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ased
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f
r
o
to
r
s
alien
cy
to
esti
m
ate
p
o
s
iti
o
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[
1
]
.
T
h
e
s
u
cc
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f
HFI
is
,
h
o
we
v
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,
s
tr
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ased
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[
2
]
.
T
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s
tatis
tical
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tech
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lar
ly
wh
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lo
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v
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s
d
y
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am
ically
[
3
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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Vo
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1
7
,
No
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2
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J
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n
e
20
2
6
:
8
7
3
-
8
8
4
874
R
ec
en
t
ac
h
iev
em
en
ts
in
c
o
n
d
i
tio
n
m
o
n
ito
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d
in
tellig
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d
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s
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s
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[
4
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R
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R
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a
n
d
s
y
s
tem
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p
tim
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[
5
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.
M
o
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,
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tific
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as
b
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tiv
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m
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ce
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p
r
o
v
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en
t
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f
elec
tr
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iv
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[
6
]
.
Mo
r
e
s
o
p
h
is
ticated
HFI
tech
n
iq
u
es
with
m
o
d
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latio
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r
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p
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s
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v
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g
th
e
lo
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s
p
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d
PMSM
ap
p
licatio
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s
[
7
]
.
R
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tly
,
HFI
co
n
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r
o
l
s
ch
em
es
h
av
e
b
ee
n
s
u
g
g
ested
b
ased
o
n
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
to
allo
w
a
d
ap
tiv
e
a
m
p
litu
d
e
ch
o
ice
i
n
en
co
d
er
less
PMSM
d
r
iv
es [
8
]
.
Ho
wev
er
,
r
eg
ar
d
less
o
f
th
ese
ad
v
an
ce
m
en
ts
,
th
e
m
ajo
r
ity
o
f
cu
r
r
en
t
m
eth
o
d
s
ar
e
b
ased
o
n
eith
er
p
r
ed
eter
m
in
e
d
in
tu
itio
n
o
r
s
o
lely
d
ata
-
d
r
iv
e
n
lear
n
in
g
,
with
o
u
t
co
n
s
id
er
ati
o
n
o
f
u
n
d
e
r
ly
in
g
m
o
to
r
p
h
y
s
ics.
I
t
h
as
also
b
ee
n
s
h
o
wn
to
b
e
a
p
p
licab
le
to
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
in
t
h
e
d
r
iv
e
s
y
s
tem
o
f
elec
tr
ic
v
eh
icles
as
a
m
ea
n
s
o
f
co
n
t
r
o
llin
g
to
r
q
u
e
a
n
d
cu
r
r
en
t,
illu
s
tr
atin
g
th
at
it
m
ay
b
e
u
s
ed
in
lim
ited
d
y
n
a
m
ic
co
n
d
itio
n
s
[
9
]
.
I
n
th
e
m
ea
n
tim
e,
o
p
tim
izatio
n
b
ased
o
n
m
ac
h
in
e
lear
n
in
g
h
as
en
h
an
ce
d
th
e
d
esig
n
o
f
ele
ctr
ic
m
ac
h
in
es
a
n
d
th
eir
p
e
r
f
o
r
m
a
n
c
e
i
n
d
i
f
f
e
r
e
n
t
co
n
d
i
t
i
o
n
s
[
1
0
]
.
Y
e
t
,
s
t
r
o
n
g
e
s
tim
a
t
i
o
n
i
n
l
o
w
-
s
p
e
e
d
r
e
g
i
m
e
s
i
s
s
t
il
l
a
n
i
s
s
u
e
,
d
es
p
i
t
e
t
h
e
s
o
p
h
is
ticated
HFI
-
b
ased
s
en
s
o
r
less
co
n
tr
o
l
m
eth
o
d
s
[
1
1
]
.
Ma
c
h
in
e
lear
n
in
g
o
p
tim
izatio
n
o
f
in
jectio
n
am
p
litu
d
e
h
as
d
em
o
n
s
tr
ated
p
o
ten
tial,
y
et
s
u
ch
to
o
ls
ten
d
t
o
b
e
lack
in
g
in
p
r
o
v
id
in
g
r
ea
l
-
tim
e
f
lex
ib
ilit
y
[
1
2
]
.
T
h
is
p
ap
er
d
is
cu
s
s
es
th
ese
s
h
o
r
tco
m
in
g
s
b
y
p
r
esen
tin
g
a
p
h
y
s
ics
-
in
f
o
r
m
e
d
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
(
PIRL)
m
o
d
el
th
at
in
c
o
r
p
o
r
ates
th
e
m
o
to
r
d
y
n
am
ics
in
to
t
h
e
p
r
o
c
e
s
s
o
f
lear
n
in
g
.
T
h
e
p
r
o
p
o
s
ed
s
o
lu
tio
n
ca
n
allo
w
co
n
tr
o
l
o
f
HFI
am
p
litu
d
e
in
en
co
d
er
less
PMSM
d
r
iv
es
in
an
ad
ap
tiv
e,
s
tab
le,
an
d
ef
f
icien
t
w
ay
b
y
in
co
r
p
o
r
atin
g
p
h
y
s
ical
co
n
s
tr
ain
ts
in
to
th
e
r
e
war
d
f
u
n
cti
o
n
.
2.
L
I
T
E
R
AT
U
RE
R
E
VI
E
W
I
n
PMSMs
at
lo
w
-
s
p
ee
d
co
n
d
itio
n
s
,
h
ig
h
-
f
r
e
q
u
en
c
y
s
ig
n
al
i
n
jectio
n
h
as
co
n
tin
u
e
d
to
b
e
o
n
e
o
f
th
e
b
est
m
eth
o
d
s
to
esti
m
ate
r
o
to
r
p
o
s
itio
n
b
ec
au
s
e
o
th
er
o
b
s
er
v
e
r
s
d
o
n
o
t
wo
r
k
u
n
d
er
lo
w
-
s
p
ee
d
co
n
d
itio
n
s
(
wea
k
b
ac
k
-
E
MF
s
ig
n
al)
[
1
3
]
.
I
n
itial
im
p
lem
en
tatio
n
s
wer
e
b
ased
o
n
co
n
s
tan
t
am
p
litu
d
e
in
jectio
n
s
ch
em
es
,
wh
ich
wer
e
ea
s
y
to
im
p
lem
e
n
t
an
d
l
ed
to
p
o
o
r
p
er
f
o
r
m
an
ce
with
ch
an
g
es
in
o
p
er
atin
g
co
n
d
itio
n
s
[
1
4
]
.
T
h
ese
f
ix
e
d
s
tr
ateg
ies ten
d
to
ca
u
s
e
m
o
r
e
t
o
r
q
u
e
r
ip
p
le
a
n
d
ac
o
u
s
tic
n
o
is
e,
wh
ich
r
estricts
th
eir
p
r
ac
tic
al
u
s
e
[
1
5
]
.
T
o
ad
d
r
ess
th
ese
p
r
o
b
lem
s
,
th
e
ad
ap
tiv
e
am
p
litu
d
e
s
elec
tio
n
alg
o
r
ith
m
b
ased
o
n
o
f
f
-
lin
e
tu
n
in
g
an
d
an
aly
tical
m
o
d
elin
g
h
as
b
ee
n
s
u
g
g
ested
to
e
n
h
an
ce
th
e
ac
c
u
r
ac
y
a
n
d
e
f
f
icien
cy
o
f
th
e
es
tim
atio
n
s
[
1
6
]
.
B
y
allo
win
g
th
e
ad
a
p
tatio
n
o
f
p
a
r
am
eter
s
in
m
o
to
r
s
y
s
t
em
s
b
y
d
ata,
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
h
as
f
u
r
th
er
e
x
p
an
d
ed
th
ese
ab
ilit
ies
[
1
7
]
.
Simu
ltan
eo
u
s
ly
,
m
ac
h
in
e
lear
n
in
g
h
as
b
ee
n
u
s
ed
to
p
r
e
d
ict
lo
ad
a
n
d
o
p
tim
ize
p
o
wer
s
y
s
tem
s
at
th
e
s
y
s
tem
lev
el,
p
r
o
v
in
g
th
e
in
cr
ea
s
ed
ap
p
licab
ilit
y
o
f
in
tellig
en
t
co
n
tr
o
l
tech
n
iq
u
es
[
1
8
]
.
I
n
tellig
en
t
p
o
wer
s
y
s
tem
s
h
av
e
also
b
ee
n
d
ev
elo
p
e
d
with
d
ig
ital
twin
tech
n
o
lo
g
ies
f
o
r
p
r
ed
ictiv
e
m
ain
ten
an
ce
an
d
o
p
tim
izatio
n
o
f
th
e
s
y
s
tem
[
1
9
]
.
I
t
h
as
b
ee
n
s
h
o
wn
t
h
at
th
e
H
FI
tech
n
iq
u
es
o
f
o
p
tim
izin
g
i
n
jecte
d
v
o
ltag
e
am
p
litu
d
e
b
y
p
u
ls
e
-
b
ased
m
eth
o
d
s
h
av
e
b
ee
n
s
tu
d
ied
r
ec
en
tly
an
d
h
av
e
d
em
o
n
s
tr
at
ed
b
etter
p
er
f
o
r
m
a
n
ce
at
lo
w
s
p
ee
d
s
[
2
0
]
.
HFI
tech
n
iq
u
es
in
4
8
V
PMSM
h
av
e
b
ee
n
ex
p
er
im
en
tally
test
ed
an
d
h
a
v
e
b
ee
n
s
h
o
w
n
to
b
e
ef
f
ec
tiv
e
in
r
ea
l
o
p
er
atin
g
co
n
d
itio
n
s
[
2
1
]
.
Mo
r
eo
v
er
,
n
etwo
r
k
e
d
d
r
i
v
e
s
y
s
tem
s
th
at
r
u
n
with
t
h
e
u
n
ce
r
tain
ties
o
f
co
m
m
u
n
icatio
n
h
av
e
b
ee
n
eq
u
ip
p
e
d
with
lear
n
in
g
-
b
ased
co
n
tr
o
l
s
tr
ateg
ies
[
2
2
]
.
Op
tim
izatio
n
o
f
elec
tr
ic
m
ac
h
in
e
d
esig
n
h
as
also
b
ee
n
ex
ten
s
iv
ely
p
er
f
o
r
m
ed
with
th
e
h
elp
o
f
m
ac
h
in
e
l
ea
r
n
in
g
,
lea
d
in
g
to
b
etter
p
er
f
o
r
m
an
ce
in
d
icato
r
s
,
in
clu
d
in
g
e
f
f
icien
cy
a
n
d
r
o
b
u
s
tn
ess
[
2
3
]
.
I
n
tellig
en
t
m
o
to
r
d
r
iv
e
s
y
s
tem
s
h
av
e
b
ee
n
s
h
o
wn
t
o
b
e
r
e
al
-
tim
e
f
ea
s
ib
le
with
em
b
ed
d
ed
lear
n
in
g
co
n
tr
o
l
-
b
ased
ar
ch
itectu
r
es
[
2
4
]
.
Mo
r
e
o
v
er
,
th
e
h
y
s
ter
esis
co
n
tr
o
l
m
eth
o
d
s
o
f
t
o
r
q
u
e
r
ip
p
le
r
ed
u
ctio
n
h
av
e
b
ee
n
in
v
esti
g
ated
to
im
p
r
o
v
e
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
th
e
d
r
iv
e
[
2
5
]
.
T
h
e
u
s
e
o
f
ad
a
p
tiv
e
c
o
n
tr
o
l
s
tr
ateg
ies
h
as
also
en
h
an
ce
d
PMSM
p
er
f
o
r
m
an
c
e
b
y
d
y
n
am
ically
r
ed
u
cin
g
t
o
r
q
u
e
r
ip
p
le
[
2
6
]
.
PMSM
d
r
iv
es h
av
e
b
ee
n
m
o
d
eled
u
s
in
g
d
ig
ital
twin
s
to
ac
cu
r
atel
y
esti
m
ate
p
ar
am
eter
s
an
d
id
en
tify
th
e
s
y
s
tem
[
2
7
]
.
Mo
d
el
p
r
e
d
ict
iv
e
co
n
tr
o
l
h
as
also
b
ee
n
co
m
b
in
ed
with
r
ein
f
o
r
ce
m
en
t le
ar
n
in
g
to
im
p
r
o
v
e
d
y
n
am
ic
p
er
f
o
r
m
an
ce
[
2
8
]
.
T
h
e
d
ev
elo
p
m
en
t
o
f
VL
SI
-
b
ased
s
y
s
tem
s
h
as
f
ac
ilit
a
te
d
th
e
r
ea
lizatio
n
o
f
in
tellig
en
t
co
n
tr
o
l
alg
o
r
it
h
m
s
wi
th
l
o
w
p
o
we
r
i
n
em
b
e
d
d
e
d
s
y
s
te
m
s
[
2
9
]
.
Mi
x
e
d
-
s
ig
n
al
a
r
ch
ite
ct
u
r
es
h
a
v
e
als
o
b
e
en
u
s
ed
i
n
r
ea
l
-
tim
e
a
d
a
p
t
iv
e
s
i
g
n
a
l
p
r
o
ce
s
s
i
n
g
i
n
m
o
to
r
c
o
n
tr
o
l
a
p
p
lic
ati
o
n
s
[
3
0
]
.
T
o
m
i
n
i
m
iz
e
t
o
r
q
u
e
r
i
p
p
le
a
n
d
e
n
h
a
n
c
e
ef
f
ic
ie
n
c
y
,
h
a
r
m
o
n
i
c
m
i
n
i
m
i
za
tio
n
m
et
h
o
d
s
b
ase
d
o
n
o
p
tim
iz
ed
p
u
ls
e
p
att
er
n
m
o
d
u
la
ti
o
n
h
a
v
e
b
ee
n
s
u
g
g
es
te
d
[
3
1
]
.
R
a
n
d
o
m
m
o
d
u
l
ati
o
n
-
b
as
e
d
n
o
is
e
s
u
p
p
r
ess
i
o
n
m
e
th
o
d
s
h
av
e
als
o
b
ee
n
e
x
p
l
o
r
ed
am
o
n
g
PMSM
d
r
i
v
es
[
3
2
]
.
E
m
b
ed
d
e
d
m
o
to
r
d
r
iv
e
s
y
s
tem
s
h
av
e
b
ee
n
s
h
o
wn
to
im
p
l
em
en
t
r
ea
l
-
tim
e
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
-
b
ased
co
n
tr
o
ller
s
,
c
o
n
f
ir
m
i
n
g
th
at
th
ey
ar
e
a
p
p
licab
le
in
a
r
ea
l
-
wo
r
ld
s
ce
n
ar
io
[
3
3
]
.
I
n
tell
ig
en
t
s
y
s
tem
s
h
av
e
also
b
ee
n
en
h
an
ce
d
b
y
em
er
g
i
n
g
n
eu
r
o
m
o
r
p
h
ic
an
d
ev
en
t
-
d
r
i
v
en
ar
ch
itectu
r
es
,
wh
ich
h
a
v
e
i
n
cr
ea
s
ed
ef
f
icien
cy
an
d
laten
c
y
[
3
4
]
.
M
o
r
e
s
o
p
h
is
ticated
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
m
eth
o
d
s
lik
e
d
ee
p
d
eter
m
in
is
tic
p
o
licy
g
r
a
d
ien
t
(
DDPG)
h
av
e
b
ee
n
e
f
f
ec
tiv
ely
u
s
ed
to
ad
d
r
ess
co
n
s
tr
ain
ed
P
MSM
co
n
tr
o
l iss
u
es [
3
5
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
P
h
ysics
-
in
fo
r
med
r
ein
fo
r
ce
me
n
t le
a
r
n
in
g
fo
r
a
d
a
p
tive
h
ig
h
-
f
r
eq
u
en
cy
in
jectio
n
in
…
(
S
u
r
en
d
a
r
A
r
a
vin
d
h
a
n
)
875
E
v
en
th
o
u
g
h
it
h
as
m
ad
e
g
o
o
d
p
r
o
g
r
ess
,
it
is
s
till
f
o
u
n
d
th
a
t
th
e
cu
r
r
en
t
m
eth
o
d
s
h
a
v
e
d
if
f
icu
lties
in
r
ea
lizin
g
co
n
cu
r
r
e
n
t
o
p
tim
izatio
n
o
f
p
o
s
itio
n
esti
m
atio
n
ac
cu
r
ac
y
,
to
r
q
u
e
r
ip
p
le
r
ed
u
ctio
n
,
a
n
d
e
n
er
g
y
ef
f
icien
cy
in
th
e
p
r
esen
ce
o
f
n
o
n
-
id
ea
l
co
n
d
itio
n
s
in
th
e
r
ea
l
wo
r
ld
.
I
n
a
d
d
itio
n
,
th
e
v
ast
m
ajo
r
ity
o
f
th
e
ap
p
r
o
ac
h
es
d
o
n
o
t
h
av
e
a
co
m
m
o
n
f
r
am
ewo
r
k
th
at
in
c
o
r
p
o
r
a
tes
p
h
y
s
ics
-
b
ased
m
o
d
elin
g
an
d
ad
ap
tiv
e
lear
n
in
g
.
T
h
e
n
ew
PIRL
f
r
am
ewo
r
k
f
ills
t
h
ese
g
ap
s
b
y
in
te
g
r
atin
g
r
ei
n
f
o
r
ce
m
e
n
t
lear
n
i
n
g
with
p
h
y
s
ical
co
n
s
tr
ain
ts
to
s
u
p
p
o
r
t r
o
b
u
s
t,
r
ea
l
-
tim
e
ad
ap
t
iv
e
co
n
tr
o
l
o
f
lo
w
-
v
o
ltag
e
PM
SM
d
r
iv
es.
3.
M
E
T
H
O
DO
L
O
G
Y
I
n
th
is
p
ar
t
,
th
e
p
r
o
p
o
s
ed
en
co
d
er
less
PMSM
co
n
tr
o
l
m
eth
o
d
will
b
e
d
escr
ib
ed
in
a
r
ep
r
o
d
u
cib
le
m
an
n
er
.
T
h
e
en
tire
en
c
o
d
er
le
s
s
PM
SM
d
r
iv
e
with
a
h
ig
h
-
f
r
eq
u
en
c
y
in
jectio
n
is
in
tr
o
d
u
c
ed
as
a
co
n
ce
p
tu
al
co
n
tr
o
l
b
lo
c
k
d
iag
r
am
in
t
h
e
f
ir
s
t
p
lace
.
T
h
en
,
a
p
h
y
s
ics
-
in
f
o
r
m
ed
r
ein
f
o
r
ce
m
en
t
lear
n
in
g
(
PIRL)
f
o
r
m
u
latio
n
is
f
o
r
m
u
lated
,
co
m
p
r
is
in
g
s
tate,
ac
tio
n
,
r
ewa
r
d
,
a
n
d
co
n
s
tr
ain
t
e
n
f
o
r
ce
m
en
t.
L
astl
y
,
th
e
en
tire
MA
T
L
AB
/S
im
u
lin
k
-
Py
th
o
n
co
-
s
im
u
latio
n
en
v
ir
o
n
m
e
n
t
a
n
d
all
th
e
s
im
u
latio
n
s
ettin
g
s
ar
e
d
escr
ib
ed
to
f
ac
ilit
ate
th
e
in
d
ep
en
d
en
t
r
e
p
r
o
d
u
cib
ilit
y
o
f
th
e
r
ep
o
r
ted
r
esu
lts
,
an
d
b
aselin
e
d
ef
in
itio
n
s
an
d
an
aly
s
is
m
ea
s
u
r
es.
3
.
1
.
P
M
SM
dy
na
m
ic
m
o
del
a
nd
hig
h
-
f
re
qu
ency
inje
ct
io
n princi
ple
T
h
e
p
er
m
a
n
en
t
m
ag
n
et
s
y
n
ch
r
o
n
o
u
s
m
o
t
o
r
th
at
is
tak
en
in
to
ac
co
u
n
t
in
th
is
p
a
p
er
is
m
o
d
e
lled
in
th
e
r
ef
er
en
ce
f
r
am
e
with
th
e
r
o
to
r
f
lu
x
.
T
h
e
eq
u
atio
n
s
o
f
s
tato
r
v
o
ltag
es a
r
e
wr
itten
in
(
1
)
a
n
d
(
2
)
.
=
+
−
(
1
)
=
+
+
(
2
)
,
an
d
,
co
m
p
o
n
e
n
ts
o
f
s
tato
r
v
o
ltag
e
an
d
cu
r
r
en
t,
is
th
e
s
t
ato
r
r
esis
tan
ce
,
,
ar
e
th
e
f
lu
x
lin
k
ag
es,
an
d
is
th
e
elec
tr
ical
an
g
u
lar
f
r
eq
u
e
n
cy
.
T
h
e
elec
tr
o
m
ag
n
etic
to
r
q
u
e
is
g
iv
en
b
y
(
3
)
.
=
3
2
(
−
)
(
3
)
p
is
p
r
o
v
id
e
d
b
y
th
e
n
u
m
b
e
r
o
f
p
o
le
p
ai
r
s
.
At
lo
w
s
p
ee
d
,
th
e
p
o
s
itio
n
o
f
a
r
o
to
r
is
esti
m
ated
b
y
m
ag
n
e
tic
s
alien
cy
,
wh
ich
m
ay
b
e
d
r
i
v
en
b
y
a
n
in
jecte
d
h
ig
h
-
f
r
eq
u
e
n
cy
v
o
ltag
e
s
ig
n
al
ℎ
in
en
co
d
er
less
o
p
er
ati
o
n
.
T
h
e
s
ig
n
al
is
s
u
p
er
im
p
o
s
e
d
to
th
e
o
r
d
e
r
ed
s
tato
r
v
o
ltag
e,
ty
p
ically
th
e
d
-
ax
is
v
o
ltag
e
as g
iv
en
in
(
4
)
.
=
∗
+
ℎ
s
in
(
ℎ
)
(
4
)
ℎ
is
an
d
ℎ
ar
e
th
e
in
jectio
n
am
p
l
itu
d
e
an
d
th
e
ca
r
r
ier
f
r
e
q
u
en
c
y
,
r
esp
ec
tiv
ely
.
T
h
e
h
i
g
h
-
f
r
eq
u
en
cy
cu
r
r
e
n
t
r
esp
o
n
s
e
in
d
u
ce
d
p
r
o
v
id
es
th
e
in
f
o
r
m
atio
n
o
n
th
e
s
alien
cy
an
g
le
o
f
th
e
r
o
to
r
,
f
r
o
m
wh
ic
h
th
e
r
o
to
r
p
o
s
itio
n
m
ay
b
e
r
ec
o
v
er
ed
.
ℎ
is
cr
itically
im
p
o
r
ta
n
t
to
th
e
q
u
ality
o
f
th
is
esti
m
atio
n
:
a
s
m
all
v
alu
e
ca
u
s
es
th
e
d
em
o
d
u
lated
s
ig
n
al
to
b
e
lo
s
t in
n
o
is
e;
a
lar
g
e
v
alu
e
ca
u
s
es a
lar
g
e
to
r
q
u
e
r
ip
p
le
an
d
o
th
er
co
p
p
er
l
o
s
s
es.
T
h
e
en
c
o
d
er
less
PMSM
d
r
iv
e
th
at
is
o
p
e
r
ated
with
h
ig
h
-
f
r
eq
u
en
cy
s
ig
n
al
in
jectio
n
an
d
am
p
litu
d
e
co
r
r
ec
tio
n
v
ia
r
ein
f
o
r
ce
m
en
t
l
ea
r
n
in
g
h
as
a
t
o
tal
co
n
t
r
o
l
s
tr
u
ctu
r
e
as
d
e
p
icted
in
Fig
u
r
e
1
.
T
h
e
h
ig
h
-
f
r
e
q
u
en
cy
v
o
ltag
e
s
ig
n
al
is
in
jecte
d
in
to
th
e
d
-
ax
is
v
o
ltag
e
co
m
m
a
n
d
o
n
ly
,
an
d
th
e
elec
tr
ical
an
d
m
ec
h
an
ical
v
ar
iab
les
ar
e
m
ea
s
u
r
ed
a
n
d
ap
p
lied
in
le
ar
n
in
g
-
b
ased
ad
ap
tatio
n
o
f
in
j
ec
tio
n
am
p
litu
d
e
,
as sh
o
wn
in
Fig
u
r
e
1
.
3
.
2
.
P
r
o
blem
f
o
rm
ula
t
io
n
T
h
is
s
tu
d
y
aim
s
t
o
id
e
n
tify
a
n
ad
ap
tiv
e
co
n
t
r
o
l
law
o
f
ℎ
th
at
will
en
s
u
r
e
h
ig
h
esti
m
atio
n
ac
cu
r
ac
y
an
d
lo
w
u
n
d
esira
b
le
s
id
e
ef
f
ec
ts
.
T
h
e
o
p
tim
izatio
n
is
ch
ar
ac
t
er
ized
b
y
a
co
m
p
o
s
ite
co
s
t f
u
n
ctio
n
in
(
5
)
.
=
1
2
+
2
Δ
ℎ
2
+
3
(
5
)
I
n
wh
ich
is
th
e
er
r
o
r
i
n
th
e
i
n
s
tan
tan
eo
u
s
r
o
t
o
r
p
o
s
itio
n
esti
m
atio
n
,
Δ
ℎ
is
th
e
m
ax
im
u
m
to
r
q
u
e
r
ip
p
l
e
ca
u
s
ed
b
y
in
jectio
n
,
a
n
d
is
th
e
ad
d
ed
lo
s
s
o
f
p
o
wer
ca
u
s
ed
b
y
th
e
h
ig
h
-
f
r
e
q
u
en
c
y
c
o
m
p
o
n
en
t
.
T
h
e
weig
h
ts
1
,
2
,
3
ar
e
p
r
o
p
o
r
tio
n
s
o
f
th
e
s
ig
n
i
f
ican
ce
o
f
ea
ch
o
f
th
e
ter
m
s
.
D
u
e
to
th
e
n
o
n
lin
ea
r
ity
o
f
th
e
s
y
s
tem
,
its
d
ep
en
d
e
n
ce
o
n
p
ar
a
m
eter
s
,
an
d
its
d
ep
en
d
en
ce
o
n
tim
e,
it
is
n
o
t
p
o
s
s
ib
le
to
p
r
o
v
id
e
an
ex
p
licit
an
aly
tical
s
o
lu
tio
n
o
f
th
e
o
p
tim
al
ℎ
is
.
T
h
u
s
,
r
ein
f
o
r
ce
m
e
n
t le
ar
n
in
g
is
u
s
ed
to
ac
q
u
ir
e
a
n
o
p
tim
al
ad
ap
t
atio
n
s
tr
ateg
y
b
y
ac
tu
ally
in
ter
ac
tin
g
with
a
s
im
u
lated
en
v
ir
o
n
m
en
t.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
1
7
,
No
.
2
,
J
u
n
e
20
2
6
:
8
7
3
-
8
8
4
876
Fig
u
r
e
1
.
B
lo
ck
d
iag
r
am
o
f
th
e
en
co
d
er
less
PMSM
d
r
iv
e
with
h
ig
h
-
f
r
eq
u
en
cy
i
n
jectio
n
an
d
r
ein
f
o
r
ce
m
en
t
-
lear
n
in
g
-
b
ased
am
p
litu
d
e
ad
ap
tatio
n
3
.
3
.
P
hy
s
ics
-
info
rm
ed
re
info
rc
em
ent
lea
rning
f
ra
m
ewo
r
k
T
h
e
r
ein
f
o
r
ce
m
en
t
-
lear
n
i
n
g
(
R
L
)
ag
en
t
in
ter
ac
ts
co
n
tin
u
o
u
s
ly
with
th
e
P
MSM
en
v
ir
o
n
m
en
t
to
ad
ap
t
th
e
in
jecte
d
h
i
g
h
-
f
r
eq
u
e
n
cy
(
HF)
v
o
ltag
e
am
p
litu
d
e.
At
ea
ch
tim
e
s
tep
,
th
e
ag
e
n
t
r
ec
ei
v
es
th
e
m
o
to
r
s
tate
v
ec
to
r
in
(
6
)
.
=
[
,
,
,
,
]
(
6
)
I
n
wh
ich
wh
er
e
,
ar
e
s
tato
r
cu
r
r
en
t
co
m
p
o
n
en
ts
,
is
th
e
elec
tr
ical
an
g
u
lar
s
p
ee
d
,
is
th
e
elec
tr
o
m
ag
n
etic
to
r
q
u
e,
an
d
is
th
e
m
ec
h
an
ical
lo
ad
ap
p
lied
.
T
h
e
ag
e
n
t
p
r
o
d
u
ce
s
an
ac
tio
n
,
wh
ich
is
a
s
m
all
ch
an
g
e
Δ
ℎ
o
f
th
e
HF
-
in
jectio
n
a
m
p
litu
d
e
ℎ
.
F
ig
u
r
e
2
d
em
o
n
s
tr
ates
th
e
g
en
er
al
s
tr
u
ctu
r
e
o
f
th
e
p
r
o
p
o
s
ed
p
h
y
s
ics
-
in
f
o
r
m
ed
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
f
r
am
e
wo
r
k
,
wh
e
r
eb
y
t
h
e
PMSM
en
v
ir
o
n
m
en
t,
ac
to
r
-
cr
itic
n
etw
o
r
k
s
,
p
h
y
s
ics
-
b
ased
r
ewa
r
d
ca
lcu
latio
n
m
o
d
u
le,
a
n
d
ex
p
er
ien
ce
r
ep
lay
m
ec
h
a
n
i
s
m
to
u
p
d
ate
th
e
p
o
licy
ar
e
in
ter
ac
tin
g
.
On
ce
th
e
en
v
ir
o
n
m
en
t
h
as
im
p
lem
en
ted
th
is
ac
tio
n
,
it
s
en
d
s
b
ac
k
th
e
f
o
llo
win
g
s
tate
+
1
an
d
a
s
ca
lar
r
e
war
d
u
s
ed
to
m
o
d
if
y
th
e
a
g
en
t p
o
licy
.
I
n
tr
a
d
itio
n
al
r
ein
f
o
r
ce
m
e
n
t
lear
n
i
n
g
,
th
e
p
er
f
o
r
m
an
ce
is
to
m
ax
i
m
ize
th
e
d
is
co
u
n
ted
cu
m
u
lativ
e
r
ewa
r
d
in
(
7
)
.
=
[
∑
+
+
1
∞
=
0
]
(
7
)
W
ith
∈
(
0
,
1
)
b
ein
g
th
e
d
is
co
u
n
t
f
ac
t
o
r
th
at
b
alan
ce
s
th
e
s
h
o
r
t
-
ter
m
an
d
th
e
lo
n
g
-
ter
m
p
e
r
f
o
r
m
a
n
ce
.
I
n
t
h
is
s
tu
d
y
,
th
e
r
ewa
r
d
is
p
h
y
s
ics
-
in
f
o
r
m
ed
an
d
is
d
e
f
in
ed
in
(
8
)
.
=
−
−
(
∥
−
−
+
∥
2
+
∥
−
−
−
(
+
)
∥
2
)
(
8
)
=
1
2
+
2
Δ
2
+
3
is
th
e
m
u
lti
-
o
b
jectiv
e
co
s
t,
is
th
e
p
h
y
s
ics
-
weig
h
t
co
ef
f
icien
t
,
an
d
th
e
s
ec
o
n
d
ter
m
is
a
p
en
alty
o
f
n
o
n
-
o
b
s
e
r
v
an
ce
o
f
th
e
PMSM
s
tato
r
v
o
ltag
e
eq
u
atio
n
s
.
T
h
e
e
x
p
lo
r
a
tio
n
b
y
th
e
ag
en
t
is
lim
ited
to
p
h
y
s
ically
f
ea
s
ib
le
o
p
er
atin
g
p
o
s
itio
n
s
b
y
em
b
ed
d
in
g
s
u
ch
r
esid
u
als,
en
h
an
cin
g
s
tab
ilit
y
,
an
d
ac
h
iev
in
g
f
aster
co
n
v
er
g
en
ce
.
T
h
e
ac
to
r
-
cr
itic
ar
c
h
itectu
r
e
o
n
th
e
d
ee
p
d
eter
m
i
n
is
tic
p
o
lic
y
g
r
a
d
ien
t
(
DDPG)
alg
o
r
ith
m
m
ee
ts
th
e
im
p
lem
en
tatio
n
o
f
t
h
e
p
o
licy
n
etwo
r
k
.
T
h
e
ac
to
r
n
etwo
r
k
c
r
ea
tes
th
e
co
n
ti
n
u
o
u
s
in
cr
em
en
ts
o
f
am
p
litu
d
e
,
an
d
t
h
e
n
u
cleu
s
o
f
th
e
cr
itic
ap
p
r
o
x
im
ates
th
e
s
tate
-
ac
tio
n
v
alu
e
(
,
)
.
T
h
ese
two
n
etwo
r
k
s
ar
e
tr
ain
ed
th
r
o
u
g
h
m
in
i
-
b
atch
g
r
ad
ie
n
t
d
escen
t
w
ith
th
e
h
el
p
o
f
e
x
p
er
ien
ce
r
ep
l
ay
to
m
ax
im
ize
t
h
e
lear
n
in
g
ef
f
icien
cy
.
T
h
e
lear
n
in
g
-
r
ate
an
d
ex
p
lo
r
atio
n
-
n
o
is
e
p
ar
am
eter
s
f
ad
e
with
ex
p
e
r
ien
ce
,
wh
ich
e
n
ab
les
s
elf
-
ev
o
lv
in
g
b
eh
av
i
o
r
to
f
o
llo
w
g
r
ad
u
al
c
h
an
g
es in
t
h
e
p
ar
a
m
eter
s
(
e.
g
.
s
tato
r
-
r
esis
tan
ce
d
r
if
t)
.
T
h
e
co
m
p
lete
p
r
o
ce
d
u
r
e
o
f
t
h
e
s
u
g
g
ested
p
h
y
s
ics
-
in
f
o
r
m
e
d
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
f
r
a
m
ewo
r
k
is
s
u
m
m
ar
ized
in
Alg
o
r
ith
m
1
.
A
lg
o
r
ith
m
1
o
u
tlin
es
th
e
in
ter
p
lay
b
etwe
en
th
e
PMSM
en
v
ir
o
n
m
e
n
t
an
d
th
e
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
ag
en
t,
th
e
s
tate
o
b
s
er
v
atio
n
,
ac
tio
n
g
en
er
atio
n
,
r
ewa
r
d
co
m
p
u
tatio
n
,
an
d
p
o
licy
u
p
d
ate.
T
h
e
alg
o
r
ith
m
a
p
p
lies
th
e
s
tate
d
ef
in
itio
n
o
f
(
6
)
,
r
ewa
r
d
cr
ea
tio
n
o
f
(
8
)
,
an
d
th
e
o
p
tim
iz
atio
n
g
o
al
o
f
to
o
p
tim
ize
th
e
lear
n
i
n
g
p
r
o
ce
s
s
.
As
s
h
o
wn
in
Alg
o
r
ith
m
1
,
th
e
ag
en
t
is
u
p
d
ated
in
th
e
HF
in
je
ctio
n
am
p
litu
d
e
b
y
en
g
ag
in
g
with
th
e
PMSM
m
o
d
el,
wh
er
ea
s
th
e
p
h
y
s
ics
-
in
f
o
r
m
ed
r
ewa
r
d
m
ak
es th
e
lear
n
in
g
p
r
o
ce
s
s
co
n
s
is
ten
t
with
th
e
m
o
to
r
d
y
n
a
m
ics p
r
escr
ib
ed
in
(
8
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
P
h
ysics
-
in
fo
r
med
r
ein
fo
r
ce
me
n
t le
a
r
n
in
g
fo
r
a
d
a
p
tive
h
ig
h
-
f
r
eq
u
en
cy
in
jectio
n
in
…
(
S
u
r
en
d
a
r
A
r
a
vin
d
h
a
n
)
877
Alg
o
r
ith
m
1
.
Ph
y
s
ics
-
in
f
o
r
m
e
d
r
ein
f
o
r
ce
m
en
t le
ar
n
in
g
f
o
r
H
F
am
p
litu
d
e
ad
ap
tatio
n
I
n
p
u
t: m
o
to
r
s
tate
s
t
=
[
i
d
,
i
q
,
ω
e
,
T
e
,
T
l
o
ad
]
;
ac
to
r
p
o
licy
π
θ
; c
r
itic
Q
ϕ
; d
is
co
u
n
t
γ
; p
h
y
s
ics
-
weig
h
t
λ
.
R
ep
ea
t f
o
r
ea
ch
e
p
is
o
d
e:
1.
Ob
s
er
v
e
cu
r
r
e
n
t state
s
t
; c
o
m
p
u
t
e
ac
tio
n
a
t
=
π
θ
(
s
t
)
(
in
cr
em
en
t
Δ
V
h
)
; a
p
p
ly
V
h
←
V
h
+
Δ
V
h
.
2.
Simu
late
PMSM
d
y
n
am
ics f
o
r
th
e
s
am
p
lin
g
in
ter
v
al
Δ
t
to
o
b
tain
n
ex
t state
s
t
+
1
.
3.
E
v
alu
ate
co
s
t
J
t
=
w
1
θ
er
r
2
+
w
2
Δ
T
HF
2
+
w
3
P
l
o
s
s
.
4.
C
o
m
p
u
te
r
esid
u
als f
r
o
m
PMSM
eq
u
atio
n
s
; f
o
r
m
r
ewa
r
d
r
t
=
−
J
t
−
λ
(
∥
r
d
∥
2
+
∥
r
q
∥
2
)
.
5.
Sto
r
e
tr
an
s
itio
n
(
s
t
,
a
t
,
r
t
,
s
t
+
1
)
in
r
ep
lay
b
u
f
f
e
r
.
6.
Up
d
ate
ac
to
r
an
d
cr
itic b
y
d
et
er
m
in
is
tic
p
o
licy
-
g
r
a
d
ien
t le
ar
n
in
g
.
Dec
ay
ex
p
lo
r
atio
n
n
o
is
e
an
d
l
ea
r
n
in
g
-
r
ate
s
ch
ed
u
les u
n
til
th
e
co
n
v
er
g
en
ce
c
r
iter
io
n
is
s
atis
f
ied
(
av
er
ag
e
r
ewa
r
d
c
h
an
g
e
<
0
.
5
% o
v
er
1
0
0
ep
is
o
d
es).
Fig
u
r
e
2
.
Ar
c
h
itectu
r
e
o
f
p
h
y
s
ics in
f
o
r
m
ed
R
L
ag
e
n
t sh
o
win
g
s
tate,
r
ewa
r
d
,
an
d
p
o
licy
u
p
d
ate
p
ath
s
3
.
4
.
Sim
ula
t
i
o
n
s
et
up
a
nd
i
m
plem
ent
a
t
io
n det
a
ils
A
MA
T
L
AB
/
Simu
lin
k
Py
th
o
n
co
-
s
im
u
latio
n
en
v
.
is
u
s
ed
to
im
p
lem
en
t
an
d
test
th
e
e
n
co
d
er
less
PMSM
co
n
tr
o
l
s
y
s
tem
o
f
p
h
y
s
ics
-
in
f
o
r
m
ed
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
(
PIRL)
.
T
h
e
P
MSM
elec
tr
ical
an
d
m
ec
h
an
ical
p
lan
t,
in
v
e
r
ter
m
o
d
el,
ca
s
ca
d
ed
s
p
ee
d
-
c
u
r
r
en
t
co
n
tr
o
l
lo
o
p
s
,
an
d
PIRL
p
o
licy
,
wh
ich
is
r
u
n
in
Py
th
o
n
an
d
in
ter
c
h
an
g
es
s
tate
ac
tio
n
d
ata
with
Simu
lin
k
at
th
e
o
u
ter
-
lo
o
p
in
ter
v
al
a
r
e
im
p
lem
en
ted
in
MA
T
L
AB
/S
im
u
lin
k
.
T
h
e
s
p
ec
if
ics
o
f
th
e
p
lan
t
a
n
d
co
n
tr
o
l
im
p
lem
en
tatio
n
,
s
u
ch
as
th
e
in
v
er
ter
s
witch
in
g
/s
atu
r
atio
n
b
lo
ck
,
dq
-
d
o
m
ain
PMSM
m
o
d
el,
m
e
asu
r
em
en
t p
o
in
ts
,
an
d
in
ter
n
al
s
ig
n
al
r
o
u
tin
g
,
ar
e
illu
s
tr
ated
in
Fig
u
r
e
3
(
a
)
in
th
e
MA
T
L
AB
/S
im
u
lin
k
f
o
r
m
at.
T
h
is
co
-
s
im
u
latio
n
ar
ch
itectu
r
e
was
f
u
r
th
er
s
im
p
lifie
d
an
d
v
is
u
alize
d
b
y
p
r
o
v
i
d
in
g
a
s
ig
n
al
-
ch
ain
(
Fig
u
r
e
3
(
b
)
)
o
f
th
e
p
lan
t
p
at
h
,
th
e
lo
ca
tio
n
o
f
th
e
HF
in
jectio
n
,
th
e
d
em
o
d
u
latio
n
/p
o
s
itio
n
-
esti
m
atio
n
ch
ain
,
th
e
f
o
r
m
atio
n
o
f
th
e
o
b
s
er
v
atio
n
v
ec
to
r
s
,
an
d
th
e
Py
th
o
n
R
L
ag
en
t
f
ee
d
b
ac
k
lo
o
p
.
T
ab
le
1
s
u
m
m
ar
izes
th
e
s
im
u
latio
n
p
a
r
am
eter
s
o
f
th
e
PMSM
m
o
d
el
,
in
v
er
ter
,
c
o
n
tr
o
l
ler
s
,
an
d
HF
in
jectio
n
an
d
h
el
d
co
n
s
tan
t th
r
o
u
g
h
o
u
t th
e
HF a
m
p
litu
d
e
s
tr
ateg
ies co
m
p
ar
is
o
n
ac
r
o
s
s
all
co
n
tr
o
ller
s
co
m
p
ar
ed
.
T
ab
le
1
.
PMSM
an
d
s
im
u
latio
n
p
ar
am
eter
s
P
a
r
a
me
t
e
r
S
y
mb
o
l
V
a
l
u
e
U
n
i
t
D
C
l
i
n
k
v
o
l
t
a
g
e
48
V
P
o
l
e
p
a
i
r
s
4
–
S
t
a
t
o
r
r
e
s
i
st
a
n
c
e
0
.
4
Ω
a
x
i
s i
n
d
u
c
t
a
n
c
e
0
.
6
mH
a
x
i
s i
n
d
u
c
t
a
n
c
e
0
.
9
mH
P
e
r
man
e
n
t
m
a
g
n
e
t
f
l
u
x
0
.
0
3
Wb
N
o
mi
n
a
l
t
o
r
q
u
e
10
Nm
S
w
i
t
c
h
i
n
g
f
r
e
q
u
e
n
c
y
12
k
H
z
H
F
i
n
j
e
c
t
i
o
n
f
r
e
q
u
e
n
c
y
ℎ
1
.
2
k
H
z
S
a
mp
l
i
n
g
t
i
me
1
0
0
µs
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
,
Vo
l.
1
7
,
No
.
2
,
J
u
n
e
20
2
6
:
8
7
3
-
8
8
4
878
(
a)
(
b
)
Fig
u
r
e
3
.
MA
T
L
AB
/Si
m
u
lin
k
-
Py
th
o
n
c
o
-
s
im
u
latio
n
f
r
am
ew
o
r
k
f
o
r
PIRL
-
b
ased
en
c
o
d
er
le
s
s
PM
SM
d
r
iv
e
co
n
tr
o
l: (
a)
MA
T
L
AB
/Si
m
u
li
n
k
im
p
lem
e
n
tatio
n
o
f
th
e
PM
SM
d
r
iv
e
an
d
c
o
n
tr
o
l lo
o
p
s
an
d
(
b
)
c
o
-
s
im
u
latio
n
s
ig
n
al
f
lo
w
f
o
r
PIRL
-
b
ased
h
ig
h
-
f
r
e
q
u
en
c
y
in
jectio
n
co
n
tr
o
l
3
.
4
.
1
.
So
lv
er
,
co
ntr
o
l lo
o
p r
a
t
es
,
a
nd
RL
h
y
perpa
ra
m
et
er
s
T
h
e
s
i
m
u
l
at
i
o
n
s
a
r
e
p
e
r
f
o
r
m
e
d
b
y
a
f
i
x
e
d
-
s
t
e
p
d
i
s
c
r
e
t
e
-
ti
m
e
s
o
l
v
e
r
.
B
as
e
s
a
m
p
l
i
n
g
t
i
m
e
i
s
T
s
=
1
0
0
μ
s
,
a
n
d
a
t
t
h
is
t
i
m
e
,
t
h
e
i
n
n
e
r
c
u
r
r
en
t
l
o
o
p
a
n
d
HF
i
n
j
ec
t
i
o
n
c
a
r
r
i
e
r
a
r
e
u
p
d
a
t
e
d
.
T
h
e
o
u
te
r
s
p
e
e
d
lo
o
p
i
s
u
p
d
a
t
e
d
w
i
t
h
a
p
e
r
i
o
d
o
f
1
m
s
,
a
n
d
t
h
e
R
L
ac
t
i
o
n
u
p
d
a
t
e
p
e
r
i
o
d
i
s
a
d
j
u
s
t
e
d
to
t
h
e
s
a
m
e
p
e
r
i
o
d
(
R
L
u
p
d
a
t
e
=
1
m
s
)
.
T
h
e
s
tep
s
u
s
ed
at
ea
ch
d
is
cr
ete
tim
e
ar
e:
i
)
th
e
d
q
c
u
r
r
e
n
t
c
o
n
tr
o
ller
ca
lc
u
lates
(
∗
,
∗
)
;
ii
)
th
e
HF
ca
r
r
ier
ℎ
(
)
=
ℎ
s
in
(
ℎ
)
is
ad
d
ed
t
o
th
e
d
-
a
x
is
co
m
m
an
d
s
u
ch
t
h
at
=
∗
+
ℎ
;
iii
)
th
e
in
v
er
ter
b
l
o
c
k
im
p
lem
en
ts
SVP
W
M
m
o
d
u
latio
n
an
d
v
o
lta
g
e
s
atu
r
atio
n
ac
co
r
d
in
g
t
o
th
e
4
8
V
DC
b
u
s
;
iv
)
th
e
PMSM
d
q
m
o
d
el
a
d
v
an
ce
s
elec
tr
ical
s
tates
an
d
th
e
m
ec
h
an
ical
s
p
ee
d
;
an
d
v
)
th
e
o
b
s
er
v
atio
n
v
ec
to
r
.
T
h
e
in
c
r
em
en
tal
ac
tio
n
Δ
ℎ
is
s
en
t o
u
t b
y
th
e
Py
th
o
n
ag
e
n
t
,
an
d
th
e
am
p
litu
d
e
is
u
p
d
ated
with
lim
its
as g
iv
en
i
n
(
9
)
.
ℎ
←
c
l
ip
(
ℎ
+
Δ
ℎ
,
ℎ
,
m
i
n
,
ℎ
,
m
ax
)
(
9
)
ℎ
r
em
ain
s
f
ix
ed
,
b
u
t
o
n
ly
th
e
s
in
u
o
id
al
ca
r
r
ier
ch
an
g
es d
u
r
in
g
th
e
b
ase
s
am
p
lin
g
tim
e
with
in
a
f
i
x
ed
1
m
s
R
L
p
er
io
d
,
s
o
t
h
at
lear
n
ed
ad
ap
tat
io
n
d
y
n
am
ics ar
e
in
d
e
p
en
d
e
n
t
o
f
th
e
ca
r
r
ier
o
s
cillatio
n
.
T
h
e
PIRL
ag
en
t
is
co
n
f
ig
u
r
ed
as
an
ac
to
r
-
cr
itic
DDPG
with
th
e
d
is
co
u
n
t
f
ac
to
r
,
γ
=
0
.
9
9
,
an
d
with
p
h
y
s
ics
-
r
eg
u
lar
iz
atio
n
weig
h
t,
λ
=
0
.
1
.
T
wo
f
u
ll
y
co
n
n
ec
ted
h
id
d
e
n
la
y
er
s
(
6
4
n
eu
r
o
n
s
ea
ch
)
o
f
t
h
e
ac
to
r
a
n
d
cr
itic
n
etwo
r
k
s
h
a
v
e
a
R
eL
U
a
ctiv
atio
n
.
Min
i
-
b
atc
h
s
ize
is
1
2
8
,
r
e
p
lay
b
u
f
f
e
r
m
em
o
r
y
is
10
6
,
a
n
d
a
s
o
f
t
u
p
d
ate
o
f
th
e
tar
g
et
n
etwo
r
k
is
=
0
.
005
.
T
h
e
ex
p
lo
r
atio
n
is
ap
p
lied
as
ad
d
it
iv
e
Gau
s
s
ian
n
o
is
e
o
n
th
e
ac
t
o
r
o
u
tp
u
t
with
a
s
tan
d
ar
d
d
e
v
iatio
n
o
f
0
.
2
V
th
at
d
ec
ay
s
lin
ea
r
l
y
to
0
.
0
1
V
d
u
r
i
n
g
th
e
tr
ain
in
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
9
4
P
h
ysics
-
in
fo
r
med
r
ein
fo
r
ce
me
n
t le
a
r
n
in
g
fo
r
a
d
a
p
tive
h
ig
h
-
f
r
eq
u
en
cy
in
jectio
n
in
…
(
S
u
r
en
d
a
r
A
r
a
vin
d
h
a
n
)
879
3
.
4
.
2
.
P
la
nt
no
n
-
idea
litie
s
,
mea
s
urem
ent
co
nd
it
io
nin
g
,
a
nd
no
is
e
m
o
del
T
o
s
im
u
late
th
e
co
n
d
itio
n
s
o
f
an
ex
tr
a
-
lo
w
-
v
o
ltag
e
d
r
iv
e
,
th
e
in
v
er
ter
s
tag
e
h
as
a
DC
-
b
u
s
li
m
itatio
n
at
4
8
V,
PW
M
s
atu
r
atio
n
(
d
u
ty
c
lam
p
)
,
a
n
d
a
d
ea
d
-
tim
e/d
is
to
r
t
io
n
ef
f
ec
t
th
at
h
as
b
ee
n
m
o
d
el
led
as
an
ef
f
ec
tiv
e
v
o
ltag
e
d
is
to
r
tio
n
th
at
is
a
f
u
n
c
tio
n
o
f
cu
r
r
e
n
t
d
ir
ec
tio
n
.
A
d
is
cr
ete
f
ir
s
t
-
o
r
d
e
r
lo
w
-
p
ass
I
I
R
f
ilter
is
u
s
ed
to
f
ilter
th
e
m
ea
s
u
r
ed
cu
r
r
e
n
ts
an
d
v
o
l
tag
es
to
r
ef
lect
a
p
r
ac
tical
s
am
p
lin
g
/co
n
d
itio
n
in
g
.
,
,
an
d
ar
e
s
u
b
ject
to
ad
d
itiv
e
m
ea
s
u
r
em
e
n
t
n
o
is
e,
wh
ich
is
o
f
ze
r
o
-
m
ea
n
Gau
s
s
ian
n
o
is
e
with
co
n
s
tan
t
v
a
r
ian
c
e
,
an
d
id
e
n
tical
n
o
is
e
s
ettin
g
s
ar
e
ap
p
lied
to
all
co
n
t
r
o
ller
s
.
3
.
4
.
3
.
T
ra
ini
ng
pro
t
o
co
l a
nd
ba
s
eline
co
ntr
o
llers
T
h
e
tr
ain
in
g
an
d
test
in
g
ar
e
d
o
n
e
o
v
er
t
h
e
s
p
ee
d
r
ef
er
e
n
ce
r
an
g
e
o
f
0
-
5
0
0
r
p
m
u
n
d
e
r
v
ar
io
u
s
lo
ad
-
to
r
q
u
e
p
r
o
f
iles
,
wh
ich
in
clu
d
e
a
s
tep
an
d
r
am
p
v
ar
iatio
n
in
th
e
lo
ad
p
r
o
f
ile
,
as
well
a
s
all
th
e
co
m
p
ar
ed
co
n
tr
o
ller
s
u
n
d
er
th
e
s
am
e
e
x
citatio
n
co
n
d
itio
n
s
.
T
h
e
s
am
e
in
n
er
/o
u
ter
l
o
o
p
g
ain
s
,
HFI
d
em
o
d
u
latio
n
an
d
p
o
s
itio
n
-
esti
m
atio
n
ch
ain
s
,
in
v
er
ter
an
d
p
lan
t
m
o
d
els
ar
e
u
s
ed
to
im
p
lem
en
t
th
e
b
aselin
e
s
ch
em
es;
th
e
o
n
ly
d
if
f
er
en
ce
is
th
e
d
if
f
er
e
n
t H
F in
jectio
n
am
p
litu
d
e
g
en
e
r
atio
n
m
ec
h
an
is
m
am
o
n
g
co
n
tr
o
ller
s
.
T
h
e
HF
in
jectio
n
am
p
litu
d
e
ℎ
an
d
g
en
e
r
ated
b
y
th
e
PIRL
ag
en
t
is
in
itialized
th
r
o
u
g
h
r
an
d
o
m
ex
p
lo
r
ato
r
y
ac
tio
n
s
r
an
d
o
m
ly
s
am
p
led
in
t
h
e
in
ter
v
al
0
.
5
-
4
V,
an
d
th
e
a
v
er
ag
e
ep
is
o
d
ic
r
e
war
d
is
d
ec
lar
e
d
to
b
e
co
n
v
er
g
ed
o
n
ce
th
e
v
ar
iat
io
n
is
less
th
an
0
.
5
%.
T
h
e
s
u
g
g
ested
co
n
tr
o
ller
will
b
e
co
n
tr
asted
with
th
r
ee
r
ef
er
en
ce
s
ch
em
es:
a)
T
h
e
f
ix
ed
am
p
litu
d
e
HF in
ject
io
n
:
ℎ
is
co
n
s
tan
t d
u
r
in
g
th
e
en
t
ir
e
r
u
n
,
wh
er
e
ℎ
=
2
V
.
b)
Heu
r
is
tic
ad
ap
tiv
e
HF
in
jectio
n
:
ℎ
is
m
o
d
if
ied
th
r
o
u
g
h
a
m
o
n
o
to
n
ic
m
ap
p
in
g
o
f
o
p
er
a
tin
g
co
n
d
itio
n
(
m
ec
h
an
ical
s
p
ee
d
an
d
/o
r
s
ta
to
r
cu
r
r
en
t
m
a
g
n
itu
d
e
)
an
d
r
estricte
d
in
th
e
s
am
e
r
an
g
e
a
s
th
e
R
L
-
b
ased
co
n
tr
o
ller
s
as g
iv
en
in
(
10
)
.
ℎ
∈
[
ℎ
,
m
i
n
,
ℎ
,
m
ax
]
(
1
0
)
c)
Stan
d
ar
d
R
L
(
n
o
n
-
r
eg
u
lar
ized
)
:
T
h
e
s
am
e
s
tate
-
v
ec
to
r
,
ac
tio
n
-
b
o
u
n
d
s
,
n
etwo
r
k
,
s
o
lv
e
r
s
ettin
g
s
,
an
d
tr
ain
in
g
s
ch
ed
u
le
as th
o
s
e
o
f
PIRL,
ex
ce
p
t th
at
th
e
p
h
y
s
ics
-
r
esid
u
al
p
en
alty
ter
m
is
o
m
itted
i
n
th
e
r
ewa
r
d
.
All th
e
co
n
tr
o
ller
s
ar
e
test
ed
i
n
th
e
s
am
e
s
o
lv
er
s
ettin
g
s
,
s
a
m
p
lin
g
,
a
n
d
d
is
tu
r
b
ed
co
n
d
itio
n
s
.
3
.
4
.
4
.
L
o
g
g
ed
s
ig
na
ls
a
nd
ev
a
lua
t
io
n m
et
rics
E
v
er
y
co
m
p
a
r
ativ
e
o
u
tco
m
e
is
ac
q
u
ir
e
d
u
s
in
g
t
h
e
s
am
e
r
ec
o
r
d
ed
s
ig
n
als
a
n
d
id
en
tical
p
o
s
t
-
p
r
o
ce
s
s
in
g
s
tep
s
am
o
n
g
th
e
co
n
tr
o
ller
s
.
T
h
e
m
ea
s
u
r
ed
v
ar
iab
les
ar
e
d
q
cu
r
r
e
n
ts
(
,
)
,
in
v
er
ter
v
o
ltag
e
co
m
m
an
d
s
(
,
)
,
HF
in
jectio
n
am
p
litu
d
e
ℎ
,
elec
tr
ical
s
p
ee
d
,
m
ec
h
an
ical
s
p
ee
d
,
elec
tr
o
m
a
g
n
etic
to
r
q
u
e
,
lo
ad
to
r
q
u
e
,
esti
m
ated
elec
tr
ical
p
o
s
itio
n
̂
an
d
th
e
s
im
u
latio
n
r
ef
er
e
n
ce
elec
tr
ical
p
o
s
itio
n
.
T
h
e
er
r
o
r
in
th
e
R
MS
r
o
to
r
p
o
s
itio
n
is
ca
lcu
lated
u
s
in
g
(
1
1
).
R
M
S
(
)
=
√
1
∫
(
(
)
−
̂
(
)
)
2
0
(
1
1
)
T
o
r
q
u
e
r
ip
p
le
f
ac
to
r
is
ca
lcu
la
ted
with
in
th
e
ev
alu
atio
n
p
e
r
io
d
u
s
in
g
(
12
).
TR
(
%
)
=
e
,
m
ax
−
e
,
m
in
,
av
g
×
100
(
1
2
)
C
u
r
r
en
t
T
HD
is
ca
lcu
lated
in
th
e
FF
T
m
ag
n
itu
d
e
s
p
ec
tr
u
m
o
f
a
s
p
ec
if
ied
an
aly
s
is
win
d
o
w
with
th
e
s
am
e
win
d
o
w
len
g
th
a
n
d
s
am
p
le
r
at
e
ac
r
o
s
s
all
co
n
tr
o
ller
s
.
3
.
4
.
5
.
Det
er
m
ini
s
m
a
nd
s
im
ula
t
io
n c
o
ns
is
t
ency
s
et
t
ing
s
Neu
r
al
n
etwo
r
k
s
ar
e
in
itialized
with
f
ix
ed
r
an
d
o
m
s
ee
d
s
,
n
eu
r
al
-
n
etwo
r
k
e
x
p
lo
r
atio
n
is
p
er
f
o
r
m
ed
with
f
ix
ed
r
a
n
d
o
m
s
ee
d
s
,
an
d
f
ix
ed
r
a
n
d
o
m
s
ee
d
s
ar
e
u
s
ed
wh
en
co
m
p
ar
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g
v
ar
iatio
n
s
o
f
R
L
.
T
h
e
f
ix
e
d
-
s
tep
d
is
cr
ete
s
o
lv
er
(
n
o
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r
iab
le
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s
tep
in
teg
r
atio
n
)
is
u
s
ed
to
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im
u
late
th
e
p
lan
t.
Var
iab
les th
at
h
av
e
b
ee
n
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ed
ar
e
s
to
r
ed
at
th
e
b
ase
s
am
p
lin
g
tim
e
T
s
in
o
r
d
er
to
p
r
e
v
en
t
d
o
wn
-
s
am
p
lin
g
ar
tef
ac
ts
d
u
r
in
g
th
e
co
m
p
u
tatio
n
o
f
to
r
q
u
e
r
ip
p
le
an
d
FF
T
/TH
D.
4.
RE
SU
L
T
S
4
.
1
.
L
ea
rning
perf
o
r
m
a
nce
a
nd
co
nv
er
g
ence
T
h
e
tr
ain
in
g
ep
is
o
d
es
ex
p
er
i
en
ce
d
b
y
th
e
r
ein
f
o
r
ce
m
en
t
l
ea
r
n
in
g
ag
e
n
t
wer
e
4
0
0
0
,
ea
ch
with
a
d
u
r
atio
n
o
f
0
.
3
s
o
f
s
im
u
lated
m
o
to
r
tim
e.
Fig
u
r
e
4
g
iv
es
t
h
e
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er
ag
e
e
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d
e
r
ewa
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o
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t
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g
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n
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at
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
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2
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t
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r
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ics
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iliz
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g
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h
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ics
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iv
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ce
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ich
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ap
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en
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ig
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th
e
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aselin
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T
h
e
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cr
ea
s
e
in
t
h
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ate
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o
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t
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ca
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ed
ited
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o
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h
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ics
ter
m
th
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lim
its
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lo
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atio
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h
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ically
co
n
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is
ten
t
v
o
ltag
e
-
cu
r
r
en
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tr
ajec
t
o
r
ies.
T
h
e
am
p
litu
d
e
o
f
t
h
e
o
u
tp
u
t
o
f
th
e
ac
t
o
r
n
etwo
r
k
lev
elled
o
f
f
at
t
h
e
r
an
g
e
o
f
1
.
2
-
2
.
8
V
at
v
ar
io
u
s
o
p
e
r
atin
g
p
o
i
n
ts
,
wh
er
ea
s
th
e
b
as
elin
e
R
L
r
an
g
ed
at
0
.
5
-
3
.
5
V
with
in
th
e
s
am
e
ti
m
e.
T
h
e
av
er
ag
e
r
ewa
r
d
r
ea
lized
at
th
e
en
d
o
f
th
e
p
r
o
p
o
s
ed
p
h
y
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ics
-
in
f
o
r
m
e
d
r
ewa
r
d
lear
n
in
g
(
PIRL)
a
g
en
t
was
lar
g
er
th
a
n
t
h
at
o
f
th
e
co
n
v
en
tio
n
al
R
L
b
y
2
2
%,
wh
ich
is
in
d
ee
d
a
s
ig
n
if
ican
t
lear
n
in
g
ef
f
icien
cy
im
p
r
o
v
e
m
en
t.
4
.
2
.
Dy
na
m
ic
a
nd
s
t
ea
dy
s
t
a
t
e
perf
o
rma
nce
T
h
e
p
o
licy
th
at
was
lear
n
ed
was
test
ed
o
n
v
ar
io
u
s
lo
ad
to
r
q
u
es
an
d
s
p
ee
d
s
.
Fig
u
r
e
5
d
em
o
n
s
tr
ates
th
e
o
r
d
er
e
d
an
d
esti
m
ated
r
o
to
r
p
o
s
itio
n
at
1
0
0
r
p
m
,
an
d
h
e
r
e
th
e
PIRL
s
ch
em
e
o
b
tain
ed
a
n
ea
r
-
id
ea
l
o
v
er
lap
.
T
h
e
r
o
o
t
m
ea
n
s
q
u
a
r
e
esti
m
atio
n
er
r
o
r
wen
t
d
o
wn
to
2
.
9
elec
tr
ical
d
eg
r
ee
s
with
th
e
f
ix
ed
am
p
litu
d
e
co
n
tr
o
ller
t
o
th
e
p
r
o
p
o
s
ed
m
eth
o
d
,
wh
ich
s
to
o
d
at
0
.
9
5
d
eg
r
ee
s
.
At
h
ig
h
s
p
ee
d
s
(
ab
o
v
e
4
0
0
r
p
m
)
,
b
o
t
h
R
L
m
eth
o
d
s
wer
e
eq
u
ally
ac
cu
r
ate,
w
h
ils
t
in
v
er
y
lo
w
s
p
ee
d
s
(
less
th
an
5
0
r
p
m
)
th
e
PIRL
was
ab
le
to
co
n
tin
u
e
esti
m
atin
g
r
eliab
ly
,
wh
er
e
th
e
b
aselin
e
R
L
wo
u
ld
s
o
m
etim
es ju
m
p
o
u
t
o
f
lo
ck
.
T
ab
le
2
in
d
icate
s
k
ey
p
er
f
o
r
m
an
ce
in
d
icato
r
s
th
at
ar
e
av
e
r
ag
ed
ac
r
o
s
s
all
th
e
test
co
n
d
itio
n
s
.
T
h
e
s
u
g
g
ested
s
o
lu
tio
n
m
in
im
ized
th
e
to
r
q
u
e
r
i
p
p
le
b
y
a
p
p
r
o
x
im
a
tely
6
5
an
d
2
5
%
c
o
m
p
ar
e
d
to
t
h
e
f
ix
e
d
am
p
litu
d
e
s
ce
n
ar
io
an
d
c
o
n
v
e
n
tio
n
al
R
L
,
r
esp
ec
tiv
ely
.
Hig
h
f
r
eq
u
e
n
cy
ex
citatio
n
ca
u
s
ed
a
f
ew
v
ar
iatio
n
s
in
th
e
in
cr
em
en
tal
p
o
wer
lo
s
s
s
in
ce
th
e
am
p
litu
d
e
t
h
at
h
ad
b
ee
n
le
ar
n
t
was
n
o
t
o
v
er
e
x
cited
wh
e
n
th
er
e
was
n
o
n
ee
d
d
u
r
in
g
lig
h
t lo
ad
co
n
d
itio
n
s
.
4
.
3
.
P
a
ra
m
et
er
v
a
ria
t
io
n r
es
po
ns
e
a
nd
no
is
e
T
h
e
s
tan
d
ar
d
d
ev
iatio
n
o
f
th
e
s
tato
r
r
esis
tan
ce
wa
s
d
o
u
b
led
,
an
d
th
e
s
tato
r
r
esis
tan
ce
was
r
aised
b
y
1
5
%
to
s
im
u
late
a
tem
p
er
at
u
r
e
in
cr
ea
s
e
to
test
r
o
b
u
s
tn
ess
.
T
h
e
f
ix
ed
am
p
litu
d
e
co
n
t
r
o
ller
w
as
f
o
u
n
d
to
in
cr
ea
s
e
th
e
p
o
s
itio
n
er
r
o
r
b
y
4
0
%
c
o
m
p
ar
ed
to
th
e
n
o
r
m
al
R
L
,
wh
ich
in
cr
ea
s
ed
b
y
2
0
%.
T
h
e
p
h
y
s
ics
-
in
f
o
r
m
e
d
co
n
tr
o
ller
was
alm
o
s
t
co
n
s
tan
t
at
a
d
e
g
r
ad
atio
n
o
f
a
litt
le
b
elo
w
5
%.
Fig
u
r
e
6
s
h
o
ws
in
s
tan
tan
eo
u
s
to
r
q
u
e
wav
ef
o
r
m
s
with
p
ar
am
eter
s
p
er
tu
r
b
ed
;
th
e
o
u
tp
u
t
o
f
PIRL
was
s
m
o
o
th
,
an
d
lo
w
-
f
r
eq
u
e
n
cy
m
o
d
u
latio
n
was
in
s
ig
n
if
ican
t.
4
.
4
.
E
nerg
y
ef
f
iciency
a
nd
curr
ent
ha
rm
o
nics
T
h
e
s
tato
r
cu
r
r
en
t
o
f
th
e
R
MS
an
d
th
e
to
tal
h
ar
m
o
n
ic
d
is
to
r
ti
o
n
(
T
HD)
wer
e
ex
am
in
ed
with
in
th
e
lo
ad
r
an
g
e.
I
n
th
e
ca
s
e
o
f
n
o
m
in
al
to
r
q
u
e,
PIRL
k
ep
t
cu
r
r
e
n
t
T
HD
at
4
%
o
r
les
s
,
as
co
m
p
ar
ed
to
f
ix
ed
am
p
litu
d
e
an
d
s
tan
d
a
r
d
R
L
c
o
n
tr
o
ller
s
at
7
%
an
d
5
%
,
r
esp
ec
tiv
ely
.
T
h
e
r
ed
u
ce
d
h
ar
m
o
n
ic
co
n
ten
t
is
b
ec
au
s
e
o
f
r
ed
u
ce
d
am
p
litu
d
e
tr
an
s
itio
n
s
o
f
v
o
ltag
e
,
wh
ich
ar
e
lear
n
e
d
b
y
th
e
ag
e
n
t.
T
h
e
en
e
r
g
y
s
av
in
g
o
f
a
b
o
u
t
1
.
8
%
o
n
a
t
y
p
ical
d
u
ty
cy
cle
ca
u
s
ed
b
y
t
h
e
r
ed
u
ctio
n
in
R
MS
cu
r
r
en
t w
as si
g
n
if
ican
t b
ec
au
s
e
it a
llo
wed
co
n
t
in
u
o
u
s
o
p
er
atio
n
in
lo
w
-
v
o
ltag
e
e
-
m
o
b
ilit
y
ap
p
licatio
n
s
.
F
i
g
u
r
e
4
.
C
o
m
p
a
r
i
s
o
n
o
f
r
e
w
a
r
d
c
o
n
v
e
r
g
e
n
c
e
b
e
t
w
e
e
n
s
t
a
n
d
a
r
d
R
L
a
n
d
p
h
y
s
i
c
s
-
i
n
f
o
r
m
e
d
R
L
a
g
e
n
t
s
Fig
u
r
e
5
.
R
o
to
r
p
o
s
itio
n
esti
m
atio
n
co
m
p
a
r
is
o
n
am
o
n
g
f
ix
ed
am
p
litu
d
e,
R
L
,
a
n
d
PIRL
co
n
tr
o
ller
s
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Po
w
E
lec
&
Dr
i Sy
s
t
I
SS
N:
2088
-
8
6
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4
P
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med
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C
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0
.
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0
0
1
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.
5
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Su
mm
a
t
i
v
e
o
n qua
ntit
a
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iv
e
im
pro
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e
m
ent
s
Fig
u
r
e
7
is
co
m
p
ar
in
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%
im
p
r
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em
en
ts
with
th
e
s
u
g
g
ested
m
eth
o
d
a
n
d
th
e
b
aselin
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tech
n
iq
u
es.
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h
e
b
est
o
f
th
em
is
th
e
in
c
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ea
s
ed
co
n
v
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g
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ce
o
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lear
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t
h
e
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ig
h
er
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o
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l
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w
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atin
g
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a
n
d
t
h
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to
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q
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ip
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ch
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n
titativ
e
f
in
d
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s
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b
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tiate
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in
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ical
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e
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atin
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ils
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th
e
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n
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er
l
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g
m
o
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aly
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ase
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at
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th
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ller
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r
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cy
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ts
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y
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y
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s
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a
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ast
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r
ier
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s
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m
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r
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HD
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%
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tan
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ar
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to
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Fig
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C
u
r
r
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t
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ag
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itu
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es
5.
DIS
CU
SS
I
O
N
Mo
to
r
p
h
y
s
ics
lear
n
in
g
as
a
p
ar
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o
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r
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ess
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tially
alter
s
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ex
p
lo
r
atio
n
an
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o
f
o
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tim
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en
co
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er
less
PMSM
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b
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th
e
ag
en
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h
e
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h
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in
f
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m
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ce
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(
PIRL)
m
eth
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ich
im
p
o
s
es
th
e
s
tato
r
v
o
ltag
e
m
o
d
el
r
esid
u
al
o
n
th
e
r
ewa
r
d
,
r
estricts
ex
p
lo
r
atio
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to
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e
p
h
y
s
ically
co
n
s
is
ten
t
o
p
er
atin
g
r
eg
im
es;
th
i
s
en
s
u
r
es
th
at
lear
n
in
g
will
b
e
s
tab
le
an
d
co
n
v
er
g
e
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
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4
I
n
t J Po
w
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lec
&
Dr
i Sy
s
t
,
Vo
l.
1
7
,
No
.
2
,
J
u
n
e
20
2
6
:
8
7
3
-
8
8
4
882
m
o
r
e
q
u
ick
ly
th
a
n
tr
a
d
itio
n
al
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
(
R
L
)
.
T
h
e
ag
e
n
t
is
a
r
ea
l
-
tim
e
ad
a
p
tiv
e
co
n
tr
o
ller
t
h
at
in
cr
ea
s
es th
e
in
jectio
n
am
p
litu
d
e
in
r
esp
o
n
s
e
to
lo
w
s
p
ee
d
s
t
o
m
ain
tain
s
ig
n
al
-
to
-
n
o
is
e
r
atio
an
d
d
ec
r
ea
s
es th
e
in
jectio
n
am
p
litu
d
e
wh
en
h
ea
v
ily
lo
ad
ed
to
m
i
n
im
ize
th
e
lo
s
s
es
in
cu
r
r
ed
to
r
ea
c
h
lev
els o
f
ex
p
er
t
-
lev
el
tu
n
i
n
g
b
eh
av
io
r
with
o
u
t
h
u
m
a
n
in
ter
v
en
tio
n
.
C
o
m
p
ar
ed
to
f
ix
ed
o
r
h
eu
r
is
tic
co
n
tr
o
ller
s
,
th
e
s
u
g
g
ested
ap
p
r
o
ac
h
h
as
f
u
lly
ad
a
p
tiv
e
p
e
r
f
o
r
m
an
ce
wi
th
n
o
p
r
e
-
d
eter
m
in
ed
m
ap
s
o
r
ca
lib
r
atio
n
,
b
u
t
ca
n
b
e
ea
s
ily
i
n
ter
p
r
eted
an
d
d
ata
-
s
p
ar
s
e
b
y
u
s
in
g
its
p
h
y
s
ics
-
in
f
o
r
m
ed
r
ewa
r
d
.
Un
lik
e
th
e
o
n
lin
e
ad
ap
tatio
n
o
f
th
e
ev
o
l
u
tio
n
a
r
y
alg
o
r
ith
m
s
b
ase
d
o
n
o
f
f
li
n
e
s
ea
r
ch
es,
PIRL
co
n
v
er
g
es
th
e
o
n
lin
e
ad
ap
tatio
n
in
ab
o
u
t
4
0
%
th
e
tim
e
,
an
d
th
e
co
m
p
u
tatio
n
o
f
p
h
y
s
ics
-
r
esid
u
al
in
cu
r
s
io
n
tak
es
o
n
ly
a
b
o
u
t
5
%
o
f
th
e
o
v
er
all
tim
e.
T
h
e
f
r
am
ewo
r
k
is
v
er
y
r
o
b
u
s
t
to
ch
an
g
es
in
p
ar
am
ete
r
s
an
d
n
o
is
e
s
in
ce
th
e
p
h
y
s
ics
p
en
alty
is
c
o
n
tin
u
o
u
s
ly
m
o
n
ito
r
ed
to
m
ain
tain
c
o
n
s
is
ten
cy
b
etwe
en
m
ea
s
u
r
ed
an
d
p
r
e
d
icted
elec
tr
i
ca
l
q
u
an
titi
es,
wh
ich
r
esu
lts
in
th
e
r
esis
tan
ce
o
r
in
d
u
ctan
ce
d
r
if
t
b
ein
g
co
r
r
ec
te
d
im
m
ed
iately
,
as
well
as
m
ain
ta
in
in
g
a
lev
el
o
f
in
v
e
r
ter
s
af
ety
m
ar
g
in
.
I
n
p
r
ac
tice,
th
e
tech
n
iq
u
e
o
f
f
e
r
s
a
f
ea
s
ib
le
p
ath
to
s
elf
-
tu
n
in
g
c
o
n
tr
o
l
o
v
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ex
tr
a
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lo
w
-
v
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ltag
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ca
s
es
lik
e
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ik
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s
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o
ter
s
,
an
d
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o
t
ic
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tu
ato
r
s
,
wh
ich
h
av
e
less
lab
o
r
io
u
s
ca
lib
r
atio
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r
eq
u
ir
em
e
n
ts
an
d
ar
e
m
o
r
e
en
er
g
y
-
ef
f
icien
t.
T
h
e
tr
ain
e
d
p
o
licy
g
en
er
ates
f
o
r
war
d
i
n
f
er
en
ce
in
ap
p
r
o
x
i
m
ately
1
8
µs
u
s
in
g
a
1
5
0
MH
z
DSP
an
d
is
f
ea
s
ib
le
in
a
cu
r
r
en
t
-
co
n
tr
o
l
cy
cle
o
f
on
ly
1
0
0
µs
,
m
ak
i
n
g
it
p
r
ac
tic
al
o
n
m
o
s
t e
m
b
e
d
d
ed
s
y
s
tem
s
.
I
n
ad
d
itio
n
to
am
p
litu
d
e
ad
a
p
tatio
n
,
th
e
id
en
tical
f
r
am
ewo
r
k
ca
n
b
e
g
en
er
alize
d
to
co
-
o
p
tim
ize
in
jectio
n
f
r
e
q
u
e
n
cy
,
to
r
q
u
e
,
an
d
f
lu
x
co
n
tr
o
l
o
r
b
e
in
teg
r
ated
in
to
th
e
s
etu
p
o
f
a
d
ig
ital
twin
t
o
f
ac
ilit
ate
p
r
ed
ictio
n
-
ad
a
p
tatio
n
an
d
n
ev
er
-
e
n
d
in
g
im
p
r
o
v
em
e
n
t.
E
x
p
lain
a
b
le
-
AI
to
o
ls
,
co
u
p
led
with
o
t
h
er
s
,
m
i
g
h
t
h
elp
to
in
c
r
ea
s
e
tr
an
s
p
ar
e
n
cy
an
d
tr
u
s
t
in
th
e
in
d
u
s
tr
y
.
All
in
all,
p
h
y
s
ical
in
s
ig
h
t
in
r
ein
f
o
r
ce
m
en
t
lear
n
i
n
g
tr
a
n
s
f
o
r
m
s
m
o
d
el
-
b
ased
an
d
d
ata
-
d
r
iv
e
n
p
a
r
ad
ig
m
s
b
y
f
a
cilitatin
g
ef
f
icien
t,
r
o
b
u
s
t
,
an
d
r
ea
l
-
tim
e
a
d
ap
tiv
e
co
n
tr
o
l in
t
h
e
n
ex
t
g
en
er
atio
n
s
en
s
o
r
less
PM
SM
d
r
iv
es.
6.
CO
NCLU
SI
O
N
AND
F
U
T
U
RE
WO
RK
T
h
is
p
ap
er
d
em
o
n
s
tr
ated
a
p
h
y
s
ics
-
in
f
o
r
m
ed
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
(
PIRL)
ar
ch
itectu
r
e
o
f
ad
ap
tiv
e
o
p
er
atio
n
o
f
h
ig
h
-
f
r
e
q
u
en
c
y
in
jectio
n
am
p
litu
d
e
co
n
t
r
o
l in
e
n
co
d
er
less
lo
w
-
v
o
ltag
e
PMSM
d
r
iv
es.
T
h
e
ar
ticle
d
ea
lt
with
th
e
o
ld
p
r
o
b
lem
o
f
f
in
d
i
n
g
th
e
co
r
r
ec
t
a
m
p
litu
d
e
o
f
in
jectio
n
at
v
a
r
y
in
g
s
p
ee
d
s
,
lo
ad
s
,
an
d
u
n
ce
r
tain
ties
in
p
ar
am
ete
r
s
a
p
r
o
b
lem
t
h
at
d
ir
ec
tly
r
elate
s
t
o
p
o
s
itio
n
esti
m
atio
n
ac
cu
r
ac
y
,
to
r
q
u
e
r
ip
p
le
,
an
d
en
er
g
y
e
f
f
icien
cy
i
n
ex
tr
a
-
lo
w
-
v
o
ltag
e
s
y
s
tem
s
.
T
h
e
p
r
o
p
o
s
ed
a
p
p
r
o
ac
h
lim
ite
d
th
e
r
ein
f
o
r
ce
m
e
n
t le
ar
n
in
g
r
ewa
r
d
s
f
u
n
ctio
n
,
with
th
e
PM
SM
v
o
ltag
e
eq
u
atio
n
s
em
b
e
d
d
ed
,
to
p
h
y
s
ically
co
n
s
is
ten
t
o
p
er
atin
g
r
e
g
io
n
s
.
T
h
is
in
te
g
r
atio
n
s
u
cc
e
s
s
f
u
lly
r
eg
u
lar
ize
d
ex
p
lo
r
atio
n
a
n
d
m
ad
e
ac
tio
n
s
in
f
ea
s
ib
le
as
well
a
s
co
n
v
er
g
en
ce
to
b
e
r
ap
id
an
d
m
o
r
e
s
tab
le
th
an
p
u
r
ely
d
ata
-
d
r
iv
en
r
ein
f
o
r
ce
m
e
n
t
lear
n
i
n
g
m
eth
o
d
s
.
T
h
e
u
s
e
o
f
s
im
u
latio
n
estab
lis
h
ed
th
at
th
e
PIRL
co
n
tr
o
ller
i
n
cu
r
r
e
d
a
co
n
s
id
er
ab
le
d
ec
r
ea
s
e
in
to
r
q
u
e
r
ip
p
le
in
ad
d
itio
n
to
in
c
r
ea
s
in
g
th
e
ac
cu
r
ac
y
o
f
p
o
s
itio
n
es
tim
atio
n
in
lo
w
s
p
ee
d
an
d
s
tab
ilit
y
to
p
ar
a
m
eter
d
r
i
f
t a
n
d
n
o
is
e
in
m
ea
s
u
r
em
en
ts
.
Alth
o
u
g
h
th
ese
ar
e
p
o
s
itiv
e
r
esu
lts
,
th
e
cu
r
r
en
t
r
esear
ch
is
co
n
f
in
ed
t
o
th
e
v
alid
atio
n
o
f
s
im
u
latio
n
-
b
ased
v
alid
atio
n
.
T
h
er
e
ar
e
o
th
er
f
ac
to
r
s
th
at
m
ay
af
f
ec
t
p
r
ac
tical
im
p
lem
en
tatio
n
,
in
clu
d
in
g
ADC
q
u
an
tizatio
n
,
non
-
lin
ea
r
ities
o
f
in
v
er
ter
s
,
P
W
M
r
eso
lu
tio
n
,
an
d
co
n
s
tr
ain
ts
o
f
r
ea
l
-
tim
e
co
m
p
u
tatio
n
,
b
u
t
t
h
ese
wer
e
n
o
t
ex
p
licitly
m
o
d
elled
.
M
o
r
eo
v
e
r
,
th
e
lear
n
in
g
p
r
o
ce
s
s
p
r
esu
p
p
o
s
es
f
air
ly
p
r
ec
is
e
in
itial
m
o
to
r
p
ar
a
m
eter
s
in
t
h
e
tr
ain
in
g
th
at
ca
n
in
f
lu
e
n
ce
g
en
er
ali
z
atio
n
in
ac
tu
al
h
a
r
d
war
e
co
n
d
itio
n
s
.
T
h
e
n
e
x
t
s
tag
e
o
f
wo
r
k
will
b
e
ca
r
r
ied
o
u
t
i
n
th
e
ex
p
er
im
en
tal
v
alid
atio
n
with
th
e
u
s
e
o
f
a
4
8
V
PMSM
p
r
o
to
ty
p
e
to
p
r
o
v
e
th
e
f
ea
s
ib
il
ity
o
f
th
e
wo
r
k
i
n
r
ea
l
tim
e
an
d
its
s
tr
en
g
th
.
Oth
er
ar
ea
s
o
f
f
u
tu
r
e
r
esear
ch
ar
e
co
m
b
in
in
g
o
p
tim
izatio
n
o
f
th
e
in
jectio
n
am
p
litu
d
e
an
d
f
r
eq
u
e
n
cy
,
th
e
in
v
e
r
ter
n
o
n
-
lin
ea
r
m
o
d
els
,
an
d
ex
p
lo
r
in
g
en
h
an
ce
d
lear
n
in
g
a
p
p
r
o
ac
h
es,
lik
e
ex
p
lain
ab
le
r
ein
f
o
r
ce
m
e
n
t
lear
n
in
g
,
to
in
cr
ea
s
e
tr
an
s
p
a
r
en
cy
an
d
in
d
u
s
tr
ial
ac
ce
p
tan
ce
.
T
h
e
s
u
g
g
ested
f
r
a
m
ewo
r
k
ca
n
b
e
a
p
p
lied
t
o
o
t
h
er
elec
tr
ic
d
r
iv
e
s
y
s
tem
s
as
well
,
in
d
u
ctio
n
an
d
s
witch
ed
-
r
elu
ctan
ce
m
ac
h
i
n
es.
ACK
NO
WL
E
DG
M
E
N
T
S
T
h
is
r
esear
ch
was su
p
p
o
r
ted
b
y
ad
v
a
n
ce
d
s
cien
tific
r
esear
ch
.
F
UNDING
I
NF
O
R
M
A
T
I
O
N
T
h
is
r
esear
ch
r
ec
ei
v
ed
n
o
s
p
e
cif
ic
g
r
an
t
f
r
o
m
an
y
f
u
n
d
in
g
a
g
en
cy
i
n
th
e
p
u
b
lic,
co
m
m
er
ci
al,
o
r
n
o
t
-
f
o
r
-
p
r
o
f
it secto
r
s
.
AUTHO
R
CO
NT
RI
B
UT
I
O
NS ST
A
T
E
M
E
N
T
T
h
is
jo
u
r
n
al
u
s
es
th
e
C
o
n
tr
ib
u
to
r
R
o
les
T
ax
o
n
o
m
y
(
C
R
ed
iT)
to
r
ec
o
g
n
ize
in
d
iv
id
u
al
au
th
o
r
co
n
tr
ib
u
tio
n
s
,
r
ed
u
ce
au
th
o
r
s
h
ip
d
is
p
u
tes,
an
d
f
ac
ilit
ate
co
llab
o
r
atio
n
.
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