Exponential Consensus of Multiagent Systems With Lipschitz Nonlinearities Using Sampled-Data Information

2018 ◽  
Vol 65 (12) ◽  
pp. 4363-4375 ◽  
Author(s):  
Junjie Fu ◽  
Guanghui Wen ◽  
Wenwu Yu ◽  
Tingwen Huang ◽  
Jinde Cao
2020 ◽  
Vol 50 (12) ◽  
pp. 5189-5200 ◽  
Author(s):  
Xin Wang ◽  
Hui Wang ◽  
Chuandong Li ◽  
Tingwen Huang ◽  
Jurgen Kurths

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Hong Xia ◽  
Ting-Zhu Huang ◽  
Jin-Liang Shao ◽  
Jun-Yan Yu

This paper considers a group consensus problem with a dynamic leader for multiagent systems in a sampled-data setting. With the leader’s state available to only a fraction of the followers, a distributed linear protocol based on sampled-data control is proposed for group consensus under fixed directed topology. On basis ofM-matrix theory, we derive a sufficient condition on the sampling period and the control parameter for ultimate boundedness of the tracking errors. Furthermore, simulation examples are provided to demonstrate the effectiveness of the theoretical results.


2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Niu Jie ◽  
Li Zhong

This paper studies the sampled-data based consensus of multiagent system with general linear time-invariant dynamics. It focuses on looking for a maximum allowable sampling period bound such that as long as the sampling period is less than this bound, there always exist linear consensus protocols solving the consensus problem. Both fixed and randomly switching topologies are considered. For systems under fixed topology, a necessary and sufficient sampling period bound is obtained for single-input multiagent systems, and a sufficient allowable bound is proposed for multi-input systems by solving theH∞optimal control problem of certain system with uncertainty. For systems under randomly switching topologies, tree-type and complete broadcasting network with Bernoulli packet losses are discussed, and explicit allowable sampling period bounds are, respectively, given based on the unstable eigenvalues of agent’s system matrix and packet loss probability. Numerical examples are given to illustrate the results.


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