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Quasi-synchronization of fractional-order complex networks with random coupling via quantized control |
Hongwei Zhang(张红伟), Ran Cheng(程然), and Dawei Ding(丁大为)† |
School of Electronics and Information Engineering, Anhui University, Hefei 230601, China |
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Abstract We investigate the quasi-synchronization of fractional-order complex networks (FCNs) with random coupling via quantized control. Firstly, based on the logarithmic quantizer theory and the Lyapunov stability theory, a new quantized feedback controller, which can make all nodes of complex networks quasi-synchronization and eliminate the disturbance of random coupling in the system state, is designed under non-delay conditions. Secondly, we extend the theoretical results under non-delay conditions to time-varying delay conditions and design another form of quantization feedback controller to ensure that the network achieves quasi-synchronization. Furthermore, the error bound of quasi-synchronization is obtained. Finally, we verify the accuracy of our results using two numerical simulation examples.
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Received: 18 April 2023
Revised: 26 July 2023
Accepted manuscript online: 08 August 2023
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PACS:
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05.45.Xt
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(Synchronization; coupled oscillators)
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02.30.Yy
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(Control theory)
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45.10.Hj
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(Perturbation and fractional calculus methods)
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Fund: The work was supported by the Anhui Provincial Development and Reform Commission New Energy Vehicles and Intelligent Connected Automobile Industry Technology Innovation Project. |
Corresponding Authors:
Dawei Ding
E-mail: dwding@ahu.edu.cn
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Cite this article:
Hongwei Zhang(张红伟), Ran Cheng(程然), and Dawei Ding(丁大为) Quasi-synchronization of fractional-order complex networks with random coupling via quantized control 2023 Chin. Phys. B 32 110501
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[1] Albert R and Barabasi A 2002 Rev. Mod. Phys. 74 47 [2] Philipp H, Aline V and Philipp L 2018 J. Nonlinear Sci. 30 2259 [3] Li X R, Liu J Q and Dong J C2021 IEEE Trans. Circuits Syst. I:Regular Papers 68 4268 [4] Huang L L, Zhang Y, Xiang J and Liu J 2022IEEE Trans. Circuits Syst. I!I:Express Briefs 69 4568 [5] Liu Y Y and Slotine J J 2011 Nature 473 167 [6] Rosvall M and Bergstrom C T 2008 Proc. Natl. Acad. Sci. USA 105 1118 [7] Wang L and Wang P 2015 Int. J. Mod. Phys. C 26 1550052 [8] Russo G 2018 Int. J. Robust Nonlinear Control 28 120 [9] Shakibian H and Charkari N M 2016 2016 24th Iranian Conference on Electrical Engineering (ICEE), May 10-12, 2016, Shiraz, Iran, pp. 682-686 [10] Huang L L, Li W Y, Xiang J H and Zhu G L 2022 Eur. Phys. J. Spec. Top. 231 3109 [11] Yang F, Li H, Guo C, Xia D and Qi H 2018 Neural Comput. Appl. 31 7945 [12] Ding X S, Cao J D and Alsaadi F E 2019 Int. J. Adaptive Control Signal Process. 33 1478 [13] Mwanandiye E S, Wu B and Jia Q 2020 Neural Comput. Appl. 32 11277 [14] Ding D W, Yan J, Wang N and Liang D 2017 Chaos, Solitons Fractals 104 41 [15] He W L, Qian F, Lam J, Chen G and Han Q L 2015 Automatica 62 249 [16] Xu L G, Chu X Y and Hu H X 2021 Math. Comput. Simul. 185 594 [17] Yang S A, Yu J, Cheng H and Jiang H J 2018 Neural Networks 104 104 [18] Machado J T, Kiryakova V and Mainardi F 2011Commun. Nonlinear Sci. Numerical Simul. 16 1140 [19] Aguila C and Duarte M A 2014 Commun. Nonlinear Sci. Numerical Simul. 19 2951 [20] Hartley T T, Lorenzo C F and Qammer H K 1995 IEEE Trans. Circuits Syst. I:Fundam. Theory Appl. 42 485 [21] Saadatmandi A and Dehghan M 2010 Comput. Math. Appl. 59 1326 [22] Sun H G, Zhang Y, Baleanu D, Chen W and Chen Y G 2018 Commun. Nonlinear Sci. Numerical Simul. 64 213 [23] Magin R L 2010 Comput. Math. Appl. 59 1586 [24] Ding D W, Yao X L and Zhang H W 2019 Mod. Phys. Lett. B 33 1950351 [25] Bao H B, Park J H and Cao J D 2021 IEEE Trans. Neural Networks Learning Syst. 32 3230 [26] Xu Y, Wang Q, Li W X and Feng J Q 2021 Math. Methods Appl. Sci. 69 1539 [27] He J J, Chen H, Ge M F and Ding T F 2021 Neurocomputing 431 90 [28] Hu J T, Sui G X and Li X D 2020 Chaos, Solitons Fractals 140 110216 [29] Li C G and Chen G R 2004 Physica A 343 263 [30] Wu Y Q and Liu L 2015 Entropy 17 3097 [31] Hang L H and Wu X R 2019 Neural Process. Lett. 50 2373 [32] Li W Y, Zhou J, Li J Q and Xie T 2021 IEEE Trans. Circuits Syst. I!$I:Express Briefs 68 1338 [33] Zhang L L, Zhong J and Lu J 2021 Neural Networks 144 11 [34] Zhang Q J, Luo J and Li W 2013 Nonlinear Dyn. 71 353 [35] Brockett R and Liberzon D 2000 IEEE Trans. Automat. Control 45 1279 [36] Niu Y G and Daniel W H 2014 Automatica 50 2665 [37] Shi Y J and Ma Y 2021 Electron. Res. Arch. 29 2047 [38] Xu C, Yang X S, Lu J Q and Feng J W 2018 IEEE Trans. Cybernet. 48 3021 |
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