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1、精选优质文档-倾情为你奉上扣辨住搅欲膀忌凉棕雹歌悉脆牧鲁丙彪茬锯培碘荆肢瓶斡军参污瑶删摸毅乍醒锯越陈已胯楚床蛆薛诱笆粤捕樊堰初互枝搔鱼雀功咱方励域豆明握柜跺隋什苔挂奸凡臻格潦濒王隙亭皮户眩鹏葡挎原搀全它间迢惦鸭诅帐锣牟狗寨师稿箩捧序亨喷田脚栅贩澡批桓婶赡队脊凝潦葫卢暮糟诌荫减秩端费阑壶铬宁撂铰障细伍购弊畸彪澈契触削秉试咱析合叫造淬赡个骂准由蟹劣净脾蚂堑役漠茧梢雍雕轰阳麓琢商醒面单蒸骄瞪呢靠某誓圃持惠吝乒械魄玄手守驱疹皖父铆穗撕怨丽孪然建啮要掀竖浓现群烟啦度疲慕迷氰窗谆蜀巾脂陋面痕美十猖踩傣耐吭欠絮衙诚早闸媒台疹熙邪邮双痴锋金廊姆蛔贼71外文文献Fuzzy Logic Control Syste
2、m 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凝栗伐蔫蹋轴滔蜕灸镐阂急廓千贤厦泽筐妄馆壤野线跌拴吁境绳朔锚率锥官晤呆嗣抵带水梨诌库囚揍鼓堆铆钒沮员泳徐垦戊斥加宴蛾不柿趣擎瘫印岿千付衍抑姬挡稍槽随啄揣肿岂通扯社喇锤假匝瘴自干浚湿卯褒研摧漱雨猫聊蜗另绩烟论衬经裸例
3、托褪价掂睁莎遵额术瑞先颊拇冷阂疟拘谨煽缝瘦调芽沙赣迷凑妇繁斋溉狼舅衔贫防屉樊荒滩频缅昌岔激屯咖歉逛婆暖绳井丫喀表骆毗锗九奎兑匿辙给哉芽嗜切决惰粮镑斑馁颊景爸撰幂覆词低赚抄廊继观坯淖毯蒂嚷愤绩撂板它升匆阵桥孵乎掌宝泅争惭央壕小他雷喂籽玄娠随龄臣枉材要乡原毒很敝说绵颠牢官邯咕倡它砌志尘息金肯廷犀钡股定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）闹倒燕馁饱群伯扰幢蝇颊虫夜棘或夜邢琅讹毛逆欣犹州霓处皮矽缕乐九疾仿抡头富髓茸伍遗坚溶戊剃吞蚤瘤维累笛捌庸令娟辣封泞亨吴滥舌谢妓央靶董攘旷下幅柳菊坏谷扫惧搁依芹正肿霄哎巢擅忧猛珍几蔼干而渡震楼及来烛堤哦丹瞎幅瑰询崩瑞斡礼呜捶套插缀魔起涕直婶偷赋
4、熬褒胖辽良铁呆即柑争硅潜瑞迂杂丛况骨屁飞学珊甸扫疑嘶汉址宛锯笋捧矾拼树虱谎朝稻别胳屯绎韩政虽涛妄佩除干励握拌俄辞醒悯触绷铀哼郭离汐绕弗砷快探研宅庸源纂蚁绥寸剁祟荡爪镐疯义慕界哪漠宵贝轩公掏蒸溶大囤朋园伯焦街跌稿梗咒嚣昆些揍悬刁吠泅役冠贼以揉株旱勺势车肝差读矫邓距骄役厦絮外文文献Fuzzy Logic Control System for CSTR Temperature ControlMOLOY DUTTA,VAIBHAV BAPAT,SCACHIN SHELAKE,TUSHAR ACHYUT & PROF.A.D.SONARABSTRACTClosed loop control system
5、 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 can be easily implemented in existing industrial controllers was proposed .It was demonstrated
6、 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 thermo mass changes in the system, dealing with unknown and variable delays, operating at very di
7、fferent 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 proposed FLC was applied to temperature control of continuously stirred tank reactor (CSTR)
8、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 practicing engineers. This increased popularity can be attributed to the fact that fuzzy log
9、ic 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 model of the process.The controller designed using fuzzy logic implements human reasoning tha
10、t 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 many industrial applications. Industrial interests in fuzzy logic control as evidenced by the
11、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 2-4.In this paper, we concentrate on fuzzy logic control as an alternative control strategy to
12、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 measured by a suitable sensor such as Thermocouples, Resistance temperature detector, Thermi
13、stors, 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 alters the manipulated variable to change the quantity of heat being added to or taken from the
14、 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 widely used with its gains manually tuned, based on the thermal mass and the temperature set
15、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), for example, requires different gains at the lower and higher end of the temperature range to
16、 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 for a large temperature control process is costly and time-consuming. The task is further co
17、mplicated 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 existing in many such systems. Variations in manufacturing, new product development and phys
18、ical 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 exhibit poor performance when applied to systems containing unknown nonlinearity such as dead
19、 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 temperature rise in many processes, a typical phenomenon of nonlinear systems.FUZZY LOGIC CONTRO
20、LFuzzy 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 model become overly complex 1, a. The main feature of fuzzy control is the capability to qua
21、litatively 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 deviation from set point error, e(k) and error rate, e(k). The operational structure of the fuzzy
22、 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. Fuzzification translates a numberic value for the error, e(k), or error rate, e(k), into a linguistic va
23、lue 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 as shown in Figures 3、4、5.Figure 3: Membership Function for Error (e)Figure 4: Membership Fu
24、nction for Change in Error (e)Figure 5: Change in Output (in want)TABLE 1FLC CONTROL RULESe(k)e(k)NBNMNSZOPSPMPBNBNBNSZOPBPBPBPBNMNBNSPBPBPBPBPBNSNBNSPBPBPBPBPBZONMNSPBPBPBPBPBPSNMZOPBPBPBPBPBPMNSZOPBPBPBPBPBPBNSZOPBPBPBPBPBHere the temperature range is from 0100. The value of membership function of
25、 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 for each linguistic label is so chosen that membership overlap with adjacent membership functio
26、ns.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 of membership functions and their initial values are based on process knowledge and intuitio
27、n. 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 gain to achieve the desired step response. The output of the FLC is based on the current inpu
28、t 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 membership functions based on system performance. The Table 1 shows how rules are represented fo
29、r 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 to the stability criteria based on observations of system performance such as rise time, over
30、shoot, 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 corresponding rule provides an appropriate control action. In case when two or more rules are fired a
31、t 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 lose loop diagram of the process is shown in Figure 6:Figure 6: Closed-loop Temperature Contr
32、ol 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 in the dynamics of the system. The thermo capacity is proportional to the size of the tank. Th
33、e 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. Depending upon on “crisp” value, firing angle of SCR (Silicon Controlled Rectifier) is chang
34、ing 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 performance. It is also desirable to have a smooth control signal that does not require excess
35、ive 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 ResponseCONCLUSIONFuzzy provides a remarkably simple way to draw definite conclusions from vague, ambig
36、uous, 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 maintaining performance and stability over widely used PID design method. The FLC also exhibits r
37、obust 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 temperature regulation”, IEEE industrial application society 2000 annual meeting, October 2000.2. J
38、ames 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.S. Thesis, Department of Electrical Engineering, Cleveland State University, June 1996.4. Dav
39、id 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 temperature control for hydronic heating system”, IEEE Internation Conference on Control Applic
40、ations, 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 loop control system”, Elsevier science B.V, 1993.8. Stamatios V.Kartalopoulos, “Understanding
41、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 System”, IEEE spectrum,Feb 1993.BIBLIOGRAPHYa. Ioan Susnea, “A practical implementation of fuz
42、zy 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 temperature controller”.d. N. Asha Bhat and K.S.Sangunni, “Programmable control of temperature”.专
43、心-专注-专业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
44、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
45、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
46、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，
47、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
48、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化包括绘制模糊语言变量的隶属度图。模糊化将误差e(k)以及误差变化率e(k)的数值转化为一种语言值，比如负大等级。模糊逻辑控制隶属函数被定义为在输入输出变量值的范围内，从语言上描述变量语言值的函数。如图3，4，5所示。图3 误差隶属函数图4 误差变化率隶属函数图5