Neural Network Model-Predictive Control for CHB Converters With FPGA Implementation

Finite control set model-predictive control appears an interesting and effective control technique for cascaded H-bridge converters but, because of its computational complexity, becomes impractical when the number of levels of the converter increases. This article proposes a neural-network-based approach capable of overcoming the computational burden of conventional predictive control algorithms. The proposed control is, then, applied to a cascaded H-bridge static synchronous compensator using a field-programmable gate array and tested via hardware in the loop. Results and analysis demonstrate that the optimal control of multilevel converters with many levels can be obtained with low computational effort.