Published in Proceedings of the 2007 IEEE International Conference Control and Automation, May 1, 2007, pages 1575-1580. Copyright © 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. The definitive version is available online at: http://dx.doi.org/10.1109/ICCA.2007.4376625.
DC-DC converters can be found in almost every power electronics device. To improve the efficiency and controller response of a DC-DC converter to dynamical ~stem changes, neural network has been chosen as an alternative to classic methods. However, no prior work has been done in the neural network approach for control of a PSFB (phase-Shifted Full-Bridge) converter yet. In this research, a multi-layer feedforward neural network controller is proposed. The neural network based controller has the advantage of adaptive learning ability, and can work under the situation when the input voltage and load current fluctuate. Levenberg-Marquardt backpropagation training algorithm is used in computer simulation. The neural controller is then implemented on hardware using a DSP (digital signal processor). Satisfactory experimental results are obtained.
Electrical and Computer Engineering