A Control-Oriented Framework for Coupling Physics-Based and Data-Driven Models
Leeroy Makusha, Preston Abadie, Donald J. Docimo
- 发表年份
- 2026
- 访问权限
- 开放获取
摘要
Design, control, and estimation for dynamic systems require accurate and analytically tractable models. However, modern engineered systems contain components that are described with heterogeneous modeling paradigms, as well as subsystems that are challenging to model from physics alone. There have been significant efforts to address this through heterogeneous coupling frameworks and data-driven modeling. However, these two paths have been pursued in parallel. This work bridges this gap by introducing a control-oriented framework to couple physics-based and data-driven models. A physics-based microgrid with a data-driven data center load model is used to demonstrate the proposed four step methodology. Application of the framework yields a coupled system that allows for rigorous assessment of control properties. Equilibrium and stability tests are conducted, and they both reveal that the coupling structure and functions play a critical role in determining physically meaningful equilibrium points and stability of the integrated system. This information could only be accessed through the proposed framework, highlighting its importance.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Fractional Differential Equations
Igor Podlubný
2025
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
Genetic Programming: On the Programming of Computers by Means of Natural Selection
John R. Koza
1992