View source for Robustness Guarantees for Structured Model Reduction of Dynamical Systems

{{Paper
|Title=Robustness Guarantees for Structured Model Reduction of Dynamical Systems
|Authors=Ayush Pandey, Richard M. Murray
|Source=IEEE Conference on Decision and Control (CDC), 2021
|Abstract=Model reduction methods usually focus on the error performance analysis; however, in presence of uncertainties, it is important to analyze the robustness properties of the error in model reduction as well. In this paper, we give robustness guarantees for structured model reduction of linear and nonlinear dynamical systems under parametric uncertainties. In particular, we consider a model reduction where the states in the reduced model are a strict subset of the states of the full model, and the dynamics for all other states are collapsed to zero (similar to quasi-steady state approximation). We show two approaches to compute a robustness metric for any such model reduction — a direct linear analysis method for linear dynamics and a sensitivity analysis based approach that also works for nonlinear dynamics. We also prove that for linear systems, both methods give equivalent results.
|URL=https://www.its.caltech.edu/~apandey/documents/cdc_2021.pdf
|Type=Conference paper
|ID=2021c
|Tag=AP21-CDC
|Funding=DARPA BioCon, NSF Cell Free
}}