This paper mainly focuses on the control strategy for phase-shifting full-bridge soft switching electrolytic silver power supply based on Zero Voltage Switching (ZVS) soft switching technology. Taking into consideration the low performance of traditional PID control for phase-shifting full-bridge soft-switching, this paper introduce a PID improved by Back Propagation (BP) neural network with one single learning rate which is used to calculate weights from the input layer to the hidden layer and weights from the hidden layer to the output layer. After testing, it is found that setting independent learning rate for calculation of weights from the input layer to the hidden layer and weights from the hidden layer to the output layer which will not have an adverse effect on the design of the controller. Instead, the learning rate can be set according to the respective characteristics of the weights between the two layers, which is called double learning rate BP neural network PID. The simulation results indicate that compared with the single learning rate BP neural network PID control, the double learning rate BP neural network control has higher response speed, less over-shoot, short time to enter the steady state and strong immunity.

中文翻译：

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相移全桥软开关电源中PID控制器的双学习率反向传播神经网络

本文主要研究基于零电压开关（ZVS）软开关技术的移相全桥软开关电解银电源的控制策略。针对移相全桥软开关传统PID控制性能低下的问题，本文介绍了一种通过反向传播（BP）神经网络改进的PID，具有单一学习率，用于从输入层计算权重到隐藏层，权重从隐藏层到输出层。经过测试发现，设置独立的学习率来计算从输入层到隐藏层的权重和从隐藏层到输出层的权重，不会对控制器的设计产生不利影响。反而，可以根据两层之间权重的各自特点设置学习率，称为双学习率BP神经网络PID。仿真结果表明，与单学习率BP神经网络PID控制相比，双学习率BP神经网络控制具有响应速度快、超调少、进入稳态时间短、抗扰性强等优点。