Combining Data-Driven and Event-Driven for Online Learning Predictive Control in Power Converters

The combination of data-driven and event-driven opens up the possibility of alleviating the long-standing research challenges for power converters in classical finite control-set model predictive control, i.e., model parametric uncertainties and unnecessary switching loss. Inspire by this, we will launch a major study on the problem of designing online learning predictive controller. Unlike most prior works in this area, it can be accomplished by an integrated data-driven and event-driven design framework. To be more precise, the design procedures rely on a combination of developing a data-driven model-free adaptive predictive control, introducing an online reinforcement learning technique, and leveraging an event-driven mechanism. Furthermore, we also provide extensions to robust model-free predictive control design based on input–output data and against unknown uncertainties under low switching frequency operation, while avoiding a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> knowledge of model information and weighting factors. Finally, we illustrate our approach and highlight its advantages on a numerical example, and the results presented are promising and motivate further research in this field.