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基于 GA-Fuzzy-PID 算法的真空退火炉温度控制系统设计。

Design of vacuum annealing furnace temperature control system based on GA-Fuzzy-PID algorithm.

机构信息

School of Electrical and Electronic Engineering, Anhui Science and Technology University, Bengbu, China.

出版信息

PLoS One. 2023 Nov 29;18(11):e0293823. doi: 10.1371/journal.pone.0293823. eCollection 2023.

DOI:10.1371/journal.pone.0293823
PMID:38019774
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10686503/
Abstract

As is well known, the metal annealing process has the characteristics of heat concentration and rapid heating. Traditional vacuum annealing furnaces use PID control method, which has problems such as high temperature fluctuation, large overshoot, and long response time during the heating and heating process. Based on this situation, some domestic scholars have adopted fuzzy PID control algorithm in the temperature control of vacuum annealing furnaces. Due to the fact that fuzzy rules are formulated through a large amount of on-site temperature data and experience summary, there is a certain degree of subjectivity, which cannot ensure that each rule is optimal. In response to this drawback, the author combined the technical parameters of vacuum annealing furnace equipment, The fuzzy PID temperature control of the vacuum annealing furnace is optimized using genetic algorithm. Through simulation and comparative analysis, it is concluded that the design of the fuzzy PID vacuum annealing furnace temperature control system based on GA optimization is superior to fuzzy PID and traditional PID control in terms of temperature accuracy, rise time, and overshoot control. Finally, it was verified through offline experiments that the fuzzy PID temperature control system based on GA optimization meets the annealing temperature requirements of metal workpieces and can be applied to the temperature control system of vacuum annealing furnaces.

摘要

众所周知，金属退火过程具有热量集中和快速加热的特点。传统的真空退火炉采用 PID 控制方法，在加热和升温过程中存在温度波动大、超调量大、响应时间长等问题。针对这种情况，国内一些学者在真空退火炉的温度控制中采用了模糊 PID 控制算法。由于模糊规则是通过大量的现场温度数据和经验总结来制定的，因此具有一定的主观性，不能保证每个规则都是最优的。针对这一缺点，作者结合真空退火炉设备的技术参数，采用遗传算法对真空退火炉的模糊 PID 温度控制进行了优化。通过仿真和对比分析，得出结论：基于 GA 优化的模糊 PID 真空退火炉温度控制系统在温度精度、上升时间和超调控制方面均优于模糊 PID 和传统 PID 控制。最后，通过离线实验验证了基于 GA 优化的模糊 PID 温度控制系统满足金属工件的退火温度要求，可以应用于真空退火炉的温度控制系统。

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