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.
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).10.1088/0031-8949/90/1/015205Suche in Google Scholar
[2] A. Liu, L. Yu, D. Zhang, and W. Zhang, J. Frankl. Inst. 350, 1944 (2013).Suche in Google Scholar
[3] J. Liu, E. Tian, Z. Gu, and Y. Zhang, Commun. Nonlinear Sci. 19, 2479 (2014).10.1016/j.cnsns.2013.11.002Suche in Google Scholar
[4] C. Ma, Q. Zeng, L. Zhang, and Y. Zhu, Neurocomputing 136, 321 (2014).10.1016/j.neucom.2013.12.028Suche in Google Scholar
[5] K. Mathiyalagan, R. Sakthivel, and H. Su, Can. J. Phys. 92, 976 (2014).10.1139/cjp-2013-0146Suche in Google Scholar
[6] R. Sakthivel, K. Mathiyalagan, S. Lakshmanan, and Ju H. Park, Nonlinear Dynam. 74, 1297 (2013).10.1007/s11071-013-1041-2Suche in Google Scholar
[7] V. Vembarasan, P. Balasubramaniam, K. Ratnavelu, and N. Kumaresan, Phys. Scripta 86, 065003 (2012).10.1088/0031-8949/86/06/065003Suche in Google Scholar
[8] W. Wang, S. Zhong, F. Liu, and J. Cheng, Int. J. Robust. Nonlin. 24, 2574 (2014).10.1002/rnc.3011Suche in Google Scholar
[9] Y. Wang, X. Zhang, and Z. Hu, Neurocomputing 166, 346 (2015).10.1016/j.neucom.2015.04.061Suche in Google Scholar
[10] Y. Zhu, Q. Zhang, Z. Wei, and L. Zhang, Neurocomputing 110, 44 (2013).10.1016/j.neucom.2012.09.033Suche 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.2015.05.011Suche in Google Scholar
[12] P. Balasubramaniam and L. J. Banu, Neurocomputing 122, 349 (2013).10.1016/j.neucom.2013.06.015Suche in Google Scholar
[13] S. He and F. Liu, Math. Comput. Simulat. 92, 1 (2013).Suche in Google Scholar
[14] H. Shen, Z. G. Wu, and J. H. Park, Int. J. Robust. Nonlin. 25, 3231 (2015).10.1002/rnc.3255Suche in Google Scholar
[15] M. K. Song, J. B. Park, and Y. H. Joo, Fuzzy Set. Syst. 277, 81 (2015).10.1016/j.fss.2015.02.004Suche in Google Scholar
[16] S. He, Neurocomputing 168, 348 (2015).10.1016/j.neucom.2015.05.091Suche in Google Scholar
[17] R. Sakthivel, M. Joby, K. Mathiyalagan, and S. Santra, J. Frankl. Inst. 352, 4446 (2015).Suche in Google Scholar
[18] W. Qi and X. Gao, Appl. Math. Lett. 46, 111 (2015).10.1016/j.aml.2015.02.016Suche in Google Scholar
[19] Z. Chen, Q. Huang, and Z. Liu, Appl. Math. Comput. 258, 138 (2015).10.1016/j.amc.2015.01.065Suche in Google Scholar
[20] Y. Ma and H. Chen, Appl. Math. Comput. 268, 897 (2015).10.1016/j.amc.2015.06.067Suche 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/s11071-013-0932-6Suche in Google Scholar
[22] R. Sakthivel, S. Selvi, K. Mathiyalagan, and P. Shi, IEEE Trans. Cybern. 45, 2720 (2015).10.1109/TCYB.2014.2382563Suche in Google Scholar
[23] J. L. Wang, H. N. Wu, and T. Huang, Automatica 56, 105 (2015).10.1016/j.automatica.2015.03.027Suche in Google Scholar
[24] L. Li and J. Jian, Neurocomputing 168, 276 (2015).10.1016/j.neucom.2015.05.098Suche in Google Scholar
[25] V. Vembarasan, G. Nagamani, P. Balasubramaniam, and J. H. Park, Math. Biosci. 244, 165 (2013).10.1016/j.mbs.2013.05.003Suche in Google Scholar
[26] B. Zheng, S. Xu, and J. Lam, Neurocomputing 142, 299 (2014).10.1016/j.neucom.2014.04.031Suche in Google Scholar
[27] G. X. Zhong and G. H. Yang, J. Process Contr. 32, 16 (2015).10.1016/j.jprocont.2015.04.013Suche in Google Scholar
[28] Y. Du, S. Zhong, J. Xu, and N. Zhou, ISA Trans. 56, 1 (2015).10.1016/j.isatra.2014.11.005Suche in Google Scholar
[29] Q. Song, Z. Zhao, and J. Yang, Neurocomputing 122, 330 (2013).10.1016/j.neucom.2013.06.018Suche in Google Scholar
[30] L. Lee, Y. Liu, J. Liang, and X. Cai, ISA Trans. 57, 172 (2015).10.1016/j.isatra.2015.02.001Suche in Google Scholar
[31] H. Shen, J. H. Park, and Z. G. Wu, Nonlinear Dynam. 77, 1709 (2014).10.1007/s11071-014-1412-3Suche in Google Scholar
[32] L. Wang, Y. Shen, and Z. Ding, Neural Networks 70, 74 (2015).10.1016/j.neunet.2015.07.008Suche 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.2015.05.006Suche in Google Scholar PubMed
[34] Z. Zhang, Z. Zhang, and H. Zhang, J. Frankl. Inst. 352, 1296 (2015).10.1016/j.jfranklin.2014.12.022Suche in Google Scholar
[35] J. Cheng, H. Zhu, S. Zhong, Y. Zhang, and Y. Li, Int. J. Syst. Sci. 46, 1080 (2015).10.1080/00207721.2013.808716Suche in Google Scholar
[36] Y. Zhang, P. Shi, and S. K. Nguang, Appl. Math. Lett. 38, 115 (2014).10.1016/j.aml.2014.07.010Suche in Google Scholar
[37] P. G. Park, J. W. Ko, and C. Jeong, Automatica 47, 235 (2011).10.1016/j.automatica.2010.10.014Suche in Google Scholar
[38] N. Jiang, X. Liu, W. Yu, and J. Shen, Neurocomputing 167, 314 (2015).10.1016/j.neucom.2015.04.064Suche in Google Scholar
[39] L. Yin, J. Appl. Math. 2014, 730292 (2014).Suche in Google Scholar
[40] M. B. Elowitz and S. Leibler, Nature 403, 335 (2000).10.1038/35002125Suche in Google Scholar PubMed
©2016 by De Gruyter
Artikel in diesem Heft
- Frontmatter
- Robust Finite-Time Passivity for Discrete-Time Genetic Regulatory Networks with Markovian Jumping Parameters
- Multi-Soliton Solutions of the Generalized Sawada–Kotera Equation
- Electrical Conduction in Transition-Metal Salts
- Importance of Unit Cells in Accurate Evaluation of the Characteristics of Graphene
- Understanding the Formation Mechanism of Two-Dimensional Atomic Islands on Crystal Surfaces by the Condensing Potential Model
- The Thermodynamic Functions in Curved Space of Neutron Star
- Spanning Trees of the Generalised Union Jack Lattice
- Prolongation Structure of a Generalised Inhomogeneous Gardner Equation in Plasmas and Fluids
- Negative Energies in the Dirac Equation
- Residual Symmetry and Explicit Soliton–Cnoidal Wave Interaction Solutions of the (2+1)-Dimensional KdV–mKdV Equation
- Multifold Darboux Transformations of the Extended Bigraded Toda Hierarchy
- Unidirectional Excitation of Graphene Plasmon in Attenuated Total Reflection (ATR) Configuration
- Completed Optimised Structure of Threonine Molecule by Fuzzy Logic Modelling
Artikel in diesem Heft
- Frontmatter
- Robust Finite-Time Passivity for Discrete-Time Genetic Regulatory Networks with Markovian Jumping Parameters
- Multi-Soliton Solutions of the Generalized Sawada–Kotera Equation
- Electrical Conduction in Transition-Metal Salts
- Importance of Unit Cells in Accurate Evaluation of the Characteristics of Graphene
- Understanding the Formation Mechanism of Two-Dimensional Atomic Islands on Crystal Surfaces by the Condensing Potential Model
- The Thermodynamic Functions in Curved Space of Neutron Star
- Spanning Trees of the Generalised Union Jack Lattice
- Prolongation Structure of a Generalised Inhomogeneous Gardner Equation in Plasmas and Fluids
- Negative Energies in the Dirac Equation
- Residual Symmetry and Explicit Soliton–Cnoidal Wave Interaction Solutions of the (2+1)-Dimensional KdV–mKdV Equation
- Multifold Darboux Transformations of the Extended Bigraded Toda Hierarchy
- Unidirectional Excitation of Graphene Plasmon in Attenuated Total Reflection (ATR) Configuration
- Completed Optimised Structure of Threonine Molecule by Fuzzy Logic Modelling