Home Robust Finite-Time Passivity for Discrete-Time Genetic Regulatory Networks with Markovian Jumping Parameters
Article
Licensed
Unlicensed Requires Authentication

Robust Finite-Time Passivity for Discrete-Time Genetic Regulatory Networks with Markovian Jumping Parameters

  • R. Sakthivel EMAIL logo , M. Sathishkumar , B. Kaviarasan and S. Marshal Anthoni
Published/Copyright: February 26, 2016

Abstract

This article addresses the issue of robust finite-time passivity for a class of uncertain discrete-time genetic regulatory networks (GRNs) with time-varying delays and Markovian jumping parameters. By constructing a proper Lyapunov–Krasovskii functional involving the lower and upper bounds of time delays, a new set of sufficient conditions is obtained in terms of linear matrix inequalities (LMIs), which guarantees the finite-time boundedness and finite-time passivity of the addressed GRNs for all admissible uncertainties and satisfies the given passive performance index. More precisely, the conditions are obtained with respect to the finite-time interval, while the exogenous disturbances are unknown but energy bounded. Furthermore, the Schur complement together with reciprocally convex optimisation approach is used to simplify the derivation in the main results. Finally, three numerical examples are provided to illustrate the validity of the obtained results.


Corresponding author: R. Sakthivel, Department of Mathematics, Sungkyunkwan University, Suwon 440-746, South Korea; and Department of Mathematics, Sri Ramakrishna Institute of Technology, Coimbatore 641 010, India, E-mail:

Acknowledgments

The work of S. Marshal Anthoni was supported by the NBHM/DAE under grant No. 2/48(4)/2013/NBHM(R.P)/R&D II/687. The work of M. Sathishkumar was supported by Rajiv Gandhi National Fellowship (RGNF), UGC, New Delhi, India [grant no. F1-17.1/2015-16/RGNF-2015-17-SC-TAM-18857/(SA-III/Website), dated: 09-01-2016].

References

[1] L. J. Banu and P. Balasubramaniam, Phys. Scripta 90, 015205 (2015).10.1088/0031-8949/90/1/015205Search in Google Scholar

[2] A. Liu, L. Yu, D. Zhang, and W. Zhang, J. Frankl. Inst. 350, 1944 (2013).Search in Google Scholar

[3] J. Liu, E. Tian, Z. Gu, and Y. Zhang, Commun. Nonlinear Sci. 19, 2479 (2014).10.1016/j.cnsns.2013.11.002Search in Google Scholar

[4] C. Ma, Q. Zeng, L. Zhang, and Y. Zhu, Neurocomputing 136, 321 (2014).10.1016/j.neucom.2013.12.028Search in Google Scholar

[5] K. Mathiyalagan, R. Sakthivel, and H. Su, Can. J. Phys. 92, 976 (2014).10.1139/cjp-2013-0146Search in Google Scholar

[6] R. Sakthivel, K. Mathiyalagan, S. Lakshmanan, and Ju H. Park, Nonlinear Dynam. 74, 1297 (2013).10.1007/s11071-013-1041-2Search in Google Scholar

[7] V. Vembarasan, P. Balasubramaniam, K. Ratnavelu, and N. Kumaresan, Phys. Scripta 86, 065003 (2012).10.1088/0031-8949/86/06/065003Search in Google Scholar

[8] W. Wang, S. Zhong, F. Liu, and J. Cheng, Int. J. Robust. Nonlin. 24, 2574 (2014).10.1002/rnc.3011Search in Google Scholar

[9] Y. Wang, X. Zhang, and Z. Hu, Neurocomputing 166, 346 (2015).10.1016/j.neucom.2015.04.061Search in Google Scholar

[10] Y. Zhu, Q. Zhang, Z. Wei, and L. Zhang, Neurocomputing 110, 44 (2013).10.1016/j.neucom.2012.09.033Search in Google Scholar

[11] Z. Zhu, Y. Zhu, L. Zhang, M. A. Yami, E. Abouelmagd, and B. Ahmad, Neurocomputing 168, 1121 (2015).10.1016/j.neucom.2015.05.011Search in Google Scholar

[12] P. Balasubramaniam and L. J. Banu, Neurocomputing 122, 349 (2013).10.1016/j.neucom.2013.06.015Search in Google Scholar

[13] S. He and F. Liu, Math. Comput. Simulat. 92, 1 (2013).Search in Google Scholar

[14] H. Shen, Z. G. Wu, and J. H. Park, Int. J. Robust. Nonlin. 25, 3231 (2015).10.1002/rnc.3255Search in Google Scholar

[15] M. K. Song, J. B. Park, and Y. H. Joo, Fuzzy Set. Syst. 277, 81 (2015).10.1016/j.fss.2015.02.004Search in Google Scholar

[16] S. He, Neurocomputing 168, 348 (2015).10.1016/j.neucom.2015.05.091Search in Google Scholar

[17] R. Sakthivel, M. Joby, K. Mathiyalagan, and S. Santra, J. Frankl. Inst. 352, 4446 (2015).Search in Google Scholar

[18] W. Qi and X. Gao, Appl. Math. Lett. 46, 111 (2015).10.1016/j.aml.2015.02.016Search in Google Scholar

[19] Z. Chen, Q. Huang, and Z. Liu, Appl. Math. Comput. 258, 138 (2015).10.1016/j.amc.2015.01.065Search in Google Scholar

[20] Y. Ma and H. Chen, Appl. Math. Comput. 268, 897 (2015).10.1016/j.amc.2015.06.067Search in Google Scholar

[21] O. M. Kwon, M. J. Park, Ju H. Park, S. M. Lee, and E. J. Cha, Nonlinear Dynam. 73, 2175 (2013).10.1007/s11071-013-0932-6Search in Google Scholar

[22] R. Sakthivel, S. Selvi, K. Mathiyalagan, and P. Shi, IEEE Trans. Cybern. 45, 2720 (2015).10.1109/TCYB.2014.2382563Search in Google Scholar

[23] J. L. Wang, H. N. Wu, and T. Huang, Automatica 56, 105 (2015).10.1016/j.automatica.2015.03.027Search in Google Scholar

[24] L. Li and J. Jian, Neurocomputing 168, 276 (2015).10.1016/j.neucom.2015.05.098Search in Google Scholar

[25] V. Vembarasan, G. Nagamani, P. Balasubramaniam, and J. H. Park, Math. Biosci. 244, 165 (2013).10.1016/j.mbs.2013.05.003Search in Google Scholar

[26] B. Zheng, S. Xu, and J. Lam, Neurocomputing 142, 299 (2014).10.1016/j.neucom.2014.04.031Search in Google Scholar

[27] G. X. Zhong and G. H. Yang, J. Process Contr. 32, 16 (2015).10.1016/j.jprocont.2015.04.013Search in Google Scholar

[28] Y. Du, S. Zhong, J. Xu, and N. Zhou, ISA Trans. 56, 1 (2015).10.1016/j.isatra.2014.11.005Search in Google Scholar

[29] Q. Song, Z. Zhao, and J. Yang, Neurocomputing 122, 330 (2013).10.1016/j.neucom.2013.06.018Search in Google Scholar

[30] L. Lee, Y. Liu, J. Liang, and X. Cai, ISA Trans. 57, 172 (2015).10.1016/j.isatra.2015.02.001Search in Google Scholar

[31] H. Shen, J. H. Park, and Z. G. Wu, Nonlinear Dynam. 77, 1709 (2014).10.1007/s11071-014-1412-3Search in Google Scholar

[32] L. Wang, Y. Shen, and Z. Ding, Neural Networks 70, 74 (2015).10.1016/j.neunet.2015.07.008Search in Google Scholar PubMed

[33] Y. Wu, J. Cao, A. Alofi, A. A. Mazrooei, and A. Elaiw, Neural Networks 69, 135 (2015).10.1016/j.neunet.2015.05.006Search in Google Scholar PubMed

[34] Z. Zhang, Z. Zhang, and H. Zhang, J. Frankl. Inst. 352, 1296 (2015).10.1016/j.jfranklin.2014.12.022Search in Google Scholar

[35] J. Cheng, H. Zhu, S. Zhong, Y. Zhang, and Y. Li, Int. J. Syst. Sci. 46, 1080 (2015).10.1080/00207721.2013.808716Search in Google Scholar

[36] Y. Zhang, P. Shi, and S. K. Nguang, Appl. Math. Lett. 38, 115 (2014).10.1016/j.aml.2014.07.010Search in Google Scholar

[37] P. G. Park, J. W. Ko, and C. Jeong, Automatica 47, 235 (2011).10.1016/j.automatica.2010.10.014Search in Google Scholar

[38] N. Jiang, X. Liu, W. Yu, and J. Shen, Neurocomputing 167, 314 (2015).10.1016/j.neucom.2015.04.064Search in Google Scholar

[39] L. Yin, J. Appl. Math. 2014, 730292 (2014).Search in Google Scholar

[40] M. B. Elowitz and S. Leibler, Nature 403, 335 (2000).10.1038/35002125Search in Google Scholar PubMed

Received: 2015-9-28
Accepted: 2016-1-16
Published Online: 2016-2-26
Published in Print: 2016-4-1

©2016 by De Gruyter

Downloaded on 23.9.2025 from https://www.degruyterbrill.com/document/doi/10.1515/zna-2015-0405/html
Scroll to top button