大规模网络系统平均状态观测器设计
Clustering-Based Average State Observer Design for Large-Scale Network Systems
摘要：
本文研究了具有少量专用传感器的大规模网络系统的聚集监测问题。由于不可观测性和/或计算不可行性，此类系统的完整状态估计通常是不可行的。因此，通过聚类和聚合，一个易于处理的表示的网络系统，称为投影网络系统，设计的最小阶平均状态观测器。该观测器估计的平均状态的集群，这是确定明确考虑估计误差。此外，给定聚类，所提出的观测器设计算法利用估计误差动态的结构来实现计算易处理性。仿真结果表明，该算法的计算速度明显快于通常的$\mathcal{H}_2/\mathcal{H}_\infty$观测器设计技术。另一方面，妥协的估计误差特性被证明是边际。
英文摘要：
This paper addresses the aggregated monitoring problem for large-scale network systems with a few dedicated sensors. Full state estimation of such systems is often infeasible due to unobservability and/or computational infeasibility. Therefore, through clustering and aggregation, a tractable representation of a network system, called a projected network system, is obtained for designing a minimum-order average state observer. This observer estimates the average states of the clusters, which are identified with explicit consideration to the estimation error. Moreover, given the clustering, the proposed observer design algorithm exploits the structure of the estimation error dynamics to achieve computational tractability. Simulations show that the computation of the proposed algorithm is significantly faster than the usual $\mathcal{H}_2/\mathcal{H}_\infty$ observer design techniques. On the other hand, compromise on the estimation error characteristics is shown to be marginal.
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