Model-free adaptive control of superheated steam temperature based on quantum-behaved particle swarm optimization

YANG Yanxi; XIE Zhouhua; DENG Yi

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Model-free adaptive control of superheated steam temperature based on quantum-behaved particle swarm optimization

Thermal Power Generation Issue 3, (2016)

更新时间：2025-09-28

- Model-free adaptive control of superheated steam temperature based on quantum-behaved particle swarm optimization
- 作者机构：
  
  西安理工大学自动化与信息工程学院,陕西,西安,710048
- 作者简介：
- 基金信息：
- DOI：
  CLC： V231.1
- Published Online：15 April 2016，
  
  Published：2016
- 稿件说明：
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YANG Yanxi, XIE Zhouhua, DENG Yi. Model-free adaptive control of superheated steam temperature based on quantum-behaved particle swarm optimization[J]. Thermal Power Generation, 2016, (3).
DOI：

YANG Yanxi, XIE Zhouhua, DENG Yi. Model-free adaptive control of superheated steam temperature based on quantum-behaved particle swarm optimization[J]. Thermal Power Generation, 2016, (3). DOI：

摘要

火力发电厂锅炉过热蒸汽温度不仅具有大滞后特性

且其模型还会随锅炉负荷的变化而变化

故依赖数学模型设计的PID控制一直未取得较好的控制品质。对此，本文提出一种基于量子粒子群的改进无模型自适应控制(QPSO-IMFAC)算法

摆脱了对模型的依赖

利用量子粒子群(QPSO)算法对IMFAC算法控制参数寻优

解决了传统无模型自适应控制(MFAC)算法控制参数难以确定的问题。仿真结果表明:采用QPSO-IMFAC算法控制锅炉过热蒸汽温度时

系统具有强扰动抑制性和高鲁棒性

QPSO-IMFAC算法对锅炉过热蒸汽温度控制是有效的；对阶跃响应和定值扰动的控制效果均优于QPSO-PID算法。因此，可以将IMFAC算法实现于DCS控制中，且使用QPSO-IMFAC算法优化出的控制参数可作为锅炉过热蒸汽温度调节的基准值。

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