基于大系统理论飞机地面专用空调优化控制

doi:10.16182/j.issn1004731x.joss.17-0216

系统仿真学报 ›› 2019, Vol. 31 ›› Issue (6): 1239-1248.doi: 10.16182/j.issn1004731x.joss.17-0216

• 短文 • 上一篇

基于大系统理论飞机地面专用空调优化控制

王修岩^1,2, 张革文^1,2, 周琛¹, 李宗帅¹, 刘艳敏¹

1. 中国民航大学电子信息与自动化学院,天津 300300;
2. 中国民航大学天津市智能信号与图像处理重点实验室,天津 300300

收稿日期:2017-05-15 修回日期:2017-09-08 出版日期:2019-06-08 发布日期:2019-12-12
作者简介:王修岩(1965-),男,吉林农安,博士,教授,研究方向为非线性系统建模与控制;张革文(1992-),男,江西吉安,硕士生,研究方向为控制理论与控制工程;周琛(1992-),女,山东东营,硕士生,研究方向为控制理论与控制工程。

Airplane Pre-conditioning Optimizing Control Based on Large-scale System Theory

Wang Xiuyan^1,2, Zhang Gewen^1,2, Zhou Chen¹, Li Zongshuai¹, Liu Yanmin¹

1. School of Electronics Information and Automation, Civil Aviation University of China, Tianjin 300300, China;
2. Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China

Received:2017-05-15 Revised:2017-09-08 Online:2019-06-08 Published:2019-12-12

摘要/Abstract

摘要： 针对飞机地面专用空调优化控制的问题,将改进OPBM (Output Predicition and Balance Method)引入到飞机地面专用空调系统优化中。以波音737飞机地面专用空调为对象,采用分散辨识法,建立空调大系统数学模型,提出了同时考虑空调系统能耗与空调区域热舒适性的优化目标,建立了空调系统全局优化运行模型,设计带局部反馈的OPBMLF对模型进行了优化求解。利用Matlab进行仿真研究,研究结果与实际数据进行对比,结果表明优化控制策略能够在满足环境需求的情况下,实现系统能耗最小,验证了其有效性。

关键词: 改进OPBM, 飞机地面专用空调, 优化控制, 大系统, 全局优化模型

Abstract: Aiming at airplane pre-conditioning optimizing control, a modified OPBM (Output Predicition and Balance Method) was introduced. Taking Boing 737 airplane pre-conditioning as the research subject, the decentralized identification algorithm was adopted for establishing the airplane pre-conditioning large-scale system model; the optimal objective considering the system energy consumption, air conditioning area human comfort, and air quality was proposed; and the global system optimization operating condition model was constructed. The OPBMLF with local feedback was designed to optimize globally airplane pre-conditioning system model. Matlab was used for achieving simulation research, and the results was compared with the real data. The results showed that the optimal control strategy can minimize the energy consumption of the system under the condition of meeting the environmental requirements, which verifies its effectiveness.

Key words: modified OPBM, airplane pre-conditioning, optimizing control, large-scale system, globally optimizing model

中图分类号:

TP273

王修岩, 张革文, 周琛, 李宗帅, 刘艳敏. 基于大系统理论飞机地面专用空调优化控制[J]. 系统仿真学报, 2019, 31(6): 1239-1248.

Wang Xiuyan, Zhang Gewen, Zhou Chen, Li Zongshuai, Liu Yanmin. Airplane Pre-conditioning Optimizing Control Based on Large-scale System Theory[J]. Journal of System Simulation, 2019, 31(6): 1239-1248.

参考文献

[1] 中华人民共和国民用航空行业标准. MH/T6109-2014 飞机地面空调机组[S]. 北京: 中国标准出版社, 2014.
People’s Republic of China Civil Aviation Industry Standard. MH/T6109-2014 Airplane Pre-conditioning Air Unit[S]. Beijing: China standard Publication, 2014.
[2] 杨世忠, 任庆昌. 大系统理论在变风量空调系统中的应用[J]. 计算机仿真, 2015(12): 360-363.
Yang Shizhong, Ren Qingchang.Application of Large Scale System Theory in Variable Air Volume Air Conditioning System[J]. Computer Simulation, 2015(12): 360-363.
[3] Zhang Zhiqin, Li Hui.Optimization of the Cooling Tower Condenser Water Leaving Temperature Using a Component-Based Model[J]. ASHRAE Transactions (S0001-2505), 2011, 117: 934-944.
[4] 李宁洲, 冯晓云, 卫晓娟. 采用动态多子群GSA-RBF神经网络的机车黏着优化控制[J]. 铁道学报, 2014, 36(12): 27-34.
Li Ningzhou, Feng Xiaoyun, Wei Xiaojuan.Optimized Locomotive Adhesion Control Based on Dynamic Multiple Sub-group GSA-RBF Neural Network[J]. Journal of The China Railway Society, 2014, 36(12): 27-34.
[5] 王日英, 罗文广, 吴小娜, 等. 中央空调冷却水系统节能优化控制研究[J]. 计算机测量与控制, 2014, 22(11): 3568-3571.
Wang Riying, Luo Wenguang, Wu Xiaona, et al.Energy Saving Optimization Control Research of Central Air Conditioning Cooling Water System[J]. Computer Measure and Control, 2014, 22(11): 3568-3571.
[6] 白克强, 刘知贵, 陈思海, 等. 工业大系统多模型-分散辨识方法[J]. 化工自动化及仪表, 2009, 36(5): 45-47.
Bai Keqiang, Liu Zhigui, Chen Sihai, et al.Multiple Models of Industrial Large Scale Systems-Decentralized Identification Algorithm[J]. Chemical Automation and Instrumentation, 2009, 36(5): 45-47.
[7] Gregor P. Henze, Anthony R.Florita. Advances in Near-Optimal Control of Passive Building Thermal Storage[J]. Journal of Solar Energy Engineering (S1528-8986), 2010, 132(5): 1-9.
[8] Zhiqin Zhang, William D Turner.Methodology for determining the optimal operating strategies for a chilled water storage system. Part1: Theoretical model[J]. HAVC & Rresearch (S1078-9669), 2011, 17(5): 737-751.
[9] 朱自伟, 王勇, 朱晓红, 等. 智能建筑节能策略与应用研究[J]. 重庆建筑大学学报, 2006(3): 99-101.
Zhu Ziwei, Wang Yong, Zhu Xiaohong, Ren Yanming.Study on Energy-Saving Strategy and Application of Intelligent Buildings[J]. Journal of Chongqing Jianzhu University, 2006(3): 99-101.
[10] Nabil Nassif, Raymond C.Tesiero. Impact of Ice Thermal Storage on Cooling Energy Cost for Commercial HVAC Systems[J]. ASHRAE Transactions (S0001-2505), 2013.
[11] Nabil Nassif.Optimal Design and Operation of Ice Thermal Storage System for a Typical Chilled Water Plant[J]. ASHRAE Transactions (S0001-2505), 2014, 120(1): 248-255.

基于大系统理论飞机地面专用空调优化控制

Airplane Pre-conditioning Optimizing Control Based on Large-scale System Theory

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	蒋建波, 李鹏, 苗爱敏, 曹敏, 张松海, 檀磊, 李柯润. GCPS时滞稳定性分析的整体建模方法[J]. 系统仿真学报, 2018, 30(9): 3274-3282.
[2]	李琦, 户杏启, 赵建敏. 基于机器学习的热源总供热量优化控制[J]. 系统仿真学报, 2018, 30(3): 1134-1143.
[3]	孟庆龙, 闫秀英. 空调系统表冷器供冷量优化控制仿真研究[J]. 系统仿真学报, 2015, 27(6): 1368-1373.