# Clustering-Based Average State Observer Design for Large-Scale Network Systems

arXiv: https://arxiv.org/abs/2205.13633 · HTML: https://mlsanity.com/paper/2205.13633/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2022.

## Built on by (most cited first, 1 of 1)

- [A system identification approach to clustering vector autoregressive time series](https://mlsanity.com/paper/2505.14421/) (2025)

## Abstract (arXiv)

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 H2/H_∞ observer design techniques. On the other hand, compromise on the estimation error characteristics is shown to be marginal.