2018/06/18 by Yang Pan, Pan Yang, Zhongpu Xu +2 · 7 citations
Computer Science · Engineering · Mathematics · Medicine · Physics and Astronomy · #Adaptive control #Artificial intelligence #Collective behavior #Complex network #Computer science #Control (management) #Control engineering #Control theory (sociology) #Dynamical systems theory #Engineering #Epidemic model #Feedback control #Lyapunov function #Lyapunov stability #Mathematical and Theoretical Epidemiology and Ecology Models #Neural Networks Stability and Synchronization #Nonlinear system #Opinion Dynamics and Social Influence #Physics #Population #Stability (learning theory) #Synchronization (alternating current) #Telecommunications #math.CA #math.OC #msc:34C60 #msc:34D20 #msc:34D23 #physics.soc-ph
paper · pdf · doi:10.1063/1.5047653
published in Chaos An Interdisciplinary Journal of Nonlinear Science 29(3), 033122 (American Institute of Physics) · 22 pages, 8 figures
arxiv created 2018/06/18 · openalex publication_date 2019/03/01 · arxiv updated 2019/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This paper investigates feedback pinning control of synchronization behaviors aroused by epidemic spread on complex networks. Based on the quenched mean field theory, epidemic control synchronization models with the inhibition of contact behavior are constructed, combined with the epidemic transmission system and the adaptive dynamical network carrying active controllers. By the properties of convex functions and the Gerschgorin theorem, the epidemic threshold of the model is obtained, and the global stability of disease-free equilibrium is analyzed. For individual's infected situation, when an epidemic disease spreads, two types of feedback control strategies depending on the diseases' information are designed: the first one only adds controllers to infected individuals, and the other adds controllers to both infected and susceptible ones. By using the Lyapunov stability theory, under designed controllers, some criteria that guarantee the epidemic controlled synchronization system achieving behavior synchronization are also derived. Several numerical simulations are performed to show the effectiveness of our theoretical results. As far as we know, this is the first work to address the controlled behavioral synchronization induced by epidemic spread under the pinning feedback mechanism. It is hopeful that we may have deeper insights into the essence between the disease's spread and collective behavior under active control in complex dynamical networks.