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Sampled-Data Based Mean Square Bipartite Consensus of Double-Integrator Multi-Agent Systems with Measurement Noises
Yifa Liu; Long Cheng
2018
Conference Name2018 Chinese Intelligent Systems conference
Conference Date13-14 Oct. 2018
Conference PlaceWenzhou
CountryChina
AbstractA distributed sampled-data based bipartite consensus protocol is proposed for double-integrator multi-agent systems with measurement noises under signed digraph. A time-varying consensus gain and the agents’ states feedback are adopted to counteract the noise effect and achieve bipartite consensus. By determining the state transition matrix of the multi-agent system, we describe the dynamic behaviour of the system. Under the proposed protocol, the states of some agents converge in mean square to one random vector while the rest of agents’ states are convergent to another random vector. It is noted that these two vector are at the same amplitude, however their signs are different. It is proved that sufficient conditions for achieving the mean square bipartite consensus are: (1) the topology graph is weighted balanced, structurally balanced and has a spanning tree; and (2) the time-varying consensus gain satisfies the stochastic approximation conditions. We verify the validity of the proposed protocol by numerical simulations.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23117
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Yifa Liu,Long Cheng. Sampled-Data Based Mean Square Bipartite Consensus of Double-Integrator Multi-Agent Systems with Measurement Noises[C],2018.
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