Reinforcement Learning-Based Event-Triggered FCS-MPC for Power Converters
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 mechanis...
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| Published in | IEEE transactions on industrial electronics (1982) Vol. 70; no. 12; pp. 11841 - 11852 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.12.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0278-0046 1557-9948 |
| DOI | 10.1109/TIE.2023.3239865 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Qiu, Lin Liu, Xing Fang, Youtong Rodriguez, Jose |
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| SubjectTerms | 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 |
| Title | Reinforcement Learning-Based Event-Triggered FCS-MPC for Power Converters |
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