Abstract
In this article, the notion of pinning control for directed networks of dynamical systems is introduced, where the nodes could be either single-input single-output (SISO) or multi-input multi-output (MIMO) dynamical systems, and could be non-identical and nonlinear in general but will be specified to be identical linear time-invariant (LTI) systems here in the study of network controllability. Both state and structural controllability problems will be discussed, illustrating how the network topology, node-system dynamics, external control inputs and inner dynamical interactions altogether affect the controllability of a general complex network of LTI systems, with necessary and sufficient conditions presented for both SISO and MIMO settings. To that end, the controllability of a special temporally switching directed network of linear time-varying (LTV) node systems will be addressed, leaving some more general networks and challenging issues to the end for research outlook.
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Acknowledgment
The author thanks Mario di Bernardo, Jian-Xi Gao, Bao- Yu Hou, Xiang Li, Yang-Yu Liu, LinWang, Xiao-FanWang, Lin-Ying Xiang and Gang Yan for their valuable comments and discussions.
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Recommended by Associate Editor Guo-Ping Liu
Guanrong Chen is a chair professor and the director of the Centre for Chaos and Complex Networks at the City University of Hong Kong, China. He was elected a Fellow of the IEEE in 1997, awarded the 2011 Euler Gold Medal from Russia, and conferred Honorary Doctor Degrees by the Saint Petersburg State University, Russia in 2011 and by the University of Normandy, France in 2014. He is a Highly Cited Researcher in Engineering (since 2009), in Physics (2014) and also in Mathematics (2015) according to Thomson Reuters. He is a member of the Academy of Europe (2014) and a Fellow of TheWorld Academy of Sciences (2015).
His research interests include complex systems and networks with regard to their modeling, dynamics and control.
ORCID iD: 0000-0003-1381-7418
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Chen, G. Pinning control and controllability of complex dynamical networks. Int. J. Autom. Comput. 14, 1–9 (2017). https://doi.org/10.1007/s11633-016-1052-9
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DOI: https://doi.org/10.1007/s11633-016-1052-9