Foundation under grant 2208085QF202, by the Key Natural Science Foundation of Higher Education Institutions of Anhui Province under Grant KJ2021A0369, in part by the Open Fund of Key Laboratory of Anhui Higher Education Institutes under Grant CS2021-01.

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Qin, Y., Li, F., Wang, J. et al. Extended Dissipative Synchronization of Reaction–Diffusion Genetic Regulatory Networks Based on Sampled-data Control. Neural Process Lett 55, 3169–3183 (2023). https://doi.org/10.1007/s11063-022-11003-4

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  • DOI: https://doi.org/10.1007/s11063-022-11003-4

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