Model predictive control and linear control of DC–DC boost converter in low voltage DC microgrid: An experimental comparative study

It is challenging to select the best control strategy to regulate the output voltage of a DC–DC boost converter in DC MicroGrids (MGs). Indeed, the controller must cope with the standard requirements: robustness, steady-state and dynamic performance. Moreover, it also needs to handle the boost converter’s intrinsic Non-Minimum Phase behavior that significantly increases the difficulty from the control point of view. This paper compares four control methods (Linear Controller-based Voltage-Mode Control, Linear Controller-based Averaged Current-Mode Control, Finite Control Set Model Predictive Controller-based Voltage-Mode Control, and Finite Control Set Model Predictive Controller-based Current-Mode Control) based on experimental results. To achieve a fair comparison of the different control methods, an optimal design of the power converter parameters is proposed to minimize their influence on the controller’s performance. It provides some guidelines to select the appropriate controller. Several tests are performed on the experimental setup to evaluate the four control strategies, considering the features of DC MGs. Qualitative and quantitative comparisons are made regarding converter models, controller design, performance evaluation and implementation issues. The experimental results show that Finite Control Set Model Predictive Controller-based Current-Mode Control and Linear Controller-based Averaged Current-Mode Control are the best compromises