用导数数据构建线性降阶模型，保持系统稳定性。
Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data
- 基于对偶函数与导数的对称赫尔米特求积法
- 保留系统厄米性，确保降阶后仍稳定
- 适合需保证稳定性的控制系统建模
数据驱动的降阶建模是控制系统的计算机辅助设计中的关键环节。本文提出一种新型对称赫尔米特形式的基于求积的平衡截断算法，该方法利用全阶系统传递函数及其导数的评估值构造线性降阶模型。重要的是，赫尔米特形式能保持生成数据所依赖系统的优良性质，如状态空间厄米性，从而确保降阶模型的渐近稳定性。该方法在数值实验中展现了良好的逼近性能和稳定性保障。
原文摘要 · Abstract (English)
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.
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