Data-driven Structured Realization

Abstract

We present a framework for constructing structured realizations of linear dynamical systems having transfer functions of the form $C({\sum }_{k=1}^K{h}_k(s)A_k{)}^{-1}B$ where $h_1,h_2,\dots ,h_K$ are prescribed functions that specify the surmised structure of the model. Our construction is data-driven in the sense that an interpolant is derived entirely from measurements of a transfer function. Our approach extends the Loewner realization framework to more general system structure that includes second-order (and higher) systems as well as systems with internal delays. Numerical examples demonstrate the advantages of this approach.

Cited by in corpus (15)

• Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms
• Projection-Based Model Reduction with Dynamically Transformed Modes
• Estimating Dispersion Curves from Frequency Response Functions via Vector-Fitting
• Model reduction for linear systems with low-rank switching
• Discontinuity propagation in delay differential-algebraic equations
• On the sample complexity of stabilizing linear dynamical systems from data
• Structured vector fitting framework for mechanical systems
• Identification of Dominant Subspaces for Linear Structured Parametric Systems and Model Reduction
• Structured barycentric forms for interpolation-based data-driven reduced modeling of second-order systems
• Context-aware controller inference for stabilizing dynamical systems from scarce data
• Adaptive choice of near-optimal expansion points for interpolation-based structure-preserving model reduction
• From Time-Domain Data to Low-Dimensional Structured Models
• The Pantelides algorithm for delay differential-algebraic equations
• Approximating a flexible beam model in the Loewner framework
• System stabilization with policy optimization on unstable latent manifolds