Abstract
In this paper, finite-time global synchronization control problem for a class of nonlinear coupling Markovian switched complex networks (NCMSCNs) is investigated. Furthermore, according to differentiability of nonlinear coupling function g(x,y), g(x,y) how to affect synchronization dynamics of the class of NCMSCNs is analyzed by two viewpoints. The first is that if g(x,y) satisfies the Lipschitz condition and is derivable, the above question is discussed by taking g(x,y) = L1x+L2y, g(x,y) =–L1x+L2y, g(x,y) = L1x–L2y and g(x,y) =–L1x–L2y, where L1 > 0, L2 > 0. The second is that if nonlinear coupling function g(x,y) only satisfies the Lipschitz condition, by analyzing the differences of synchronization control rules for the class of NCMSCNs and a class of linear coupling Markovian switched complex networks (LCMSCNs), the problem is explored. Comparing the previous works [12,21,22,26,33,34], the main improvement of this paper is that the works of this paper extend the existed analyzing ideas of the finite-time global synchronization for nonlinear coupling complex networks, including NCMSCNs.
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Xin Wang received his M.S. degree in control theory and control engineering from Lanzhou University of Technology, Lanzhou, China, in 2006 and his Ph.D. degree in control theory and control engineering from Donghua University, Shanghai, China, in 2015. During October 2016 and December 2016, January 2017 and April 2017, he was the visiting scholar with the Xavier University of Louisiana, USA and the University of New Orleans, USA, respectively. Currently, he is an Associate Professor at Zhejiang Business Technology Institute, China. His current research interests include synchronization, control of neural networks and coupled system control.
Bin Yang received the B.S. and M.S. degrees in control theory and control engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2004 and Guangxi Teachers Education University, Nanning, China, in 2009, and the Ph.D. degree in control theory and control engineering from Donghua University, Shanghai, China, in 2016, respectively. He is currently a Lecturer with the the School of Mathematics Science, Huaiyin Normal University, China. His current research interests include synchronization, intelligent control, complex networks and optimization algorithm.
Kun Gao received the B.S. degree from Aviation University Air Force, Changchun, China, in 1993, the M.S. degree from Jilin University, Changchun, China, in 2000, and the Ph.D. degree from Donghua University, Shanghai, China, in 2006. He is currently the Director of the Big Data Institute, Zhejiang Business Technology Institute, China. He is leading a large scientific research team in the fields of wise health, smart city, and Internet financial to develop big data related scientific research. His research interests include big data and coupled system control.
Jian-an Fang received the B.S., M.S. and Ph.D. degrees in electrical engineering from Donghua University (China Textile University), Shanghai, China, in 1988, 1991 and 1994 respectively. Subsequently, he joined the College of Information Science and Technology, Donghua University, Shanghai, China, where he became a Dean and Professor in 2001. During February 1998 and May 1998, he was the visiting scholar in the University of Michigan at Ann Arbor. During May 1998 and February 1999, he was the visiting scholar in the University of Maryland at College Park. During May 2005 and August 2005, he was the senior visiting scholar in the University of Southern California. In 2005 and 2006, Prof. Fang was elected as a Council Member of Shanghai Automation Association and a Council Member of Shanghai Microcomputer Applications, respectively. His research interests are mainly in complex system modeling and control, intelligent control systems, chaotic system control and synchronization, and digitalized technique for textile and fashion.
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Wang, X., Yang, B., Gao, K. et al. Finite-time Synchronization Control Relationship Analysis of Two Classes of Markovian Switched Complex Networks. Int. J. Control Autom. Syst. 16, 2845–2858 (2018). https://doi.org/10.1007/s12555-018-0157-z
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DOI: https://doi.org/10.1007/s12555-018-0157-z