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第二十六届中国控制会议

作者： Li Tao,Zhang Jifeng Academy of Mathematics and Systems Science,Chinese Academy of Sciences,Beijing 100080,P.R.China

In this paper,sampled-data based average-consensus control is considered for networks consisting of continuous- time first-order integrator agents under a noisy distributed communication *** impact of the sampling size and the number of network nodes on the system performances is *** control input of each agent is based only on the information measured at the sampling instants from its neighborhood rather than the complete continuous process,and the measurement of its neighbors’ states are corrupted by communication *** probability limit theory and the property of graph Laplacian,it is shown that for a connected network,when the sampling size is sufficiently small,the static mean square error between the individual state and the average initial states of all nodes is arbitrarily ***,by choosing properly the consensus gains the almost sure consensus can be *** is worth pointing out that an uncertainty principle of Gaussian networks is obtained,which tells us that in the case of white Gaussian noises,no matter what the sampling size is,the product of the static and transient performance indexes is always equal to or larger than a constant depending on the noise intensity,network topology and the number of network nodes.

关键词： Multi-agent system average consensus stochastic system sampled-data based control distributed stochastic ap- proximation uncertainty principle

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sampled-data based average consensus with measurement noises: convergence analysis and uncertainty principle

引用

Science in China(Series F) 2009年第11期52卷 2089-2103页

作者： LI Tao ZHANG JiFeng Key Laboratory of Systems and control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China

In this paper, sampled-data based average-consensus control is considered for networks consisting of continuous-time first-order integrator agents in a noisy distributed communication environment. The impact of the sampling size and the number of network nodes on the system performances is analyzed. The control input of each agent can only use information measured at the sampling instants from its neighborhood rather than the complete continuous process, and the measurements of its neighbors＇ states are corrupted by random noises. By probability limit theory and the property of graph Laplacian matrix, it is shown that for a connected network, the static mean square error between the individual state and the average of the initial states of all agents can be made arbitrarily small, provided the sampling size is sufficiently small. Furthermore, by properly choosing the consensus gains, almost sure consensus can be achieved. It is worth pointing out that an uncertainty principle of Gaussian networks is obtained, which implies that in the case of white Gaussian noises, no matter what the sampling size is, the product of the steady-state and transient performance indices is always equal to or larger than a constant depending on the noise intensity, network topology and the number of network nodes.

关键词： multi-agent systems average consensus stochastic systems sampled-data based control distributed stochastic approximation,uncertainty principle

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