Design of Temperature Control System for Electromagnetic Stir-Frying Machine Based on Intelligent PID of Disturbance Observer

doi:10.7652/xjtuxb202105015

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Design of Temperature Control System for Electromagnetic Stir-Frying Machine Based on Intelligent PID of Disturbance Observer

更新时间：2025-07-09

- Design of Temperature Control System for Electromagnetic Stir-Frying Machine Based on Intelligent PID of Disturbance Observer
- Vol. 55, Issue 5, Pages: 133-142(2021)
- 作者机构：
  
  山东理工大学电气与电子工程学院,山东,淄博,255022
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202105015
  CLC： TP273
- Online First：10 May 2021，
  
  Published：2021
- 稿件说明：
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Design of Temperature Control System for Electromagnetic Stir-Frying Machine Based on Intelligent PID of Disturbance Observer[J]. 2021, 55(5): 133-142.
DOI：

Design of Temperature Control System for Electromagnetic Stir-Frying Machine Based on Intelligent PID of Disturbance Observer[J]. 2021, 55(5): 133-142. DOI： 10.7652/xjtuxb202105015.

摘要

针对阿胶珠炮制对电磁炒药机温度控制精度和响应速度的要求

提出了一种基于干扰观测器的改进粒子群优化(PSO)径向基函数神经网络(RBFNN)PID的控制方法。根据阿胶珠电磁炒药机结构

建立阿胶珠电磁炒药机温度控制系统数学模型

通过对控制系统结构分析

构建RBF神经网络结构。利用RBF神经网络的自学习能力

采用梯度下降法对自身参数进行适当调整

实现PID参数动态调整

使系统惯性和时滞性有效抑制。为降低外部干扰影响

分析并构建干扰观测器模型

使干扰量得到实时观测和有效补偿。为弥补RBF神经网络模型参数精度不足

以系统误差瞬时值为适应度函数

利用改进PSO算法对RBF神经网络模型参数寻优

获取最佳控制性能。仿真结果表明:与传统PID控制方法和RBFNN-PID控制方法相比

所提控制方法使调节时间分别减少35 s和19 s

超调量分别降低19.2%和13.1%; 与无干扰观测器相比

所提控制方法对外部干扰抑制能力平均提高50%; 所提控制方法满足阿胶珠炮制工艺的要求。

Abstract

In view of the requirements of temperature control accuracy and response rate of electromagnetic stir-frying machine for producing the Chinese drug beads

an improved particle swarm optimization(PSO)radial basis function neural network(RBFNN)PID control method is proposed based on interference observer. According to the structure of the electromagnetic stir-frying machine of Asini Coii Colla beads

the mathematical model of temperature control system of the special electromagnetic stir-frying machine is established. Analyzing the structure of the control system

the RBF neural network structure is constructed. Adopting the self-learning ability of the RBF neural network

the gradient descent method is chosen to adjust its own parameters adequately to realize the dynamic adjustment of PID parameters

thus the system inertia and time lag are suppressed efficiently. Analyzing and constructing a disturbance observer model

the interference is real-time observed and effectively compensated to reduce the influence of external interference. To obtain the best control performance

the RBF neural network model parameters are optimized by the improved particle swarm optimization algorithm with the system error instantaneous values for the fitness function to make up for the RBF neural network model parameters' accuracy. Simulation results show that the regulating time of this control method is reduced by 35 s and 19 s

and the overshoot is reduced by 19.2% and 13.1%

respectively

compared with the traditional PID control method and RBFNN-PID control method. The external interference suppression ability is increased by 50% on average compared with the case without interference observer.

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