外文翻譯 基于多數(shù)據(jù)融合傳感器的分布式溫度控制系統(tǒng)

1、DISTRIBUTED TEMPERATURE CONTROL SYSTEM BASED ON MULTI-SENSOR DATA FUSION Abstract: Temperature control system has been widely used over the past decades. In this paper, a general architecture of distributed temperature control system is put forward based on multi-sensor data fusion and CAN bus. A ne

2、w 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 generality, which is suitable for many fields of large scale temperature control. Experiment shows that this system possesses higher a

3、ccuracy, reliability, good realtime characteristic and wide application prospectKeywords: Distributed control system; CAN bus; intelligent CAN node; multi-sensor data fusion.1. Introduction Distributed temperature control system has been widely used in our daily life and production, including intell

4、igent building, greenhouse, constant temperature workshop, large and medium granary, depot, and so on1. This kind of system should ensure that the environment temperature can be kept between two predefined limits. In the conventional temperature measurement systems we build a network through RS-485

5、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, when the monitoring area is much more widespread and transmission distance becomes farther, the disadvantages of RS-485 Bus become more

6、 obvious. In this situation, the transmission and response speed becomes lower, the anti-interference ability becomes worse. Therefore, we should seek out a new communication method to solve the problems produced by RS-485 Bus.During all the communication manners, the industrial control-oriented fie

7、ld 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 centralized management system. As a serial communication protocol supporting distributed real-time control, CAN bus has much more merits

8、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 implementation of distributed measurement and control domains. With the development of sensory technology, more and more systems begin to adopt mul

9、ti-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 synthesize the new information so that the whole is greater than the sum of its parts . And it is a critical task both in the contemporary

10、and future systems which have distributed networks of low-cost, resource-constrained sensors2. Distributed architecture of the temperature control system The distributed architecture of the temperature control system is depicted in the Figure 1. As can be seen, the system consists of two modulesseve

11、ral intelligent CAN nodes and a main controller. They are interconnected with each other through CAN bus. Each module performs its part into the distributed architecture. The following is a brief description of each module in the architecture. 31main controllerAs the systems main controller, the hos

12、t PC can communicate with the intelligent CAN nodes. It is devoted to supervise and control the whole system, such as system configuration, displaying running condition, parameter initialization and harmonizing the relationships between each part. Whats more, we can print or store the systems histor

13、y 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 system includes five units: MCUa single chip, A/D conversion unit, temperature monitoring unitsensor group, digital display unit and actua

14、torsa 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 region of the control objective into many cells, and lay the intelligent CAN nodes in some of the typical cells. In each node, MCU collects

15、temperature data from the temperature measurement sensor groups with the aid of the A/D conversion unit. Simultaneously, it performs basic data fusion algorithms 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,

16、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 intelligent CAN node can implement the degenerative feedback control of each cell through enabling the corresponding heating or cooling device

17、s. 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 result 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 tempe

18、rature, 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 frame to the CAN bus which will notify the main controller the temperature value in the cell so that controller can conveniently make decisi

19、ons 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 homogeneous. Whats more, we can also control the intelligent node by modifying the temperatures setting value on the host PC.Generally, the

20、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 system. In the posterior section, we will introduce a data fusion method which is suitable for the intelligent CAN nodes。4. Multi-sensor

21、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 the arithmetical mean of the measured data from finite sensors. Furthermore, when some of the sensors become invalid in the temperature se

22、nsor groups, the intelligent CAN node can still obtain the accurate temperature value by fusing the information from the other valid sensors. 4.1. Consistency verification of the measured data During the process of temperature measurement in our designed distributed temperature control system, measu

23、rement error comes into being inevitably because of the influence of the paroxysmal disturb or the equipment fault. So we should eliminate the careless mistake before data fusion. We can eliminate the measurement errors by using scatter diagram method in the system equipped with little amount of sen

24、sors. Parameters to represent the data distribution structure include medianTM, upper quartile numberFv, lower quartile numberFL and quartile dispersiondF. It is supposed that each sensor in the temperature control system proceeds temperature measurement independently. In the system, there are eight

25、 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 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

26、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, TM.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, t

27、hat 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 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

28、 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 applying the distributed temperature control system to a greenhouse, we obtain an array of eight temperature values from eight sensors as

29、 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, we can know that the measurement error is +. After we eliminate the careless error from the fifth sensor using the method introduced befor

30、e, 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 sensor 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

31、 the standard deviation are as follows respectively:According to formula (13), we can educe the temperature fusion value with the seven measured temperature value. The error of the fusion temperature result is . It is obvious that the measurement result from data fusion is more close to the true val

32、ue than that from arithmetical mean. In the practical application, the measured temperature value may be very dispersive as the monitoring area becomes bigger, data fusion will improve the measuring precision much more obviously.6. Conclusions The distributed temperature control system based on mult

33、i-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 the intelligent CAN node, and system management is implemented in the main controller (host PC)

34、. By using CAN bus and data fusion technology the reliability and real-time ability of the system is greatly improved. We are sure that it will be widely used in the future.References 1 Waltz E. Liinas J, Multi-sensor Data Fusion, Artech House, New York, 1990. 2 Philips Semiconductors, (1995b). “P82

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36、or Integration and Fusion in Intelligent Systems, IEEE Trans. on Systems, Man, and Cybernetics, Vol. 19, No. 5, pp.901-931 September/October, 1989. 5 Pau LF, Sensors data fusion, Journal of Intelligent and Robotic System, pp. 103-106, 1998. 6 Thomopoulos S C., Sensor integration and data fusion, Jou

37、rnal of Robotic Systems, pp.337-372, 1990. 7 Rao B S Y, Durrant-Whyte H F, Sheen J A, A fully decentralized multi-sensor system for tracking and surveillance, The International Journal of Robotics Research, Massachusetts Institute of Technology, Vol 12, No. 1, pp. 20-44, Feb 1993. 8 Tenney R R, Jr s

38、andell N R, Detection with distributed sensors, AES, Vol 17, pp.501-510, 1981 基于多數(shù)據(jù)融合傳感器的分布式溫度控制系統(tǒng)摘要： 在過去的幾十年，溫度控制系統(tǒng)已經(jīng)被廣泛的應(yīng)用。對于溫度控制提出了一種基于多傳感器數(shù)據(jù)融合和CAN總線控制的一般結(jié)構(gòu)。一種新方法是基于多傳感器數(shù)據(jù)融合估計算法參數(shù)分布式溫控系統(tǒng)。該系統(tǒng)的重要特點是其共性，其適用于很多具體領(lǐng)域的大型的溫度控制。實驗結(jié)果說明該系統(tǒng)具有較高的準確性、可靠性，良好的實時性和廣泛的應(yīng)用前景。關(guān)鍵詞： 分布式控制系統(tǒng)；CAN總線控制；智能CAN節(jié)點；多數(shù)據(jù)融合傳感器。1介紹

39、 分布式溫度控制系統(tǒng)已經(jīng)被廣泛的應(yīng)用在我們?nèi)粘Ｉ詈蜕a(chǎn),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(huán)境溫度能被保持在兩個預(yù)先設(shè)定的溫度間。在傳統(tǒng)的溫度測量系統(tǒng)中，我們用一個基于溫度傳感器的單片機系統(tǒng)建立一個RS-485局域網(wǎng)控制器網(wǎng)絡(luò)。借助網(wǎng)絡(luò),我們能實行集中監(jiān)控和控制.然而,當(dāng)監(jiān)測區(qū)域分布更廣泛和傳輸距離更遠,RS-485總線控制系統(tǒng)的劣勢更加突出。在這種情況下，傳輸和響應(yīng)速度變得更低，抗干擾能力更差。因此，我們應(yīng)當(dāng)尋找新的通信的方法來解決用RS-485總線控制系統(tǒng)而產(chǎn)生的問題。在所有的通訊方式中，適用于工業(yè)控制系統(tǒng)的總線控制技術(shù)，我們可以突破傳統(tǒng)點對點通信方式的限制、

40、建立一個真正的分布式控制與集中管理系統(tǒng)，CAN總線控制比RS-485總線控制系統(tǒng)更有優(yōu)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(yīng)用于實現(xiàn)分布式測量和范圍控制。 隨著傳感器技術(shù)的開展，越來越多的系統(tǒng)開始采用多傳感器數(shù)據(jù)融合技術(shù)來提高他們的實現(xiàn)效果。多傳感器數(shù)據(jù)融合是一種范式對多種來源整合數(shù)據(jù),以綜合成新的信息，比其他局部的總和更加強大。無論在當(dāng)代和未來，系統(tǒng)的低本錢，節(jié)省資源都是傳感器中的一項重要指標(biāo)。2分布式架構(gòu)的溫度控制系統(tǒng) 分布式架構(gòu)溫度控制系統(tǒng)如圖中所示的圖1?？梢钥闯觯@系統(tǒng)由兩個模塊兩個智能CAN節(jié)點和一個主要的控制器組成。每個模塊局部執(zhí)行進入分布式架構(gòu)。

41、下面的是簡短的描述下各模塊。主要控制器 作為系統(tǒng)的主要控制器，這主pc能和智能CAN節(jié)點通信。它致力于監(jiān)督和控制整個系統(tǒng)，系統(tǒng)配置、顯示運行狀況、參數(shù)初始化和協(xié)調(diào)各局部間的關(guān)系。更重要的是，我們能打印或儲存系統(tǒng)的歷史溫度的數(shù)據(jù)，這對分析系統(tǒng)性能是非常有用的。智能CAN節(jié)點 每一個溫度控制系統(tǒng)的智能CAN節(jié)點有五個局部：MCU一個單片機，A/D轉(zhuǎn)換單元，溫度監(jiān)測單元傳感器群，數(shù)字顯示器，激發(fā)器一個冷卻單元和供暖單元。接下來介紹智能CAN節(jié)點的工作原理。 在實際操作中，我們劃分控制的目標(biāo)進入一些單元，儲存智能CAN節(jié)點在一些典型的單元。在每個節(jié)點，單片機借助A / D轉(zhuǎn)換單位從溫度測量傳感器收集溫度數(shù)據(jù)。同時，它執(zhí)行根本的數(shù)據(jù)融合運算獲得運算的結(jié)果,更接近實際。數(shù)字顯示器及時顯示融合節(jié)點的結(jié)果，所以我們能及時了解在每個控制單元所處的環(huán)境溫度。 通過比擬融合值用主控制器構(gòu)建一個，這樣智能CAN節(jié)點可以通過相應(yīng)的加熱或冷卻裝置實現(xiàn)反應(yīng)控制各單元。如