Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data
This work provides a novel method for constructing reduced-order models that maintain qualitative properties of the original system, benefiting control system design.
The paper introduces a symmetric Hermite quadrature-based balanced truncation method for learning linear dynamical systems from derivative data, which preserves system properties like Hermiticity and asymptotic stability.
Data-driven reduced-order modeling is an essential component in the computer-aided design of control systems. In this work, we present a novel symmetric Hermite formulation of the quadrature-based balanced truncation algorithm that constructs linear reduced-order models from evaluations of the full-order system's transfer function and its derivative. Significantly, the Hermite formulation preserves desirable qualitative properties of the system used to generate the data, such as state-space Hermiticity and, consequently, asymptotic stability.