Weighting Factor Design for FS-MPC in VSCs: A Brain Emotional Learning-Based Approach

Finite set model predictive control (FS-MPC) has been identified as one of the most favorable controllers for power electronic applications due to its capability over real-time solutions to multiple objectives and constraints. However, the main challenge in the FS-MPC is the choice of appropriate weighting factors in the cost function to reach the best switching state of the inverter. This study proposes an approach based on brain emotional learning (BEL) to provide online tuning of weighting factors in FS-MPC of a power converter, which prevents the dependency of the converter control system on the various uncertainties coming from operating conditions and loading conditions. The proposed BEL approach is fully model-free, indicating that the weighting factors are adjusted without previous knowledge of the system model and parameters. Simulation and experimental results validate the proposed control scheme's effectiveness under different load conditions.