A Control-Oriented Framework for Coupling Physics-Based and Data-Driven Models

TL;DR

This paper introduces a control-oriented framework that effectively couples physics-based and data-driven models, enabling rigorous analysis of equilibrium and stability in complex systems like microgrids.

Contribution

It presents a novel four-step methodology to integrate heterogeneous models, bridging the gap between physics-based and data-driven approaches for system control.

Findings

Coupled system allows for rigorous control property assessment.

Coupling structure influences equilibrium points and stability.

Framework reveals critical role of coupling functions in system behavior.

Abstract

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.