论文标题
通过强大的加速延迟自我加强，基于共识的网络进行快速过渡
Rapid Transitions with Robust Accelerated Delayed Self Reinforcement for Consensus-Based Networks
论文作者
论文摘要
快速过渡对于将基于共识的多代理网络快速响应对外部刺激的快速响应很重要。尽管高增益可以提高响应速度，但潜在的不稳定倾向于限制最大可能的增益，因此，在过渡期间将最大收敛率限制为共识。由于具有对称图的多代理网络的更新定律可以被视为其拉普拉斯势函数的梯度，因此来自优化理论的Nesterov-type加速梯度方法可以进一步提高此类网络的收敛速率。加速级别方法的一个优点是，可以使用加速的延迟自我增强（A-DSR）来实现，该方法不需要网络中的新信息，也不需要网络连接中的修改。但是，由于更新定律不是laplacian-potential函数的梯度，因此加速梯度方法并不直接适用于通用有向图。这项工作的主要贡献是将加速梯度方法扩展到通用的定向图网络，而无需密切连接图。此外，尽管在加速梯度方法中的动量项和过时的反馈项都很重要，但通常表明，单独的动量项足以实现平衡的鲁棒性和在主导模式下没有振荡而没有振荡的快速过渡，但对于其图形laplacian具有真实谱的网络。与没有A-DSR的情况相比，提出的结构鲁棒性的稳健a-DSR在结构鲁棒性方面为40％，收敛速度为50％，以说明了仿真结果，以说明性能提高。此外，与没有A-DSR的情况相比，提出了实验结果，该结果表明，与鲁棒A-DSR相比，与健壮A-DSR的收敛速度更快。
Rapid transitions are important for quick response of consensus-based, multi-agent networks to external stimuli. While high-gain can increase response speed, potential instability tends to limit the maximum possible gain, and therefore, limits the maximum convergence rate to consensus during transitions. Since the update law for multi-agent networks with symmetric graphs can be considered as the gradient of its Laplacian-potential function, Nesterov-type accelerated-gradient approaches from optimization theory, can further improve the convergence rate of such networks. An advantage of the accelerated-gradient approach is that it can be implemented using accelerated delayed-self-reinforcement (A-DSR), which does not require new information from the network nor modifications in the network connectivity. However, the accelerated-gradient approach is not directly applicable to general directed graphs since the update law is not the gradient of the Laplacian-potential function. The main contribution of this work is to extend the accelerated-gradient approach to general directed graph networks, without requiring the graph to be strongly connected. Additionally, while both the momentum term and outdated-feedback term in the accelerated-gradient approach are important in general, it is shown that the momentum term alone is sufficient to achieve balanced robustness and rapid transitions without oscillations in the dominant mode, for networks whose graph Laplacians have real spectrum. Simulation results are presented to illustrate the performance improvement with the proposed Robust A-DSR of 40% in structural robustness and 50% in convergence rate to consensus, when compared to the case without the A-DSR. Moreover, experimental results are presented that show a similar 37% faster convergence with the Robust A-DSR when compared to the case without the A-DSR.
