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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,
65
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
67
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
70
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.
73
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
74
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
75
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
Steady State Error (rpm)
Not mentioned
Not mentioned +20 ±9
77
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.
79
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.
Figure 4.15 Speed variation for the step change in load torque 50% to
100%applied at t=2.5 sec with the speed of 1800 rpm.
80
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
81
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.
82
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
83
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.
84
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
85
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
Settling Time (sec) 0.36 0.34 0.33
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 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.
87
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
Recovery Time (sec) 0.015 0.019 0.11
Steady State Error (rpm) 6 5 +1
The Overall time domain parameters of developed PID controller
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
88
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
Settling Time (sec) 1.21 0.82
Max. Over Shoot (%) 2.73 1.33
Steady State Error (rpm) +10 8
Set Speed Change from 500 to 1000rpm
Max. Over Shoot (%) 4.3 1.4
Settling Time (sec) 2.9 0.34
Load Change from 25% to 50% Max. Speed Drop (%) 1.5 0.46
Recovery Time (sec) 0.09 0.019
Steady State Error (rpm) +20 5
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.
90
Table 4.14 Performance comparison of developed FLC for DC
Separately Excited Motor
Controller Developed
PID Developed
FLC During rated speed and 10% load
Rise Time (sec) 0.90 0.89
Settling Time (sec) 2.41 1.12
Max. Over Shoot (%) 8.8 0.61
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
91
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
Settling Time (sec) 0.34 0.33 0.60
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.
92
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 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.
93
Figure 4.26 Speed variation for the step change in load torque 50% to
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
Recovery Time (sec) 0.060 0.075 0.18
Steady State Error (rpm) 12 8 +2.5
94
The Overall time domain parameters of developed PID controller
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
Controller Developed
PID Developed
FLC
During rated speed and 10% load
Rise Time (sec) 0.90 0.89
Settling Time (sec) 2.41 1.12
Max. Over Shoot (%) 8.8 0.61
Steady State Error (rpm) +23 ±12
Set Speed Change from 500 to 1000rpm
Max. Over Shoot (%) 1.6 0.83
Settling Time (sec) 0.42 0.33
Load Change from 25% to 50%
Max. Speed Drop (%) 1.27 0.77
Recovery Time (sec) 0.43 0.075
Steady State Error (rpm) +13 8
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
95
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.
96
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
98
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.
99
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
Steady State Error (rpm)
+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.