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基于模糊温度控制的MATLAB仿真


Based On Fuzzy Controler On MATLAB Simulink Simulation (基于模糊控制的 matlab simulink 仿真)
Abstract — For improving the temperature control precision as the industry require. In this paper we i

ntroduce how to design Fuzzy controller in detail and how to model in MATLAB and use Fuzzy Toolbox and SIMULINK in MATLAB to realize the computer simulation of parameters control system. Using the algorithm of Fuzzy control in the system,the temperature was controlled in good state.At present,the system has been used in the phase of the application and the pilot of the resistance furnace temperature in the actual industrial , and satisfying results were achieved . Practice shows that Fuzzy control method improves the leal—time performance 、 stability and accuracy of controlling and makes the operation simplified.The use for reference of the method was obviously in industrial application. 摘要:为提高工业上所需温度的控制精度,本文 介绍如何设计模糊控制器,以及如何在具体的模型在 MATLAB 中 , 使 用 模 糊 工 具 箱 和 SIMULINK 在 MTLAB 实现参数的计算机模拟控制系统。在该系统 中,通过采用模糊控制算法对温度实现了很好的控 制,并且该系统正处于实际工业电阻炉温度控制的应 用和试行阶段,也达到了满意的控制效果。实践表 明,模糊控制方法提高了控制的实时性稳定性和精确 度,并且实现了操作过程的简化,对于工程实际应用 具有较强的借鉴意义。 is an Intelligent Control Department. It uses linguistic rules and fuzzy sets for fuzzy reasoning. In order to solve complex systems, including nonlinearity, uncertainty and accurate mathematical model is difficult to establish the problem, fuzzy control technology to become widely used. Temperature, often using the traditional PID control algorithm is less obvious [1]: conditions change. Also will change the system parameters, PID parameters need to be adjusted, otherwise it would be worse dynamic characteristics, control accuracy decreased: the temperature deviation is large, prone to the phenomenon of integral saturation, resulting in control for too long and so on. in the same Time, fuzzy toolbox and SIMULINK in MATLAB to achieve the parameter control system computer simulation, to promote efficiency and system design [2] for accuracy. The whole system mainly by the AT89S51 microcontroller, temperature data acquisition circuit, the zero crossing detection and trigger circuit, keyboard and display circuit, memory circuit (CF card), sound and light alarm circuit, reset circuit and the corresponding control software of several parts.

Keywords:Fuzzy Controler; MATLAB; SIMULINK;simulation; 关 键 词 : 模 糊 控 制 ; SIMULINK ; MATLAB;仿真
I.

INTRODUCTION (介绍系统)

MATLAB / Simulink is a universal language of scientific computing and simulation, and the establishment of MATLAB, Simulink is a system block diagram and block diagram-based system-level simulation environment, the environment provides a number of specialized modules library: such as CDMA Reference Blockset, DSP (Digital Signal Processor) module library and so on. It is a dynamic system modeling, simulation and analysis of simulation results package has the following characteristics: (1) to invoke the preparation of the agent module to the module block diagram of the system is connected into, making the modeling and engineering simulation system block diagram of unified, more comprehensive research communication systems with high openness. (2) allows the user to freely modify the module parameters, and can seamlessly use all the analysis tool MATLAB with high interactivity. (3) simulation results can be almost "real time " to be displayed in graphical or data, which is the same laboratory. Fuzzy logic control, automation development and the future strategy, in which great attention has been paid,

Block diagram of the system
II.

EASE OF USE(控制器设计)

In theory, the higher dimension fuzzy controller, the control precision is higher. But the higher dimension, Control algorithm is also more difficult to achieve. Currently, the widely used two-dimensional fuzzy controller Nonlinear control law will help ensure system stability. Reduce the response process overshoot. Fuzzy controller includes fuzzification, fuzzy reasoning fuzzy three-part settlement. A. Fuzzy linguistic variables and membership functions to determine Fuzzy controller and dual-input, single output structure, the input linguistic variables as temperature, rate of change of error e and error e, the output variable duty cycle for the SCR-time changes in the amount of ¨.

Temperature error e = t-T, where t is the actual temperature, the temperature settings. The basic domain of the error e is [a 30 ~ C, +30 ~ C], e in the fuzzy domain of: X = [-6, -5, -4, -3, -2, -1,0 , +1, +2, +3, +4, +5, +6], the error e of the quantization factor

D.

The assignment table of Linguistic variable U

Ke = 6 / 30 = 0.2. Linguistic variables E selected 7 language value: [PB, PM, PS, 0, NS, NM, NB]. Error rate of change of the basic domain of e is [-24, +24], ec in the fuzzy domain of y = [-6, -5, -4, -3, -2, -1,0, +1 , +2, +3, +4, +5, +6], the error rate of change of the
quantization factor e

Kec

= 6 / 24 = 0.25. Linguistic

variables Ec selected 7 language value: [PB, PM, P3, 0, NS, NM, NB] Control the amount of change in the basic domain of u is [-0.6,0.6], u in the fuzzy domain of Z = [-6, -5, -4, -3, -2, -1,0, +1, +2, +3, +4, +5, +6], control the amount of change Ku scale factor = 0.6 / 6 = 0.1. Linguistic variables selected 7 language value: [PB, PM, PS, 0, NS, NM, NB]. Lessons learned through practice. Determine the language variable fuzzy set membership function, thus establishing the language variable. Lessons learned through practice. Determine the language variable fuzzy set membership function, thus establishing the language E Ec table. See Table 1 for aTable2. B. Design of fuzzy control rules Design principles of fuzzy control rules is the system output response to dynamic and static characteristics of the best: When the error is large or larger, the Selection Control the amount of the error as soon as possible to eliminate the main; and the error is small, the selection control input to be taken to avoid overshoot, The stability of the system as a starting point. Test based on actual operating experience, analysis, induction, resistance furnace temperature control to determine the rules as shown in Table 4, the table in the space X that can not happen.

U variable assignment

The assignment table of Linguistic variable E

The table 4:the fuzzy control rule C. The assignment table of Linguistic variable EC E. The establishment of fuzzy control query table Table 4 contains the control rules can be written in the

form of the following statements : IF

EC = Bj THEN U = CIj ( i=1,2,…,7;j=1,2,…,7 ) , Where Aj , Bj , CIj was error, error change and

E = Aj AND

control the amount of change in their respective domain of the fuzzy sets. For the 45 rules. The overall fuzzy relation [3] to:

The membership function R:

When the error, error change were to take output control the amount of rules can be synthesized

Aj Bj

Uij by the fuzzy inference

and set a new FIS document and choose "Mamdani" as a type of controller. According to analyzing above, The universe range of e and ec from -6 to 6,while u from 0 to 6.Input and output variables can be set and control rules table is filled in the manner of "if···then". Fig.2 and Fig.3 depict fuzzy membership function curve of input and output variables. FigA shows other settings as follows [4]. We control the temperature of resistance furnace to be simulated map:

On the field for X, Y all combinations of all the elements to strike the appropriate amount of control variable changes in the language of fuzzy sets, and the method by which the maximum fuzzy membership of fuzzy set of judgments. To obtain the domain Z of the elements to control the amount of change that value u. The system is based on off-line calculation, we can establish the fuzzy controller in Table 5 lookup table. After computing the lookup table. Its pre-stored in the computer storage unit. In the actual control. Fuzzy controller changes the value of first quantization error and the error to the appropriate language variable on the domain. Find according to quantify the results of fuzzy control query table directly to obtain the control volume. To achieve real-time control system quickly.

Figure 1 Fuzzy simulating model

omposite

parameters'controller

The table 5: Fuzzy controller lookup table
III.

REPARE YOUR PAPER BEFORE

STYLING(仿真结果)
SIMULINK in Matlab is system modeling and simulation platform for users, adopting agile module combination to create dynamic system with the main characteristics of fast and accuracy [4]. So it is a more effective method to gain better performance with SIMULINK in complex nonlinear system. Run Matlab7.0 and open command window, click "Start" in the left-hand comer and "Toolboxes". Now Fuzzy Logic can be found. An alternative method: input "fuzzy" in command window, then entry fuzzy logic editor

IV.

RESULTS AND CONCLUSION(结 论)

This paper introduces the fuzzy control of the resistance furnace with a temperature control system,practice shows that the fuzzy control method can improve the real-time control, stability and accuracy, and simplify the process of realization of the operation. Currently, the system is in practical industrial application of Temperature Control and the pilot phase. Achieved good results. V.

RERENCES(参考文献)

[I] Pan Xinmin, Wang Yanfang. Micro-computer control technology [M]. Beijing: Electronic Industry Press. 2003.pp:278-299 [2]Xianghong Tang, Hengli Xue,Xuefeng Zheng, MA TLAB and application in the course of electronic information, Beijing: Electronic Industry Press,2006,pp. 275-291. [3] Zhu Jing. Fuzzy control theory and system theory [M]. Beijing: Mechanical Industry Press, 2005.pp:209-211. [4]Ying Li,Boli Zhu,Wei Zhang,The modeling and simulation of dynamic system based on SIMULINK,Xian:Xian University of Electronic Science & Technology Press,2004.


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