Pinning control of scale-free dynamical networks

By Xiaofan Wang, Guanrong Chen. 2002

992 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3bfe871c1a44ef283a287b58828e81a54c133cab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Guanrong Chen
2. Wenwu Yu
3. Jinde Cao
4. Jürgen Kurths
5. Jinhu Lu
6. Zidong Wang
7. Frank L. Lewis
8. Tianping Chen

In Statistical and Nonlinear Physics

1. Guanrong Chen
2. Wenwu Yu
3. Jinde Cao
4. Jürgen Kurths
5. Jinhu Lu
6. Zidong Wang
7. Tianping Chen
8. Xiwei Liu

Citations in the corpus

• Synchronization of Coupled Reaction-Diffusion Neural Networks with Time-Varying Delays via Pinning-Impulsive Controller (2013, methodology): “Therefore, we can utilize an existing method concerning the state feedback pinning control strategy or adaptive pinning control strategy to determine which nodes should be controlled first, for instance, the methods in [50] and [51].”
• Synchronization via Pinning Control on General Complex Networks (2013, methodology): “To reduce the number of controlled vertices, some local feedback injections may be applied to a fraction of network vertices, which is known as pinning control [7, 9, 12, 15, 16, 17, 25, 26, 32, 33, 34, 38, 42, 43, 45, 46, 54, 62, 63].”
• On pinning synchronization of complex dynamical networks (2009, methodology): “Therefore, a large amount of work has been devoted to the study of synchronization in various largescale complex networks (Cao, Li, & Wang, 2006; W. Lu & T. Chen, 2004; Wang & Cao, 2006; Wang & Chen, 2002a,b; Yu, Cao, & Lü, 2008).”
• Control Centrality and Hierarchical Structure in Complex Networks (2012, background): “While these principles have significantly enriched our understanding of many networks that affect our lives, our ultimate goal is to develop the capability to control them [5–17].”
• Synchronization Control for Nonlinear Stochastic Dynamical Networks: Pinning Impulsive Strategy (2012, background): “Hence, in many cases, controllers are necessary to be designed to force the unpredicted final synchronous state into a certain required objective trajectory [18]–[22].”
• Distributed Cooperative Optimal Control for Multiagent Systems on Directed Graphs: An Inverse Optimal Approach (2015, background): “This problem is also known as cooperative tracking problem [20], consensus to a leader [21], model reference consensus [22], pinning control [23], or synchronized tracking control [24], [25].”
• Cooperative Adaptive Fuzzy Tracking Control for Networked Unknown Nonlinear Multiagent Systems With Time-Varying Actuator Faults (2014, background): “This problem is known as leader-following consensus [10], synchronization to a leader [11], model reference consensus [12], leader-following control [13], or pinning control [14].”
• Control Principles of Complex Networks (2015, background): “…δi = 1 for pinned nodes and 0 otherwise, and ui(t) = σ[pi(s(t))− pi(xi(t))] (100) is the d-dimensional linear feedback controller (Li et al., 2004; Wang and Chen, 2002a), pi(x(t)) is the pinning function that controls the input of node i, and s(t) is the desired synchronization…”
• Pinning Complex Networks by a Single Controller (2006, background): “Remark 4: In [20], the authors pinned 50 Chen’s oscillators coupled by a coupled small-world network by adding two controllers with control gain .”
• Multi-agent differential graphical games (2011, background): “On the other hand, the problem of cooperative tracking requires that all nodes synchronize to a leader or control node (Hong, Hu, & Gao, 2006; Li, Wang, & Chen, 2004; Ren, Moore, & Chen, 2007; Wang & Chen, 2002).”
• Consensus of second‐order multi‐agent systems with exogenous disturbances (2011, background): “Since it is much more effective to pin some most highly connected nodes than some common nodes in a large-scale network [26, 27], we choose two nodes with high degrees pinned.”
• Adaptive consensus with a virtual leader of multiple agents governed by locally Lipschitz nonlinearity (2013, methodology): “By applying local feedback control to a small fraction of network nodes, synchronization of a scale-free nonlinear dynamic network is investigated by virtue of pinning control in [16].”