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Tytuł artykułu

Chaos synchronization of uncertain coronary artery systems through sliding mode

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The chaotic phenomena of coronary artery systems are hazardous to health and may induce illness development. From the perspective of engineering, the potential harm can be eliminated by synchronizing chaotic coronary artery systems with a normal one. This paper investigates the chaos synchronization problem in light of the methodology of sliding mode control (SMC). Firstly, the nonlinear dynamics of coronary artery systems are presented. Since the coronary artery systems suffer from uncertainties, the technique of derivative-integral terminal SMC is employed to achieve the chaos synchronization task. The stability of such a control system is proven in the sense of Lyapunov. To verify the feasibility and effectiveness of the proposed method, some simulation results are illustrated in comparison with a benchmark.
Rocznik
Strony
455--462
Opis fizyczny
Bibliogr. 28 poz., wykr.
Twórcy
autor
  • School of Control and Computer Engineering, North China Electric Power University, Beijing, 102206, P.R. China
  • State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, P.R. China
autor
autor
  • College of Robotics, Beijing Union University, Beijing, 100101, P.R. China
Bibliografia
  • [1] G.K. Hansson, “Mechanisms of disease – Inflammation, atherosclerosis, and coronary artery disease”, N. Engl. J. Med. 352 (16), 1685–1695 (2005).
  • [2] K. Ozaki and T. Tanaka, “Molecular genetics of coronary artery disease”, J. Hum. Genet. 61 (1), 71–77 (2016).
  • [3] G.W. He and D.P. Taggart, “Spasm in arterial grafts in coronary artery bypass grafting surgery”, Ann. Thorac. Surg. 101 (3), 1222–1229 (2016).
  • [4] T. Schauer, N.O. Negard, F. Previdi, H.J. Hunt, M.H. Fraser, E. Ferchland, and J. Raisch, “Online identification and nonlinear control of the electrically stimulated quadriceps muscle”, Control Eng. Practice 13 (9), 1207–1219 (2005).
  • [5] W.L. Li, “Tracking control of chaotic coronary artery system”, Int. J. Syst. Sci. 43 (1), 21–30 (2012).
  • [6] L.M. Pecora and T.L. Carroll, “Synchronization in chaotic systems”, Phys. Rev. Lett. 64 (8), 821–824 (1990).
  • [7] M. Rafikov and J.M. Balthazar, “On control and synchronization in chaotic and hyperchaotic systems via linear feedback control”, Commun. Nonlinear Sci. Numer. Simul. 13, 1246–1255 (2008).
  • [8] Y.G. Yu, H.X. Li, and J. Duan, “Chaos synchronization of a unified chaotic system via partial linearization”, Chaos Solitons Fractals 41 (1), 457–463 (2009).
  • [9] D. Ghosh and A.R. Chowdhury, “Nonlinear observer-based impulsive synchronization in chaotic systems with multiple attractors”, Nonlinear Dyn. 60 (4), 607–613 (2010).
  • [10] A. Chithra and I.R. Mohamed, “Synchronization and chaotic communication in nonlinear circuits with nonlinear coupling”, J. Comput. Electron. 16 (3), 833–844 (2017).
  • [11] W.S. Wu, Z.S. Zhao, J. Zhang, and L.K. Sun, “State feedback synchronization control of coronary artery chaos system with interval time-varying delay”, Nonlinear Dyn. 87 (3), 1773–1783 (2017).
  • [12] C. Gong, Y. Li, and X. Sun, “Backstepping control of synchronization for biomathematical model of muscular blood vessel”, J. Applied Sci. 24 (6), 604–607 (2006).
  • [13] V.I. Utkin, Sliding modes in control and optimization, 2nd edn., Springer-Verlag, Berlin, 1992.
  • [14] D.W. Qian, S.W. Tong, H. Liu, and X.J. Liu, “Load frequency control by neural-network-based integral sliding mode for nonlinear power systems with wind turbines”, Neurocomputing 173, 875–885 (2016).
  • [15] D.W. Qian, S.W. Tong, and C.D. Li, “Observer-based leaderfollowing formation control of uncertain multiple agents by integral sliding mode”, Bull. Pol. Ac.: Tech. 65 (1), 35–44 (2017).
  • [16] D.W. Qian, C.D. Li, S.G. Lee, and C. Ma, “Robust Formation Maneuvers through Sliding Mode for Multi-agent Systems with Uncertainties”, IEEE/CAA Journal of Automatica Sinica 5 (1), 342–351 (2018).
  • [17] J. Yang, M. Dou, and D. Zhao, “Iterative sliding mode observer for sensorless control of five-phase permanent magnet synchronous motor”, Bull. Pol. Ac.: Tech. 65 (6), 845– 857 (2017).
  • [18] Y. Wang, M. Sun, S. Du, and Z. Chen, “Comparative investigations of nonlinear and linear observers for a highly manoeuvrable target in sliding mode guidance”, Bull. Pol. Ac.: Tech. 65 (2), 233–245 (2017).
  • [19] C.J. Lin, S.K. Yang, and H.T. Yau, “Chaos suppression control of a coronary artery system with uncertainties by using variable structure control”, Comput. Math. Appl. 64 (5), 988–995 (2012).
  • [20] Z.S. Zhao, X.M. Li, J. Zhang, and Y.Z. Pei, “Terminal sliding mode control with self-tuning for coronary artery system synchronization”, Int. J. Biomath. 10 (3) (2017), DOI: 10.1142/ S1793524517500413.
  • [21] A.M. Zou, K.D. Kumar, Z.G. Hou, and X. Liu, “Finite-Time attitude tracking control for spacecraft using terminal sliding mode and Chebyshev neural network”, IEEE Trans. Syst. Man Cybern. Part B-Cybern. 41 (4), 950–963 (2011).
  • [22] C.D. Li, Z.X. Ding, J.Q. Yi, Y.S. Lv, and G.Q. Zhang, “Deep belief network based hybrid model for building energy consumption prediction”, Energies 11 (1), 2018, DOI: 10.3390/en11010242.
  • [23] C.S. Chiu and C.T. Shen, “Finite-time control of DC-DC buck converters via integral terminal sliding modes”, Int. J. Electron. 99 (5), 643–655 (2012).
  • [24] Z.S. Zhao, J. Zhang, G. Ding, and D.K. Zhang, “Chaos synchronization of coronary artery system based on higher order sliding mode adaptive control”, Acta Phys. Sin. 64 (21), (2015), DOI: 10.7498/aps.64.210508.
  • [25] C.S. Chiu, “Derivative and integral terminal sliding mode control for a class of MIMO nonlinear systems”, Automatica 48 (2), 316– 326 (2012).
  • [26] C.D. Li, Z.X. Ding, D.W. Qian, and Y.S. Lv, “Data-driven design of the extended fuzzy neural network having linguistic outputs”, J. Intell. Fuzzy Syst. 34 (1), 349–360 (2018).
  • [27] C.D. Li, J.L. Gao, J.Q. Yi, and G. Zhang, “Analysis and design of functionally weighted single-input-rule-modules connected fuzzy inference systems”, IEEE Trans. Fuzzy Syst. 26 (1), 56–71 (2018).
  • [28] C.D. Li, Z. Ding, D.B. Zhao, J.Q. Yi, and G.Q. Zhang, “Building energy consumption prediction: an extreme deep learning approach”, Energies 10 (10), 1–20 (2017).
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-709270e4-0a7f-4697-8c94-e77b9050ba66
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