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.docx

1、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 uniq

2、ue 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 in both software simulation and hardware test in an industrial setting that the fuzzy logic controller (FL

3、C) 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 different temperature set points without returning etc. It is achieved by implementing, in FLC, a classical c

4、ontrol 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) and significant improvements in the system performance are observed.INTRODUCTIONWhile modern control theory

5、 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 logic control provides a powerful vehicle that allows engineers to incorporate human reasoning in the control

6、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 that has been programmed into fuzzy logic language (membership functions, rule and the rule interpretation).It

7、 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 many publications on the subject in the control literature have created awareness of its increasing importa

8、nce 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 the current proportion-integral-derivative (PID) method used widely in industry. Consider a typical tempera

9、ture 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, Thermistors, etc and converted to a signal acceptable to the controller. The controller compares the temperature

10、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 process. The objective of the controller is to regulate the temperature as close as possible to the set po

11、int.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 point. Equipment with large thermal capacities require different PID gains than equipment with small therma

12、l 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 avoid overshoots and oscillations. This is necessary since even brief temperature overshoots initiate nuis

13、ance 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 complicated when incorrect PID constants are sometimes entered due to lack of understanding of temperature co

14、ntrol 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 physical constraints place the Resistance Temperature Detector (RTD) temperature sensor at different locations,

15、 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 zones, saturation and hysteresis.It is further understood that many temperature control process are nonlin

16、ear. 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 CONTROLFuzzy logic control is an appealing alternative to conventional control methods when systems follow some g

17、eneral 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 qualitatively capture the attributes of a control system based on observable phenomenon a, b.Fuzzy Logic Contr

18、ol 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 controller is shown in Figure 2:Figure 2: Structure of Fuzzy ControllerFuzzificationFuzzification involves

19、 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 value such as positive large with a membership grade.The FLC membership functions are defined over the range

20、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 Function for Change in Error (e)Figure 5: Change in Output (in want)TABLE 1FLC CONTROL RULESe(k)e(k)NBNMNSZOP

21、SPMPBNBNBNSZOPBPBPBPBNMNBNSPBPBPBPBPBNSNBNSPBPBPBPBPBZONMNSPBPBPBPBPBPSNMZOPBPBPBPBPBPMNSZOPBPBPBPBPBPBNSZOPBPBPBPBPBHere 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 f

22、or 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 functions.The output membership functions for the labels are zero, small, medium and large. Both the input and out

23、put 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 intuition. The main idea is to define partition of operating regions that will represent the process variables.Rule

24、s 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 input e(k) and e(k), and without any knowledge of the previous input and output data. The rules developed in th

25、is 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 for CSTR 8.DefuzzificationDefuzzification takes the fuzzy output of the rules and generates a “crisp” numberi

26、c 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, overshoot, steady state error. According to the resolution needed, number of membership function increases. The

27、 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 at the same time, the dominant rule is tuned first. Once input membership rule tuning is completed, fine-tun

28、ing 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 Control SystemIn this paper, the application of fuzzy logic is to control the temperature of water. For sensing

29、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. The time delay in the system is quite sensitive to the placement of the RTD. The RTD senses the temperature o

30、f 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 changing and eventually control the power supplied to the heater through interfacing card.TEST RESULTSIn tempera

31、ture 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 excessive on and off actions in the heater. The results are shown in the Figure 7. In each case, the FLC was able

32、 to successfully meet all design 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 find precise solution. The results show significant improvement in maintaining performanc