Knowledge Commons of Institute of Automation,CAS
Sampled-Data Based Mean Square Bipartite Consensus of Double-Integrator Multi-Agent Systems with Measurement Noises | |
Liu, Yifa1,2; Cheng, Long1,2 | |
2018-10 | |
会议名称 | 2018 Chinese Intelligent Systems Conference |
会议日期 | 2018.10.13-14 |
会议地点 | Wenzhou |
会议举办国 | China |
摘要 | A 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. |
关键词 | Bipartite consensus Multi-agent systems Measurement noises Double-integrator Sampled-data |
收录类别 | EI |
是否为代表性论文 | 否 |
七大方向——子方向分类 | 人工智能基础理论 |
国重实验室规划方向分类 | 小样本高噪声数据学习 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/23117 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Cheng, Long |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Liu, Yifa,Cheng, Long. Sampled-Data Based Mean Square Bipartite Consensus of Double-Integrator Multi-Agent Systems with Measurement Noises[C],2018. |
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