PH Control Technique using Fuzzy Logic

Seminar Report’03 PH Control Technique using Fuzzy Logic

INTRODUCTION

Fuzzy control is a practical alternative for a variety of challenging

control applications since it provides a convenient method for constructing

non-linear controllers via the use of heuristic information. Since heuristic

information may come from an operator who has acted as “a human in the

loop” controller for a process. In the fuzzy control design methodology, a

set of rules on how to control the process is written down and then it is

incorporated into a fuzzy controller that emulates the decision making

process of the human. In other cases, the heuristic information may come

from a control engineer who has performed extensive mathematical

modelling, analysis and development of control algorithms for a particular

process. The rest of the process is the same as the earlier case. The ultimate

objective of using fuzzy control is to provide a user-friendly formalism for

representing and implementing the ideas we have about how to achieve high

performance control. Apart from being a heavily used technology these

days, fuzzy logic control is simple, effective and efficient. In this paper, the

structure, working and design of a fuzzy controller is discussed in detail

through an in-depth analysis of the development and functioning of a fuzzy

logic pH controller.

Dept. of AEI MESCE Kuttippuram1


FUZZY CONTROL - The Basics

The primary goal of control engineering is to distill and apply

knowledge about how to control a process so that the resulting control

system will reliably and safely achieve high performance operation. Here,

the effort made is to show how fuzzy logic provides a methodology for

representing and implementing our knowledge about how best to control a

process.

Figure 1. Fuzzy Controller

The general block diagram of a fuzzy controller is shown in figure 1.

The controller is composed of four elements, viz

A Rule Base

An Inference Mechanism

A Fuzzification Interface

A Defuzzification Interface



RULE BASE

This is a set of “If ……..then…..” rules which contains a fuzzy logic

quantification of the expert’s linguistic description of how to achieve good

control.

INFERENCE MECHANISM

This emulates the expert’s decision making in interpreting and

applying knowledge about how best to control the plant.

FUZZIFICATION INTERFACE

This converts controller inputs into information that the inference

mechanism can easily use to activate and apply rules.

DEFUZZIFICATION INTERFACE

It converts controller inputs into information that the inference

mechanism into actual inputs for the process.

SELECTION OF INPUTS AND OUTPUTS

It should be made sure that the controller will have the proper

information available to be able to make good decisions and have proper

control inputs to be able to steer the system in the directions needed to be

able to achieve high-performance operation.



The fuzzy controller is to be designed to automate how a human

expert who is successful at this task would control the system. Such a fuzzy

controller can be successfully developed using high-level languages like C,

Fortran, etc. Packages like MATLAB® also support Fuzzy Logic.

CRISP SETS Vs FUZZY SETS

CRISP SETS

The word ‘crisp sets’ denote ordinary sets or classical sets. Examples

for crisp sets are set of natural numbers ‘N’, set of all real numbers ‘R’, etc.

FUZZY SETS AND MEMBERSHIP FUNCTIONS

Given a linguistic variable Ui with a linguistic value Aij and

membership function μ Aij(Ui) that maps Ui to [0,1], a ‘fuzzy set is defined as

Aij= {(Ui, μ Ai

j (Ui)); Ui ε υi}

The above written concept can be clearly understood by going

through the following example. Suppose we assign Ui=“temperature” and

linguistic value A11=“hot”, then A1

1 is a fuzzy set whose membership

function describes the degree of certainty that the numeric value of the

temperature, Ui ε υi, possesses the property characterized by A11. This is

made even clearer by the figure 2.



Figure 2: Membership Function of Temperature.

In the above example, the membership function chosen is triangular.

There are some other membership functions lime Gaussian, Trapezoidal,

Sharp peak, Skewed triangle, etc. Depending on the application and choice

of the designer, the required one can be chosen.

Figure 3: 1)Triangular, 2)Trapezoidal, 3)Skewed triangular, 4)Sharp peak



P H CONTROL

To illustrate the application of fuzzy logic, the remaining section of

the paper is directed towards the design and working of a pH control system

using fuzzy logic.

PH is an important variable in the field of production especially in

chemical plants, sugar industries, etc. PH of a solution is defined as the

negative of the logarithm of the hydrogen ion concentration, to the base 10.

I.e., PH= -log 10 [H+]

Figure 4: Block Diagram of pH Control System

Let us consider the stages of operation of a sugar industry, where PH

control is required. The main area of concern is the clarification of raw juice

of sugarcane. The raw juice will be having a PH of 5.1 to 5.5. The clarified

juice should ideally be neutral. I.e., the set point should be a PH of 7. The



process involves addition of lime and SO2 gas for clarifying the raw juice.

The addition of these two are called liming and sulphitation respectively.

Since the process involves continuous addition of lime and SO2 ; lime has a

property of increasing the PH of the clarified juice. This is the principle used

for PH control in sugar industries. The PH of the raw juice is measured and

this value is compared to the set point and this is further used for changing

the diameter of the lime flow pipe as per the requirement. The block diagram

of the process is given in figure 1.

The whole process can be summarised as follows. The PH sensor

measures the PH. This reading is amplified and recorded. The output of the

amplifier is also fed to the PH indicator and interface. The output of this

block is fed to the fuzzy controller. The output of fuzzy controller is given to

the stepper motor drive. This inturn adjusts the diameter of lime flow pipe as

per the requirement. Thus, the input to the fuzzy controller is the PH reading

of the raw juice. The output of the fuzzy controller is the diameter of the

lime flow pipe valve or a quantity that controls the diameter of the lime flow

pipe valve like a DC current, voltage, etc. The output obtained from the

fuzzy controller is used to drive a stepper motor which inturn controls the

diameter of the value opening of the lime flow pipe. This output tends to

maintain the pH value of sugar juice to a target value. A detailed description

of the design and functioning of the fuzzy controller is given in the following

section.

The different sections in the fuzzy controller used in this PH

controller are

Fuzzification Section

Rule Base

Inference Mechanism

Defuzzification Section



FUZZIFICATION SECTION

The variables ‘PH’ and ‘LFPD’ or Lime-flow-pipe-diameter are

selected for Fuzzification. In this section, the action performed is obtaining a

value of the input variable and finding the numerical values of the

membership function defined for that variable. As a result of Fuzzification,

the situation currently sensed (input) is converted into such a form that, it can

be used by the inference mechanism to trigger the rules in the rule base.

As explained earlier, the set point for the process is normally 7. Let the

diameter set point be 15mm and this corresponds to a pH of 7. Let the input

range be taken from 4 to 10 and other variable, ie.output range from 1 to

18mm.

After fuzzification, the fuzzy sets obtained are labelled using the

following term set, T={LAD, MAD, SAD, SP, SAL, MAL, LAL, LDD,

MDD, SDD, SD, SID, MID, LID}

LAD = large acidic

MAD = medium acidic

SAD = small acidic

SP = set point pH

SAL = small alkaline

MAL = medium alkaline

LAL = large alkaline

LDD = large decrease diameter

MDD = medium decrease diameter

SDD = small decrease diameter



SD = set diameter

SID = small increase diameter

MID = medium increase diameter

LID = large increase diameter

The membership functions of input variable PH and output variable

LFPD is shown in figures 5 and 6 respectively. μi is the membership

function of output. Here, we use triangular membership functions.

Figure 5: Membership Function of pH

Figure 6: Membership Function of LFPD



As a result of Fuzzification, we get the names of fuzzy sets to which the

input belongs and to what extend they belong to these sets i.e., their

membership functions.

RULE BASE

Rule base stores the different rules that are to be fired or used according

to the input. These rules are either gathered from experienced human

operator or from careful study of existing pH control systems. The rule base

thus represents the control strategy employed in the pH control. Table 1

gives the set of fuzzy action rules in our particular application.

Table 1: The Rule Base

The incoming pH value from sensor fires the rules from rule-base. This

is done by the inference mechanism.

INFERENCE MECHANISM



The inference mechanism employed in pH control is based on

individual rule firing. In this scheme, contribution of each rule is evaluated

and overall decision is derived.

During inference process, each rule that is fired by a crisp value pf pH is

summed up after giving the weightages decided by the fuzzification unit.

This weightage is called degree of satisfaction (DoS). DoS is decided by the

fuzzification module. The process of inference is illustrated in figure below.

Figure 7: Inference Mechanism and Defuzzification

In the above drawn case, the input pH is 6.25. The set point pH is 7.

After fuzzification, we understand that the input belongs to the fuzzy set

SAD with a degree of membership DoM=0.7 and at the same time, it belongs

to SP with a DoM=0.3. Since the membership function chosen is symmetric

(triangular), the DoS=DoM. The work until now was done by the



fuzzification module. Now, the control switches to the inference

mechanism.

In the inference mechanism, depending on which all fuzzy sets the input

belongs to, the corresponding rules are fired. This describes the functioning

of the three out of four blocks in the fuzzy controller. Now comes the

defuzzification section.

DEFUZZIFICATION SECTION

This section performs the task of converting the output of inference

mechanism, i.e., the rules that are fired, and the DoS given by the

fuzzification module into a crisp value of LFPD. For this, it uses “height

defuzzification” which is computationally simple and fast. The crisp rate of

lime flow depends on the crisp value of lime flow pipe diameter LFPD,

which is given by the formula

LFPD=(P1H1+P2H2) / (H1+H2)

In our example, a maximum of two sets are clipped simultaneously.

The resulting LFPD will be LFPD=(15X0.3 +16X0.7)/ (0.3+0.7)

= 15.7 mm

PRACTICAL CONTROL CHARACTERISTICS

Practically, the above-described method is an iterative method. After a

certain period of damped oscillations, the pH tends to maintain the desired

value.



SUGAR PLANT WITH AND WITHOUT AUTOMATIC

pH CONTROL

In sugar plants, pH control is achieved either manually or automatically.

A comparative study is given below.

MANUAL CONTROL

These are many strategies used in manual pH control, out of which a

couple are explained below. One of them is that the operator keeps the juice

flow and SO2 gas flow rates constant and adjusts the lime control value to get

the desired value of pH. The lime flow rate is adjusted by changing the

diameter of an outlet pipe of lime milk tank. This adjustment is based on the

operator’s experience. In such a control, the pH of clear juice obtained is in

the range 6.9 to 7.2. This is observed using pH indicator paper.

In another method, the lime is added to heated raw juice until the pH is

9.5 to 10.5. By maintaining the juice flow constant. Here, lime flow rate is

varied manually. By adjusting the airflow to the Sulphur burner, SO2 gas is

passed through limed juice and pH is maintained in the range 6.9 to 7.1.

Here also, pH is observed using pH indicator paper.

AUTOMATIC CONTROL

The earlier discussed fuzzy logic pH control technique can be used for

sugar factory automatic. To implement this controller, many methods can be

adopted. It may be implemented using a PC, a microprocessor, or a micro

controller. The first two methods are discussed in brief and we focus on the



third method in detail. In the first two methods, a program is written down

based on the fuzzy algorithm. If we are using a PC, any high level language

can be used. If we are using a microprocessor, we have to either write the

program in a high level language provided, we have a compiler to convert

this program into the assembly language of the microprocessor or the

program has to be written down in the assembly language itself. In these two

methods, the input values and output values are taken in and given out

through the ports of the μp or pc.

In the third method, we are using a microcontroller of PIC series. We

are using the μc PIC 16F73 chip, to be specific. In addition to this μc, we are

using an USART, input buffer, output latches, etc. A block diagram of the

whole system is given below.



HARDWARE BLOCK DIAGRAM

Figure 8: Hardware Block Diagram

PIC MICROCONTROLLER

The PIC Microcontroller that is used here to realize the

fuzzy controller is a general-purpose eight-bit microcontroller

developed by the MICROCHIP INC. The programming of this

microcontroller can be done in two ways. One is by

assembly language programming of the micro controller.

The other is by using a compiler MPLAB for converting the

high-level language program written by the programmer into

the assembly language of the microcontroller.

INPUT UNITS



The inputs to the microcontroller are crisp values of pH

measured by the sensors. This input can be either digital or

analog. Separate units are provided for analog and digital

inputs. Buffer is used for holding the input for sampling.

Buffer holds the input only for a small period. According to

the required accuracy, the inputs are taken i.e., sampled

from the input terminal continuously since the buffer can

hold only for a fraction of time. If less accuracy is needed,

sampling rate is decreased.

OUTPUT UNITS

The output of our fuzzy logic controller is crisp value of

lime flow pipe diameter. This output is obtained through the

output latch. The latch used here is 74373.

USART AND MAX232

USART or universal synchronous receiver transmitter is

used for communicating with the computer. MAX232 is used

for converting TTL level data to RS232 level.

OTHER APPLICATIONS OF FUZZY LOGIC

Modern consumer goods like washing machine, owen,

etc.

Expert systems

Fault tolerant aircraft control



Genetic engineering

Fuzzy logic also finds application in the fields of Robotics,

civil engineering, industrial engineering, mechanical

engineering, economics, medicine, etc.

MERITS OF FUZZY LOGIC CONTROLLER

The main advantage of a fuzzy controller is that a

model is not required to develop such a controller.

The method of using rule base is more lucid and

natural t o people.

Fussy logic methods can be used for tuning of

conventional PID controllers.

DEMERITS OF FUZZY LOGIC CONTROLLER

Since the fuzzy controllers have no mathematical

model, it is difficult to analyse the system.

The working of the controller heavily depends on the

judgement of the human expert who formulated the

rules.



CONCLUSION

Fuzzy logic is a relatively new technology, which is increasingly

finding applications in the present scenario. With its ability to simulate a

human decision making process, the technology seems to be lucid and

natural to the human. In short, fuzzy logic is a car with an engineer in its

driver’s seat. With proper design of its four components along with a wisely

chosen set of rules, fuzzy control is all set to crack the most complex of

control problems- thanks to Lotfi A. Zedah who is regarded as the father of

Fuzzy logic.



BIBLIOGRAPHY

1. B. T. JADHAV, R.R. MUDHOLKAR & S.R. SAWANT,

“FUZZY LOGIC pH CONTROL TECHNIQUE”,

INDUSTRIAL AUTOMATION- APRIL 2003, pp 42-46.

2. KEVIN M. PASINO & STEPHEN YURKOVICH,

“FUZZY CONTROL”

ADDISON WESLEY, pp 21-65

3. GEORGE J. KLIR & BO YUAN,

“FUZZY SETS AND FUZZY LOGIC-THEORY AND APPLICATION”,

PHI, pp 1-32.



ABSTRACT

Fuzzy logic, a relatively new cutting edge technology, is nowadays

gaining popularity. Fuzzy control is a practical alternative for a variety of

challenging control applications since it provides a convenient method for

constructing no-linear controllers via the use of heuristic information. Such

heuristic information may come from an operator who has acted as a “human

in the loop” controller for a process. A fuzzy controller emulates human

decision-making process. Fuzzy control provides a user-friendly formalism

for representing and implementing the ideas we have about how to achieve

high performance control. This paper discusses the automation of sugar

factory by the use of fuzzy controller for controlling the pH of sugar

solution. The structure, working, and design of a fuzzy controller for this

purpose is very well dealt with in this paper. With its ability to simulate a

human decision making process, the technology seems to be lucid and

natural to the human. In short, fuzzy logic is a car with an engineer in its

driver’s seat. With proper design of its four components along with a wisely

chosen set of rules, fuzzy control is all set to crack the most complex of

control problems- thanks to Lotfi A. Zedah, who is regarded as the father of

Fuzzy Logic.



ACKNOWLEDGEMENT

I extend my sincere gratitude towards Prof . P.Sukumaran Head of

Department for giving us his invaluable knowledge and wonderful technical

guidance

I express my thanks to Mr. Muhammed kutty our group tutor and

also to our staff advisor Ms. Biji Paul for their kind co-operation and

guidance for preparing and presenting this seminar.

I also thank all the other faculty members of AEI department and my

friends for their help and support.



CONTENTS

1. INTRODUCTION 1

2. FUZZY CONTROL - The Basics 2

RULE BASE

INFERENCE MECHANISM

FUZZIFICATION INTERFACE

DEFUZZIFICATION INTERFACE

SELECTION OF INPUTS AND OUTPUTS

3. CRISP SETS Vs FUZZY SETS 4

CRISP SETS

FUZZY SETS AND MEMBERSHIP FUNCTIONS

4. PH CONTROL 6

FUZZIFICATION SECTION

RULE BASE

INFERENCE MECHANISM

DEFUZZIFICATION SECTION

PRACTICAL CONTROL CHARACTERISTICS

5. SUGAR PLANT WITH AND WITHOUT

AUTOMATIC pH CONTROL 13

MANUAL CONTROL

AUTOMATIC CONTROL

6. HARDWARE BLOCK DIAGRAM 15

PIC MICROCONTROLLER

INPUT UNITS

OUTPUT UNITS

USART AND MAX232

7. OTHER APPLICATIONS OF FUZZY LOGIC 16

8. MERITS OF FUZZY LOGIC CONTROLLER 17

9. DEMERITS OF FUZZY LOGIC CONTROLLER 17

10. CONCLUSION 18

11. BIBLIOGRAPHY 19




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PH Control Technique using Fuzzy Logic