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1]
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ng
i
n
c
om
pr
o
m
i
s
e
d
t
r
a
ns
i
e
nt
be
ha
v
i
or
a
nd di
m
i
ni
s
he
d r
obus
t
ne
s
s
[7
]
,
[
8]
.
T
o
c
i
r
c
u
m
v
e
nt
t
he
s
e
c
ons
t
r
a
i
nt
s
,
m
e
t
a
he
u
r
i
s
t
i
c
a
ppr
oa
c
he
s
,
pa
r
t
i
c
ul
a
r
l
y
g
e
ne
t
i
c
a
l
g
or
i
t
hm
s
(
G
A
s
)
,
ha
v
e
ga
i
ne
d
w
i
de
s
pr
e
a
d
a
t
t
e
nt
i
on
f
or
t
uni
ng
P
I
g
a
i
ns
.
G
A
-
or
i
e
nt
e
d
f
r
a
m
e
w
or
ks
e
nha
nc
e
t
h
e
dr
i
v
e
’
s
d
y
na
m
i
c
f
e
a
t
ur
e
s
v
i
a
popul
a
t
i
on
-
dr
i
v
e
n
e
v
ol
ut
i
ona
r
y
opt
i
m
i
z
a
t
i
on
[
9]
.
H
o
w
e
v
e
r
,
a
v
a
s
t
m
a
j
or
i
t
y
of
e
xi
s
t
i
ng
l
i
t
e
r
a
t
ur
e
r
e
l
i
e
s
on
i
de
a
l
i
z
e
d
m
ot
or
s
t
r
uc
t
ur
e
s
t
ha
t
om
i
t
i
r
on
l
os
s
c
ha
r
a
c
t
e
r
i
s
t
i
c
s
,
m
e
a
ni
n
g
t
he
y
r
a
r
e
l
y
e
v
a
l
ua
t
e
t
he
r
obus
t
ne
s
s
of
s
e
ns
or
l
e
s
s
v
e
c
t
or
dr
i
v
e
s
a
c
r
os
s
r
e
a
l
i
s
t
i
c
ope
r
a
t
i
ng
pr
of
i
l
e
s
[
10]
.
C
on
s
e
que
nt
l
y
,
a
c
l
e
a
r
r
e
s
e
a
r
c
h
g
a
p
r
e
m
a
i
ns
i
n
de
v
e
l
opi
ng
a
G
A
-
opt
i
m
i
z
e
d
P
I
c
ont
r
ol
l
e
r
t
ha
t
i
nt
e
g
r
a
t
e
s
c
or
e
l
os
s
m
ode
l
i
ng
w
i
t
hi
n
a
s
e
ns
or
l
e
s
s
v
e
c
t
or
c
ont
r
ol
f
r
a
m
e
w
or
k
w
hi
l
e
s
i
m
ul
t
a
ne
ous
l
y
e
nha
nc
i
n
g
d
y
na
m
i
c
pe
r
f
or
m
a
nc
e
a
nd
e
n
e
r
gy
e
f
f
i
c
i
e
nc
y
.
T
hi
s
pa
pe
r
a
ddr
e
s
s
e
s
t
hi
s
g
a
p
b
y
pr
opos
i
ng
a
r
ot
or
f
l
ux
-
or
i
e
nt
e
d
s
e
ns
or
l
e
s
s
c
ont
r
ol
s
t
r
a
t
e
g
y
i
nc
or
por
a
t
i
ng
c
or
e
l
os
s
m
ode
l
i
ng
a
nd
a
G
A
-
ba
s
e
d
opt
i
m
i
z
a
t
i
on
pr
oc
e
dur
e
f
or
P
I
g
a
i
n
t
uni
ng
.
T
he
m
a
i
n
c
ont
r
i
but
i
ons
a
r
e
s
um
m
a
r
i
z
e
d a
s
f
ol
l
o
w
s
:
D
e
v
e
l
op
m
e
nt
of
a
s
e
ns
or
l
e
s
s
i
ndi
r
e
c
t
v
e
c
t
or
c
ont
r
ol
s
c
he
m
e
i
nc
l
udi
ng
c
or
e
l
os
s
r
e
s
i
s
t
a
nc
e
.
GA
-
ba
s
e
d o
pt
i
m
i
z
a
t
i
on
of
P
I
s
pe
e
d
c
on
t
r
o
l
l
e
r
pa
r
a
m
e
t
e
r
s
c
o
ns
i
de
r
i
ng
dy
na
m
i
c
a
nd
e
ne
r
g
y
-
r
e
l
a
t
e
d c
r
i
t
e
r
i
a
.
C
om
pa
r
a
t
i
v
e
e
v
a
l
ua
t
i
on a
ga
i
ns
t
c
l
a
s
s
i
c
a
l
P
I
c
ont
r
ol
.
P
e
r
f
or
m
a
n
c
e
v
a
l
i
da
t
i
on i
n t
e
r
m
s
of
t
r
a
ns
i
e
nt
r
e
s
pons
e
,
t
or
que
a
c
c
ur
a
c
y
,
a
nd e
f
f
i
c
i
e
nc
y
i
m
p
r
o
v
e
m
e
nt
.
T
he
s
t
r
uc
t
ur
a
l
l
a
y
out
of
t
hi
s
a
r
t
i
c
l
e
i
s
c
onf
i
g
ur
e
d
a
s
f
ol
l
ow
s
:
T
he
m
a
t
he
m
a
t
i
c
a
l
f
or
m
ul
a
t
i
on
of
t
he
IM
a
c
c
ount
i
ng
f
o
r
c
or
e
l
os
s
e
s
i
s
de
r
i
v
e
d
i
n
s
e
c
t
i
on
2.
T
he
i
m
pl
e
m
e
nt
a
t
i
on
de
t
a
i
l
s
of
t
he
G
A
-
b
a
s
e
d
P
I
a
dj
us
t
m
e
nt
a
ppr
oa
c
h
a
r
e
out
l
i
ne
d
i
n
s
e
c
t
i
on
3.
S
e
c
t
i
on
4
e
v
a
l
ua
t
e
s
a
nd
di
s
c
us
s
e
s
t
he
o
bt
a
i
ne
d
s
i
m
ul
a
t
i
on
out
c
om
e
s
, w
hi
l
e
s
e
c
t
i
on
5 pr
o
v
i
de
s
t
he
c
onc
l
udi
ng
r
e
m
a
r
ks
of
t
hi
s
r
e
s
e
a
r
c
h.
2.
D
Y
N
A
M
I
C
M
O
D
E
L
I
N
G
O
F
T
H
E
IM
WI
T
H
C
O
R
E
L
O
S
S
I
n
t
he
c
ont
e
xt
of
G
A
-
ba
s
e
d
r
ot
or
f
l
ux
e
s
t
i
m
a
t
i
on
f
or
i
nduc
t
i
on
m
a
c
hi
ne
s
,
v
a
r
i
ous
m
a
t
he
m
a
t
i
c
a
l
r
e
pr
e
s
e
nt
a
t
i
ons
c
a
n
be
i
m
pl
e
m
e
nt
e
d
a
c
r
os
s
di
f
f
e
r
e
nt
r
e
f
e
r
e
n
c
e
f
r
a
m
e
s
,
i
nc
l
udi
ng
t
hos
e
a
l
i
g
ne
d
w
i
t
h
t
he
s
t
a
t
or
or
r
ot
or
f
l
ux
l
i
nka
ge
s
.
N
e
v
e
r
t
he
l
e
s
s
,
t
he
s
t
a
t
i
ona
r
y
r
e
f
e
r
e
n
c
e
f
r
a
m
e
i
s
hi
g
hl
y
p
r
e
f
e
r
r
e
d
he
r
e
t
o
a
v
oi
d
unne
c
e
s
s
a
r
y
c
o
m
put
a
t
i
ona
l
bur
de
ns
a
nd
nonl
i
ne
a
r
m
a
ppi
n
g
c
o
m
pl
e
xi
t
i
e
s
[
11]
.
T
hi
s
f
r
a
m
e
s
e
l
e
c
t
i
on
i
s
pr
i
m
a
r
i
l
y
m
ot
i
v
a
t
e
d
b
y
i
t
s
c
a
pa
c
i
t
y
t
o
l
ow
e
r
c
a
l
c
ul
a
t
i
on
e
xe
c
ut
i
on
t
i
m
e
,
s
uppor
t
r
e
duc
e
d
s
a
m
pl
i
ng
i
nt
e
r
v
a
l
s
,
a
nd
pr
o
v
i
de
e
l
e
v
a
t
e
d
a
c
c
ur
a
c
y
a
l
ong
w
i
t
h
r
e
i
nf
or
c
e
d
s
y
s
t
e
m
s
t
a
bi
l
i
t
y
[
12]
.
C
ons
e
que
nt
l
y
,
t
hi
s
s
t
udy
ut
i
l
i
z
e
s
t
he
s
t
a
t
i
ona
r
y
r
e
f
e
r
e
nc
e
f
r
a
m
e
t
o c
ons
t
r
uc
t
t
he
c
or
e
-
l
os
s
-
a
w
a
r
e
IM
m
ode
l
de
pi
c
t
e
d i
n F
i
g
ur
e
1.
F
i
g
ur
e
1
.
E
qui
v
a
l
e
nt
c
i
r
c
ui
t
of
t
he
IM
c
ons
i
de
r
i
ng
c
or
e
l
os
s
B
y
c
ons
i
de
r
i
ng
t
ha
t
i
r
on
l
os
s
pr
i
m
a
r
i
l
y
s
t
e
m
s
f
r
o
m
e
dd
y
c
ur
r
e
nt
phe
no
m
e
na
,
t
he
e
qui
v
a
l
e
nt
r
e
pr
e
s
e
nt
a
t
i
on of
t
he
IM
c
a
n b
e
s
t
r
uc
t
ur
e
d a
s
de
pi
c
t
e
d i
n F
i
g
ur
e
1. U
nd
e
r
t
hi
s
c
onf
i
g
ur
a
t
i
on, t
he
e
ddy
c
ur
r
e
nt
s
a
r
e
m
ode
l
e
d
a
s
f
l
ow
i
n
g
di
r
e
c
t
l
y
a
c
r
os
s
t
he
d
-
q
a
xi
s
w
i
ndi
ng
s
.
C
ons
e
que
nt
l
y
,
i
n
a
c
c
or
da
nc
e
w
i
t
h
F
i
g
ur
e
1,
t
he
IM
v
ol
t
a
g
e
f
or
m
ul
a
t
i
ons
t
ha
t
i
nc
or
por
a
t
e
s
t
a
t
or
c
or
e
l
os
s
c
om
pone
nt
s
a
r
e
e
xpr
e
s
s
e
d
vi
a
(
1)
w
i
t
hi
n
t
he
s
y
nc
hr
onous
r
ot
a
t
i
ng
f
r
a
m
e
[
13]
.
{
̄
=
̄
+
̄
+
̄
+
(
̄
+
̄
)
0
=
̄
+
̄
+
̄
+
(
̄
+
̄
)
(
1)
T
he
t
or
que
i
s
e
xpr
e
s
s
e
d b
y
(
2)
:
=
3
2
(
.
−
.
)
(
2)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2502
-
4752
I
ndone
s
i
a
n J
E
l
e
c
E
n
g
&
C
om
p S
c
i
, V
ol
.
4
3
, N
o.
1
,
Ju
ly
20
26
:
28
-
38
30
C
or
e
l
os
s
r
e
s
i
s
t
a
nc
e
de
pe
nds
on
bot
h
f
r
e
que
nc
y
a
nd
f
l
ux
l
e
v
e
l
,
but
i
t
i
s
f
a
r
m
or
e
s
e
ns
i
t
i
v
e
t
o
f
r
e
que
nc
y
c
ha
nge
s
t
ha
n
t
o
r
ot
or
f
l
ux
v
a
r
i
a
t
i
ons
.
T
hus
,
i
s
a
ppr
oxi
m
a
t
e
d
a
s
a
f
unc
t
i
on
of
f
r
e
que
n
c
y
a
nd
t
he
g
a
i
ns
A
a
nd B
.
=
.
+
.
2
(
3)
W
he
r
e
:
=
1
−
2
,
=
,
=
(
4)
T
he
IM
m
ode
l
,
i
nc
or
por
a
t
i
ng
c
or
e
l
os
s
e
f
f
e
c
t
s
,
i
s
de
s
c
r
i
be
d
us
i
ng
a
s
ui
t
a
bl
e
s
t
a
t
e
-
s
pa
c
e
f
o
r
m
w
i
t
hi
n
t
he
P
a
r
k
r
e
f
e
r
e
nc
e
f
r
a
m
e
[
14]
:
{
=
(
+
)
+
+
+
=
(
+
)
+
+
+
0
=
(
−
)
+
+
+
+
0
=
(
−
)
+
+
+
+
(
5)
3.
F
I
E
L
D
-
O
R
I
E
N
T
E
D
C
O
N
T
R
O
L
M
O
D
E
L
T
he
i
m
pl
e
m
e
nt
e
d
F
O
C
i
s
ba
s
e
d
on t
he
a
l
i
g
nm
e
nt
of
a
r
ot
a
t
i
ng
d
-
q
r
e
f
e
r
e
nc
e
f
r
a
m
e
s
o
t
ha
t
t
he
d
-
a
xi
s
i
s
a
l
i
g
ne
d
w
i
t
h
t
he
r
ot
or
f
l
ux
v
e
c
t
or
φ
r
[
15]
.
W
i
t
h
φ
r
di
r
e
c
t
e
d
a
l
ong
t
he
d
-
a
xi
s
,
t
he
s
t
a
t
e
e
qua
t
i
ons
a
l
l
ow
t
he
e
xpr
e
s
s
i
on of
v
ds
,
v
qs
,
φ
r
, a
nd
ω
r
unde
r
t
he
c
ondi
t
i
ons
φ
qr
=
0 a
nd
φ
′
qr
=
0.
B
y
i
nc
or
por
a
t
i
ng
t
he
s
e
c
ondi
t
i
ons
, t
he
i
nduc
t
i
on m
a
c
hi
ne
dy
na
m
i
c
m
od
e
l
c
a
n be
de
r
i
v
e
d a
s
[
16]
.
{
=
′
+
+
−
+
=
′
+
+
+
{
=
−
′
1
+
=
(
−
′
)
(
6)
W
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g
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3.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
ndone
s
i
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4752
A
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M
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)
31
F
i
g
ur
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2
.
E
s
t
i
m
a
t
i
on pr
oc
e
s
s
of
t
he
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t
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g
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s
F
i
g
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3
.
A
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c
hi
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ur
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bl
oc
k of
t
he
i
ndi
r
e
c
t
r
ot
or
F
O
C
4.
T
U
N
I
N
G
C
O
N
T
R
O
L
L
E
R
S
U
S
I
N
G
A
G
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I
T
H
M
4.1.
P
r
o
p
or
t
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on
a
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e
gr
al
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e
gu
l
at
or
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PI
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ba
s
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o
m
pe
ns
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t
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on
s
y
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s
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m
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ne
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y
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pl
o
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e
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w
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hi
n
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t
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i
a
l
a
ppl
i
c
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t
i
ons
t
o
e
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nc
e
t
he
pe
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f
or
m
a
n
c
e
of
e
l
e
c
t
r
i
c
dr
i
v
e
s
[
17]
.
T
he
c
on
v
e
nt
i
ona
l
a
ppr
oa
c
h
t
o
s
e
l
e
c
t
i
ng
t
he
i
r
t
uni
ng
pa
r
a
m
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t
e
r
s
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l
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on
a
t
r
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r
or
a
ppr
oa
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ur
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z
e
r
o
s
t
e
a
d
y
-
s
t
a
t
e
e
r
r
o
r
[
18]
.
H
o
w
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e
r
,
a
dopt
i
ng
i
na
ppr
opr
i
a
t
e
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poor
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y
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l
e
c
t
e
d
c
ont
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ol
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oe
f
f
i
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nt
s
c
a
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ga
t
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v
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y
i
m
pa
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t
t
he
o
v
e
r
a
l
l
s
y
s
t
e
m
be
ha
v
i
or
,
m
a
ki
ng
pr
ope
r
de
s
i
g
n
c
r
uc
i
a
l
[
19]
.
T
r
a
di
t
i
ona
l
l
y
,
t
he
de
s
i
g
n
v
a
l
ue
s
f
o
r
t
he
s
e
c
o
m
pe
ns
a
t
or
s
a
r
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de
t
e
r
m
i
n
e
d
i
m
m
e
di
a
t
e
l
y
f
r
o
m
t
h
e
m
ot
or
da
t
a
by
m
e
a
ns
of
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s
t
a
bl
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s
he
d
de
t
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m
i
ni
s
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c
(
c
l
a
s
s
i
c
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l
)
m
e
t
hodol
og
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e
s
.
T
he
s
e
a
l
g
or
i
t
hm
s
c
a
l
l
f
or
pr
e
c
i
s
e
c
a
l
c
ul
a
t
i
ons
pl
us
a
t
hor
oug
h
unde
r
s
t
a
ndi
ng
of
e
v
e
r
y
s
i
ng
l
e
m
ot
or
a
t
t
r
i
but
e
.
H
o
w
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r
,
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he
y
ha
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g
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f
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c
a
nt
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s
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d
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nt
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g
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,
w
hi
c
h
c
a
n
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ol
v
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d
t
hr
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m
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y
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r
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t
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g
i
e
s
,
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pe
c
i
f
i
c
a
l
l
y
GA
[
20]
.
F
i
g
ur
e
4
de
pi
c
t
s
t
he
c
ont
r
ol
a
r
c
hi
t
e
c
t
ur
e
f
or
t
he
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e
nde
d
IM
s
pe
e
d
dr
i
v
e
.
T
he
r
e
f
e
r
e
n
c
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s
pe
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d
Ω
r
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f
i
s
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ubt
r
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d f
r
o
m
t
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d s
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Ω
t
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s
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, w
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P
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r
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K
ps
+
Ki
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v
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ous
f
r
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c
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i
on
c
oe
f
f
i
c
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e
nt
.
A
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
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:
2502
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4752
I
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4
3
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1
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Ju
ly
20
26
:
28
-
38
32
e
xt
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r
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s
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ur
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m
Cr
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e
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oa
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a
ppl
i
e
d
t
o
t
he
s
ha
f
t
.
T
hi
s
s
t
r
uc
t
ur
e
s
e
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v
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s
a
s
t
he
ba
s
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or
c
o
m
pa
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on
v
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ona
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P
I
t
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w
i
t
h t
he
G
A
-
opt
i
m
i
z
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d P
I
c
ont
r
ol
l
e
r
.
I
f
=
0
,
t
he
c
or
r
e
s
pondi
ng
c
l
os
e
d
-
l
oop e
xpr
e
s
s
i
on be
c
o
m
e
s
:
(
)
=
+
2
+
(
+
)
+
(
10)
B
y
c
o
m
pa
r
i
ng
t
he
de
no
m
i
na
t
or
w
i
t
h t
ha
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of
a
s
e
c
ond
-
or
de
r
s
y
s
t
e
m
,
w
e
obt
a
i
n:
{
=
2
=
2
−
(
11)
F
i
g
ur
e
4
.
B
l
oc
k
di
a
g
r
a
m
o
f
s
pe
e
d c
ont
r
ol
4.2.
P
r
i
n
c
i
p
l
e
s
of
GA
o
p
e
r
at
i
on
G
A
s
r
e
pr
e
s
e
nt
s
m
e
t
a
h
e
ur
i
s
t
i
c
s
e
a
r
c
h
a
nd
opt
i
m
i
z
a
t
i
on
r
out
i
ne
s
t
ha
t
m
a
i
nt
a
i
n
a
pool
of
c
a
ndi
da
t
e
s
,
dr
a
w
i
ng
i
ns
pi
r
a
t
i
on
f
r
o
m
e
v
ol
ut
i
ona
r
y
m
e
c
ha
ni
s
m
s
[
21]
.
T
he
y
i
t
e
r
a
t
i
v
e
l
y
e
v
ol
v
e
a
c
ol
l
e
c
t
i
on
of
pr
os
pe
c
t
i
v
e
a
l
t
e
r
na
t
i
v
e
d
e
s
i
g
ns
(
c
h
r
o
m
os
o
m
e
s
)
t
hr
oug
h
s
e
l
e
c
t
i
on,
c
r
os
s
o
v
e
r
,
c
o
m
bi
ne
d
w
i
t
h
m
ut
a
t
i
on
t
o
m
i
ni
m
i
z
e
a
pr
e
de
f
i
ne
d
f
i
t
ne
s
s
f
unc
t
i
on.
E
v
e
r
y
i
ndi
v
i
dua
l
c
hr
o
m
os
o
m
e
c
or
r
e
s
ponds
t
o
a
c
a
ndi
da
t
e
s
ol
ut
i
on,
e
v
a
l
ua
t
e
d
qua
l
i
t
a
t
i
v
e
l
y
t
hr
oug
h
t
he
obj
e
c
t
i
v
e
f
unc
t
i
on
[
22]
.
O
v
e
r
s
uc
c
e
s
s
i
v
e
g
e
ne
r
a
t
i
ons
,
t
he
pop
ul
a
t
i
on
c
onv
e
r
ge
s
t
ow
a
r
d
hi
g
hl
y
opt
i
m
i
z
e
d
or
qu
a
s
i
-
opt
i
m
a
l
out
put
s
[
23]
.
T
he
p
a
r
a
m
ount
m
e
r
i
t
of
e
xe
c
ut
i
ng
G
A
a
ppr
oa
c
h
e
s
i
ns
i
de
c
ont
r
o
l
s
y
s
t
e
m
de
s
i
g
n
i
s
i
t
s
a
bi
l
i
t
y
t
o
ha
nd
l
e
nonl
i
ne
a
r
,
m
ul
t
i
m
oda
l
,
a
nd
c
om
p
l
e
x
s
e
a
r
c
h
s
pa
c
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s
w
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t
hout
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e
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r
i
ng
de
r
i
v
a
t
i
v
e
i
nf
or
m
a
t
i
on
[
24]
.
I
n
t
hi
s
s
t
udy
,
t
he
G
A
m
e
t
hod
i
s
a
dop
t
e
d
t
o
f
i
nd
t
he
be
s
t
pa
r
a
m
e
t
e
r
s
f
or
t
he
pr
opor
t
i
ona
l
(
K
p
v
)
a
nd
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nt
e
g
r
a
l
(
K
i
v
)
g
a
i
ns
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s
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a
t
e
d
w
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t
h
a
v
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l
oc
i
t
y
P
I
c
ont
r
ol
l
e
r
f
or
a
n
IM
,
w
hi
l
e
e
xpl
i
c
i
t
l
y
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ons
i
de
r
i
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c
or
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e
f
f
e
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t
s
.
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h
i
s
e
ns
ur
e
s
bot
h
i
m
pr
o
v
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d
d
y
na
m
i
c
pe
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f
or
m
a
nc
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a
n
d
e
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nc
e
d e
ne
r
gy
e
f
f
i
c
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e
nc
y
[
25]
.
T
he
f
unda
m
e
nt
a
l
G
A
m
e
c
ha
ni
s
m
c
ons
i
s
t
s
of
t
hr
e
e
m
a
i
n s
t
e
ps
:
a)
E
v
a
l
ua
t
i
on of
i
ndi
v
i
dua
l
f
i
t
ne
s
s
–
a
s
s
e
s
s
i
ng
t
he
qua
l
i
t
y
of
e
a
c
h c
a
ndi
da
t
e
s
ol
ut
i
on.
b)
S
e
l
e
c
t
i
on a
nd f
or
m
a
t
i
on of
t
he
g
e
ne
pool
–
c
hoos
i
ng
pr
o
m
i
s
i
ng
s
ol
ut
i
ons
f
or
r
e
pr
oduc
t
i
on.
c)
R
e
c
o
m
bi
na
t
i
on
a
nd
m
ut
a
t
i
on
–
g
e
ne
r
a
t
i
ng
ne
w
s
ol
ut
i
ons
t
o
e
xpl
or
e
t
he
s
e
a
r
c
h
s
pa
c
e
a
n
d
a
v
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d
l
oc
a
l
m
i
ni
m
a
.
F
i
g
ur
e
5 i
l
l
us
t
r
a
t
e
s
t
he
s
t
r
uc
t
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e
of
a
s
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m
pl
e
GA
.
F
i
g
ur
e
5
.
T
he
b
a
s
i
c
ope
r
a
t
or
s
of
G
A
Evaluation Warning : The document was created with Spire.PDF for Python.
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i
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E
l
e
c
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:
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4752
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(
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M
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)
33
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e
f
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opt
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m
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he
P
I
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t
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pe
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pe
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t
a
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t
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pe
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f
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x f
or
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on gi
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he
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a
t
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m
a
t
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c
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l
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e
l
a
t
i
on:
=
∫
2
(
)
=
∫
(
(
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0
0
−
(
)
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2
(
12)
T
he
s
e
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e
d
r
e
a
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s
c
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s
m
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\
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t
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c
a
l
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r
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t
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s
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m
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ppl
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o
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a
t
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t
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t
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i
m
ul
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nk
m
od
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l
.
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he
a
r
c
hi
t
e
c
t
ur
a
l
f
r
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m
e
w
or
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r
a
c
t
e
r
i
z
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ng
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he
P
I
c
ont
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r
opt
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m
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z
a
t
i
on
a
ppr
oa
c
h
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ng
GA
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s
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l
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t
r
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t
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d i
n
F
i
g
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6.
F
i
g
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6
.
S
t
r
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t
ur
e
r
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pr
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s
e
nt
i
ng
t
he
P
I
pa
r
a
m
e
t
e
r
opt
i
m
i
z
a
t
i
on a
l
g
or
i
t
h
m
G
A
s
a
r
e
e
m
pl
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e
d
t
o
de
t
e
r
m
i
ne
t
he
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oe
f
f
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c
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e
nt
s
of
t
he
P
I
D
g
a
i
ns
t
hr
oug
h
m
i
ni
m
i
z
a
t
i
on
of
t
he
m
e
a
n
s
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r
i
t
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S
E
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.
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hos
e
P
I
D
p
a
r
a
m
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t
e
r
s
opt
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m
i
z
e
d
us
i
ng
G
A
a
r
e
t
he
i
nt
e
g
r
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l
g
a
i
n
(
K
I
)
a
nd
pr
opor
t
i
ona
l
g
a
i
n (
K
P
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. T
h
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s
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ng
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l
a
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out
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n T
a
bl
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1,
w
hi
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h i
s
us
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d f
or
opt
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m
i
z
i
n
g
t
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P
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D
c
ont
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ol
l
e
r
.
T
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bl
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1
.
O
pe
r
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t
i
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l
s
e
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t
i
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s
of
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s
N
um
be
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n
e
r
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t
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s
20
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of
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t
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h
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m
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e
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r
oba
bi
l
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t
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r
os
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ove
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r
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bi
l
i
t
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of
m
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t
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08
O
nc
e
t
he
a
f
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e
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t
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t
he
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pa
r
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m
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t
e
r
s
c
a
n
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s
t
r
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i
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f
or
w
a
r
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y
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dj
us
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e
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t
he
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e
b
y
e
nha
n
c
i
ng
o
v
e
r
a
l
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s
y
s
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e
m
be
ha
v
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opt
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a
l
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e
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c
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m
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t
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on
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hod
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r
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s
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m
m
a
r
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bl
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bl
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2
g
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v
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v
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l
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t
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t
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m
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z
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t
i
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bl
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2
.
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g
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t
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l
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P
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pa
r
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r
s
V
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l
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s
K
I
20.
287
KP
1.
641
F
i
g
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7
s
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t
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n be
obs
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r
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a
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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F
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7. F
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be
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F
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8. S
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m
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on di
a
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of
VC
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O
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of
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i
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F
i
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9
.
F
i
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u
r
e
9
i
l
l
us
t
r
a
t
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s
t
he
e
f
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c
t
of
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r
on
l
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s
e
s
on
m
a
c
hi
ne
c
ont
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ol
:
S
pe
e
d
a
s
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how
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i
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F
i
g
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9(
a)
c
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r
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c
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b)
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c
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9
(
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t
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t
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n
F
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g
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10
.
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10
(a
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–
(
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[
1]
F
.
B
e
t
i
n
,
M
.
M
o
gh
a
da
s
i
a
n,
V
.
L
a
n
f
r
a
n
c
h
i
,
a
n
d
G
.
-
A
.
C
a
pol
i
n
o,
“
F
a
ul
t
-
t
ol
e
r
a
n
t
c
o
n
t
r
o
l
of
s
i
x
-
p
h
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n
duc
t
i
o
n
m
a
c
h
i
n
e
s
us
i
n
g
c
om
b
i
n
e
d
f
uz
z
y
l
o
g
i
c
a
n
d
g
e
n
e
t
i
c
a
l
g
or
i
t
hm
s
,
”
i
n
2013
I
E
E
E
W
or
k
s
hop
on
E
l
e
c
t
r
i
c
a
l
M
ac
h
i
ne
s
D
e
s
i
gn,
C
ont
r
ol
and
D
i
agnos
i
s
(
W
E
M
D
C
D
)
,
M
a
r
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2013
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–
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doi
:
10.
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e
m
dc
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[
2]
D
.
B
i
s
w
a
s
,
K
.
M
uk
h
e
r
j
e
e
,
a
n
d
N
.
C
.
K
a
r
,
“
A
n
o
ve
l
a
ppr
oa
c
h
t
o
w
a
r
ds
e
l
e
c
t
r
i
c
a
l
l
os
s
m
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n
i
m
i
z
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t
i
o
n
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n
ve
c
t
or
c
o
n
t
r
ol
l
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d
i
n
duc
t
i
o
n
m
a
c
h
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n
e
d
r
i
ve
f
o
r
E
V
/
H
E
V
,
”
i
n
2
012
I
E
E
E
T
r
ans
por
t
at
i
on
E
l
e
c
t
r
i
f
i
c
at
i
on
C
onf
e
r
e
nc
e
and
E
x
p
o
(
I
T
E
C
)
,
2012,
pp.
1
–
5
,
doi
:
10.
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t
e
c
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2012.
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[
3]
A
.
A
.
C
a
be
z
a
s
R
e
bol
l
e
do
a
nd
M
.
A
.
V
a
l
e
nz
ue
l
a
,
“
E
x
pe
c
t
e
d
s
a
vi
ng
s
us
i
ng
l
os
s
-
m
i
n
i
m
i
z
i
ng
f
l
u
x
o
n
i
m
d
r
i
ve
s
—
pa
r
t
i
:
opt
i
m
u
m
f
l
u
x
a
n
d
pow
e
r
s
a
vi
ng
s
f
or
m
i
n
i
m
u
m
l
os
s
e
s
,
”
I
E
E
E
T
r
ans
ac
t
i
o
ns
on
I
ndus
t
r
y
A
ppl
i
c
at
i
ons
,
vol
.
51,
n
o.
2,
pp.
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–
1416,
M
a
r
.
2015,
doi
:
10.
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t
i
a
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2014.
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[
4]
S
.
C
h
a
c
ko,
C
.
N
.
B
h
e
n
de
,
S
.
J
a
i
n,
a
n
d
R
.
K
.
N
e
m
a
,
“
A
n
ove
l
o
n
l
i
n
e
r
ot
o
r
r
e
s
i
s
t
a
n
c
e
e
s
t
i
m
a
t
i
o
n
t
e
c
hn
i
que
us
i
ng
E
A
t
u
n
e
d
f
u
z
z
y
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on
t
r
ol
l
e
r
f
or
ve
c
t
or
c
o
n
t
r
ol
l
e
d
i
n
duc
t
i
o
n
m
ot
or
dr
i
ve
,
”
i
n
2015
I
E
E
E
I
nt
e
r
nat
i
onal
C
onf
e
r
e
nc
e
on
E
l
e
c
t
r
i
c
al
,
C
om
put
e
r
and
C
om
m
uni
c
a
t
i
on
T
e
c
hnol
ogi
e
s
(
I
C
E
C
C
T
)
,
M
a
r
.
2015,
pp.
1
–
11,
doi
:
10.
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c
e
c
c
t
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2015.
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[
5]
F
.
L
i
n
a
nd
P
.
H
ua
ng,
“
R
e
c
ur
r
e
n
t
f
uz
z
y
n
e
ur
a
l
n
e
t
w
or
k
us
i
ng
g
e
n
e
t
i
c
a
l
g
or
i
t
hm
f
or
l
i
n
e
a
r
i
n
duc
t
i
o
n
m
ot
or
s
e
r
vo
dr
i
ve
,
”
i
n
200
6
1ST
I
E
E
E
C
o
nf
e
r
e
nc
e
on
I
ndus
t
r
i
al
E
l
e
c
t
r
oni
c
s
and
A
ppl
i
c
a
t
i
ons
,
M
a
y
2006,
pp.
1
–
6,
doi
:
10.
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c
i
e
a
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2006.
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[
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M
.
W
a
h
e
e
da
B
e
e
vi
,
A
.
S
uke
s
h
K
u
m
a
r
,
a
n
d
H
.
S
.
S
i
bi
n
,
“
L
os
s
m
i
n
i
m
i
z
a
t
i
o
n
o
f
ve
c
t
or
c
on
t
r
ol
l
e
d
i
n
duc
t
i
o
n
m
ot
or
dr
i
ve
us
i
ng
g
e
n
e
t
i
c
a
l
g
o
r
i
t
hm
,
”
i
n
20
12
I
nt
e
r
nat
i
o
nal
C
onf
e
r
e
nc
e
on
G
r
e
e
n
T
e
c
hnol
ogi
e
s
(
I
C
G
T
)
,
D
e
c
.
2012
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pp.
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–
257,
do
i
:
10.
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c
g
t
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[
7]
D
.
D
.
P
i
n
h
e
i
r
o,
C
.
M
.
O
.
S
t
e
i
n,
J
.
P
.
C
os
t
a
,
R
.
C
a
r
dos
o,
a
n
d
E
.
G
.
C
a
r
a
t
i
,
“
C
o
m
pa
r
i
s
o
n
of
s
e
n
s
or
l
e
s
s
t
e
c
hn
i
que
s
ba
s
e
d
on
m
ode
l
r
e
f
e
r
e
n
c
e
a
da
p
t
i
ve
s
ys
t
e
m
f
o
r
i
n
duc
t
i
o
n
m
ot
or
d
r
i
ve
s
,
”
i
n
2015
I
E
E
E
13t
h
B
r
az
i
l
i
a
n
P
ow
e
r
E
l
e
c
t
r
oni
c
s
C
onf
e
r
e
nc
e
and
1s
t
Sout
he
r
n
P
ow
e
r
E
l
e
c
t
r
oni
c
s
C
onf
e
r
e
nc
e
(
C
O
B
E
P
/
S
P
E
C
)
,
N
ov.
2015,
pp.
1
–
6,
doi
:
10.
1109/
c
obe
p.
2015.
7420106.
[
8]
E
.
M
.
R
a
s
h
a
d,
T
.
S
.
R
a
dw
a
n,
a
n
d
M
.
A
.
R
a
h
m
a
n,
“
A
m
a
x
i
m
u
m
t
o
r
que
pe
r
a
m
pe
r
e
ve
c
t
or
c
o
n
t
r
ol
s
t
r
a
t
e
g
y
f
or
s
yn
c
h
r
o
n
o
us
r
e
l
uc
t
a
n
c
e
m
ot
or
s
c
on
s
i
de
r
i
ng
s
a
t
ur
a
t
i
o
n
a
nd
i
r
o
n
l
os
s
e
s
,
”
i
n
C
onf
e
r
e
nc
e
R
e
c
or
d
of
t
he
2004
I
E
E
E
I
ndus
t
r
y
A
ppl
i
c
at
i
ons
C
onf
e
r
e
nc
e
,
2004.
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h
I
A
S
A
n
nual
M
e
e
t
i
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F
.
R
a
z
a
vi
a
n
d
B
.
G
h
a
di
r
i
,
“
I
m
pe
r
i
a
l
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s
t
c
o
m
pe
t
i
t
i
ve
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l
g
o
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(
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pt
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m
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z
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d
P
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s
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t
r
ol
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n
t
h
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c
t
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l
d
-
or
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e
n
t
e
d
c
on
t
r
ol
of
a
n
I
M
dr
i
ve
,
”
i
n
2011
I
E
E
E
C
ol
l
oqui
um
on
H
um
ani
t
i
e
s
,
Sc
i
e
nc
e
and
E
ngi
ne
e
r
i
ng
,
D
e
c
.
2011
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pp.
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–
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doi
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hus
e
r
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[
10]
Z
.
R
oua
ba
h,
F
.
Z
i
da
n
i
,
a
nd
B
.
A
bde
l
h
a
di
,
“
E
f
f
i
c
i
e
n
c
y
opt
i
m
i
z
a
t
i
o
n
of
i
n
duc
t
i
o
n
m
ot
o
r
dr
i
ve
us
i
ng
f
uz
z
y
l
o
g
i
c
a
n
d
g
e
n
e
t
i
c
a
l
g
o
r
i
t
hm
s
,
”
i
n
2008
I
E
E
E
I
nt
e
r
nat
i
onal
Sy
m
pos
i
um
on
I
n
dus
t
r
i
al
E
l
e
c
t
r
oni
c
s
,
2008
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737
–
742,
do
i
:
10.
1109/
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s
i
e
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[
11]
S
.
S
e
l
vi
a
n
d
S
.
G
opi
n
a
t
h,
“
V
e
c
t
or
c
o
n
t
r
ol
o
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