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
)
Vol.
10, No.
1, Mar
ch 2019,
pp.
83~92
IS
S
N
: 2088-
86
94,
D
O
I
:
10.11
5
9
1
/ij
ped
s
.
v10
.
i
1.pp
8
3
-9
2
83
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
Parameter estimation of
D
C
motor through w
hale
optimization algorithm
By
a
m
ak
esh
N
a
yak
, Sa
ng
ee
ta
Sa
h
u
Scho
o
l
o
f
E
l
e
c
t
r
i
cal
Eng
i
n
eering
, KIIT Un
i
v
e
rsi
t
y
,
Ind
i
a
Art
i
cl
e In
fo
ABSTRACT
A
r
tic
le hist
o
r
y
:
R
e
ce
i
v
e
d
Jul 3,
2018
Re
vise
d S
e
p 4,
201
8
Ac
ce
p
t
ed
No
v
1
0
,
2
018
T
h
i
s
a
r
t
i
c
l
e
e
s
t
i
m
a
t
e
s
t
h
e
u
n
k
n
o
w
n
d
c
m
o
t
o
r
p
a
r
a
m
e
t
e
r
s
b
y
a
d
a
p
t
in
g
t
h
e
adap
ti
ve
m
od
el
w
it
h
th
e
ref
e
rence
m
o
d
e
l
cr
eat
ed
b
y
exp
e
rim
e
ntal
d
ata
o
n
to
arm
a
tu
re
c
u
rrent
a
nd
s
p
e
ed
r
esp
o
n
s
e
f
r
om
s
eparatel
y
ex
cit
e
d
d
c
mo
to
r
.
T
h
e
fiel
d
flu
x
dyn
am
ics,
w
h
i
ch
i
s
u
s
ual
l
y
i
g
n
o
red,
i
s
in
c
l
ud
ed
t
o
m
od
e
l
t
he
dy
nam
i
cs
o
f
t
h
e
m
o
t
o
r.
T
he
b
l
o
ck
d
i
a
g
r
am
i
n
c
lud
i
n
g
t
he
f
lu
x
dyn
am
i
c
s
and
m
o
d
e
l
p
a
ram
e
t
e
rs
i
s
co
nsidered
a
s
t
h
e
ad
aptiv
e
m
o
del
.
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h
e
i
n
t
eg
ral
t
i
m
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sq
uare
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rror
b
e
tw
e
e
n
th
e
instant
ex
perimen
t
al
d
ata
an
d
t
h
e
corr
es
po
ndi
ng
adap
ti
ve
m
od
el
d
at
a
i
s
t
aken
a
s
c
o
st
f
unc
t
i
on.
T
he
W
h
a
le
o
p
timi
zati
on
algorithm
is
u
se
d
t
o
m
i
n
imize
t
h
e
cost
f
unct
i
on
.
Additi
ona
l
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y,
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o
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m
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o
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h
e
perf
o
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man
ces
o
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o
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n
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l
gorith
m
an
d
f
o
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acc
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resu
lt,
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h
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nt
al
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ata
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s
w
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i
c
h
f
o
rm
t
h
e
th
ree
in
equ
a
lit
y
c
on
st
raint
s
.
A
f
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xed
pe
n
a
lt
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valu
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a
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d
ed
t
o
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t
f
u
n
c
ti
o
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f
or
vi
ol
a
t
i
ng
th
ese
co
ns
traints.
T
he
e
ffect
iv
eness
of
e
stim
a
t
io
n
w
i
t
h
t
w
o
d
i
f
f
e
r
e
n
t
m
e
th
ods is v
a
li
d
a
ted
b
y
co
nverg
ence cu
rv
e.
K
eyw
ord
:
Cost f
unct
ion
F
l
ux d
y
n
a
m
ics
mode
l
Integral ti
m
e
s
quare error
S
e
para
t
e
l
y
e
xc
ite
d D
C
m
otor
W
h
al
e op
ti
mi
zat
ion
al
g
o
r
i
t
h
m
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:
S
a
ngeeta
S
a
hu,
S
c
hoo
l
o
f
Ele
c
t
rica
l
En
gine
erin
g,
KIIT University,
Ca
mp
us
–
3,
Pa
ti
a
,
B
hu
ba
ne
sw
a
r
, O
d
is
ha,
Ind
i
a.
Em
ail:
sa
h
u
.sange
e
t
a
@
gm
ai
l.com
1.
I
N
TR
OD
U
C
TI
O
N
F
o
r
desi
g
n
in
g
a
pro
p
er
c
o
n
t
r
oller
to
a
c
h
iev
e
t
he
s
pec
i
fic
re
sp
o
nses
w
i
t
h
o
u
t
a
f
fe
ct
ing
t
h
e
sta
b
i
l
ity
o
f
t
h
e
sy
st
em
r
e
q
u
i
res
t
h
e
dyn
ami
c
m
o
d
e
l
of
a
n
y
s
y
st
e
m
.
Th
i
s
d
eman
ds
t
he
e
xa
ct
v
a
l
ue
o
f
syste
m
p
a
r
am
ete
r
s.
D
a
ta
s
hee
t
o
f
som
e
s
ys
tem
para
me
t
e
rs
c
an
b
e
use
d
t
o
m
ode
l
the
s
yst
e
m
if
g
i
v
en
,
ot
he
rw
i
s
e
i
t
c
a
n
b
e
deter
m
i
n
ed
t
h
r
ou
gh
e
x
peri
m
e
nts.
H
ow
e
v
er,
i
t
i
s
d
i
ffi
c
u
l
t
t
o
d
e
t
ermi
n
e
a
ll
t
h
e
p
ara
m
e
t
ers
t
h
ro
ugh
expe
r
i
me
n
t
s.
I
n
a
d
d
iti
o
n
t
o
t
h
at,
the
ac
cura
c
y
o
f
de
term
in
ed
p
a
ram
e
ter
s
m
a
i
nly
de
pen
d
s
o
n
the
acc
urac
y
o
f
me
asurin
g
i
n
st
rum
e
nts.
I
n
or
der
to
overc
om
e
the
a
b
ove
d
i
f
fic
u
l
t
i
e
s
,
t
h
e
a
c
t
u
a
l
s
y
s
t
e
m
c
a
n
b
e
d
e
s
i
g
n
e
d
i
n
t
h
e
f
o
rm
o
f
b
l
o
c
k
d
i
ag
ram
con
s
id
eri
n
g
a
l
l
dyn
ami
c
b
eh
av
iours
an
d
t
h
e
v
a
l
u
e
s
a
r
e
o
p
t
i
m
i
s
e
d
i
n
s
u
c
h
a
w
a
y
t
h
a
t
the resp
o
n
ses
f
r
om
b
loc
k
d
i
a
g
r
am
m
odel na
m
e
d as
a
dap
tiv
e
m
odel
wi
ll
e
x
a
ct
ly
m
a
t
ch
w
i
t
h
th
e
co
rre
s
p
o
ndi
ng
response
from
actual
(re
fer
e
nce
)
s
ystem
m
odel.
T
he
p
er
form
anc
e
s
o
f
para
me
ters
e
st
ima
t
i
o
n
de
p
e
nd
on
a
c
c
u
rac
y
o
f
ada
p
ti
v
e
m
o
d
e
l
,
a
cc
u
r
at
e
exp
e
rime
n
t
a
l
d
at
a
o
f
r
ef
e
r
ence
m
od
el, the se
lect
i
o
n
of c
os
t
fu
nc
tion a
n
d
abi
l
i
t
y
f
or
m
i
n
im
isa
t
i
o
n
of
o
ptim
i
s
a
tio
n
al
g
o
ri
t
h
m
s
.
The
e
s
t
i
ma
tio
n
by
m
ea
surin
g
s
ys
te
m
da
t
a
i
s
ca
l
l
e
d
a
n
expe
r
i
me
n
t
a
l
(
in
d
u
ct
i
v
e)
i
de
nt
i
f
ica
tio
n.
T
h
e
re
a
r
e
t
w
o
m
e
t
ho
ds
of
e
xp
e
r
i
m
en
t
a
l
est
i
m
a
t
ion
on
e
on
lin
e
and
ot
her offli
n
e. In offli
n
e i
d
e
n
t
i
f
ica
tio
n the
m
easur
ing
of the
s
yste
m data is rec
o
rded,
a
nd a m
a
them
atica
l
mode
l
is
c
rea
t
e
d
a
fter
t
he
w
h
o
l
e
m
easuri
n
g
pr
oce
ss
has
fin
i
s
h
e
d
.
The
o
n
l
i
ne
i
de
nti
f
ica
t
i
o
n
is
w
he
re
t
he
c
om
p
u
ta
t
i
o
n
of
t
h
e
a
dap
t
ive
para
me
t
e
r
mode
l
oc
c
u
rs
t
o
t
h
e
on
line
m
e
a
s
ur
ing
proc
es
s
da
t
a
.
In
o
nli
n
e,
t
he
s
tab
i
lit
y
criter
i
o
n
must be
formu
l
ate
d
be
f
ore
onl
ine
pr
oce
s
s
be
gi
ns o
t
h
erw
i
se
t
h
e
s
ystem
w
i
l
l
not be
co
n
v
er
ged.
Th
is
a
r
t
ic
le
i
s
exc
l
us
i
v
el
y
foc
u
se
d
on
t
h
e
o
ffline
es
t
i
m
a
ti
o
n
o
f
dc
s
epa
r
a
t
e
l
y
e
x
c
i
te
d
mo
t
o
r.
T
ho
ug
h
80%
of
i
ndus
tries
use
three
phase
i
ndu
c
ti
on
m
otor
s
,
s
t
ill
DC
m
o
tors
p
l
a
ys
a
n
imp
o
r
t
a
n
t
ro
le
i
n
i
ndus
trial
con
t
ro
l
sy
stem
due
t
o
e
a
s
y
c
on
tro
l
.
D
a
ta
p
ro
vide
d
b
y
m
a
nufac
tur
e
r
m
ay
n
ot
b
e
ad
e
q
u
a
t
e
a
nd
acc
u
r
at
e,
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN: 2088-
8694
I
nt
J
P
ow
Elec
& Dr
i
S
y
st, Vol. 10,
N
o.
1, Mar
c
h 2
0
1
9
:
83
–
92
84
espec
i
al
ly
f
or
c
he
aper
D
C
m
o
t
o
rs
w
he
re
t
h
e
t
oler
a
n
ce
l
e
v
el
i
n
e
l
e
ct
ri
c
a
l
a
n
d
me
ch
an
i
c
al
p
ara
m
e
t
e
r
s
may
b
e
very
h
i
gh.
I
n
thi
s
case
t
h
e
p
a
r
a
m
e
ters
a
re
i
de
n
tifie
d
b
y
v
ario
u
s
t
echn
i
qu
es.
Th
e
sl
op
e,
f
req
u
en
cy
r
e
s
p
onse
ana
l
ys
is
w
i
t
h
e
nerg
y
mode
l
w
a
s
us
ed for
i
de
nt
ifi
c
a
t
io
n
o
f
p
ara
m
et
e
r
s
[
1
]-[
4
].
L
ea
st
s
qu
a
r
e
an
d
re
c
u
rs
iv
e
l
e
ast
sq
ua
re
m
e
t
hods
w
e
r
e
u
s
e
d
f
or
i
d
e
nt
i
f
i
c
a
t
io
n
of
p
ara
m
e
t
e
r
s
i
n
[
5
],
[6].
T
he
i
n
v
e
r
se
t
heory
w
a
s
a
l
s
o
a
p
p
l
i
ed
f
or
est
i
ma
t
i
o
n
o
f
pa
ram
e
ter
s
[
7].
Mom
e
n
t
m
et
ho
d
w
a
s
use
d
f
or
i
de
n
t
i
ficat
i
o
n
o
f
p
ar
am
et
ers
i
n
[
8].
P
a
ra
me
ters
e
s
t
i
m
a
t
i
o
n
a
n
d
c
o
n
t
r
o
l
v
a
r
i
a
b
l
e
o
f
d
c
m
o
t
o
r
w
e
r
e
d
e
t
e
r
m
i
n
e
d
b
y
neur
a
l
n
e
t
w
o
rk
[
9].
A
l
gebra
i
c
ide
n
ti
fi
ca
t
i
o
n
t
e
c
hni
qu
e
wa
s
u
s
ed
f
o
r
p
arame
t
er
e
st
i
m
a
t
io
n
of
d
c
mo
tor
i
n
[
10
]
,
[
1
1
]
.
DC
m
oto
r
p
aramet
er
i
d
e
n
t
ifi
c
ati
on
appr
oa
ches
b
as
e
d
o
n
t
h
e
Ta
yl
or
s
erie
s
e
xpa
n
s
ion
of
t
he
m
o
t
or
s
p
ee
d
r
e
s
p
o
n
se
w
as
p
rese
n
t
e
d
i
n
[1
2].
Inte
gral
ti
m
e
s
quar
e
err
o
r
(IT
S
E
)
and
Integral
tim
e
abs
o
l
u
te
e
rror
(I
TAE
)
we
re
u
sed
f
o
r
parameter
estima
t
i
o
n
and
des
i
gn
o
f
control
var
i
a
b
les
in
[
13]-[15].
I
n
t
his
articl
e
,
e
f
fo
rt
h
as
b
e
e
n
ma
d
e
t
o
f
i
nd
t
h
e
s
ev
e
n
unk
no
wn
parameters
o
f
separ
a
tely
e
xcited
d
c
mo
t
o
r
nam
e
d
as
:
arm
a
ture
r
e
sista
n
ce,
a
rm
at
ure
i
n
duc
ta
nc
e
,
f
ie
ld
resista
n
ce
,
fie
l
d
i
n
duc
ta
nc
e
fic
t
it
ious
m
ut
u
a
l
rea
c
t
anc
e
,
a
nd
M
om
en
t
of
I
ner
tia
a
nd
V
i
sc
ous
f
r
i
c
t
io
n
coe
f
fic
i
e
n
t.
T
h
e
e
xperim
e
nta
l
d
a
t
a
o
n
t
o
a
r
m
a
ture
c
ur
rent
a
nd
s
p
eed
respon
ses
w
i
th
r
espect
t
o
ti
m
e
a
r
e
col
l
ec
te
d
t
h
rou
gh
data
a
c
quis
i
t
i
o
n
b
oard
f
r
o
m
separ
a
tely
e
xci
t
e
d
m
o
t
o
r
w
h
ic
h
ru
ns
f
or
f
ul
l
r
a
te
d
v
o
lta
ge
w
i
t
h
loa
d
t
orq
u
e
of
50 N
-
m
using
c
u
rre
nt
s
ens
o
r a
nd v
o
lta
ge
s
e
n
sor r
espect
i
v
e
l
y.
The
dc
m
o
t
or
m
ode
l
i
n
c
l
u
d
i
n
g
t
h
e
effec
t
o
f
fl
ux
dy
nam
i
cs
i
s
b
u
i
lt
in
M
A
TLA
B
e
n
vir
onme
n
t
w
i
t
h
rand
om
ly
c
h
o
s
e
n
in
it
ia
l
para
me
t
e
rs.
The
e
r
ror
be
t
w
een
r
e
s
po
nse
fro
m
ex
p
e
ri
me
n
t
al
d
at
a
a
n
d
t
h
at
o
f
ad
a
p
ti
v
e
para
me
ters
o
f
adap
t
i
ve
m
o
d
e
l
i
s
use
d
t
o
f
o
r
m
ul
a
t
e
the
cos
t
f
u
n
c
t
i
on
.
Th
e
c
o
st
f
un
cti
o
n
i
s
b
a
s
ed
o
n
diff
ere
n
t
in
t
e
gral
c
r
iter
i
on
i
s
w
e
l
l
d
e
f
ine
d
i
n
c
ontro
l
e
n
g
i
nee
r
i
n
g
[1
6].
T
he
S
im
pso
n
'
s
o
n
e-
thir
d
ru
le
i
s
use
d
f
or
in
t
e
grat
io
n
of
o
b
j
ec
t
i
ve
f
u
n
c
t
ion.
T
he
m
i
n
i
m
isat
io
n
o
f
t
h
e
c
ost
f
u
n
c
t
i
o
n
i
n
o
rder
t
o
a
d
apt
t
h
e
r
e
spo
n
s
e
fr
om
ada
p
t
i
ve
m
o
d
e
l
t
o
t
h
e
e
x
peri
m
e
ntal
r
es
po
n
s
e
can
b
e
re
a
lise
d
t
hr
o
u
gh
re
c
e
nt
p
ub
l
i
s
h
e
d
W
ha
le
o
p
tim
i
s
a
t
i
o
n
a
l
go
rith
m
[1
7].
Th
e
me
c
h
an
i
cal
a
nd
e
l
e
c
t
ri
cal
p
a
r
amet
e
r
s
o
f
d
c
m
o
tor
are
iden
t
i
fie
d
by
Co
n
s
tra
i
n
t
O
p
t
i
m
i
z
a
t
i
o
n
T
e
c
h
n
i
q
u
e
u
s
i
n
g
M
A
T
L
A
B
c
o
d
e
a
n
d
M
A
T
L
A
B
P
a
r
a
m
e
t
e
r
I
den
t
ifica
t
i
o
n
T
o
o
l
bo
x
[18]
.
A
n
over
v
iew
of
d
i
f
fere
n
t
o
pt
imiz
a
t
i
o
n
a
l
gori
t
hm
s
tha
t
i
s
use
d
t
o
a
c
h
ieve
o
p
t
i
m
al
d
es
ig
n
o
f
a
n
e
l
ec
t
r
i
c
al
m
a
c
hi
ne
i
s
m
e
n
t
i
o
n
e
d
i
n
[
1
9
]
.
P
S
O
i
s
u
s
e
d
f
o
r
p
a
r
a
m
e
t
e
r
e
s
t
i
m
a
t
i
o
n
o
f
a
N
onl
in
e
a
r
Au
t
o
-
R
eg
re
ssi
v
e
wit
h
E
xogen
o
u
s
(N
A
R
X
)
m
odel
for
dc
m
ot
or
[
2
0
].
G
rey
Wo
l
f
O
p
tim
i
z
a
t
i
o
n
[
21]
a
n
d
B
io
–
I
nsp
i
red
O
p
timiz
a
t
ion
A
l
g
o
ri
thm
[2
2]
i
s
used
f
o
r
p
a
r
am
eter
e
stima
t
i
o
n
o
f
P
M
D
C
core
l
e
s
s
m
icr
o
-m
o
t
o
r
.
T
h
e
d
c
m
o
t
o
r
p
a
r
a
m
e
t
e
r
i
s
e
v
a
l
u
a
t
e
d
a
c
c
u
rat
e
l
y
by
th
e
re
ce
ntl
y
p
u
b
li
sh
ed
F
lo
we
r
Po
ll
i
n
a
t
io
n
Alg
o
r
i
t
hm
(
FP
A
)
[
23]
a
nd
N
el
der
–
Me
a
d
O
p
t
i
m
i
sa
tio
n
[24].
I
n
t
h
i
s
a
r
t
i
c
l
e
,
t
he
d
yna
mic
m
ode
l
of
D
C
mo
tor
i
s
m
od
ifie
d
to
i
ncl
u
de
t
he
f
i
e
ld
f
lu
x
d
y
na
mics
w
hic
h
ot
herwise
a
f
fe
cts
the
ac
cura
c
y
o
f
e
s
t
i
m
a
t
i
o
n
b
eca
use
t
h
e
fl
u
x
d
y
n
a
m
ics
affec
t
t
he
t
ran
s
ie
nt
r
es
po
nse
of
t
he
motor.
S
ec
ond
l
y
, t
he e
xper
i
m
e
nta
l
da
t
a
is d
i
v
i
d
e
d
in
t
o thre
e
i
n
t
e
rv
al
s:
ri
s
e
ti
me, s
et
t
l
i
n
g
ti
me
a
n
d
st
ead
y s
t
at
e,
w
h
ic
h
form
t
h
e
t
hre
e
i
ne
qua
l
i
t
y
c
o
n
s
t
rain
ts
a
nd
a
f
i
xe
d
pe
n
a
l
t
y
i
s
a
d
ded
to
t
he
c
os
t
f
unc
ti
on
f
or
v
i
o
la
tin
g
the
s
e
co
ns
tra
i
n
t
s.
H
e
r
e
two
meth
o
d
s
are
use
d
:
1
st
m
e
t
h
o
d
i
s
w
i
t
h
w
h
o
l
e
d
a
t
a
a
n
d
2
nd
m
e
t
h
o
d
i
s
w
i
t
h
t
h
e
d
a
t
a
di
vide
d
int
o
t
h
r
ee
i
nte
r
va
ls.
A
com
p
aris
on
o
f
t
h
e
tw
o
m
e
t
h
o
d
s
s
how
s
th
a
t
t
he
2
nd
m
e
t
ho
d
gi
v
e
s
b
e
tt
e
r
r
e
s
u
l
t
tha
n
t
he
1
st
.
2.
MODEL
O
F
S
EPARATELY EX
CITE
D
DC MACHIN
E
The
m
o
del
o
f
s
e
p
ara
t
e
l
y
ex
cite
d
dc
m
o
t
or
i
s
de
ve
lo
pe
d
b
y
i
nc
orp
ora
tin
g
the
i
n
it
i
a
l
d
y
n
am
ic
beha
v
i
o
u
r
o
f
t
h
e
f
i
e
ld
c
ur
ren
t
w
hi
c
h
e
x
h
i
b
i
t
t
he
f
l
ux
d
yna
m
i
cs.
T
ho
ug
h
t
h
e
fl
u
x
i
s
co
ns
ta
n
t
u
n
d
er
s
tea
dy sta
t
e
c
o
ndi
ti
o
n
,
b
ut
d
yn
a
m
i
c
s
of
f
lu
x
exi
s
t
i
n
it
i
a
ll
y
du
e
t
o
f
i
e
l
d
i
n
du
ct
a
n
ce
wh
i
c
h
aff
e
c
t
s
t
h
e
resp
on
s
e
s
of
s
p
e
ed
and
arm
a
ture
c
ur
rent.
F
o
r
a
c
c
ura
t
e
e
s
tim
at
i
on
o
f
m
o
t
or
p
ara
m
e
t
e
rs,
t
h
e
ef
f
ect
o
f
dyn
ami
c
s
of
f
lux
mu
st
b
e
inc
l
ude
d
w
h
i
l
e
m
ode
l
lin
g
the
dc
m
a
c
h
i
ne.
T
h
e
m
o
d
i
f
i
e
d
m
ode
l
w
h
i
ch
i
nc
l
u
de
s
t
h
e
ef
fec
t
o
f
dy
nam
i
cs
o
f
f
l
ux
as show
n
in
F
igure
1.
1
aa
Ls
R
af
L
af
L
f
f
f
V
Ls
R
1
J
sB
f
f
f
V
Ls
R
L
T
F
i
gure
1.
D
yna
m
i
c
m
odel of s
epa
r
ate
l
y e
x
c
i
t
e
d dc
m
oto
r
i
n
c
lu
d
i
n
g
fiel
d f
l
ux
d
y
n
am
i
c
s
Evaluation Warning : The document was created with Spire.PDF for Python.
Int J
P
o
w
El
e
c
&
D
ri S
yst
I
S
S
N
:
2088-
86
94
Pa
ra
m
e
t
e
r est
i
ma
ti
on
of
DC mo
to
r th
roug
h
wh
al
e op
ti
mi
zat
i
o
n
a
l
go
ri
th
m (Bya
ma
k
e
s
h
Na
ya
k
)
85
The
de
ve
l
ope
d
m
odi
f
i
e
d
m
od
els ba
se
d on t
h
e
equa
ti
on
s a
r
e
repre
s
ented
as
fol
l
o
ws:
The
bac
k
e
m
f
ind
uce
d
c
an be
w
r
itte
n
as:
E
ℜ
i
ω
wher
e
Φ
i
s
t
h
e
fl
ux
per
pole
an
d
can
b
e
repr
esente
d
as:
Φ
ℜ
w
h
er
e
N
is
t
he
num
ber
of
t
urns
i
n
the
fiel
d
w
i
n
d
i
n
g
and
ℜ
i
s
the
re
l
u
c
t
a
n
c
e
of
m
agne
tic
m
ateria
l
.
P
,
Z
and
A
are
po
le,
t
o
ta
l numbe
r
of
a
rm
ature
c
o
n
d
u
ct
o
r
s
and nu
m
b
er
o
f
par
al
le
l
pa
th
s
of
a
rm
at
ure
w
i
n
d
i
n
g
re
spec
ti
ve
ly.
The
fic
t
i
t
i
o
us m
utua
l i
n
d
u
c
t
a
n
ce
is de
n
o
te
d
as
L
ℜ
t
h
e
dyn
ami
c
b
eh
a
v
io
u
r
o
f
ex
cit
a
ti
o
n
c
u
rre
n
t
i
can be
w
r
itte
n
in
t
he
for
m
of:
L
R
i
V
(
1)
the freque
n
cy
dom
ain f
o
rm
o
f
(1)
i
s
a
s:
i
S
(
2
)
the d
y
n
a
m
ic
e
q
u
at
i
o
n
s
go
v
er
ne
d by
the
ar
mature
can
b
e e
x
p
r
essed
i
n the form as:
R
i
L
L
i
ω
V
(
3
)
L
i
i
T
J
B
ω
(
4
)
wher
e
L
i
i
i
s t
h
e torq
u
e
d
ev
e
l
op
e
d
i
n
t
h
e ma
chi
n
e
.
The
fre
q
u
e
nc
y
do
m
a
i
n
f
orm
o
f
3
a
nd 4 c
a
n
b
e
r
epre
se
nted a
s:
R
L
S
i
S
L
ω
S
VS
(
5
)
L
i
S
T
S
J
S
B
ω
S
(
6
)
wher
e
i
a
,
ω
m
,
T
a
nd
V
a
r
e
t
h
e
ar
ma
t
u
r
e
c
urre
n
t
(
am
pere
),
m
e
c
ha
n
i
c
a
l
s
p
eed
(ra
d
/
s
ec
)
,
l
o
ad
t
o
r
qu
e
a
n
d
arm
a
ture
vol
ta
ge
(
V
o
lt) r
espec
t
i
v
e
l
y.
The
b
l
oc
k
dia
g
ram of D
C m
o
to
r
us
i
ng 4 a
n
d 5
i
s
r
epre
sente
d
a
s s
how
n i
n
F
igure
1.
The
se
ve
n
u
n
k
now
n
para
me
t
e
rs
R
a
,
L
a
,
L
,
J
,
B
,
L
a
nd
R
a
re
a
r
m
ature
resistance(Ω
)
,
a
rmature
in
duc
ta
nce
(
H
)
,
fic
t
i
t
i
o
us
m
u
t
ual
in
duc
t
a
nce,
m
om
ent
of
i
ne
rtia
(kg-m
2
),
v
isc
o
us
f
r
i
c
tio
n
(V
ol
t-sec
/
r
a
d
.
),
f
iel
d
in
duc
ta
nce
an
d
fie
l
d
re
s
i
sta
n
c
e
r
es
pec
t
i
v
e
l
y.
T
he
b
loc
k
d
ia
gra
m
o
f
d
c
m
otor
w
i
t
h
t
he
se
s
e
v
e
n
unk
n
o
w
n
para
me
ters
i
s
repr
esen
ted
as
a
dap
t
i
v
e
m
o
de
l.
I
n
th
is
a
r
tic
le
,
e
f
f
o
r
t
s
a
r
e
m
a
d
e
t
o
d
e
t
e
r
m
i
n
e
t
h
e
a
b
o
v
e
s
e
v
e
n
para
me
ters
t
hr
o
u
g
h
a
n op
t
i
m
i
sat
i
o
n
a
lgor
it
h
m
.
3.
EXPE
RIMENTAL DATA C
O
LLECTI
ON
S
i
nce
the
e
s
t
i
ma
t
i
o
n
o
f
par
a
m
e
ters
i
s
ba
se
d
o
n
t
he
e
xperi
m
e
ntal
r
esp
ons
es
f
rom
spee
d
and
a
r
ma
ture
curr
ent i
n
th
i
s ar
ti
c
l
e
, the
a
cc
ur
acy
o
f e
s
tim
ati
o
n of
p
ara
m
e
t
e
rs m
ain
l
y de
pen
d
s o
n
the
e
xpe
r
i
me
n
t
a
l
re
s
po
nses
t
o
s
p
e
e
d
a
n
d
a
r
m
a
t
u
r
e
c
u
r
r
e
n
t
.
T
h
i
s
d
e
m
a
n
d
s
t
h
e
a
c
c
u
r
a
t
e
c
o
l
l
e
c
t
i
on
of
s
pee
d
a
n
d
arm
a
ture
c
urre
nt
d
a
t
a
.
T
h
e
separ
a
te
ly
e
xci
t
ed
m
o
t
or
r
uns
f
or
t
he
r
ate
d
a
rm
ature
vol
ta
ge
u
n
der
n
o
l
oad
c
o
n
d
i
t
i
on
and
50
N
-
m
l
o
ad
e
d
c
o
n
d
i
t
i
o
n
s
.
T
h
e
o
p
t
i
c
a
l
e
n
c
o
d
e
r
w
a
s
u
s
e
d
t
o
m
e
a
s
u
r
e
t
h
e
s
p
e
e
d
r
esp
o
n
s
e.
A
rm
ature
c
u
rre
nt
r
esp
o
n
se
w
as
mea
s
u
r
ed
b
y
Ag
il
e
n
t1
146
A
Hal
l
-ef
f
ect
p
ro
b
e
.
Da
t
a
ont
o
sp
ee
d
re
s
p
ons
es
a
nd
c
urr
e
nt
r
es
po
nse
s
w
e
r
e
col
l
ec
te
d
t
h
ro
u
gh
t
h
e data
a
c
q
uisi
t
i
o
n
boar
d
of
LA
BV
IEW deve
l
o
p
er
.
A
h
i
g
h
p
a
ss
fi
l
t
er
w
as
u
sed to
f
i
l
t
e
r
o
u
t
the
n
o
ise
c
onte
n
t
o
f
t
h
e
d
a
t
a
a
n
d
se
nd
t
o
c
o
m
put
e
r
.
T
h
e
c
onve
rs
i
o
n
ra
t
i
o
w
a
s
a
do
pt
ed
f
or
s
p
e
ed
a
nd
a
rmat
u
r
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN: 2088-
8694
I
nt
J
P
ow
Elec
& Dr
i
S
y
st, Vol. 10,
N
o.
1, Mar
c
h 2
0
1
9
:
83
–
92
86
curr
ent
da
ta
t
o
m
a
tc
h
w
i
t
h
t
he
e
xa
c
t
v
a
l
ue
s
m
e
a
s
ured
b
y
t
h
e
am
m
e
t
e
r
a
nd
tac
h
oge
ne
ra
t
o
r
o
n
s
tead
y
s
t
at
e
con
d
i
t
i
on.
A
l
oo
k
up
ta
ble
for
bo
t
h
a
rm
ature
c
u
rre
nt
a
nd
s
pee
d
w
a
s
de
vel
o
pe
d
in
M
A
TLA
B
s
i
m
u
lin
k
env
i
ro
nm
en
t. The loo
k u
p
ta
b
le
wi
ll ac
t as
r
efe
r
enc
e
mo
d
e
l
.
The
re
f
er
ence
m
ode
l
(
l
o
o
k
u
p
tab
le)
a
n
d
a
d
apt
i
v
e
mode
l
(b
loc
k
d
i
a
gram
o
f dc m
otor)
is p
lac
e
d
in
one
s
im
uli
n
k f
i
l
e.
The
sev
e
n
un
k
now
n pa
ram
e
ter
s
ar
e
a
dapte
d
th
oug
h
an
o
p
t
i
m
isat
io
n
a
l
g
o
ri
thm
s
b
y
c
r
ea
t
i
ng
a
c
o
st
f
u
n
c
t
i
on.
The
va
lu
es
a
re
a
dopte
d
t
h
o
u
gh
t
h
e
i
t
e
r
a
tion
proce
s
s
of
o
p
t
i
m
i
s
a
tio
n
a
l
g
o
r
ithm
i
n
s
uc
h
a
w
a
y
tha
t
t
he
r
esp
o
n
se
from
ada
p
t
i
ve
m
o
d
e
l
w
ill
te
n
d
t
o
m
a
tch
wi
t
h
t
h
e
c
o
r
re
sp
ondi
ng
r
e
s
po
n
s
e
t
o
r
ef
ere
n
ce
m
od
el
t
h
e
m
o
d
e
l
i
s
r
e
p
eated
l
y
c
a
l
led
thr
o
u
g
h
a
M
A
TLA
B
com
m
a
nd 's
im'.
4.
CONVE
N
T
IONAL METHOD
The
un
kn
ow
n
par
a
m
e
ter
s
c
an
b
e
ide
n
t
i
f
i
e
d
t
hr
ou
gh
m
a
t
h
em
atica
l
f
or
mula
t
i
o
n
u
sin
g
e
xper
i
m
e
nt
a
l
data
o
f
ar
ma
t
u
re
c
urre
nt,
spe
e
d
an
d
fiel
d
c
u
rre
nt
w
it
h
tw
o
diff
er
e
n
t
loa
d
c
on
d
i
t
i
on
s.
O
nly
t
h
r
ee
da
ta
p
oi
n
t
s
o
f
ea
ch
r
espo
nse, two
c
o
n
sec
u
ti
v
e
d
a
t
a at
s
tart
i
n
g
t
i
me
f
or
d
e
t
e
r
m
i
n
a
t
i
o
n
of
e
le
c
t
ri
cal
,
f
i
el
d
a
n
d
me
ch
an
i
c
a
l
t
i
m
e
con
s
ta
n
t
w
ith
t
he
s
tea
d
y
sta
t
e
da
t
a
p
o
i
n
t
s
fo
r
tw
o
differ
en
t
l
o
a
d
s
a
r
e
re
quired.
T
he
s
t
a
rt
ing
s
l
o
p
e
o
f
a
rma
t
ure
curr
ent,
f
iel
d
c
urre
n
t
a
nd
spe
e
d
r
e
sp
onse
s
a
re
t
e
r
m
e
d
as
e
l
e
ctr
ica
l
f
i
e
l
d
a
nd
m
e
c
h
a
n
ica
l
tim
e
co
nsta
n
t
s
and
d
e
not
e
d
a
s
,
a
nd
r
e
spec
ti
ve
ly.
The
ste
a
d
y
s
tate
c
urre
nt
a
n
d
s
p
e
ed
d
a
t
a
w
i
th
t
w
o
d
i
f
f
ere
n
t
l
o
ads
ar
e
d
e
not
e
d
a
s
,
,
,
an
d
respec
t
i
ve
l
y
.
The
is
t
he
s
te
ad
y
s
t
ate
f
i
e
l
d
c
u
rr
ent
a
n
d
a
n
d
are
the
two
d
i
ffe
r
ent
l
o
ad
t
orque
s.
T
he
e
rror
l
e
s
s
of
p
ar
am
eter
est
i
m
a
t
i
on
d
e
p
e
nd
s
on
t
he
p
re
c
i
sen
e
s
s
o
f
d
a
t
a
in
form
ation
a
n
d
sa
mpl
i
ng
ti
me
.
H
o
w
e
ver
,
t
he
a
cc
ura
c
y
o
f
p
a
r
am
et
er
e
st
ima
tio
n
i
s
a
ls
o
affec
t
e
d
b
e
c
a
u
se
o
f
arm
a
ture
r
e
a
c
t
i
on,
n
o
i
n
form
ati
on
of
b
rus
h
c
on
t
a
c
t
d
ro
p,
e
f
f
ec
t
o
f
l
e
ak
ag
e
fl
ux
a
nd
n
onl
in
e
a
r
e
ff
e
c
t
of
B
-H
curve
.
T
he m
athe
ma
t
i
ca
l for
m
ula
t
io
n for
esti
ma
t
i
o
n
o
f pa
ram
e
ter
i
s
a
s
f
o
l
lo
w
s
.
⎭
⎪
⎪
⎪
⎪
⎪
⎬
⎪
⎪
⎪
⎪
⎪
⎫
(
7
)
5.
COST FUNCT
ION
Tw
o
m
e
th
od
s
are
use
d
t
o
de
fi
ne
t
he
c
ost
func
t
i
o
n
.
In
1
st
m
e
tho
d
t
he
w
ho
l
e
d
ata
is
p
r
o
cesse
d
b
y
defi
ni
n
g
o
ne
c
ost
f
unc
ti
on
w
h
e
r
ea
s
in
2
nd
m
etho
ds
t
he
d
ata
is
d
i
v
ide
d
i
nt
o
t
h
ree
i
n
t
e
r
v
al
s.
T
he
1
st
int
e
rv
a
l
i
s
up
to
r
ise
t
i
me
,
the
2
nd
o
ne
i
s
t
h
e
o
s
c
i
l
l
a
t
in
g
r
e
gi
on
a
nd
thir
d
o
n
e
i
s
t
he
s
te
a
dy
s
t
a
t
e
re
gi
on
.
Th
e
ti
me
l
imi
t
o
f
three
c
o
st
f
un
cti
o
ns
u
se
d
in
2
n
d
m
eth
od
is
d
iffere
n
t
,
a
nd
the
o
vera
l
l
c
ost
f
unc
t
i
on
i
s
sum
of
t
he
t
hre
e
in
de
pen
d
e
n
t
c
o
s
t
f
u
n
c
tio
n.
T
he
t
hre
e
i
ne
qua
l
ity
c
o
n
s
t
ra
i
n
ts
a
re
d
e
f
in
ed
c
o
n
s
i
d
erin
g
ea
ch
i
nd
ep
e
n
d
e
nt
fu
nc
ti
o
n
.
The vio
l
a
t
i
o
n of
i
n
d
e
p
en
de
nt c
o
s
t
fu
nct
i
on i
s
pena
l
i
s
ed
b
y fi
xed
pe
nal
t
y va
lue
.
5.1.
1
st
m
etho
d
To
m
a
t
c
h
t
he
a
da
p
tive
re
sp
ons
e
w
i
t
h
t
he
c
orre
spo
n
d
i
n
g
e
x
perim
e
n
ta
l
r
e
sp
on
se
t
he
o
p
tim
isa
tio
n
alg
o
ri
t
h
m
i
s
u
se
d
w
h
ich
m
i
ni
m
i
ses
t
h
e
co
st
f
unc
t
i
on.
I
n
th
i
s
p
r
obl
e
m
the
integral
o
f
time
squared
er
ror
b
e
t
w
e
e
n
ex
pe
ri
me
n
t
al
d
at
a
an
d
ad
apt
i
v
e
d
a
t
a
i
s
con
s
id
e
r
e
d
a
s
t
h
e
c
o
s
t
f
unc
tio
n.
M
a
t
h
e
ma
t
i
ca
lly
i
t
c
a
n
be
w
r
itte
n a
s
:
ITS
E
=
∗
.
(
8
)
S
i
m
p
son
1/3
rule
i
s
use
d
h
e
r
e
t
o
d
e
v
e
l
o
p
M
A
TLA
B
pro
g
ra
m
for
int
eg
ra
t
i
on.
T
he
X
r
epr
e
sents
the
seve
n
u
nkn
ow
n
varia
b
le
s
of
d
c
m
o
tor
.
T
he
d
a
t
a
is
c
ol
le
c
t
e
d
f
or
2
s
e
c
o
n
d
s.
T
he
w
h
o
le
d
a
t
a
is
u
s
e
d
for
ada
p
t
i
o
n
i
n 1s
t m
e
tho
d
.
Evaluation Warning : The document was created with Spire.PDF for Python.
Int J
P
o
w
El
e
c
&
D
ri S
yst
I
S
S
N
:
2088-
86
94
Pa
ra
m
e
t
e
r est
i
ma
ti
on
of
DC mo
to
r th
roug
h
wh
al
e op
ti
mi
zat
i
o
n
a
l
go
ri
th
m (Bya
ma
k
e
s
h
Na
ya
k
)
87
5.2.
2n
d
meth
o
d
I
n
o
r
d
er
t
o
im
prove
t
he
p
e
r
form
anc
e
s
(m
ini
m
i
s
at
ion
of
e
rror)
,
t
he
d
ata
of
s
pe
e
d
r
espo
nse
ar
e
ana
l
yse
d
an
d
d
i
v
i
d
ed
i
nt
o thr
ee
intervals. The p
e
n
al
ty
i
s
ad
d
e
d
for v
io
la
t
i
on
of c
ons
t
r
ai
n
t
s.
The
t
hree
in
t
erva
l
s
beha
ve
a
s
thre
e
inde
pe
nde
n
t
f
u
n
c
t
i
o
ns.
The
c
o
s
t
fu
n
ct
i
o
n
is
w
ri
tt
en
as
:
(
9)
The
ine
q
ua
lit
y
con
s
t
r
ai
n
t
s
are
:
1
0
2
0
3
0
(
10)
F
o
r
vio
l
a
t
io
n
of
(
1
1
)
in
a
n
y
iter
a
tio
n
o
f
o
pt
imisa
t
io
n
a
l
gor
it
h
m
,
t
h
e
p
e
n
a
l
t
y
i
s
i
m
p
o
s
e
d
i
n
c
o
s
t
fu
nc
ti
o
n
a
nd
i
t
is
s
h
o
w
n
as:
∗
1
2
3
(
11)
The
pe
na
l
t
y
v
a
l
u
e
,
a
,
b,
c
m
ust
be
c
are
f
ul
ly
c
h
o
se
n
so
t
ha
t
the
o
pt
im
isat
ion
w
i
l
l
b
e
co
nve
r
g
ed
tow
a
rds
zer
o. The
t
hre
e
func
t
i
o
n
s
are
re
prese
n
te
d base
d
o
n
the
t
h
r
ee
i
n
t
e
r
v
al o
f spee
d re
sponse
as
:
∗
.
.
∗
.
.
∗
.
⎭
⎪
⎬
⎪
⎫
(
12)
6.
WHA
LE OP
TIM
I
Z
A
TION
A
L
GORI
T
H
M
(
WOA)
The
hum
p
b
ac
k
w
h
a
l
e
is
k
n
o
w
n
f
or
t
he
ir
s
pec
i
al
h
un
t
i
n
g
m
et
ho
d.
T
he
y
sear
ch
t
h
e
p
re
y.
O
nc
e
sea
r
ch
of
p
re
y
is
o
ve
r,
e
ncirc
lin
g
a
n
d
a
ttac
k
in
g
t
h
e
pre
y
w
as
c
ar
ried
o
u
t
.
Th
e
m
a
them
at
ica
l
m
o
d
el
o
f
sea
r
chi
ng,
enc
i
r
c
lin
g
a
n
d
a
t
tac
k
ing
is
e
x
p
ressed
a
s
f
o
l
l
o
w
s
:
S
e
a
r
chi
n
g
for
prey
i
s
t
h
e
e
x
p
l
or
at
ion
p
h
ase
w
h
ic
h
is
car
ried
ou
t b
y
w
ha
l
e
s ra
nd
oml
y
a
ccor
d
in
g
t
o
t
he p
os
it
ion
s
of e
ach
o
t
h
e
r
. Th
e
mat
h
emat
i
cal
mo
d
e
l i
s
as f
o
ll
o
w
s:
⃗
⃗
.
⃗
⃗
(
13)
The
p
o
si
t
i
o
n
i
s
upda
te
d
by
⃗
1
⃗
⃗
.
⃗
(
14)
If
r
a
ndom
val
ues
'
A
' gre
a
ter
tha
n
1
o
r le
ss
t
ha
n
−1 ar
e
t
o
forc
e se
arc
h
age
nt
t
o m
ove far
a
w
a
y fr
om
a
reference whale.
The
be
ha
v
i
our
of
enc
i
rc
l
i
ng
c
a
n
be
r
epre
sent
ed by
t
h
e f
o
ll
o
w
in
g
equa
tio
ns
:
⃗
⃗
.
∗
⃗
⃗
(
15)
aft
e
r the
b
e
st s
ea
rch
age
n
t
i
s
d
efi
n
ed,
the
o
t
he
r se
arc
h
age
nt
s
w
ill
hence
t
r
y to
u
pda
te t
h
e
ir
pos
i
t
i
o
n
s
to
w
ards
the be
st
s
ear
ch a
gen
t
.
⃗
1
∗
⃗
⃗
.
⃗
(
16)
wher
e'
t'
in
d
i
c
a
te
s
t
h
e
c
u
rre
n
t
i
tera
tio
n,
⃗
a
n
d
⃗
ar
e
coe
f
fic
i
en
t
vec
t
or
s,
∗
⃗
is
t
he
p
os
i
t
i
o
n
v
ector
o
f
t
h
e
be
st
sol
u
ti
on
o
bt
ai
ne
d
so
f
a
r
,
⃗
i
s
the
pos
it
ion
ve
ctor,
|
|
i
s
t
he
a
bs
ol
u
t
e
val
u
e,
a
nd
'
ꞏ
'
is
an
e
l
e
me
nt-
by-e
l
e
m
ent
mu
l
tip
l
i
cati
o
n
.
Th
e v
ect
o
r
s
⃗
a
nd
⃗
a
r
e fo
rmu
l
at
ed
as
f
o
ll
o
w
s:
⃗
2
⃗.
⃗
⃗
(
17)
⃗
2
.
⃗
(
18)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN: 2088-
8694
I
nt
J
P
ow
Elec
& Dr
i
S
y
st, Vol. 10,
N
o.
1, Mar
c
h 2
0
1
9
:
83
–
92
88
wher
e
⃗
i
s
l
i
n
e
a
r
l
y
d
e
c
r
e
a
s
e
d
f
r
o
m
2
t
o
0
o
v
e
r
t
h
e
c
o
u
r
s
e
o
f
i
t
e
r
a
t
i
o
n
s
(in
bo
t
h
e
xp
l
o
rat
i
on
an
d
exp
l
oi
ta
tio
n
pha
ses)
a
nd
⃗
is a
ra
ndom
v
ect
or
i
n
(0,
1
).
There
a
r
e
tw
o
a
t
t
a
c
k
i
ng
a
ppr
oache
s
(
e
x
p
l
o
i
tat
i
o
n
phas
e
s)
.
O
n
e
i
s
S
h
rink
in
g
e
n
c
i
rcl
i
ng
m
e
c
h
ani
s
m
for
‘
a
’ ly
in
g
b
etwee
n
0
t
o
1
in
2
D
s
p
a
ce.
I
n
th
is
c
ase
t
h
e
15
an
d
16
are
u
s
e
d
.
T
h
e
s
e
c
o
n
d
i
s
S
p
i
r
a
l
u
p
d
a
t
i
n
g
pos
it
io
n base
d on va
lue
of
r
a
ndom
no
'
p'
.
T
h
e
m
a
them
atica
l
m
ode
l is as
fo
l
l
ow
s
:
⃗
1
∗
⃗
⃗
.
⃗
0.5
⃗
.
cos
2
∗
⃗
0
.5
(
19)
wher
e
⃗
∗
⃗
⃗
an
d
in
di
ca
t
e
s t
h
e di
st
a
n
c
e
o
f
th
e
i
th
w
hale
t
o
t
h
e
pre
y
(be
st
s
o
l
ut
i
o
n ob
ta
ine
d
so
f
a
r),
b
is a
c
onsta
nt
f
or
d
efi
n
in
g the s
h
a
p
e o
f
t
he
l
o
g
a
r
i
t
hm
ic sp
i
ra
l
,
l
i
s
a
r
andom
num
ber
in
(
−1
,
1
).
The
WO
A
s
t
a
r
ts
w
i
t
h
a
set
of
r
and
o
m
un
kn
ow
n
para
me
ters
o
f
dc
m
a
chi
n
e.
T
h
e
p
o
s
i
t
i
on
of
e
ac
h
wh
a
l
e
mu
st
h
av
e
se
v
e
n
d
c
m
ac
h
i
n
e
p
a
r
amet
ers
whi
c
h
are
up
d
a
t
e
d
t
o
find
o
u
t
t
he
b
e
s
t
s
o
lu
ti
o
n
t
ha
t
is
t
he
ada
p
t
i
ve
r
e
s
p
onse
w
i
l
l
m
a
t
c
h
w
it
h
the
co
rr
espon
d
i
n
g
e
xpe
r
i
me
nt
a
l
dat
a
r
e
s
pon
se
.
Th
e
paramet
e
r
‘a’
i
s
d
e
cre
a
s
e
d
f
r
om
2
t
o
0
to
p
ro
vi
d
e
e
xp
lo
ra
ti
on
a
nd
e
xp
l
o
it
a
t
i
o
n
w
i
t
h
i
nc
r
ease
of
iter
a
tion.
F
or
upda
t
i
n
g
th
e
pos
it
io
n o
f
sear
ch agen
t
, the tw
o
proce
d
u
res are
adop
te
d.
F
or |
⃗
|
> 1
t
he r
and
o
m
s
o
l
u
tio
n is
s
e
l
ec
te
d
(
14)
b
u
t
for
|
⃗
|
<
1
t
h
e
best
s
ol
u
tio
n
i
s
s
e
l
ecte
d
(
16)
b
ased
o
n
co
st
f
u
n
c
t
i
o
n
a
s
i
n
E
qu
a
t
io
n
(8/
9
).
D
ep
e
n
din
g
o
n
t
h
e
val
u
e
of
p,
t
h
e
s
e
a
r
c
h
a
g
e
n
t
i
s
a
b
l
e
t
o
s
w
i
t
c
h
b
e
t
w
e
e
n
e
i
t
h
e
r
a
s
p
i
r
a
l
o
r
a
c
i
rcular
m
ove
me
n
t
a
s
in
(
19)
.
F
i
nal
l
y
,
t
h
e
W
O
A
a
l
gor
ithm
i
s
term
i
nate
d
b
y
the
s
e
l
ect
i
on
of
a
t
e
rm
i
n
a
t
i
o
n c
r
iteri
on or
fi
x
ed
ite
rat
i
on.
7.
PERFO
R
M
ANCE EVALUAT
ION S
T
A
T
ISTICS
Th
e
p
r
e
c
i
sen
e
ss
o
f
esti
mati
on
of
p
a
r
amet
e
r
c
an
b
e
j
u
dg
ed
b
y
t
h
e
s
ta
tis
t
i
c
s
b
e
t
w
e
e
n
t
he
e
x
p
er
i
m
en
ta
l
data
a
n
d
a
da
p
t
i
v
e da
t
a
va
l
ue
s
and
ex
pre
s
se
d
as:
standard de
v
ia
tion error(SDE)
∑
.
(
20)
Mean Er
r
or (ME)
∑
.
(
21)
wher
e
i
s
t
h
e
nu
mb
e
r
o
f
d
a
t
a
p
oin
t
.
8.
RESULT
S
A
N
D
DISCU
SSIO
N
The
para
me
ter
s
o
f
se
para
te
l
y
e
xc
i
t
ed
d
c
motor
can
b
e
deter
m
in
ed
b
y
pe
rform
in
g
the
var
i
ou
s
expe
r
i
me
n
t
s
in
l
ab
orat
ory
a
n
d
mig
h
t
a
l
s
o
b
e
g
i
v
e
n
i
n
m
a
c
h
ine
s
p
ec
i
f
i
c
a
t
i
o
n
da
ta
s
hee
t
.
Bu
t,
h
ow
e
v
er,
it
is
di
ffic
ul
t
to
a
s
c
e
r
tai
n
t
he
e
x
a
ct
v
a
l
ue
o
f
pa
ram
e
ter
s
b
ec
ause
o
f
i
nac
c
u
rac
y
o
f
m
easur
ing
instr
u
m
e
nts.
There
f
ore
,
t
he
a
d
a
p
tive
m
e
t
h
o
d
is t
h
e
best s
o
l
u
t
io
n for
dete
rmi
na
ti
on o
f
u
n
k
n
o
w
n
pa
r
am
eter
s
of
t
he
sys
te
m.
The
da
t
a
o
f s
p
eed re
s
po
nse
a
nd c
u
rr
ent
r
e
sp
onse
c
o
rre
spo
n
d
i
n
g
t
o tim
e are
c
o
llec
t
ed fro
m
sepa
ratel
y
exc
ite
d
dc
m
o
t
or
a
t
r
a
te
d
v
o
ltage
a
nd
w
i
th
l
oa
d
torq
ue
o
f
5
0
N
-
m
b
y
u
si
ng
t
he
s
pee
d
e
nc
o
d
e
r
a
n
d
c
urren
t
sens
or
f
or
2
s
ec
on
d
s
.
Tw
o
l
ook
u
p
t
a
b
les
are
forme
d
b
a
s
e
d
o
n
th
e
sp
e
e
d
a
n
d
cu
rre
n
t
re
spon
se
d
at
a
in
MA
TLA
B
sim
u
l
i
nk
e
n
v
i
r
o
n
m
e
n
t
.
T
he
b
lo
c
k
d
ia
gra
m
o
f
D
C
m
ot
or
i
s
s
how
n
i
n
F
i
g
u
r
e
2
is
a
lso
pl
ace
d
in
sam
e
M
A
TLA
B
sim
u
lin
k
fil
e
.
The
b
l
oc
k
dia
g
r
a
m
o
f
d
c
m
o
tor
w
i
l
l
a
c
t
a
s
ad
apt
i
v
e
mo
d
e
l
b
e
cause
o
f
un
k
now
n
para
me
ters
t
hat
a
r
e
up
date
d
at
e
a
c
h
i
t
era
tio
n.
T
he
r
e
s
p
o
n
se
s
of
l
oo
k
up
t
a
b
l
e
m
ode
l
w
ill
a
c
t
as
refere
nce
m
o
d
e
l.
T
he
c
urr
e
n
t
r
esp
onse
s
o
f
re
fere
nce
a
n
d
ada
p
ti
v
e
m
ode
ls
a
re
s
t
o
re
d
i
n
t
he
w
or
ksp
a
c
e
'
arm
_
current'
,
whereas
the
s
p
eed
res
p
onses
are
store
d
i
n
'
sp
eed
'
.
The
m
odel
sh
o
u
ld
b
e
s
t
ored
b
y
c
e
rtai
n
nam
e
.
By
c
a
l
l
i
ng
t
h
is
n
am
e
thro
ug
h
'
sim'
c
om
ma
n
d
t
he
r
esp
onse
s
a
r
e
p
a
s
se
d
to
t
he
c
o
s
t
fun
c
t
i
on.
T
h
e
c
os
t
fu
nc
ti
o
n
a
nd
W
O
A
func
t
i
o
n
a
r
e
buil
t
in m
-file
of
the
Ma
t
l
a
b
env
i
r
on
ment
.
Evaluation Warning : The document was created with Spire.PDF for Python.
Int J
P
o
w
El
e
c
&
D
ri S
yst
I
S
S
N
:
2088-
86
94
Pa
ra
m
e
t
e
r est
i
ma
ti
on
of
DC mo
to
r th
roug
h
wh
al
e op
ti
mi
zat
i
o
n
a
l
go
ri
th
m (Bya
ma
k
e
s
h
Na
ya
k
)
89
F
i
gure
2.
R
e
f
er
ence
and
ad
a
p
t
i
ve
m
ode
l
Th
e
op
t
i
m
i
s
e
d
a
d
a
pt
i
v
e
va
lu
es are
f
o
und
o
ut b
y
c
o
nsid
e
r
ing
t
h
e
tw
o ob
jec
t
i
v
e
func
t
i
o
n
s.
F
1
a
nd F
2
=
=
t
e
t
dt and
t
e
t
dt
(
22)
F
F
∗F
(
23)
wher
e,
|
e
t
|
a
n
d
|
e
t
|
i
s
the
differe
n
ce
o
f
refere
n
c
e
spee
d
an
d
ada
p
ti
ve
s
pee
d
a
nd
re
ference
armature
curr
ent a
n
d ad
a
p
ti
ve
c
urr
e
nt r
espec
t
i
v
e
l
y.
T
h
e
t
im
e
i
n
c
r
em
ent (
h) is c
hose
n
to 0.
00
5.
I
n
o
r
d
e
r
t
o
g
e
t
the
be
t
t
e
r
r
esu
l
t,
t
he
c
urre
nt
a
nd
spe
e
d
r
es
p
o
n
se
o
f
e
x
perim
e
nta
l
d
a
t
a
are
di
v
i
de
d
int
o
t
h
r
e
e
i
n
t
e
r
v
a
l
s
.
T
h
e
c
o
s
t
f
u
n
c
t
i
o
n
f
o
r
c
u
r
r
e
n
t
r
e
s
p
o
n
s
e
s
w
i
t
h
c
o
n
s
t
ra
in
ts
f
or
t
hr
ee
inter
v
als
i
s
s
how
n
in
(
11
)
.
S
i
m
i
la
rl
y,
t
he
s
pe
e
d
r
e
s
po
nse
cost
f
unc
ti
on
sho
u
l
d
be
d
e
f
i
n
e
d
.
Th
e
ov
e
r
al
l
co
st
f
un
ct
ion
i
s
f
o
r
mu
l
a
t
e
d
a
s
i
n
(19).
The
pe
n
a
l
t
y
v
a
lue
a
d
o
p
t
e
d
her
e
i
s
100
0.
T
he
t
o
t
a
l
n
um
b
e
rs
o
f
Wha
l
es
a
re
1
0
an
d
t
h
e
n
u
m
b
er
o
f
i
t
erat
ion
s
c
o
n
s
i
d
e
r
ed
h
e
r
e
i
s
1
00
.
The
be
st
v
a
l
ue
s
of
R
a
,
L
a
,
L
af
,
R
f
,
L
f
,
J
and
B
ob
t
a
ine
d
b
y
1
st
m
et
hod
implem
ented
thr
o
ugh
WOA
are
0.0
05622915,
0
.4809508
,
1.
239847
0
.
42
826
78
,
0
.
005
6
229
15
,1
0
.
0
952
4
a
n
d
19
4.5
9
9
4
,
w
h
e
r
e
a
s
i
n
2
nd
m
et
h
o
d
t
h
e
v
a
lu
es
a
re
0
.0
08
2
429
03
,
0
.
49
076
78
,
1
.
2
199
,
0
.
40
752
4
5,
0
.0
36
90
0
02,
14.1
3
5
7
9
a
n
d
27
9.13
8
8
i
n
give
n
order.
T
he
e
st
im
ate
d
v
a
l
ue
s
a
r
e
tab
u
l
at
ed
i
n
Tab
l
e
1
a
n
d
where,
|
e
t
|
a
nd
|
e
t
|
is the d
i
ffe
r
en
ce
of re
fer
e
nce
spee
d a
n
d ada
p
ti
ve spee
d
an
d
re
ference armat
u
r
e
c
urrent
and
ada
p
t
i
ve
c
urr
e
nt re
s
pec
t
i
v
e
l
y.
T
he tim
e inc
r
e
m
ent (
h
) is c
ho
se
n
t
o 0.
00
5.
I
n
o
r
d
e
r
t
o
g
e
t
the
be
t
t
e
r
r
esu
l
t,
t
he
c
urre
nt
a
nd
spe
e
d
r
es
p
o
n
se
o
f
e
x
perim
e
nta
l
d
a
t
a
are
di
v
i
de
d
int
o
t
h
r
e
e
i
n
t
e
r
v
a
l
s
.
T
h
e
c
o
s
t
f
u
n
c
t
i
o
n
f
o
r
c
u
r
r
e
n
t
r
e
s
p
o
n
s
e
s
w
i
t
h
c
o
n
s
t
ra
in
ts
f
or
t
hr
ee
inter
v
als
i
s
s
how
n
in
(
11
)
.
S
i
m
i
la
rl
y,
t
he
s
pe
e
d
r
e
s
po
nse
cost
f
unc
ti
on
sho
u
l
d
be
d
e
f
i
n
e
d
.
Th
e
ov
e
r
al
l
co
st
f
un
ct
ion
i
s
f
o
r
mu
l
a
t
e
d
a
s
i
n
(19).
The
pe
n
a
l
t
y
v
a
lue
a
d
o
p
t
e
d
her
e
i
s
100
0.
T
he
t
o
t
a
l
n
um
b
e
rs
o
f
Wha
l
es
a
re
1
0
an
d
t
h
e
n
u
m
b
er
o
f
it
e
r
a
t
i
o
ns
c
on
side
red
her
e
i
s
1
00.
T
h
e
b
e
st
v
al
ues
of
R
a
,
L
a
,
L
af
,
R
f
,
L
f
,
J
and
B
ob
ta
in
ed
b
y
1
st
me
tho
d
im
p
l
em
ente
d
t
h
rou
gh
WO
A
are
0.00
56
2
2
9
1
5
,
0.
480
95
0
8
,
1.23
9
8
4
7
0
.
42
826
78
,
0.00
5
6
2
2
9
1
5
,1
0.09
5
24 a
nd 1
94.
59
94,
w
he
rea
s
in 2
nd
m
e
t
ho
d
t
h
e
v
a
l
u
e
s
a
re
0
.0
0
824
2903
, 0
.4
90
767
8
, 1
.2
199
,
0
.
4
075
245
, 0
.03
690
002
,
14
.13
5
7
9
an
d 27
9
.
1
388
i
n
giv
e
n o
r
d
e
r.
T
h
e
estimat
e
d
v
al
u
e
s
are
t
a
bu
l
a
t
e
d
in
Tabl
e
1
and
per
u
n
i
t
err
o
r
of
e
a
c
h
es
tima
t
e
d
v
a
l
ue
w
it
h
r
e
spect
t
o
data
s
hee
t
v
alue
a
re
i
nc
or
por
ated
in Ta
b
le
1.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SSN: 2088-
8694
I
nt
J
P
ow
Elec
& Dr
i
S
y
st, Vol. 10,
N
o.
1, Mar
c
h 2
0
1
9
:
83
–
92
90
Ta
ble
1.
Est
i
m
a
ti
o
n
of
D
C
m
otor
p
ara
m
e
t
er by 1
st
a
nd
2
nd
me
t
h
od
E
s
ti
m
a
tion
of
d
c
m
o
tor
pa
ram
e
t
e
rs
si
m
u
lati
on
run time
:
2s
econds
i
n
c
r
e
m
e
nt
t
i
m
e
(h)
:0.
005
DC
m
o
t
o
r
p
ar
ame
t
e
r
s
Ma
ch
in
e
d
a
t
a
s
h
e
e
t
1
st
m
e
t
hod
2
nd
m
e
t
hod
V
a
l
u
e
D
i
f
f
er
en
c
e
i
n
ma
ch
in
e
d
a
t
a
shee
t
a
nd
obse
r
ve
d
va
lu
e
in
pu
Va
l
u
e
D
i
f
f
er
en
ce
i
n
ma
ch
i
n
e
d
a
t
a
shee
t
a
n
d
obse
r
ve
d
va
lu
e
in pu
R
a
(Ω)
0
.5
0
.4809
508
0.
0380
984
0.4907
678
0
.0184644
L
a
(H)
0
.01
0
.
0056
229
15
0.4377
085
0.0082
429
03
0
.1757097
L
af
(H)
1
.23
1
.
2398
47
-
0
.
00
800
569
1
.
2199
0.
0082113
8
R
f
(
Ω)
240
1
94.
59
94
0
.
1891
69
279.
13
88
-
0.
16
307
L
f
(
H)
1
2
1
0
.095
24
0
.1587
14.135
79
-
0.17
79
J (N-
m
)
0.4
0
.
4282
678
-0
.07
0
6
0.4075
245
-
0
.01
8
8
B (N-
r
a
d
/
s
)
0.02
0
.0056
229
15
0.7188
0
.0369
000
2
-
0.84
5
C
onve
r
g
e
v
a
l
u
e
--
-
-
-
1
3
.
3
9
6.
6082
The
best
o
pt
i
m
a
l
c
on
ver
g
e
val
u
e
of
t
he
c
os
t
func
t
i
o
n
f
o
u
n
d
by
W
OA
are
1
3
.
39
i
n
1st
me
th
od
a
nd
6
.
6
082
i
n
2
nd
me
t
h
o
d.
T
o ge
t
t
h
e
bes
t
opt
im
al
c
o
nverge
val
u
e,
b
o
t
h
the
m
e
th
o
ds
r
u
n
for numbe
r o
f
tim
es.
The
sma
l
le
r
numbe
r
of
r
uns
i
s
requ
ire
d
t
o
ge
t
t
h
e
be
st
o
pt
i
m
al
c
o
n
v
er
ge
v
al
ue
i
n
2
nd
m
eth
od
c
o
m
p
are
d
t
o
1
s
t
me
tho
d
.
The
run
time
for
eac
h
run
is
d
i
ffe
re
nt.
The
a
v
era
g
e
r
u
n
time
i
n
1
st
m
e
t
h
od i
s
1
4
55
se
c
o
n
d
,
bu
t
in
2
n
d
me
tho
d
i
t
is les
s
a
n
d
f
o
u
n
d
t
o be 7
2
0
s
ec
on
d. The num
ber
o
f
i
t
e
rat
i
ons
r
e
q
ui
r
e
d i
s
l
e
ss i
n
2
nd
w
i
t
h
c
o
mp
ari
s
on
to
1
st
m
eth
od.
T
he
m
ean
e
rror
a
nd
sta
n
da
r
d
d
e
v
ia
t
i
o
n
e
rr
or
o
f
spe
e
d
r
e
s
p
o
n
s
e
a
r
e
-
1
.0
1
9
1
a
n
d
2
.
8
180
re
sp
ec
ti
v
e
ly
i
n
1
st
m
et
ho
d,
w
her
eas
i
n
2
nd
m
et
hod
t
h
e
a
bove
c
o
e
f
f
i
c
i
e
nt
s
are
f
o
u
n
d
t
o
b
e
-0
.4
71
1
and
1
.
0
7
96
.
Si
mi
l
a
rly
,
f
o
r
c
u
rren
t
r
e
s
ponse
,
t
h
e
m
ea
n
e
rro
r
a
n
d
st
a
n
d
a
rd
d
e
via
t
i
o
n
e
rror
value
s
a
r
e
(
-29.40
94,
4
2.44
8
8
)
for
1
st
m
e
t
h
o
d
a
nd
(-1
2.
685
2,
13.4
9
26)
f
or
2
nd
m
e
t
h
o
d
.
T
h
e
s
t
a
t
i
s
t
i
c
a
l
e
r
r
o
r
a
n
a
l
y
s
i
s
w
i
t
h
r
e
s
p
e
c
t
t
o
t
h
e
d
a
t
a
shee
t
o
f
m
a
c
h
i
ne
c
on
firm
s
t
h
e
2n
d
m
e
t
h
od
gi
ve
s
be
tter
pa
ra
me
ter
e
st
i
m
at
ion
co
mp
a
r
ed
t
o
th
e
1
s
t
me
th
od
.
Th
e
cu
rre
n
t
re
sp
on
se
a
nd
s
pe
e
d
r
espon
se
o
f
bot
h
met
h
od
s
a
r
e
co
m
pare
d
w
i
t
h
e
xper
i
m
e
ntal
d
ata
p
o
i
n
t
s
a
s
show
n
i
n
F
igur
es
3
(
a
,
b).
The
co
n
v
e
r
ge
nc
e
c
u
rves
o
f
c
o
st
f
u
n
c
t
i
o
n
for
bo
t
h
m
et
ho
ds
w
i
t
h
d
i
ffe
r
ent
in
de
pen
d
e
n
t
ru
ns ar
e
r
epre
se
nted i
n F
i
g
u
res
4(a,
b).
F
i
gure
3
(a)
.
A
dap
t
i
v
e
c
u
r
r
en
t
r
e
sponses t
o t
h
e
expe
r
i
me
nt
a
l
d
a
ta
u
sin
g
W
O
A
algori
t
hm
F
i
gur
e 3
(b).
Ada
p
tive
an
d sp
e
e
d r
e
spo
n
ses t
o
the
e
x
p
e
r
ime
n
ta
l
da
ta
u
sin
g
W
O
A
a
l
gor
ith
m
Evaluation Warning : The document was created with Spire.PDF for Python.
Int J
P
o
w
El
e
c
&
D
ri S
yst
I
S
S
N
:
2088-
86
94
Pa
ra
m
e
t
e
r est
i
ma
ti
on
of
DC mo
to
r th
roug
h
wh
al
e op
ti
mi
zat
i
o
n
a
l
go
ri
th
m (Bya
ma
k
e
s
h
Na
ya
k
)
91
F
i
gure
4 (a
). Con
v
e
rge
cur
v
e
of cos
t f
u
nc
t
i
o
n
o
f 1
st
me
t
ho
d res
p
ec
t
i
v
e
l
y
usin
g
W
O
A
a
l
go
rit
h
m
F
i
g
u
r
e
4
(
b).
Con
v
erge
c
urve
o
f c
o
st
f
unc
t
i
on
of
2
nd
m
eth
o
d
r
espec
tive
l
y us
in
g WO
A
a
l
gor
i
t
hm
9.
CONCL
U
S
ION
The
pa
ram
e
ter
s
o
f
separ
a
te
l
y
e
xc
it
e
d
D
C
m
o
tor
can
b
e
determ
ine
d
by
perf
orm
i
ng
the
r
e
q
u
ire
d
numbe
r of
e
x
p
e
rim
e
nts. The a
cc
u
r
ate es
tim
a
t
i
o
n
m
a
y be a
ffec
te
d
d
u
e t
o
e
rror in
t
he me
a
suri
ng i
n
str
u
me
nts or
b
y
a
pp
roxi
ma
ti
on
o
f
ma
th
ema
t
i
c
al
a
n
a
ly
si
s.
I
n
a
d
d
i
ti
on
,
so
me
p
a
ram
e
ters
a
r
e
d
if
fic
u
lt
t
o
m
e
asur
e
as
f
or
exa
m
ple
t
h
e
v
i
sc
ous
f
rict
i
o
n
.
T
he
c
on
ven
t
i
o
na
l
m
e
th
o
d
g
ive
n
i
n
t
h
is
a
rtic
le
m
ay
b
e
the
2
nd
c
ho
ic
e
of
ide
n
tif
i
c
at
i
on
of
p
ar
am
eters.
H
ow
e
v
er,
this
m
etho
d
a
l
s
o
doe
s
n
o
t
a
c
c
u
r
a
te
ly
d
e
t
er
mine
t
he
p
ar
am
eter
s
bec
a
u
s
e
of
w
ron
g
est
i
ma
ti
on
o
f
tim
e
c
o
nsta
nt
s,
p
art
i
c
u
lar
l
y
t
h
e
a
rm
ature
tim
e
c
o
nsta
n
t
w
hic
h
i
s
a
f
fe
ct
ed
b
y
cou
p
l
i
ng ef
fec
t
of fie
l
d
be
ha
v
i
our
. The o
p
t
i
m
i
sa
ti
o
n
a
l
gor
ith
m
c
a
n
be
u
t
i
l
i
s
ed t
o
a
d
ap
t t
h
e para
me
t
e
rs
t
hr
ou
g
h
a
c
o
st
f
u
n
c
t
i
o
n
to
t
rac
k
t
he
e
xpe
r
i
me
nt
a
l
r
espo
nse
da
ta
b
y
runn
i
n
g
th
e
ma
ch
in
e
at
r
at
ed
v
a
l
u
e
.
Thi
s
r
e
q
ui
re
s
the
a
ccur
a
t
e
dyna
mic
mode
l.
T
he
d
y
n
am
ic
s
of
f
lu
x
be
ha
vio
u
r
is
t
h
e
m
a
j
o
r
r
e
s
pon
si
ble
f
o
r
a
ffe
ct
ing
t
h
e
elec
tr
ical
a
nd
m
e
c
h
ani
c
a
l
tim
e
consta
n
t
s.
T
her
e
fore
,
thi
s
a
rtic
l
e
p
ro
po
se
s
t
h
e
dyn
amic
m
o
d
e
l
of
s
ep
ara
t
el
y
exc
ite
d
dc
m
otor
s
w
h
ic
h
ta
ke
s
c
a
re
o
f
the
d
ynam
i
c
s
o
f
fl
u
x
b
e
h
a
vi
o
u
r
.
In
t
h
i
s
a
r
t
i
c
l
e
,
t
h
e
Wh
al
e
op
ti
mi
sati
on
alg
o
ri
t
h
m
is
u
s
e
d
t
o
m
in
imise
the
cos
t
f
u
n
ct
i
on
-
on
e
for
co
m
p
l
ete
peri
ods
o
f
ex
per
i
m
e
n
t
al
d
a
t
a
a
nd
o
t
h
e
r
by
ma
kin
g
t
he
w
h
o
l
e
p
er
io
d
of
e
xpe
r
i
me
nt
a
l
d
a
t
a
on
to
d
i
f
fere
nt
i
n
t
erva
ls.
T
h
e
s
t
a
tist
i
ca
l
a
n
al
ys
i
s
a
n
d
e
s
tim
ated
parameters
c
om
pared
w
ith
t
he
d
a
t
a
sheet
p
a
ram
e
ters
o
f
different
i
n
t
e
r
va
l
segm
en
t
m
e
t
h
o
d
s
ho
w
be
t
t
er
r
esu
l
t
s
in com
par
i
so
n to
t
he
w
ho
le
p
e
r
io
d of ex
p
e
r
i
m
e
n
t
a
l d
a
ta.
REFE
RENCES
[1]
L
.
L
jiung, “Syst
em
I
den
t
if
ication,
”
i
n
T
heo
r
y f
o
r
th
e Us
er
(Pren
t
i
ce Hall, 2nd edition,
1999.
[2]
H
.
U
nbeh
a
uen
,
G
.
P.
R
ao
,
“A
r
evi
e
w
of
i
dentif
icati
on
i
n
conti
n
uous-tim
e
syst
e
m
s,”
An
nu
al
R
eviews
i
n
Co
n
t
r
o
l
,
22
,
1
45–
17
1,
1998.
[
3
]
G
.
F
.
F
r
a
n
k
l
i
n
,
J
.
D
.
P
o
w
e
l
l
,
M
.
L
.
W
o
r
k
m
a
n
,
“
D
i
g
i
t
a
l
C
o
n
t
rol
o
f
D
yn
amic
S
yst
e
m
s
”
Ad
d
i
s
onW
e
s
l
e
y,
2
n
d
e
d
it
io
n
, 1
99
0.
[4]
J
.
C.
B
a
s
il
io,
M.
V
.
M
o
re
i
r
a,
“
S
t
ate-s
p
ace
par
a
m
e
ter
i
d
en
t
ific
a
t
io
n
in
a
s
e
c
on
d
c
o
n
t
ro
l
la
bo
ra
to
ry
,”
I
E
E
E
T
r
a
n
s
.
o
n
Educat
ion,
4
7
,
204–
21
0,
200
4.
[5
]
R
.
Krneta,
S.
A
nti
c
,
D.
S
to
jan
o
v
i
c,
“
Recursiv
e
l
east
s
q
u
a
r
e
me
th
o
d
i
n
pa
ra
me
te
rs
i
de
n
t
i
f
ic
a
tion
of
D
C
mo
tors
mo
dels
,
”
Facta
Un
i
v
ers
i
tati
s.
18
, 46
7
–
4
7
8
, 2
00
5.
[6]
M
.
Ruderm
an,
J.
K
ret
t
ek,
F.
H
o
f
f
m
a
n
,
T
.
B
e
t
r
a
n
,
“
O
p
t
i
m
a
l
s
tate
s
pace
c
ontrol
o
f
DC
m
ot
or
,”
i
n
Proceed
in
gs
of
t
h
e
17
th
Wo
rld
Co
ng
re
ss I
F
AC
,
S
eou
l
,
K
o
rea,
pp.
5
7
9
6
–
5
801
,
2
00
8.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N: 2
0
8
8
-
86
94
I
nt
J
P
ow
E
l
e
c
&
Dr
i
S
y
st,
Vol.
10,
N
o.
1
,
Mar
c
h
2
0
1
9
:
83
–
92
92
[7
]
M
.
H
a
def,
M
.
R.
M
ek
idech
e,
“
P
a
ramet
e
r
id
en
tificat
io
n
of
a
s
e
p
a
r
a
t
e
l
y
e
x
c
i
t
e
d
D
C
m
o
t
o
r
v
i
a
i
n
v
e
r
s
e
p
r
o
b
l
e
m
m
e
t
hod
o
l
og
y,
”
in
Pr
oceedi
n
g
s
o
f
t
h
e Ecol
ogic V
e
hi
cles
and R
e
ne
wab
l
e E
n
er
g
i
e
s
, M
o
n
aco
, F
rance,
200
9.
[8]
M
.
Ha
de
f,
A
.
B
o
uro
u
i
n
a
,
M
.
R
.
Me
kide
c
h
e
,
“
P
a
r
a
me
te
r
id
e
n
ti
fi
cati
on
of
a
D
C
m
o
to
r
via
m
o
m
e
nt
s
m
e
th
od
,
”
Int
.
J.
o
f
E
l
ectri
c
a
l and
P
o
wer Engin
eeri
n
g
,
1
,
2
1
0
–
214
, 2
00
8.
[9
]
A
.
Ru
baai,
R.
K
o
t
a
ru,
“O
n
line
id
ent
i
fi
catio
n
and
con
t
rol
of
a
d
c
mo
tor
usi
n
g
le
a
r
n
i
n
g
a
d
a
pta
tio
n
of
n
e
u
ra
l
n
e
tw
orks
,
”
IEEE
T
r
ans.
on
Ind
u
str
y
Ap
plicatio
ns
,
3
6
,
9
35
–9
42,
2000
.
[1
0]
G
.
M
am
ani,
J
.
Beced
as
,
H.
S
ira-Ram
i
rez,
V
.
F
e
liu
B
atl
l
e,
“Open-l
o
o
p
a
lg
ebrai
c
i
den
t
if
ication
m
e
th
od
f
o
r
DC
moto
r
s
,”
i
n
P
r
o
c
eedi
ngs
o
f
t
h
e Eur
o
p
e
an
Contro
l Co
nf
erence
,
Kos, Greece, 2
007
.
[1
1]
G
.
M
am
ani,
J
.
Beced
as,
V.
F
eli
u
B
atl
l
e,
“
On-lin
e
f
a
s
t
a
l
g
eb
raic
p
aram
eter
a
n
d
st
ate
estim
ati
o
n
f
o
r
a
DC
m
ot
o
r
a
p
p
l
ie
d
to a
d
a
pt
iv
e
16
c
on
trol
,
”
i
n
Pr
oceed
ing
s
of
th
e
W
o
rld Co
ng
r
e
ss
on En
g
i
neer
ing
,
L
on
do
n
,
UK,
2
0
08.
[12]
W
e
i
Wu,
“DC
Motor
Paramet
e
r
Iden
t
i
fi
catio
n
U
s
i
ng
S
p
eed
S
tep
Res
po
nse
s
,”
i
n
M
o
dellin
g a
n
d
Sim
u
l
a
t
i
o
n
i
n
En
gin
e
e
r
ing
Vo
lu
me
20
12
,
Arti
cle
ID
1897
57,
5
p
ages
d
oi:1
0.
1
1
5
5
/
2
0
12/
18
97
57
,
Hi
nd
awi P
ubli
s
hin
g
Corporat
i
o
n
, 20
1
2.
[1
3]
F
.
G
.
M
a
rti
n
s
,
“
Tu
n
i
ng
P
ID
C
on
tro
l
l
e
rs
u
sing
t
h
e
IT
AE
C
ri
ter
i
on,”
Int.
J. o
f
E
ngi
neer
ing Ed
uca
t
i
o
n
,
2
1,
p
p
.
867
–8
73
,
2
0
05
.
[1
4]
M
.
Z
h
u
an
g,
D
.
P
.
A
th
eri
on,
“
Tunin
g
P
ID
C
ontro
ll
e
r
s
with
I
ntegral
P
e
rf
o
r
man
ce
Cri
t
eri
a
,”
i
n
Pr
o
ceed
in
gs o
f
IEEE
In
t.
Conferen
ce Con
t
r
o
l
’
91,
Edin
bu
rgh,
U
K
,
p
p.
481
–4
86
,
1
9
9
1
.
[1
5]
S
.
O
zana,
T
.
D
o
cekal
,
“
P
ID
c
on
tro
ller
desig
n
b
ased
o
n
gl
o
bal
optimi
zati
on
t
echn
i
qu
e
w
i
t
h
a
dd
itional
co
ns
traints,
”
J.
of El
ectri
cal En
gi
neer
ing
,
6
7,
1
6
0
–
168
,
2
01
6.
[16
]
S. Lev
i
n
e, “The
Co
ntro
l
Ha
nd
bo
ok
”
,
S
e
c
o
n
d
Ed
i
tion
,
C
RC P
r
e
s
s,
2
01
0.
[17]
S.
M
ir
j
a
l
ili,
A
.
Lewis,
“
T
h
e
Whale
Opti
mi
zati
o
n
Algor
i
t
h
m
,”
i
n
Adva
nces
i
n
En
g
i
neeri
n
g
S
o
ftwa
r
e
,
95,
pp
.
5
1
–
6
7
,
20
16
.
[1
8]
S
.
Ad
e
wusi,
“Mod
elin
g and P
a
ra
met
e
r
Estim
ati
o
n
of
a
DC
M
o
to
r
Us
in
g
Co
nstraint Optim
i
zati
on Tech
ni
que,
”
IOSR
J.
of
M
ech
ani
cal an
d Ci
vil
En
gg
in
eerin
g,
13,
4
6
-
56
,
2
01
6.
[1
9]
S
.
S
t
i
p
e
ti
c,
W
.
M
i
ebach,
D.
Z
arko,
“
Optimi
zati
o
n
in
D
esi
g
n
o
f
E
le
c
t
r
i
c
Ma
c
h
ine
s
:
Me
th
od
olog
y
a
n
d
W
o
rkflo
w
,”
A
C
EMP
– OP
TIM
–
El
ectromo
tiom
Joi
n
t
Conf.,
20
15.
[2
0]
M
.
S
.
A
.
M
oh
am
ad,
I.
M
.
Y
a
ss
in
,
A
.
Z
ab
i
d
i,
M
.
N
.
T
aib
,
R
.
A
dn
an,
“
C
om
pa
ri
so
n
bet
w
een
P
S
O
a
nd
O
LS
f
o
r
N
A
RX
P
aramet
e
r
E
stim
ati
o
n
of
a
D
C
M
o
t
o
,”
IEEE
S
y
mpo
s
i
u
m
on
In
du
stria
l
Elect
ro
ni
cs
&
Ap
pli
c
at
io
n
s
,
Ku
ch
i
n
g,
M
a
la
y
s
ia
,
20
13
.
[2
1]
Y
a
nn
is
L
.
K
a
rn
av
as,
Ioan
ni
s
D.
C
has
i
o
t
i
s
,
“
P
MDC
Coreles
s
Micro-Motor
Pa
ramet
e
rs
E
stimati
o
n
through
Gre
y
W
o
lf Optim
i
zer,”
IE
EE
con
f
. o
n
E
l
ect
ri
cal M
a
c
h
in
es,
p
p.
8
65
–
8
7
0
, 2
01
6.
[2
2]
V
.
S
an
kardo
ss,
P
. Geeth
a
n
j
ali,
“
P
M
DC
M
oto
r
P
aram
eter Esti
mation
Usi
ng
Bio-In
spired
O
p
timization
Algor
i
th
ms,”
in
IE
EE
Acces
s
,
5
, 11
2
4
4
–
11
2
5
4
, 20
1
7
.
[2
3]
D
.
P
uan
g
d
o
w
n
reon
g,
S
.
Hl
un
g
n
am
tip
,
C
.
Tha
m
m
a
rat,
A
.
Na
w
i
k
av
atan
,
“
A
ppl
ication
of
F
lo
wer
P
o
lli
n
ation
A
l
g
o
rit
h
m
to
P
aram
eter
I
den
tif
ic
ati
o
n
of
D
C
M
o
to
r
M
o
del,
”
i
n
5th
Inter
n
a
t
i
o
n
a
l El
ectrica
l En
g
i
n
eeri
n
g
Co
ngr
ess
,
P
a
t
t
aya,
Th
a
il
an
d,
p
p
.
1
–
4
,
2
017.
[2
4]
B.
N
ay
ak,
S.
S
a
h
u,
T
.
R.
C
ho
udh
ury
,
“
P
a
ram
e
ter
Estim
atio
n
o
f
DC
M
o
t
or
u
s
i
n
g
Ada
p
tive
T
r
a
ns
fe
r
Fu
nc
tion
b
a
se
d
o
n
N
eald
e
r
–
M
ead
O
pt
imis
ati
on,”
In
don
esian
J.
of
El
e
c
tri
c
al
En
gi
neer
in
g a
n
d
Com
p
u
t
er
Sci
e
nce
,
9,
p
p
.
696
-
70
2
,
2018
.
BIOGRAPHI
E
S
OF
AUT
HORS
He
i
s
b
o
rn
i
n
O
d
is
ha
i
n
1
9
6
5
,
Ind
i
a.
H
e
receiv
e
d
t
h
e
M
a
s
t
er
D
e
g
ree
i
n
E
lectri
cal
E
ngin
e
e
r
i
n
g
f
r
o
m
I
n
s
tit
u
t
e
o
f
T
ech
no
lo
gy,
B
an
ara
s
H
indu
U
niversity
(IT-B
H
U
)
,
Ba
n
a
ra
s,
I
nd
ia
a
nd
P
h.D.
deg
r
ee
i
n
E
l
ectrical
E
ng
in
eerin
g
f
r
o
m
t
h
e
K
IIT
U
n
i
v
e
rsity,
Bh
ub
a
n
es
war,
I
nd
ia.
Sin
ce
19
99,
h
e
is
w
o
r
ki
ng
a
s
Ass
o
ciat
e
P
r
of
esso
r
in
E
l
ectri
cal
E
ngi
neeri
n
g
Dep
artm
ent
o
f
K
IIT
Un
i
v
ers
i
t
y
o
f
Bhu
b
anes
war.
H
e
h
a
s
a
vast
k
n
o
w
l
e
dg
e
o
f
E
lect
rical
E
ng
ineeri
ng
with
i
nd
us
try
e
x
pe
rie
n
c
e
.
His
m
a
in
r
esearch
a
reas
a
re
P
ow
er
E
l
ectro
n
i
cs
a
n
d
E
lect
ri
cal
D
rives
,
hy
brid
v
eh
ic
l
e
,
ren
e
w
a
bl
e
energ
y
an
d
ap
p
licati
o
n
o
f
P
IC Microco
nt
roll
ers i
n
s
peci
al d
riv
e
ap
p
li
cati
ons.
Sh
e
is
b
o
r
n
Od
isha
,
In
dia
,
i
n
A
p
ril
,
1
9
8
1
.
S
he
g
ra
d
u
a
t
e
d
i
n
E
l
e
ctri
ca
l
En
g
i
n
eerin
g
and
M.
Tech.
In
P
ow
er
a
n
d
E
n
e
rg
y
S
y
s
t
e
m
f
rom
th
e
K
IIT
U
niv
e
rs
it
y,
B
h
uban
e
sw
a
r.
S
h
e
i
s
pursu
in
g
her
P
h
D
d
e
gre
e
i
n KIIT
Univ
e
rsity
.
Evaluation Warning : The document was created with Spire.PDF for Python.