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8
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s
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[
1
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2
]
T
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ash
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No
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[
3
]
in
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[
5
,
7
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.
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[
6
,
8
,
9
]
.
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I
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P
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w
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Vo
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9
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1
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Ma
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433
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2
434
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[
1
1
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3
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.
Am
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Kal
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o
r
less
DT
C
I
MD
,
E
K
F e
s
ti
m
ates
th
e
r
o
to
r
f
l
u
x
lin
k
ag
es,
s
ta
to
r
cu
r
r
en
t
s
an
d
r
o
to
r
s
p
ee
d
.
[
1
4
-
1
8
]
.
Pre
v
io
u
s
r
esear
ch
e
s
s
h
o
w
s
t
h
at
r
o
to
r
s
p
ee
d
is
esti
m
ated
in
E
K
F
co
n
s
id
er
in
g
it
as
a
co
n
s
ta
n
t
[
1
]
.
Fro
m
liter
at
u
r
e
s
u
r
v
e
y
it
i
s
o
b
s
er
v
ed
t
h
at
s
p
ee
d
in
f
o
r
m
atio
n
ca
n
b
e
ex
tr
ac
ted
f
r
o
m
t
h
e
eq
u
a
tio
n
o
f
m
o
tio
n
,
r
elatin
g
lo
ad
to
r
q
u
e
an
d
elec
tr
o
m
a
g
n
et
ic
to
r
q
u
e.
E
lectr
o
m
a
g
n
etic
to
r
q
u
e
ca
n
b
e
o
b
tain
ed
u
s
in
g
th
e
r
o
to
r
f
lu
x
li
n
k
ag
e
s
an
d
s
tato
r
c
u
r
r
en
ts
o
b
tai
n
ed
f
r
o
m
t
h
e
E
KF
s
tate
v
ar
iab
le
esti
m
ate.
T
h
e
lo
ad
to
r
q
u
e
r
eq
u
ir
ed
f
o
r
th
e
s
p
ee
d
es
ti
m
atio
n
is
tak
e
n
as
a
n
ad
d
itio
n
al
s
tate
v
ar
iab
l
e
in
E
K
F
a
n
d
co
n
s
id
er
ed
it
as
a
co
n
s
ta
n
t
[
1
9
]
.
B
u
t
th
is
m
eth
o
d
o
f
f
er
s
s
lu
g
g
i
s
h
p
er
f
o
r
m
a
n
ce
d
u
r
in
g
lo
w
s
p
ee
d
o
p
er
atio
n
.
A
n
e
w
m
e
th
o
d
w
as
p
r
o
p
o
s
ed
b
y
th
e
a
u
t
h
o
r
s
[
2
0
]
w
h
ich
al
s
o
u
s
es
t
h
e
eq
u
atio
n
o
f
m
o
t
i
o
n
f
o
r
r
o
to
r
s
p
ee
d
esti
m
atio
n
b
y
E
KF
b
u
t
t
h
e
lo
ad
to
r
q
u
e
r
eq
u
ir
ed
f
o
r
s
p
ee
d
esti
m
atio
n
i
s
f
ed
as
an
in
p
u
t
to
th
e
E
KF
f
o
r
esti
m
ati
n
g
th
e
s
p
ee
d
.
T
h
is
m
eth
o
d
o
f
f
er
s
f
a
s
t
co
n
v
er
g
en
ce
an
d
r
ed
u
ce
d
e
s
ti
m
atio
n
e
v
en
at
v
er
y
lo
w
s
p
ee
d
in
cl
u
d
in
g
ze
r
o
s
p
ee
d
.
T
h
e
s
o
f
t
w
ar
e
v
al
id
atio
n
o
f
n
e
w
ap
p
r
o
ac
h
o
f
esti
m
at
in
g
s
p
ee
d
u
s
i
n
g
E
KF
o
b
s
er
v
er
is
ca
r
r
ied
o
u
t
u
s
in
g
M
A
T
L
A
B
-
S
i
m
u
li
n
k
s
o
f
t
w
ar
e
a
n
d
o
b
s
er
v
ed
th
e
ef
f
ec
ti
v
en
e
s
s
o
f
th
e
tec
h
n
iq
u
e
f
o
r
lo
w
s
p
ee
d
esti
m
atio
n
in
s
e
n
s
o
r
less
DT
C
I
MD
.
I
n
t
h
is
p
ap
er
r
ea
l ti
m
e
v
alid
atio
n
o
f
th
e
n
e
w
E
KF a
p
p
r
o
ac
h
u
s
in
g
th
e
lo
ad
p
r
o
f
ile
in
p
u
t
to
E
KF
esti
m
ato
r
f
o
r
r
o
to
r
s
p
ee
d
esti
m
atio
n
is
ca
r
r
ied
o
u
t
u
s
in
g
P
r
o
ce
s
s
o
r
-
In
-
T
h
e
-
L
o
o
p
(
P
I
L
)
r
ea
l
ti
m
e
v
al
id
atio
n
tec
h
n
iq
u
e
u
s
in
g
OP
AL
-
R
T
r
ea
l
ti
m
e
s
i
m
u
lato
r
OP
4
5
0
0
.
T
h
e
r
esu
l
ts
a
n
d
an
al
y
s
i
s
ar
e
p
r
esen
ted
.
2.
SE
NSO
R
L
E
SS
SVM
DT
C
I
NDUC
T
I
O
N
M
O
T
O
R
DRIVE
DT
C
is
a
p
o
w
er
f
u
l
co
n
tr
o
l
tech
n
iq
u
e
w
h
ic
h
ca
n
d
ir
ec
tl
y
a
n
d
i
n
d
ep
en
d
en
tl
y
co
n
tr
o
l
t
h
e
elec
tr
o
m
ag
n
etic
to
r
q
u
e
an
d
s
t
ato
r
f
lu
x
lin
k
a
g
es
o
f
an
i
n
d
u
ctio
n
m
o
to
r
.
Sp
ac
e
v
ec
to
r
m
o
d
u
latio
n
(
SV
M)
i
s
u
s
ed
f
o
r
th
e
o
p
ti
m
al
s
elec
tio
n
o
f
in
v
er
ter
v
o
ltag
e
s
w
i
tch
i
n
g
s
p
ac
e
v
ec
to
r
.
I
n
co
n
v
e
n
ti
o
n
al
DT
C
o
p
ti
m
a
l
s
w
itc
h
in
g
-
v
o
ltag
e
v
ec
to
r
s
ar
e
to
b
e
s
elec
ted
f
r
o
m
t
h
e
o
p
ti
m
u
m
s
w
i
tch
in
g
-
v
o
lta
g
e
v
ec
to
r
l
o
o
k
-
u
p
tab
le.
B
u
t
in
co
n
v
e
n
tio
n
al
DT
C
h
y
s
ter
esi
s
b
an
d
co
n
tr
o
ller
s
th
e
o
p
ti
m
u
m
s
elec
tio
n
o
f
v
o
lta
g
e
s
p
ac
e
v
ec
t
o
r
f
r
o
m
th
e
lo
o
k
u
p
tab
le
an
d
ca
u
s
e
to
r
q
u
e
r
ip
p
le
s
an
d
g
iv
e
s
v
ar
iab
le
s
w
itc
h
i
n
g
f
r
eq
u
e
n
c
y
.
I
n
SV
M
P
I
co
n
tr
o
ller
s
r
ep
lace
th
e
h
y
s
ter
esi
s
b
an
d
co
n
tr
o
ller
s
an
d
o
f
f
er
s
co
n
s
tan
t
s
w
itc
h
i
n
g
f
r
e
q
u
en
c
y
a
n
d
r
ed
u
ctio
n
i
n
to
r
q
u
e
r
ip
p
le.
Sen
s
o
r
les
s
d
r
iv
es
r
ed
u
ce
t
h
e
c
o
m
p
le
x
it
y
an
d
co
s
t
o
f
s
y
s
te
m
b
u
t
s
en
s
o
r
less
d
r
iv
e
p
er
f
o
r
m
a
n
ce
g
r
ea
tl
y
d
ep
en
d
s
o
n
th
e
s
p
ee
d
esti
m
at
o
r
.
B
asic
o
p
en
lo
o
p
esti
m
ato
r
s
h
av
e
lo
w
s
p
ee
d
esti
m
atio
n
is
s
u
es
d
u
e
to
s
tato
r
r
esis
ta
n
ce
p
ar
a
m
eter
v
ar
iatio
n
d
u
r
i
n
g
lo
w
s
p
ee
d
o
p
er
atio
n
o
f
t
h
e
d
r
iv
e.
Alo
n
g
w
it
h
t
h
at
s
y
s
te
m
an
d
m
ea
s
u
r
e
m
e
n
t
n
o
is
e,
d
r
if
t,
d
c
o
f
f
s
et
etc
p
r
esen
t
in
t
h
e
s
y
s
te
m
w
ill
a
f
f
ec
t
t
h
e
lo
w
s
p
ee
d
esti
m
atio
n
.
E
KF
is
a
clo
s
ed
lo
o
p
o
b
s
er
v
er
w
h
ic
h
ca
n
b
e
u
s
ed
f
o
r
an
y
n
o
n
li
n
ea
r
ad
ap
tiv
e
s
y
s
te
m
to
es
t
i
m
ate
t
h
e
p
ar
am
eter
f
r
o
m
a
n
y
n
o
is
y
en
v
ir
o
n
m
e
n
t
u
s
i
n
g
s
tate
s
p
ac
e
tech
n
iq
u
e
a
n
d
r
ec
u
r
s
i
v
e
alg
o
r
ith
m
a
n
d
h
e
n
ce
E
KF
e
s
ti
m
ato
r
is
an
id
ea
l
ch
o
ice
f
o
r
m
it
ig
at
in
g
lo
w
s
p
ee
d
esti
m
atio
n
is
s
u
e
s
in
s
en
s
o
r
less
DT
C
I
MD
.
3.
I
NDUC
T
I
O
N
M
O
T
O
R
M
O
DE
L
D
E
S
CRIP
T
I
O
N
Sp
ee
d
esti
m
a
tio
n
u
s
i
n
g
E
KF
n
ee
d
s
s
tate
s
p
ac
e
m
o
d
el
o
f
i
n
d
u
ctio
n
m
o
to
r
f
o
r
t
h
e
p
r
ed
ictio
n
o
f
s
tates
at
an
y
in
s
ta
n
t
f
r
o
m
p
r
ev
io
u
s
l
y
est
i
m
a
ted
v
al
u
es.
T
h
e
t
wo
ax
is
s
tate
s
p
ac
e
m
o
d
el
o
f
in
d
u
ctio
n
m
o
to
r
in
s
tatio
n
ar
y
r
e
f
er
en
ce
f
r
a
m
e
ca
n
b
e
u
s
ed
w
h
er
e
th
e
s
tato
r
cu
r
r
e
n
t
an
d
r
o
to
r
f
lu
x
lin
k
a
g
es
ar
e
th
e
s
tate
v
ar
iab
les
an
d
r
o
to
r
s
p
ee
d
is
au
g
m
e
n
ted
as
th
e
f
i
f
t
h
s
tate
v
ar
iab
le
an
d
i
s
esti
m
ated
u
s
i
n
g
eq
u
at
io
n
o
f
m
o
tio
n
.
T
h
e
m
o
d
el
d
ev
elo
p
ed
in
th
is
p
ap
er
is
f
ee
d
in
g
t
h
e
lo
ad
to
r
q
u
e
p
r
o
f
ile
a
s
an
i
n
p
u
t
to
E
KF
e
s
ti
m
ato
r
in
s
tead
o
f
co
n
s
id
er
in
g
lo
ad
to
r
q
u
e
as
co
n
s
ta
n
t
as
g
i
v
en
b
y
p
r
ev
io
u
s
r
esear
ch
er
s
.
T
h
e
m
a
th
e
m
atica
l
m
o
d
el
o
f
t
h
e
i
n
d
u
ctio
n
m
o
to
r
w
it
h
f
i
v
e
s
tate
v
ar
iab
le
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
P
o
w
er
E
lectr
o
n
&
Dr
i
S
y
s
t
I
SS
N:
2
0
8
8
-
8
694
R
ea
l Time
V
a
lid
a
tio
n
o
f E
K
F
E
s
tima
to
r
fo
r
Lo
w
S
p
ee
d
E
s
ti
ma
tio
n
…
(
Min
i R
)
435
[
ψ
dr
ψ
qr
ω
r
]
=
[
-
(
′
+
′
L
m
2
′
(
′
)
2
)
0
L
m
′
′
T
r
ω
r
L
m
′
′
0
0
-
(
′
+
′
L
m
2
′
(
′
)
2
)
−
ω
r
L
m
′
′
L
m
′
′
T
r
0
L
m
T
r
0
−
1
−
ω
r
0
0
L
m
T
r
ω
r
−
1
0
−
2
∗
3
2
∗
2
∗
ψ
qr
2
∗
3
2
∗
2
∗
ψ
dr
0
0
0
]
[
ψ
dr
ψ
qr
ω
r
]
+
[
1
L
s'
0
0
0
1
L
s'
0
0
0
0
0
0
0
0
0
−
2
]
[
]
(
1
)
[
]
=
[
1
0
0
0
0
0
1
0
0
0
]
[
ψ
dr
ψ
qr
ω
r
]
(
2
)
T
h
e
e
lec
tro
m
a
g
n
e
ti
c
to
rq
u
e
re
q
u
i
re
d
f
o
r
e
stim
a
ti
n
g
th
e
ro
t
o
r
sp
e
e
d
is
d
e
riv
e
d
f
ro
m
sta
to
r
c
u
rre
n
ts
a
n
d
ro
to
r
f
lu
x
li
n
k
a
g
e
s
in
Eq
u
a
ti
o
n
(3
)
.
=
3
2
2
′
(
−
)
(3
)
T
h
e
lo
ad
to
r
q
u
e
r
eq
u
ir
ed
to
b
e
f
ed
to
E
KF
e
s
ti
m
ato
r
f
o
r
r
o
to
r
s
p
ee
d
esti
m
a
tio
n
i
s
i
n
co
r
p
o
r
ated
in
t
h
e
m
at
h
e
m
a
tical
m
o
d
el
o
f
m
ac
h
i
n
e
b
y
f
ee
d
in
g
th
e
lo
ad
p
r
o
f
ile
as
th
e
th
ir
d
ele
m
en
t
to
th
e
in
p
u
t
m
atr
ix
alo
n
g
w
it
h
t
h
e
s
tato
r
v
o
lta
g
es.
4.
E
XT
E
ND
E
D
K
A
L
M
AN
F
I
L
T
E
R
A
L
G
O
RI
T
H
M
E
x
ten
d
ed
k
al
m
a
n
f
ilter
al
g
o
r
ith
m
(
E
K
F
)
is
a
s
to
c
h
asti
c
a
n
d
r
ec
u
r
s
i
v
e
ad
ap
tiv
e
o
b
s
er
v
er
w
h
ich
ca
n
b
e
u
s
ed
f
o
r
j
o
in
t
s
tate
an
d
p
ar
a
m
eter
esti
m
atio
n
o
f
a
n
o
n
li
n
ea
r
d
y
n
a
m
ic
s
y
s
te
m
.
E
KF
w
i
ll
ta
k
e
ca
r
e
o
f
t
h
e
n
o
is
e
in
t
h
e
s
y
s
te
m
d
u
r
i
n
g
esti
m
at
io
n
b
y
u
s
i
n
g
co
v
ar
ia
n
ce
m
atr
ice
s
o
f
t
h
e
s
tate
v
ar
iab
le
(
P
)
,
s
y
s
te
m
n
o
is
e
(
Q)
a
n
d
v
o
ltag
e
m
ea
s
u
r
e
m
e
n
t
n
o
is
e
(
R
u
)
an
d
t
h
e
c
u
r
r
en
t
m
ea
s
u
r
e
m
en
t
n
o
i
s
e
(
R
e
)
.
T
h
ese
n
o
is
e
s
o
u
r
ce
s
ta
k
e
ca
r
e
o
f
m
ea
s
u
r
e
m
e
n
t
a
n
d
m
o
d
elin
g
i
n
ac
cu
r
ac
ie
s
.
E
KF
alg
o
r
it
h
m
c
o
n
s
is
ts
o
f
t
w
o
s
ta
g
e
s
o
f
ca
lcu
latio
n
,
f
ir
s
t
s
ta
g
e
i
s
p
r
ed
ictio
n
o
f
s
tates
u
s
i
n
g
m
ath
e
m
atica
l
m
o
d
el
w
h
ic
h
co
n
tain
s
p
r
ev
io
u
s
e
s
ti
m
ates
a
n
d
i
n
s
ec
o
n
d
s
ta
g
e
t
h
e
p
r
ed
icted
s
tates
ar
e
co
n
tin
u
o
u
s
l
y
co
r
r
ec
ted
b
y
a
f
ee
d
b
ac
k
c
o
r
r
ec
tio
n
s
ch
e
m
e.
T
h
e
p
r
ed
ictio
n
s
ta
g
e
n
ee
d
s
th
e
d
is
cr
etize
d
m
o
d
el
o
f
t
h
e
I
M.
T
h
e
w
ei
g
h
ted
d
if
f
er
en
ce
o
f
t
h
e
m
ea
s
u
r
ed
an
d
e
s
ti
m
ated
o
u
t
p
u
t
is
ad
d
ed
to
th
e
p
r
ed
icted
v
alu
es.[
2
0
]
.
’
[
(
+
1
)
(
+
1
)
ψ
dr
(
+
1
)
ψ
qr
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+
1
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ω
r
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+
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[
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′
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[
i
ds
(
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i
qs
(
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ψ
dr
(
)
ψ
qr
(
)
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r
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)
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+
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T
L
s'
0
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T
L
s'
0
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0
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0
0
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−
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[
(
)
(
)
(
)
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(
4
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[
i
ds
(
)
i
qs
(
]
=
[
1
0
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0
1
0
0
0
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[
i
ds
(
)
i
qs
(
)
ψ
dr
(
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ψ
qr
(
)
ω
r
(
)
]
(
5
)
T
h
e
E
KF
alg
o
r
it
h
m
w
as
d
ev
el
o
p
ed
to
esti
m
ate
t
h
e
s
tates
an
d
th
e
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ti
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ated
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tate
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o
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o
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n
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ta
n
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an
d
th
e
s
ta
to
r
v
o
ltag
es
ar
e
u
s
ed
f
o
r
p
r
ed
ictin
g
th
e
v
a
lu
e
s
o
f
th
e
s
ta
te
v
ar
iab
les
at
t
h
e
p
r
esen
t
i
n
s
ta
n
t.
T
h
e
d
ev
iatio
n
o
f
p
r
ed
icted
v
alu
e
s
f
r
o
m
ac
t
u
al
v
al
u
es
ar
e
o
b
tain
e
d
b
y
co
m
p
ar
in
g
t
h
e
p
r
ed
icted
s
tato
r
cu
r
r
en
ts
a
n
d
th
e
m
ea
s
u
r
ed
s
ta
to
r
cu
r
r
en
t
s
.
T
h
is
d
i
f
f
er
en
ce
in
v
alu
e
is
t
h
e
er
r
o
r
w
h
ich
is
t
u
n
ed
u
s
in
g
a
co
r
r
ec
tio
n
f
ac
to
r
to
g
et
a
n
ac
c
u
r
ate
e
s
ti
m
ate
o
f
th
e
s
tate
s
.
T
h
e
co
r
r
ec
ted
er
r
o
r
is
t
h
en
s
u
m
m
ed
u
p
w
it
h
t
h
e
p
r
ed
icted
v
alu
e
s
f
o
r
esti
m
ati
n
g
t
h
e
v
al
u
es
o
f
t
h
e
s
tate
v
ar
iab
les.
E
KF
is
u
s
ed
to
esti
m
ate
t
h
e
r
o
to
r
s
p
ee
d
o
f
th
e
s
en
s
o
r
less
DT
C
I
MD
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
694
I
n
t J
P
o
w
er
E
lectr
o
n
&
Dr
i S
y
s
t
,
Vo
l.
9
,
No
.
1
,
Ma
r
ch
2
0
1
8
:
433
–
44
2
436
A
2
0
h
p
DT
C
I
MD
u
s
in
g
E
K
F
esti
m
ato
r
is
d
e
v
elo
p
ed
in
M
AT
L
A
B
-
Si
m
u
l
in
k
s
o
f
t
w
ar
e
a
n
d
v
alid
ated
th
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
d
r
i
v
e
f
o
r
s
p
ee
d
esti
m
atio
n
f
r
o
m
r
at
ed
s
p
ee
d
to
v
er
y
lo
w
s
p
ee
d
i
n
clu
d
in
g
ze
r
o
s
p
ee
d
.
Si
m
u
latio
n
r
es
u
lts
p
r
o
v
es
th
e
ef
f
ec
tiv
e
n
e
s
s
o
f
t
h
i
s
n
e
w
lo
ad
p
r
o
f
ile
in
p
u
t
f
ee
d
E
KF
es
ti
m
ato
r
f
o
r
lo
w
s
p
ee
d
esti
m
atio
n
i
n
DT
C
I
MD
an
d
p
r
esen
ted
t
h
e
r
e
s
u
l
ts
in
[
2
0
]
.
T
h
e
m
o
to
r
p
ar
a
m
eter
s
ar
e
g
iv
e
n
in
T
ab
le
1
.
I
n
t
h
i
s
w
o
r
k
a
r
ea
l
ti
m
e
v
alid
atio
n
o
f
th
e
co
n
tr
o
ller
u
s
i
n
g
P
r
o
ce
s
s
o
r
-
In
-
T
h
e
-
L
o
o
p
(
P
I
L
)
r
ea
l
ti
m
e
v
al
id
atio
n
tech
n
iq
u
e
i
s
ca
r
r
ied
o
u
t
a
n
d
r
ea
l
ti
m
e
p
er
f
o
r
m
an
ce
o
f
t
h
e
co
n
tr
o
ller
is
v
al
id
ated
an
d
co
m
p
ar
ed
w
it
h
s
i
m
u
lat
io
n
r
es
u
lts
.
Ta
b
le
1
.
Mo
t
o
r
P
ar
am
eter
s
P
o
w
e
r
(
k
w
)
15
R
s
(
o
h
m)
0
.
2
1
4
7
F
r
e
q
u
e
n
c
y
(
H
z
)
50
R
r
'
(
o
h
m)
0
.
2
2
0
5
J(
k
g
/
m
2
)
0
.
1
0
2
L
ls
(H)
0
.
0
0
0
9
9
1
B
(
N
m/
r
a
d
/
s)
.
0
0
9
5
4
1
′
(H)
0
.
0
0
0
9
9
1
N
o
o
f
p
o
l
e
s P
2
L
m
(H)
0
.
0
6
4
1
9
V
o
l
t
a
g
e
(
V
)
4
0
0
N
m
(
r
p
m)
1
4
6
0
C
u
r
r
e
n
t
(
A
)
36
T
l
(
N
m)
98
5.
RE
A
L
T
I
M
E
SI
M
UL
AT
I
O
N
Mo
d
el
d
r
iv
en
d
ev
elo
p
m
e
n
t
a
p
p
r
o
ac
h
h
as
g
ai
n
ed
tr
e
m
e
n
d
o
u
s
d
e
m
an
d
i
n
test
in
g
a
n
d
v
al
id
atin
g
th
e
co
n
tr
o
ller
in
r
ea
l
ti
m
e
s
i
m
u
l
ato
r
en
v
ir
o
n
m
e
n
t.
T
h
is
h
e
lp
s
to
r
ed
u
ce
t
h
e
ti
m
e
tak
e
n
f
o
r
d
ev
elo
p
m
e
n
t
o
f
e
m
b
ed
d
ed
s
y
s
te
m
a
n
d
to
p
r
o
d
u
ce
r
ap
id
an
d
r
eliab
le
p
r
o
d
u
ct
in
a
s
h
o
r
t
in
ter
v
al
o
f
t
i
m
e.
R
T
-
P
I
L
is
a
v
er
if
ica
tio
n
an
d
v
alid
atio
n
tech
n
iq
u
e
u
s
ed
to
w
ar
d
s
t
h
e
d
ev
elo
p
m
e
n
t
o
f
a
h
ar
d
w
a
r
e
p
r
o
to
ty
p
e.
T
h
e
p
er
f
o
r
m
a
n
ce
a
n
al
y
s
i
s
o
b
tain
e
d
f
r
o
m
t
h
is
r
ea
l
t
i
m
e
P
I
L
w
i
ll
p
r
o
v
id
e
th
e
d
etails
r
eq
u
ir
e
d
f
o
r
h
ar
d
w
ar
e
a
n
d
co
m
p
u
tatio
n
al
r
es
o
u
r
ce
s
f
o
r
t
h
e
i
m
p
le
m
en
ta
tio
n
o
f
f
u
t
u
r
e
p
r
o
to
ty
p
e.
T
h
e
ad
v
a
n
ta
g
es
o
f
t
h
is
ap
p
r
o
ac
h
ar
e
th
e
s
i
m
u
lat
io
n
ti
m
e
s
ca
le
i
s
t
h
e
s
a
m
e
to
clo
ck
ti
m
e
s
ca
le,
t
h
e
r
ea
l
ti
m
e
co
n
tr
o
ller
co
d
e
g
en
er
atio
n
f
r
o
m
t
h
e
s
i
m
u
lated
al
g
o
r
ith
m
ca
n
b
e
d
o
n
e
au
to
m
atica
ll
y
,
t
h
e
ti
m
e
ta
k
e
n
f
o
r
d
ev
elo
p
i
n
g
,
co
s
t
to
d
esig
n
a
n
d
p
r
o
to
ty
p
e
th
e
co
n
tr
o
ller
an
d
te
s
ti
n
g
o
f
al
g
o
r
ith
m
at
e
x
tr
e
m
e
co
n
d
itio
n
s
w
it
h
h
i
g
h
ac
c
u
r
ac
y
ca
n
b
e
ac
h
iev
ed
.
I
n
P
I
L
t
h
e
s
p
ec
if
ic
d
r
iv
er
f
u
n
ctio
n
s
i
n
s
ta
lled
in
a
s
i
m
u
la
tio
n
in
teg
r
ate
d
en
v
ir
o
n
m
en
t
o
f
t
h
e
h
o
s
t
P
C
w
ill
co
m
m
u
n
ica
te
w
it
h
t
h
e
n
o
n
r
ea
l ti
m
e
e
n
v
ir
o
n
m
en
t ta
r
g
et
p
r
o
ce
s
s
o
r
.
5
.
1
.
Str
uct
ure
o
f
O
pa
l
-
RT
re
a
l t
i
m
e
s
i
m
ula
t
io
n sy
s
t
e
m
Op
al
-
R
T
's
r
ea
l
t
i
m
e
s
i
m
u
lato
r
en
ab
les
to
lin
k
s
o
f
t
w
ar
e
s
i
m
u
latio
n
s
m
ad
e
i
n
M
A
T
L
AB
/Si
m
u
li
n
k
Si
m
P
o
w
er
S
y
s
te
m
s
m
o
d
els
o
n
a
d
ed
icate
d
r
ea
l
-
ti
m
e
co
m
p
u
t
in
g
p
lat
f
o
r
m
.
R
ea
l
ti
m
e
s
i
m
u
l
atio
n
h
elp
s
to
lin
k
th
e
co
n
tr
o
ller
b
o
ar
d
to
th
e
s
i
m
u
latio
n
m
o
d
el.
T
h
e
s
tr
u
c
tu
r
e
o
f
R
T
lab
r
ea
l
ti
m
e
s
i
m
u
la
to
r
is
s
h
o
w
n
i
n
t
h
e
b
lo
ck
d
iag
r
a
m
Fi
g
u
r
e
1
.
Fig
u
r
e
1
.
P
r
o
ce
s
s
o
r
in
th
e
lo
o
p
b
lo
ck
d
iag
r
a
m
T
ar
g
et
m
ac
h
i
n
e
is
eq
u
ip
p
ed
w
ith
p
r
o
g
r
a
m
m
ab
le
DSP
/FP
G
A
an
d
I
/O
ch
an
n
els
to
co
m
m
u
n
icate
w
i
t
h
th
e
s
i
m
u
latio
n
h
o
s
t
co
m
p
u
ter
an
d
th
e
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[
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.
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2
.
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o
difica
t
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equired f
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t
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ex
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o
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3
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s
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tio
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r
u
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n
i
n
g
[
2
1
-
22]
Nex
t
s
tep
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to
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m
m
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u
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h
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p
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An
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I
SS
N
:
2
0
8
8
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8
694
I
n
t J
P
o
w
er
E
lectr
o
n
&
Dr
i S
y
s
t
,
Vo
l.
9
,
No
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1
,
Ma
r
ch
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:
433
–
44
2
438
Op
ctr
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lo
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ee
d
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in
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is
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u
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s
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te
m
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id
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n
ter
f
ac
e
to
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e
O
p
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R
T
ev
al
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atio
n
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o
ar
d
t
o
co
n
tr
o
l a
ll th
e
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/O
lin
es.
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u
r
e
4
.
R
T
lab
r
ea
l ti
m
e
s
i
m
u
latio
n
d
ia
g
r
a
m
o
f
SS
(
s
la
v
e)
s
u
b
s
y
s
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m
T
h
e
SS
(
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u
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co
n
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ts
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t
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e
m
ai
n
co
n
tr
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ller
o
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s
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n
s
o
r
less
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C
SVM
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MD
w
it
h
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o
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o
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ile
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p
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t
f
ee
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E
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as
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e
esti
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ato
r
.
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h
e
co
n
tr
o
ller
w
il
l
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ei
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e
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h
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ee
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to
r
v
o
lt
ag
es
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n
d
t
w
o
s
tato
r
cu
r
r
en
ts
r
eq
u
ir
ed
f
o
r
th
e
esti
m
atio
n
t
h
r
o
u
g
h
th
e
an
a
lo
g
i
n
b
lo
ck
.
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h
e
s
i
g
n
al
s
r
ec
ei
v
ed
f
r
o
m
a
n
alo
g
in
b
lo
c
k
h
as
to
b
e
r
escaled
b
ef
o
r
e
g
iv
in
g
to
th
e
co
n
tr
o
ller
.
T
h
e
b
lo
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s
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s
ed
to
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en
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ate
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W
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s
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n
a
ls
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d
s
to
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d
b
y
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T
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VE
NT
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h
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in
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g
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r
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5
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ated
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ato
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u
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5
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all
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er
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ter
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s
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id
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s
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lid
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ain
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e
n
u
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d
er
r
u
n
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i
n
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co
n
d
iti
o
n
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
P
o
w
er
E
lectr
o
n
&
Dr
i
S
y
s
t
I
SS
N:
2
0
8
8
-
8
694
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ea
l Time
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a
lid
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tio
n
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f E
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F
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s
tima
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w
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i
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R
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e
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ts
6.
RE
A
L
T
I
M
E
P
RO
CE
SS
O
R
-
IN
-
T
H
E
L
O
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P
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M
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AT
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SU
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F
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RO
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e
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ea
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al
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o
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e
w
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ad
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ile
in
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u
t
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ato
r
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en
s
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r
less
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o
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ed
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m
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s
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-
T
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p
(
R
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P
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u
s
i
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Op
al
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R
T
d
ig
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m
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lato
r
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I
n
th
is
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t
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h
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g
e
r
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l
ti
m
e
s
i
g
n
als
v
ia
lo
o
p
b
ac
k
ca
b
les
(
h
ar
d
w
ir
ed
)
th
r
o
u
g
h
t
h
e
I
/Os
o
f
t
h
e
s
i
m
u
lato
r
.
R
ea
l
T
im
e
-
P
I
L
r
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lt
s
ar
e
p
r
ese
n
ted
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to
p
r
o
v
e
th
e
ef
f
ec
tiv
e
n
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o
f
n
e
w
E
K
F
esti
m
a
to
r
f
o
r
lo
w
s
p
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d
esti
m
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i
n
DT
C
I
MD
.
T
o
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f
ec
ti
v
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s
o
f
t
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g
o
r
ith
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d
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s
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n
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tr
an
s
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t
h
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d
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d
t
o
r
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e
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s
al
ar
e
test
ed
f
o
r
all
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an
g
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p
ee
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s
f
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m
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ated
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p
ee
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w
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p
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d
i
n
g
ze
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p
ee
d
.
Ta
b
le
2
.
P
r
o
f
ile
o
f
R
ef
er
e
n
ce
Sp
ee
d
an
d
A
p
p
lied
L
o
ad
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me
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s)
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me
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s)
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5
1
.
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d
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p
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o
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q
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e
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N
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0
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Fig
u
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7
.
P
lo
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tak
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th
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o
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g
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o
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w
r
ite
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ato
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at
5
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p
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u
b
j
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ted
to
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to
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q
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f
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8
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p
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d
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al
a)
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ee
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lo
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T
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r
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e
p
lo
ts
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I
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N
:
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8
8
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I
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P
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E
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Dr
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Vo
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1
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:
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g
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r
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7
an
d
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u
r
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8
c
lear
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ad
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ti
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ato
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o
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id
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ick
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e
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p
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n
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e,
ac
c
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r
ate
s
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e
ed
an
d
to
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q
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e
esti
m
atio
n
at
v
er
y
lo
w
s
p
ee
d
s
at
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ated
to
r
q
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e
w
it
h
s
p
ee
d
a
n
d
to
r
q
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e
r
ev
er
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al.
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h
e
s
p
ee
d
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d
to
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m
ated
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y
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s
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y
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llo
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in
g
t
h
e
r
ef
er
en
c
e
s
p
ee
d
an
d
to
r
q
u
e.
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h
is
p
r
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th
e
e
f
f
ec
ti
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en
e
s
s
o
f
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KF
est
i
m
ato
r
f
o
r
s
p
ee
d
esti
m
a
tio
n
f
r
o
m
r
ated
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p
ee
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y
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w
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ee
d
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ate
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er
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m
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ce
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th
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n
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er
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9
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et
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Fig
u
r
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9
.
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d
(
b
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o
r
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es a
t 1
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(
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Fig
u
r
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10
.
MA
T
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si
m
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m
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in
g
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d
itio
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s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
P
o
w
er
E
lectr
o
n
&
Dr
i
S
y
s
t
I
SS
N:
2
0
8
8
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8
694
R
ea
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a
lid
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n
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E
s
tima
to
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r
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w
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p
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d
E
s
ti
ma
tio
n
…
(
Min
i R
)
441
F
ig
u
r
e
11
.
R
ea
l ti
m
e
Si
m
u
lato
r
o
u
tp
u
t d
is
p
la
y
ed
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D
SO
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h
a
n
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f
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2
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5
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b
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F
ig
u
r
e
12
.
R
ea
l ti
m
e
Si
m
u
lato
r
o
u
tp
u
t d
is
p
la
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SO
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2
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s
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cted
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a
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r
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4
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h
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Fig
u
r
e
13
.
R
ea
l ti
m
e
Si
m
u
lat
o
r
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u
tp
u
t d
is
p
la
y
ed
in
D
SO a
t 0
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p
m
s
u
b
j
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ted
to
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ll lo
ad
to
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q
u
e
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f
9
8
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m
(
s
p
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8
Nm
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T
h
e
r
ea
l
ti
m
e
o
u
tp
u
t
s
ee
n
in
th
e
D
SO
v
alid
ates
t
h
e
e
x
ce
ll
en
t
p
er
f
o
r
m
an
ce
o
f
t
h
e
d
r
i
v
e
w
it
h
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KF
esti
m
ato
r
w
it
h
L
o
ad
p
r
o
f
ile
f
e
d
to
E
KF a
s
an
in
p
u
t f
o
r
esti
m
atio
n
.
7.
CO
NCLU
SI
O
N
T
o
im
p
r
o
v
i
s
e
th
e
p
er
f
o
r
m
a
n
c
e
o
f
th
e
s
en
s
o
r
less
DT
C
I
M
d
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iv
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d
u
r
in
g
lo
w
s
p
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p
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ati
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n
an
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KF
esti
m
ato
r
w
ith
lo
ad
to
r
q
u
e
r
eq
u
ir
ed
f
o
r
s
p
ee
d
esti
m
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n
is
f
ed
to
t
h
e
E
KF
m
o
d
el
is
s
i
m
u
lated
i
n
OP
AL
-
R
T
r
ea
l
ti
m
e
s
i
m
u
lato
r
u
s
in
g
t
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P
r
o
ce
s
s
o
r
-
In
-
T
h
e
L
o
o
p
tech
n
i
q
u
e
to
ev
alu
a
te
th
e
r
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l
ti
m
e
p
er
f
o
r
m
a
n
ce
o
f
t
h
e
E
KF
esti
m
ato
r
f
o
r
lo
w
s
p
ee
d
esti
m
a
tio
n
.
T
h
e
o
u
tp
u
t
r
es
u
l
ts
o
b
tain
ed
f
r
o
m
r
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m
e
s
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s
h
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w
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t
h
e
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f
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tiv
e
s
o
f
t
h
e
E
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f
o
r
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w
s
p
ee
d
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m
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n
at
v
ar
io
u
s
to
r
q
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e
an
d
s
p
ee
d
co
n
d
itio
n
s
.
T
h
e
s
tead
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s
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d
tr
an
s
ie
n
t
s
tate
p
er
f
o
r
m
a
n
ce
at
r
ated
f
u
ll
lo
ad
to
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q
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e
an
d
v
ar
y
in
g
lo
ad
co
n
d
itio
n
s
ar
e
v
er
if
ie
d
an
d
o
b
s
er
v
ed
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e
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esti
m
ato
r
g
iv
e
s
e
x
ce
lle
n
t
p
er
f
o
r
m
an
ce
f
r
o
m
r
ated
s
p
ee
d
to
v
er
y
lo
w
s
p
ee
d
in
cl
u
d
i
n
g
ze
r
o
s
p
ee
d
.
T
h
e
P
r
o
ce
s
s
o
r
-
In
-
T
h
e
-
L
o
o
p
(
P
I
L
)
v
alid
at
io
n
s
ch
e
m
e
h
elp
s
to
an
al
y
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t
h
e
p
er
f
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r
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a
n
ce
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I
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Dr
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Vo
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9
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1
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2
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co
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Har
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.
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lam
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ith
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Mr
.
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it
Ku
m
ar
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o
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R
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an
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F
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R
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NC
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S
[1
]
P
.
V
a
s,
S
e
n
s
o
rle
ss
Vec
to
r a
n
d
Dir
e
c
t
T
o
rq
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e
C
o
n
tr
o
l
.
Ne
w
Yo
rk
:
Ox
f
o
rd
Un
iv
.
P
re
ss
,
1
9
9
8
.
[2
]
Bim
a
l.
K.Bo
se
,
M
o
d
e
rn
P
o
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r E
lec
tro
n
ics
a
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d
AC
Dr
ive
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p
e
r
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a
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v
e
r,
NJ
:
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n
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ice
Ha
ll
P
T
R,
2
0
0
2
.
[3
]
T
a
k
a
h
a
sh
i
a
n
d
T
.
No
g
u
c
h
i,
“
A
N
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Qu
ick
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Re
sp
o
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ficie
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S
trate
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n
In
d
u
c
ti
o
n
M
o
to
r”
,
IEE
E
T
ra
n
.
O
n
I
n
d
u
stry
Ap
p
li
c
a
ti
o
n
s
,
v
o
l
.
IA
-
22,
N
o
.
5
,
p
p
.
8
2
0
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8
2
7
.
S
e
p
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-
Oc
t.
1
9
8
6
.
[4
]
M
.
De
p
e
n
b
ro
c
k
,
“
Dire
c
t
se
lf
c
o
n
t
ro
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ig
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d
y
n
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m
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e
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o
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a
n
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o
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in
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e
rter
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e
d
AC
m
a
c
h
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e
s,”
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Z
Arc
h
.
.
,
v
o
l
.
7
,
n
o
.
7
,
p
p
.
2
1
1
–
2
1
8
,
1
9
8
5
.
[5
]
Cristi
a
n
L
a
sc
u
,
Io
n
B
o
l
d
e
a
a
n
d
F
r
e
d
e
Blaa
b
jerg
,
“
A
M
o
d
if
ied
Dire
c
t
T
o
rq
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e
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o
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tr
o
l
f
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n
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o
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r
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e
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s
Driv
e
”
,
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E
T
ra
n
.
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n
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stry
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li
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ti
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n
s
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.
3
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,
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o
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1
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p
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1
2
2
-
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3
0
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n
/F
e
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.
2
0
0
0
.
[6
]
J.
Ho
lt
z
,
“
S
e
n
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rles
s
c
o
n
tr
o
l
o
f
in
d
u
c
ti
o
n
m
o
to
rs
—
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e
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rm
a
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li
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it
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n
s,”
in
Pro
c
.
IE
EE
-
IS
IE
A
n
n
u
.
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ti
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g
,
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ico
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o
l.
1
,
p
p
.
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L
1
2
–
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L
2
0
.
[7
]
K.
Ra
jas
h
e
k
a
ra
,
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e
n
so
rle
ss
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n
tr
o
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o
rs
.
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isc
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taw
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y
,
N
J: I
EE
E
P
re
ss
,
1
9
9
6
.
[8
]
R.
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g
a
,
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Ba
ra
b
a
n
o
v
,
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.
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o
b
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n
d
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.
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ld
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a
,
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e
c
o
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tr
o
l
o
f
in
d
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ti
o
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rs:
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ty
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n
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ly
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e
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rm
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n
c
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ro
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m
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n
t,
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E
T
ra
n
s.
A
u
to
m
.
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tro
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l
.
4
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p
.
1
2
0
9
–
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2
2
,
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g
.
2
0
0
1
.
[9
]
J.
Ho
lt
z
,
“
S
e
n
so
rles
s
c
o
n
tro
l
o
f
in
d
u
c
ti
o
n
m
a
c
h
in
e
s
—
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it
h
o
r
w
it
h
o
u
t
sig
n
a
l
in
jec
ti
o
n
?
”
IEE
E
T
ra
n
s
.
In
d
.
E
lec
tro
n
.
,
v
o
l.
5
3
,
n
o
.
1
,
p
p
.
7
–
3
0
,
F
e
b
.
2
0
0
6
.
[1
0
]
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.
Cir
rin
c
i
o
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e
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n
d
M
.
P
u
c
c
i,
“
A
n
M
RA
S
-
b
a
se
d
s
e
n
so
rles
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ig
h
p
e
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rm
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n
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e
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c
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n
m
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to
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e
with
a
p
re
d
ictiv
e
a
d
a
p
ti
v
e
m
o
d
e
l,
”
IEE
E
T
ra
n
s.
I
n
d
.
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e
c
tro
n
.
,
v
o
l.
5
2
,
n
o
.
2
,
p
p
.
5
3
2
–
5
5
1
,
A
p
r.
2
0
0
5
.
[1
1
]
M
.
Cirri
n
c
io
n
e
,
M
.
P
u
c
c
i,
G
.
Cir
rin
c
io
n
e
,
a
n
d
G
.
-
A
.
Ca
p
o
li
n
o
,
“
An
a
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a
p
ti
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e
sp
e
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o
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se
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r
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se
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n
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st
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re
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ro
n
f
o
r
in
d
u
c
t
io
n
m
a
c
h
in
e
d
riv
e
s,”
IEE
E
T
r
a
n
s.
In
d
.
A
p
p
l.
,
v
o
l.
4
2
,
n
o
.
1
,
p
p
.
8
9
–
1
0
4
,
Ja
n
.
/F
e
b
.
2
0
0
6
.
[1
2
]
G
.
Ed
e
lb
a
h
e
r,
K.
Je
z
e
rn
ik
,
a
n
d
E
.
Urle
p
,
“
L
o
w
-
sp
e
e
d
se
n
so
rles
s
c
o
n
tr
o
l
o
f
in
d
u
c
ti
o
n
m
a
c
h
in
e
,
”
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E
T
ra
n
s
.
In
d
.
El
e
c
tro
n
.
,
v
o
l.
5
3
,
n
o
.
1
,
p
p
.
1
2
0
–
1
2
9
,
F
e
b
.
2
0
0
6
.
[1
3
]
W
a
d
e
,
M
.
W
.
Du
n
n
ig
a
n
,
a
n
d
B.
W
.
W
il
li
a
m
s,
“
Co
m
p
a
riso
n
o
f
st
o
c
h
a
stic
a
n
d
d
e
term
in
isti
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p
a
ra
m
e
ter
id
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n
ti
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ica
ti
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n
a
lg
o
rit
h
m
s
f
o
r
in
d
irec
t
v
e
c
to
r
c
o
n
tr
o
l,
”
in
I
EE
Co
l
lo
q
.
—
Vec
to
r
Co
n
tro
l
a
n
d
Dir
e
c
t
T
o
rq
u
e
Co
n
tro
l
I
n
d
u
c
ti
o
n
M
o
to
rs
,
L
o
n
d
o
n
,
U.K.,
1
9
9
5
,
v
o
l
.
2
,
p
p
.
1
–
5.
[1
4
]
F
.
Ch
e
n
a
n
d
M
.
W
.
D
u
n
n
ig
a
n
,
“
Co
m
p
a
ra
ti
v
e
stu
d
y
o
f
a
slid
in
g
-
m
o
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se
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d
Ka
lm
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n
f
il
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f
o
r
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ll
sta
te
e
sti
m
a
ti
o
n
in
a
n
i
n
d
u
c
ti
o
n
m
a
c
h
in
e
,
”
Pro
c
.
I
n
st.
E
lec
tr.
En
g
.
—
E
lec
tr.
Po
we
r
Ap
p
l.
,
v
o
l.
1
4
9
,
n
o
.
1
,
p
p
.
5
3
–
6
4
,
Ja
n
.
2
0
0
2
.
[1
5
]
A
lso
fy
a
n
i,
I.
M
.
,
Id
ris,
N.R.
N.
,
A
la
m
ri,
Y.A
.
,
S
u
ti
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n
o
,
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.
,
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a
n
d
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.
,
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.
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m
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riso
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Esti
m
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ted
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o
rq
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e
s
Us
in
g
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o
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P
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ss
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il
ter
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n
d
Ex
ten
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e
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Ka
lma
n
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il
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Driv
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t
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ti
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.
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6
]
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.
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p
e
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o
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A
.
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z
i,
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.
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,
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n
d
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.
S
tas
i,
“
In
d
u
c
ti
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ra
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g
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-
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n
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KF
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b
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se
d
a
lg
o
rit
h
m
s,”
in
Pr
o
c
.
IEE
E
-
I
S
IE
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Ble
d
,
S
lo
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e
n
ia,
1
9
9
9
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l.
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,
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p
.
1
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4
4
–
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2
4
9
.
[1
7
]
K.
L
.
S
h
i,
T
.
F
.
Ch
a
n
,
Y.
K.
W
o
n
g
,
a
n
d
S
.
L
.
Ho
,
“
S
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e
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e
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si
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a
n
o
p
ti
m
ize
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e
x
ten
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e
d
Ka
lm
a
n
f
il
ter,”
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E
T
r
a
n
s.
I
n
d
.
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e
c
tro
n
.
,
v
o
l
.
4
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o
.
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p
.
1
2
4
–
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3
3
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e
b
.
2
0
0
2
.
[1
8
]
G
o
d
p
ro
m
e
ss
e
Ke
n
n
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e
,
T
a
r
e
k
A
h
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d
-
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li
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ra
n
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a
m
n
a
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Lag
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rrig
u
e
,
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n
d
Am
ir
A
r
z
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n
d
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e
,
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Re
a
l
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T
i
m
e
S
p
e
e
d
a
n
d
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lu
x
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d
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p
ti
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In
d
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c
ti
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o
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g
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r
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sist
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n
c
e
a
n
d
L
o
a
d
T
o
rq
u
e
,
”
IEE
E
T
ra
n
s.E
n
e
rg
y
C
o
n
v
e
rs
io
n
,
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l.
2
4
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o
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p
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9
,
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(2
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4
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.
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