Consensus analysis and design for high-order linear swarm systems with time-varying delays

2011 ◽  
Vol 390 (23-24) ◽  
pp. 4114-4123 ◽  
Author(s):  
Jianxiang Xi ◽  
Zongying Shi ◽  
Yisheng Zhong
2014 ◽  
Vol 8 (18) ◽  
pp. 2162-2170 ◽  
Author(s):  
Xiwang Dong ◽  
Zongying Shi ◽  
Geng Lu ◽  
Yisheng Zhong

2015 ◽  
Vol 298 ◽  
pp. 36-52 ◽  
Author(s):  
Xiwang Dong ◽  
Zongying Shi ◽  
Geng Lu ◽  
Yisheng Zhong

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Fangcui Jiang

This paper focuses on the consensus problem for high-order multiagent systems (MAS) with directed network and asymmetric time-varying time-delays. It is proved that the high-order multiagent system can reach consensus when the network topology contains a spanning tree and time-delay is bounded. The main contribution of this paper is that a Lyapunov-like design framework for the explicit selection of protocol parameters is provided. The Lyapunov-like design guarantees the robust consensus of the high-order multiagent system with respect to asymmetric time-delays and is independent of the exact knowledge of the topology when the communication linkages among agents are undirected and connected.


Author(s):  
Jianxiang Xi ◽  
Zongying Shi ◽  
Yisheng Zhong

By using dynamic output feedback consensus protocols, consensus analysis, and design, problems for swarm systems with external disturbances and time-varying delays are dealt with. First, two subspaces, namely, a consensus subspace and a complement consensus subspace, are defined. Based on the state projection onto the two subspaces, L2-consensus and L2-consensualization problems are introduced. Then, a necessary and sufficient condition for consensus is presented and an explicit expression of the consensus function is given. Especially, it is shown that the time-varying delay does not influence the consensus function. Finally, in terms of linear matrix inequalities, sufficient conditions for L2-consensus and L2-consensualization are presented, respectively, which possess less calculation complexity, since they are independent of the number of agents, and numerical simulations are shown to demonstrate theoretical results.


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