Design and Fuzzy Logic Control Implementation for Embedded Systems Using Microcontrollers.

Application for Drive two Electric Furnaces.

H. FEKHAR and A. KHELASSI

Laboratory of applied automation Faculty of hydrocarbons and chemistry

Boumerdes University E-mail :hfekhar1952@yahoo.fr, madjidk@hotmail.com

Abstract : This article describes the application of the microcontroller by using fuzzy logic algorithm

to drive two electric furnaces. The first part of the paper consists of presentation hardware device.

We have five distinct sections. A Pic18f4620 microcontroller, keypad unit, electronic drivers and

alphanumeric liquid crystal display(LCD). The keypad allows the user to input the required

temperatures and controllers parameters. The LCD display allows a better user interface with text

message, measurement of physical variables and display all controllers parameters. The external

integrated circuit is used to drive a power amplifier (SSR) .The software was written in C

language dedicated for microcontrollers. A fuzzy logic control approach has been proposed to

control the temperature of furnaces. This algorithm is implemented to reduce the performance

degradation due to parameters variations and disturbances.

Keywords: Microcontroller, keypad, sensor, shift registers, implementation, fuzzy logic control

1.Introduction

The use of microcontrollers with a closed loop

control incorporating fuzzy logic has been

developed for a class of industrial

temperature control problem [1]. Fuzzy logic

control(FLC) has been rapidly gaining

popularity among practicing engineers. This

increased popularity can be attributed to

2. Hardware description [2]

In this part we present the system hardware.

The system is composed of PIC board,

integrated circuits, two electric furnaces,

sensors and software package.

The overall system hardware is shown in

figure1.It consist of the following sub systems:

-(1) is microcontroller pic 18F4620 with flash

the fact that fuzzy logic provide a powerful

vehicle that allows engineers to incorporate

human reasoning in the control algorithm. The

use of microcontrollers is in full expansion

nowadays. This embedded system has been

used in many industrial applications.

Currently these devices with programmable

memory and many ports with Alternate

function pins facilitate the connection to

external devices. This present paper consists

to develop the embedded system using

Microchip PIC18F4620 to control two electric

furnaces. Fuzzy microcontroller PIC is used for

the implementation.

-(2) and ( 3) are the CMOS 4094 serial in

parallel out shift register. This IC is used to

control DAC0800[3]. Its inputs require three

lines (pins rd0 , rd1 and rd2).

(4) and (5) are the DAC0800(digital to analog

converter). These circuits are used to drive

power amplifiers.

(6) and (7) represent the power source to

heater actuated by a solid state relay(SSR)

-(10), (11) and (12) are temperatures

sensors(AD590 and Pt100) and measurement

amplifiers. These devices produces 100 mv/C.

-(14) and (15) represent the 16 buttons

keypad and LCD display. These devices provide

an Interface between the user and the

Microcontroller development system. The

keyboard allows the user to input the required

temperatures and other operating parameters

as required by the application program. The

LCD module displays all controllers

parameters.

3.Design and implementation of fuzzy

controller [4 ,5 ,6 ].

3.1.Fuzzy controllers structure.

Fuzzy logic has been found to be very suitable

for embedded control application. The bloc

diagram is presented in figure2.

The three principal elements to a FLC are:

- Fuzzification module

Rule base and inference engine.

Defuzzification module.

A.Fuzzification .

On of the key part in the design of FLC is

choosing the number of fuzzy sets. The inputs

are the errors (e1, e2) between the references

(Rf1 , Rf2) and actual temperatures ( Tm1,

Tm2) and the changes in errors ( Ce1 , Ce2).

The variables can be expressed as fellows:

e1(k) = Rf1(k) Tm1(k) (1)

e2(k)= Rf2(k) Tm2(k) (2)

Ce1(k)=e1(k)-e1(k-1) (3)

Ce2(k)=e2(k)-e2(k-1) (4)

Where (k) is the time index.

The representation of the variables in

terms of per unit values permits flexibility

in design and tuning of controller. The

following normalized equations can be

written

e1n(k)=Ge1*e1(k) ( 5)

e2n(k)=Ge2*e2(k) (6)

Ce1n(k)=Gd1*Ce1(k) (7)

Ce2n(k)=Gd2*Ce2(k)(8)

Ge1, Ge2, Gd1 and Gd2 define the scaling

factors respectively errors and change of

errors. The universe of discourse for errors

and the change errors may be normalized

from -1 to 1. The changing of scaling

factors , changes the normalized universes

of discourse. These parameters are used

for tuning the Fuzzy-Logic-Controllers

(FLC1 and FLC2).The most popular choices

for the shape are the membership

functions (MF) include triangle-trapezoidal

function. For symmetrical (MF) there are

some optimal values for the cross-point

level and ratio. Each two adjacent MF

have a cross-point level 0.5, this

provides for significantly less overshoot,

faster rise-time and less undershoot. This

MF are chosen owing to the simplicity. In

this paper, for errors and change errors

seven fuzzy levels are defined as:

fellow(figure 3):

NB(negative big)

NM(negative medium)

NS(negative small)

ZE(zero equal)

PS(positive small)

PM(positive medium)

PB(positive big).

Each MF are labeled A1(e1n),B1(Ce1n)

A2(e2n) B2(Ce2n).

Where A1, A2, B1, B2 define MF errors

and change errors respectively.

B.Inference rule and defuzzification

In the fuzzy logic control the most

commonly used method for inferring the

rule output is so-called Mamdanis

method. For a Mamdani-type FLC fuzzy

rules are in the form:

Ri :if en is A1 and en is Bi then un is Ci

where Ai and Bi are fuzzy subsets in their

universe of discourse and Ci is a fuzzy

singleton. In this paper each universe of

discourse is divided into seven fuzzy

subset (NB NM NS ZE PS PM PB).The

inference result of each rule consist of two

parts, the weighting factors (Wi) of the

individual rule and degree of change in

duty ratio (Ci) according to the rule. (Wi) is

obtained by mean of product[6 7].

The expert experience (figure4) has been

incorporate into a knowledge base with

7x7 rules. The inferred output of each rule

are :

Wi1 =A1(e1n)*B1(Ce1n) (8)

Wi2=A2(e2n) * B2(Ce2n) (9)

Zi1=Wi1 * Ci1 ( 10 )

Zi2 = Wi2 *Ci2 ( 11)

Where Zi1, Zi2 denote the fuzzy

representation of change in duty ratio

inferred by the ith rule. Index 1 and 2

denote the furnace N1 and N2.The

defuzzification operation is performed

next to obtain a crisp result. Here the

center of gravity (COG) method is

preferred.

u1n = / (12)

u2n = (13)

where u1n and u2n are the results of

change in duty ratio. The outputs un is

incremental command. It is apparent that

FLC1 and FLC2 are integral-type fuzzy

controllers and force the steady-state

temperatures error to zero. Consequently

we have;

U1(k)=u1(k-1)+Gu1*u1(k) (14)

U2(k)=u2(k-1)+Gu2*u2(k) (15)

3.2.Control Implementation

The microcontroller Pic18f4620 is used for

the experimental application [7,8]. Pic

with this feature provide 10-bit conversion

giving a resolution of 1 to 1024.This is

good enough for all but the most

demanding applications (Pic incorporates

13 multiplexed A/D channel and running

at 40 Mhz).The CPU is driven by the

software instructions to perform specific

tasks. The instructions are written in high-

level language C dedicated for

microcontrollers.

DACs gives outputs U1(k) and U2(k).

In our case we adopted serial-in/parallel

out technique by using CMOS4094 shift

registers.

The idea is to put the serial data on

the input line, LSB first(pin rd0) and

clock(pin rd1) the shift registers 16

times(for 2*8 bit registers), stop

(pin rd2) and read the parallel data from

the Q0 to Q15 outputs. After 16 clocks

pulses all 16 serial data bits have shifted

In their appropriate pin. After initialization

of the software, the steps for temperature

control can be summarized as fellows:

-The user enters command/dat, set points

(Rf1, Rf2) and parameters FLC1 and FLC2

(ge1, Ge2, gde1, gde2, Gu1 and Gu2).The

pic has to scan the keys regularly and

check the keypad. If the key is pressed, the

routine under execution display its

response on the LCD.

-The program has measure the physical

variables(temperature Tm1,Tm2) by using

the feedback sensors via 10 bits

multiplexed A/D converter. The time

acquisition(clock-conversion) equal fosc/2.

The system operates on the average

temperature reading from three sensors

to give a more accurate representation of

the overall temperature in the enclosure.

-if a particular key is pressed, the CPU

calculates the outputs control U1 and U2

than send the command via CMOS4094-

DACS.The scaling factors and sample

period Ts can be modified by using the

appropriate keys for tuning the FLC1 and

FLC2.

In our implementation the total length of

the fuzzy algorithm code is 29 kb which is

54 % of the picc program memory. There

still remain 35 kb of the Pic18f4620

program memory, that are available for

others purposes.

4.Experimental results

In this section, an experiment is set up to

demonstrate the performances of the FLC.

Execution of the software that we have

developed and implemented allows us to

plot the graphs in a digital scope. Figure(8)

shows the experimental results of the

proposed FLC. In this figure the

temperature-response and output

command of the furnace1 and Furnace2

are represented. The first test is related to

starting the command. A time responses

of about 760s(furnace1) and

275s(furnace2) Is observed during the

starting period.

The controllers tracks the commanded

temperatures reasonably well with a small

Steady-state errors. The second test

examines the disturbance rejection

capabilities of each controller between

370s and 470s.The FLC quickly return the

Temperature to the set point within 10s .

5 Conlusion

A Fuzzy Logic Control(FLC) approach has

been proposed to control the temperature

of electric furnaces. The microcontroller

PIC18F4620 is used for the

implementation. To test and demonstrate

the effectiveness of the proposed control,

a prototype experimental setup has been

developed (figure 6 , and figure7).

Implementation of fuzzy logic control

algorithm in embedded microcomputers

for dedicated application has shown good

control.

References

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International journal of engineering

science and technology V3 N4 2011

pp276-283

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Microcontrollers for embedded systems

Using an anti-windup P.I controllers .

The 6th international symposium on

hydrocarbons & chemistry Algier oct 2012.

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modeling and control .John wiley & sons

1994.

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theory based control of a phase-controlled

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indust applic V30 N1 Jan/fev 1994

pp 34-44.

[6] H.Buhler;Rglage par logique floue

Presses polytechnique romandes 1994

[7] J.Parab, S.A.Shinde,Practical aspect of

embedded systems design using

microcontrollers ,Springer Science 2008.

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and applications.Tata McGraw-hill New-

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[9]A.M.Ibrahim ;Fuzzy logic for embedded

systems applications Elsevier Science 2004

(u.s.a)

(6) (8) (10)

(7)

(12) (9)

(13)

Rf(k) Figure 1. Schematic diagram

microcontroller en

Pic18f4620 Uout

un(k) un(k)

- Cen

-Tm(k)

Figure 2. Block diagram fuzzy logic controllers ( FLC1 and FLC2)

(1)

PB

P

I

C

1

8 rd0

F rd1

4 rd2

6

2

0

pc

ra0

ra1

6

2

(13) L C D

Display

4094

(2)

4094

(3)

Keyboard

(14)

DAC1

(4)

DAC2

(5)

(11)

1/z Gde1,

2

Ge1,2

FLC1

And

FLC2

1/Z

Gu

P

L

A

N

T

sensors Signal

conditionning

ADC

Figure 3. Membership function error (e1n,e2n) and change error (Ce1n,Ce2n)

Figure 5 Membership functions Outputs u1n(k) and u2n(k)

Figure4. Rule base for (u1n) and (u2n)

Figure 6. Furnace N1 Sensors PIC18F4620 Conditionning circuit Lcd display

Electronics drivers (CMOS4094-DAC ) Keypad Furnace N2

Figure 7. Fuzzy Microcontroller Board

(a) Starting periode (b) Disturbance rejection

Output control-temperature,Temperature response, Disturbance, Output control

Rf1=75 C ,Rf2=50C, Ge1=Ge2=0.01, Gde1=Gde2=0.01,Gu1=Gu2=150,

Figure 8. Graphs control-Temperatures