CHAPTER 4 FUZZY LOGIC CONTROLLER -...

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CHAPTER 4

FUZZY LOGIC CONTROLLER

4.1 INTRODUCTION

Unlike digital logic, the Fuzzy Logic is a multivalued logic. It deals

with approximate perceptive rather than precise. The effective and efficient

control using fuzzy logic has emerged as a tool to deal with uncertain,

imprecise or qualitative decision making problems. Fuzzy Logic derived from

fuzzy set theory. Fuzzy logic was first proposed by Lotfi Zadeh in 1965.

Recently the Fuzzy Logic is utilized in many applications, such as adjustable

speed drive, aircraft engines, helicopter control, missile guidance, automatic

transmission, wheel slip control, auto focus cameras, washing machines,

railway engines for smoother drive and fuel consumption and many industrial

processes. Many literatures say that the Fuzzy Logic Control provides better

results than the conventional PID controllers.

The Fuzzy set theory represents the human reasoning with

knowledge that is almost impossible to represent in quantitative measures or

for that control plants that are hard to control or ill defined. Fuzzy inference

system models the system using if-then rules. Fuzzy set theory proposed the

membership function at range of numbers (0, 1) or False or True membership

function. This theory provides the mathematical strength to check the

uncertainty connected with human thinking or reasoning. Fuzzy logic is

suitable for model that is hard to control or non-linear models. This system

also provides over MIMO systems and also allows decision making with

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incomplete information. Human reasoning can also be known as multi valued

4.2 DESIGN OF FUZZY LOGIC CONTROLLER

In Fuzzy Logic controller design, the first step is to understand and

characterize the system behavior by using knowledge and experience. The

second step is to directly design the control algorithm using fuzzy rules,

which describe the principles of the controller's regulation in terms of the

relationship between its inputs and outputs. The last step is to simulate and

debug the design. The fuzzy logic controller (FLC) can be designed without

the exact model of the system. For FLC, it is sufficient to understand the

general behavior of the system. Such a FLC is designed and implemented for

DC-DC converter fed DC motor.

The FLC involves three stages namely Fuzzification, Rule-Base

and Defuzzification. The Sugeno type controller is performed for present

control because it has singleton membership in the output variable. Moreover

it can be easily implemented and number of calculations can be reduced. The

general structure of Fuzzy Logic controller is given in Figure 4.1.

Figure 4.1 Structure of Fuzzy Logic Controller

Fuzzification

Preprocessing

Defuzzification

Post processing

Rule Base

Inference Engine

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4.2.1 Fuzzification

In Fuzzy logic system the linguistic variables are used instead of

numerical variables. The process of converting a numerical variable (real

number or crisp variables) in to a linguistic variable (fuzzy number or fuzzy

variable) is called fuzzification.

In this work, the motor variables are speed and current (ia). The

speed is controlled by FLC. The error e(k) and change in error e(k) is given

as input to the FLC. The error is found by comparing the actual speed (k)

with reference speed r(k). From the error e(k) and pervious error eprevious(k)

the change in error is calculated and then it is normalized, in order to use the

same FLC for different reference speed. This process stage is called as

preprocessing which is shown in Figure 4.1. Then the error and change in

error are fuzzified.

Seven linguistic variables are used for the input variable e(k) and

e(k). That are negative big (NB), negative medium (NM), negative small

(NS), zero (Z), positive small (PS), positive medium (PM) and positive big

(PB). There are many types of membership functions, such as triangular-

shaped, Gaussian, sigmoidal, pi-shaped trapezoidal-shaped, bell-shaped etc.

the triangular membership function is used for simplicity and also to reduce

the calculations.

(4.1)

(4.2)

4.2.2 Defuzzification

The reverse process of fuzzification is called defuzzification. The

linguistic variables are converted in to a numerical variable. As the weighted

sum method is considered to be the best well-known defuzzification method,

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it is utilized in the present model. The defuzzified output is the duty cycle

dc(k). The change in duty cycle dc (k) can be obtained by adding the

pervious duty cycle pdc(k) with the duty cycle dc(k) which is given in

equation 8. This process stage is called as post processing which is also

shown in Figure 4.1.

(4.3)

The input and output fuzzy membership functions are shown in

Figure 4.2.

Figure 4.2 Fuzzy memberships used for simulation

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4.2.3 Rule Table and Inference Engine

The control rules that relate the fuzzy output to the fuzzy inputs are

derived from general knowledge of the system behavior, also the perception

l

e (k) is Y, then

e(k) and

dc(k) respectively. The rule table for the designed fuzzy controller is given

in the Tab

Table 4.1 Fuzzy rule table for seven membership functions

Error

Change in

Error

NB NM NS Z PS PM PB

NB NB NB NB NB NM NS Z

NM NB NB NB NM NS Z PS

NS NB NB NM NS Z PS PM

Z NB NM NS Z PS PM PB

PS NM NS Z PS PM PB PB

PM NS Z PS PM PB PB PB

PB Z PS PM PB PB PB PB

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4.3 SIMULATION OF FLC IN MATLAB

The detailed design procedure for the development of Fuzzy Logic

Controller using MATLAB is given here. As mentioned in the previous

section there are three variables chosen, two for input variables Error and

Change in Error the third one is for output variable duty cycle.

The general procedures to develop the FLC are

Step I : Identify the inputs and their ranges and name them

Step II : Identify the outputs and their ranges and name them

Step III: Create the degree of fuzzy membership function for each input and

output

Step IV: Construct the rule base that the system will operate under

Step V : Decide how the action will be executed by assigning strengths to the

rules

Step VI: Combine the rules and defuzzify the output

Table 4.2 shows the membership function names and ranges of

input variable Error. Here Seven triangular membership function were used

and ranges between -1 to +1. The triangular membership function is simple

and easy to implement. Figurs 4.2 represents the input membership function

for Error. The range of membership function shows that the maximum

possible normalised speed error is +1 and minimum is -1. This range is

possible for controlling the speed of the motor. From many litrerature the

seven membership function is the suitable choice of selection and the shape of

the membership function is selected.

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Linguistic variable for Error Linguistic Value Notation Numerical Value

Negative Big NB [-1.333 -1 -0.6665]

Negative Medium NM [-1 -0.6665 -0.3334]

Negative Small NS [-0.6665 -0.3334 0]

Zero Z [-0.3334 0 0.3334]

Positive Small PS [0 0.3334 0.6665]

Positive Medium PM [0.3334 0.6665 1]

Positive Big PB [0.6665 1 1.334]

Figure 4.3 Input membership function for Error

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Linguistic variable for Change in Error Linguistic Value Notation Numerical Value

Negative Big NB [-5.334 -4 -2.666]

Negative Medium NM [-4 -2.666 -1.333]

Negative Small NS [-2.666 -1.333 0]

Zero Z [-1.333 0 1.333]

Positive Small PS [0 1.333 2.666]

Positive Medium PM [1.333 2.666 4]

Positive Big PB [2.666 4 5.334]

Figure 4.4 Input membership function for Change in Error

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Similarly the membership function is chosen for the change in

error. The membership function range for change in error is maximum +2 and

minimum is -2. Change in error is the difference between present error and

previous error. Table 4.3 shows the membership function names and ranges of

input variable Cahnge in Error. Figure 4.4 represents the input membership

function for Change in Error.

Likewise the membership function is chosen for the output variable

Table 4.4 shows the membership function names and ranges of

output variable Duty Cycle. Figure 4.5 represents the output membership

function for Duty Cycle. Figure 4.6 and 4.7 shows the rule viewer and surface

viewer of the designed Fuzzy Logic Controller respectively.

Linguistic variable for Duty Cycle

Linguistic Value Notation Numerical Value

Negative Big NB -1.00

Negative Medium NM -0.33

Negative Small NS -0.66

Zero Z 0.00

Positive Small PS 0.33

Positive Medium PM 0.66

Positive Big PB 1.00

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Figure 4.5. Output membership function for Duty Cycle

Figure 4.6 Rule viewer of FLC

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Figure 4.7 Surface viewer of FLC

4.4 SIMULINK MODEL OF THE SYSTEM WITH FUZZY LOGIC CONTROLLER

The complete simulation model of the DC series motor drive system with Fuzzy Logic Controller is given in Figure 4.8. The fuzzy controller block from fuzzy logic toolbox is used to test and evaluate the FLC. As mentioned in the PID controller here also the actual speed and the set

speed is given to the FLC preprocessing to generate the error and change in error signals. The error and change in error are given as the input to the FLC, the controller produce the duty cycle, during post processing the change in

duty cycle is obtained and it is given to the PWM generator unit. The PWM generator unit generates the PWM with the switching frequency of 1KHz by comparing the repeating sequence signal with the FLC output. Then the PWM

is given to the current controller, the current controller allows the PWM if the actual motor current is within the limits of the set current value. Further the PWM is given to the DC chopper unit to give the variable DC voltage to the DC series motor. There by the motor speed is controlled.

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Figure 4.8 Simulink Model of DC series motor with FLC

The Structure of the fuzzy controller including preprocessing and

postprocessing using MATLAB/Simulink is shown in Figure 4.9. In

preprocessing stage error is calculated by subtracting the actual speed from

the reference speed ref. The error is normalized by dividing with reference

speed. The range of normalized speed is from 0 to 1. Then the change in error

is calculated from the present error with the previous error using the memory

block. The error and change in error is given as input to the FLC through a

mux block. The output of the FLC is duty cycle. In postprocessing stage the

change in duty cycle is obtained by adding the present duty cycle with

previous duty cycle.

Figure 4.9 Simulink model of FLC

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4.5 RESULTS AND DISCUSSION FOR THE FLC WITH SEVEN

MEMBERSHIP FUNCTION

The DC series motor model through the DC-DC converter

including FLC was simulated using MATLAB simulation. The fuzzy

controller was designed and DC-DC converter fed DC series motor was

tested. The simulated waves of gate pulse, output voltage, motor current and

speed with respect to time for r=1800 rpm are shown in Figure 4.10. The

expanded view is shown in Figure 4.11.

Figure 4.10 Pulse, Output Voltage, Motor Current and Speed Variation

with respect to Time Response for r=1800 rpm

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Figure 4.11 Expanded view of Pulse, Output Voltage, Motor Current

and Speed Variation with respect to Time at r=1800 rpm

From the Figure 4.11 it is clearly seen that the time duration

between each pulse is 0.001 sec means that the switching frequency is 1 KHz.

When the pulse is ON the motor current is increasing and decreasing when

the pulse is OFF due to the chopping action of the DC-DC converter. The

FLC regulate the speed at 1800 rpm. The performance comparison of

developed FLC for DC-DC converter fed 220V DC series motor with PID

controller and reported result in Yousef et al (1995) is given in Table 4.5.

From the table 4.5 it is seen that all the performance parameter has been

reduced considerable amount, which shows that the FLC is superior out of

other controllers shown.

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Table 4.5 Performance comparison of developed FLC for 220V DC

series motor with rated speed

Controller Classical PI Yousef et al

(1995)

Fuzzy Yousef et al

(1995)

Developed PID Developed

FLC

During rated speed and 10% load Rise Time

(sec) Not

mentioned Not

mentioned 0.8 0.8

Settling Time (sec)

2.67 1.7 1.55 1

Max. Over Shoot (%)

6.72 3.21 3.06 0.36

Steady State Error (rpm)

Not mentioned

Not mentioned

+10 ±2

Load Change from 25% to 50% Max. Speed

Drop (%)

5.26 3.21 1.5 0.47

Recovery Time (sec)

2.82 2.4

0.18 0.030


Not mentioned

Not mentioned +20 ±9

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Figure 4.12 Speed variation for the step change in reference speed at

different interval with 10% load torque

The Figure 4.12 shows the speed variation and current variation for

the step change in reference speed from 500rpm to 1000rpm at 4 sec and

1000rpm to 1800rpm at 7 sec with 10% load torque. The current is always

chopping between maximum to minimum. It is seen from figure that when the

speed is increased from 500rpm to 1000rpm the motor takes 0.32 sec whereas

in the initial stage it took almost 0.35 sec to reach 500rpm. This may be due to

the inertia in the beginning. The FLC provides proper speed regulation for all

the speed changes. The comparative time domain parameters of Speed

variation for various set speed changes are depicted in Table 4.6.

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Table 4.6 Time domain parameter of FLC for different set speed

change with 10% load

Set Speed Changes 0 to 500rpm 500 to 1000rpm 1000 to 1800rpm

Max. Over Shoot (%) 1.00 0.77 0.61

Settling Time (sec) 0.35 0.32 0.47

The simulated result of speed regulation for a step change in the

load torque from 10% to 25%, 25% to 50% and 50% to 100% applied at t=2.5

sec is shown in Figure 4.13, 4.14 and 4.15 respectively. The FLC gives proper

response to the system for the load changes from 10% to 100%. At 100% load

there is a small dip in the speed response and it is recover the speed with in

1.1 sec. The expanded part of different load changes is given in Figure 4.16

for comparison. The comparative time domain parameters of Speed variation

for various load changes are depicted in Table 4.7.

Figure 4.13 Speed variation for the step change in load torque from 10% to 25% applied at t=2.5 secs with the speed of 1800 rpm.

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Figure 4.14 Speed variation for the step change in load torque 25% to

50% applied at t=2.5secs with the speed of 1800 rpm.


100%applied at t=2.5 sec with the speed of 1800 rpm.

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Figure 4.16 Comparison of Speed variation for the step change in load torque applied at t=2.5 sec with rated speed

Figure 4.16 provides the comparative analysis for the FLC with various load torque changes. When the load changes from 50% to 100%, the speed variations completely abolished and the speed drop is more than the lesser load conditions.

Table 4.7 Time domain parameter of FLC for the load changes for 220V DC Series Motor with rated speed

Load Variations 10% to 25% 25% to 50% 50% to 100% Max. Speed Drop

(%) 0.31 0.47 0.72

Recovery Time (sec) 0.025 0.030 1.1

Steady State Error (rpm) ±10 ±9 +3.3

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The Overall time domain parameters of developed PID controller

and FLC for 220V DC series motor for rated speed with 10% load, set speed

changes and the load torque changes are illustrated in the Table 4.8. From the

Table 4.8 it is seen that comparatively the FLC is good in all the aspects.

Table 4.8 Overall time domain parameter of developed FLC for 220V

DC series motor

Controller Developed PID

Developed FLC

During rated speed and 10% load Rise Time (sec) 0.8 0.8 Settling Time (sec) 1.55 1 Max. Over Shoot (%) 3.06 0.36 Steady State Error (rpm) +10 ±2

Set Speed Change from 500 to 1000rpm Max. Over Shoot (%) 6.88 0.77 Settling Time (sec) 1.6 0.32

Load Change from 25% to 50% Max. Speed Drop (%) 1.5 0.47 Recovery Time (sec) 0.18 0.030 Steady State Error (rpm) +20 ±9

4.6 DESIGN OF MODIFIED FUZZY LOGIC CONTROLLER

Initially the FLC is designed with seven triangular membership

function of equal width and then it was reduced to five of variable width

membership function. The width of the membership function is varied in

order to reduce the number of membership function from seven to five. In this

five membership function, the width of the center membership function is

considered to be narrow and it has been wide towards outer.

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Five linguistic variables are used for the input variable e(k) and

e(k). That are negative big (NB), negative small (NS), zero (Z), positive

small (PS) and positive big (PB). There are many types of membership

functions, such as triangular-shaped, Gaussian, sigmoidal, pi-shaped

trapezoidal-shaped, bell-shaped etc. the triangular membership function is

used for simplicity and also to reduce the calculations.

Figure 4.17. Modified Fuzzy memberships used for simulation

In most of the work seven membership functions were preferred for

accurate result. In this work only five membership functions were used for the

input, error and change in error. To reduce the number of membership

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function the width of the membership functions were kept different. The

membership function width for the center membership functions is considered

narrow and wide towards outer. The input and output fuzzy membership

functions are shown in Figure 4.17.

The rule table for the designed fuzzy controller is given in the

Table 4.9. The element in the first row and first column means that

If error is NB, and change in error is NB then output is NB.

Table 4.9 Fuzzy rule table for five membership functions

Error

Cha

nge

in E

rror

NB NS Z PS PB NB NB NB NB NS Z

NS NB NB NS Z PS

Z NB NS Z PS PB

PS NS Z PS PB PB

PB Z PS PB PB PB

4.7 RESULTS AND DISCUSSIONS FOR MODIFIED FLC WITH

110V DC SERIES MOTOR

The FLC performance was also analyzed in different aspects as in

the PID controller analysis in the previous section. In this section the motor

parameter for 110V DC series motor is considered for analysis. The same

MATLAB/Simulink model shown in Figure 4.8 was utilized to test the

performance by replacing the 220V motor model parameter with 110V motor

parameter given in Table 3.8. The simulated waves of gate pulse, output

voltage, motor current and speed with respect to time for r=1500rpm are

shown in Figure 4.18. The expanded view is shown in Figure 4.19.

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Figure 4.18 Pulse, Output Voltage, Motor Current and Speed Variation with respect to Time Response for r=1500 rpm

Figure 4.19 Expanded view of Pulse, Output Voltage, Motor Current and Speed Variation with respect to Time Response for

r=1500rpm

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The switching frequency of PWM is 1 KHz. The FLC regulate the

speed at 1500rpm. The performance comparison of developed FLC for

DC-DC converter fed 110V DC series motor with PID controller is given in

Table 4.10. From the Table 4.10 it is seen that all the value of performance

parameters are less for FLC than the PID controller, which shows that the

superiority of FLC.

Table 4.10 Performance comparison of developed Fuzzy Logic Controller for 110V DC Series Motor with PID controller

Controller Developed PID Developed FLC Rise Time (sec) 0.71 0.67

Settling Time (sec) 1.21 0.82

Max. Over Shoot (%) 2.73 1.33

Steady State Error (rpm) +10 8

Figure 4.20 Speed variation for the step change in reference speed at different interval with 10% load torque

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The Figure 4.20 shows the speed variation for the step change in

reference speed from 500rpm to 1000rpm at 3 sec and 1000rpm to 1500 rpm

at 7 sec with 10% load torque. It is seen from the Figure 4.20 due to the

inertia in the beginning the motor takes 0.36 sec to reach the speed from 0 to

500rpm whereas in the second step it took 0.34 sec only to reach from 500 to

1000rpm. The FLC provides proper speed regulation for all the step speed

changes. The comparative time domain parameters of speed variation for

various set speed changes are depicted in Table 4.11.

Table 4.11 Time domain parameter of FLC for different set speed

change with 10% load

Set Speed Changes 0 to 500rpm 500 to 1000rpm 1000 to 1800rpm

Max. Over Shoot (%) 1.6 1.4 1.2



load torque from 10% to 25%, 25% to 50% and 50% to 100% applied at 3

sec, 5.5 sec and 8 sec are shown in Figure 4.21. The FLC provides proper

regulation to the system for the load changes from 10% to 100%. At 100%

load the oscillations in speed is eliminated due to high load torque. The

comparative time domain parameters of Speed variation for various load

changes are represented in Table 4.12.

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Figure 4.21 Performance of DC series motor with FLC for load

variation at 3sec, 5.5sec and 8sec with rated speed

Table 4.12 Time domain parameter of FLC for the load changes for

110V DC Series Motor with rated speed

Load Variations 10% to 25% 25% to 50% 50% to 100%

Max. Speed Drop (%) 0.40 0.46 0.60


Steady State Error (rpm) 6 5 +1


and FLC for 110V DC series motor for rated speed with 10% load, set speed

changes and the load torque changes are illustrated in the Table 4.13. From

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the Table 4.13 it is seen that comparatively the FLC is superior in all the

aspects.

Table 4.13 Overall time domain parameter of developed FLC for 110V

DC series motor

Controller Developed

PID Developed

FLC

During rated speed and 10% load

Rise Time (sec) 0.71 0.67




Set Speed Change from 500 to 1000rpm



Load Change from 25% to 50% Max. Speed Drop (%) 1.5 0.46

Recovery Time (sec) 0.09 0.019


4.8 RESULTS AND DISCUSSIONS FOR MODIFIED FLC WITH

DC SEPARATELY EXCITED MOTOR

In this section the DC separately excited motor is considered for

analysis. Then the same MATLAB/Simulink model shown in Figure 4.8 was

utilized to test the performance by replacing the 220V DC series motor model

with DC separately excited motor shown in Figure 2.8. The simulated

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waves of speed response with respect to time for r=1800rpm is shown in

Figure 4.22.

Figure 4.22 Speed response with respect to time of DC Separately

Excited Motor with Fuzzy controller for rated speed and

10% load torque

The switching frequency of PWM selected for this case is also the

same 1 KHz. The FLC regulate the speed at rated value of 1800rpm. The

performance comparison of developed FLC for DC-DC converter fed DC

separately excited motor with PID controller for rated condition is given in

Table 4.14.

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Table 4.14 Performance comparison of developed FLC for DC

Separately Excited Motor


PID Developed

FLC During rated speed and 10% load




Steady State Error (rpm) +23 ±12

Figure 4.23 Speed variation for the step change in reference speed at

different interval with 10% load torque

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The Figure 4.23 shows the speed variation for the step change in

reference speed from 500rpm to 1000rpm at 4 sec and 1000rpm to 1500rpm at

7 sec with 10% load torque for 220V DC series motor and DC separately

excited motor. The modified FLC provides proper speed regulation for all the

step speed changes for both the motor. The comparative time domain

parameters of speed variation for various set speed changes are depicted in

Table 4.15.

Table 4.15 Time domain parameter of FLC for different set speed change with 10% load

Set Speed Changes 0 to 500rpm 500 to

1000rpm 1000 to

1800rpm

Max. Over Shoot (%) 0.94 0.83 0.55


Figure 4.24 Speed variation for the step change in load torque 10% to 25% applied at t=3 sec when the speed is 1800rpm.

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load torque from 10% to 25%, 25% to 50% and 50% to 100% applied at 3sec

for 220V DC series motor and DC separately excited motor are shown in

Figure 4.24, 4.25 and 4.26 respectively. The FLC provides appropriate speed

regulation to both DC series and DC separately excited motor for the load

changes from 10% to 100%. At 100% load the speed drop of DC series motor

is 0.72% and it takes 1.1 sec to recover the original speed where as in DC

separately excited motor the speed drop is 0.5%, it is almost equal to speed

drop in series motor but it takes 0.18 sec only to recover the speed. While

seeing this case the modified FLC is more suited for DC separately excited

motor than DC series motor. The comparative time domain parameters of

Speed variation for various load changes are represented in Table 4.16.

Figure 4.25 Speed variation for the step change in load torque ( TL=50%) applied at t=3 sec when the speed is 1800rpm.

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100% applied at t=3 sec when the speed is 1800rpm

Table 4.16 Time domain parameter of FLC for the load changes for DC

separately excited motor with rated speed

Load Variations 10% to 25% 25% to 50% 50% to 100%

Max. Speed Drop (%) 1.05 0.77 0.50


Steady State Error (rpm) 12 8 +2.5

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and FLC for DC separately excited motor for rated speed with 10% load, set

speed changes and the load torque changes are illustrated in the Table 4.17.

From the Table 4.17 it is seen that comparatively the FLC is superior in all the

aspects than the PID controller.

Table 4.17 Overall time domain parameter of developed FLC for DC

Separately Excited Motor


PID Developed

FLC

During rated speed and 10% load




Steady State Error (rpm) +23 ±12

Set Speed Change from 500 to 1000rpm



Load Change from 25% to 50%

Max. Speed Drop (%) 1.27 0.77

Recovery Time (sec) 0.43 0.075


4.9 HARDWARE IMPLEMENTATION WITH FLC

The developed modified Fuzzy Logic Controller was implemented

by using a NXP 80C51 based microcontroller (P89V51RD2BN). A DC-DC

buck converter was built with the MOSFET using IRFP450, and the

controllers were tested with DC series motor and DC separately excited

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motor. The speed of the motor was sensed by a pulse type digital speed sensor

and to feed back the signal to the controller. The Figure 4.29 shows the

experimental setup of the proposed system with DC series motor.

The microcontroller (P89V51RD2BN) has an 80C51 compatible

core with the following features: 80C51 Central Processing Unit, 5 V

Operating voltage from 0 to 40 MHz, 64 kB of on-chip Flash program

memory. It also has an PCA (Programmable Counter Array) with PWM and

Capture/Compare functions. The PWM is generated at a frequency of 10 kHz.

A LEM make current sensor LTS25NP is used to sense the motor current and

it is compared with the reference current using the comparator LM 399. The

AND gate is used to allow the PWM waveform when the actual current is less

than the reference current.

The PWM from the microcontroller was then amplified for a level

through the open collector optocoupler CYN 17-1 and fed to the DC DC

power converter through an isolator and driver chip IR2110. The DC-DC

buck converter output was given to the DC series motor whose speed is to be

controlled. The speed sensor connected to the motor shaft gives the pulse

output which again converted in to voltage using f/v converter and this DC

voltage is fed to the ADC available in the microcontroller.

The implementation of FLC in a microcontroller was done using

to develop and compile the C programming for FLC. The C program is

compiled and converted into hex file. Finally the hex code was embedded in

to the microcontroller used. The hex code is downloaded by using the

magic is given in Figure 4.27 and 4.28 respectively.

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Figure 4.27 Screen shot for Keil uvision compiler software

Figure 4.28 Screen shot for Flash Magic software to download the hex code

97

The Figure 4.29 shows the experimental setup of the system with

FLC. The experimental response of the DC series motor and DC separately

excited motor for the step change in reference speed are given in Figure 4.30

and 4.31 respectively.

Figure 4.29 Hardware setup of the system with FLC

Figure 4.30 Experimental graph of speed variation for the step change

in reference speed r=1800rpm using fuzzy controller for

DC Series Motor

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Figure 4.31 Experimental graph of speed variation for the step change

in reference speed r=1800rpm using fuzzy controller for

DC Separately Excited Motor

Figure 4.30 shows the speed response with the set speed of

1800rpm for Modified FLC controller for DC series motor and Figure 4.31

shows the speed response with the set speed of 1800rpm for Modified FLC

controller for DC separately excited motor. From the Figurers it is noted that

the DC series motor is taking the settling time of 6 sec and for separately

excited motor is 3.2 sec. The modified FLC has produced more oscillations in

the response, but it is due to the nature of the FLC. The Table 4.18 exposes

the performance comparison of hardware of proposed system with Fuzzy

controller.

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Table 4.18 Hardware Performance Comparison of developed FLC with

PID controller

Controller

Developed PID controller

Developed FLC

Series Motor

Sep. Ext. Motor

Series Motor

Sep. Ext. Motor

Settling Time (sec) 10.25 4 6 3.2

Max. Over Shoot (%) 5 3 0.9 0.8


+30 +15 ±17 ±15

4.10 CONCLUSION

In this chapter the performance of Fuzzy Logic controller and

modified Fuzzy Logic Controller for DC series motor with 220V and 110V

motor parameter and DC separately excited motor were analyzed. The

performances were analyzed with different load torque and different set speed

changes for both DC series and separately excited motor and found that the

speed can be controlled effectively with the modified FLC for all the motors.

Also in modified FLC the number of membership function is less. Hence the

memory required is less during the implementation. The modified FLC

reduces the peak overshoot, settling time and steady state error of the system

for all the cases. All the response of the system with modified FLC is found to

be satisfactory but still it is needed to be reduced the settling time and the

speed variations.

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