1、基于多數據融合傳感器的分布式溫度控制系統摘要： 在過去的幾十年，溫度控制系統已經被廣泛的應用。對于溫度控制提出了一種基于多傳感器數據融合和CAN總線控制的一般結構。一種新方法是基于多傳感器數據融合估計算法參數分布式溫控系統。該系統的重要特點是其共性，其適用于很多具體領域的大型的溫度控制。實驗結果說明該系統具有較高的準確性、可靠性，良好的實時性和廣泛的應用前景。關鍵詞： 分布式控制系統；CAN總線控制；智能CAN節點；多數據融合傳感器。1介紹 分布式溫度控制系統已經被廣泛的應用在我們日常生活和生產,包括智能建筑、溫室、恒溫車間、大中型糧倉、倉庫等。這種控制保證環境溫度能被保持在兩個預先設定的溫度

2、間。在傳統的溫度測量系統中，我們用一個基于溫度傳感器的單片機系統建立一個RS-485局域網控制器網絡。借助網絡,我們能實行集中監控和控制.然而,當監測區域分布更廣泛和傳輸距離更遠,RS-485總線控制系統的劣勢更加突出。在這種情況下，傳輸和響應速度變得更低，抗干擾能力更差。因此，我們應當尋找新的通信的方法來解決用RS-485總線控制系統而產生的問題。在所有的通訊方式中，適用于工業控制系統的總線控制技術，我們可以突破傳統點對點通信方式的限制、建立一個真正的分布式控制與集中管理系統，CAN總線控制比RS-485總線控制系統更有優勢。比方更好的糾錯能力、改善實時的能力，低本錢等。目前，它正被廣泛的應

3、用于實現分布式測量和范圍控制。 隨著傳感器技術的開展，越來越多的系統開始采用多傳感器數據融合技術來提高他們的實現效果。多傳感器數據融合是一種范式對多種來源整合數據,以綜合成新的信息，比其他局部的總和更加強大。無論在當代和未來，系統的低本錢，節省資源都是傳感器中的一項重要指標。2分布式架構的溫度控制系統 分布式架構溫度控制系統如圖中所示的圖1。可以看出，這系統由兩個模塊兩個智能CAN節點和一個主要的控制器組成。每個模塊局部執行進入分布式架構。下面的是簡短的描述下各模塊。主要控制器 作為系統的主要控制器，這主pc能和智能CAN節點通信。它致力于監督和控制整個系統，系統配置、顯示運行狀況、參數初始化

4、和協調各局部間的關系。更重要的是，我們能打印或儲存系統的歷史溫度的數據，這對分析系統性能是非常有用的。智能CAN節點 每一個溫度控制系統的智能CAN節點有五個局部：MCU一個單片機，A/D轉換單元，溫度監測單元傳感器群，數字顯示器，激發器一個冷卻單元和供暖單元。接下來介紹智能CAN節點的工作原理。 在實際操作中，我們劃分控制的目標進入一些單元，儲存智能CAN節點在一些典型的單元。在每個節點，單片機借助A / D轉換單位從溫度測量傳感器收集溫度數據。同時，它執行根本的數據融合運算獲得運算的結果,更接近實際。數字顯示器及時顯示融合節點的結果，所以我們能及時了解在每個控制單元所處的環境溫度。 通過比

5、擬融合值用主控制器構建一個，這樣智能CAN節點可以通過相應的加熱或冷卻裝置實現反應控制各單元。如果在特別的智能CAN節點融合結果大于設定值，冷卻單位將開始工作。相反,如果在節點融合的結果低于設定值加熱單位將開始工作。用這種方法,我們不僅能監控環境溫度,還能做相應的觸發器來實現溫度的自動調節。與此同時，每個CAN節點發送數據幀到CAN總線，CAN總線將告知在著單元中的主控制器這溫度值，那么這控制器能便利的作出是否修改這參數的決定。自從這CAN節點有調節溫度的單元在，整個房間的溫度將保持均勻。更重要的是,我們也可以通過在主pc上修改溫度的設定值來控制這智能節點。 一般來說,處理器不擅長即時的復雜的

6、數據處理和數據融合,所以如何選擇適宜的數據融合算法對系統變得至關重要。后一節中,我們將介紹適合于智能CAN節點的數據融合方法。4.多傳感器數據融合 旨在利用數據融合在分布式溫度控制系統中來消除不確定性,獲得更精確、可靠是比從限定的傳感器的測量數據的算數平均值更重要。當一些傳感器的溫度傳感器變為無效的，這智能CAN節點還可以通過熔斷這些信息而從有用的傳感器獲得精確溫度。實測數據的一致性核實 在我們設計的分布式溫度控制系統的溫度測量的過程中,突發性干擾或設備故障的影響不可防止的產生測量誤差。所以在數據融合前我們應該消除錯誤的誤差。 我們可以利用系統中配備的少量傳感器用散點圖發消除這個測量誤差。用參

7、數來代表數據分布結構包括中值TM,上四位數 Fv,下四位數FL和分散四位數dF. 人們認為每個傳感器在溫度控制系統的溫度測量所得獨立。在系統中，有八個傳感器在各智能CAN節點的溫度傳感器群。所以我們在每個CAN節點同一時刻能獲得8個溫度值。我們安排收集到的溫度數據序列由小到大:T1, T2, , T8 在序列中，T1是最低位而T8是最高位。我們定義TM為： 上四位數Fv是區間TM, T8的中值，低四位數 Fl是區間T1, TM的中值，這四位數的離散是：。 該公式,一個是常數,取決于系統測量誤差, 通常值是0.5,1.0,2.0等等。在數列中其余的測量值都被看作是于有效值一致的。在智能CAN節點

8、的單片機智將把一致的測量值融合。5. 溫度測量的數據融合的舉例 分布式溫度控制系統運用于一間溫室, 我們從8個溫度傳感器獲得一組8個溫度值如下八個溫度測量值的結果, 測量誤差是.這剩下的七個傳感器被分成兩個傳感器組，S1, S3, S7 是第一組，S2, S4, S6, S8 是第二組。兩組測量數據的算術平均和標準偏差分別如下： 根據公式(13), 我們可以用七個測量溫度確定溫度融合值。融合溫度的結果的誤差是。 很明顯,數據融合測量結果比算術的平均值更接近于實際值。在實際操作中,測量溫度可能是很分散的變得更大的監測區域,數據融合將更加明顯提高了測量精度。 這基于多數據融合傳感器的分布式溫度控制

9、系統是通過CAN總線構建。它充分利用了FDCS即時總線控制系統的特點。數據采集，數據融合，系統控制用智能CAN節點得到實現，而系統管理通過主控制器host PC被實現。通過使用CAN總線與數據融合技術系統的可靠性和實時的能力被大大提高了。我們確定它在將來會得到廣泛的應用。DISTRIBUTED TEMPERATURE CONTROL SYSTEM BASED ON MULTI-SENSOR DATA FUSIONAbstract: Temperature control system has been widely used over the past decades. In this pap

10、er, a general architecture of distributed temperature control system is put forward based on multi-sensor data fusion and CAN bus. A new method of multi-sensor data fusion based on parameter estimation is proposed for the distributed temperature control system. The major feature of the system is its

11、 generality, which is suitable for many fields of large scale temperature control. Experiment shows that this system possesses higher accuracy, reliability, good realtime characteristic and wide application prospectKeywords: Distributed control system; CAN bus; intelligent CAN node; multi-sensor dat

12、a fusion.1. Introduction Distributed temperature control system has been widely used in our daily life and production, including intelligent building, greenhouse, constant temperature workshop, large and medium granary, depot, and so on1. This kind of system should ensure that the environment temper

13、ature can be kept between two predefined limits. In the conventional temperature measurement systems we build a network through RS-485 Bus using a single-chip metering system based on temperature sensors. With the aid of the network, we can carry out centralized monitoring and controlling. However,

14、when the monitoring area is much more widespread and transmission distance becomes farther, the disadvantages of RS-485 Bus become more obvious. In this situation, the transmission and response speed becomes lower, the anti-interference ability becomes worse. Therefore, we should seek out a new comm

15、unication method to solve the problems produced by RS-485 Bus.During all the communication manners, the industrial control-oriented field bus technology can ensure that we can break through the limitation of traditional point to point communication mode and build up a real distributed control and ce

16、ntralized management system. As a serial communication protocol supporting distributed real-time control, CAN bus has much more merits than RS-485 Bus, such as better error correction ability, better real-time ability, lower cost and so on. Presently, it has been extensively used in the implementati

17、on of distributed measurement and control domains. With the development of sensory technology, more and more systems begin to adopt multi-sensor data fusion technology to improve their performances. Multi-sensor data fusion is a kind of paradigm for integrating the data from multiple sources to synt

18、hesize the new information so that the whole is greater than the sum of its parts 345. And it is a critical task both in the contemporary and future systems which have distributed networks of low-cost, resource-constrained sensors2. Distributed architecture of the temperature control system The dist

19、ributed architecture of the temperature control system is depicted in the Figure 1. As can be seen, the system consists of two modulesseveral intelligent CAN nodes and a main controller. They are interconnected with each other through CAN bus. Each module performs its part into the distributed archi

20、tecture. The following is a brief description of each module in the architecture. 31main controllerAs the systems main controller, the host PC can communicate with the intelligent CAN nodes. It is devoted to supervise and control the whole system, such as system configuration, displaying running con

21、dition, parameter initialization and harmonizing the relationships between each part. Whats more, we can print or store the systems history temperature data, which is very useful for the analysis of the system performance3.2. Intelligent CAN node Each intelligent CAN node of the temperature control

22、system includes five units: MCUa single chip, A/D conversion unit, temperature monitoring unitsensor group, digital display unit and actuatorsa cooling unit and a heating unit. The operating principle of the intelligent CAN node is described as follows. In the practical application, we divide the re

23、gion of the control objective into many cells, and lay the intelligent CAN nodes in some of the typical cells. In each node, MCU collects temperature data from the temperature measurement sensor groups with the aid of the A/D conversion unit. Simultaneously, it performs basic data fusion algorithms

24、to obtain a fusion value which is more close to the real one. And the digital display unit displays the fusing result of the node timely, so we can understand the environment temperature in every control cell separately. By comparing the fusion value with the set one by the main controller, the inte

25、lligent CAN node can implement the degenerative feedback control of each cell through enabling the corresponding heating or cooling devices. If the fusion result is bigger than the set value in the special intelligent CAN node, the cooling unit will begin to work. On the contrary, if the fusion resu

26、lt is less than the set value in the node the heating unit will begin to work. By this means we can not only monitor the environment temperature, but also can make the corresponding actuator work so as to regulate the temperature automatically. At the same time every CAN node is able to send data fr

27、ame to the CAN bus which will notify the main controller the temperature value in the cell so that controller can conveniently make decisions to modify the parameter or not. Since the CAN nodes can regulate the temperature of the cell where they are, the temperature in the whole room will be kept ho

28、mogeneous. Whats more, we can also control the intelligent node by modifying the temperatures setting value on the host PC.Generally, the processors on the spot are not good at complex data processing and data fusing, so it becomes very critical how to choose a suitable data fusion algorithm for the

29、 system. In the posterior section, we will introduce a data fusion method which is suitable for the intelligent CAN nodes。4. Multi-sensor data fusion The aim to use data fusion in the distributed temperature control system is to eliminate the uncertainty, gain a more precise and reliable value than

30、the arithmetical mean of the measured data from finite sensors. Furthermore, when some of the sensors become invalid in the temperature sensor groups, the intelligent CAN node can still obtain the accurate temperature value by fusing the information from the other valid sensors. 4.1. Consistency ver

31、ification of the measured data During the process of temperature measurement in our designed distributed temperature control system, measurement error comes into being inevitably because of the influence of the paroxysmal disturb or the equipment fault. So we should eliminate the careless mistake be

32、fore data fusion. We can eliminate the measurement errors by using scatter diagram method in the system equipped with little amount of sensors. Parameters to represent the data distribution structure include medianTM, upper quartile numberFv, lower quartile numberFL and quartile dispersiondF. It is

33、supposed that each sensor in the temperature control system proceeds temperature measurement independently. In the system, there are eight sensors in each temperature sensor group of the intelligent CAN node. So we can obtain eight temperature values in each CAN node at the same time. We arrange the

34、 collected temperature data in a sequence from small to large: T1, T2, , T8 In the sequence, T1 is the limit inferior and T8 is the limit superior. We define the medianTM as: (1) The upper quartileFv is the median of the interval TM, T8.The lower quartile numberFL is the median of the interval T1, T

35、M.The dispersion of the quartile is: 2We suppose that the data is an aberration one if the distance from the median is greater than adF, that is, the estimation interval of invalid data is: (3) In the formula, a is a constant, which is dependent on the system measurement error, commonly its value is

36、 to be 0.5, 1.0, 2.0 and so on. The rest values in the measurement column are considered as to be the valid ones with consistency. And the Single-Chip in the intelligent CAN node will fuse the consistent measurement value to obtain a fusion result 5. Temperature measurement data fusion experiment By

37、 applying the distributed temperature control system to a greenhouse, we obtain an array of eight temperature values from eight sensors as followsThe mean value of the eight measurement temperature result isComparing the mean value (8)T with the true temperature value in the cell of the greenhouse,

38、we can know that the measurement error is +. After we eliminate the careless error from the fifth sensor using the method introduced before, we can obtain the mean value of the rest seven data (7)T=, the measurement error is . The seven rest consistent sensor can be divided into two groups with sens

39、or S1, S3, S7 in the first group and sensor S2, S4, S6, S8 in the second one. The arithmetical mean of the two groups of measured data and the standard deviation are as follows respectively:According to formula (13), we can educe the temperature fusion value with the seven measured temperature value

40、. The error of the fusion temperature result is . It is obvious that the measurement result from data fusion is more close to the true value than that from arithmetical mean. In the practical application, the measured temperature value may be very dispersive as the monitoring area becomes bigger, da

41、ta fusion will improve the measuring precision much more obviously.6. Conclusions The distributed temperature control system based on multi-sensor data fusion is constructed through CAN bus. It takes full advantage of the characteristics of field bus control system-FDCS. Data acquisition, data fusion and system controlling is carried out in t