Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to have recently successfully tackled challenging applications. However, such methods often obscure the structure dynamics and behind black-box over-parameterized representations, thus limiting our ability understand closed-loop behavior. This art...

Abstract The concept of “hybrid twin” (HT) has recently received a growing interest thanks to the availability powerful machine learning techniques. This twin combines physics-based models within model order reduction framework—to obtain real-time feedback rates—and data science. Thus, main idea HT is develop on-the-fly data-driven correct possible deviations between measurements and prediction...