]> 2026-09-05T00:37:46+00:00 Robustness Guarantees for Structured Model Reduction of Dynamical Systems 0 en 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. Ayush Pandey Richard M. Murray 2021c IEEE Conference on Decision and Control (CDC), 2021 AP21-CDC Conference paper 2022-10-09T17:11:01Z 2459862.2159838 Robustness Guarantees for Structured Model Reduction of Dynamical Systems Title 102 en Title