基于模糊神经网络PID的串级温度控制系统研究

为解决传统PID 控制存在控制效果不够理想、性能欠佳和很难满足系统精度要求的问题，提出基于模糊 神经网络的自适应PID 控制算法对系统进行控制。采用Labview 构建模糊神经PID 控制器，对环控引气系统温度进 行动态控制，进行仿真研究，并将此控制策略与经典PID 控制进行仿真比较。结果表明：基于模糊神经网络的PID 控制算法在系统的超调量和调节时间上都小于经典PID，能提高系统的快速性和准确性，改善系统特性。

Research on Cascade Temperature Control System Based on Fuzzy Neural Network PID

In order to solve the problem that the control effect of the traditional PID control is not ideal, the performance is poor and it is difficult to meet the requirements of the accuracy of the system, an adaptive PID control algorithm based on fuzzy neural network is proposed to control the system. The fuzzy neural PID controller is constructed by using Labview to dynamically control the temperature of the loop controlled air entraining system. The simulation research is carried out, and the control strategy is compared with the classical PID control. The results show that the PID control algorithm based on fuzzy neural network is less than the classical PID in the overshoot and regulation time of the system, and can improve the rapidity and accuracy of the system, and improve the system characteristics.