In this article, a novel method is proposed to investigate the $ H_{\infty} $ state observer design problem for genetic regulatory networks with time-varying delays and disturbances. First, the structure of the desired state observer is constructed, and the parameterized bounds of the error system solutions are given. Then, a bounded real lemma is established to provide the existence of the state observer and ensure that the error system is globally exponentially stable at an $ H_{\infty} $ performance level. Third, based on the obtained bounded real lemma, the explicit expressions of the $ H_{\infty} $ state observer can be obtained by solving several matrix inequalities. The effectiveness of the proposed novel observer design method is illustrated via a numerical example.
Citation: Xue Zhang, Yu Xue. A novel $ H_{\infty} $ state observer design method for genetic regulatory networks with time-varying delays[J]. AIMS Mathematics, 2024, 9(2): 3763-3787. doi: 10.3934/math.2024185
In this article, a novel method is proposed to investigate the $ H_{\infty} $ state observer design problem for genetic regulatory networks with time-varying delays and disturbances. First, the structure of the desired state observer is constructed, and the parameterized bounds of the error system solutions are given. Then, a bounded real lemma is established to provide the existence of the state observer and ensure that the error system is globally exponentially stable at an $ H_{\infty} $ performance level. Third, based on the obtained bounded real lemma, the explicit expressions of the $ H_{\infty} $ state observer can be obtained by solving several matrix inequalities. The effectiveness of the proposed novel observer design method is illustrated via a numerical example.
[1] | F. L. Ren, J. D. Cao, Asymptotic and robust stability of genetic regulatory networks with time-varying delays, Neurocomputing, 71 (2008), 834–842. https://doi.org/10.1016/j.neucom.2007.03.011 doi: 10.1016/j.neucom.2007.03.011 |
[2] | J. D. Cao, F. L. Ren, Exponential stability of discrete-time genetic regulatory networks with delays, IEEE Trans. Neural Netw., 19 (2008), 520–523. https://doi.org/10.1109/TNN.2007.911748 doi: 10.1109/TNN.2007.911748 |
[3] | X. Y. Lou, Q. Ye, B. T. Cui, Exponential stability of genetic regulatory networks with random delays, Neurocomputing, 73 (2010), 759–769. https://doi.org/10.1016/j.neucom.2009.10.006 doi: 10.1016/j.neucom.2009.10.006 |
[4] | Y. He, J. Zeng, M. Wu, C. K. Zhang, Robust stabilization and $H_{\infty}$ controllers design for stochastic genetic regulatory networks with time-varying delays and structured uncertainties, Math. Biosci., 236 (2012), 53–63. https://doi.org/10.1016/j.mbs.2012.01.005 doi: 10.1016/j.mbs.2012.01.005 |
[5] | J. Q. Hu, J. L. Liang, J. D. Cao, Stability analysis for genetic regulatory networks with delays: The continuous-time case and the discrete-time case, Appl. Math. Comput., 220 (2013), 507–517. https://doi.org/10.1016/j.amc.2013.06.003 doi: 10.1016/j.amc.2013.06.003 |
[6] | L. Wang, Z. P. Luo, H. L. Yang, J. D. Cao, Stability of genetic regulatory networks based on switched systems and mixed time-delays, Math. Biosci., 278 (2016), 94–99. https://doi.org/10.1016/j.mbs.2016.06.004 doi: 10.1016/j.mbs.2016.06.004 |
[7] | H. M. Jiao, L. P. Zhang, Q. K. Shen, J. W. Zhu, P. Shi, Robust gene circuit control design for time-delayed genetic regulatory networks without SUM regulatory logic, IEEE ACM T. Comput. Bi., 15 (2018), 2086–2093. https://doi.org/10.1109/TCBB.2018.2825445 doi: 10.1109/TCBB.2018.2825445 |
[8] | H. M. Jiao, M. Shi, Q. K. Shen, J. W. Zhu, P. Shi, Filter design with adaptation to time-delay parameters for genetic regulatory networks, IEEE ACM T. Comput. Bi., 15 (2018), 323–329. https://doi.org/10.1109/TCBB.2016.2606430 doi: 10.1109/TCBB.2016.2606430 |
[9] | X. B. Wan, Z. D. Wang, Q. L. Han, M. Wu, Finite-time $H_{\infty}$ state estimation for discrete time-delayed genetic regulatory networks under stochastic communication protocols, IEEE T. Circuits-I, 65 (2018), 3481–3491. https://doi.org/10.1109/TCSI.2018.2815269 doi: 10.1109/TCSI.2018.2815269 |
[10] | D. Y. Chen, W. L. Chen, J. Hu, H. J. Liu, Variance-constrained filtering for discrete-time genetic regulatory networks with state delay and random measurement delay, Int. J. Syst. Sci., 50 (2019), 231–243. https://doi.org/10.1080/00207721.2018.1542045 doi: 10.1080/00207721.2018.1542045 |
[11] | L. N. Zhang, X. Y. Zhang, Y. Xue, X. Zhang, New method to global exponential stability analysis for switched genetic regulatory networks with mixed delays, IEEE T. Nanobiosci., 19 (2020), 308–314. https://doi.org/10.1109/TNB.2020.2971548 doi: 10.1109/TNB.2020.2971548 |
[12] | Y. Xue, L. N. Zhang, X. Zhang, Reachable set estimation for genetic regulatory networks with time-varying delays and bounded disturbances, Neurocomputing, 403 (2020), 203–210. https://doi.org/10.1016/j.neucom.2020.03.113 doi: 10.1016/j.neucom.2020.03.113 |
[13] | H. Shen, Y. Z. Men, J. D. Cao, J. H. Park, $H_{\infty}$ filtering for fuzzy jumping genetic regulatory networks with round-robin protocol: A hidden-Markov-model-based approach, IEEE T. Fuzzy Syst., 28 (2020) 112–121. https://doi.org/10.1109/TFUZZ.2019.2939965 |
[14] | Q. Wang, H. Wei, Z. W. Long, A non-reduced order approach to stability analysis of delayed inertial genetic regulatory networks, J. Exp. Theor. Artif. In., 33 (2021), 227–237. https://doi.org/10.1080/0952813X.2020.1735531 doi: 10.1080/0952813X.2020.1735531 |
[15] | S. S. Xiao, Z. S. Wang, Stability analysis of genetic regulatory networks via a linear parameterization approach, Complex Intell. Syst., 8 (2022), 743–752. https://doi.org/10.1007/s40747-020-00245-1 doi: 10.1007/s40747-020-00245-1 |
[16] | Y. Xue, C. Liu, X. Zhang, State bounding description and reachable set estimation for discrete-time genetic regulatory networks with time-varying delays and bounded disturbances, IEEE T. Syst, Man, Cy-S., 52 (2022), 6652–6661. https://doi.org/10.1109/TSMC.2022.3148715 doi: 10.1109/TSMC.2022.3148715 |
[17] | J. J. Chen, P. Jiang, B. S. Chen, Z. G. Zeng, Global stability of delayed genetic regulatory networks with wider hill functions: A mixing monotone semiflows approach, Neurocomputing, 526 (2023), 39–47. https://doi.org/10.1016/j.neucom.2023.01.057 doi: 10.1016/j.neucom.2023.01.057 |
[18] | J. L. Liang, J. Lam, Z. D. Wang, State estimation for Markov-type genetic regulatory networks with delays and uncertain mode transition rates, Phys. Lett. A, 373 (2009), 4328–4337. https://doi.org/10.1016/j.physleta.2009.09.055 doi: 10.1016/j.physleta.2009.09.055 |
[19] | B. Lv, J. L. Liang, J. D. Cao, Robust distributed state estimation for genetic regulatory networks with Markovian jumping parameters, Commun. Nonlinear Sci., 16 (2011), 4060–4078. https://doi.org/10.1016/j.cnsns.2011.02.009 doi: 10.1016/j.cnsns.2011.02.009 |
[20] | J. L. Liu, E. G. Tian, Z. Gu, Y. Y. Zhang, State estimation for Markovian jumping genetic regulatory networks with random delays, Commun. Nonlinear Sci., 19 (2014), 2479–2492. https://doi.org/10.1016/j.cnsns.2013.11.002 doi: 10.1016/j.cnsns.2013.11.002 |
[21] | Q. Li, B. Shen, Y. R. Liu, F. E. Alsaadi, Event-triggered state estimation for discrete-time stochastic genetic regulatory networks with Markovian jumping parameters and time-varying delays, Neurocomputing, 174 (2016), 912–920. https://doi.org/10.1016/j.neucom.2015.10.017 doi: 10.1016/j.neucom.2015.10.017 |
[22] | L. P. Tian, V. Palgat, F. X. Wu, M-matrix-based state observer design for genetic regulatory networks with mixed delays, IEEE T. Circuits-II., 65 (2018), 1054–1058. https://doi.org/10.1109/TCSII.2017.275130 doi: 10.1109/TCSII.2017.275130 |
[23] | X. B. Wan, Z. D. Wang, M. Wu, X. H. Liu, State estimation for discrete time-delayed genetic regulatory networks with stochastic noises under the round-robin protocols, IEEE T. Nanobiosci., 17 (2018), 145–154. https://doi.org/10.1109/TNB.2018.2797124 doi: 10.1109/TNB.2018.2797124 |
[24] | X. Zhang, Y. Y. Han, L. G. Wu, Y. T. Wang, State estimation for delayed genetic regulatory networks with reaction-diffusion terms, IEEE T. Neur. Net. Lear., 29 (2018), 299–309. https://doi.org/10.1109/TNNLS.2016.2618899 doi: 10.1109/TNNLS.2016.2618899 |
[25] | X. Zhang, X. F. Fan, L. G. Wu, Reduced- and full-order observers for delayed genetic regulatory networks, IEEE T. Cybernetics, 48 (2018), 1989–2000. https://doi.org/10.1109/TCYB.2017.2726015 doi: 10.1109/TCYB.2017.2726015 |
[26] | R. Manivannan, J. D. Cao, K. T. Chong, Generalized dissipativity state estimation for genetic regulatory networks with interval time-delay signals and leakage delays, Commun. Nonlinear Sci., 89 (2020), 105326. https://doi.org/10.1016/j.cnsns.2020.105326 doi: 10.1016/j.cnsns.2020.105326 |
[27] | X. N. Song, X. R. Li, S. Song, C. K. Ahn, State observer design of coupled genetic regulatory networks with reaction-diffusion terms via time-space sampled-data communications, IEEE ACM T. Comput. Bi., 19 (2022), 3704–3714. https://doi.org/10.1109/TCBB.2021.3114405 doi: 10.1109/TCBB.2021.3114405 |
[28] | X. B. Wan, L. Xu, H. J. Fang, G. Ling, Robust non-fragile $H_{\infty}$ state estimation for discrete-time genetic regulatory networks with Markov jump delays and uncertain transition probabilities, Neurocomputing, 154 (2015), 162–173. https://doi.org/10.1016/j.neucom.2014.12.008 doi: 10.1016/j.neucom.2014.12.008 |
[29] | Q. Li, B. Shen, Y. R. Liu, F. E. Alsaadi, Event-triggered $H_{\infty}$ state estimation for discrete-time stochastic genetic regulatory networks with Markovian jumping parameters and time-varying delays, Neurocomputing, 174 (2016), 912–920. https://doi.org/10.1016/j.neucom.2015.10.017 doi: 10.1016/j.neucom.2015.10.017 |
[30] | W. L. Chen, D. Y. Chen, J. Hu, J. L. Liang, A. M. Dobaie, A sampled-data approach to robust $H_{\infty}$ state estimation for genetic regulatory networks with random delays, Int. J. Control Autom. Syst., 16 (2018), 491–504. https://doi.org/10.1007/s12555-017-0106-2 doi: 10.1007/s12555-017-0106-2 |
[31] | Z. G. Huang, J. W. Xia, J. Wang, Y. L. Wei, Z. Wang, J. Wang, Mixed $H_{\infty}{/}l_{2}-l_{\infty}$ state estimation for switched genetic regulatory networks subject to packet dropouts: A persistent dwell-time switching mechanism, Appl. Math. Comput., 355 (2019), 198–212. https://doi.org/10.1016/j.amc.2019.02.081 doi: 10.1016/j.amc.2019.02.081 |
[32] | H. Shen, S. C. Huo, H. C. Yan, J. H. Park, V. Sreeram, Distributed dissipative state estimation for Markov jump genetic regulatory networks subject to round-robin scheduling, IEEE T. Neur. Net. Lear., 31 (2020), 762–771. https://doi.org/10.1109/TNNLS.2019.2909747 doi: 10.1109/TNNLS.2019.2909747 |
[33] | L. Sun, J. Wang, X. Y. Chen, K. B. Shi, H. Shen, $H_\infty$ fuzzy state estimation for delayed genetic regulatory networks with random gain fluctuations and reaction-diffusion, J. Franklin I., 358 (2021), 8694–8714. https://doi.org/10.1016/j.jfranklin.2021.08.047 doi: 10.1016/j.jfranklin.2021.08.047 |
[34] | F. E. Alsaadi, Y. R. Liu, N. S. Alharbi, Design of robust $H_{\infty}$ state estimator for delayed polytopic uncertain genetic regulatory networks: Dealing with finite-time boundedness, Neurocomputing, 497 (2022), 170–181. https://doi.org/10.1016/j.neucom.2022.05.018 doi: 10.1016/j.neucom.2022.05.018 |
[35] | J. Wang, H. T. Wang, H. Shen, B. Wang, J. H. Park, Finite-time $H_{\infty}$ state estimation for PDT-switched genetic regulatory networks with randomly occurring uncertainties, IEEE ACM T. Comput. Bi., 19 (2022), 1651–1660. https://doi.org/10.1109/TCBB.2020.3040979 doi: 10.1109/TCBB.2020.3040979 |
[36] | Z. H. Ye, D. Zhang, C. Deng, H. C. Yan, G. Feng, Finite-time resilient sliding mode control of nonlinear UMV systems subject to DOS attacks, Automatica, 156 (2023), 111170. https://doi.org/10.1016/j.automatica.2023.111170 doi: 10.1016/j.automatica.2023.111170 |
[37] | A. B. Israel, T. N. E. Greville, Generalized inverses: Theorey and applications, New York, 1974. |