Supervisory intelligent control system design for forward DC-DC converters

Abstract

A supervisory intelligent control system is developed. The supervisory intelligent control system is comprised of a neural controller and a supervisory controller. The neural controller is investigated to mimic an ideal controller and the supervisory controller is designed to compensate for the approximation error between the neural controller and the ideal controller. In the proposed control scheme, an online parameter training methodology is developed based on the gradient descent method and the Lyapunov stability theorem, so that the control system can guarantee system stability. Finally, to investigate the effectiveness of the proposed control scheme, it is applied to control a forward DC-DC converter. A comparison between a PI controller, a fuzzy controller, a fuzzy neural network controller and the supervisory intelligent controller is made. Experimental results show that the proposed control system can achieve favourable regulation performances even for different input voltages and under load resistance variations.