4 research outputs found
Distributed Robust Stability Analysis of Interconnected Uncertain Systems
This paper considers robust stability analysis of a large network of
interconnected uncertain systems. To avoid analyzing the entire network as a
single large, lumped system, we model the network interconnections with
integral quadratic constraints. This approach yields a sparse linear matrix
inequality which can be decomposed into a set of smaller, coupled linear matrix
inequalities. This allows us to solve the analysis problem efficiently and in a
distributed manner. We also show that the decomposed problem is equivalent to
the original robustness analysis problem, and hence our method does not
introduce additional conservativeness.Comment: This paper has been accepted for presentation at the 51st IEEE
Conference on Decision and Control, Maui, Hawaii, 201
Distributed Robustness Analysis of Interconnected Uncertain Systems Using Chordal Decomposition
Large-scale interconnected uncertain systems commonly have large state and
uncertainty dimensions. Aside from the heavy computational cost of solving
centralized robust stability analysis techniques, privacy requirements in the
network can also introduce further issues. In this paper, we utilize IQC
analysis for analyzing large-scale interconnected uncertain systems and we
evade these issues by describing a decomposition scheme that is based on the
interconnection structure of the system. This scheme is based on the so-called
chordal decomposition and does not add any conservativeness to the analysis
approach. The decomposed problem can be solved using distributed computational
algorithms without the need for a centralized computational unit. We further
discuss the merits of the proposed analysis approach using a numerical
experiment.Comment: 3 figures. Submitted to the 19th IFAC world congres