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

• 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)
• ISSN: 2644-1284; 2644-1284
• DOI: 10.1109/OJIES.2021.3058411

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.