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Guanghui Wen
Researcher at Southeast University
Publications - 367
Citations - 14454
Guanghui Wen is an academic researcher from Southeast University. The author has contributed to research in topics: Consensus & Computer science. The author has an hindex of 54, co-authored 312 publications receiving 9957 citations. Previous affiliations of Guanghui Wen include RMIT University & Nanjing University of Aeronautics and Astronautics.
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Designing Fully Distributed Consensus Protocols for Linear Multi-Agent Systems With Directed Graphs
TL;DR: A distributed adaptive consensus protocol is designed to achieve leader-follower consensus in the presence of a leader with a zero input for any communication graph containing a directed spanning tree with the leader as the root node.
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Consensus Tracking of Multi-Agent Systems With Lipschitz-Type Node Dynamics and Switching Topologies
TL;DR: It is proved that consensus tracking in the closed-loop multi-agent systems with a fixed topology having a directed spanning tree can be achieved if the feedback gain matrix and the coupling strength are suitably selected.
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Containment of Higher-Order Multi-Leader Multi-Agent Systems: A Dynamic Output Approach
TL;DR: A new class of distributed observer-type containment protocols based only on the relative output measurements of the neighboring agents is proposed, removing the impractical assumption in some of the existing approaches that the observers embedded in the multiple dynamic agents have to share information with their neighbors.
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Consensus of multi-agent systems with nonlinear dynamics and sampled-data information: a delayed-input approach
TL;DR: Theoretical analysis on this time-delayed system shows that consensus with asymptotic time-varying velocities in a strongly connected network can be achieved over some suitable sampled-data intervals.
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Consensus in multi-agent systems with communication constraints
TL;DR: It is proved that second-order consensus can be reached if the general algebraic connectivity of the communication topology is larger than a threshold value and the mobile agents communicate with their neighbors frequently enough as the network evolves.