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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Bibliographic Details
Published in:IEEE transactions on circuits and systems. I, Regular papers Vol. 68; no. 4; pp. 1760 - 1768
Main Authors: Dong, Weizhen, Li, Shuhui, Fu, Xingang, Li, Zhongwen, Fairbank, Michael, Gao, Yixiang
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
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Summary: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 error signals and integrals of the error signals, enabling the ANN to have PI control ability; 2) The ANN receives voltage feedback signals from the dc/dc converter, making the combined system equivalent to a recurrent neural network; 3) The ANN is trained to minimize a cost function over a long time horizon, making the ANN have a stronger predictive control ability than a conventional predictive controller; 4) The ANN is trained offline, preventing the instability of the network caused by weight adjustments of an on-line training algorithm. The ANN performance is evaluated through simulation and hardware experiments and compared with conventional control methods, which shows that the ANN controller has a strong ability to track rapidly changing reference commands, maintain stable output voltage for a variable load, and manage maximum duty-ratio and current constraints properly.
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ISSN:1549-8328
1558-0806
DOI:10.1109/TCSI.2021.3053468