Hierarchical Robust Analysis for Identified Systems in Network

TL;DR

This paper presents a hierarchical robustness analysis method for large-scale networked systems with uncertain parameters, balancing computational efficiency and result conservatism in worst-case robustness evaluation.

Contribution

It introduces a hierarchical approach to robustness analysis tailored for large-scale uncertain networked systems, reducing computational complexity while maintaining accuracy.

Findings

Efficient robustness analysis for large-scale networks

Trade-off between computation time and conservatism addressed

Applicable to systems with ellipsoid-identified uncertainties

Abstract

This technical report considers worst-case robustness analysis of a network of locally controlled uncertain systems with uncertain parameter vectors belonging to the ellipsoid sets found by identification procedures. In order to deal with computational complexity of large-scale systems, an hierarchical robustness analysis approach is adapted to these uncertain parameter vectors thus addressing the trade-off between the computation time and the conservatism of the obtained result.