The discrete-time first-order multi-agent networks with communication noises are under consideration. Based on the noisy observations, the consensus control is given for networks with both fixed and time-varying topol...The discrete-time first-order multi-agent networks with communication noises are under consideration. Based on the noisy observations, the consensus control is given for networks with both fixed and time-varying topologies. The states of agents in the resulting closed-loop network are updated by a stochastic approximation （SA） algorithm, and the consensus analysis for networks turns to be the convergence analysis for SA. For networks with fixed topologies, the proposed consensus control leads to consensus of agents with probability one if the graph associated with the network is connected. In the case of time-varying topologies, the similar results are derived if the graph is jointly connected in a fixed time period. Compared with existing results, the networks considered here are in a more general setting under weaker assumptions and the strong consensus is established by a simpler proof.展开更多
In this paper,a distributed stochastic approximation algorithm is proposed to track the dynamic root of a sum of time-varying regression functions over a network.Each agent updates its estimate by using the local obse...In this paper,a distributed stochastic approximation algorithm is proposed to track the dynamic root of a sum of time-varying regression functions over a network.Each agent updates its estimate by using the local observation,the dynamic information of the global root,and information received from its neighbors.Compared with similar works in optimization area,we allow the observation to be noise-corrupted,and the noise condition is much weaker.Furthermore,instead of the upper bound of the estimate error,we present the asymptotic convergence result of the algorithm.The consensus and convergence of the estimates are established.Finally,the algorithm is applied to a distributed target tracking problem and the numerical example is presented to demonstrate the performance of the algorithm.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.60774020, 60821091,and 60874001
文摘The discrete-time first-order multi-agent networks with communication noises are under consideration. Based on the noisy observations, the consensus control is given for networks with both fixed and time-varying topologies. The states of agents in the resulting closed-loop network are updated by a stochastic approximation （SA） algorithm, and the consensus analysis for networks turns to be the convergence analysis for SA. For networks with fixed topologies, the proposed consensus control leads to consensus of agents with probability one if the graph associated with the network is connected. In the case of time-varying topologies, the similar results are derived if the graph is jointly connected in a fixed time period. Compared with existing results, the networks considered here are in a more general setting under weaker assumptions and the strong consensus is established by a simpler proof.
基金This work was supported by the National Key Research and Development Program of China under Grant 2018YFA0703800the National Natural Science Foundation of China under Grant 61822312This work was also supported(in part)by the Strategic Priority Research Program of Chinese Academy of Sciences under Grant No.XDA27000000.
文摘In this paper,a distributed stochastic approximation algorithm is proposed to track the dynamic root of a sum of time-varying regression functions over a network.Each agent updates its estimate by using the local observation,the dynamic information of the global root,and information received from its neighbors.Compared with similar works in optimization area,we allow the observation to be noise-corrupted,and the noise condition is much weaker.Furthermore,instead of the upper bound of the estimate error,we present the asymptotic convergence result of the algorithm.The consensus and convergence of the estimates are established.Finally,the algorithm is applied to a distributed target tracking problem and the numerical example is presented to demonstrate the performance of the algorithm.
