Application of Mixture of Experts in Machine Learning-Based Controlling of DC-DC Power Electronics Converter

Regardless of the application in which power electronic converters are deployed, their desired performances crucially depend on the controlling strategy while different impressive parameters are varied. This paper offers a novel controlling strategy originated from the mixture of two well-known controlling techniques, namely feedback (FBC) and model predictive (MPC) controllers. It uses the advantages of the above-mentioned controllers while their drawbacks or limitations are covered by each other using the mixture of experts (MoE) technique. Two neural networks for capturing the features of MPC and FBC along with a gating network as the main tool of MoE are employed in order to optimize the controlling of the DC-DC power electronic converters. These networks are trained through a set of pair data as the input vector and the target data. The results reveal that better performance can be obtained via benefit exploitation of both controlling techniques using a comprehensive MoE. The dynamic and steady state errors are decreased by 5% and 8%, respectively which demonstrate a global enhancement in the controlling of the DC-DC power electronic converters.