English Scientific Conference paper (Chapter in Book)

Published: IEEE [ed.]. 2004 43rd IEEE Conference on Decision and Control (CDC): Proceedings of
the 43rd IEEE Conference. (2004) ISBN:0780386825 pp. 1794-1798

Identifiers

- MTMT: 2836282
- DOI: 10.1109/CDC.2004.1430306
- Scopus: 14344253073
- Teljes dokumentum: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1430306
- Google scholar: 1656424610570476922
- Google scholar hash: ep1EPmjN_BYJ

Uncertainty propagation in complex, interconnected dynamical systems can be performed
more efficiently by decomposing the network based on the hierarchy and/or the strength
of coupling. In this paper, we first present a structural decomposition method that
identifies the hierarchy of subsystems. We briefly review the notion of horizontal-vertical
decomposition (HVD) or strongly connected components (SCC) decomposition of a dynamical
system and describe algorithms based on Markov chain theory and graph theory to obtain
the HVD from the equation graph of the system. We also present a non-structural decomposition
method to identify the weakly connected subsystems of a system based on the Laplacian
of a graph derived from the Jacobian. While most of prior efforts in this direction
concentrated on stability, robustness and concrete results were limited to linear
systems, we use it for uncertainty propagation and study of asymptotic behavior of
nonlinear interconnected systems. We illustrate the two methods using a fuel cell
system example. These two methods provide a framework for efficient propagation of
uncertainty in complex nonlinear systems.

2021-12-04 20:14