Adaptive energy feedback control for resonant converters using neural networks

Abstract

A neural controller implementing an energy feedback control law is proposed as an alternative to classic control of resonant converters. The energy feedback control, and particularly the optimal trajectory control law (OTCL), is introduced. As a result, the state space is considered to be divided into two subspaces. An analog neural network (ANN) learns to classify these two classes by means of a learning algorithm. An easy implementation of this controller is proposed and applied to a series resonant converter (SRC). Simulation results show a good improvement in the SRC response and confirm the validity of the controller. >