A Control-Oriented Framework for Coupling Physics-Based and Data-Driven Models
The work develops a framework to combine physics-based models with data-driven Artificial Neural Networks (ANNs) for dynamic systems like microgrids. It transforms both models into a common discrete-time state space representation and defines specific coupling terms between them. This allows engineers to analyze critical control properties like equilibrium points and stability in the integrated system.