Design and Development and Hardware Implementation of Fuzzy Knowledge Based Intelligent Temperature Control System

Design and Development and Hardware

Implementation of Fuzzy Knowledge Based

Intelligent Temperature Control System

Deepak Bharadwaj

Mechanical & Automobile Enginering Department, itm University Gurgaon, Sector 23A, Haryana

Email –id: [email protected]

L.N Das

Electrical & Electronics Engineering Department, itm University Gurgaon, Sector 23A, Haryana

Email-id: [email protected]

Abstract:

Fuzzy logic approach for temperature control is adopted for solving these difficulties encountered in conventional PID controller. In this approach temperature error is considered and corresponding firing angle of rectifier is controlled to regulate the power supply inside the temperature control process. The control scheme regulate the power supply in accordance with the difference between set point and desired point .Mamdani fuzzy rule base is considered for controlling the firing angle of the controlled rectifier. Fuzzy chip which consists of microcontroller is the heart of controller, which decides how much power is required by firing the power rectifier.. The control program developed in C language is embedded into the microcontroller. .The rule is made by the operator and accuracy of the system totally depends on the operator data, which is feed in the memory of microcontroller. Result is obtained from the hardware setup and it has been observed in Pico scope, where the change in firing angle versus error voltage, graph is plotted. The hardware is meant for any type of system where precisely temperature controlled is important like industrial processes, chemical mixing, buffer solution preparation, etc. Peak overshoot of the temperature is reduced within ±2oc inside the temperature process. Temperature of system is affected by outside environment condition, due to sudden atmospheric change in pressure and temperature.

Keywords: Mamdani, Fuzzy chip, microcontroller, firing angle, Temperature Control, Hardware.

1. Introduction

This project presents mixed-signal IC techniques where the best of analog and digital worlds are combined to offer optimum fuzzy microcontroller chips. The analog circuitry provides the required computing power, while the digital part allows a linear and systematic way to program the fuzzy chip with a simple user interface. Magnetic permeability and resistivity of the materials experience large variations during heating in furnaces. Power unit operating at mid frequencies is expected to observe these variations and keep the output power constant and in the active region. It is not easy to obtain heat, pressure and position control while controlling other units, by using microcontroller based system in power units .These require a separate control unit, such as Programmable Logic Controller (PLCs).

New generation PLC systems are equipped to provide all the necessary control functions needed in a furnace, as well pulse width modulation signal generator that can drive the switches of a dc/ac converter. This provides the necessary tools for various control techniques such as classical control, artificial intelligence, fuzzy logic and adaptive control. The recent success of the application of fuzzy logic for controlling nonlinear systems has motivated the development of low cost hardware based system in this kind of approach. On the other hand; there is a widespread use of microcontroller in industry automation.

fuzzy controller. Secondly, the basic structure of the software is written in Embedded C language and stored in microcontroller chip.

2. Problem Definition

Many chemical reactions, industrial process and experiments require maintaining temperature at predetermined value. While mixing of drug in pharmaceutical industry it is very important to maintain the temperature of buffer solution .If there is change in PH value mixing is not done properly, because properties of buffer solution change. In industrial processes, while converting the raw material from solid state to liquid state, temperature plays an important role for phase transformation. If crystallization temperature of system goes high, then material properties like hardness, yield stress, ductility, magnetic properties also change. If there is excess heating above crystallization temperature then raw materials get evaporated and waste. If there is variation in temperature, then thermal distortion affects the molecule of the raw material.

2.1 Problem Statement

Intelligent temperature control system for furnace temperature control.

2.2 Objective

Peak overshoot of the temperature is reduced within ±2oc inside the temperature process. Temperature of system is affected by outside environment condition, due to sudden atmospheric change in pressure and temperature.

2. 3 Methodology adopted

A fuzzy logic control approach has been proposed to control the temperature of furnace. Furnace temperature is converted in the form of voltage by thermocouple sensor. This voltage converted into digital form with the help of ADC converter and stores in the microcontroller. With the help of Mamdani fuzzy rule structure, the rule base is stored in the memory of microcontroller. Microcontroller compares the date received which is stored in the EPROM of microcontroller. Microcontroller generates the controlled output and give to the DAC converter.DAC converts the digital date into analog data for controlling the conduction of triac through optoisolator.

Programmed EPROM is called fuzzy chip which consists basically three parts –the CPU, the input circuit and the output circuit as shown in Figure1. The main unit of the controller includes the microprocessor 89C51 had a 64K byte EPROM and 32K byte static RAM .The fuzzy program is stored in the EPROM of the microcontroller. The static RAM is used for running the program and storing temporarily data during the control operation. The circuit is designed with all the necessary components for the correct operation of the microcontroller. The fuzzy logic routines are completely software implemented in the controller. It consists of a C language programmed system that includes, basically, routines for the adjustment of the rule base, the membership functions and the control operation.

3. Fuzzy Sets and Rules

3.1Definition of the Fuzzy Sets Used

Figures 2 & Figure3 shows the fuzzy sets defined on the measurable values error, and firing angle of Duty cycle respectively, for two different fuzzy sets. The same fuzzy sets have similar mean values have the shapes triangular.

3.2 Determining the Rules

Given that there are nine fuzzy variables for each input, we can have a total of 9 different fuzzy rules that do not conflict. If humans were to determine the rules for this system they would likely pick an output for each possible input combination, giving a total of 9 outputs.

We could have more than 9 possible rules, however, with some of the input combinations being used more than once. For example, we might have the nine rules as follows;

1. If error is NL then firing angle is DNL 2. If error is NB then firing angle is DNB 3. If error is NS then firing angle is DNS 4. If error is N then firing angle is DN 5. If error is ZE then firing angle is DZE 6. If error is P then firing angle is DP 7. If error is PS then firing angle is DPS 8. If error is PL then firing angle is DPL 9. If error is PL then firing angle is DPL

Where NL,NB,NS,N stands for negative large, negative big, negative small and negative respectively. ZE stand for zeoro and P, PS, PB and PL stands for positive, positive small. Positive big and positive large. Same for dutty cycle starting from D.

4

In Appendix A the program is written in C language and then Embedded C Program has been implemented with the help of logic. The logic behind C program is If-else rule, which is simple conditional statement in C program. The C program consists one main program and one sub-program .Main program consists simple fuzzy rule structure and the sub-program consist, how the error is read from ADC0848 IC. On the Keil-software prompt this embedded program has been written and simulation is done for infinite time. Keil compiler generates the hex file in (Appendix B) of embedded C program and this hex file is burnt on the microcontroller chip.

5. Hardware Interface

Figure 4 shows the main controller unit of work which is developed on the breadboard. In this Hardware the following components are used;

1. Thermocouple

2. Amplifier

3. ADC0848 Chip

4. Microcontroller ATMEL89C51

5. DAC0808

6. Timer IC555

7. Optocoupler IC (MOC3011) 8. Triac IC (BT139)

Figure 4. Main controller unit of work which is developed on the breadboard

5.1 Functional Block Diagram

5.2 Working Principle

Figure 4 shows the hardware implementation of intelligent control system. Temperature is sense by thermocouple K-type, which gives the voltage proportional to temperature. Since the voltage receives in terms of millivolt so its need to be amplified. For amplification two stage of operational amplifier is used .Now the set temperature 5volt is applied at inverting input of operational –amplifier and at non-inverting input the actual voltage from thermocouple after two stage amplification is applied. In this hardware op-amp is used as comparator also. Now the error from the comparator element is sense in the ADC.ADC converts this analog error signal into digital form .Microcontroller sense this error signal and produce the controlled output in the form of digital data by processing the algorithm in EPROM proportional to error voltage, which generates the digital output. This signal is sent to the DAC .DAC produce this controlled data in the form of analog current which is converted to proportional voltage in a current to voltage amplifier. This manipulated voltage variable goes to the voltage controlled oscillator built around Timer IC 555.Voltage controlled oscillator which generates the oscillation controlled by DAC voltage generated from the fuzzy chip and controlled the oscillation. The output of VCO goes to the base of transistor for switching action. The collector voltage of this transistor activates the optocoupler .In this hardware optocoupler is provided for isolation. If there is reverse voltage from source flows to circuit, it damages the fuzzy chip component. Optocoupler generates the current proportional to the applied voltage and through suitable resistance it goes to the terminal 2 of the triac .Now the other pin4 of optocoupler goes to the terminal 3 gate of the triac. Gate voltage fires of triac proportional to the phase angle of supply voltage to the furnace. The power supply to the furnace is proportional to the error voltage sense by the fuzzy controller. So in this design the fuzzy controller works like a fuzzy proportional controller.

6. Results

Result is obtained from the hardware with the help of Picoscope.The variation in proportional firing angle is obtained by changing in error voltage in Figure 5 & Figure 6.

ms

125 150 175 200 225 250 275 300 325 350 375

mV

-50 -40 -30 -20 -10 0 10 20 30 40 50

x=202.7ms

14May2010 16:09

x

Figure6. When there is no error in the system

ms

125 150 175 200 225 250 275 300 325 350 375

mV

-50 -40 -30 -20 -10 0 10 20 30 40 50

x=202.7ms

14May2010 16:08

x

Figure 7. When there is error in the system

Since DAC produce the manipulated variable current proportional to error which is passing through resistance and amplifier, it is converted into proportional voltage through a current to voltage converter. This voltage is applied at the controller input pin of voltage controlled oscillation, which generates a controlled frequency of oscillations. This voltage frequency signal fires the triac through an optocoupler in proportional phase angle firing mode, which regulates the power input to the furnace till set temperature is achieved. This is how the entire control system operates in maintaining the selected set temperature.

7. Conclusions and Recommendations

In this project temperature is controlled in one stage only. For better and fast response temperature control, we have divided the operation zone with three areas namely fast increasing temperature, medium temperature zone and steady state zone. However the work can be further improved and refined by better selective fuzzy membership functions and rule bases to cover entire zone of temperature control operation.

References

[1] V B Kulkarni “Smart Sensors and Intelligent Instrumentation: Neuro-furnace Controller” Department of Electronics Engineering Finolex Academy of Management and Technology, P-60/P-61, Mirjole Block MIDC, Ratnagiri 415639. Vol 85, July 2004 [2] W.J. Lee, Y.H. Park, B.G. Park, I.M. Park, Y.H. Park* “Thermal residual stress investigation in AS52/ Al18B4O33 magnesium

matrix composite by thermal cycling test” School of Materials Science and Engineering, Pusan National University, san 30 Jangjeon-dong, Geumjeong-gu, Busan 609-735, Republic of Korea, International Scientific Journal published monthly by the World Academy of Materials and Manufacturing Engineering, Volume 32, Issue 1, July 2008 Pages 17-20

[3] C. J. Jiménez, S. Sánchez Solano, A. Barriga “HARDWARE IMPLEMENTATION OF A GENERAL PURPOSE FUZZY CONTROLLER” Instituto de Microelectrónica de Sevilla - Centro Nacional de Microelectrónica Avda. Reina Mercedes s/n, (Edif. CICA) E-41012, Sevilla, Spain, Sixth International Fuzzy Systems Association World Congress (IFSA’95), Vol. 2, pp. 185-188, Sao Paulo - Brazil, July 21-28, 1995.

[4] J. L. Huertas, S. Sánchez Solano, I. Baturone, A. Barriga, “INTEGRATED CIRCUIT IMPLEMENTATION OF FUZZY CONTROLLERS” Instituto de Microelectrónica de Sevilla - Centro Nacional de Microelectrónica Avda. Reina Mercedes s/n, (Edif. CICA)

[5] E-41012, Sevilla, Spain, Twenty-First European Solid-State Circuits Conference (ESSCIRC’95), pp. 130-133 , Lille - France, September 19-21, 1995.

[6] Jan Jantzen “Design Of Fuzzy Controllers” Technical University of Denmark, Department of Automation, Bldg 326, DK-2800 Lyngby, DENMARK. Tech. report no 98-E 864 (design), 19 Aug 1998.

[7] Chin-Teng Lin_, Chia-Feng Juang, Chung-Ping Li “Water bath temperature control with a neural fuzzy inference network” Department of Electrical and Control Engineering, National Chiao-Tung University, Hsinchu, Taiwan, ROC Received July 1996; received in revised form March 1998, Fuzzy Sets and Systems Elsevier.

[8] Edilberto Teixeira,Marcelo Araujo,Adriano Breurning ,Haroldo Azevedo and Gilson Correa, “HARDWARE IMPLEMENTATION OF A FUZZY CONTROLLER FOR NONLINEAR APPLICATIONS”Univesity Federal de Uberlandia,Department de Engenharia Eletrica,IEEE,1995

[9] P.P .Bhogle ,B.M .Patre,L.M.Waghmare and V.M.Panchade, “Neuro Fuzzy Temperature Controller” International Conference on Mechatronics and Automation,August 5-8 ,2007 ,Harbin,China

[10] HU Yarryu,GUI Wer hua,TANG Zhao-hui,TANG Ling, “Intelligent temperature control system of quench furnace”,Trans.Nonferrous Met.Soc.China,Vol 14,No 4

[11] [10] HE Jian-jun and YU Shou-yi, “Temperature Intelligent Control System of Large –Scale Standing Quench Furnace”College if Information Science and Engineering ,Central South University Changsha,China,Journal of Electronic Science and Technology of China,Vol 3 ,No1

[12] Tongxin Zheng and Elham B.Makram, “An Adaptive Arc Furnace model”,IEEE Transcation ON POWER DELIVERY ,VOL 15 ,NO3,JULY 2000

[13] Shukun Cao,Lei Shi,Xiangbo,and Heng Zhang, “Continuously Sintering Furnace Temperature Control System Based on Intelligent PID Adjustment”,School of Mechanical Engineering,University of Jinan,Jinan ,P.R China,2008 International Conference on Computer and Electriacl Engineering.

[14] H.M.Unver and M.T .Aydemir, “Power and Frequency Control in a 60 KW Induction Steel Heating Furnaces through PLC” Department of Electrical and Electronics Engineering,Kirikkale University,Kirikale ,Turkey

[15] Teo Lian Seng,Marzuki Khalid and Rubiyah Yusof, “Tuning of a Neuro-fuzzy controller by Genetic Algorithms with an application to a coupled tank liquid level control system”, Centre for Artificial Intelligence and Robotics,University Teknology Malaysia,International Journal of Engineering on Artificial Intelligence Vol11(1998) pp.517-529,Pergamon Press

[16] Muhammad Ali Mazidi,Janice Gillipie Mazidi and Rolin D.McKinlay, “The 8051 Microcontroller and Embedded Systems”Using Assembly and C,Second Edition,Person Education

[17] Todd D.Morton, “Embedded Microcontrollers”Pearson Education,First Indian Reprint ,2003

[18] John Yen and Reza Langari, “Fuzzy Logic Intelligence ,Control ,and Information”Centre for Fuzzy Logic ,Robotics ,and Intelligence Systems ,Texas A &M University,Second Impression,2007,Pearson Education.

[19] Robert L. Boylestad & Louis Nashelsky, “Electronic Devices & Circuit Theory”, Prentice-Hall of India,Eight Edition.

Appendix A

Embedded C program for fuzzy chip #include<reg51.h>

#include<stdio.h> sbit INTR=P2^7; sbit CS=P2^4; char error();

void MSDelay(unsigned int); char error1();

void main() {

float p,q,f;

unsigned char error; error=error1();

p=error/5; q=1-p;

if(error >=-2 && error<0) {

f=(75*p+65*q/(p+q)); printf("value of f %f",f); }

else if(error>=1 && error<2) {

f=(70*p+65*q/(p+q)); printf("value of f %f\n",f); }

if(error>=3 && error<=5) {

f=(55*p+40*q/(p+q)); printf("value of f %f\n",f); }

else if(error>=6||error<-3) {

printf("error in temperature ");

}

}

char error1() { unsigned char error; INTR=1;

CS=1; RD=1; WR=1; while(1) {

P1=0x0A; CS=0; WR=0; MSDelay(250); WR=1; CS=1; P1=0xFF; while(INTR==1); RD=0;

CS=0;

MSDelay(250); RD=1;

error=P1; CS=1; }

return error; }

void MSDelay(unsigned int itime) {

unsigned int i,j; for(i=0;i<itime;i++) for(j=0;j<1275;j++); }

Appendix B

Hex file generated in Keil compiler

:100BC30025660076616C7565206F66206620256654 :100BD3000A006572726F7220696E2074656D7065AC :080BE300726174757265200057

:1009D600120BEB8F2E7BFF7A0B79A3852E32120535 :1009E600C9E52E75F05084FCE41202AB8F258E24E7 :1009F6008D238C22AB25AA24A923A822E4FFFE7D01 :100A0600807C3F1200068F298E288D278C26E52EA6 :100A1600C3941E406BE52E94285065AB25AA24A9E5 :100A260023A82212000AC004C005C006C007E4FFBE :100A3600FE7D827C42AB29AA28A927A8261200FBA4 :100A4600D003D002D001D000120204C004C005C0F9 :100A560006C007E4FFFE7D967C42AB25AA24A923A7 :100A6600A8221200FBD003D002D001D00012000A47 :100A76008F2D8E2C8D2B8C2A7BFF7A0B79B8807864 :100A8600E52EC39428407CE52E943C5076AF29AEE3 :100A960028AD27AC26AB25AA24A923A82212000A32 :100AA600C004C005C006C007E4FFFE7D827C42ABE1 :100AB60029AA28A927A8261200FBD003D002D00114 :100AC600D000120204C004C005C006C007E4FFFE41 :100AD6007D8C7C42AB25AA24A923A8221200FBD038 :100AE60003D002D001D00012000A8F2D8E2C8D2B40 :100AF6008C2A7BFF7A0B79C68F358E348D338C32F8 :100B06001205C9E52EC3943C407BE52E94505075E2 :100B1600AF29AE28AD27AC26AB25AA24A923A82247 :100B260012000AC004C005C006C007E4FFFE7D200F :100B36007C42AB29AA28A927A8261200FBD003D0FD :100B460002D001D000120204C004C005C006C007CE :100B5600E4FFFE7D5C7C42AB25AA24A923A82212D1 :100B660000FBD003D002D001D00012000A8F2D8ED8 :100B76002C8D2B8C2A7BFF7A0B79C68F358E348D84 :100B8600338C328014E52EC394505007E52EC3945F :0C0B96001D50097BFF7A0B79D51205C9B0 :010BA2002230

:100BEB00D2A7D2A4D2B7D2B675900AC2A4C2B67F8E :100BFB00FA7E00120C46D2B6D2A47590FF20A7FD48 :100C0B00C2B7C2A47FFA7E00120C46D2B785902FD2 :040C1B00D2A480D40B

:100C4600E4FDFCC3ED9FEC9E5015E4FBFA0BBB00E4 :0F0C5600010ABA04F8BBFBF50DBD00010C80E4E8 :010C6500226C

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

:10077700E00205DBD0E00480C51208E360A0E5488D :10078700041203297845EAF6E548200502600104CA :0507970024041209001A

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:1002DF00E9D2E7C933E833F892D5EDD2E7CD33EC65 :0702EF0033FC5002B2D522DE

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:10041800E4C3CF33CFCE33CECD33CD33DBF37B073D :1004280075F00A846004F6081B0AE5F0F6088FF0F8 :10043800740AA4FFACF08EF0740AA42CFEACF05041 :10044800010C8DF0740AA42CFDE435F0F608DBDE0F :0304580074072204

:10045B003F8000004120000042C80000447A0000A9 :10046B00461C400047C35000497424004B1896802B :10047B004CBEBC205A0E1BCA6753C21C749DC5AE22 :10048B00FB60065407601C23231204B9EB5438603D :10049B00091204A51204B41200FBECF8EDF9EEFA04 :1004AB00EFCB22EB1204B480F1543803241C9004DC :1004BB005BFE93FC0EEE93FD0EEE930ECE93FF229E :03000000020C6689

:0C0C6600787FE4F6D8FD75814A0209D6BB

:1004CB00BB010689828A83E0225002E722BBFE022F :0904DB00E32289828A83E4932262

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