[1]周洪煜,陈晓煜,徐春霞..预测控制在中央空调净化系统中的应用[J].郑州大学学报(工学版),2008,29(03):73-75.[doi:10.3969/j.issn.1671-6833.2008.03.019]
 ZHOU Hongyu,CHEN Xiaoyu,Xu Chunxia.Application of predictive control in central air conditioning purification system[J].Journal of Zhengzhou University (Engineering Science),2008,29(03):73-75.[doi:10.3969/j.issn.1671-6833.2008.03.019]
点击复制

预测控制在中央空调净化系统中的应用()
分享到:

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
29卷
期数:
2008年03期
页码:
73-75
栏目:
出版日期:
1900-01-01

文章信息/Info

Title:
Application of predictive control in central air conditioning purification system
作者:
周洪煜陈晓煜徐春霞.
重庆大学,动力工程学院,重庆,400030, 重庆大学,动力工程学院,重庆,400030, 重庆大学,动力工程学院,重庆,400030
Author(s):
ZHOU Hongyu; CHEN Xiaoyu; Xu Chunxia
关键词:
神经网络 预测控制 空气净化器 中央空调
Keywords:
neural networks predictive control air purifiers Central air conditioning
DOI:
10.3969/j.issn.1671-6833.2008.03.019
文献标志码:
A
摘要:
针对常规PID控制在非线性、大惯性系统中存在滞后、精度低等弱点,构建了神经网络与预测算法相结合的控制系统.采用预控算法,充分利用预测控制的滚动优化和反馈校正的特性,采用神经网络建立系统的动态模型作为预测控制器的预测模型,实现了对大滞后系统的自适应控制,具有实时控制和预测性能,有效地提高了控制精度和可靠性,增强了稳定性.现场运行结果表明,在空气净化器系统中使用该方法效果良好,易于推广.
Abstract:
Aiming at the weaknesses of conventional PID control such as hysteresis and low accuracy in nonlinear and large inertial systems, a control system combining neural network and prediction algorithm is constructed. The pre-control algorithm is adopted, which makes full use of the rolling optimization and feedback correction characteristics of predictive control, and uses the neural network to establish the dynamic model of the system as the prediction model of the prediction controller, which realizes the adaptive control of the large lag system, which has real-time control and prediction performance, effectively improves the control accuracy and reliability, and enhances the stability. The field operation results show that the method used in the air purifier system has good effect and is easy to promote.

相似文献/References:

[1]蒋建东,张豪杰,王静.基于HHT的电力负荷组合预测应用[J].郑州大学学报(工学版),2015,36(04):1.[doi:10.3969/ j. issn.1671 - 6833.2015.04.001]
 JIANG Jian-dong,ZHANG Hao-jie,WANG Jing.Research and Application of HHT-Based Power Load Combination Forecasting[J].Journal of Zhengzhou University (Engineering Science),2015,36(03):1.[doi:10.3969/ j. issn.1671 - 6833.2015.04.001]
[2]邓万宇,李力,牛慧娟.基于Spark的并行极速神经网络[J].郑州大学学报(工学版),2016,37(05):47.[doi:10.3969/ j.issn.1671 -6833.2016.05.010]
 Deng Wanyu,Li Li,Niu Huijuan.Sparked-based Parallel Extreme Learning Machine[J].Journal of Zhengzhou University (Engineering Science),2016,37(03):47.[doi:10.3969/ j.issn.1671 -6833.2016.05.010]
[3]肖斌,张恒宾,刘宏伟.改进PSO-BPNN算法在管道腐蚀预测中的应用[J].郑州大学学报(工学版),2022,43(01):27.[doi:10.13705/j.issn.1671-6833.2022.01.008]
 XIAO Bin,ZHANG Hengbin,LIU Hongwei.Application of Improved PSO-BPNN Algorithm in Corroded Pipelines Prediction[J].Journal of Zhengzhou University (Engineering Science),2022,43(03):27.[doi:10.13705/j.issn.1671-6833.2022.01.008]
[4]杨华芬,杨有,尚晋..一种改进的进化神经网络优化设计方法[J].郑州大学学报(工学版),2010,31(05):116.[doi:10.3969/j.issn.1671-6833.2010.05.028]
[5]王杰,陈锴鹏..基于决策函数及PSO优化的SVM预测控制应用研究[J].郑州大学学报(工学版),2013,34(02):53.[doi:10.3969/j.issn.1671-6833.2013.02.014]
[6]郭克希,谭佩莲,唐进元..基于人工神经网络的螺旋锥齿轮磨削加工表面粗糙度预测[J].郑州大学学报(工学版),2009,30(03):65.
 GUO Kexi,TAN Peilian,TANG Jinyuan.Surface Roughness Forecasting of Spiral Bevel Gear Based on Artificial Neural Network[J].Journal of Zhengzhou University (Engineering Science),2009,30(03):65.
[7]刘伟,刘赞,王玲玲..神经网络与结构编码法预测直馏汽油色谱保留指数[J].郑州大学学报(工学版),2004,25(03):26.[doi:10.3969/j.issn.1671-6833.2004.03.007]
 LIU Wei,LIU Zan,Wang Lingling.Neural network and structural coding method to predict the chromatographic retention index of straight-run gasoline[J].Journal of Zhengzhou University (Engineering Science),2004,25(03):26.[doi:10.3969/j.issn.1671-6833.2004.03.007]
[8]樊亚军,曲仕茹..利用BP神经网络实现三维飞机目标识别[J].郑州大学学报(工学版),2004,25(04):56.[doi:10.3969/j.issn.1671-6833.2004.04.015]
 Fan Yajun,Qu Shiru.BP neural network is used to realize three-dimensional aircraft target recognition[J].Journal of Zhengzhou University (Engineering Science),2004,25(03):56.[doi:10.3969/j.issn.1671-6833.2004.04.015]
[9]王少波,柴艳丽,梁醒培..神经网络学习样本点的选取方法比较[J].郑州大学学报(工学版),2003,24(01):63.[doi:10.3969/j.issn.1671-6833.2003.01.014]
 Wang Shaobo,Chai Yanli,Liang Xingpei.Comparison of the selection methods of neural network learning sample points[J].Journal of Zhengzhou University (Engineering Science),2003,24(03):63.[doi:10.3969/j.issn.1671-6833.2003.01.014]
[10]刘应梅,杨宛辉,章健,等.基于人工神经网络的变电站故障诊断[J].郑州大学学报(工学版),1999,20(04):86.[doi:10.3969/j.issn.1671-6833.1999.04.027]
 LIU Yingmei,Yang Wanhui,ZHANG Jian,et al.Substation fault diagnosis based on artificial neural network[J].Journal of Zhengzhou University (Engineering Science),1999,20(03):86.[doi:10.3969/j.issn.1671-6833.1999.04.027]

更新日期/Last Update: 1900-01-01