Context: The DataPower project has developed a software-defined power converter (TRL 9), but its online data acquisition platform—critical for intelligent control and smart maintenance—remains at TRL 2-3. The internship aims to advance this platform within the ANR-funded project ANR-21-CE05-0011, which explores power converters as both energy processors and data sources for machine learning.Objective: Develop and integrate an online data acquisition platform to enable closed-loop intelligent control and smart maintenance, bridging model-based and data-driven approaches.Key Tasks:Understand the existing power hardware, control firmware, and Python/MATLAB-based modules for smart maintenance and generic control.Implement a model-driven control database and define open-loop excitation procedures for data collection.Validate the closed-loop interaction: data acquisition → smart maintenance (parameter identification, anomaly detection, fault diagnosis, life estimation) → control system update.Methodology: Use data-driven methods like Virtual Reference Feedback Tuning (VRFT) to bypass traditional modeling, relying instead on experimental input-output data for rapid controller synthesis.Deliverables:A functional prototype of the online data acquisition platform integrated with the power converter.A case study demonstrating the intelligent control and smart maintenance loop.Documentation of excitation protocols, data requirements, preprocessing, and control database strategy.Skills Required: Background in control systems and power electronicsProficiency in Python and MATLAB/SimulinkFamiliarity with embedded systems is a plus.Contact: Luiz Villa and Pauline Kergus