Inter
national
J
our
nal
of
P
o
wer
Electr
onics
and
Dri
v
e
System
(IJPEDS)
V
ol.
16,
No.
4,
December
2025,
pp.
2186
∼
2196
ISSN:
2088-8694,
DOI:
10.11591/ijpeds.v16.i4.pp2186-2196
❒
2186
Adapti
v
e
fuzzy
logic
contr
oller
based
BLDC
motor
to
impr
o
v
e
the
dynamic
perf
ormance
f
or
electric
tractor
application
Ashwini
Y
enegur,
Mungamuri
Sasikala
Department
of
Electrical
and
Electronics
Engineering,
F
aculty
of
Engineering
and
T
echnology
(Exclusi
v
ely
for
W
omen),
Sharnbasv
a
Uni
v
ersity
,
Kalab
uragi,
India
Article
Inf
o
Article
history:
Recei
v
ed
Jan
22,
2025
Re
vised
Aug
18,
2025
Accepted
Sep
2,
2025
K
eyw
ords:
Adapti
v
e
FLC
BLDC
motor
Dynamic
performance
Electric
tractor
MA
TLAB/Simulink
ABSTRA
CT
Permanent
magnet
brushless
DC
(PMBLDC)
motors
are
widely
used
in
a
v
ariety
of
industrial
applications
due
t
o
their
high-po
wer
density
and
ease
of
re
gulation.
The
three-phase
po
wer
semiconductors
bridge
is
the
standard
w
ay
for
controlling
these
motors.
In
order
to
initiate
the
in
v
erter
bridge
and
switch
on
the
po
wer
de
vices,
rotor
position
sensors
must
be
pro
vided
with
the
correct
commutation
sequence.
The
po
wer
de
vices
commutate
progressi
v
ely
60
de
grees,
depending
on
the
location
of
the
rotor
.
The
right
speed
controllers
are
necessary
for
the
motor
to
run
as
ef
ciently
as
possible.
PI
controllers
are
commonly
emplo
yed
with
permanent
magnet
motors
to
achie
v
e
speed
control
in
simple
manner
.
Ne
v
ertheless,
these
controllers
pro
vide
challenges
in
managing
control
comple
xity
,
including
nonlinearity
,
parametric
uctuations,
and
load
disturbances.
PI
controllers
need
accurate
linear
mathematical
models.
T
o
o
v
ercome
this,
in
this
paper
adapti
v
e
fuzzy
logic
controller
(FLC)
for
controlling
the
speed
of
a
BLDC
motor
is
presented.
When
the
motor
dri
v
e
system
uses
the
adapti
v
e
FLC
technology
for
speed
control,
it
e
xhibits
better
dynamic
beha
vior
and
is
more
resistant
to
changes
in
parameters
and
load
disturbances.
The
main
objecti
v
es
of
this
w
ork
are
to
analyze
and
appraise
the
functi
oning
of
an
electric
tractor
dri
v
en
by
a
PMBLDC
motor
dri
v
e
using
adapti
v
e
FLC.
The
PMBLDC
motor
dri
v
e
controllers
are
simulated
using
MA
TLAB/Simulink
softw
are.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Ashwini
Y
ene
gur
Department
of
Electrical
and
Electronic
Engineering
F
aculty
of
Engineering
and
T
echnology
(Exclusi
v
ely
for
W
omen),
Sharnbasv
a
Uni
v
ersity
Kalab
uragi,
Karnataka,
India
Email:
ashwiniyene
gur@gmail.com
1.
INTR
ODUCTION
Brushless
DC
motors
are
mostl
y
used
for
rotary
,
actuation,
positioning,
and
v
ariable
speed
uses
that
need
to
be
stable
and
ha
v
e
accurate
motion
control.
In
order
to
pro
vide
continuous
control
under
v
aried
operating
conditions,
an
ef
fecti
v
e
controller
must
be
designed
[1].
Due
to
their
x
ed
g
ain
parameters,
classic
motor
controllers
lik
e
PI
and
PID
controllers
are
sensiti
v
e
to
changes
in
load,
speed
uctuations,
and
parameter
v
ariations
[2]-[4].
Sliding
mode
control
(SMC)-based
algorithms
ha
v
e
been
in
v
estig
ated
by
researchers
for
use
in
electric
dri
v
e
applications.
One
of
SMC’
s
main
shortcomings
is
that
the
chattering
issues
which
causes
the
system’
s
performance
to
deteriorate.
These
problems
with
con
v
entional
control
algorithms
ha
v
e
piqued
the
curiosity
of
researchers,
who
are
l
oo
ki
ng
to
b
uild
and
analyze
electric
motor
s
y
s
tem
controllers
using
articial
J
ournal
homepage:
http://ijpeds.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Po
w
Elec
&
Dri
Syst
ISSN:
2088-8694
❒
2187
intelligence
(AI)
methods.
The
y
ha
v
e
created
AI
control
methods
for
speed
control,
such
as
neural
netw
ork
approaches
and
fuzzy
logic,
as
a
result
of
their
model-free
design
philosoph
y
.
Se
v
eral
controllers
for
BLDC
motor
dri
v
es
were
compared
in
terms
of
resilience
[5].
Along
with
con
v
entional
fuzzy
and
ANFIS,
self-tuning
FLC
approaches
ha
v
e
also
been
suggested
to
mak
e
FLC
more
resilient
to
outside
disturbances
[6].
When
the
motor’
s
parameters
change,
the
system
adapts
by
adjusting
the
scaling
f
actors,
rule
base,
and
membership
function
of
the
parameters.
A
”self-tuning
fuzzy
logic
controller”
(FLC)
describes
this
approach
[7].
By
setting
the
parameters
of
membership
function
(MF),
which
has
a
major
inuence
on
parameter
uctuations
among
the
dif
ferent
self-tuning
methods,
this
research
aims
to
b
uild
a
resilient
and
adaptable
controller
.
Serv
o,
re
gulatory
,
and
steady-state
responses
are
among
the
areas
the
proposed
controller’
s
adv
antages
are
in
v
estig
ated.
Here
is
the
outline
of
proposed
paper:
i)
Section
2
deals
into
the
mathematical
modeling
of
BLDC
and
ho
w
the
BLDC
motor
dri
ving
system
w
orks;
ii)
Section
3
deals
with
the
FLC
design
and
also
discuss
es
the
proposed
controller’
s
adapti
v
e
FLC
approach;
iii)
Section
4
v
alidates
the
simulation
results,
sho
ws
the
ndings
compared
to
other
controllers;
and
i
v)
Section
5
gi
v
es
the
conclusion.
2.
METHOD
Figure
1
illustrates
a
speed
control
system
for
a
BLDC
motor
used
in
an
electric
tra
ctor
,
emplo
ying
an
adapti
v
e
fuzzy
logic
controller
(FLC)
for
precise
speed
control.
The
process
be
gins
with
a
3-phase
A
C
supply
,
which
is
con
v
erted
to
DC
through
a
rectier
[8].
This
DC
supply
is
then
modulated
using
a
PWM
rectier
to
pro
vide
controlled
v
oltage
to
the
motor
.
Figure
1.
The
general
conguration
of
adapti
v
e
FLC-based
speed
control
of
BLDC
motor
The
motor’
s
speed
is
monitored
via
a
shaft
encoder
,
which
feeds
back
the
actual
speed
(
ω
m
)
to
the
control
system.
The
adapti
v
e
FLC
compares
the
actual
speed
with
the
reference
speed
(
ω
r
ef
)
and
calculates
the
error
.
Based
on
this
error
and
its
rate
of
change,
the
FLC
adjusts
the
reference
current
(
i
r
ef
).
This
current
is
passed
through
the
reference
current
generator
,
which
gener
ates
the
appropriate
phase
currents
(
i
r
r
ef
,
i
y
r
ef
,
i
br
ef
)
to
control
the
motor
[9].
The
PWM
modulator
translates
these
reference
currents
into
switching
signals,
which
are
fed
into
the
BLDC
motor
which
will
dri
v
e
the
tractor
.
The
entire
process
is
a
closed-loop
system,
with
continuous
feedback
ensuring
that
the
motor
operates
at
the
desired
speed
by
adapting
to
load
changes
and
e
xternal
disturbances,
resulting
in
smooth
and
ef
cient
operation
[10].
2.1.
Modeling
of
BLDC
motor
A
permanent
magnet
with
three
stator
winding
mak
es
up
the
BLDCM
rotor
.
It
is
possible
to
disre
g
ard
currents
produced
by
the
rotor
due
to
the
high
resistance
of
magnets
and
stainless
steel.
There
is
no
damper
winding
simulation
[11].
By
solving
the
circuit
equation
for
the
three
windings,
we
get
the
phase
v
ariables.
Adaptive
fuzzy
lo
gic
contr
oller
based
BLDC
motor
to
impr
o
ve
the
dynamic
performance
...
(Ashwini
Y
ene
gur)
Evaluation Warning : The document was created with Spire.PDF for Python.
2188
❒
ISSN:
2088-8694
V
r
st
V
y
st
V
bst
=
R
st
0
0
0
R
st
0
0
0
R
st
i
r
st
i
y
st
i
bst
+
d
dt
L
r
r
L
r
y
L
y
b
L
y
r
L
y
y
L
y
b
L
br
L
by
L
bb
i
r
st
i
y
st
i
bst
+
e
r
e
r
e
b
(1)
Where
the
stator
phase
v
oltages
are
represented
by
v
ast
,
v
bst
,
and
v
cst
,
and
the
stator
resistance
is
represented
by
Rs
.
The
stator’
s
three-phase
currents
are
i
cst
,
i
ast
,
and
i
bst
.
Each
phase
has
its
o
wn
self-inductance,
which
are
L
aa
,
L
bb
,
and
L
cc
.
The
phase-to-phase
inductances
are
L
ab
,
L
bc
,
and
L
ac
.
Phases
Ea,
Eb,
and
Ec
are
associated
with
electromoti
v
e
pressures.
The
resistance
of
each
winding
has
been
assumed
to
be
equal
[12].
Furthermore,
it
is
thought
that
the
rotor
reluctance
does
not
change
with
angle
because
there
is
no
conspicuous
rotor
.
L
r
r
=
L
y
y
=
L
bb
=
L
(2)
L
r
r
=
L
y
r
=
L
r
b
=
L
br
=
L
y
b
=
L
by
=
M
(3)
The
PMBDCM
model
is
constructed
by
replacing
(1)
with
(2)
and
(3).
V
r
st
V
y
st
V
bst
=
R
st
0
0
0
R
st
0
0
0
R
st
i
r
st
i
y
st
i
bst
+
d
dt
L
M
M
M
L
M
M
M
L
i
r
st
i
y
st
i
bst
+
e
r
e
y
e
b
t
(4)
The
source
v
oltages,
which
may
be
thought
of
as
v
ast
,
v
bst
,
and
v
cst
.
v
r
st
=
v
r
o
−
v
no
,
v
y
st
=
v
y
o
−
v
no
andv
bst
=
v
bo
−
v
no
(5)
Where
v
no
is
the
zero-reference
potenti
al
at
the
midpoint
of
dc
connecti
on
and
v
r
o
,
v
y
o
,
v
bo
,
and
v
no
are
the
three-phase
and
neutral
v
oltages,
respecti
v
ely
.
The
stator
phase
currents
are
limited
to
be
balanced,
as
in
(6).
i
r
st
+
i
y
st
+
i
bst
=
0
(6)
Because
of
this,
the
inductance
grid
is
made
easier
to
understand.
M
iy
+
M
ib
=
−
M
ir
(7)
Consequently
,
in
the
domain
of
state
space,
as
(8).
V
ast
V
bst
V
cst
=
R
st
0
0
0
R
st
0
0
0
R
st
i
ast
i
b
st
i
cst
+
d
dt
L
−
M
0
0
0
L
−
M
0
0
0
L
−
M
i
ast
i
bst
i
cst
+
e
a
e
b
e
c
(8)
The
assumption
has
been
that
the
back
EMFs
(
e
r
,
e
y
,
and
e
b
)
ha
v
e
a
trapezoidal
w
a
v
e
from
(9).
e
r
e
y
e
b
=
ω
m
λ
m
f
r
s
(
θ
r
)
f
y
s
(
θ
r
)
f
bs
(
θ
r
)
(9)
In
this
conte
xt,
ω
m
represents
the
angular
rotor
speed
in
rad/sec,
m
stands
for
the
ux
linkage,
and
r
is
the
rotor
position
in
radians.
The
functions
f
r
s
(
θ
r
)
,
f
y
s
(
θ
r
)
and
f
bs
(
θ
r
)
are
identical
to
e
r
,
e
y
and
e
b
,
with
a
maximum
magnitude
of
±1.
Induced
emfs
don’
t
ha
v
e
sharp
corners
because
the
y’
re
trapezoidal.
A
continuous
function,
electromagnetic
elds
are
generated
by
the
deri
v
ati
v
es
of
ux
connections
[13].
Because
of
fringes,
the
ux
density
f
u
nc
tion
is
smooth
and
de
v
oid
of
sharp
edges.
The
electromagnetic
toque,
according
to
Ne
wton,
is
as
(10).
T
e
=
e
a
i
r
+
e
b
i
y
+
e
a
i
b
(10)
The
inertia
is
dened
as
(11).
J
=
J
m
+
J
l
(11)
Int
J
Po
w
Elec
&
Dri
Syst,
V
ol.
16,
No.
4,
December
2025:
2186–2196
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Po
w
Elec
&
Dri
Syst
ISSN:
2088-8694
❒
2189
The
(12)
includes
the
v
ariables
inertia
(J),
friction
coef
cient
(B),
and
load
torque
Tl.
J
dω
m
dt
+
B
ω
m
=
(
T
e
−
T
l
)
(12)
The
speed-position
relationship
of
the
electrical
rotor
.
dθ
r
dt
=
p
2
ω
m
(13)
The
damping
coef
cient
B
inuences
the
system
despite
its
usually
ne
gli
gible
size.
Each
2
ϕ
c
ycles,
the
rotor
position,
represented
by
the
equation
”
θ
r
is
repeated.
One
must
tak
e
into
account
the
potential
of
the
point
of
neutrality
with
re
g
ard
to
zer
o
potential
(
v
no
)
in
order
to
a
v
oid
a
v
oltage
imbalance
and
maintain
dri
v
e
ef
cienc
y
[14].
By
substituting
(6)
for
(8)
in
the
VI
equation,
we
get
(14).
v
r
0
+
v
y
0
+
v
b
0
−
3
v
n
0
=
R
s
(
i
r
+
i
y
+
i
b
)
+
(
L
−
M
)
(
pi
r
+
pi
y
+
pi
b
)
+
(
e
r
+
e
y
+
e
b
)
(14)
Changing
(14)
to
(6)
yields
the
follo
wing
as
(15).
v
r
0
+
v
y
0
+
v
b
0
−
3
v
n
0
=
(
e
r
+
e
y
+
e
b
)
(15)
Thus
v
n
0
=
[
v
r
0
+
v
y
0
+
v
b
0
]
−
(
e
r
+
e
y
+
e
b
)]
/
3
(16)
There
is
a
connection
between
the
dif
ferential
(8),
(12),
and
(13).
describes
the
model
by
making
use
of
v
ariables
that
are
independent
i
r
st
,
i
y
st
,
i
bst
,
ω
m
and
θ
r
.
Combining
all
rele
v
ant
equations
yields
(17)-(23).
˙
x
=
Ax
+
B
u
+
C
e
(17)
Where,
x
=
i
r
st
i
y
st
i
bst
ω
m
θ
r
t
(18)
A
=
−
R
st
L
−
M
0
0
−
λ
m
J
f
r
s
(
θ
r
)
0
0
−
R
st
L
−
M
0
−
λ
m
J
f
y
s
(
θ
r
)
0
0
0
−
R
st
L
−
M
−
λ
m
J
f
bs
(
θ
r
)
0
λ
m
J
f
r
s
(
θ
r
)
λ
m
J
f
y
s
(
θ
r
)
λ
m
J
f
bs
(
θ
r
)
−
B
J
0
0
0
0
P
2
0
(19)
B
=
−
1
L
−
M
0
0
0
0
−
1
L
−
M
0
0
0
0
−
1
L
−
M
0
0
0
0
−
1
L
−
M
(20)
C
=
−
1
L
−
M
0
0
0
−
1
L
−
M
0
0
0
−
1
L
−
M
0
0
0
(21)
u
=
v
r
st
v
y
st
v
bst
T
l
t
(22)
e
=
e
r
e
y
e
b
t
(23)
A
three-phase
winding
stator
and
a
permanent-magnet
rotor
characterize
a
BLDC
motor
,
which
is
analogous
to
an
induction
motor
.
This
g
adget
has
a
trapezoidal
back
emf.
When
it
comes
to
commutation,
the
in
v
erter
steps
in
as
an
electronic
commutator
by
controlling
the
conduction’
s
switching
sequence
[15].
T
o
adjust
the
BLDC
motor’
s
speed,
one
must
be
a
w
are
of
its
rotor’
s
location.
One
w
ay
to
learn
this
is
to
use
hall
Adaptive
fuzzy
lo
gic
contr
oller
based
BLDC
motor
to
impr
o
ve
the
dynamic
performance
...
(Ashwini
Y
ene
gur)
Evaluation Warning : The document was created with Spire.PDF for Python.
2190
❒
ISSN:
2088-8694
ef
fect
sensors.
The
standard
conguration
for
BLDCMs
consists
of
three
hall
ef
fect
sensors
spaced
by
120
de
grees.
A
high
logic
state
is
sho
wn
e
v
ery
time
the
rotor
passes
the
sensor
.
If
not
,
it
will
stay
in
the
lo
w
logic
state.
At
the
end
of
each
c
ycle,
each
sensor
has
completed
one
full
round
[16].
The
phase
v
oltage
changes
in
relation
to
the
hall
sensors’
states,
and
Figure
2(a)
demonstrates
that
the
produced
back
EMF
has
a
trapezoidal
shape.
In
order
to
spin
the
rotor
,
the
stator
coils
are
ener
gized
by
Hall
ef
fect
sensors
that
detect
its
position.
Located
in
Figure
2(b)
are
the
BLDCM’
s
rotor
and
stator
coils.
Ev
ery
sixty
de
grees,
the
data
is
collected
from
the
hall
ef
fect
sensors.
There
are
six
distinct
phases
to
a
whole
c
ycle
[17],
[18].
T
o
k
eep
the
motor
turning
after
starting,
current
o
ws
in
a
series
from
the
tw
o
poles
that
are
opposite
to
each
other
.
The
current
o
ws
across
tw
o
coils
simultaneously
.
(a)
(b)
Figure
2.
Process
of
BLDC
motor
with
(a)
w
a
v
eforms
of
phase
and
hall
sensor
signals
and
(b)
six-step
commutation
process
of
BLDCM
in
a
sequential
manner
3.
CONTR
OL
STRA
TEGY
3.1.
Design
of
con
v
entional
FLC
The
dri
v
e
is
composed
of
a
BLDC
motor
,
v
oltage
source
in
v
erter
,
hall
sensor
,
fuzzy
logic
(FL)
speed
controller
,
and
h
ysteresis
current
controller
.
T
w
o
inputs
to
the
FLC
are
error
(e)
and
change
in
error
(ce),
which
are
e
xpressed
as
(24)
and
(25).
e
(
k
)
=
ω
r
ef
(
k
)
−
ω
m
(
k
)
(24)
ce
(
k
)
=
e
(
k
)
−
e
(
k
−
1)
(25)
The
controller
inputs
e
and
ce
ha
v
e
membership
functions
that
are
dened
as
[-1,
1]
on
the
normalized
domain.
T
o
acquire
the
crisp
output,
the
defuzzication
procedure
yields
the
true
control
output.
The
a
v
ailable
literature
indicates
that
the
s
election
of
MF
parameters
has
an
impact
on
FLC
performance.
Proper
tweaking
of
these
f
actors
can
enhance
FLC’
s
performance
[19].
The
follo
wing
section
present
the
ideal
tuning
mechanism
for
MF
(membership
function)
parameters.
The
error
(
ω
r
ef
−
ω
m
)
serv
es
as
the
input
to
the
FLC
while
the
changes
in
the
error
form
the
basis
for
the
FLC’
s
operation.
Since
the
FLC
continuously
measures
and
calculates
the
i
np
ut
s,
fuzzy
logic
rules
are
emplo
yed
to
modify
the
PI
controller
parameters
online
in
order
to
attain
the
optimal
PI
parameters
[20].
At
dif
ferent
periods,
the
error
and
change
in
error
and
output
may
satisfy
the
PI
self-calibration
requirements
because
of
the
FLC
inputs
of
the
adapti
v
e
fuzzy
PI
controller
[21]-[23].
Figure
3
sho
ws
the
block
diagram
of
the
fuzzy
logic
controller
.
Figure
4
sho
ws
the
membership
functions
of
the
input
v
ariables
error
and
change
in
error
,
respecti
v
ely
.
The
membership
functions
for
the
proportional
g
ain
K
P
and
the
inte
gral
g
ain
K
i
are
de
v
eloped,
as
sho
wn
in
Figure
5,
respecti
v
ely
.
The
o
wchart
for
the
recommended
control
technique
is
sho
wn
in
Figure
6.
Int
J
Po
w
Elec
&
Dri
Syst,
V
ol.
16,
No.
4,
December
2025:
2186–2196
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Po
w
Elec
&
Dri
Syst
ISSN:
2088-8694
❒
2191
Figure
3.
Block
diagram
of
fuzzy
logic
controller
(a)
(b)
Figure
4.
Membership
functions
of
(a)
error
and
(b)
change
in
error
(a)
(b)
Figure
5.
Membership
functions
of
(a)
K
P
and
(b)
K
i
The
follo
wing
are
the
main
properties
of
fuzzy
logic
control:
Se
v
en
triangular
membership
functions
are
utilized
for
both
inputs
and
outputs,
fuzzy
inference
rules
number
49,
and
are
7
by
7.
F
or
fuzzication,
we
emplo
y
the
continuous
uni
v
erse
of
discourse,
and
for
implication,
we
use
the
”min”
operator
[24].
Fuzzy
implications
pro
vide
the
basis
of
inference.
F
or
defuzzication,
the
”Centroid”
method
is
emplo
yed.
The
membership
functions
for
all
fuzzy
v
ariables
are
ne
g
ati
v
e
big
(NB),
ne
g
ati
v
e
medium
(NM),
ne
g
ati
v
e
small
(NS),
zero
(ZE),
positi
v
e
small
(PS),
positi
v
e
medium
(PM),
and
positi
v
e
big
(PB)
[25].
Adaptive
fuzzy
lo
gic
contr
oller
based
BLDC
motor
to
impr
o
ve
the
dynamic
performance
...
(Ashwini
Y
ene
gur)
Evaluation Warning : The document was created with Spire.PDF for Python.
2192
❒
ISSN:
2088-8694
3.2.
Adapti
v
e
fuzzy
logic
contr
oller
The
system
in
Figure
7
is
an
adapti
v
e
fuzzy
logic
controller
for
BLDC
motor
speed
control
.
It
combines
FLC
with
a
traditional
PI
controller
.
The
FLC
dynamically
adjusts
the
PI
controller’
s
g
ains
(Kp
and
Ki)
based
on
the
motor’
s
speed
error
and
rate
of
change
of
error
,
ensuring
adapti
v
e
tuning.
The
PI
controller
uses
these
adapti
v
e
g
ains
to
control
the
motor’
s
speed
by
adjusting
the
PWM
duty
c
ycle,
which
re
gulates
the
motor’
s
v
oltage
which
resulting
into
a
change
in
speed
[25].
This
adapti
v
e
approach
mak
es
the
system
more
rob
ust,
impro
ving
performance
under
changing
conditions
lik
e
load
v
ariations.
Figure
6.
Flo
wchart
Figure
7.
Block
diagram
of
adapti
v
e
fuzzy
logic
controller
for
BLDC
motor
4.
RESUL
TS
AND
DISCUSSION
4.1.
W
ithout
load
The
speed
response
of
the
BLDC
motor
dri
v
e
system
with
FLC
is
sho
wn
in
Figure
8(a).
It
is
noticeable
that
the
motor
reaches
a
constant
speed
of
1000
rpm
at
0.4
sec.
Figure
8(b)
displays
the
stator
v
oltage
and
current
of
the
BLDC
motor
.
Stator
current
stabilize
s
after
0.1
sec.
The
w
a
v
eform
sho
ws
the
back
electromoti
v
e
force
of
the
BLDC
motor
without
a
load.
The
result
re
v
eals
that
the
3-phase
back
emf
v
oltages
are
set
at
100
V
after
0.050
sec.
The
speed
response
of
the
BLDC
motor
dri
v
e
system
with
adapti
v
e
FLC
is
sho
wn
in
Figure
9(a).
It
is
noticeable
that
the
motor
reaches
a
constant
speed
of
1000
rpm
at
a
time
equals
to
0.1
sec.
Figure
9(b)
displays
the
stator
v
oltage
and
current
of
the
BLDC
motor
.
The
stator
current
st
abilizes
after
0.2
seconds;
the
w
a
v
eforms
sho
w
the
back
electromoti
v
e
force
(emf)
and
current
of
the
BLDC
motor
without
a
load.
The
result
re
v
eals
that
the
3-phase
back
emf
v
oltages
are
set
at
100
V
at
time
0.001
sec.
Figure
Int
J
Po
w
Elec
&
Dri
Syst,
V
ol.
16,
No.
4,
December
2025:
2186–2196
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Po
w
Elec
&
Dri
Syst
ISSN:
2088-8694
❒
2193
10(a)
sho
ws
the
comparison
performances
of
all
suggested
controllers
for
BLDC
motors
in
terms
of
rise
time,
settling
time,
and
o
v
ershoot
percentage.
According
to
T
able
1,
summary
data
for
the
controllers,
adapti
v
e
FLC
has
the
lo
west
rise
time
(0.03
sec),
settling
time
(0.1
sec),
and
o
v
ershoot
percentage
(8%).
T
able
1.
comparisons
of
the
dynamic
performance
of
the
BLDC
motor
on
no
load
S.No.
Controllers
Rise
time
(msec)
Settling
time
(msec)
Peak
o
v
ershoot
(%)
1
W
ith
PI
controller
80
850
15
2
W
ith
FLC
70
250
13
3
W
ith
Adapti
v
e
FLC
30
100
8
(a)
(b)
Figure
8.
The
results
of
(a)
speed
response
with
FLC
and
(b)
phase
v
oltage
and
phase
current
with
FLC
(a)
(b)
Figure
9.
Results
of
(a)
speed
response
and
(b)
phase
v
oltage
and
current
with
adapti
v
e
FLC
(a)
(b)
Figure
10.
V
ariation
of
(a)
speed
and
(b)
torque
with
FLC
and
adapti
v
e
FLC
Adaptive
fuzzy
lo
gic
contr
oller
based
BLDC
motor
to
impr
o
ve
the
dynamic
performance
...
(Ashwini
Y
ene
gur)
Evaluation Warning : The document was created with Spire.PDF for Python.
2194
❒
ISSN:
2088-8694
The
torque
responses
obtained
from
the
PI,
fuzzy
logic,
and
adapti
v
e
FL
controllers
are
displayed
in
Figure
10(b).
The
torque
response
of
the
controller
is
contingent
upon
the
conditions
of
the
input
torque.
But
then
it
suddenly
slo
wed
do
wn
for
a
split
second
before
heading
back
to
its
original
position.
It
has
been
noted
that
the
torque
oscillates
less
when
adapti
v
e
FLC
is
used.
4.2.
W
ith
50%
load
Figure
11
depicts
the
BLDC
motor’
s
speed
at
50%
full
load.
Figure
11
sho
wn
the
comparison
performance
of
all
suggested
controllers
for
the
BLDC
motor
in
terms
of
settling
time
and
o
v
ershoot
percentage.
According
to
T
able
2,
summary
data
for
the
cont
rollers,
adapti
v
e
FLC
has
the
lo
west
settling
time
(0.18
sec),
and
o
v
ershoot
percentage
(7.4%).
W
ith
comparison
of
fuzzy
logic
cont
roller
and
PI
controllers,
the
adapti
v
e
FLC
g
a
v
e
better
performance
at
50%
of
load.
Figure
11.
The
v
ariation
of
motor
speed
with
controllers
T
able
2.
Control
performance
comparison
of
BLDC
motor
at
50%
load
S.No.
Controllers
Settling
time
(msec)
Peak
o
v
ershoot
(%)
1
W
ith
PI
controller
320
12.5
2
W
ith
FLC
280
9.3
3
W
ith
adapti
v
e
FLC
180
7.4
5.
CONCLUSION
The
study
e
xamines
the
speed
control
of
a
PMBLDC
motor
using
an
adapti
v
e
fuzzy
logic
controller
and
e
v
aluates
its
ef
fecti
v
eness
through
modeling
and
simulation
in
MA
TLAB/Simulink.
The
entire
motor
dri
v
e
system
w
as
modeled,
and
its
performance
w
as
analyzed
at
dif
ferent
load
conditions
to
assess
the
controller’
s
ability
to
control
speed
ef
ciently
.
The
results
are
compared
ag
ainst
the
performance
of
the
traditional
FLC
and
PI
controller
.
The
ndings
re
v
ealed
that
the
adapti
v
e
FL
controller
signicantly
outperformed
both
FLC
and
PI
controllers
at
no
load
and
50%
of
full
load.
Specically
,
it
pro
vided
f
aster
response
with
decreased
rise
time,
reduced
o
v
ershoot,
and
a
lo
wer
settling
time.
This
highlights
the
adapti
v
e
fuzzy
logic
controller’
s
superior
ability
to
quickly
stabilize
the
motor
speed
while
maintaining
accurac
y
and
ef
cienc
y
,
making
it
a
more
ef
fecti
v
e
solution
for
controlling
PMBLDC
motors
in
dynamic
en
vironments.
FUNDING
INFORMA
TION
Authors
state
no
funding
in
v
olv
ed.
A
UTHOR
CONTRIB
UTIONS
ST
A
TEMENT
This
journal
uses
the
Contrib
utor
Roles
T
axonomy
(CRediT)
to
recognize
indi
vidual
author
contrib
utions,
reduce
authorship
disputes,
and
f
acilitate
collaboration.
Int
J
Po
w
Elec
&
Dri
Syst,
V
ol.
16,
No.
4,
December
2025:
2186–2196
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Po
w
Elec
&
Dri
Syst
ISSN:
2088-8694
❒
2195
Name
of
A
uthor
C
M
So
V
a
F
o
I
R
D
O
E
V
i
Su
P
Fu
Ashwini
Y
ene
gur
✓
✓
✓
✓
✓
✓
✓
✓
✓
✓
Mung
amuri
Sasikala
✓
✓
✓
✓
✓
✓
C
:
C
onceptualization
I
:
I
n
v
estig
ation
V
i
:
V
i
sualization
M
:
M
ethodology
R
:
R
esources
Su
:
Su
pervision
So
:
So
ftw
are
D
:
D
ata
Curation
P
:
P
roject
Administration
V
a
:
V
a
lidation
O
:
Writing
-
O
riginal
Draft
Fu
:
Fu
nding
Acquisition
F
o
:
F
o
rmal
Analysis
E
:
Writing
-
Re
vie
w
&
E
diting
CONFLICT
OF
INTEREST
ST
A
TEMENT
Authors
state
no
conict
of
interest.
D
A
T
A
A
V
AILABILITY
Data
a
v
ailability
is
not
applicable
to
this
paper
as
no
ne
w
data
were
created
or
analyzed
in
this
study
.
REFERENCES
[1]
V
.
K
umarasamy
,
V
.
Karumanchetty
,
Thottam
Ramasamy
,
G.
Chandraseka
ran,
G.
Chinnaraj,
P
.
Si
v
aling
am,
and
N.
S.
K
umar
,
“
A
re
vie
w
of
inte
ger
order
PID
and
fractional
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PID
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optimization
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speed
control
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brushless
DC
motor
dri
v
e,
”
International
J
ournal
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System
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ance
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g
ement
,
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ol.
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4,
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1139–1150,
2023,
doi:
10.1007/s13198-023-01952-x.
[2]
J.
Zhou,
S.
Ebrahimi,
and
J.
Jatsk
e
vich,
“Extended
operation
of
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hless
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motors
be
yond
120°
under
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”
IEEE
T
r
ansactions
on
Ener
gy
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ver
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Adaptive
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dynamic
performance
...
(Ashwini
Y
ene
gur)
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