  Resonant power converters offer improved levels of efficiency and power
density. In order to implement such systems, advanced control techniques are
required to take the most of the power converter. In this context, model
predictive control arises as a powerful tool that is able to consider
nonlinearities and constraints, but it requires the solution of complex
optimization problems or strong simplifying assumptions that hinder its
application in real situations. Motivated by recent theoretical advances in the
field of deep learning, this paper proposes to learn, offline, the optimal
control policy defined by a complex model predictive formulation using deep
neural networks so that the online use of the learned controller requires only
the evaluation of a neural network. The obtained learned controller can be
executed very rapidly on embedded hardware. We show the potential of the
presented approach on a Hardware-in-the-Loop setup of an FPGA-controlled
resonant power converter.

Optimization and Control