Electric heated water bath cascade control system research based on improved differential evolution algorithm-radial basis function neural network

被引：0

作者：

Yu M. ^{[1
]}

Zou Z. ^{[1
]}

机构：

[1] Institute of Chemical Defense, Academy of Military Sciences, Beijing

来源：

Huagong Xuebao/CIESC Journal | 2019年 / 70卷 / 12期

关键词：

Cascade control; Differential evolution; Electric heated water bath; Neural network; Process control;

D O I：

10.11949/j.issn.0438-1157.20190885

中图分类号：

学科分类号：

摘要：

Aiming at the large inertia, nonlinearity and large delay of the controlled object in the temperature control of the electric heated water bath device, RBF (radial basis function) neural network cascade control system based on the IDE(improved differential evolution) algorithm is designed. The IDE algorithm is used to optimize the initial parameters of the RBF neural network. The optimized RBF neural network is used to identify the Jacobian information of the controlled object of the main control loop, and then the online adjustment of the parameters of the main control loop PID (proportional integration differentiation) controller is realized. Aiming at the problem that the main control loop controller contains output noise which leads to the decline of control performance, the Kalman filter is introduced to redesign the main loop of the cascade control. The output value of the control object is processed by the Kalman filter algorithm and then returned to the closed loop control system. The simulation test of the IDE-RBF-PID-PI cascade control system is carried out for the common electric heated water bath device in the micro chemical industry. The results show that the IDE-RBF-PID-PI cascade control system greatly improves control performance compared to conventional cascade control. The Kalman filtering algorithm introduced in the main control loop effectively reduces the output noise of the control system, and the control effect is close to the ideal state without noise. © All Right Reserved.

引用

页码：4680 / 4688

页数：8

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