Finite Control-Set Learning Predictive Control for Power Converters

This letter concentrates on introducing a learning methodology that extends and improves classical finite control-set model predictive control approach, which is able to significantly mitigate the inherent limitations of system uncertainties and unknown perturbations subject to robustness characteristics. To this end, in our work, a finite control-set learning predictive control architecture, which is addressed as an unsupervised learning technique, is presented. In this control task, we define a single neural network to learn the tracking control part online, and a robustifying control term is embedded into the suggested control solution so as to handle the approximator error and/or external disturbances, thereby leading to considerable enhancement of robustness. Dissimilar to classical finite control-set model predictive control, we establish that this method does not require a priori knowledge of model information and weighting factors, making our approach applicable to a variety of power converter systems. Finally, we highlight its advantages with a case study.

Automated summary

This letter introduces an improved control method that extends and improves the classical predictive control approach, addressing the limitations of system uncertainties and unknown perturbations. The method uses unsupervised learning technique to learn the control part online, and incorporates a robustifying control term to enhance the robustness. Unlike traditional methods, this approach does not require prior knowledge of model information and weighting factors, making it applicable to various power converter systems.