FPGA-Based Model Predictive Control for Power Converters

Model predictive control is an optimization-based control approach that uses the mathematical description of the system. The predictive controller determines an ideal input sequence by calculating an objective function including nonlinear dynamics. Even though the predictive controller usually has a large control bandwidth and good steady-state performance, it entails a high computational burden. This shortcoming is the major problem for performing the predictive control method. In this work, the real-time implementation strategy of the predictive controller using field-programmable gate array is discussed. Three different power converters are considered as case studies and the presented strategy shows the effectiveness of field-programmable gate array to decrease the required execution time. The experimental results verify the theoretical investigations and inclusive comparison results are reported for different power converter topologies.