Reinforcement Learning-Based Event-Triggered FCS-MPC for Power Converters

• Published in: IEEE transactions on industrial electronics (1982) Vol. 70; no. 12; pp. 11841 - 11852
• Main Authors: Liu, Xing, Qiu, Lin, Fang, Youtong, Rodriguez, Jose
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Capacitors; Control methods; Control systems; Cost function; Dynamic programming; Event-triggered mechanism; finite control-set model predictive control; neural network; Performance tests; Power converters; Predictive control; reinforcement learning; Switches; Uncertainty; Voltage control
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2023.3239865

This article aims to first focus on an improvement of finite control-set model predictive control strategy for power converters that is based on reinforcement learning event-triggered predictive control architecture with the help of adaptive dynamic programming technique and event-triggered mechanism subject to system uncertainties. Our development, endowed with the merits of reinforcement learning and event-triggered control as well as a predictive control solution, is able to alleviate the issues of parametric uncertainties and high switching frequency inherent in the existing scheme, while retaining the merits of the finite control-set model predictive control. Finally, this proposal is experimentally evaluated, where robust performance tests confirm the interest and applicability of the proposed control methodology.