Bipartite Consensus for Multi-Agent Systems with Differential Privacy Constraint

Author(s):  
Zhiqiang Zuo ◽  
Ran Tian ◽  
Yijing Wang
IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Jiaju Yu ◽  
Jiashang Yu ◽  
Pengfei Zhang ◽  
TingTing Yang ◽  
Xiurong Chen

2018 ◽  
Vol 2018 ◽  
pp. 1-9
Author(s):  
Cui-Qin Ma ◽  
Yun-Bo Zhao ◽  
Wei-Guo Sun

Event-triggered bipartite consensus of single-integrator multi-agent systems is investigated in the presence of measurement noise. A time-varying gain function is proposed in the event-triggered bipartite consensus protocol to reduce the negative effects of the noise corrupted information processed by the agents. Using the state transition matrix, Ito^ formula, and the algebraic graph theory, necessary and sufficient conditions are given for the proposed protocol to yield mean square bipartite consensus. We find that the weakest communication requirement to ensure the mean square bipartite consensus under event-triggered protocol is that the signed digraph is structurally balanced and contains a spanning tree. Numerical examples validated the theoretical findings where the system shows no Zeno behavior.


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