In
te
r
n
ation
a
l Jou
rn
al
o
f Po
we
r
Elec
tron
ic
s an
d
D
r
ive S
y
stem
(IJ
PED
S
)
V
o
l.
10, N
o.
3, S
ep 2019,
pp.
1
1
4
8
~1
1
5
6
ISSN: 2088-
8694,
DOI
:
10.11591
/ijpeds.
v10.
i
3.pp1148-1156
1148
Jou
rn
a
l
h
o
me
pa
ge
:
ht
tp:
//i
a
e
score
.
com
/
j
o
u
r
na
l
s
/
i
n
d
e
x
.
p
hp/IJ
PED
S
Reduced-order observ
er for real-time imp
lementation
sp
eed
sensorless
control
of indu
ct
ion using RT-LA
B
software
Man
s
o
u
r
B
e
ch
ar
1
,
A
b
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e
l
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j
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ba
r
H
a
zz
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Mo
ha
med ha
b
b
a
b
3
,
Pi
er
re
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d
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1,
2
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Lab
orat
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of
Research
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tro
l
,
A
n
al
y
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is
and
Op
t
i
m
i
zati
o
n
o
f
E
lect
ro-En
e
rget
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y
s
t
e
m
s
,
Univers
i
ty o
f
T
a
hri
Moha
mmed,
A
lgeri
a
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G
r
ou
pe d
e Rec
h
erche en
El
ectro
ni
que Indus
t
rie
lle (GR
E
I) Unive
r
s
ité d
u Q
u
éb
ec
à
Tro
is
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v
i
è
res, Can
ada
Art
i
cl
e In
fo
ABSTRACT
A
r
tic
le hist
o
r
y
:
R
e
c
e
i
v
e
d
Dec
2
7
,
2
018
Re
vise
d F
e
b 7,
201
9
A
c
c
e
pte
d
A
pr 8,
201
9
In thi
s paper,
R
edu
ced-Ord
er
O
b
s
erv
e
r F
o
r
Real
-
T
im
e Implem
en
tat
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ontro
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o
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ndu
cti
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s
i
ng
RT-L
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S
oftw
areis
p
r
esent
ed.
S
p
eed
est
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m
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s
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rmed
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hro
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erver.
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ased
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u
ct
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r
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L
A
B
S
of
t
w
a
r
e
Co
pyri
gh
t © 2
019 In
stit
u
t
e
of Advanced
En
gi
neeri
n
g
an
d
S
c
ien
ce.
All
rights
res
e
rv
ed.
Corres
pon
d
i
n
g
Au
th
or:
Ma
nso
u
r Bec
h
ar,
Lab
o
ra
toire
de
Rec
he
rche
C
o
m
m
a
nde
, A
nal
y
se
e
t
O
p
t
i
m
i
z
a
t
i
o
n de
s
Systè
m
es
E
l
e
ctro-
éne
r
gé
ti
q
u
es,
U
n
i
v
ersi
té Ta
h
ri Mo
h
am
ed
d
e
Bec
h
ar,
BP
417,
B
e
c
har
(080
00),
A
L
G
ERIA
Em
ail:
ma
nso
u
r
b
e
cha
r
@
g
ma
i
l
.
c
om
1.
I
N
TR
OD
U
C
TI
O
N
In
p
o
w
e
r
e
l
ectro
ni
c
s
c
o
n
t
r
ol
o
f
A
C
m
a
c
hi
ne
d
riv
e
s
many
m
e
t
ho
d
s
em
p
l
oye
d
of
c
o
n
t
ro
l
in
v
ar
io
us
hi
gh
pe
rform
anc
e
i
nd
us
t
r
i
a
l
a
p
p
lic
a
tio
ns
[
1
]
.
This
h
a
s
b
e
e
n
c
o
n
ve
n
tio
n
a
ll
y
ac
h
i
e
v
e
d
by
usin
g
D
C
m
otors
w
ith
t
h
e
ir
s
imple
c
o
n
t
ro
l
s
t
r
u
ct
ure.
A
C
m
a
chi
n
es
a
r
e
g
e
n
er
ally
ine
x
pen
s
i
v
e,
c
ompa
c
t
a
nd
r
o
bus
t
w
i
t
h
l
ow
ma
int
e
na
nce
re
qu
i
r
em
ents c
o
m
par
e
d to
D
C
ma
chine
s
bu
t
r
equ
i
re
c
o
m
p
le
x c
o
n
t
r
o
l
[2].
Hi
gh
p
e
rfo
rman
c
e
sc
al
ar
c
ont
ro
l
of
i
ndu
c
tio
n
moto
r
me
t
h
o
d
req
u
i
re
s
pe
ed
o
r
po
si
tio
n
i
n
for
m
a
t
i
o
n
for
its
ope
ra
t
i
o
n
.
G
e
ner
a
l
l
y
speed
o
r
p
o
s
i
t
i
o
n
tra
n
sd
uc
ers
prov
ide
th
is
i
n
f
orm
a
ti
on.
H
ow
ever
t
hese
me
cha
n
ic
al
s
e
n
sor
s
a
r
e
c
o
s
tl
y
an
d
frag
ile.
On
t
he
o
t
h
er
h
and,
s
e
n
sor
l
e
s
s
dri
v
es
o
pe
rat
i
ng
w
i
t
h
ou
t
s
p
e
e
d
or
pos
it
io
n
t
r
a
n
sd
ucer
s
ha
ve
t
he
a
dva
n
t
a
g
e
of
r
e
d
uc
e
d
h
ar
dw
are
com
p
l
e
x
it
y
and
l
o
w
e
r
c
o
st,
reduc
ed
s
i
z
e
of
t
h
e
dri
v
e
ma
chi
n
e
,
e
lim
i
n
a
tio
n
of
t
he
s
ens
o
r
ca
b
l
e
,
b
e
t
t
e
r
no
i
s
e
im
mu
ni
t
y
,
in
c
r
e
a
s
ed
r
el
i
a
b
i
li
ty
,
a
n
d
l
e
ss
ma
int
e
na
nce
r
e
qu
irem
ents
[
3].
D
u
e
t
o
t
h
e
se
r
e
a
sons
s
pee
d
s
e
n
sor
l
e
s
s
s
ys
tem
s
,
i
n
w
hic
h
r
o
t
or
s
pee
d
m
e
a
s
u
r
e
m
e
n
t
s
a
r
e
n
o
t
a
v
a
i
l
a
b
l
e
,
a
r
e
p
r
e
f
e
r
r
e
d
a
n
d
f
i
n
d
a
p
p
l
i
c
a
t
i
ons
i
n
ma
ny
a
r
ea
s
for
sp
ee
d
reg
u
l
at
i
on,
l
oad
tor
que
r
ejec
t
i
o
n
a
nd s
p
ee
d tra
c
k
i
ng
pur
pose
s
.
Est
i
ma
tio
n
o
f
unme
a
sura
ble
stat
e
var
i
a
b
le
s
is
c
om
monl
y
ca
lle
d
o
b
s
erva
tio
n.
A
d
ev
ic
e
(or
a
com
p
u
t
e
r
p
rog
r
am) t
h
a
t
es
t
ima
t
es
or observe
s
the
s
tates
i
s
cal
l
e
d
a
st
a
t
e
-o
b
s
erve
r or
simp
l
y a
n
obser
ver.
If t
h
e
st
a
t
e-ob
ser
v
er
obse
r
ves
a
l
l
s
t
ate
varia
b
les
of
t
he
s
ys
tem
,
r
ega
rdl
e
ss
of
w
h
e
t
h
e
r
s
o
m
e
st
at
e
v
a
ri
abl
e
s
are
ava
ila
b
l
e
for
d
i
r
ect
m
e
a
sure
me
nt
,
i
t
i
s
c
a
l
l
ed
a
f
u
l
l-order
sta
te
-o
bserve
r
.
A
n
observe
r
t
h
a
t
e
stim
ate
s
f
ew
er
tha
n
t
he
d
im
ens
i
o
n
o
f
t
h
e
s
t
a
t
e-
ve
ct
or
i
s
ca
l
l
e
d
r
e
d
uce
d
-
o
rder
sta
t
e-
obse
r
ver
or
s
impl
y
a
re
duc
e
d
-or
d
er
obs
erve
r.
If
t
h
e
order
of
t
he
r
educe
d
-
o
rder
s
ta
t
e
-
obser
ve
r
is
t
he
m
in
i
m
um
possi
b
l
e,
t
he
o
bser
ver
is
call
e
d
minim
u
m-
orde
r
st
a
t
e-obser
v
e
r [4].
Evaluation Warning : The document was created with Spire.PDF for Python.
Int J
P
o
w
E
l
e
c
&
D
ri S
yst
IS
S
N
:
2088-
86
94
Red
u
c
e
d-o
r
der
obse
r
ver for re
al-
t
im
e
im
p
l
e
m
e
n
tat
i
on
s
p
ee
d sens
orle
ss co
nt
ro
l
…
(M
ans
our
Be
c
h
a
r
)
1
149
Ma
ny
r
esea
rch
e
rs
h
av
e
foc
u
s
e
d
on
t
he
d
e
s
ign
o
f
s
ens
o
rles
s
co
n
t
r
ol
a
l
g
o
r
i
t
h
m
s
f
o
r
i
n
duct
i
on
m
ot
o
r
.
Re
duce
d
-
o
rder
obse
r
ver
is
u
s
e
d
for
spe
e
d
e
stima
t
io
n,
one
o
f
i
t
s
d
isa
d
v
a
n
t
ag
e
s
i
s
th
e
s
e
ns
it
ivi
t
y
t
o
p
a
r
a
m
et
er
varia
t
i
o
n
e
s
pe
c
i
al
l
y
a
t
low
sp
ee
d
or
unde
r
l
o
ad
a
pp
l
i
c
a
t
i
on.
V
a
ri
o
u
s
m
et
h
odo
log
i
es
h
av
e
b
e
en
e
xpl
oi
t
e
d
f
o
r
spee
d
e
s
t
i
m
a
tion
:
A
dap
t
i
v
e
O
b
ser
v
ers
[5]
,
S
lid
i
ng
Mo
de
T
e
c
h
n
i
q
u
e
[
6
],
E
xt
e
nded
Kalm
an
F
i
l
ter
[
7
],
M
RAS
obs
erve
rs [8]
. F
igure
1 sh
ow
s a
ch
a
r
t of the
d
i
f
fere
nt spee
d
s
e
nsorless e
s
t
i
m
a
tio
n
stra
teg
i
es:
Figure
1.
S
pee
d
sens
o
rle
s
s
es
ti
m
a
t
i
o
n
strate
g
i
e
s
The
pro
b
lem
of
s
e
n
sor
l
ess
c
o
ntr
o
l
l
e
d
i
n
d
u
c
tio
n
m
o
tor
relat
e
d
to
t
he
s
ta
bi
lit
y
of
t
he
c
ontr
o
l
m
e
th
o
d
.
Th
is
i
s
us
ual
l
y
f
ac
ed
w
it
h
d
i
r
ect
f
ie
l
d
o
rie
n
ted
c
o
ntr
o
l
a
nd
d
i
rec
t
t
or
que
c
on
tro
l
s
tr
ate
g
ie
s,
c
om
bi
ne
d
w
ith
s
p
e
e
d
-
f
l
u
x
o
b
s
e
r
v
e
r
.
T
h
e
a
i
m
o
f
t
h
i
s
p
a
p
e
r
i
s
t
o
t
e
s
t
a
r
e
d
u
c
e
d
o
rder
obse
r
ver
for
speed
s
e
n
so
rl
es
s
co
ntro
l
of
IM
a
nd
i
t
s
sta
b
il
ity
ove
r
a
w
i
de
s
pe
ed
r
a
nge.
F
o
r
the
d
e
t
a
ile
d
sta
b
i
l
i
t
y
a
n
a
l
ysis
t
he
r
e
a
de
r
coul
d
refer
to
[9-
1
2]
.
In
t
hi
s
re
se
arch
p
ap
e
r
,
re
d
u
c
e
d
-o
rd
er
o
bs
erv
e
r
fo
r
sp
ee
d
se
n
s
o
r
l
e
ss
s
ca
l
a
r
co
nt
rol
of
i
ndu
ct
io
n
mot
o
r
h
a
s
b
een
d
e
s
ign
e
d
a
n
d
i
m
pl
e
m
en
t
e
d
in
r
eal-t
i
m
e
u
s
in
g
R
T
-LAB
p
a
c
ka
g
e
.
The
pr
op
ose
d
obser
ve
r
e
s
ti
m
a
tes
the
r
o
tor
spee
d
,
t
he
s
t
a
tor
cur
r
ents.
For
dev
e
lo
p
i
n
g
t
he
s
e
n
sor
less
c
o
n
t
ro
l
a
l
gor
ithm
,
m
ode
l
i
n
g
o
f
i
n
d
u
c
t
io
n
motor
i
s
p
re
sente
d
.
Lya
p
un
o
v
’s
s
ta
b
ili
ty
c
riter
i
o
n
i
s
em
p
l
oye
d
t
o
e
s
tim
a
t
e
ro
tor
s
p
ee
d.
T
he
e
x
p
er
im
ental
resul
t
s sh
ow
th
e
e
ffec
ti
vene
s
s
o
f the
pro
p
o
se
d
se
ns
orless co
ntr
ol m
eth
o
d
b
a
s
ed o
n
a
reduce
d
-orde
r
obse
r
ver.
2.
INDUCTIO
N MOT
O
R
MODE
L
The
d
ynam
i
c
m
odel
of
i
n
d
u
c
ti
o
n
m
ot
or
i
n
sta
tio
nar
y
r
ef
ere
n
ce
fr
a
m
e
α,
β
m
ay
b
e
wri
t
t
e
n
as
g
ive
n
[1
3-
1
5
]
:
rβ
rα
rβ
rα
r
r
φ
r
L
r
R
wr.
isβ
r
L
r
.R
m
L
dt
rβ
dφ
wr.φ
φ
r
L
r
R
isα
r
L
r
.R
m
L
dt
rα
dφ
Vsβ
s
σL
1
rβ
φ
2
r
L
s
σL
r
.R
m
L
wr.
Lr
s
σL
Lm
sβ
i
r
.R
2
r
L
m
L
s
R
s
σL
1
dt
sβ
di
Vsα
s
σL
1
wr.
Lr
s
σL
Lm
rα
φ
2
r
L
s
σL
r
.R
m
L
sα
i
r
.R
2
r
L
m
L
s
R
s
σL
1
dt
sα
di
(
1
)
wher
e :
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN: 2088-
8694
Int J
P
o
w
El
e
c
&
D
ri S
yst
,
V
ol.
10,
N
o.
3
, S
e
p
2
0
1
9
:
114
8
– 1
156
1
150
m
L
,
r
L
,
s
L
are m
a
gne
t
i
z
i
n
g
,
rotor
se
lf-
l
ea
kage
a
n
d
sta
t
o
r
self-lea
kag
e
i
nd
uc
t
a
nc
e
s
,
r
R
,
s
R
are r
o
t
o
r
and
st
a
t
or re
s
i
s
ta
nc
es,
σ
i
s
l
e
a
ka
ge
c
oeffic
ie
n
t
s
L
.
2
m
L
1
r
L
,
The
elec
t
r
oma
g
ne
t
i
c
tor
que
c
an
b
e
expre
s
se
d by
:
)
.i
φ
.i
.(
φ
2.Lr
3.P.Lm
T
sα
rβ
sβ
rα
e
(
2
)
3.
DESIGN OF
REDUCED-
ORDER OBSERVER
I
n
t
h
i
s
pa
pe
r
t
h
e
r
o
tor
speed
a
nd
t
h
e
sta
t
o
r
c
ur
rents
s
i
ˆ
,
s
i
ˆ
w
e
re
e
st
im
ated
u
si
ng
re
duc
ed-
o
r
d
e
r
obs
erve
r,
t
he
state
e
qua
t
i
on o
f
t
he in
d
u
ct
i
o
n m
achine
is gi
v
en
as f
o
llo
ws:
)
ˆ
(
ˆ
ˆ
Y
Y
K
BU
X
A
X
(3)
Tr
w
w
Tr
A
1
ˆ
ˆ
1
;
Tr
m
L
Tr
m
L
B
0
0
;
1
2
2
1
k
k
k
k
K
w
h
er
e A
is the
e
stim
ated
v
a
l
u
e
a
nd K
is
t
he obs
e
r
ver
gai
n
m
atri
x
w
i
t
h
:
r
ˆ
r
ˆ
ˆ
X
s
s
i
i
Y
s
s
V
V
U
s
s
i
ˆ
ˆ
ˆ
i
Y
The
est
i
m
a
t
i
o
n
e
r
ror
can
b
e
e
xpre
s
se
d as
f
o
l
l
o
w
s
:
T
r
r
A
e
KB
A
e
)
(
ˆ
(4)
T
e
e
e
r
r
wh
ere
A
A
A
ˆ
The
fo
l
l
ow
in
g
Lya
p
u
n
ov func
t
i
o
n
is de
fi
ne
d:
2
)
ˆ
.(
.
w
w
l
e
T
e
V
(5)
w
h
er
e l
i
s
a
posi
tive
c
onst
a
n
t
.
The
o
b
se
rver
gain
ma
t
r
ix
K
is c
hose
n
such
th
a
t
the
d
er
iva
t
iv
e o
f a
posit
iv
e
defi
ni
t
e
Lya
pu
no
v
func
t
i
o
n
V
bec
o
me
s
ne
gat
i
ve
d
e
f
in
i
t
e
a
s
exp
la
ine
d
i
n
[1
6].
The
rot
o
r
speed
o
f i
n
duc
t
i
o
n
m
oto
r
c
an be
est
i
ma
ted b
y
P
I con
t
r
oll
e
r:
r
r
r
r
e
e
s
Ki
Kp
w
ˆ
ˆ
*
ˆ
(6)
w
h
er
e
K
p
a
n
d
K
i
a
re
t
he
p
rop
o
rt
i
o
n
a
l
a
n
d
inte
gr
al
c
ons
t
a
nt
s,
r
e
spe
c
t
i
v
e
l
y
.
T
h
e
bl
oc
k
dia
g
r
a
m
of
s
p
e
e
d
est
i
ma
t
i
o
n
of i
n
d
u
c
tio
n
mot
o
r
ca
n be
c
ons
ide
r
e
d
a
s
show
n
i
n
F
ig
ure
2.
Evaluation Warning : The document was created with Spire.PDF for Python.
Int J
P
o
w
E
l
e
c
&
D
ri S
yst
IS
S
N
:
2088-
86
94
Red
u
c
e
d-o
r
der
obse
r
ver for re
al-
t
im
e
im
p
l
e
m
e
n
tat
i
on
s
p
ee
d sens
orle
ss co
nt
ro
l
…
(M
ans
our
Be
c
h
a
r
)
1
151
F
i
gure
2.
Bloc
k
dia
gra
m
o
f r
e
duce
d
-orde
r
o
b
s
er
v
e
r
4.
SCALAR
C
O
N
T
R
OL S
CH
EME
D
u
e
to
i
t
s
s
impl
ic
it
y,
s
ca
la
r
c
o
n
t
ro
l
is
one
o
f
the
m
o
st
c
om
m
o
n
ly
u
sed
m
e
t
hod
s
in
i
ndu
st
ry
m
ac
hin
e
d
r
i
v
e
s
.
H
o
w
e
v
e
r
,
i
t
s
d
y
n
a
m
i
c
p
e
r
f
o
r
m
a
n
c
e
i
s
l
i
m
i
t
e
d
,
e
v
e
n
i
n
c
l
ose
d
l
o
o
p,
p
art
i
cu
lar
l
y
w
h
e
n
ope
ra
ti
n
g
i
n
regi
ons o
f l
o
w
spee
d [1
7]
.
The
es
senc
e
o
f
c
on
tro
l
t
o
ma
in
t
a
i
n
a
c
o
n
st
ant
sc
a
l
ar
V
olta
ge
/Fr
e
que
nc
y
r
a
t
i
on
(V
/f)
i
n
o
r
d
er
t
o
ma
int
a
in
t
he
m
agne
t
i
c
fl
u
x
i
n
a
i
r
-
ga
p
c
o
n
s
ta
nt
a
t
m
a
x
i
m
u
m
val
u
e
.
If
t
h
e
v
o
l
ta
ge
d
oes
no
t
ha
ve
a
p
r
o
per
rela
tio
ns
hip
w
i
th
t
he
freq
u
e
n
cy.
The
ma
ch
i
n
e
ca
n
opera
te
i
n
the
s
a
t
u
r
a
tio
n
o
r
f
i
e
ld
w
e
a
k
eni
n
g
re
gi
on
[18
]
.
The
elec
trom
a
gne
tic
f
lu
x
prod
uc
e
d
c
an
b
e
ca
lcu
l
a
t
ed
b
y
usi
n
g
t
h
e
re
la
t
i
o
n
s
h
ip
b
e
t
w
e
e
n
t
he
v
o
lta
g
e
a
nd
elec
tr
oma
gne
t
i
c
fl
u
x
,
expr
ess
e
d a
s
:
)
.
.
(
1
))
.
.
.
(
1
(
)
.
.
.
(
*
*
2
2
2
Tr
r
Ls
s
Rs
Tr
r
Tr
r
Ls
s
Rs
s
s
Vs
(
7
)
C
l
ose
d
l
oo
p
c
o
n
t
ro
l
of
t
he
s
pe
ed
o
f
a
n
A
C
in
duc
t
i
o
n
m
ot
or
c
a
n
b
e
imp
l
e
m
e
n
t
a
e
d
b
ased
o
n
the
con
s
ta
n
t
V
ol
ta
ge/F
req
u
e
n
c
y
ra
tio
n
(V
/f)
prin
cip
l
e [
19].
I
ndee
d
,
in
p
ra
c
t
i
c
e,
w
e
are
usu
a
l
l
y
sa
t
i
s
f
i
e
d
w
ith
a
s
im
pl
i
f
ie
d
c
on
t
r
ol
l
a
w
,
c
o
rre
spo
n
d
i
ng
t
o
t
he
neg
l
ige
n
ce
o
f
the
ohm
ic dr
o
p
(Rs=
0
) i
n
(
22),
t
o
g
ive u
s
:
s
s
s
V
*
(
8
)
H
e
nce
t
h
e
re
lat
i
o
n
s
h
ip
v
o
l
tag
e
a
nd
fre
que
nc
y,
by
ma
i
n
ta
ini
ng
t
h
e
con
s
t
a
nt
s
t
a
to
r
f
l
ux
.
The
p
r
i
n
cip
a
l
schem
e
o
f
s
p
e
e
d
s
en
sorle
ss
con
t
ro
l
of
i
n
d
u
ct
i
on
m
o
t
o
r
ba
se
d
o
n
a
re
duce
d
-
o
rder
o
bser
v
e
r
is
s
h
o
w
n
i
n
F
i
gure
3
.
F
i
gure
3.
B
l
o
c
k
d
i
a
gram
of spee
d se
nsor
les
s
c
on
t
r
ol
o
f i
n
d
u
ct
i
o
n m
o
t
o
r b
a
se
d o
n
a
re
duce
d
-order
obse
r
ve
r.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN: 2088-
8694
Int J
P
o
w
El
e
c
&
D
ri S
yst
,
V
ol.
10,
N
o.
3
, S
e
p
2
0
1
9
:
114
8
– 1
156
1
152
5.
DESCRIPTIO
N
O
F
REA
L
-
T
I
M
E
S
IM
ULATI
O
N
AND
S
E
TUP
LABORAT
OR
Y
The
s
i
m
u
l
a
ti
o
n
o
f
sens
orles
s
c
o
nt
r
o
l
al
g
o
ri
thm
is
e
xec
u
te
d
on
O
P5
60
0
re
al
-t
i
m
e
d
i
gi
ta
l
si
mul
a
to
r
us
i
n
g one of
a
dua
l qu
ad-c
ore
com
p
u
t
e
r
. Tab
l
e 1, S
um
m
a
rize
s
t
h
e
char
a
c
t
e
r
ist
i
cs of t
h
e R
T
-LA
B
s
ys
t
e
m
use
d
in thi
s
research paper.
Table
1. RT-LAB simulator charac
t
e
ristics
Item
s
Qua
n
tit
y
De
s
c
ripti
o
n
Op
er
at
in
g
s
y
s
t
e
m
1
Red
h
a
t
C
h
a
ssis
1
O
P
5
600
C
h
a
i
ssis
(
O
P5142
)
CPU
1
1*(
4
c
o
re
s
2.
4 G
H
Z
)
Me
m
o
ry
1
4G
B
M
o
the
r
boa
r
d
1
X8
DTL
-
I
-
O
O
P
5340
A
i
n
1
16C
h
O
P
5330
A
out
1
16C
h
O
P
5353
D
i
n
1
32C
h
O
P
5354
D
out
1
32C
h
RT-LA
B
s
im
u
l
a
t
or
a
l
s
o
i
s
e
q
u
i
p
ped
w
ith
X
i
l
i
n
x
S
p
arta
n
3
pr
ogr
am
ma
ble
F
P
GA
c
a
r
d.
T
he
F
P
GA
ca
rd
can
b
e
pr
ogram
m
e
d
with
X
il
i
nx
sys
t
e
m
g
ener
ator
b
l
o
ck
set
fo
r
S
i
m
u
l
i
nk
e
n
ab
l
i
ng
i
mple
me
n
t
a
t
i
on
of
com
p
le
x se
n
s
o
r
m
od
e
l
s
l
i
ke
re
s
o
l
ve
rs or
eve
n
com
ple
x
m
ot
or dr
i
v
e
s
[20,
2
1].
The
de
vel
o
pm
ent pr
oc
ess of
t
h
e
in
t
e
g
ra
t
e
d S
i
m
u
l
i
n
k
m
ode
l
inc
l
ud
e f
o
l
l
ow
i
ng s
t
eps
:
-Con
s
t
ruct
b
l
o
ck
d
iagra
m
m
odels
o
f
the
inte
gra
t
e
d
s
ens
o
r
l
e
s
s
co
n
t
r
o
l
a
l
g
o
r
ithm
uti
l
i
z
e
Mat
l
ab/Simul
i
n
k,
a
nd
th
e
n
verify
fea
s
ib
ilit
y of
t
he
a
lgorithm
throug
h offl
ine simul
a
tio
n
.
-Cov
e
r
t S
i
m
u
l
i
nk m
odels
i
n
t
o RT-LA
B
com
p
a
tible
mode
l
s
, based
o
n
R
T-LA
B m
odel design specifica
tio
n.
-
U
s
e
(
R
TW)
a
n
d
m
ode
l
separa
t
i
o
n
t
o
ge
nera
t
e
r
e
a
l-ti
m
e
C
c
ode,
a
nd
u
p
l
o
a
d
ed
t
he
C
c
o
d
e
i
nto
O
P
560
0
di
gital
si
m
u
lat
o
r
to perform rea
l
-
t
i
m
e
simulatio
n.
- Exe
c
uti
ng t
h
e mode
l
by
l
au
n
c
hi
n
g
t
he
rea
l-t
i
m
e
S
imulation
on
all the node
s (
p
arallel e
x
e
c
ut
i
o
n).
The
RT-
L
A
B
p
l
a
tform
i
s
c
o
m
pose
d
o
f
one
h
ost
P
C
a
nd
o
n
e
rea
l
-t
i
m
e
ta
rget
c
om
pu
ter
a
s
s
how
n
in
F
i
gure
4.
T
he
n
etw
o
rk
c
o
nne
cti
o
n
T
C
P
/
I
P
p
r
o
toc
o
l
is
u
se
d
for
co
m
m
unic
a
tio
n
be
tw
een
c
ompu
ter
s
;
t
h
e
host
P
C
c
o
n
tr
o
l
s
s
i
m
u
l
a
ti
on
t
hro
u
g
h
RT-LA
B
c
ons
ole
s
u
bsy
s
t
e
m
.
T
he
m
a
st
e
r
s
ubs
yste
m
mode
l
o
f
s
e
n
s
o
rle
s
s
con
t
ro
l a
l
gor
it
hm
is c
onve
r
t
e
d
in
t
o C c
ode
u
si
ng Ma
t
l
ab
rea
l
-
ti
m
e
W
orks
ho
p (
R
TW
) fac
i
l
ity,
th
is ge
n
er
ated
C
code
i
s
dow
nl
o
a
de
d
t
o
t
ar
ge
t
com
p
u
t
er
v
ia
E
the
r
net
l
i
n
k
[
22].
S
ubs
yst
e
m
conso
l
e
ru
ns
i
n
t
h
e
h
o
st
P
C
use
d
t
o
di
sp
la
y
the r
eal
-tim
e se
nsor
le
ss
con
t
rol a
l
g
o
ri
thm
re
sul
t
s.
F
i
gure
4.
S
tructure
of t
h
e R
T
-LA
B
s
im
u
l
at
or w
ith t
he
a
t
t
ach
e
d
r
ea
l
-
p
l
ant
.
Th
e
e
x
p
e
ri
me
nt
a
l
s
et
up
o
f
the
rea
l
-t
i
m
e
senso
r
l
e
ss
c
ont
rol
o
f
i
n
duc
tio
n
motor
base
d
o
n
a
r
e
d
u
c
e
d
order
o
b
se
rver
i
s
sh
ow
n
i
n
F
i
gure
5.
T
he
e
x
p
erim
en
tal
t
e
s
t
h
as
bee
n
c
ar
rie
d
o
ut
t
o
ve
r
i
fy
t
h
e
e
ffec
t
i
v
e
n
ess
o
f
the
pro
pose
d
r
educ
e
d
o
r
d
er
o
bse
r
ver,
a
3
-ph
in
duc
t
i
o
n
m
o
t
or
f
ed
b
y
a
t
h
ree
pha
se
D
riv
e
lab
B
o
ard
i
n
ve
rter
i
s
cho
s
en.
The
sw
it
c
h
in
g
fre
q
u
e
ncy
w
a
s se
t
at
19kH
z
,
w
hil
e
t
he
tim
e-ste
p
w
as fixe
d
to 1
0
0
μ
s
.
Evaluation Warning : The document was created with Spire.PDF for Python.
Int J
P
o
w
E
l
e
c
&
D
ri S
yst
IS
S
N
:
2088-
86
94
Red
u
c
e
d-o
r
der
obse
r
ver for re
al-
t
im
e
im
p
l
e
m
e
n
tat
i
on
s
p
ee
d sens
orle
ss co
nt
ro
l
…
(M
ans
our
Be
c
h
a
r
)
1
153
F
i
gur
e 5.
Expe
r
i
m
e
nta
l
s
et
up
of
R
T-
LA
B
pla
t
form
at
CA
O
S
EE
Labor
a
t
ory.
6.
EXPE
RIMENTAL
R
ESULT
S
The
re
al-
time
im
plime
n
t
a
tio
n
of
r
ed
uc
ed-
o
rde
r
o
bser
ver
r
e
su
lt
s,
h
ave
be
en
obta
i
ne
d
b
y
us
ing
G
W
-
Inste
k
num
er
ic
al
o
sc
i
l
l
o
sc
op
e
w
i
ch
w
as
l
i
nke
d
w
i
t
h
t
he
r
e
a
l-tim
e
a
n
al
og
o
u
t
pu
t
s
i
nt
erf
a
ce
s.
F
i
g
u
r
e
6
(
a
)
d
e
p
i
c
t
s
t
h
e
a
c
t
u
a
l
m
o
t
o
r
s
p
e
e
d
a
n
d
t
h
e
e
s
t
i
m
a
t
e
d
s
p
e
e
d
,
w
h
i
l
e
F
i
g
ur
e
6(b)
s
h
o
w
s
t
h
e
z
oome
d
v
ersio
n
o
f
F
i
gure
6(a
)
.
I
t
c
a
n
b
e
n
o
t
i
c
e
d
t
h
a
t
t
h
e
e
s
t
i
m
a
t
e
d
s
p
e
e
d
f
o
l
l
o
w
s
t
h
e
r
e
a
l
s
p
e
e
d
a
t
d
i
f
f
eren
t
p
o
i
n
t
s
o
f
s
p
eed
r
a
nge
(23
88,
2
8
66,
3
34
4,
1
91
0rpm
)
t
h
e
est
i
m
a
t
i
o
n
e
rror
c
onver
g
es
t
o
z
er
o.
I
t’s
clea
r
tha
t
t
he
d
rive
s
ys
t
e
m
w
o
r
k
s
a
t
a
w
i
de
r
a
nge
o
f
spee
ds,
t
o
r
e
v
ea
l
t
h
e
e
ffe
c
t
ive
n
e
ss
of
t
he
p
rop
ose
d
r
e
duc
e
d
o
r
d
er
o
bse
r
v
e
r
four
s
te
ps
c
han
g
e
app
l
ied
t
o
s
pe
ed
r
efere
n
ce,
t
he
m
ode
l
wa
s
com
p
i
l
e
d
a
n
d
e
xc
u
t
e
d
w
i
th
s
a
m
pli
n
g
t
i
me
o
f
50
µ
s
o
n
O
P
560
0
rea
l
-ti
m
e
dig
i
t
a
l
s
i
mu
lat
o
r,
t
he
opera
tio
n
w
i
t
h
c
ha
nge
i
n
refer
en
c
e
sp
eed
i
s
t
h
e
ma
in
f
oc
u
s
o
f
t
h
i
s
p
ape
r
a
n
d
the
gre
a
t
cha
l
l
e
nge
f
or
e
st
i
m
ati
o
n
a
l
g
o
ri
t
h
ms
d
e
s
i
g
ned
f
o
r
t
h
e
s
p
e
e
d-s
e
ns
orless
c
o
nt
r
o
l
o
f
i
nd
uc
ti
on
m
ot
ors.
F
i
gure
6(
a)
i
n
d
i
c
a
t
es
t
he
g
o
od
trac
kin
g
c
h
a
r
acte
r
ist
i
cs
a
n
d
m
or
e
effec
t
i
v
ene
s
s,
t
he
e
st
im
ated
s
pe
e
d
a
lway
s
fo
l
l
ow
e
d
t
he
r
efere
n
ce.
T
he
s
te
p
c
h
an
ge
o
f
t
h
e
re
fer
e
nce
sp
e
e
d
doesn’t
a
ffect
t
he
p
erfor
m
ance
of
t
he
s
ys
tem.
The
a
dva
n
t
a
g
e
of
t
h
i
s
w
o
rk
i
n
t
h
a
t
t
he
r
e
d
uce
d
o
rder
o
bse
r
ver
i
s
s
i
m
p
l
e
t
o
i
m
p
l
em
ente
i
n
re
al-
tim
e
an
d
d
o
e
sn
’t
c
ont
a
i
n
c
o
mp
l
e
x
e
c
al
cu
l
a
t
i
o
n
s
a
nd
h
as
g
ood
p
erf
o
rma
n
ce
s
c
om
p
a
r
e
d
t
o
a
no
t
h
er
f
u
l
l
or
der
o
b
s
e
r
v
ers
tha
t
re
q
u
i
r
e
s more
ca
l
cu
lat
i
on
t
i
m
e
and
not
e
asy
to
i
m
p
lem
e
nt t
h
o
se
f
u
l
l
order
observe
r in
r
ea
l
-
t
i
m
e
.
(a)
(b
)
F
i
g
u
r
e
6
. S
pee
d
c
ur
v
e
s,
(
a)
T
he
r
efere
n
ce,
actua
l a
n
d est
i
ma
ted
s
pe
ed w
i
t
h
e
st
im
atio
n
err
o
r,
(
b)
Z
oome
d
ver
s
i
on o
f
(a
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN: 2088-
8694
Int J
P
o
w
El
e
c
&
D
ri S
yst
,
V
ol.
10,
N
o.
3
, S
e
p
2
0
1
9
:
114
8
– 1
156
1
154
F
i
gur
e 7.
S
tato
r c
u
rrents: isa,
is
b,
i
sc
[A
]
A
s
s
how
n
i
n
F
igure
8
the
e
s
tim
ated
s
ta
t
o
r
c
u
rre
nt
c
om
p
one
n
t
s
c
o
n
v
er
ge
t
o
t
h
e
re
al
s
ta
t
o
r
c
u
rrent
com
p
o
n
e
n
t
s
w
ith sma
l
l
phas
e
s
hif
t
,
it’s c
l
e
a
r tha
t
t
he
w
avef
orm
s
of
c
urre
nts
are
sinus
o
i
d.
F
i
gure
8.
Real
a
nd e
s
tim
ate
d
c
ompo
ne
nts
of stat
o
r
curr
ent i
n
th
e
fixed fram
e
(α
,
β)
F
r
om
t
he
a
bo
ve
e
x
p
erim
en
t
a
l
re
su
lt
s
w
e
c
onc
l
ude
d
th
at
t
ha
t
the
sensor
less
control
schem
e
a
sso
ci
a
t
ed
w
it
h
re
du
ced
-o
rd
e
r
o
b
s
erv
e
r
h
a
s
a
fast
r
e
s
po
nse
t
i
m
e
a
nd
goo
d
est
i
ma
tio
n
a
c
c
u
rac
y
o
v
e
r
a
wid
e
spee
d
range
.
7.
CONCL
U
S
ION
I
n
t
h
i
s
pa
per,
a
r
educ
ed-or
d
e
r
obse
r
ver
for
spe
e
d
s
ens
o
rle
ss
sa
ca
l
a
r
con
t
rol
o
f
i
nd
uc
ti
o
n
m
ot
or
i
s
prese
n
t
e
d
a
nd
im
plem
e
n
te
d
i
n
r
e
a
l-t
i
me
.
The
drive
sys
t
em
w
ith
t
he
p
r
o
pose
d
o
bserve
r
is
b
u
ilt
o
f
f
line
usin
g
Mat
l
a
b/
Si
mu
lin
k
bl
o
c
kse
t
s
an
d
e
x
ecut
e
d
in
r
e
a
l
-
t
i
me
u
si
ng
R
T-LA
B
pa
c
k
age
an
d
a
n
O
P
560
0
targe
t
.
D
i
g
i
ta
l
si
m
u
lat
i
ons
a
n
d
e
xperim
e
nta
l
s
e
t
u
p
h
a
v
e
bee
n
car
ried
o
u
t
i
n
ord
er
t
o
val
i
d
ate
t
h
e
pro
p
o
s
ed
s
en
sorle
s
s
dia
g
ra
m.
T
he
e
xper
i
m
e
nta
l
r
e
s
u
lts
s
how
t
ha
t
t
h
e
d
r
ive
sy
ste
m
w
o
rks
at
a
w
ide
range
o
f
speeds,
t
hi
s
indi
cates
the g
o
od
acc
ur
ac
y and
ro
bus
tne
s
s
of
t
he
des
i
gne
d
obse
r
ver
.
APPENDIX
INDUC
T
I
ON
M
O
T
OR PARAM
E
T
ERS
The
pa
ram
e
ter
s
o
f
t
h
e
thre
e-
ph
a
s
e
Ind
u
ct
io
n
m
o
tor,
e
m
p
l
oye
d
for
r
e
a
l-time
im
pl
e
m
e
n
ta
t
i
o
n
,
in
S
I
un
its
a
r
e
:
120
W
,
1
33H
z,
P
=
2
,
Rs=
1
.0
5
oh
m
,
R
r=
1.
7
0
5
ohm
,
Ls=
0
.02
939
H
,
Lr=0
.0
293
9
H,
L
m=
0
.
02
526
H
,
J=0.
00
03
7
K
g
.m
^2, fr=0.0
0
0
06 S
I
.
Evaluation Warning : The document was created with Spire.PDF for Python.
Int J
P
o
w
E
l
e
c
&
D
ri S
yst
IS
S
N
:
2088-
86
94
Red
u
c
e
d-o
r
der
obse
r
ver for re
al-
t
im
e
im
p
l
e
m
e
n
tat
i
on
s
p
ee
d sens
orle
ss co
nt
ro
l
…
(M
ans
our
Be
c
h
a
r
)
1
155
REFE
RENCES
[1
]
A.
G
ouich
ich
e
,
M
.
S
.
Bo
uch
e
rit,
A
.
S
a
f
a
,
Y.
M
ess
l
em,
“Sen
so
rles
sS
li
d
i
ng
M
od
e
V
ecto
r
C
on
tro
l
o
f
In
du
cti
on
M
o
t
o
r
Driv
es”
Inter
n
a
t
io
na
l Jo
ur
na
l of
P
o
wer
El
ectro
nics a
n
d
D
r
ive S
y
st
ems (
I
J
P
ED
S
)
, Vo
l
.
2,
No
. 3
, 20
1
2
.
[2
]
J.
W
.
F
i
n
c
h
a
n
d
D.
G
ia
o
u
ris,
“
C
ont
ro
lled
AC
E
l
ectrical
D
ri
v
e
s,
”
IE
EE
Transa
c
ti
on
s
on
Indust
r
i
a
l
Elect
ro
n
i
cs
,
v
o
l
.
55
,
n
o
1
,
p
p
.
1
-11
, Feb
. 20
0
8
.
[3
]
Joach
im
H
o
ltz,
“
S
ens
o
rles
s
Con
t
rol
of
I
nd
uction
M
o
tor
Dri
v
es,
”
p
r
oceed
in
gs of
t
h
e
IE
EE
,
v
o
l.
90,
no.
8
,
aug
u
st
200
2.
[4
]
Bila
l
Ak
i
n
,
“S
tate
E
st
imat
i
o
n
Te
c
h
ni
qu
es
f
o
r
S
p
eed
S
ens
o
rles
s
F
iel
d
O
riented
Co
ntro
l
o
f
I
nd
uc
ti
o
n
M
o
t
ors,
”
thesis,
The
M
i
dd
le E
as
t Tech
ni
ca
l
U
n
iversi
ty, Au
gu
s
t
2
0
0
3
.
[5
]
H.
K
ub
o
t
a
an
d
K.
M
at
suse,
“S
peed
s
en
so
rless
f
i
e
l
d
-
orien
t
ed
c
o
n
t
ro
l
o
f
i
nduct
i
o
n
m
oto
r
w
it
h
ro
to
r
resist
a
n
c
e
adapt
a
ti
on
,”
I
E
EE Tr
ans
.
Ind
.
Ap
pl
., v
o
l
. 3
0,
n
o.
5
, pp
.
1
2
1
9
–1
22
4
, Sep
. 1
99
4.
[6
]
T.
C
hern
,
J.
C
h
a
ng
,
and
K
.
T
s
a
i,
“
In
tegral
-vari
a
ble-s
t
ru
cture-c
o
n
trol-b
ased
a
dapt
iv
e
s
p
eed
e
sti
m
a
t
o
r
a
n
d
r
es
ist
a
nc
e
ident
i
f
i
er
f
or
ind
uction motor,”
In
t. J.
Contr
o
l
, v
ol. 6
9,
n
o.
1,
pp
.
3
1
–
4
7
, 19
9
8
.
[7
]
Y.
K
im
,
S
.
S
u
l
,
and
M
.
P
ark,
“
Sp
eed
s
enso
rles
s
v
ector
c
o
n
t
r
ol
o
f
i
nduct
i
o
n
motor
using
extended
K
alman
filter,
”
IEE
E
Tran
s.
In
d
.
Ap
pl
.
,
vol.
3
0
,
n
o
.
5
,
p
p
.
1
22
5–123
3,
S
ep
.
1
9
9
4
.
[8
]
C.
S
ch
aud
e
r,
“
A
d
ap
ti
ve
s
p
eed
i
d
e
nt
ifi
cati
on
f
o
r
vecto
r
c
ont
rol
o
f
i
nd
uc
t
i
on
m
o
t
o
r
s
w
i
th
ou
t
ro
ta
t
i
o
n
a
l
tra
n
sd
uc
e
r
s,”
IEE
E
Tran
s.
In
d
.
Ap
pl
.
,
vol.
2
8
,
n
o
.
5
,
p
p
.
1
05
4–106
1,
S
ep
./
Oc
t.
1
9
9
2
.
[9
]
Y.
B
e
d
di
a
f
,
F
.
Z
i
d
an
i,
L
.
Chrifi-Al
oui,
“
M
odif
i
e
d
s
peed
s
en
so
rl
ess
i
ndirect
f
iel
d
-o
ri
ent
e
d
cont
rol
o
f
i
n
d
u
c
ti
on
m
o
tor
driv
e,”
In
t.
J.
Mo
del
l
in
g.
Iden
tifica
t
i
o
n
an
d
Co
n
t
ro
l
,
v
o
l
.
2
5
,
no.
4,
pp
.
27
3–
28
6,
201
6.
[1
0]
M.
M
ontan
ari,
S
erg
e
i
M
.
P
eresada,
“
S
p
eed
s
en
so
rless
con
t
rol o
f
i
n
d
u
cti
o
n
m
o
t
o
rs
b
as
ed on a
r
e
du
c
e
d
-ord
er
a
d
a
p
t
i
v
e
obs
erver,
”
I
E
E
E
Tran
s. Co
n
t
ro
l S
y
ste
m
s
Te
c
h
no
lo
gy
., vo
l
.
15
,
no
.
6
,
p
p
. 1
04
9–
10
6
4
, No
v
. 2
00
7
.
[1
1]
S
.
A
lireza
Dav
a
ri
,
D.
A
rab
Khab
uri
,
F
.
W
a
n
g
,
R.
M
.
Ken
n
el,
“Us
i
n
g
f
u
l
l
o
r
der
and
r
e
du
ce
d
o
r
der
o
b
s
e
rvers
f
o
r
robu
st
s
enso
rless
predi
c
ti
ve
c
o
n
trol
o
f
in
du
cti
on
m
o
t
o
rs
,
”
IE
EE Tra
n
s
.
P
o
wer
El
ectr
oni
cs
.,
vo
l.
2
7
,
n
o.
7
,
pp
.
3
4
2
4
–
343
3,
J
uly.
2
0
12.
[1
2]
A.
A
.
Z
.
D
iab
,
V
.
N. An
o
sov
, “I
m
ple
m
entat
i
o
n
o
f
f
u
ll
ord
e
r
ob
se
rv
er f
or
s
peed
sen
so
rless
v
ector
c
ont
rol
o
f
i
nd
uc
t
i
on
motor
dr
ive,”
15
th
In
tern
at
io
na
l
Con
f
eren
ce on
M
i
cro
/
Na
notechn
o
l
o
g
i
es a
n
d
Elect
ro
n Devi
ce
s
EDM
,
p
p
.
3
47
–3
52
,
201
4.
[1
3]
G.
T
archal
a,
T
.
Orl
o
wska-K
o
w
a
lsk
a
,
“Slidin
g
mod
e
s
peed
o
b
s
er
v
e
r
f
or
t
h
e
i
nd
u
c
t
i
on
m
oto
r
d
rive
w
ith
d
iff
e
ren
t
fu
nctio
n
ap
pro
x
imatio
n f
o
rm
s
an
d
g
a
in
a
dap
t
ati
o
n
,
”
Przeg
l
.
El
ektro
tech
-ni
c
z
n
y
,
v
o
l
8
9
,
pp
.
1-6,
201
3.
[1
4]
I.
B
end
aas,
F
.
N
aceri, “A
n
e
w
m
e
th
od
t
o
mi
n
i
m
i
ze
t
h
e
ch
att
e
ring
p
hen
o
m
e
no
n
i
n
s
l
i
ding
m
ode
c
o
n
t
r
ol
b
as
ed
o
n
int
e
lligent contr
o
l
f
o
r in
duction
motor
dri
v
es,”
S
e
r
b
. J.
Electr.
Eng
,
vol
1
0
,
pp.
231-2
4
6
,
J
une.
2013
.
[1
5]
M.
H
abb
a
b,
A
.
H
azza
b
,
P
.
Si
card,
“
Real-T
im
e
Im
pl
e
m
ent
a
tio
n
o
f
F
uzzy
A
dap
tive
P
I-S
li
d
i
ng
M
o
d
e
Co
nt
roll
er
f
or
Indu
cti
o
n
M
a
ch
in
e
Con
t
rol
”
,
Intern
at
io
na
l
Jou
r
n
a
l
of
Elect
ri
cal an
d Com
p
u
t
er
Eng
i
n
eeri
ng (IJ
E
C
E)
,
v
o
l
8,
n
o
5,
201
8.
[1
6]
Y.
B
edd
i
af,
F
.
Z
id
ani
,
L
arbi
.Chrifi.Alaou
i
a
nd
S
.
Dri
d
,
“
M
odif
i
ed
S
peed
S
ens
o
rl
e
ss
Ind
i
rect
F
i
e
ld
-O
rien
ted
Con
t
ro
l
of
I
ndu
cti
o
n
M
o
to
r
D
r
ive,
”
In
tern
at
io
nal Jou
r
nal M
o
d
elli
ng,
Id
en
t
i
ficatio
n an
d con
t
r
o
l
,
vo
l
25
,
n
o
4
,
p
p.
2
73
-
2
8
6
,
201
6.
[1
7]
M.
S
u
e
tak
e
,
I.N
.
S
ilv
a,
A
.
Go
edtel,
“
Em
bedd
ed
d
s
p
-b
ased
c
o
m
pact
f
u
zzy
s
y
s
t
e
m
an
d
it
s
ap
p
l
i
cation
f
o
r
in
ductio
n
-
mo
to
r
v/f
sp
eed
c
o
n
t
r
ol
,
”
IEE
E
Tr
an
sa
ctio
ns on
I
n
d
u
s
trial Elect
roni
cs,
vo
l 5
8
,
no
.
3
, pp
. 7
50
-7
60
,
2
0
1
1
.
[1
8]
B.K.
B
o
s
e, M
ode
rn
p
ower el
ectro
nics
and
A
C
drives,
P
r
en
t
i
ce-Hal
l,
N
ew
J
ersey
,
2001
.
[1
9]
K.
Vino
th
K
um
ar,
S.
S
u
res
h
K
u
m
ar,
Kis
h
o
r
e
Redd
y,
“
Im
p
l
e
m
ent
a
ti
on
o
f
S
calar
C
o
n
t
r
o
l
T
ech
niq
u
e
i
n
S
V
P
WM
S
w
it
ched
T
h
r
ee
–L
evel
I
nvert
e
r
F
ed
I
n
d
u
c
ti
on
M
o
to
r
Usin
g
D
S
P
C
o
ntrol
l
er,
”
In
te
rna
tio
na
l J
o
urna
l
o
f
Pow
e
r
Elect
ro
n
i
cs
a
n
d
Dr
ive S
y
s
t
em
(
I
JPEDS)
, vo
l
.1
, n
o
.2
, p
p
.
83~9
3
.
D
ecem
ber 2
0
1
1
.
[2
0]
C.
D
u
f
our,
J
.
Bé
l
a
nger,
V.
L
apoi
nt
e,
‘
‘
F
PG
A-Ba
sed
Ultra-Low
Lat
ency
H
IL
F
ault
Tes
t
i
ng
of
a
P
e
r
m
a
nen
t
M
agn
e
t
M
o
tor
Dr
ive
using
RT-
L
AB-
X
SG’’,
S
i
mula
ti
on: T
r
a
n
s
a
c
t
i
o
n
s
of
t
h
e
S
o
ci
ety for
M
o
d
e
lin
g an
d Si
mul
a
tio
n
Inter
n
a
t
i
onal,
S
A
GE
Pu
blications
,
vol.
8
4
,
i
s
s
u
e
2/
3
,
F
ebruray
/
March
20
08,
p
p
.
161-1
7
2
.
[2
1]
C.
D
u
f
ou
r,
J
.
Bé
lang
er,
S.
A
bou
rid
a
,
V.
L
apo
i
nte,
‘‘F
P
G
A-Bas
e
d
Re
al
-Time
S
i
mulation
of
F
i
n
i
t
e-
El
e
m
ent
Ana
l
ys
is
P
e
rm
an
ent
M
a
g
n
et
S
y
n
ch
ro
no
us
M
ach
in
e
D
r
iv
es’’,
Pr
oceed
ing
s
o
f
th
e 38
th
A
nnu
al
IEEE
Po
wer
El
e
c
t
r
o
n
i
c
s
Speci
al
it
s
Co
nference (
P
ES
C’07
)
,
O
rl
ando
,
Fl
orida,
U
SA,
Jun
e
17-21
,
2
01
7.
[2
2]
M.
B
ech
ar,
A
.
H
azz
ab,
M
.
H
abbab,
‘‘Real-T
im
e
S
c
a
l
ar
C
o
n
t
r
ol
o
f
Induction
Motor
usi
n
g
R
T
-
L
AB
S
o
f
tware,’’
The
5
th
Int
e
rnati
o
n
a
l
Con
f
er
e
n
ce on
Electrica
l
E
ngineer
in
g
(
I
CE
E-B)
,
Oct
ober.
2
0
17.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN: 2088-
8694
Int J
P
o
w
El
e
c
&
D
ri S
yst
,
V
ol.
10,
N
o.
3
, S
e
p
2
0
1
9
:
114
8
– 1
156
1
156
B
I
OGRAPHIES
O
F AUTHO
RS
M
a
n
s
ou
r
Be
ch
ar
w
as
b
o
r
n
in
B
e
c
h
a
r,
A
lg
eria.
He
r
e
ceiv
e
d
th
e
M
.
S
.
d
e
gree
in
e
l
ectrical
en
gi
neerin
g
f
r
o
m
T
a
h
ri
M
o
h
amm
e
d
Un
iv
ersity
i
n
20
15
.
Wh
ere
h
e
i
s
c
u
rrentl
y
w
orki
ng
t
o
w
a
rd
t
h
e
P
h
.
D
.
d
egree
in
e
lect
rical
e
ngi
neeri
ng.
H
is
r
esearch
i
nte
r
e
s
ts
i
n
c
lu
de
p
owe
r
el
ectron
i
cs
,
proces
s
con
t
rol.
O
b
s
erv
e
r
an
d
est
i
mato
r
d
e
si
gn
f
o
r
i
nduction
motor
drive
s
y
s
t
e
m
s,
renew
able
e
n
e
rgy
.
H
azzab A
bdel
d
j
e
bar recei
ved
his
S
t
at
e En
gi
neer, M.
S
.,
an
d P
h
.D
de
g
r
e
e
s
in
E
l
ect
rical
E
ng
i
n
eeri
n
g
f
r
o
m
t
he
E
lectri
cal
E
n
g
ineeri
ng
In
st
itut
e
of
T
h
e
U
nive
r
s
ity
o
f
S
c
i
e
nces
a
nd
T
echn
o
l
ogy
o
f
Oran
(
U
S
TO),
A
l
g
eria
i
n
1
9
95,
1
9
99,
and
20
06
,
res
p
ecti
v
el
y.
H
e
i
s
c
u
rrent
ly
a
P
rof
e
ss
or
o
f
E
l
ec
tri
cal
E
ngin
e
e
rin
g
a
t
the
Unive
r
sit
y
o
f
Bech
ar
(
A
l
g
e
ria),
w
h
ere
he
h
as
b
een
t
h
e
D
irect
or
o
f
the
Res
earc
h
Laborat
o
ry
o
f
Co
m
m
an
d,
A
na
ly
ses
,
a
nd
Op
timizat
ion
of
Elect
ro-E
nerget
ic
S
ystem
s
si
nce
2009.
His
res
earch
i
nt
erests
i
n
c
lud
e
p
ow
er
q
uali
ty,
mo
de
l
i
n
g
,
m
o
d
e
rn
c
on
t
roller
and
obs
e
r
v
er
d
e
si
gn
f
o
r
no
n
lin
ear
s
yst
e
m
s
,
con
t
ro
l
o
f
p
o
w
er
e
l
ectro
nics
a
n
d
mu
lt
id
r
i
ve
s
y
s
t
e
m
s
,
cont
rol
of
p
owe
r
e
lect
ronics,
multidr
i
ve
s
ystems
a
nd
e
lectri
cal
v
e
h
icl
e
,
and
a
d
apt
i
v
e
co
nt
rol
and
no
nl
in
ear
s
y
s
t
e
ms
d
iag
nos
ti
c,
a
d
a
pt
ive
con
t
rol
,
n
eu
ral
net
w
o
r
ks
a
nd
f
uzzy
l
o
g
i
c s
y
stem
s
.
M
o
ham
e
d
Hab
b
ab
w
as
b
o
r
n
in
196
9
at
R
el
izane-A
l
geri
a,
r
eceived
t
he
s
tate
e
ngineer
d
e
gree
i
n
e
l
ectron
i
c
en
gi
neerin
g
i
n
1
99
4
f
r
o
m
t
h
e
U
n
i
vers
it
y
o
f
S
ciences
a
nd
T
e
c
h
n
o
l
o
g
y
o
f
O
r
a
n
(
U
S
T
O
)
,
A
l
g
e
r
i
a
t
h
e
M
.
S
c
.
d
e
g
r
e
e
f
r
o
m
t
h
e
U
n
ivers
i
t
y
o
f
Bech
ar.
H
e
’s cu
rrentl
y
prepari
ng
hi
s
P
h
.
d.
d
eg
ree in
elect
roni
c eng
i
n
e
e
ring.
P
i
erre
S
icard
receiv
e
d
the
bach
elo
r
d
eg
ree
in
t
echn
o
lo
gy
o
f
ele
ct
rici
ty
f
rom
t
h
e
Ecol
e
d
e
T
echn
o
lo
gi
e
S
u
péri
e
u
re,
M
ont
réal
Q
C,
C
an
ada,
i
n
1
9
8
5
,
t
h
e
M
.
S
c
.
d
e
gree
in
i
nd
us
trial
el
ectron
i
cs
f
ro
m
th
e
U
n
i
v
ersi
ty
o
f
Q
u
ébec
in
T
roi
s
-Riv
ières
,
T
r
oi
s-Riv
i
ères,
Q
C
,
Can
a
da,
in
1
99
0,
a
n
d
t
he
P
h
.
D
.
degree
i
n
e
lectri
cal
e
ng
in
ee
ri
n
g
f
r
o
m
R
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ter
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w
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two
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