A Control-Oriented Framework for Coupling Physics-Based and Data-Driven Models
For control engineers, this framework bridges the gap between heterogeneous coupling and data-driven modeling, enabling stability analysis of hybrid systems that was previously inaccessible.
This work introduces a control-oriented framework to couple physics-based and data-driven models, demonstrated on a microgrid with a data-driven data center load model. The framework enables rigorous assessment of control properties, revealing that coupling structure critically affects equilibrium and stability.
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.