Deep Learning Implementation of Model Predictive Control for Multioutput Resonant Converters

Abstract

Flexible-surface induction cooktops rely on multi-coil structures which are powered by means of advanced resonant power converters that achieve high versatility while maintaining high efficiency and power density. The study of multi-output converters has led to cost-effective and reliable implementations even if they present complex control challenges to provide high performance. For this scenario, model predictive control arises as a modern control technique that is capable of handling multivariable problems while dealing with nonlinearities and constraints. However, these controllers are based on the computationally-demanding solution of an optimization problem, which is a challenge for high-frequency real-time implementations. In this context, deep learning presents a potent solution to approximate the optimal control policy while achieving a time-efficient evaluation, which permits an online implementation. This paper proposes and evaluates a multi-output-resonant-inverter model predictive controller and its implementation on an embedded system by means of a deep neural network. The proposal is experimentally validated by a resonant converter applied to domestic induction heating featuring a twocoil 3.6 kW architecture controlled by means of a FPGA.

Variable Frequency Resonant Controller Based on Generalized Predictive Control for Biased-Sinusoidal Reference Tracking and Multi-Layer Perceptron

“…An IIR filter, a repetitive control system and virtual variable sampling were applied in [24] to control an active power filter. The application of deep learning to develop a resonant controller is explained in [29]. The resonant controller in [30] uses a fuzzy logic system to obtain adaptive gains for the controller.…”

“…A multi-layer perceptron (MLP) calculates the GPC gain matrix as a function of the PLL estimation. Different from [29], the proposed controller calculates the control law using a few artificial neurons. The GPC state vector and the plant input are also calculated using the estimated frequency function.…”

A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems

“…Indeed, there are a few papers dealing with Wireless Power Transfer based on the use of CNNs: these deep learning methods are usually applied to other kinds of usage or to different applications. In [22], a CNN is trained for estimating the overlapping area between a pot and a multi-coil system in the frame of domestic induction heating appliances: knowing the measured data for each coil (output power, current and quality factor), the area coverage is predicted using the CNN.…”