粒子群优化模糊PID在燃烧器温度控制中的应用

李雪吉; 程海鹰; 胡志勇; 张勇; 蒋新春

doi:10.13433/j.cnki.1003-8728.20200044

粒子群优化模糊PID在燃烧器温度控制中的应用

doi: 10.13433/j.cnki.1003-8728.20200044

内蒙古工业大学机械工程学院，呼和浩特　010051

基金项目: 国家自然科学基金项目（51665043）与内蒙古自治区重大基础研究开放课题项目（2017030256）

详细信息

作者简介:
李雪吉（1992−），硕士研究生，研究方向为机电装备设计，2223237282@qq.com

通讯作者:
程海鹰，教授，硕士生导师，博士，chynmgchy@sina.com

中图分类号: TH16
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- 文章访问数: 266
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- 被引次数: 0
出版历程
- 收稿日期: 2019-09-20
- 刊出日期: 2021-02-02

Application of Particle Swarm Optimization Fuzzy PID in Burner Temperature Control

College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot 010051, China

摘要

摘要: 针对燃烧器温度控制系统是一个时变、大延迟和非线性的控制系统，无法建立准确数学模型，难以进行精确控制的问题，常使用模糊PID算法实现对此类系统的控制。但模糊PID算法需要专家给出模糊规则并调节参数，且参数调节过程中存在误差，控制性能较差。本文采用粒子群优化模糊PID算法中的量化因子K_e、K_ec和比例因子K_u，快速整定模糊PID参数，然后利用MATLAB对控制系统进行仿真。结果表明，通过粒子群优化的模糊PID，系统的响应速度更快、超调量更小、达到稳态的时间更短。
- 温度控制 /
- 模糊PID /
- 粒子群优化 /
- MATLAB
Abstract: Because the temperature control system of burner is a time-varying, large delay and non-linear control system, it is difficult to establish an accurate mathematical model and control temperature accurately. Fuzzy PID algorithm is often used to control this kind of system. But the fuzzy PID algorithm needs experts to give the fuzzy rules and adjust the parameters, and there are errors in the process of parameter adjustment, so the control performance is often poor. In this paper, the quantization factors K_e, K_ec and proportional factor K_u in the particle swarm optimization fuzzy PID algorithm are used to rapid setting parameters, and then the control system is simulated with MATLAB. The results show that the response speed of the system is faster, the overshoot is smaller and the time to reach the steady state is shorter with the fuzzy PID optimized than particle swarm optimization.
- burner temperature control /
- fuzzy PID control /
- particle swarm optimization /
- MATLAB

HTML全文

图 1 燃烧器温度控制流程

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图 2 模糊PID控制结构图

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图 3 粒子群优化模糊PID结构

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图 4 燃烧器温度控制仿真结构图

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图 5 粒子群迭代曲线图

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图 6 控制方法曲线图

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