A Novel PID Control Strategy Based on PSO-BP Neural Network for Phase-Shifted Full-Bridge Current-Doubler Synchronous Rectifying Converter

Abstract

In this paper, a phase-shifted full-bridge rectifier (PSFB) based on MOSFET and its control strategies are studied. Aiming at its high loss, low efficiency and large ripple coefficient, a phase-shifted full-bridge based on IGBT is proposed to replace MOSFET phase-shifted full bridge to further reduce the on-state power loss. The current-doubler synchronization technology is applied to the rectifying converter to reduce the ripple factor further. In the control strategy, various double closed-loop PI control effects achieved by BP neural network and Particle Swarm optimization BP neural network (PSO-BP) are compared and analysed through modelling and conducting simulation. The simulation results demonstrate that PSO-BP neural network control strategy has the advantages of the fastest response speed, the smallest overshoot and the shortest steady-state time. Comprehensive test results indicate that the proposed IGBT phase-shift full bridge current-doubler synchronous rectifying converter based on PSO-BP neural network control has a good performance of excellent waveform, a fast dynamic response, a wide range of output voltage.