Finite Control-Set Learning Predictive Control for Power Converters

• Published in: IEEE transactions on industrial electronics (1982) Vol. 71; no. 7; pp. 1 - 7
• Main Authors: Liu, Xing, Qiu, Lin, Fang, Youtong, Wang, Kui, Li, Yongdong, Rodriguez, Jose
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial intelligence; Control systems; Control tasks; Electrical engineering; Finite control-set model predictive control (FCS-MPC); neural network (NN); Neural networks; Power converters; Predictive control; Robustness; Task analysis; Tracking control; Uncertainty; Unsupervised learning
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2023.3303646

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.