基于无线传感技术的室内空气品质系统辨识
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摘要
随着建筑物结构和功能复杂程度的增加以及建筑智能化程度的提高,室内空气品质的舒适度越来越依赖于空调系统的控制。而在空调系统控制中,室内空气品质参数的有效性和室内空气品质模型的精确性则是影响系统控制效果的关键因素。目前,室内空气品质参数检测存在着布线麻烦、检测范围小等缺陷,而且室内品质模型的精度往往不能达到控制系统的要求。为此,作者参与了北京市自然科学基金项目(8072008,Z07004)开发了基于无线传感器网络的室内空气品质检测系统,研究了基于多变量系统辨识理论的室内空气品质模型。具体内容如下：
1、无线传感器网络方面
1)基于JN5148无线通信模块设计制作了无线传感器网络节点硬件电路,包括无线通信模块的外围电路、电源电路和功能模块电路。
2)基于PIC单片机设计了硬件定时控制电路,采用控制负载电源通断的方式降低了近70%的节点平均功耗。
3)基于Zigbee PRO协议和JenOS操作系统开发了的节点软件应用程序,实现了协调节点、路由节点和底层节点的相应功能。
4)基于ARM9开发板和WinCE操作系统开发了网络的主控制器用于提高协调节点的处理能力。2、室内空气品质建模方面
1)提出了基于微粒群-滑模变结构控制(Particle Swarm Optimization-Slide mode Control, PSO-SMC)的神经网络训练算法,相比于单一的滑模变结构控制算法,降低了网络的训练误差,提高了网络的泛化能力。
2)针对模型的结构辨识,采用Guidorzi方法和神经网络时滞算法分别估计了模型的阶数和时滞系数。
3)针对模型的参数辨识,通过PSO-SMC算法训练的神经网络估计系统模型参数,并最终得到了拟合度较高的室内空间空气品质模型。
As the complexity of the building structure and function increases, as well as building intelligent improves, indoor air quality is increasingly dependent on the control of the air-conditioning systems. In air-conditioning control systems, the effectiveness of indoor air parameters and the accuracy of indoor air quality model are key factors which effect the control result. At present, there are many problems in the detection of indoor air parameters, such as route complexity, small test range. And most indoor air quality models can not meet the qualification for control system. Therefore, the author of this paper developed a wireless sensor network based indoor air parameters detection system and identified a multivariable system identification based indoor air quality model. The research was supported by Beijing Municipal Natural Science Foundation. The main taks are listed as follows:
1、In the wireless sensor network aspect
1) Designed the JN5148based circuit of wireless sensor network node which include communication module's peripheral circuit, power supply circuit and function module circuit.
2) Designed the PIC based hardware timing control circuit which controls the make-break of load switch in a assigned time span.
3) Developed the Zigbee PRO and JenOS based node software programs which realize the corresponding functions of coordinator, router and end device.
4) Developed the ARM9and WinCE OS based main controller which improves the coordinator's processing ability.
2、In the indoor air quality identification aspect
1) Proposed a PSO-SMC (Particle Swarm Optimization-Slide Mode Variable Structure Control) based artificial neural network training algorithm. Comparing with SMC, this algorithm reduces training error and improves network's generalization ability
2) In order to determine indoor air quality model'structure, Guidorzi and neural network time delay identification algorithm were used to identify system's order and time delay parameter.
3) In order to determine indoor air quality model'coefficient, PSO-SMC algorithm was adopted to estimate model'paremeters. Finally, indoor air quality model is established with a high fitting degree.
1、无线传感器网络方面
1)基于JN5148无线通信模块设计制作了无线传感器网络节点硬件电路,包括无线通信模块的外围电路、电源电路和功能模块电路。
2)基于PIC单片机设计了硬件定时控制电路,采用控制负载电源通断的方式降低了近70%的节点平均功耗。
3)基于Zigbee PRO协议和JenOS操作系统开发了的节点软件应用程序,实现了协调节点、路由节点和底层节点的相应功能。
4)基于ARM9开发板和WinCE操作系统开发了网络的主控制器用于提高协调节点的处理能力。2、室内空气品质建模方面
1)提出了基于微粒群-滑模变结构控制(Particle Swarm Optimization-Slide mode Control, PSO-SMC)的神经网络训练算法,相比于单一的滑模变结构控制算法,降低了网络的训练误差,提高了网络的泛化能力。
2)针对模型的结构辨识,采用Guidorzi方法和神经网络时滞算法分别估计了模型的阶数和时滞系数。
3)针对模型的参数辨识,通过PSO-SMC算法训练的神经网络估计系统模型参数,并最终得到了拟合度较高的室内空间空气品质模型。
As the complexity of the building structure and function increases, as well as building intelligent improves, indoor air quality is increasingly dependent on the control of the air-conditioning systems. In air-conditioning control systems, the effectiveness of indoor air parameters and the accuracy of indoor air quality model are key factors which effect the control result. At present, there are many problems in the detection of indoor air parameters, such as route complexity, small test range. And most indoor air quality models can not meet the qualification for control system. Therefore, the author of this paper developed a wireless sensor network based indoor air parameters detection system and identified a multivariable system identification based indoor air quality model. The research was supported by Beijing Municipal Natural Science Foundation. The main taks are listed as follows:
1、In the wireless sensor network aspect
1) Designed the JN5148based circuit of wireless sensor network node which include communication module's peripheral circuit, power supply circuit and function module circuit.
2) Designed the PIC based hardware timing control circuit which controls the make-break of load switch in a assigned time span.
3) Developed the Zigbee PRO and JenOS based node software programs which realize the corresponding functions of coordinator, router and end device.
4) Developed the ARM9and WinCE OS based main controller which improves the coordinator's processing ability.
2、In the indoor air quality identification aspect
1) Proposed a PSO-SMC (Particle Swarm Optimization-Slide Mode Variable Structure Control) based artificial neural network training algorithm. Comparing with SMC, this algorithm reduces training error and improves network's generalization ability
2) In order to determine indoor air quality model'structure, Guidorzi and neural network time delay identification algorithm were used to identify system's order and time delay parameter.
3) In order to determine indoor air quality model'coefficient, PSO-SMC algorithm was adopted to estimate model'paremeters. Finally, indoor air quality model is established with a high fitting degree.
引文
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[2]杜晓明,陈岩.无线传感器网络研究现状与应用[J].北京：工商大学学报.2008年01期.p41-44
[3]方崇智,萧德云.过程辨识[M].北京：清华大学出版社,1988
[4]樊德玺.变风量(VAV)空调系统各环节的Simulink建模与仿真[D].硕士学位论文.哈尔滨：哈尔滨工业大学,2007.P10-15.
[5]高为炳.变结构控制理论基础[M].北京：科学出版杜,1990,P22-73.
[6]高为炳.变结构控制的理论及设计方法[M].北京：科学出版杜,1998
[7]顾成奎,王正欧.基于前向神经网络的非线性时变系统辨识[J].管理科学学报,2001,V4(3)：36-39
[8]韩志刚.多层递阶方法理论与应用的进展[J].北京：控制与决策,2001,16(2),pp129-132.
[9]韩志刚,蒋爱平.自适应辨识、预报和控制多层递阶途径[M].哈尔滨：黑龙江教育出版社,1995.
[10]何芬,马承伟.温室湿度环境的主成分分析人工神经网络建模研究[J].上海：上海交通大学学报(农业科学版).2008,1 0(5).P129-131
[11]李文仲,段朝玉Zigbee 2007/PRO协议栈实验与实践[M].北京：北京航空航大学出版社,2009.P5-21.
[12]刘金琨.滑模变结构控制MATLAB仿真[M].北京：清华大学出版社,2005.10.P67-68.
[13]路立平,鹿晓力,金楠.系统辨识在空调房间建模上的应用[J].山东：山东建筑工程学院院报,2005.8,20(3).P36-38.
[14]马祖长,孙怡宁,梅涛.无线传感器网络综述[J].通信学报.2004,25(4).P114-124
[15]瞿雷,刘胜德,胡咸斌ZigBee技术及应用[M],第1版.北京：北京航空航天大学出版社,2007,P15-216.
[16]于建明,李训铭.变风量系统空调房间建模与特性参数估算[J].北京：计算机仿真.2002,2(19)：69-72.
[17]王晖,何新贵.BP网络泛化能力改进研究[J].北京：系统工程与电子技术,2001,3(23)：P85-87.
[18]王亚慧.离散时间线性SISO系统的拟滑模VS-MRAC策略研究[D].太原科技大学,硕士学位论文,1990.
[19]魏海坤,徐嗣鑫,宋文忠.神经网络的泛化理论和泛化方法[J].北京：自动化学报,2001,27(06)：806-815.
[20]徐小平,王峰,胡钢.系统辨识研究的现状[J].北京：现代电子技术,2007,15(245),p112-116
[21]徐小平,钱富才,刘丁,王峰.基于PSO算法的系统辨识方法[J].北京：系统仿真学报.2008.7(20),p3525-3528.
[22]杨宁,田辉.无线传感器网络拓扑结构研究[J].信息系统与网络,2006,36(2)：11-14.
[23]姚琼荟,黄继起,吴汉松.变结构控制系统[M].重庆：重庆大学出版社,1997：5-47.
[24]张昌凡.滑模变结构的智能控制理论与应用研究[D],湖南大学,博士学位论文,2001：15-45.
[25]张子慧.热工测量与自动控制[M].北京：中国建筑工业出版社,1998,pp135-138.
[26]赵亚丹,王亚慧.多变量系统辨识在空调房间数学建模中的应用研究[D].北京建筑工程学院,硕士学位论文,2008：8-58.
[27]曾建潮,介婧,崔志华.微粒群算法[M].北京：科学出版社,2004.P6-13
[28]A. Nied, S. I. S. Junior, G. G. Parma, B. R. Menezes. On-line Adaptive Neural Training Algorithm for an Induction Motor Flux Observer[C]. IEEE Annual Power Electronics Specialists Conference, v 2005, p 110-115
[29]AF Al-Ajlouni. R.J.Schilling,S.L.Harris. Identification of Nonlinear Discrete-time Systems using Raised-cosine Radial Basis Function Networks [J]. International Journal of Systems Science.2004,35(4):211-221
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