基于模糊逻辑控制的反应釜温度控制系统外文文献翻译英译汉

1、外文文献fuzzy logic control system for cstr temperature controlmoloy dutta,vaibhav bapat,scachin shelake,tushar achyut & prof.a.d.sonarabstractclosed loop control system incorporating fuzzy logic has been developed for a class of industrial temperature control problem. a unique fuzzy logic controller (f

2、lc) structure with an efficient realization and a small rule base that can 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

3、 the current temperature controller. this includes compensating for thermo 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 adapt

4、ation mechanism to compensate for the dynamic changes in the system. the 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

5、 practice, fuzzy logic control has been rapidly gaining popularity among 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 mod

6、ern control theory, fuzzy logic design is not based on the mathematical 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

7、 the success of fuzzy logic control is largely due to awareness to its many 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 communit

8、y. the research results over the last few years have been reported in 2-4.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 sho

9、wn in figure 1:figure 1: a typical temperature controlthe temperature is 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 po

10、int temperature and actuates the control element. the control element alters 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 studycurre

11、ntly, the classical pid (proportional, integral, derivative) control is 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, eq

12、uipment operation over wide ranges of temperature (140 to 500 degrees), 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 shutd

13、owns to the process being controlled.generally, tuning the pid constants 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 difficul

14、ty in dealing with such problems is compounded with variable time delays 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 del

15、ay (dead time) in the system.it is also well known that pid controllers 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 hea

16、t input, for example, do not necessarily produce equal increments in temperature 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 characteris

17、tics and detailed process understanding is unknown or traditional system 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 h

18、ere is a two-input and single-output controller. the inputs are the deviation 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

19、 of interests to “crisp” numbers used by the control system. fuzzification 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

20、 values and linguistically describes the variables universe of discourse 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)nbnmnszopspmpbnbnbnszopbpbpbpbnmnbnsp

21、bpbpbpbpbnsnbnspbpbpbpbpbzonmnspbpbpbpbpbpsnmzopbpbpbpbpbpmnszopbpbpbpbpbpbnszopbpbpbpbpbhere 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 zer

22、o, small, medium and large. the left and right half of the triangular for 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 fun

23、ctions are symmetric with respect to the origin. selection of the number 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 developme

24、nt strategy for systems with time delay is to regulate the overall loop 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 t

25、o compensate for varying time delays online by tuning the flc output membership 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

26、 to plant.tuning of membership functionthe membership functions subject 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 in

27、put membership functions in each region is adjusted so that the corresponding 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 fu

28、nction is performed.applicationcstr temperature control hardware setupa 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 (resista

29、nce temperature detector) is used as sensor. there are many variations in 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

30、to the flc (fuzzy logic controller) and it calculates the “crisp” value. 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

31、 is important to prevent overshoots, which seriously affect the system performance. 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 de

32、sign specifications without operator tuning.figure 7: process responseconclusionfuzzy 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

33、 find precise solution. the results show significant improvement in maintaining 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

34、, “a stable self-tuning fuzzy logic control system for industrial temperature 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. tho

35、mas a.trautzsch, “self-tuning temperature control using fuzzy logic”, m.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, clevela

36、nd state university, june 1997.5. haissing, christine “adaptive fuzzy temperature 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, 199

37、2.7. chen jiangui and chen laijiu, “study on stability of fuzzy closed loop 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 cont

38、roller”, elsevier science b.v, june, 1994.10. unknown, “adaptive fuzzy system”, 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 c

39、ontrol” 2040 kinton place, oct, 1996.c. m. razaz and j. king “fuzzy temperature controller”.d. n. asha bhat and k.s.sangunni, “programmable control of temperature”.7中文翻译基于模糊逻辑控制的反应釜温度控制系统moloy dutta, vaibhav bapat, scachin shelake, tushar achyut & prof.a.d.sonar摘要基于模糊逻辑的闭环控制系统已经发展到可以解决一系列工业温度控制问题。其中

40、一种独特的模糊逻辑控制器（flc）结构得到了提议，此种模糊逻辑控制器是基于在现有工业控制器中易于有效实现且小型的控制规则上实现的。在现有的工业设备中，无论在软件仿真还是硬件检测上，它都有力的阐明了：模糊逻辑控制器（flc）比目前的温度控制器控制效果更加精确。这种更加精确的控制包括有系统中热量变化补偿、应对未知的变量滞后以及无返回的运行在不同的温度设定值等等。它通过在模糊逻辑控制器中执行一种典型的控制策略和系统中为补偿动态变化的自适应机制而实现的。所提议的模糊逻辑控制器(flc)被应用到带搅拌的连续釜式反应器（cstr）温度控制系统中并且在系统的观测演示中得到了有重大意义的改进与提高。引言当现代

41、控制理论在最大程度上被应用于实践上时，模糊逻辑控制在实际的工程中也得到了快速的普及。这种不断增长的普及则是来源于模糊控制作为一种强大的媒介，使得工程师们将人类的推理合并到控制算法中得以实现。而与现代控制理论相反，模糊逻辑设计并不是基于过程数学模型。该模糊逻辑控制器的设计是将人类循序渐进的推理转化为模糊逻辑语言,这类语言包括有隶属函数，隶属函数语言规则以及隶属函数赋值。我们很容易就能注意到，模糊逻辑控制的成功很大程度上要归于它在很多工业应用上的认识。工业生产上之所以对模糊逻辑控制感兴趣，就像在很多出版物上关于这一方面所附带的控制文献一样，是因为学术委员会对于它的认识得到了不断的提高。这项基于过去

42、几年的研究已经在2-4被报道了。在本文中，相对于目前来说在工业控制中具有广泛应用的比例-积分-微分（pid）控制方法而言，我们专注于研究供选择控制策略的模糊逻辑控制方法。先考虑典型的温度控制系统，如图1所示。图1 温度控制系统框图温度是由一种特定的传感器如热电偶、温度辅助检测器、热敏电阻等测量，然后将测量值转化为控制器能够接收的信号。控制器将测量转化后的温度信号与期望的设定值做比较，并且作用于控制元件。接着控制元件通过改变操纵量来使过程处理过程中所吸收的或者减少的热量发生变化。总之，控制器的目标是调节温度使得尽可能接近给定值。问题研究目前而言，典型的pid（比例，积分，微分）控制因为能够手动的

43、调节各个环节的增益而且是在基于热量以及温度设定值的基础上调节实现，从而被广泛的应用。相对于小热容量设备而言，较大热容量的设备则需要不同的pid增益。此外，比如在温度变化范围由140500基础上运行的设备，则在温度较低和较高时需要不同的增益，以此用来避免过度超调和振荡。这是必需的，因为即使短暂的温度超调也会引起警报，并会使控制过程中断。一般情况下，对于一个大型的温度过程控制系统而言，将其pid参数调节到合适是昂贵的、耗时的。如果是因为缺乏对温度过程控制的理解而加入了修正pid参数，那么控制任务将会更加的复杂1。处理这类问题的难度就在于在很多系统中都存在有易变的时间滞后。在生产制造、新型产品的发展

44、以及物理约束上变化，在不同位置上所装有的辅助温度探测器（rtd）以及温度传感器的变化，包括系统中可变的时间滞后（死时间）。众所周知，在含有未知的非线性比如死区特性、饱和特性以及滞后特性系统中，pid控制器的控制效果比较差。而很多温度控制过程都是非线性的。相等增量的热输入，例如，对于一个典型的非线性系统而言，在很多过程中并不需要产生相等的温度上升增量。这些问题的复杂性和在执行传统控制器时忽略pid参数变化上的难度促使我们向智能控制技术方向上研究，比如模糊逻辑控制，并作为被标记的含有时间延迟、非线性和手动调节程序的控制系统的解决方法。模糊逻辑控制设计当系统遵循一些一般的运行特性和未知的详细的过程解答或者传统的系统模型变得过于复杂时，相对于传统控制方法而言，模糊逻辑控制是一种很有吸引力的选择1,a。模糊控制的主要特点是它具有一种能力，即从质量上可以捕获基于可观测控制系统的属性a,b。模糊逻辑控制器设计这里所设计的模糊逻辑控制器是一个双输入单输出的控制器。双输入来源于设定值误差e(k)和误差变化率e(k)。模糊控制器的运行构造如图2所示。图2 模糊控制器结构模糊化模糊