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Finite-time synchronization of intermittently controlled reaction-diffusion systems with delays: a weighted LKF method. (English) Zbl 1527.35160

Summary: Considering the fact that existing methodologies for finite-time control are difficult to simultaneously overcome the difficulties induced by the effects of reaction-diffusion and time delay when intermittent control is confronted, this paper explores a novel Lyapunov-Krasovskii functional (LKF) method to investigate the finite-time synchronization of delayed reaction-diffusion systems. By designing a simple intermittent control and a weighted LKF, a general finite-time stability criterion is established first. Then, sufficient conditions for the finite-time synchronization of the interested system are given, where the weight factor of the LKF has a heavy influence on the settling time. Several important corollaries are also given to specify the usefulness and generality of the weighted LKF method and the finite-time stability criterion. Finally, a numerical example is provided to verify the new findings, and an image encryption algorithm is presented to validate the useful application of theoretical results.

MSC:

35K57 Reaction-diffusion equations
35K51 Initial-boundary value problems for second-order parabolic systems
93B52 Feedback control
Full Text: DOI

References:

[1] Lacitignola, D.; Bozzini, B.; Frittelli, M.; Sgura, I., Turing pattern formation on the sphere for a morphochemical reaction-diffusion model for electrodeposition. Commun Nonlinear Sci Numer Simul, 484-508 (2017) · Zbl 1510.92034
[2] Maslovskaya, A.; Moroz, L.; Chebotarev, A. Y.; Kovtanyuk, A. E., Theoretical and numerical analysis of the landau-khalatnikov model of ferroelectric hysteresis. Commun Nonlinear Sci Numer Simul (2021) · Zbl 1457.82448
[3] Chen, W. H.; Luo, S.; Zheng, W. X., Impulsive synchronization of reaction-diffusion neural networks with mixed delays and its application to image encryption. IEEE Trans Neural Netw Learn Syst, 12, 2696-2710 (2016)
[4] Shanmugam, L.; Mani, P.; Rajan, R.; Joo, Y. H., Adaptive synchronization of reaction-diffusion neural networks and its application to secure communication. IEEE Trans Cybern, 3, 911-922 (2018)
[5] Wang, L.; He, B.; Zeng, Z., Global synchronization of fuzzy memristive neural networks with discrete and distributed delays. IEEE Trans Fuzzy Syst, 9, 2022-2034 (2020)
[6] Hu, C.; Yu, J.; Jiang, H.; Teng, Z., Exponential synchronization for reaction-diffusion networks with mixed delays in terms of p-norm via intermittent driving. Neural Netw, 1-11 (2012) · Zbl 1245.93122
[7] Chen, W. H.; Liu, L.; Lu, X., Intermittent synchronization of reaction-diffusion neural networks with mixed delays via razumikhin technique. Nonlinear Dynam, 1, 535-551 (2017) · Zbl 1371.34071
[8] Wu, T.; Xiong, L.; Cao, J.; Park, J. H.; Cheng, J., Synchronization of coupled reaction-diffusion stochastic neural networks with time-varying delay via delay-dependent impulsive pinning control algorithm. Commun Nonlinear Sci Numer Simul (2021) · Zbl 1467.93160
[9] Yang, X.; Song, Q.; Cao, J.; Lu, J., Synchronization of coupled Markovian reaction-diffusion neural networks with proportional delays via quantized control. IEEE Trans Neural Netw Learn Syst, 3, 951-958 (2018)
[10] Lhachemi, H.; Shorten, R., Boundary feedback stabilization of a reaction-diffusion equation with robin boundary conditions and state-delay. Automatica (2020) · Zbl 1440.93202
[11] Lhachemi, H.; Malik, A.; Shorten, R., Integral action for setpoint regulation control of a reaction-diffusion equation in the presence of a state delay. Automatica (2021) · Zbl 1478.93204
[12] Wang, L.; Zhang, C., Exponential synchronization of memristor-based competitive neural networks with reaction-diffusions and infinite distributed delays. IEEE Trans Neural Netw Learn Syst (2022)
[13] Haimo, V. T., Finite time controllers. SIAM J Control Optim, 4, 760-770 (1986) · Zbl 0603.93005
[14] Wang, J. L.; Zhang, X. X.; Wu, H. N.; Huang, T.; Wang, Q., Finite-time passivity and synchronization of coupled reaction-diffusion neural networks with multiple weights. IEEE Trans Cybern, 9, 3385-3397 (2018)
[15] Liu, X.; Wei, Y., Finite-time synchronization under aperiodically intermittent control and its application on spatially coupled reaction-diffusion neural networks, 1-7
[16] Qiu, Q.; Su, H., Finite-time output synchronization of multiple weighted reaction-diffusion neural networks with adaptive output couplings. IEEE Trans Neural Netw Learn Syst (2022)
[17] Wang, S.; Guo, Z.; Wen, S.; Huang, T.; Gong, S., Finite/fixed-time synchronization of delayed memristive reaction-diffusion neural networks. Neurocomputing, 1-8 (2020)
[18] Duan, L.; Wang, Q.; Wei, H.; Wang, Z., Multi-type synchronization dynamics of delayed reaction-diffusion recurrent neural networks with discontinuous activations. Neurocomputing, 182-192 (2020)
[19] Wei, R.; Cao, J.; Kurths, J., Fixed-time output synchronization of coupled reaction-diffusion neural networks with delayed output couplings. IEEE Trans Netw Sci Eng, 1, 780-789 (2021)
[20] Duan, L.; Shi, M.; Huang, L., New results on finite-/fixed-time synchronization of delayed diffusive fuzzy HNNs with discontinuous activations. Fuzzy Sets and Systems, 141-151 (2021) · Zbl 1467.93014
[21] Moulay, E.; Dambrine, M.; Yeganefar, N.; Perruquetti, W., Finite-time stability and stabilization of time-delay systems. Systems Control Lett, 7, 561-566 (2008) · Zbl 1140.93447
[22] Efimov, D.; Polyakov, A.; Fridman, E.; Perruquetti, W.; Richard, J. P., Comments on finite-time stability of time-delay systems. Automatica, 7, 1944-1947 (2014) · Zbl 1296.93150
[23] Wang, Q.; Duan, L.; Wei, H.; Wang, L., Finite-time anti-synchronisation of delayed hopfield neural networks with discontinuous activations. Internat J Control, 9, 2398-2405 (2022) · Zbl 1500.93121
[24] Yang, X., Can neural networks with arbitrary delays be finite-timely synchronized?. Neurocomputing, 275-281 (2014)
[25] Tang, R.; Yang, X.; Wan, X.; Zou, Y.; Cheng, Z.; Fardoun, H. M., Finite-time synchronization of nonidentical BAM discontinuous fuzzy neural networks with delays and impulsive effects via non-chattering quantized control. Commun Nonlinear Sci Numer Simul (2019) · Zbl 1476.93143
[26] Yang, X.; Song, Q.; Liang, J.; He, B., Finite-time synchronization of coupled discontinuous neural networks with mixed delays and nonidentical perturbations. J Franklin Inst B, 10, 4382-4406 (2015) · Zbl 1395.93354
[27] Wang, Q.; Fu, B.; Lin, C.; Li, P., Exponential synchronization of chaotic lur’e systems with time-triggered intermittent control. Commun Nonlinear Sci Numer Simul (2022) · Zbl 1536.93734
[28] Hu, X.; Wang, L.; Zhang, C.; Wan, X.; He, Y., Fixed-time stabilization of discontinuous spatiotemporal neural networks with time-varying coefficients via aperiodically switching control. Sci China Inf Sci, 5 (2023)
[29] Duan, L.; Wei, H.; Huang, L., Finite-time synchronization of delayed fuzzy cellular neural networks with discontinuous activations. Fuzzy Sets and Systems, 56-70 (2019) · Zbl 1423.93190
[30] Mei, J.; Jiang, M.; Xu, W.; Wang, B., Finite-time synchronization control of complex dynamical networks with time delay. Commun Nonlinear Sci Numer Simul, 9, 2462-2478 (2013) · Zbl 1311.34157
[31] Li, L.; Tu, Z.; Mei, J.; Jian, J., Finite-time synchronization of complex delayed networks via intermittent control with multiple switched periods. Nonlinear Dynam, 1, 375-388 (2016) · Zbl 1349.93167
[32] Yang, F.; Mei, J.; Wu, Z., Finite-time synchronisation of neural networks with discrete and distributed delays via periodically intermittent memory feedback control. IET Control Theory Appl, 14, 1630-1640 (2016)
[33] Liu, M.; Jiang, H.; Hu, C., Finite-time synchronization of delayed dynamical networks via aperiodically intermittent control. J Franklin Inst B, 13, 5374-5397 (2017) · Zbl 1395.93348
[34] Xu, C.; Yang, X.; Lu, J.; Feng, J.; Alsaadi, F. E.; Hayat, T., Finite-time synchronization of networks via quantized intermittent pinning control. IEEE Trans Cybern, 10, 3021-3027 (2018)
[35] Zhang, D.; Shen, Y.; Mei, J., Finite-time synchronization of multi-layer nonlinear coupled complex networks via intermittent feedback control. Neurocomputing, 15, 129-138 (2017)
[36] Zhou, Y.; Wan, X.; Huang, C.; Yang, X., Finite-time stochastic synchronization of dynamic networks with nonlinear coupling strength via quantized intermittent control. Appl Math Comput (2020) · Zbl 1475.93115
[37] Chen, S.; Song, G.; Zheng, B. C.; Li, T., Finite-time synchronization of coupled reaction-diffusion neural systems via intermittent control. Automatica (2019) · Zbl 1429.92010
[38] Tang, R.; Su, H.; Zou, Y.; Yang, X., Finite-time synchronization of markovian coupled neural networks with delays via intermittent quantized control: Linear programming approach. IEEE Trans Neural Netw Learn Syst, 10, 5268-5278 (2022)
[39] Liu, Y.; Wang, Z.; Yuan, Y.; Liu, W., Event-triggered partial-nodes-based state estimation for delayed complex networks with bounded distributed delays. IEEE Trans Syst Man Cybern: Syst, 6, 1088-1098 (2017)
[40] Kammler, D. W., A first course in fourier analysis (2000), Cambridge University Press
[41] Lu, J.; Cao, J.; Ho, D. W., Adaptive stabilization and synchronization for chaotic lur’e systems with time-varying delay. IEEE Trans Circuits Syst I Regul Pap, 5, 1347-1356 (2008)
[42] Stallings, W.; Tahiliani, M. P., Cryptography and network security: principles and practice, Vol. 6 (2014), Pearson: Pearson London
[43] Tan, X.; Xiang, C.; Cao, J.; Xu, W.; Wen, G.; Rutkowski, L., Synchronization of neural networks via periodic self-triggered impulsive control and its application in image encryption. IEEE Trans Cybern, 8, 8246-8257 (2021)
[44] Tang, R.; Yang, X., Finite-time synchronization of complex networks with intermittent event-triggered control, 1-6
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