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Fixed time synchronization of delayed chaotic neural networks by using active adaptive control. (English) Zbl 1543.34083

Summary: This article aims to investigate the fixed time synchronization of a class of chaotic neural systems by way of adaptive control method. Using Lyapunov stability theory, a new fixed time stability theorem which plays an important role on the synchronization scheme is presented at first. Then, combining the fixed time stability theorem and adaptive control technique, an adaptive control scheme has been developed to achieve the fixed time synchronization of chaotic neural systems. The proposed controllers assure the global convergence of the error dynamics in fixed-time based on the Lyapunov stability theory. Furthermore, the proposed control strategy cannot only provide a fast convergence rate, but also afford a bounded convergence time which is unrelated to the initial values and easy to work out by using the simple time calculation formula. Finally, numerical simulations are presented by taking a typical two-order chaotic neural system as an example to verify and demonstrate the effectiveness of the proposed scheme.
{© 2021 John Wiley & Sons Ltd.}

MSC:

34H10 Chaos control for problems involving ordinary differential equations
34D06 Synchronization of solutions to ordinary differential equations
93C40 Adaptive control/observation systems
Full Text: DOI

References:

[1] YuWW, CaoJD. Synchronization of switched system and application in communication. Phys Lett A. 2008;372:4438‐4445. · Zbl 1221.94096
[2] UpadhyayRK, RaiV. Complex dynamics and synchronization in two non‐identical chaotic ecological systems. Chaos Soliton Fract. 2009;40:2233‐2241. · Zbl 1198.37133
[3] SalariehH, AlastyA. Chaos control in an economic model via minimum entropy strategy. Chaos Soliton Fract. 2009;40:839‐847. · Zbl 1197.91141
[4] LuoRZ, ZengYH. The control of chaotic systems with unknown parameters and external disturbance via backstepping‐like scheme. Complexity. 2016;21:573‐583.
[5] LuoRZ. The robust adaptive control of chaotic systems with unknown parameters and external disturbance via a scalar input. Int J Adapt Control Signal Process. 2015;29:1296‐1307. · Zbl 1330.93074
[6] DadrasS, MomeniHR, MajdVJ. Sliding mode control for uncertain new chaotic dynamical system. Chaos Soliton Fract. 2009;41:1857‐1862. · Zbl 1198.34114
[7] WeiZM, LuoRZ. Impulsive control and modified projective synchronization of the hyper‐chaotic lorenz system. Math Pract Theory. 2011;41:163‐172.
[8] LiXD, PengDX, CaoJD. Lyapunov stability for impulsive systems via event‐triggered impulsive control. IEEE Trans Autom Control. 2020;65:4908‐4913. https://doi.org/10.1109/TAC.2020.2964558 · Zbl 1536.93638 · doi:10.1109/TAC.2020.2964558
[9] LiXD, YangXY, CaoJD. Event‐triggered impulsive control for nonlinear delay systems. Automatica. 2020;117:108981. · Zbl 1441.93179
[10] YuJY, LeiJW, WangLL. Backstepping synchronization of chaotic system based on equivalent transfer function method. Optik. 2017;130:900‐913.
[11] SuHP, LuoRZ, ZengYH. The observer‐based synchronization and parameter estimation of a class of chaotic system via a single output. Pramana‐J Phys. 2017;89:78.
[12] TabasiM, BalochianS. Synchronization of the chaotic fractional‐order Genesio ‐ Tesi systems using the adaptive sliding mode fractional‐order controller. J Control Automat Electr Syst. 2018;29:15‐21.
[13] LuoRZ, HuangMC, SuHP. Robust control and synchronization of 3‐D uncertain fractional‐order chaotic systems with external disturbances via adding one power integrator control. Complexity. 2019;2019:1‐11. · Zbl 1417.34149
[14] LuHT. Chaotic attractors in delayed neural networks. Phys Lett A. 2002;298:109‐116. · Zbl 0995.92004
[15] LouXY, CuiBT. Robust adaptive synchronization of chaotic neural networks by slide technique. Chin Phys B. 2008;17:520‐528.
[16] MaQ, XuSY, ZouY, ShiGD. Synchronization of stochastic chaotic neural networks with reaction‐diffusion terms. Nonlinear Dyn. 2012;67:2183‐2196. · Zbl 1243.93106
[17] ZhangYJ, HanQL. Network‐based synchronization of delayed neural networks. IEEE Trans Circuits Syst. 2013;60:676‐689. · Zbl 1468.94011
[18] ZengHB, TeoKL, HeY, XuHL, WangW. Sampled‐data synchronization control for chaotic neural networks subject to actuator saturation. Neurocomputing. 2017;260:25‐31.
[19] YeQ, JiangZX, ChenTN. Adaptive feedback control for synchronization of chaotic neural systems with parameter mismatches. Complexity. 2018;2018:1‐8. · Zbl 1405.93141
[20] LiY, HouB. Observer‐based sliding mode synchronization for a class of fractional‐order chaotic neural networks. Adv Differ Equ. 2018;2018:146. · Zbl 1446.93031
[21] ZhangWL, YangSJ, LiCD, ZhangW, YangXS. Stochastic exponential synchronization of memristive neural networks with time‐varying delays via quantized control. Neural Netw. 2018;104:93‐103. · Zbl 1441.93334
[22] AhmadI, ShafiqM, ShahzadM. Robust finite‐time global synchronization of chaotic systems with different orders. Optik‐Int J Light Electron Opt. 2016;127:8172‐8185.
[23] AhmadI, ShafiqM, ShahzadM. Global finite‐time multi‐switching synchronization of externally perturbed chaotic oscillators. J Circuits Syst Signal Process. 2018;37:5253‐5278.
[24] AhmadI, ShafiqM. A generalized analytical approach for the synchronization of multiple chaotic systems in the finite time. Arab J Sci Eng. 2020;45:2297‐2315.
[25] AhmadI, ShafiqM. Oscillation free robust adaptive synchronization of chaotic systems with parametric uncertainties. Trans Inst Meas Control. 2020;42(11):1977‐1996.
[26] HeW, XueCQ, YuXB, LiZJ, YangCG. Admittance‐based controller design for physical human‐robot interaction in the constrained task space. IEEE Trans Autom Sci Eng. 2020;17(4):1937‐1949.
[27] GaoHJ, HeW, ZhangYM, SunCY. Adaptive finite‐time fault‐tolerant control for uncertain flexible flapping wings based on rigid finite element method. IEEE Trans Cybern. 2020;1‐12. https://doi.org/10.1109/TCYB.2020.3045786 · doi:10.1109/TCYB.2020.3045786
[28] PolyakovA. Nonlinear feedback design for fixed‐time stabilization of linear control systems. IEEE Trans Autom Control. 2012;57:2106‐2110. · Zbl 1369.93128
[29] M. X.Liu, J.Wu and Y. Z.Sun, Fixed‐time stability analysis of permanent magnet synchronous motors with novel adaptive control, Math Probl Eng Volume 2017, 2017 Article ID 4903963, 11 pages. · Zbl 1426.93283
[30] C. Y.Ma, F.Wang, Z. J.Li, et al, Adaptive fixed‐time fast terminal sliding mode control for chaotic oscillation in power system, Math Probl Eng Volume 2018, 2018 Article ID 5819428, 10 pages. · Zbl 1427.93212
[31] ZhangWL, YangXS, LiCD. Fixed‐time stochastic synchronization of complex networks via continuous control. IEEE Trans Cybern. 2019;49:3099‐3104.
[32] LiuXG, LiaoXF. Fixed‐time H_∞ control for port‐controlled Hamiltonian systems. IEEE Trans Autom Control. 2019;64:2753‐2765. · Zbl 1482.93188
[33] C.He, J.Wu , J. Y.Dai , Z.Zhang, L. B.Xu and P. W.Li, Approximation‐based fixed‐time adaptive tracking control for a class of uncertain nonlinear pure‐feedback systems, Complexity2020, 2020, Article ID 4205914, 1-17. · Zbl 1435.93073
[34] AoWG, MaTD, SanchezeRV, GanfHT. Finite‐time and fixed‐time impulsive synchronization of chaotic systems. J Frankl Inst. 2020;357:11545‐11557. · Zbl 1450.93052
[35] WeiYF, QingX, XieCR, XuYH. Fixed‐time synchronization of the new single‐parameter chaotic system. Complexity. 2020;2020:1067863 8 pages. · Zbl 1445.37030
[36] PoznyakAS, SanchezEN. Nonlinear systems approximation by neural networks: error stability analysis. Intell Automat Soft Compt Int J. 1995;01:247‐258.
[37] ZhangC, GuoQ, WangJ. Finite‐time synchronizing control for chaotic neural networks. Abstr Appl Anal. 2014;2014:938612 9pages. · Zbl 1406.93184
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