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.INDEX TERMS Model predictive control, embedded deployment, neural networks, dc-dc converters.

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