基于多数据融合传感器的分布式温度控制系统中英文对照论文.docx

1、基于多数据融合传感器的分布式温度控制系统中英文对照论文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 da

2、ta 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 generality, which is suitable for many fields of large scale temperature control. Experiment shows that thi

3、s system possesses higher accuracy, 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 p

4、roduction, including intelligent 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 buil

5、d 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, when the monitoring area is much more widespread and transmission distance becomes farther, the disadvantage

6、s 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 communication method to solve the problems produced by RS-485 Bus.During all the communication manners, the indu

7、strial 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 centralized management system. As a serial communication protocol supporting distributed real-time control, CA

8、N 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 implementation of distributed measurement and control domains. With the development of sensory technology, more and more

9、 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 synthesize the new information so that the whole is greater than the sum of its parts . And it is a critical tas

10、k 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 distributed architecture of the temperature control system is depicted in the Figure 1. As can be seen, the system

11、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 architecture. The following is a brief description of each module in the architecture. 31main controllerAs the syste

12、ms 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 condition, parameter initialization and harmonizing the relationships between each part. Whats more, we can print

13、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 system includes five units: MCUa single chip, A/D conversion unit, temperature monitoring unitsensor group, dig

14、ital 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 region of the control objective into many cells, and lay the intelligent CAN nodes in some of the typical cells.

15、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 to obtain a fusion value which is more close to the real one. And the digital display unit displays the fusing

16、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 intelligent CAN node can implement the degenerative feedback control of each cell through enabling the correspondin

17、g 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 result is less than the set value in the node the heating unit will begin to work. By this means we can not only mo

18、nitor 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 frame to the CAN bus which will notify the main controller the temperature value in the cell so that controller c

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

20、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 system. In the posterior section, we will introduce a data fusion method which is suitable for the intelligent

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

22、valid 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 verification of the measured data During the process of temperature measurement in our designed distributed temper

23、ature 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 before data fusion. We can eliminate the measurement errors by using scatter diagram method in the system equippe

24、d 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 supposed that each sensor in the temperature control system proceeds temperature measurement independently. In

25、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 collected temperature data in a sequence from small to large: T1, T2, , T8 In the sequence, T1 is the limit in

26、ferior 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, TM.The dispersion of the quartile is: （2）We suppose that the data is an aberration one if the distance from the

27、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 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 o

28、nes 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 applying the distributed temperature control system to a greenhouse, we obtain an array of eight temperature

29、 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, we can know that the measurement error is +0.5. After we eliminate the careless error from the fifth sensor u

30、sing the method introduced before, we can obtain the mean value of the rest seven data (7)T=29.6, the measurement error is -0.4. 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 mea

31、n 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. The error of the fusion temperature result is -0.3. It is obvious that the measurement result fr

32、om 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, data 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 the inte