The Model Predictive Controller (MPC) is one of the most widely used controllers in power electronic converters due to its operational simplicity and low computational burden. However, determining the prediction and control horizons of this control strategy, particularly in power converter interfaced microgrids (MGs), is a fundamental challenge. Furthermore, it is difficult to extract an exact model of the system that can demonstrate its behavior under various conditions, such as when external disturbances and uncertainties are introduced. Numerous factors contribute to the controller’s inefficiency, including uncertainty in the load and input voltage on the demand and supply sides, respectively, as well as insufficient model extraction when considering system performance parameters. The purpose of this paper is to introduce a developed MPC technique for addressing the aforementioned difficulties in a data-driven manner. The proposed controller is a data-driven model reference predictive controller that regulates the output voltage of a buck converter connected to an R-load with a variable load and input voltage via the iterative feedback tuning (IFT) process. Simulation studies are presented to validate the proposed data-driven method by comparing it to the model-based MPC.
