Frequency-Limited Reduced Models for Linear and Bilinear Systems on the Riemannian Manifold

• 成果类型: Article
• 署名作者: Jiang, Yao-Lin; Xu, Kang-Li
• 署名单位: Xi'an Jiaotong University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2020.3027643
• 发表日期: 2021
• 页码: 3938-3951
• 关键词: Manifolds; optimization; Nonlinear systems; Linear systems; Frequency control; geometry; Time-frequency analysis; Finite frequency; frequency-limited controllability and observability Gramians; iterative algorithm; model order reduction (MOR); product manifold; Riemannian Optimization

摘要

In this article, we propose two new iterative algorithms to solve the frequency-limited Riemannian optimization model order reduction problems of linear and bilinear systems. Different from the existing Riemannian optimization methods, we design a new Riemannian conjugate gradient scheme based on the Riemannian geometry notions on a product manifold, and then generate a new search direction. Theoretical analysis shows that the resulting search direction is always descent with depending neither on the line search used, nor on the convexity of the cost function. The proposed algorithms are also suitable for generating reduced systems over a frequency interval in bandpass form. Finally, two numerical examples are simulated to demonstrate the efficiency of our algorithms.