基于DBO优化模糊PID的高低温试验箱温度控制方法
DOI:
CSTR:
作者:
作者单位:

1.安徽理工大学机电工程学院淮南232001;2.安徽理工大学安徽省矿山智能装备与技术重点实验室淮南232001

作者简介:

通讯作者:

中图分类号:

TN06;TP273

基金项目:

国家自然科学基金(51675004)、安徽省高等学校科学研究项目(自然科学类)(2022AH050805)、2022年安徽省智能矿山技术与装备工程研究中心开放基金项目(AIMTEEL202206)资助


Temperature control method of high and low temperature test chamber based on DBO optimization fuzzy PID
Author:
Affiliation:

1.School of Mechanical and Electrical Engineering, Anhui University of Science and Technology, Huainan 232001, China; 2.Anhui Key Laboratory of Mine Intelligent Equipment and Technology, Anhui University of Science and Technology, Huainan 232001, China

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    高低温试验箱温控系统具有非线性、时滞性。传统采用的PID控制超调量高、调节时间长,而模糊PID控制效果受量化因子与比例因子拟定的影响。为了提高试验箱温控系统响应速度与稳定性,提出了一种基于DBO算法优化模糊PID量化因子与比例因子的高低温试验箱温控方法。首先建立了高低温试验箱加热模型传递函数,在MATLAB/Simulink中搭建传统PID、模糊PID、PSO优化的模糊PID以及DBO优化的模糊PID模型进行仿真,并利用PLC、触摸屏和温控箱搭建实验装置开展实际温控实验。仿真结果表明,DBO优化的模糊PID相较于PSO优化的模糊PID的超调量降低了1.02%,调节时间降低了106 s。实验结果表明,DBO优化的模糊PID相较于PSO优化的模糊PID超调量降低了1.1%,调节时间减少了120 s,验证了DBO算法优化模糊PID量化因子与比例因子相较于PSO效果更佳。补充测试DBO优化出的最佳量化因子与比例因子在不同温度下的温控效果,表明了DBO算法优化模糊PID控制方案的可行性。

    Abstract:

    The temperature control system of high and low temperature test chamber has nonlinear and time-delay. The traditional PID control has high overshoot and long adjustment time, but the effect of fuzzy PID control is affected by the formulation of quantization factor and scale factor. In order to improve the response speed and stability of the temperature control system of the test chamber, a method of temperature control of the high and low temperature test chamber based on DBO algorithm was proposed to optimize the fuzzy PID quantization factor and scale factor. Firstly, the transfer function of the heating model of the high and low temperature test chamber was established, and the traditional PID, fuzzy PID, PSO optimized fuzzy PID and DBO optimized fuzzy PID models were built in MATLAB/Simulink for simulation. In addition, the PLC, touch screen and temperature control box were used to build experimental devices to carry out actual temperature control experiments. The simulation results show that the overshoot of DBO optimized fuzzy PID is reduced by 1.02% and the adjustment time is reduced by 106 s compared with that of PSO optimized fuzzy PID. The experimental results show that the overshoot of the fuzzy PID optimized by DBO is reduced by 1.1% and the adjustment time is reduced by 120 s compared with that of the fuzzy PID optimized by PSO, which verifies that the DBO algorithm has a better effect than PSO in optimizing the quantization factor and scale factor of fuzzy PID. The temperature control effect of the optimal quantization factor and scale factor optimized by DBO at different temperatures is tested, which indicates the feasibility of optimizing the fuzzy PID control scheme by DBO algorithm.

    参考文献
    相似文献
    引证文献
引用本文

杨洪涛,金磊,姜西祥,秦鹏飞,田杭州.基于DBO优化模糊PID的高低温试验箱温度控制方法[J].电子测量与仪器学报,2024,38(10):235-243

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2024-12-16
  • 出版日期:
文章二维码
×
《电子测量与仪器学报》
财务封账不开票通知