Control of a Buck DC/DC Converter Using Approximate Dynamic Programming and Artificial Neural Networks

This paper proposes a novel artificial neural network (ANN) based control method for a dc/dc buck converter. The ANN is trained to implement optimal control based on approximate dynamic programming (ADP). Special characteristics of the proposed ANN control include: 1) The inputs to the ANN contain e...

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Veröffentlicht in:	IEEE transactions on circuits and systems. I, Regular papers Jg. 68; H. 4; S. 1760 - 1768
Hauptverfasser:	Dong, Weizhen, Li, Shuhui, Fu, Xingang, Li, Zhongwen, Fairbank, Michael, Gao, Yixiang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.04.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms approximate dynamic programming artificial neural network Artificial neural networks Buck converters Control methods Control stability Control systems Controllers Cost function dc/dc buck converter Dynamic programming Electric potential Error signals Inductors Neural networks Optimal control Predictive control Recurrent neural networks Switches Switching frequency Transfer functions Voltage Voltage control Voltage converters (DC to DC)
ISSN:	1549-8328, 1558-0806
Online-Zugang:	Volltext
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