Abstract
Using recurrent high order neural networks for identification, a new scheme for pinning control of complex networks with changing unknown coupling strengths is proposed for achieving synchronization. The robust behavior of the control system is investigated via simulations.
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Edgar N. Sanchez was born in 1949, in Sardinata, Colombia, South America. He obtained the BSEE, major in Power Systems, from Universidad Industrial de Santander (UIS), Bucaramanga, Colombia in 1971, the MSEE from CINVESTAV-IPN (Advanced Studies and Research Center of the National Polytechnic Institute), major in Automatic Control, Mexico City, Mexico, in 1974 and the Docteur Ingenieur degree in Automatic Control from Institut Nationale Polytechnique de Grenoble, France in 1980. In 1971, 1972, 1975 and 1976, he worked for different Electrical Engineering consulting companies in Bogota, Colombia. In 1974 he was professor of Electrical Engineering Department of UIS, Colombia. From January 1981 to November 1990, he worked as a researcher at the Electrical Research Institute, Cuernavaca, Mexico. He was a professor of the graduate program in Electrical Engineering of the Universidad Autonoma de Nuevo Leon (UANL), Monterrey, Mexico, from December 1990 to December 1996. Since January 1997, he has been with CINVESTAV-IPN, Guadalajara Campus, Mexico, as a Professor of Electrical Engineering graduate programs. His research interest center in Neural Networks and Fuzzy Logic as applied to Automatic Control systems. He has been the advisor of 18 Ph.D. thesis and 40 M.Sc Thesis. He was granted an USA National Research Council Award as a research associate at NASA Langley Research Center, Hampton, Virginia, USA (January 1985 to March 1987). He is also member of the Mexican National Research System (promoted to highest rank, III, in 2005), the Mexican Academy of Science and the Mexican Academy of Engineering. He has published 4 books, more than 150 technical papers in international journals and conferences, and has served as reviewer for different international journals and conferences. He has also been member of many international conferences IPCs, both IEEE and IFAC ones.
Guanrong Chen has been a Chair Professor and the Director of the Centre for Chaos and Complex Networks at the City University of Hong Kong since year 2000, prior to that he was a tenured Full Professor at the University of Houston, Texas, USA. He was elected IEEE Fellow in 1997, awarded the 2011 Euler Gold Medal, Russia, and conferred Honorary Doctorate by the Saint Petersburg State University, Russia in 2011 and by the University of Le Havre, France in 2014. He is a Member of the Academia of Europe and a Fellow of The World Academy of Sciences, and is a Highly Cited Researcher in Engineering as well as in Mathematics according to Thomson Reuters.
David I. Rodriguez-Castellanos received his Ph.D. in Electrical Engineering at the Cinvestav unidad Guadalajara, Mexico in 2016, where his work was oriented to complex network control and optimization. He also graduates from M.Sc. in Electronic and Computer Engineering at University of Guadalajara, Mexico, in 2011. His research interests include nonlinear dynamical systems, complex networks, chaos, biological systems, inverse optimal control and neural network control.
Riemann Ruiz-Cruz was born in Oaxaca, Oaxaca, Mexico, in 1983. He earned the BSEE from Instituto Tecnologico de Oaxaca, Oaxaca, Mexico, in 2006; and the MSEE and D.Sc. on EE from the Advanced Studies and Research Center of the National Polytechnic Institute (CINVESTAV-IPN), Guadalajara campus, Mexico, in 2009 and 2013, respectively. Since August 2013, he has been with Instituto Tecnológico y de Estudios Superiores de Occidente (ITESO), Guadalajara, Jalisco, Mexico. He is also a member of the Mexican National Research System (Rank C). His research interests center on neural control, block control, inverse optimal control, and discrete-time sliding modes, and their applications to electrical machines and power systems.
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Sanchez, E.N., Rodriguez-Castellanos, D.I., Chen, G. et al. Pinning control of complex network synchronization: A recurrent neural network approach. Int. J. Control Autom. Syst. 15, 1405–1414 (2017). https://doi.org/10.1007/s12555-016-0364-4
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DOI: https://doi.org/10.1007/s12555-016-0364-4