Internati
o
nal
Journal of P
o
wer Elect
roni
cs an
d
Drive
S
y
ste
m
(I
JPE
D
S)
V
o
l.
4, N
o
. 3
,
Sep
t
em
b
e
r
2014
, pp
. 39
3
~
39
9
I
S
SN
: 208
8-8
6
9
4
3
93
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJPEDS
Neuro-Genetic Adaptive Optima
l Cont
roll
er for DC M
o
t
o
r
M
a
hmoud M
.
Elkholy
*, M.
A.
Elhame
e
d
**
* El
ectr
i
c
a
l P
o
w
e
r and
M
ach
ines
Departm
e
nt
, F
a
cult
y
of
Engine
e
r
ing,
Zag
azig
Un
ivers
i
t
y
,
Za
gaz
i
g,
E
g
y
p
t
** Electr
i
cal
En
gineer
ing Dep
a
rtment, Faculty
of
Engin
eering
,
Ki
ng Khalid
Unive
r
sit
y
, Abha
, Sau
d
i Arabi
a
Article Info
A
B
STRAC
T
Article histo
r
y:
Received Feb 6, 2014
R
e
vi
sed M
a
r
3,
2
0
1
4
Accepted
Mar 26, 2014
Conventional sp
eed
controllers o
f
DC motors suffer from being n
o
t ad
aptiv
e;
this is because
of the nonline
a
r
i
t
y
in
the m
o
to
r m
odel due to saturation
.
Structure of DC
motor speed con
t
roller should v
a
r
y
according
to its operating
conditions
, s
o
that the tr
ans
i
ent
perform
ance is
acc
eptab
l
e
.
In this
paper an
adaptive and optimal Neuro-Genetic contro
ll
er is used to control a DC
m
o
tor
speed. GA will
be used first to
obtain th
e optim
al contro
ll
er par
a
m
e
ter for
each lo
ad torque
and m
o
tor reference s
p
eed
. The
data obta
i
ned fr
om
GA is
used to
train
a n
e
ural network
;
the inpu
ts for
the neural n
e
twork
are
the load
torque
and th
e
motor refer
e
nce speed
and th
e outputs
are
th
e con
t
roller
parameters
. Th
is neural n
e
twor
k is
us
ed on l
i
n
e to
adapt
the
control
l
er
parameters
acco
rding to
oper
a
tin
g conditi
ons.
Th
is contro
ller
is
tested with
a
sudden chang
e
in the operating
conditions and
could
adapt its
elf for
the
conditions
and g
a
ve
an op
tim
al
tr
ans
i
ent
perform
a
n
ce.
Keyword:
DC m
o
tors
Spee
d c
ont
rol
l
er
Gen
e
tic
Neu
r
al
net
w
or
k
Copyright ©
201
4 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
M
a
hm
oud
M
.
El
kh
ol
y
,
Electrical Power a
n
d M
achines De
partm
e
nt,
Facu
lty of En
gin
eering
,
Zag
a
zig
Un
iv
ersity, Zag
azi
g
,
Eg
y
p
t.
Em
a
il: melk
h
o
ly7
1
@
yah
o
o
.
co
m
1.
INTRODUCTION
D
C
m
o
to
rs
h
a
v
e
a v
e
ry
g
ood con
t
ro
l ab
ility and
are
u
s
ed
l
o
ng
tim
e ag
o
as adju
stab
le speed
d
r
i
v
es, for
exam
ple they
are use
d
in traction and ele
c
tric cars
[1
], th
e con
t
ro
l
of th
ese m
o
to
rs
m
a
in
ly d
e
p
e
nd
s on
co
n
t
ro
lling
the con
v
erter circu
its th
at
fed the field or t
h
e arm
a
tu
re wind
i
n
g
s
[2
],
[3
]. C
o
nv
en
tio
n
a
l
con
t
ro
llers
can
b
e
d
e
si
g
n
e
d
op
tim
al
ly at
certain
op
eratin
g
con
d
ition
,
bu
t its p
e
rform
a
n
ce will no
t b
e
o
p
tim
al fo
r ano
t
h
e
r
o
p
e
rating
po
int. In
[4
], a PID con
t
ro
ller is d
e
sign
ed
and tu
n
e
d
b
y
traditio
n
a
l
m
e
th
o
d
at certain
o
p
e
rating
p
o
i
n
t
, th
e perform
a
n
ce will n
o
t b
e
th
e sam
e
at a d
i
fferen
t
p
o
in
t.
Accurate m
o
d
e
l fo
r th
e
DC mo
tor is essen
tial wh
en
d
e
si
g
n
i
n
g
t
h
e sp
eed
co
n
t
ro
ller to
m
i
mic th
e actu
a
l
m
o
t
o
r per
f
o
r
m
ance. De
si
g
n
i
n
g
or
di
na
ry
co
nt
r
o
l
l
e
rs
d
e
pen
d
s
o
n
s
o
m
e
sim
p
l
i
f
i
cati
ons
suc
h
as
m
odel
lin
earizatio
n
an
d
n
e
g
l
ecting
iron
satura
tio
n. Artificial in
tell
ig
en
ce b
a
sed
t
ech
n
i
q
u
e
s are u
s
ed
to
d
e
sign
sp
eed
co
n
t
r
o
ller fo
r
th
e
D
C
m
o
to
r [5
]-
[1
2
]
, also op
timizatio
n
techniques
s
u
c
h
as
ge
netic a
l
gorithm
are used t
o
o
p
tim
ize th
e
tran
sien
t
p
e
rfor
mance [12], [13]. Methods
used either
de
p
e
nd
o
n
t
h
e l
i
n
eari
zed m
odel
of t
h
e
m
o
t
o
r or t
h
e c
ont
rol
l
e
r l
ack t
h
e ada
p
t
i
v
e p
r
ope
rt
y
.
In
[1
4]
,
[1
5]
sl
i
d
i
ng m
ode c
ont
rol
l
e
r
i
s
desi
gne
d t
o
cont
rol
th
e
m
o
to
r sp
eed
,
bu
t slid
in
g
m
o
d
e
co
n
t
ro
ller is su
ffering fro
m
th
e ch
atterin
g
p
h
e
no
men
o
n
.
In
th
is pap
e
r a
SIMULINK m
o
d
e
l
for th
e
DC
m
o
to
r is d
e
velo
p
e
d
th
at con
s
id
er th
e m
o
to
r sat
u
ration
an
d
t
h
e v
a
riation
of th
e
mag
n
e
tizin
g characteristics wi
th
th
e
sp
eed
.
A PID sp
eed
co
n
t
ro
ller is used
with
t
h
e arm
a
tu
re
wind
i
n
g, th
e parameter of t
h
e PID co
n
t
ro
ller is
optim
ized by
genetic alg
o
rit
h
m
(GA)
fo
r e
ach re
fere
nce
sp
eed
and
lo
ad to
rqu
e
to
g
e
t th
e b
e
st tran
sien
t and
steady state perform
a
nce, the
objectiv
e fo
r th
e GA is to
min
i
m
i
ze
the sum of squa
re errors in spee
d taking
in
to
con
s
iderat
io
n
th
e lim
i
t
s o
n
th
e arm
a
tu
re
v
o
ltag
e
. Th
e op
erating
co
nd
itio
n
s
are v
a
ried for a wid
e
rang
e of
refe
rence s
p
ee
ds a
nd l
o
ad t
o
rq
ues,
f
o
r eac
h
poi
nt
G
A
get
s
t
h
e co
nt
r
o
l
l
e
r
param
e
t
e
rs wh
i
c
h are t
h
e
re
q
u
i
r
e
d
g
a
in
s
for th
e
PID con
t
ro
ller. Data ob
tain
ed
fro
m
GA ar
e use
d
t
o
t
r
ai
n
a neu
r
al
net
w
or
k, t
h
i
s
net
w
or
k i
s
sim
u
lated in SIMUL
I
NK, the function of it
is to
m
a
k
e
the controller ada
p
tive according to operating
Evaluation Warning : The document was created with Spire.PDF for Python.
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.
3
,
Sep
t
em
b
e
r
2
014
:
39
3 – 399
39
4
co
nd
itio
ns. The syste
m
is test
ed
at
d
i
fferen
t
co
nd
itio
ns
an
d th
e sp
eed
con
t
ro
ller is fo
und
to
b
e
op
timize
d
and
adaptive
acc
ording to
each operating
point.
2.
MOTOR MODELING
The
m
a
the
m
atical
m
odel of DC
m
o
tor
can
be
ex
pres
sed
by
t
h
e e
quat
i
o
ns:
V
i
R
L
(1)
V
i
R
L
K
ω
(2)
T
J
B
ω
T
(3)
Whe
r
e:
R
: Field
winding
resistance =
1
11
Ω
L
: Field
winding inductance
=
10
H
R
:
A
r
m
a
t
u
re wi
ndi
ng
resi
st
a
n
c
e
= 0
.
2
4
Ω
L
:
A
r
m
a
t
u
re wi
ndi
ng
i
n
duct
a
n
ce = 1
8
m
H
K
:
V
o
l
t
a
ge c
o
ns
t
a
nt
an
d ca
n
be
o
b
t
a
i
n
ed
f
r
om
m
a
gnet
i
zat
i
on
cur
v
e
T
: Electro
m
a
g
n
e
tic
m
o
to
r torqu
e
ω
: Ro
t
o
r
Sp
eed in
rad
/
sec
J
:
M
o
m
e
nt
of
i
n
ert
i
a
=
0.
5
k
g
.
m
2
B
:
R
o
t
o
r
Fri
c
t
i
o
n
=
0.
00
1
k
g
.
m
2/
sec
V
:
Fi
el
d
wi
n
d
i
n
g
vol
t
a
ge
V
: Arm
a
tu
re term
in
al v
o
ltag
e
The
bl
oc
k
di
ag
ram
of s
p
eed
c
ont
rol
of
DC
m
o
t
o
r
usi
n
g c
o
n
v
ent
i
o
nal
P
I
D
cont
rol
l
e
r i
s
sh
ow
n i
n
Fi
gu
re
1.
Fi
gu
re
1.
B
l
oc
k
di
ag
ram
of D
C
m
o
t
o
r
3.
PROP
OSE
D
NEURO
-
GENETIC
ADA
PT
IVE OPTIMAL CONTROL
LER
GA
will b
e
u
s
ed
to
ob
tain
the o
p
tim
u
m
co
n
t
ro
ller p
a
rameters fo
r each
o
p
e
rating
po
int i.e., fo
r
any
load torque a
n
d s
p
eed. The objective
f
unct
i
on
o
f
G
A
i
s
t
o
m
i
nim
i
ze
t
h
e sum
of sq
uare
err
o
r i
n
t
h
e t
r
a
n
si
en
t
r
e
spon
se of
the
m
o
to
r
sp
eed, th
e ou
tpu
t
s o
f
G
A
ar
e K
p
, K
d
and K
i
. F
i
gu
re 2 s
h
o
w
s
t
h
e fl
ow c
h
ar
t
t
h
at
descri
bes t
h
e
p
r
oces
s o
f
t
h
i
s
GA
. F
o
r eac
h
gene
rat
i
o
n, t
h
e
SIM
U
LI
NK
m
odel
i
s
run
a
nd
G
A
sea
r
che
s
fo
r t
h
e
optim
u
m
PID cont
roller pa
ra
meters. Fi
gure 3 shows the objec
tive function variation wi
t
h
each ge
ne
ration for
a refe
re
nce s
p
e
e
d
of
1
0
0
0
r.
p.
m
and a l
o
a
d
t
o
r
q
ue
of
1
0
0
N
-
m
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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4
Neu
r
o-Gen
e
tic Adap
tive Op
tima
l
C
o
n
t
ro
ller
f
o
r DC
M
o
t
o
r
(
M
ah
mo
u
d
M
.
El
khol
y)
39
5
GA i
s
r
un f
o
r
a wi
de ran
g
e o
f
m
o
t
o
r spee
d
(f
orm
500 t
o
1
0
0
0
r
p
m
)
and l
o
ad t
o
r
que (
f
r
o
m
0 t
o
10
0
N-m
)
and a t
r
a
i
ni
ng
dat
a
i
s
o
b
t
a
i
n
ed t
h
at
us
ed t
o
t
r
ai
n a ne
ural
net
w
o
r
k.
The r
o
l
e
of t
h
e
neu
r
al
net
w
or
k i
s
t
o
adapt t
h
e c
o
ntroller
param
e
ters accordi
n
g to t
h
e
ope
rating c
o
nditions as
shown i
n
Fi
gure
4.
Fi
gu
re
2.
Fl
o
w
cha
r
t
o
f
GA
u
s
ed t
o
obt
ai
n
o
p
t
i
m
u
m
cont
r
o
l
l
e
r param
e
t
e
rs
Fi
gu
re
3.
Va
ri
at
i
on
of
t
h
e
o
b
je
ct
i
v
e f
unct
i
o
n
wi
t
h
gene
rat
i
o
ns
fo
r a
re
fere
n
ce spee
d
of
1
0
0
0
r
p
m
an
d a lo
ad
t
o
rqu
e
o
f
100
N
-
m
0
5
10
15
20
25
30
35
40
45
50
11
6
0
11
8
0
120
0
122
0
124
0
126
0
128
0
130
0
132
0
G
ener
at
i
on no.
O
b
j
e
c
t
i
v
e F
unc
t
i
o
n
re
f
Neural Network
T
L
K
p
K
d
K
i
Fi
gu
re
4.
F
unct
i
on
o
f
t
h
e
ne
ur
al
net
w
or
k
Evaluation Warning : The document was created with Spire.PDF for Python.
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I
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.
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,
Sep
t
em
b
e
r
2
014
:
39
3 – 399
39
6
The neu
r
al
net
w
o
r
k
i
s
a feed
fo
r
w
ar
d net
w
or
k wi
t
h
back
pr
o
p
agat
i
o
n
l
e
arni
ng r
o
l
e
an
d
c
onsi
s
t
s
o
f
t
w
o
hi
d
d
e
n
l
a
y
e
rs o
f
t
a
n
si
g
m
oi
d act
i
v
at
i
on f
u
nct
i
o
n
an
d
an
out
put
l
a
y
e
r o
f
l
i
n
ea
r act
i
v
at
i
on
fu
nct
i
o
n. T
h
e
num
bers
of
hi
dde
n
neu
r
o
n
s
are 2
1
an
d
2 r
e
spect
i
v
el
y
.
T
h
e ne
ural
net
w
o
r
k i
s
use
d
on l
i
n
e t
o
ge
n
e
rat
e
t
h
e
appropriate controller param
e
ters acco
rding to loa
d
ing conditio
ns of the
m
o
tor, Figure 5 shows the
bloc
k
di
ag
ram
of t
h
e
sy
st
em
wi
t
h
t
h
e ne
ural
net
w
o
r
k
.
Fi
gu
re
5.
B
l
oc
k
di
ag
ram
of D
C
m
o
t
o
r wi
t
h
p
r
o
p
o
sed
t
ech
ni
que
4.
RESULTS
A
N
D
DI
SC
US
S
I
ON
Fi
gu
re 6
-
8 s
h
ow t
h
e va
ri
at
i
on
obt
ai
ne
d b
y
GA fo
r t
h
e
cont
r
o
l
l
e
r pa
ram
e
t
e
rs K
P
, K
I
and
K
D
resp
ectiv
ely
with
m
o
to
r referen
ce sp
eed
and
load to
rqu
e
.
It
i
s
not
e
d
t
h
at
co
nt
r
o
l
l
e
r
par
a
m
e
t
e
rs ha
ve a
wi
d
e
vari
at
i
o
n wi
t
h
ope
rat
i
n
g co
nd
i
t
i
ons, f
o
r e
x
a
m
pl
e at
800 r
p
m
t
h
e val
u
e of
K
p
i
s
350 at
n
o
l
o
ad
, 8
0
at
20 N-m
an
d
26
0 at 40
N-m
.
Th
e largest v
a
riation
in K
i
is f
o
r
5
0
0
r
p
m
reference
s
p
eed
. A
wi
de v
a
riation in
K
d
i
s
with
5
0
0
,
600
an
d 70
0rp
m
.
Fi
gu
re
6.
Va
ri
at
i
on
of
o
p
t
i
m
u
m
val
u
es of
K
p
with
l
o
ad t
o
r
q
ue at
di
ffe
re
nt
spee
d
s
Fi
gu
re
7.
Va
ri
at
i
on
of
o
p
t
i
m
u
m
val
u
es of
K
i
with
l
o
ad t
o
r
q
ue at
di
ffe
re
nt
spee
d
s
0
10
20
30
40
50
60
70
80
90
100
50
100
150
200
250
300
350
400
L
o
a
d
T
o
rq
u
e
(N
.
m
)
Kp
n
=
700 r
p
m
n
=
500 r
p
m
n
=
1000 r
p
m
n
=
600 r
p
m
n
=
800 r
p
m
n
=
900 r
p
m
0
10
20
30
40
50
60
70
80
90
10
0
8
10
12
14
16
18
20
22
24
26
L
o
a
d
T
o
rq
u
e
(N
.
m
)
Ki
n=
50
0
r
p
m
n=
8
0
0
r
p
m
n=
9
0
0
n
=
10
00
r
p
m
n=
600
r
p
m
n
=
70
0 r
p
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PED
S
I
S
SN
:
208
8-8
6
9
4
Neu
r
o-Gen
e
tic Adap
tive Op
tima
l
C
o
n
t
ro
ller
f
o
r DC
M
o
t
o
r
(
M
ah
mo
u
d
M
.
El
khol
y)
39
7
Fi
gu
re
8.
Va
ri
at
i
on
of
o
p
t
i
m
u
m
val
u
es of
K
d
wi
t
h
l
o
ad
t
o
rq
ue at
di
ffe
re
nt
spee
ds
Fi
gu
re 9 s
h
ow
s t
h
e vari
at
i
o
n
of t
h
e s
u
m
squa
re er
ro
r wi
t
h
t
h
e n
u
m
b
er of e
poc
hs
obt
ai
ned
whi
l
e
t
r
ai
ni
n
g
t
h
e
p
r
op
ose
d
ne
ural
net
w
or
k,
t
h
e e
r
ro
r
goal
i
s
1
0
-8
.
Fi
gu
re
9.
Va
ri
at
i
on
of
m
ean squa
re e
r
r
o
r
o
f
neu
r
al
net
w
or
k
To
test th
e effectiv
en
ess
o
f
t
h
e co
n
t
ro
ller, it is u
s
ed
with
t
h
e
m
o
to
r m
o
d
e
l as sho
w
n
i
n
Fig
u
re 5, and
m
o
to
r respo
n
se with
th
is contro
ller is co
m
p
ared
to
t
h
e resp
on
se
with
conv
en
tion
a
l PID
co
n
t
ro
ller. Fi
gu
re
1
0
sho
w
s t
r
ansi
e
n
t
resp
o
n
se
of
m
o
t
o
r spee
d w
i
t
h
a ref
e
re
nce
spee
d c
o
m
m
a
nd
o
f
60
0,
8
0
0
an
d 1
0
0
0
r
p
m
an
d
a
l
o
ad t
o
r
que
of
10
0
N-m
.
Th
e resp
o
n
se wi
t
h
t
h
e
pr
op
ose
d
co
nt
r
o
l
l
e
r i
s
bet
t
e
r t
h
a
n
t
h
at
wi
t
h
co
nve
nt
i
onal
co
n
t
ro
ller i
n
al
l cases,
o
v
e
rsho
o
t
, settlin
g
ti
me an
d
rise ti
me are greatly en
h
a
n
c
ed
.
On
e
pu
rpo
s
e
of th
e con
t
ro
ller is to
b
e
ad
ap
tiv
e,
s
o
t
h
at the c
ontroller s
t
ructure c
h
anges with t
h
e
m
o
t
o
r operat
i
n
g p
o
i
n
t
an
d be
cam
e
opt
im
al for t
h
e ne
w o
p
e
r
at
i
ng p
o
i
n
t
.
Fi
gu
re 1
1
sh
ow
s
t
h
e resp
onse
o
f
t
h
e
m
o
t
o
r wi
t
h
t
h
e pr
o
pose
d
a
n
d co
n
v
ent
i
o
nal
cont
rol
l
e
rs
w
h
en a st
e
p
c
h
a
nge
fr
om
100
0 r
p
m
t
o
500
rpm
i
s
occurre
d at 100 N-m
load. It is noti
ced
th
at
th
e p
r
op
osed
co
n
t
ro
ller ch
anged
th
e co
n
t
ro
ller p
a
ram
e
ters fo
r the
new s
p
ee
d an
d
m
o
t
o
r s
p
eed
f
o
l
l
o
we
d t
h
e c
h
ange i
n
re
fere
nce spee
d c
o
m
m
a
nd i
n
m
i
nim
u
m
t
i
m
e
and err
o
r
,
wh
ile m
o
to
r speed
with
co
nv
en
tio
n
a
l con
t
ro
ller is sl
o
w
er and
h
a
s
ov
ersho
o
t.
0
10
20
30
40
50
60
70
80
90
10
0
0
2
4
6
8
10
12
L
o
a
d
T
o
rq
ue
(
N
.
m
)
Kd
n
=
50
0 r
p
m
n
=
70
0 r
p
m
n
=
60
0 r
p
m
n
=
90
0 r
p
m
n
=
80
0 r
p
m
n
=
10
00
r
p
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-86
94
I
J
PED
S
Vo
l.
4
,
No
.
3
,
Sep
t
em
b
e
r
2
014
:
39
3 – 399
39
8
Fi
gu
re 1
0
. Vari
at
i
on of
m
o
t
o
r spee
d wi
t
h
c
o
n
v
ent
i
o
nal
C
ont
rol
l
e
r
a
n
d Neu
r
o Genet
i
c
c
ont
rol
l
e
r
Fi
gu
re
1
1
.
Vari
at
i
on
of
m
o
t
o
r
spee
d
wi
t
h
c
o
nve
nt
i
o
nal
C
o
n
t
rol
l
e
r a
n
d
P
r
o
pos
ed
co
nt
r
o
l
l
e
r wi
t
h
st
ep
cha
nge
in s
p
eed
5. CO
N
C
L
U
S
I
ON
In
th
is
p
a
p
e
r an
op
tim
a
l
-ad
a
p
tiv
e con
t
ro
ller for a DC mo
tor is d
e
sign
ed
, th
e co
n
t
ro
ll
er ch
an
g
e
s its
param
e
ters according to m
o
tor
ope
rating conditions, na
m
e
ly
m
o
tor refe
rence spee
d and load torque
. The
p
r
op
o
s
ed
con
t
ro
ller
d
e
p
e
nd
s on
GA to
i
n
su
re th
at it is
o
p
tim
al an
d
a n
e
u
r
al
n
e
two
r
k
to in
su
re that it is
adaptive
.
Mot
o
r transient a
n
d
st
eady
st
at
e respo
n
se
wi
t
h
t
h
e pr
o
pose
d
c
o
nt
r
o
l
l
e
r has a s
upe
ri
o
r
pe
rf
o
r
m
a
nce
th
an
con
v
e
n
tion
a
l con
t
ro
ller.
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ES
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Des
i
gn a
nd Developmen
t
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d
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o
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r
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e
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[2]
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Modern Speed
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1
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5
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-2
0
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100
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Ti
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M
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C
o
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v
en
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P
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m
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5
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25
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50
0
75
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100
0
125
0
Ti
m
e
(
S
e
c
)
M
o
t
o
r
S
p
e
ed (
r
pm
)
R
e
f
e
r
e
n
c
e speed
C
o
n
v
en
t
i
on
a
l
m
e
t
h
od
P
r
oposed m
e
t
h
od
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Neu
r
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e
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C
o
n
t
ro
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f
o
r DC
M
o
t
o
r
(
M
ah
mo
u
d
M
.
El
khol
y)
39
9
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ong, Siy
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ce.
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91.
BIOGRAP
HI
ES OF
AUTH
ORS
M
a
hmoud M
.
Elkholy
received Bachelor of Engineer
ing (B.E)
degree (with honors) from Zagazig
University
, Eg
ypt in 1994 under the specialization of
Electrical Machines a
nd Power Engineer
in
g,
Master of Science degree from
Zagazig Univers
i
ty
, Eg
y
p
t 1998
under the speci
alization of Electr
i
cal
Machines
and D
o
ctor of
Philoso
ph
y
(Ph
.
d) in
th
e
y
e
ar 2001
fro
m Zagazig Univ
ersity
, Eg
y
p
t in
the
Dept. of
E
l
ec
tri
c
al power
and
M
a
chines
Engine
eri
ng. H
e
has
18
years
of
exper
i
en
ce
in
acad
em
ia
a
n
d
res
earch
at
diff
e
r
ent pos
it
ions
. C
u
rrentl
y
h
e
is
an
Assistant Professor, Colleg
e
of
Engineering, Kin
g
Khalid University
, Abha, KSA and Faculty
of
Engin
eer
ing,
Zag
azig Univ
ersit
y
,
Eg
ypt. His
inte
rest
includ
es
control
the s
t
ead
y s
t
at
e and d
y
n
a
m
i
c
perform
ance o
f
elec
tric
al m
a
c
h
ines
and artif
it
ial
intel
ligen
ce
.
M. A. Elh
a
mee
d
was born
in Eg
y
p
t in 1973
. He receiv
e
d the B.E. degre
e
(with
honors) from
Za
gazig
University
-faculty
o
f
Engin
eer
in
g, Zag
a
zig,
Eg
ypt
in e
l
e
c
tri
cal
power and m
a
c
h
ines
engin
eeri
n
g i
n
1996, Master degree in 2000 in the fiel
d of elect
r
ic
al power s
y
st
em
from the sam
e
institute, and
th
e
Ph. D. degree fr
om Zagazig University
,
Eg
y
p
t,
in 2004,
in the f
i
eld of electr
ical p
o
wer s
y
stem. He has
been as
s
i
s
t
an
t p
r
ofes
s
o
r, F
acul
t
y
of
Engine
erin
g, King Khal
id
Univers
i
t
y
,
KS
A and F
acult
y
of
Engine
ering,
Za
gazig Univ
ers
i
t
y
, Eg
ypt
.
His
cur
r
ent int
e
res
t
incl
udes
ele
c
tri
c
a
l
m
achines
m
odel
i
ng
and con
t
rol
,
a
r
tif
itia
l
inte
llig
enc
e
and FACTS devi
ces.
Evaluation Warning : The document was created with Spire.PDF for Python.