Stability Enforcement in Multivariate Rational Approximation of Parametric Transfer Functions
For researchers in model order reduction of dynamical systems, this provides a robust stability enforcement method for parameterized models, improving upon existing conservative approaches.
This paper introduces a necessary and sufficient criterion for stability of parameterized rational models and develops a rational approximation algorithm that enforces stability via convex optimization without conservatism, offering increased flexibility in model structure.
Preserving stability is a central problem in data-driven model order reduction of dynamical systems. For linear systems whose dynamics depend on geometric or physical parameters, multivariate rational approximation algorithms such as the Parameterized Sanathanan-Koerner iteration and the pAAA algorithm construct parameterized reduced models from sampled transfer function data. In this setting, stability must be enforced robustly across the parameter domain. This paper introduces a necessary and sufficient criterion for characterizing the stability of parameterized models. Within a unified framework, the results apply to models with general rational as well as polynomial dependence on the parameters. Building on this criterion, we develop and demonstrate a rational approximation algorithm that includes robust stability constraints through convex optimization. Relative to the state of the art, the approach enforces stability without conservatism while allowing increased flexibility in the choice of model structure.