Embedded PWM Predictive Control of DC-DC Power Converters Via Piecewise-Affine Neural Networks

Embedded PWM Predictive Control of DC-DC Power Converters Via Piecewise-Affine Neural Networks

Predictive control is a flexible control methodology that can optimize performance while satisfying current and voltage constraints. Its application in the power electronics domain is however hampered by the high computational demands associated with it. In this paper, piecewise-affine neural networ...

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Bibliographic Details
Published in:IEEE open journal of the Industrial Electronics Society Vol. 2; pp. 199 - 206
Main Authors: Maddalena, Emilio T., Specq, Martin W. F., Wisniewski, Viviane L., Jones, Colin N.
Format: Journal Article
Language:English
Published: New York IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Computer architecture
Control algorithms
Control methods
Control theory
dc-dc converters
embedded deployment
Frequency control
Inductors
Industrial electronics
Mathematical model
Microcontrollers
Model predictive control
Neural networks
Power converters
Predictive control
Switches
Transient response
Voltage control
Voltage converters (DC to DC)
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Summary:Predictive control is a flexible control methodology that can optimize performance while satisfying current and voltage constraints. Its application in the power electronics domain is however hampered by the high computational demands associated with it. In this paper, piecewise-affine neural networks are explored to greatly simplify these controllers and allow for an inexpensive implementation in commercial hardware. More specifically, we tackle the problem of enhancing the start-up transient response of a step-down dc-dc converter while also satisfying inductor current constraints. We analyze the neural network architecture, and detail its training and validation procedures. The learned controller is then embedded on an inexpensive 80-MHz microcontroller, and experimental results are provided showing that the whole control algorithm can be executed in under 30 microseconds.
ISSN:2644-1284
2644-1284
DOI:10.1109/OJIES.2021.3058411