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

Hemiao Liu,Mahmoud Al Shurafa,Jing Wu,Cheng Liu,Yulian Zhao,Ilkyoo Lee,Jaesang Cha,Yanming Cheng,Minwoo Lee,Zhiwen Chen

doi:10.1155/2021/6654997

Abstract

In this paper, a phase-shifted full-bridge current-doubler synchronous rectifying converter (PSFB-CDSRC) based on IGBT and its control strategies are studied. In the main circuit, a current doubling synchronous rectifying circuit based on IGBT is presented to further reduce the power loss of power devices. Moreover, in the control strategy, in view of the existing researches, the basic BP neural network PID control performance of the rectifying converter still can be further improved. Therefore, this paper combines the quasi-Newton algorithm and traditional GA to propose an improved GA-BP (IGA-BP) neural network to further improve PID control performance. The simulation results demonstrate that the maximum efficiency of 5 V/500 A rectifying converter based on the proposed circuit scheme can reach 94.1%, and the rectifying converter has a good performance of excellent waveform and wide range of load. IGA-BP neural network PID control responds fast and reaches the stable state quickly in comparison with that controlled by the GA-BP neural network control strategy, and the steady-state time can be reduced by 10.5% through using IGA-BP neural network control strategy. This study can provide a valuable guidance and reference, not only in circuit scheme but also in the optimal PID control strategy for design of the high-efficiency DC/DC rectifying converter with higher power in the future.

Highlights

In this paper, a phase-shifted full-bridge current-doubler synchronous rectifying converter (PSFB-current-doubler synchronous rectifying converters (CDSRC)) based on IGBT and its control strategies are studied
In the control strategy, in view of the existing researches, the basic BP neural network PID control performance of the rectifying converter still can be further improved. erefore, this paper combines the quasi-Newton algorithm and traditional GA to propose an improved genetic algorithm BP (GA-BP) (IGA-BP) neural network to further improve PID control performance. e simulation results demonstrate that the maximum efficiency of 5 V/500 A rectifying converter based on the proposed circuit scheme can reach 94.1%, and the rectifying converter has a good performance of excellent waveform and wide range of load
IGA-BP neural network PID control responds fast and reaches the stable state quickly in comparison with that controlled by the GA-BP neural network control strategy, and the steady-state time can be reduced by 10.5% through using IGA-BP neural network control strategy. is study can provide a valuable guidance and reference, in circuit scheme and in the optimal PID control strategy for design of the high-efficiency DC/DC rectifying converter with higher power in the future

Summary

Circuit System Design

E circuit of the transformer secondary consists of synchronous rectifiers Q5 and Q6, filter inductors Lf1 and Lf2, and output filter capacitance Cf. In phase-shifting control, the forward arm consists of Q1 and Q2, and the lag arm consists of Q3 and Q4. The current closed-loop transfer function can be further equivalent as. Gvd(s) is the transfer function of duty ratio d(s) of PSFB-CDSRC to output V0(s), Gi(s) is t the current closed-loop transfer function, Kv(s) is the transfer function of feedback network, and Gvc(s) is the transfer function of compensation network of voltage loop. E Bode diagram of the system after correction can be obtained in Figure 4. e phase margin of the system is 22 deg, and the crossing frequency Wc is 6.48 kHz, which meets the requirements of stability for the system

Design of BP Neural Network PID Control Based on Improved GA

Principle and Performance Analysis of IGA

Simulation and Analysis

Findings

Conclusions