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

Abstract

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 paper 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 FPGA and tested via hardware in the loop. Results and analysis demonstrate that optimal control of multilevel converters with many levels can be obtained with low computational effort.