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Tytuł artykułu

Robust hybrid synchronization control of chaotic 3-cell CNN with uncertain parameters using smooth super twisting algorithm

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents the control design framework for the hybrid synchronization (HS) and parameter identification of the 3-Cell Cellular Neural Network. The cellular neural network (CNN) of this kind has increasing practical importance but due to its strong chaotic behavior and the presence of uncertain parameters make it difficult to design a smooth control framework. Sliding mode control (SMC) is very helpful for this kind of environment where the systems are nonlinear and have uncertain parameters and bounded disturbances. However, conventional SMC offers a dangerous chattering phenomenon, which is not acceptable in this scenario. To get chattering-free control, smooth higher-order SMC formulated on the smooth super twisting algorithm (SSTA) is proposed in this article. The stability of the sliding surface is ensured by the Lyapunov stability theory. The convergence of the error system to zero yields hybrid synchronization and the unknown parameters are computed adaptively. Finally, the results of the proposed control technique are compared with the adaptive integral sliding mode control (AISMC). Numerical simulation results validate the performance of the proposed algorithm.
Rocznik
Strony
art. no. e146474
Opis fizyczny
Bibliogr. 27 poz., rys.
Twórcy
  • University of Gujrat, Gujrat, Pakistan
autor
  • Capital University of Science and Technology, Islamabad, Pakistan
autor
  • University of Lahore, Lahore, Pakistan
autor
  • University of Gujrat, Gujrat, Pakistan
  • University of Lahore, Lahore, Pakistan
Bibliografia
  • [1] L.O. Chua and L. Yang, “Cellular neural networks: Theory,” IEEE Trans. Circuits Syst., vol. 35, no. 10, pp. 1257–1272, 1988.
  • [2] J.M. Zurada, “Analog implementation of neural networks,” IEEE Circuits Devices, vol. 8, no. 5, pp. 36–41, 1992.
  • [3] G. Liñán Cembrano, Á.B. Rodríguez Vázquez, R. Carmona Galán, F.J. Jiménez Garrido, S.C. Espejo Meana, and R. Domínguez Castro, “A 1000 fps at 128×128 vision processor with 8-bit digitized i/o,” IEEE J. Solid-State Circuits, vol. 37, no. 7, pp. 1044–1055, 2004.
  • [4] P. Arena, A. Basile, M. Bucolo, and L. Fortuna, “An object oriented segmentation on analog cnn chip,” IEEE Trans. Circuits I-Fundam. Theor. Appl., vol. 50, no. 7, pp. 837–846, 2003.
  • [5] E. Tlelo-Cuautle, A.M. González-Zapata, J.D. Díaz-Muñoz, L.G. de la Fraga, and I. Cruz-Vega, “Optimization of fractional-order chaotic cellular neural networks by metaheuristics,” Eur. Phys. J.-Spec. Top., vol. 231, pp. 2037–2043, 2022.
  • [6] C. Xiu, R. Zhou, and Y. Liu, “New chaotic memristive cellular neural network and its application in secure communication system,” Chaos Solitons Fractals, vol. 141, p. 110316, 2020.
  • [7] B. Belean, “Active contours driven by cellular neural networks for image segmentation in biomedical applications,” Stud. Inform. Control, vol. 30, no. 3, pp. 109–120, 2021.
  • [8] X. Meng, Z. Wu, C. Gao, B. Jiang, and H.R. Karimi, “Finite-time projective synchronization control of variable-order fractional chaotic systems via sliding mode approach,” IEEE Trans. Circuits Syst. II- Express Briefs, vol. 68, no. 7, pp. 2503–2507, 2021.
  • [9] F. Aliabadi, M.-H. Majidi, and S. Khorashadizadeh, “Chaos synchronization using adaptive quantum neural networks and its application in secure communication and cryptography,” Neural Comput. Appl., vol. 34, no. 8, pp. 6521–6533, 2022.
  • [10] H. Su, R. Luo, M. Huang, and J. Fu, “Practical fixed time active control scheme for synchronization of a class of chaotic neural systems with external disturbances,” Chaos Solitons Fractals, vol. 157, p. 111917, 2022.
  • [11] I. Ahmad, “A lyapunov-based direct adaptive controller for the suppression and synchronization of a perturbed nuclear spin generator chaotic system,” Appl. Math. Comput., vol. 395, p. 125858, 2021.
  • [12] D. Qian, Y. Xi, and S. Tong, “Chaos synchronization of uncertain coronary artery systems through sliding mode,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 67, no. 3, pp. 455–462, 2019.
  • [13] N. Siddique and F.U. Rehman, “Parameter identification and hybrid synchronization in an array of coupled chaotic systems with ring connection: An adaptive integral sliding mode approach,” Math. Probl. Eng., vol. 2018, p. 6581493, 2018.
  • [14] M. Liu, H. Jiang, C. Hu, Z. Yu, and Z. Li, “Pinning synchronization of complex delayed dynamical networks via generalized intermittent adaptive control strategy,” Int. J. Robust Nonlinear Control, vol. 30, no. 1, pp. 421–442, 2020.
  • [15] G.M. Mahmoud, T. Aboelenen, T.M. Abed-Elhameed, and A.A. Farghaly, “On boundedness and projective synchronization of distributed order neural networks,” Appl. Math. Comput., vol. 404, p. 126198, 2021.
  • [16] G.M. Mahmoud, M.E. Ahmed, and T.M. Abed-Elhameed, “On fractional-order hyperchaotic complex systems and their generalized function projective combination synchronization,” Optik, vol. 130, pp. 398–406, 2017.
  • [17] J. Sun, Z. Shan, P. Liu, and Y. Wang, “Backstepping synchronization control for three-dimensional chaotic oscillatory system via dna strand displacement,” IEEE Trans. Nanobiosci., vol. 22, no. 3, pp. 511–522, 2023.
  • [18] M. Yuan, X. Luo, X. Mao, Z. Han, L. Sun, and P. Zhu, “Event-triggered hybrid impulsive control on lag synchronization of delayed memristor-based bidirectional associative memory neural networks for image hiding,” Chaos Solitons Fractals, vol. 161, p. 112311, 2022.
  • [19] X. Zhao, J. Liu, F. Zhang, and C. Jiang, “Complex generalized synchronization of complex-variable chaotic systems,” Eur. Phys. J.-Spec. Top., vol. 230, no. 7, pp. 2035–2041, 2021.
  • [20] X. Mao et al., “Instability of optical phase synchronization between chaotic semiconductor lasers,” Opt. Lett., vol. 46, no. 12, pp. 2824–2827, 2021.
  • [21] R. Guo and Y. Qi, “Partial anti-synchronization in a class of chaotic and hyper-chaotic systems,” IEEE Access, vol. 9, pp. 46 303–46 312, 2021.
  • [22] S.Z. Mirrezapour, A. Zare, and M. Hallaji, “A new fractional sliding mode controller based on nonlinear fractional-order proportional integral derivative controller structure to synchronize fractional-order chaotic systems with uncertainty and disturbances,” J. Vib. Control, vol. 28, no. 7-8, pp. 773–785, 2022.
  • [23] N. Siddique and F.U. Rehman, “Hybrid synchronization and parameter estimation of a complex chaotic network of permanent magnet synchronous motors using adaptive integral sliding mode control,” Bull. Pol. Acad. Sci. Tech. Sci., p. e137056, 2021.
  • [24] V. Utkin, J. Guldner, and J. Shi, Sliding mode control in electromechanical systems. CRC press, 2017.
  • [25] R. Xu and Ü. Özgüner, “Sliding mode control of a class of under-actuated systems [j],” Automatica, vol. 44, no. 1, pp. 233–241, 2008.
  • [26] P. Arena, R. Caponetto, L. Fortuna, and D. Porto, “Bifurcation and chaos in noninteger order cellular neural networks,” Int. J. Bifurcation Chaos, vol. 8, no. 7, pp. 1527–1539, 1998.
  • [27] G. Manganaro, P. Arena, and L. Fortuna, Cellular neural networks: chaos, complexity and VLSI processing. Springer Science & Business Media, 2012, vol. 1.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e55be183-4848-4a5e-be75-aa8fb6dae250
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