基于模糊邏輯控制的反應(yīng)釜溫度控制系統(tǒng)外文文獻(xiàn)翻譯英譯漢

1、外文文獻(xiàn)Fuzzy Logic Control System for CSTR Temperature ControlABSTRACTClosed loop control system incorporating fuzzy logic has been developed for a class of industrial temperature control problem. A unique fuzzy logic controller (FLC) structure with an efficient realization and a small rule base that c

2、an be easily implemented in existing industrial controllers was proposed .It was demonstrated in both software simulation and hardware test in an industrial setting that the fuzzy logic controller (FLC) is much more capable than the current temperature controller. This includes compensating for ther

3、mo mass changes in the system, dealing with unknown and variable delays, operating at very different temperature set points without returning etc. It is achieved by implementing, in FLC, a classical control strategy and an adaptation mechanism to compensate for the dynamic changes in the system. The

4、 proposed FLC was applied to temperature control of continuously stirred tank reactor (CSTR) and significant improvements in the system performance are observed.INTRODUCTIONWhile modern control theory has made modest inroad into practice, fuzzy logic control has been rapidly gaining popularity among

5、 practicing engineers. This increased popularity can be attributed to the fact that fuzzy logic control provides a powerful vehicle that allows engineers to incorporate human reasoning in the control algorithm. As opposed to modern control theory, fuzzy logic design is not based on the mathematical

6、model of the process.The controller designed using fuzzy logic implements human reasoning that has been programmed into fuzzy logic language (membership functions, rule and the rule interpretation).It is interesting to note that the success of fuzzy logic control is largely due to awareness to its m

7、any industrial applications. Industrial interests in fuzzy logic control as evidenced by the many publications on the subject in the control literature have created awareness of its increasing importance by the academic community. The research results over the last few years have been reported in 2-

8、4.In this paper, we concentrate on fuzzy logic control as an alternative control strategy to the current proportion-integral-derivative (PID) method used widely in industry. Consider a typical temperature control application shown in Figure 1:Figure 1: A typical Temperature ControlThe temperature is

9、 measured by a suitable sensor such as Thermocouples, Resistance temperature detector, Thermistors, etc and converted to a signal acceptable to the controller. The controller compares the temperature signal to the desired set point temperature and actuates the control element. The control element al

10、ters the manipulated variable to change the quantity of heat being added to or taken from the process. The objective of the controller is to regulate the temperature as close as possible to the set point.PROBLEM UNDER STUDYCurrently, the classical PID (proportional, integral, derivative) control is

11、widely used with its gains manually tuned, based on the thermal mass and the temperature set point. Equipment with large thermal capacities require different PID gains than equipment with small thermal capacities.In addition, equipment operation over wide ranges of temperature (140 to 500 degrees),

12、for example, requires different gains at the lower and higher end of the temperature range to avoid overshoots and oscillations. This is necessary since even brief temperature overshoots initiate nuisance alarms and costly shutdowns to the process being controlled.Generally, tuning the PID constants

13、 for a large temperature control process is costly and time-consuming. The task is further complicated when incorrect PID constants are sometimes entered due to lack of understanding of temperature control process 1.The difficulty in dealing with such problems is compounded with variable time delays

14、 existing in many such systems. Variations in manufacturing, new product development and physical constraints place the Resistance Temperature Detector (RTD) temperature sensor at different locations, including variable time delay (dead time) in the system.It is also well known that PID controllers

15、exhibit poor performance when applied to systems containing unknown nonlinearity such as dead zones, saturation and hysteresis.It is further understood that many temperature control process are nonlinear. Equal increments of heat input, for example, do not necessarily produce equal increments in tem

16、perature rise in many processes, a typical phenomenon of nonlinear systems.FUZZY LOGIC CONTROLFuzzy logic control is an appealing alternative to conventional control methods when systems follow some general operating characteristics and detailed process understanding is unknown or traditional system

17、 model become overly complex 1, a. The main feature of fuzzy control is the capability to qualitatively capture the attributes of a control system based on observable phenomenon a, b.Fuzzy Logic Control DesignThe FLC developed here is a two-input and single-output controller. The inputs are the devi

18、ation from set point error, e(k) and error rate, e(k). The operational structure of the fuzzy controller is shown in Figure 2:Figure 2: Structure of Fuzzy ControllerFuzzificationFuzzification involves mapping the fuzzy variables of interests to “crisp” numbers used by the control system. Fuzzificati

19、on translates a numberic value for the error, e(k), or error rate, e(k), into a linguistic value such as positive large with a membership grade.The FLC membership functions are defined over the range of input and output variable values and linguistically describes the variables universe of discourse

20、 as shown in Figures 3、4、5.Figure 3: Membership Function for Error (e)Figure 4: Membership Function for Change in Error (e)Figure 5: Change in Output (in want)TABLE 1FLC CONTROL RULESe(k)e(k)NBNMNSZOPSPMPBNBNBNSZOPBPBPBPBNMNBNSPBPBPBPBPBNSNBNSPBPBPBPBPBZONMNSPBPBPBPBPBPSNMZOPBPBPBPBPBPMNSZOPBPBPBPBP

21、BPBNSZOPBPBPBPBPBHere the temperature range is from 0100. The value of membership function of error varies from -5 to 75 and for the error change is -5 to 0.The triangular input membership functions for the linguistic labels zero, small, medium and large. The left and right half of the triangular fo

22、r each linguistic label is so chosen that membership overlap with adjacent membership functions.The output membership functions for the labels are zero, small, medium and large. Both the input and output variables membership functions are symmetric with respect to the origin. Selection of the number

23、 of membership functions and their initial values are based on process knowledge and intuition. The main idea is to define partition of operating regions that will represent the process variables.Rules developmentRules development strategy for systems with time delay is to regulate the overall loop

24、gain to achieve the desired step response. The output of the FLC is based on the current input e(k) and e(k), and without any knowledge of the previous input and output data. The rules developed in this paper for CSTR are able to compensate for varying time delays online by tuning the FLC output mem

25、bership functions based on system performance. The Table 1 shows how rules are represented for CSTR 8.DefuzzificationDefuzzification takes the fuzzy output of the rules and generates a “crisp” numberic value use as control input to plant.Tuning of membership functionThe membership functions subject

26、to the stability criteria based on observations of system performance such as rise time, overshoot, steady state error. According to the resolution needed, number of membership function increases. The center and slopes of the input membership functions in each region is adjusted so that the correspo

27、nding rule provides an appropriate control action. In case when two or more rules are fired at the same time, the dominant rule is tuned first. Once input membership rule tuning is completed, fine-tuning of output, membership function is performed.APPLICATIONCSTR temperature control hardware setupA

28、lose loop diagram of the process is shown in Figure 6:Figure 6: Closed-loop Temperature Control SystemIn this paper, the application of fuzzy logic is to control the temperature of water. For sensing the temperature RTD (Resistance Temperature Detector) is used as sensor. There are many variations i

29、n the dynamics of the system. The thermo capacity is proportional to the size of the tank. The time delay in the system is quite sensitive to the placement of the RTD. The RTD senses the temperature of water and give the signal to the FLC (Fuzzy Logic Controller) and it calculates the “crisp” value.

30、 Depending upon on “crisp” value, firing angle of SCR (Silicon Controlled Rectifier) is changing and eventually control the power supplied to the heater through interfacing card.TEST RESULTSIn temperature control application, it is important to prevent overshoots, which seriously affect the system p

31、erformance. It is also desirable to have a smooth control signal that does not require excessive on and off actions in the heater. The results are shown in the Figure 7. In each case, the FLC was able to successfully meet all design specifications without operator tuning.Figure 7: Process ResponseCO

32、NCLUSIONFuzzy provides a remarkably simple way to draw definite conclusions from vague, ambiguous, imprecise information. In a sense, fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solution. The results show significant improvement in main

33、taining performance and stability over widely used PID design method. The FLC also exhibits robust performance for plants with significant variations in dynamics.REFERENCES1. Zhiqiang Gao, Thomas A. Trautzsch and James G. Dawson, “A stable self-tuning fuzzy logic control system for industrial temper

34、ature regulation”, IEEE industrial application society 2000 annual meeting, October 2000.2. James Dawson, “Fuzzy Logic Control of Linear System with Variable Time Delay”, M.S. Thesis, Cleveland State University, June 1994.3. Thomas A.Trautzsch, “Self-tuning Temperature Control Using Fuzzy Logic”, M.

35、S. Thesis, Department of Electrical Engineering, Cleveland State University, June 1996.4. David J.Elliot, “Fuzzy Logic Position Swrvo Motor Control Development Platform”, M.S. Thesis,Department of Electrical Engineering, Cleveland State University, June 1997.5. Haissing, Christine “Adaptive fuzzy te

36、mperature control for hydronic heating system”, IEEE Internation Conference on Control Applications, Hawaii August 1999, volume 1.6. Daniel G. Schwaretz and George J. Klir, “Fuzzy Logic Flowers in Japan”, IEEE Spectrum July, 1992.7. Chen Jiangui and Chen Laijiu, “Study on stability of fuzzy closed l

37、oop control system”, Elsevier science B.V, 1993.8. Stamatios V.Kartalopoulos, “Understanding neural network and Fuzzy Logic”, PHI.9. Soo Yeong Yi and Myung Jin Chung, “Systematic Design and Stability Analysis of Fuzzy Logic Controller”, Elsevier Science B.V, June, 1994.10. Unknown, “Adaptive Fuzzy S

38、ystem”, IEEE spectrum,Feb 1993.BIBLIOGRAPHYa. Ioan Susnea, “A practical implementation of fuzzy logic controller with Motorola 68HC11”, University Dunarea De Jos of Galati, Romania.b. Aptronix Incorporated “Reactor temperature control” 2040 Kinton Place, Oct, 1996.c. M. Razaz and J. King “Fuzzy temp

39、erature controller”.d. N. Asha Bhat and K.S.Sangunni, “Programmable control of temperature”.中文翻譯基于模糊邏輯控制的反應(yīng)釜溫度控制系統(tǒng)MOLOY DUTTA, VAIBHAV BAPAT, SCACHIN SHELAKE, TUSHAR摘要基于模糊邏輯的閉環(huán)控制系統(tǒng)已經(jīng)發(fā)展到可以解決一系列工業(yè)溫度控制問(wèn)題。其中一種獨(dú)特的模糊邏輯控制器（FLC）結(jié)構(gòu)得到了提議，此種模糊邏輯控制器是基于在現(xiàn)有工業(yè)控制器中易于有效實(shí)現(xiàn)且小型的控制規(guī)則上實(shí)現(xiàn)的。在現(xiàn)有的工業(yè)設(shè)備中，無(wú)論在軟件仿真還是硬件檢測(cè)上，它都有力的闡明

40、了：模糊邏輯控制器（FLC）比目前的溫度控制器控制效果更加精確。這種更加精確的控制包括有系統(tǒng)中熱量變化補(bǔ)償、應(yīng)對(duì)未知的變量滯后以及無(wú)返回的運(yùn)行在不同的溫度設(shè)定值等等。它通過(guò)在模糊邏輯控制器中執(zhí)行一種典型的控制策略和系統(tǒng)中為補(bǔ)償動(dòng)態(tài)變化的自適應(yīng)機(jī)制而實(shí)現(xiàn)的。所提議的模糊邏輯控制器(FLC)被應(yīng)用到帶攪拌的連續(xù)釜式反應(yīng)器（CSTR）溫度控制系統(tǒng)中并且在系統(tǒng)的觀測(cè)演示中得到了有重大意義的改進(jìn)與提高。引言當(dāng)現(xiàn)代控制理論在最大程度上被應(yīng)用于實(shí)踐上時(shí)，模糊邏輯控制在實(shí)際的工程中也得到了快速的普及。這種不斷增長(zhǎng)的普及則是來(lái)源于模糊控制作為一種強(qiáng)大的媒介，使得工程師們將人類的推理合并到控制算法中得以實(shí)現(xiàn)。而與

41、現(xiàn)代控制理論相反，模糊邏輯設(shè)計(jì)并不是基于過(guò)程數(shù)學(xué)模型。該模糊邏輯控制器的設(shè)計(jì)是將人類循序漸進(jìn)的推理轉(zhuǎn)化為模糊邏輯語(yǔ)言,這類語(yǔ)言包括有隸屬函數(shù)，隸屬函數(shù)語(yǔ)言規(guī)則以及隸屬函數(shù)賦值。我們很容易就能注意到，模糊邏輯控制的成功很大程度上要?dú)w于它在很多工業(yè)應(yīng)用上的認(rèn)識(shí)。工業(yè)生產(chǎn)上之所以對(duì)模糊邏輯控制感興趣，就像在很多出版物上關(guān)于這一方面所附帶的控制文獻(xiàn)一樣，是因?yàn)閷W(xué)術(shù)委員會(huì)對(duì)于它的認(rèn)識(shí)得到了不斷的提高。這項(xiàng)基于過(guò)去幾年的研究已經(jīng)在2-4被報(bào)道了。在本文中，相對(duì)于目前來(lái)說(shuō)在工業(yè)控制中具有廣泛應(yīng)用的比例-積分-微分（PID）控制方法而言，我們專注于研究供選擇控制策略的模糊邏輯控制方法。先考慮典型的溫度控制系統(tǒng)

42、，如圖1所示。圖1 溫度控制系統(tǒng)框圖溫度是由一種特定的傳感器如熱電偶、溫度輔助檢測(cè)器、熱敏電阻等測(cè)量，然后將測(cè)量值轉(zhuǎn)化為控制器能夠接收的信號(hào)。控制器將測(cè)量轉(zhuǎn)化后的溫度信號(hào)與期望的設(shè)定值做比較，并且作用于控制元件。接著控制元件通過(guò)改變操縱量來(lái)使過(guò)程處理過(guò)程中所吸收的或者減少的熱量發(fā)生變化?？傊?，控制器的目標(biāo)是調(diào)節(jié)溫度使得盡可能接近給定值。問(wèn)題研究目前而言，典型的PID（比例，積分，微分）控制因?yàn)槟軌蚴謩?dòng)的調(diào)節(jié)各個(gè)環(huán)節(jié)的增益而且是在基于熱量以及溫度設(shè)定值的基礎(chǔ)上調(diào)節(jié)實(shí)現(xiàn)，從而被廣泛的應(yīng)用。相對(duì)于小熱容量設(shè)備而言，較大熱容量的設(shè)備則需要不同的PID增益。此外，比如在溫度變化范圍由140500基礎(chǔ)上運(yùn)

43、行的設(shè)備，則在溫度較低和較高時(shí)需要不同的增益，以此用來(lái)避免過(guò)度超調(diào)和振蕩。這是必需的，因?yàn)榧词苟虝旱臏囟瘸{(diào)也會(huì)引起警報(bào)，并會(huì)使控制過(guò)程中斷。一般情況下，對(duì)于一個(gè)大型的溫度過(guò)程控制系統(tǒng)而言，將其PID參數(shù)調(diào)節(jié)到合適是昂貴的、耗時(shí)的。如果是因?yàn)槿狈?duì)溫度過(guò)程控制的理解而加入了修正PID參數(shù)，那么控制任務(wù)將會(huì)更加的復(fù)雜1。處理這類問(wèn)題的難度就在于在很多系統(tǒng)中都存在有易變的時(shí)間滯后。在生產(chǎn)制造、新型產(chǎn)品的發(fā)展以及物理約束上變化，在不同位置上所裝有的輔助溫度探測(cè)器（RTD）以及溫度傳感器的變化，包括系統(tǒng)中可變的時(shí)間滯后（死時(shí)間）。眾所周知，在含有未知的非線性比如死區(qū)特性、飽和特性以及滯后特性系統(tǒng)中，P

44、ID控制器的控制效果比較差。而很多溫度控制過(guò)程都是非線性的。相等增量的熱輸入，例如，對(duì)于一個(gè)典型的非線性系統(tǒng)而言，在很多過(guò)程中并不需要產(chǎn)生相等的溫度上升增量。這些問(wèn)題的復(fù)雜性和在執(zhí)行傳統(tǒng)控制器時(shí)忽略PID參數(shù)變化上的難度促使我們向智能控制技術(shù)方向上研究，比如模糊邏輯控制，并作為被標(biāo)記的含有時(shí)間延遲、非線性和手動(dòng)調(diào)節(jié)程序的控制系統(tǒng)的解決方法。模糊邏輯控制設(shè)計(jì)當(dāng)系統(tǒng)遵循一些一般的運(yùn)行特性和未知的詳細(xì)的過(guò)程解答或者傳統(tǒng)的系統(tǒng)模型變得過(guò)于復(fù)雜時(shí)，相對(duì)于傳統(tǒng)控制方法而言，模糊邏輯控制是一種很有吸引力的選擇1,a。模糊控制的主要特點(diǎn)是它具有一種能力，即從質(zhì)量上可以捕獲基于可觀測(cè)控制系統(tǒng)的屬性a,b。模糊邏輯控制器設(shè)計(jì)這里所設(shè)計(jì)的模糊邏輯控制器是一個(gè)雙輸入單輸出的控制器。雙輸入來(lái)源于設(shè)定值誤差e(k)和誤差變化率e(k)。模糊控制器的運(yùn)行構(gòu)造如圖2所示。圖2 模糊控制器結(jié)構(gòu)模糊化模糊化包括繪制模糊語(yǔ)言變