论文标题
通过选择最佳边缘权重和节点时间尺度的模型减少共识网络系统
Model Reduction of Consensus Network Systems via Selection of Optimal Edge Weights and Nodal Time-Scales
论文作者
论文摘要
本文提出了基于基础图的给定聚类的共识网络系统的模型减少方法。也就是说，给定有时间尺度的代理的共识网络系统，这些网络系统通过加权的无向图和图形聚类演变而来，参数化的还原共识网络系统由其边缘权重和节点时间尺度作为要优化的参数构建。提出了基于H-赋值和H-2的优化方法来选择还原的网络参数，以便将相应的近似误差（即，误差系统的H-荷兰和H-2-核心）最小化。通过数值示例说明了提出的模型还原方法的有效性。
This paper proposes model reduction approaches for consensus network systems based on a given clustering of the underlying graph. Namely, given a consensus network system of time-scaled agents evolving over a weighted undirected graph and a graph clustering, a parameterized reduced consensus network system is constructed with its edge weights and nodal time-scales as the parameters to be optimized. H-infinity- and H-2-based optimization approaches are proposed to select the reduced network parameters such that the corresponding approximation errors, i.e., the H-infinity- and H-2-norms of the error system, are minimized. The effectiveness of the proposed model reduction methods is illustrated via a numerical example.
