论文标题
使用神经网络预测控制器对降压转换器的电压调节
The Voltage Regulation of a Buck Converter Using a Neural Network Predictive Controller
论文作者
论文摘要
在本文中，提出了神经网络预测控制器（NNPC）来控制降压转换器。常规控制器（例如比例积分（PI）或比例积分衍生物（PID））是基于工作点附近的线性小信号模型而设计的。因此，由于系统模型通过更改操作点而更改，因此控制器在启动，负载更改或参考更改中的性能不是最佳的。神经网络预测控制器通过遵循传统模型预测控制器的概念来最佳地控制降压转换器。 NNPC的优点是神经网络系统识别降低了使用不准确参数的系统模型的不准确性。具有良好训练的神经网络的NNPC可以作为BUCK转换器的最佳控制器执行。为了比较传统的雄鹿转换器和NNPC的有效性，提供了仿真结果。
In this paper, a neural network predictive controller (NNPC) is proposed to control a buck converter. Conventional controllers such as proportional integral (PI) or proportional integral derivative (PID) are designed based on the linearized small signal model near the operating point. Therefore, the performance of the controller in the start up, load change, or reference change is not optimal since the system model changes by changing the operating point. The neural network predictive controller optimally controls the buck converter by following the concept of the traditional model predictive controller. The advantage of the NNPC is that the neural network system identification decreases the inaccuracy of the system model with inaccurate parameters. A NNPC with a well trained neural network can perform as an optimal controller for the buck converter. To compare the effectiveness of the traditional buck converter and the NNPC, the simulation results are provided.
