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[
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[
5
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ec
r
ea
s
ed
p
o
wer
f
ac
to
r
(
PF
)
,
DC
m
o
to
r
s
d
o
n
o
t
em
it
an
y
h
ar
m
o
n
ics.
B
ec
au
s
e
o
f
th
eir
s
tr
aig
h
tf
o
r
war
d
co
n
s
tr
u
ctio
n
,
DC
m
o
to
r
s
ar
e
s
im
p
le
to
s
er
v
ice
an
d
m
ai
n
tain
.
DC
m
o
to
r
s
h
a
v
e
a
p
p
licatio
n
s
in
d
iv
er
s
e
a
r
ea
s
s
u
ch
as r
en
ewa
b
le
en
e
r
g
y
s
y
s
t
em
s
,
r
o
b
o
tics
,
au
to
m
o
tiv
e,
a
n
d
in
d
u
s
tr
ial
au
to
m
atio
n
.
Pro
p
o
r
tio
n
al
in
teg
r
al
d
er
iv
ati
v
e
(
PID
)
co
n
tr
o
l
is
a
co
n
tr
o
l
tech
n
iq
u
e
co
m
m
o
n
ly
u
s
ed
in
v
ar
io
u
s
r
esear
ch
to
p
ics,
in
clu
d
in
g
DC
m
o
to
r
s
p
ee
d
c
o
n
tr
o
l.
I
n
PID
co
n
tr
o
l,
th
e
s
tead
y
s
tate
er
r
o
r
(
er
r
o
r
t
h
at
d
o
es
n
o
t
ch
an
g
e)
ca
n
b
e
ad
ju
s
ted
b
y
s
elec
tin
g
th
e
ap
p
r
o
p
r
iate
p
r
o
p
o
r
tio
n
al
(
P
)
,
in
teg
r
al
(
I
)
,
an
d
d
er
iv
ativ
e
(
D
)
co
r
r
elatio
n
s
[
1
3
]
.
I
n
PID
co
n
tr
o
l,
th
e
co
r
r
elatio
n
P,
I
,
an
d
D
ar
e
u
s
ed
to
g
eth
er
to
r
eg
u
late
th
e
s
tead
y
s
tate
er
r
o
r
,
r
is
e
tim
e,
an
d
s
ettlin
g
tim
e
(
th
e
tim
e
r
eq
u
ir
e
d
to
r
ea
ch
th
e
s
tead
y
s
tate
er
r
o
r
)
[
1
4
]
.
T
h
e
wea
k
n
ess
o
f
co
n
v
en
tio
n
al
DC
m
o
to
r
co
n
tr
o
l w
ith
PID
is
th
at
PID
ca
n
ca
u
s
e
o
v
er
-
s
h
o
o
t,
PID
ca
n
ca
u
s
e
an
u
n
s
tab
le
s
y
s
tem
,
n
am
ely
a
s
y
s
tem
th
at
ca
n
n
o
t
r
eg
u
late
s
p
ee
d
co
r
r
ec
tl
y
an
d
PID
ca
n
r
eq
u
ir
e
co
r
r
ec
t
tu
n
i
n
g
to
r
eg
u
late
th
e
co
r
r
elatio
n
o
f
P,
I
,
an
d
D,
w
h
ich
r
eq
u
i
r
es tim
e
an
d
s
k
ill
[
1
5
]
.
Sev
er
al
DC
m
o
to
r
co
n
tr
o
l
t
ec
h
n
iq
u
es
u
s
in
g
o
p
tim
ized
P
I
D
h
av
e
b
ee
n
p
r
esen
ted
.
Op
tim
izatio
n
tech
n
iq
u
es
u
s
in
g
co
m
p
u
tin
g
h
av
e
b
ee
n
wid
ely
ap
p
lied
,
s
u
c
h
as
p
ar
ticle
s
war
m
o
p
tim
izatio
n
(
PS
O)
[
1
6
]
,
[
1
7
]
,
f
ir
ef
ly
alg
o
r
ith
m
[
1
8
]
–
[
2
0
]
,
eq
u
ilib
r
iu
m
o
p
tim
izer
[
2
1
]
,
g
r
ay
w
o
lf
o
p
tim
izatio
n
[
2
2
]
,
an
d
tr
an
s
it
s
ea
r
ch
o
p
tim
izatio
n
alg
o
r
ith
m
[
2
3
]
.
T
h
er
e
is
s
till
m
u
ch
t
o
lear
n
ab
o
u
t
DC
m
o
to
r
co
n
t
r
o
l,
d
esp
ite
th
e
p
r
esen
tatio
n
o
f
m
u
ltip
le
o
p
tim
al
co
n
tr
o
l
ex
p
er
im
en
ts
.
T
o
s
et
PID
s
e
ttin
g
s
f
o
r
DC
m
o
to
r
s
,
th
is
ar
ticle
p
r
o
p
o
s
es
a
co
n
tr
o
l
ap
p
r
o
ac
h
b
ased
o
n
th
e
h
o
r
n
ed
lizar
d
o
p
tim
izatio
n
alg
o
r
ith
m
-
aq
u
ila
o
p
tim
izer
(
HL
AO)
m
et
h
o
d
.
T
h
er
e
ar
e
tw
o
HL
AO
p
er
f
o
r
m
a
n
ce
m
ea
s
u
r
e
m
en
ts
u
s
ed
in
t
h
is
ar
ticle.
T
h
e
C
E
C
2
0
1
7
b
e
n
ch
m
ar
k
f
u
n
cti
o
n
test
is
co
m
p
ar
e
d
with
h
o
r
n
ed
lizar
d
o
p
tim
iza
tio
n
(
HL
O)
,
a
co
m
p
a
r
is
o
n
ap
p
r
o
ac
h
,
t
o
d
eter
m
in
e
th
e
f
ir
s
t
p
e
r
f
o
r
m
an
ce
m
ea
s
u
r
em
en
t.
I
n
th
e
m
ea
n
tim
e,
HL
AO
was
p
u
t
to
th
e
test
f
o
r
PID
-
b
ased
DC
m
o
to
r
co
n
tr
o
l
in
th
e
s
ec
o
n
d
test
.
I
n
th
e
s
ec
o
n
d
test
,
HL
O
an
d
th
e
tr
ad
itio
n
al
PID
a
p
p
r
o
ac
h
wer
e
em
p
lo
y
ed
as
co
m
p
ar
is
o
n
tech
n
i
q
u
es.
T
h
e
ap
p
licatio
n
o
f
PID
co
n
tr
o
l to
a
DC
m
o
to
r
with
HL
AO
is
th
e
ar
ticle's co
n
tr
ib
u
tio
n
.
T
h
e
s
tr
u
ctu
r
e
o
f
th
is
ar
ticle
is
as
f
o
llo
ws:
th
e
HL
O
alg
o
r
ith
m
,
DC
m
o
to
r
,
an
d
HL
AO
ar
e
d
escr
ib
ed
in
s
ec
tio
n
2
.
Sectio
n
3
is
th
e
p
r
o
p
o
s
ed
HL
AO
f
o
r
tu
n
in
g
PI
D
in
DC
m
o
to
r
.
I
n
s
ec
tio
n
4
t
h
er
e
ar
e
d
is
cu
s
s
io
n
s
an
d
s
im
u
latio
n
s
.
T
h
e
c
o
n
clu
s
i
o
n
is
p
r
esen
ted
in
th
e
last
s
ec
tio
n
.
2.
M
E
T
H
O
D
2
.
1
.
H
o
rned
liza
rd
o
ptim
iza
t
io
n a
lg
o
rit
hm
T
h
e
HL
O
is
a
m
etah
eu
r
is
tic
o
p
tim
izatio
n
s
y
s
tem
th
at
s
im
u
lates
cr
y
p
s
is
,
s
k
in
lig
h
ten
in
g
o
r
d
ar
k
en
in
g
,
b
lo
o
d
s
p
r
ay
in
g
,
a
n
d
m
o
b
ile
d
ef
en
s
e
s
tr
ateg
ies
f
o
r
escap
e
b
y
m
at
h
em
atica
l
m
ea
n
s
[
2
4
]
.
W
h
en
a
lizar
d
en
g
a
g
es
in
cr
y
p
s
is
b
eh
av
io
r
,
it
tu
r
n
s
tr
an
s
p
ar
e
n
t
to
e
lu
d
e
d
etec
tio
n
b
y
p
o
te
n
tial
p
r
ed
ato
r
s
.
I
n
g
en
e
r
al,
HL
OA
h
as 6
m
ain
s
tr
ateg
ies u
s
ed
.
2
.
1
.
1
.
F
irst
t
a
ct
ic
:
cr
y
ptic
co
nd
uct
C
r
y
p
s
is
is
a
co
n
c
e
p
t
i
n
b
i
o
l
o
g
y
t
h
a
t
r
ef
er
s
t
o
a
b
il
it
y
o
f
an
o
r
g
a
n
is
m
t
o
h
i
d
e
i
ts
el
f
o
r
b
ec
o
m
e
i
n
v
is
i
b
le
to
p
r
e
d
at
o
r
s
o
r
p
r
e
y
.
C
r
y
p
s
is
is
a
n
i
m
p
o
r
ta
n
t
s
u
r
v
i
v
al
s
tr
a
teg
y
in
n
at
u
r
e,
wh
ic
h
h
el
p
s
o
r
g
an
is
m
s
to
a
v
o
id
p
r
e
d
at
o
r
s
an
d
i
n
c
r
e
ases
t
h
ei
r
c
h
a
n
c
es
o
f
s
u
r
v
iv
al
a
n
d
r
e
p
r
o
d
u
cti
o
n
.
T
h
is
s
t
r
at
eg
y
c
an
b
e
m
o
d
el
e
d
i
n
(
1
)
to
(
9
)
.
∗
=
{
+
,
in
dic
a
te
s
R
e
d
−
,
;
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=
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+
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−
,
(
1
)
∗
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+
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(
∗
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)
(
2
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c
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(
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(
3
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s
in
(
ℎ
)
(
4
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I
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8
9
3
8
Hyb
r
id
h
o
r
n
ed
liz
a
r
d
o
p
timiz
a
tio
n
a
lg
o
r
ith
m
-
a
q
u
ila
o
p
timiz
er fo
r
DC
mo
to
r
(
Wid
i A
r
ib
o
w
o
)
1675
2
=
∗
−
∗
+
∗
−
∗
(
6
)
In
(
5
)
an
d
(
6
)
ca
n
b
e
r
ep
r
esen
t
ed
in
o
n
e
eq
u
atio
n
,
as sh
o
wn
(
7
)
.
=
∗
−
∗
±
[
∗
−
∗
]
(
7
)
T
h
e
in
v
er
s
e
f
o
r
m
o
f
(
7
)
is
as
(
8
)
.
=
1
s
in
(
ℎ
)
−
1
c
os
(
ℎ
)
±
[
2
s
in
(
ℎ
)
−
2
s
in
(
ℎ
)
]
(
8
)
W
h
er
e
th
e
an
g
les (
h
u
e)
f
ill
ℎ
≠
ℎ
≠
ℎ
≠
ℎ
an
d
ch
r
o
m
a
1
≠
2
.
1
=
1
[
s
in
(
ℎ
)
−
1
c
os
(
ℎ
)
]
±
2
[
c
os
(
ℎ
)
−
s
in
(
ℎ
)
]
(
9
)
Fro
m
(
9
)
,
th
e
p
o
s
itio
n
o
f
t
h
e
n
ew
s
ea
r
ch
ag
en
t
(
h
o
r
n
ed
li
za
r
d
)
in
th
e
s
ea
r
ch
s
o
lu
tio
n
s
p
ac
e
1
⃗
⃗
⃗
(
+
1
)
i
s
o
b
tain
ed
in
(
1
0
)
.
1
⃗
⃗
⃗
(
+
1
)
=
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
)
+
(
−
.
)
[
1
[
si
n
(
1
⃗
⃗
⃗
⃗
⃗
(
)
)
−
1
cos
(
2
⃗
⃗
⃗
⃗
⃗
(
)
)
]
−
(
−
1
)
(
c
o
s
(
3
⃗
⃗
⃗
⃗
⃗
(
)
)
−
si
n
(
4
⃗
⃗
⃗
⃗
⃗
(
)
)
)
]
(
1
0
)
W
h
er
e
a*
an
d
b
*
ar
e
th
e
c
h
r
o
m
atic
co
o
r
d
in
ates.
c*
an
d
h
v
alu
es
co
r
r
esp
o
n
d
to
c
h
r
o
m
a
(
o
r
s
atu
r
atio
n
)
a
n
d
h
u
e.
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
)
is
th
e
b
est
s
ea
r
ch
ag
e
n
t
f
o
r
th
e
g
en
er
atio
n
t.
1
,
2
,
3
,
4
ar
e
i
n
teg
er
r
an
d
o
m
n
u
m
b
e
r
s
g
en
er
ated
b
etwe
en
1
an
d
t
h
e
u
tm
o
s
t
n
u
m
b
er
o
f
s
ea
r
ch
ag
e
n
ts
with
1
≠
2
≠
3
≠
4
.
r
ep
r
esen
ts
th
e
u
tm
o
s
t n
u
m
b
e
r
o
f
iter
atio
n
s
.
is
a
b
in
ar
y
v
alu
e.
2
.
1
.
2
.
Seco
nd
t
a
ct
ic:
da
r
k
ening
o
r
lig
hting
o
f
t
he
s
k
in
B
ased
o
n
wh
eth
er
it
n
ee
d
s
to
r
ed
u
ce
o
r
b
o
o
s
t
its
s
o
lar
th
er
m
al
g
ain
,
th
e
h
o
r
n
ed
lizar
d
ca
n
ch
an
g
e
th
e
co
lo
r
o
f
its
s
k
in
.
T
h
e
s
ec
o
n
d
t
ac
tic
ca
n
b
e
m
o
d
eled
in
(
11
)
a
n
d
(
12
)
.
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
)
=
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
)
+
1
2
ℎ
1
sin
(
1
⃗
⃗
⃗
⃗
⃗
(
)
−
2
⃗
⃗
⃗
⃗
⃗
(
)
)
−
(
−
1
)
1
2
ℎ
1
sin
(
3
⃗
⃗
⃗
⃗
⃗
(
)
−
4
⃗
⃗
⃗
⃗
⃗
(
)
)
(
1
1
)
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
)
=
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
)
+
1
2
1
sin
(
1
⃗
⃗
⃗
⃗
⃗
(
)
−
2
⃗
⃗
⃗
⃗
⃗
(
)
)
−
(
−
1
)
1
2
1
sin
(
3
⃗
⃗
⃗
⃗
⃗
(
)
−
4
⃗
⃗
⃗
⃗
⃗
(
)
)
(
1
2
)
W
h
er
e
L
ig
h
ten
in
g
1
(
0
v
alu
e
)
an
d
L
ig
h
th
en
in
g
2
(
0
.
4
0
4
6
6
6
1
v
alu
e)
ar
e
two
r
an
d
o
m
n
u
m
b
e
r
s
cr
ea
ted
b
etwe
en
th
em
.
An
alo
g
o
u
s
ly
,
Dar
k
1
a
n
d
Dar
k
2
a
r
e
ar
b
itra
r
y
v
alu
es
th
at
ar
e
p
r
o
d
u
ce
d
b
y
d
i
v
id
in
g
Dar
k
e
n
in
g
1
(
v
alu
e=
0
.
5
4
4
0
5
1
0
)
b
y
Dar
k
e
n
in
g
2
(
1
v
alu
e
)
.
2
.
1
.
3
.
T
hird
t
a
ct
ic:
s
qu
irt
ing
blo
o
d
T
h
e
h
o
r
n
ed
lizar
d
s
h
o
o
ts
b
l
o
o
d
o
u
t
o
f
its
ey
es
to
war
d
o
f
f
en
e
m
ies.
On
e
way
to
v
is
u
alize
th
e
s
h
o
o
tin
g
b
lo
o
d
p
r
o
tectio
n
m
e
ch
an
is
m
is
as
a
p
r
o
jectile
ac
t
io
n
.
I
t
d
iv
id
es
t
h
e
p
r
o
jectile
m
o
tio
n
in
t
o
its
two
co
m
p
o
n
en
ts
,
th
e
X
-
ax
is
(
h
o
r
izo
n
tal)
an
d
th
e
Y
-
ax
is
(
v
er
tical)
,
to
g
et
th
e
eq
u
atio
n
s
o
f
m
o
tio
n
.
I
n
th
e
h
o
r
izo
n
tal
d
ir
ec
tio
n
,
it c
a
n
b
e
m
o
d
eled
in
(
13
)
.
=
0
+
∫
0
=
0
+
(
1
3
)
T
h
e
eq
u
atio
n
f
o
r
th
e
v
e
r
tical
d
ir
ec
tio
n
is
as (
1
4
)
a
n
d
(
1
5
)
.
=
0
+
∫
(
0
+
)
0
=
0
+
0
+
1
2
2
(
1
4
)
0
=
0
⃗
(
1
5
)
I
n
(
1
6
)
an
d
(
1
7
)
ea
c
h
o
f
w
h
ich
is
a
v
ec
to
r
eq
u
atio
n
o
f
lo
ca
tio
n
an
d
v
elo
city
.
0
=
0
c
os
(
)
+
(
(
0
s
in
(
)
−
1
2
2
)
(
1
6
)
=
=
(
0
c
os
(
)
)
+
(
(
0
s
in
(
)
−
)
⃗
(
1
7
)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2252
-
8
9
3
8
I
n
t J Ar
tif
I
n
tell
,
Vo
l.
14
,
No
.
2
,
Ap
r
il
2
0
2
5
:
1
6
7
3
-
1
6
8
2
1676
L
astl
y
,
th
e
tr
ajec
to
r
y
h
as th
e
f
o
llo
win
g
ex
p
r
ess
io
n
as (
1
8
)
.
1
⃗
⃗
⃗
(
+
1
)
=
[
0
c
os
(
)
+
]
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
)
+
[
0
s
in
(
)
−
+
]
⃗
⃗
⃗
(
)
(
1
8
)
W
h
er
e
0
is
s
et
to
1
s
eg
.
is
s
e
t to
2
.
is
s
et
to
1
e
-
6
.
g
is
g
r
av
ity
o
f
th
e
ea
r
th
(
0
.
0
0
9
8
0
7
k
m
/s
2
).
2
.
1
.
4
.
F
o
urt
h t
a
c
t
ic:
m
o
v
e
t
o
g
et
a
wa
y
I
n
th
is
tactic,
h
o
r
n
ed
lizar
d
s
m
ak
e
f
ast,
r
an
d
o
m
m
o
v
em
en
ts
ar
o
u
n
d
t
h
e
en
v
ir
o
n
m
en
t
to
a
v
o
id
p
r
ed
ato
r
s
.
T
h
e
tactic
ca
n
b
e
f
o
r
m
u
lated
in
(
19
)
.
1
⃗
⃗
⃗
(
+
1
)
=
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
)
+
(
1
2
−
)
⃗
⃗
⃗
(
)
(
1
9
)
2
.
1
.
5
.
F
if
t
h t
a
c
t
ic:
∝
-
m
el
a
no
ph
o
re
s
t
im
ula
t
ing
ho
rm
o
ne
Ho
r
n
ed
lizar
d
s
k
in
ca
n
ch
a
n
g
e
s
k
in
b
y
r
o
tatin
g
d
u
e
to
th
e
in
f
lu
e
n
ce
o
f
tem
p
er
at
u
r
e
o
n
t
h
e
∝
-
m
elan
o
p
h
o
r
e
s
tim
u
latin
g
h
o
r
m
o
n
e.
T
h
is
tactica
l f
o
r
m
u
la
i
s
f
o
r
m
u
lated
in
(
20
)
.
ℎ
(
)
=
−
(
)
−
(
2
0
)
W
h
er
e
an
d
ar
e
th
e
wo
r
s
t
a
n
d
b
est
f
tn
ess
v
alu
e
i
n
th
e
c
u
r
r
en
t
g
e
n
er
atio
n
.
Af
ter
ca
lcu
latin
g
(
20
)
,
t
h
e
ℎ
(
)
v
alu
e
v
ec
to
r
is
n
o
r
m
alize
d
with
in
t
h
e
in
ter
v
al
[
0
,
1
]
.
I
n
(
21
)
,
s
ea
r
ch
ag
en
ts
ar
e
r
ep
lace
d
b
y
a
lo
w
∝
−
r
ate,
less
th
an
0
.
3
.
1
⃗
⃗
⃗
(
)
=
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
)
+
1
2
[
1
⃗
⃗
⃗
⃗
⃗
(
)
−
(
−
1
)
2
⃗
⃗
⃗
⃗
⃗
(
)
]
(
2
1
)
2
.
2
.
DC
m
o
t
o
r
I
n
th
is
p
ar
t,
a
lin
ea
r
m
o
d
el
o
f
a
DC
m
o
to
r
is
cr
ea
ted
u
s
in
g
th
e
m
ec
h
an
ical
an
d
elec
tr
ical
e
q
u
atio
n
s
in
co
n
ju
n
ctio
n
.
T
h
er
e
is
a
n
e
x
p
la
n
atio
n
o
f
m
ath
em
atica
l
m
o
d
el
s
an
d
m
o
d
el
co
n
ce
p
ts
.
T
h
e
m
o
d
el'
s
co
r
r
ec
tn
ess
is
th
e
m
ain
f
ac
to
r
in
c
o
n
tr
o
l
d
esig
n
.
E
f
f
icien
cy
is
in
cr
ea
s
ed
,
an
d
tim
e
an
d
m
o
n
ey
ar
e
s
av
ed
.
T
h
e
id
ea
o
f
cr
ea
tin
g
th
e
id
ea
l
m
o
d
el
is
a
cr
u
cial
f
ir
s
t
s
tep
.
T
h
e
im
ag
e
in
Fig
u
r
e
1
illu
s
tr
ates
th
e
wid
ely
r
ec
o
g
n
ized
p
r
in
cip
le
o
f
DC
m
o
to
r
m
o
d
elin
g
: th
e
in
teg
r
atio
n
o
f
elec
tr
ical
an
d
m
ec
h
a
n
ical
eq
u
atio
n
s
.
Fig
u
r
e
1
.
T
h
e
DC
m
o
to
r
s
ch
e
m
atic
[
2
5
]
2
.
3
.
H
o
rned
liza
rd
o
ptim
iza
t
io
n a
lg
o
rit
hm
-
a
qu
ila
o
ptim
i
ze
r
T
h
i
s
a
r
ti
c
l
e
p
r
es
e
n
ts
a
m
o
d
i
f
i
ca
t
i
o
n
o
f
H
L
O
A
u
s
i
n
g
t
h
e
a
q
u
il
a
o
p
t
i
m
i
z
e
r
e
q
u
a
t
i
o
n
.
T
h
e
i
s
u
s
e
d
t
o
c
o
n
t
r
o
l
t
h
e
e
x
t
e
n
d
e
d
s
ea
r
c
h
(
e
x
p
l
o
r
a
t
i
o
n
)
t
h
r
o
u
g
h
t
h
e
n
u
m
b
e
r
o
f
i
t
e
r
a
t
i
o
n
s
a
n
d
c
a
n
b
e
f
o
r
m
u
l
a
t
e
d
i
n
(
22
)
.
=
(
1
−
)
(
2
2
)
Ad
d
itio
n
ally
,
th
e
m
ea
n
v
al
u
e
o
f
th
e
cu
r
r
en
t
s
o
lu
tio
n
co
n
n
ec
ted
at
th
e
-
th
iter
atio
n
is
a
p
p
lied
an
d
ca
n
b
e
f
o
r
m
u
lated
in
(
2
3
)
.
(
)
=
1
∑
(
)
=
1
,
∀
=
1
,
2
…
.
(
23
)
T
h
is
r
esear
ch
m
o
d
i
f
ies
(
10
)
b
y
ad
d
in
g
(
22
)
an
d
(
23
)
.
So
,
it c
a
n
b
e
f
o
r
m
u
lated
as
(
2
4
)
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
Hyb
r
id
h
o
r
n
ed
liz
a
r
d
o
p
timiz
a
tio
n
a
lg
o
r
ith
m
-
a
q
u
ila
o
p
timiz
er fo
r
DC
mo
to
r
(
Wid
i A
r
ib
o
w
o
)
1677
1
⃗
⃗
⃗
(
+
1
)
=
(
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
⃗
(
)
∗
+
(
)
)
+
(
−
.
)
[
1
[
s
in
(
1
⃗
⃗
⃗
⃗
⃗
(
)
)
−
1
c
os
(
2
⃗
⃗
⃗
⃗
⃗
(
)
)
]
−
(
−
1
)
(
c
os
(
3
⃗
⃗
⃗
⃗
⃗
(
)
)
−
s
in
(
4
⃗
⃗
⃗
⃗
⃗
(
)
)
)
]
(
24
)
3.
T
H
E
P
RO
P
O
SE
D
H
L
AO
F
O
R
T
UNING
P
I
D
I
N
DC
M
O
T
O
R
T
h
is
r
esear
ch
aim
s
to
im
p
r
o
v
e
HL
O
ca
p
ab
ilit
ies
b
y
ad
d
in
g
th
e
aq
u
ila
o
p
tim
izer
m
e
th
o
d
.
T
h
e
p
r
o
p
o
s
ed
m
eth
o
d
is
u
s
ed
to
o
b
tain
PID
p
ar
am
eter
s
f
o
r
ad
a
p
tiv
e
co
n
tr
o
l
o
f
DC
m
o
to
r
s
.
T
o
o
b
tain
t
h
e
id
ea
l
tem
p
o
r
ar
y
r
esp
o
n
s
e
p
o
in
t,
th
e
s
tep
s
illu
s
tr
ated
in
Fig
u
r
e
2
ar
e
ca
r
r
ied
o
u
t.
T
h
e
in
itial
s
tep
f
o
llo
ws
th
e
f
lo
w
o
f
th
e
HL
O
m
eth
o
d
t
o
(
10
)
.
I
n
(
10
)
,
it is
ch
an
g
e
d
u
s
in
g
t
h
e
(
24
)
.
Fig
u
r
e
2
.
Pro
p
o
s
ed
o
f
HL
AO
f
o
r
DC
m
o
to
r
4.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
4
.
1
.
Co
nv
er
g
ence
curv
e
pro
f
ile
T
o
r
u
n
s
im
u
latio
n
s
an
d
wr
ite
co
d
e,
a
lap
to
p
with
a
n
I
n
tel
I
5
-
5
2
0
0
2
.
1
9
GHz
p
r
o
ce
s
s
o
r
s
p
ec
if
icatio
n
an
d
8
GB
o
f
R
AM
m
em
o
r
y
is
u
tili
ze
d
to
g
eth
e
r
with
a
MA
T
L
AB
/Si
m
u
lin
k
p
r
o
g
r
am
.
T
h
e
b
en
ch
m
ar
k
f
u
n
ctio
n
is
u
s
ed
to
m
ea
s
u
r
e
th
e
HL
AO
alg
o
r
ith
m
'
s
p
er
f
o
r
m
an
ce
.
T
h
i
s
is
to
ascer
tain
h
o
w
well
th
e
s
u
g
g
ested
ap
p
r
o
ac
h
p
er
f
o
r
m
s
.
2
3
f
u
n
ctio
n
s
m
ak
e
u
p
th
e
b
en
ch
m
ar
k
f
u
n
ctio
n
.
T
en
f
ix
ed
-
d
im
en
s
io
n
al
m
u
ltimo
d
al
f
u
n
ctio
n
s
(
F1
4
–
F2
3
)
,
s
ix
m
u
ltimo
d
al
f
u
n
ctio
n
s
(
F8
–
F1
3
)
,
an
d
s
ev
en
u
n
im
o
d
al
f
u
n
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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I
m
ple
m
ent
ing
H
L
AO
f
o
r
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m
o
t
o
r
PID
-
b
ased
DC
m
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to
r
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n
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o
l
n
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ess
itates
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t
an
d
ac
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r
ate
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ar
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eter
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s
tm
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o
o
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o
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tim
al
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ar
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eter
s
th
r
o
u
g
h
th
e
im
p
lem
e
n
tatio
n
o
f
HL
AO,
its
p
er
f
o
r
m
an
ce
m
u
s
t
also
b
e
v
er
if
ied
.
Fig
u
r
e
4
s
h
o
ws
th
e
o
u
tc
o
m
es
o
f
th
e
PID
co
n
tr
o
l
f
o
r
DC
m
o
t
o
r
s
u
s
in
g
HL
AO.
A
co
n
tr
o
l'
s
p
er
f
o
r
m
an
ce
ca
n
b
e
ev
alu
ated
u
s
in
g
a
v
ar
iety
o
f
t
h
eo
r
ies.
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er
al
wid
ely
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o
g
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ized
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r
ies,
in
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d
e
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r
al
o
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tim
e
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weig
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ted
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s
o
lu
te
er
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r
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T
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d
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eg
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ated
o
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tim
e
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ted
s
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ar
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r
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T
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T
SE
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d
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T
AE
ar
e
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tili
ze
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as
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er
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h
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k
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=
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2
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)
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∞
0
(
2
5
)
=
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(
)
.
∞
0
(
2
6
)
Fig
u
r
e
4
.
T
h
e
r
esp
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n
s
e
o
f
DC
m
o
to
r
B
y
test
in
g
th
e
HL
AO
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b
ased
PID
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n
a
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m
o
to
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with
a
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ef
er
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ce
s
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ee
d
o
f
1
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u
,
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e
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T
SE
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r
o
m
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is
0
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0
0
3
3
.
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h
is
v
alu
e
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8
9
.
2
5
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d
5
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1
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3
%
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etter
th
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PID
an
d
HL
O
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PI
D.
Me
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ile,
th
e
o
v
er
s
h
o
o
t
v
alu
e
o
f
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PI
D
is
b
etter
b
y
th
e
d
etailed
r
e
s
u
lts
o
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th
e
p
er
f
o
r
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ce
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o
r
ea
ch
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o
r
ith
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ca
n
b
e
s
ee
n
in
T
ab
le
5
.
T
ab
le
5
.
R
esp
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n
s
e
DC
m
o
to
r
with
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C
o
n
t
r
o
l
l
e
r
O
v
e
r
sh
o
o
t
R
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se
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me
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t
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me
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E
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I
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.
0
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2
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CO
NCLU
SI
O
N
A
m
etah
eu
r
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tic
o
p
tim
izatio
n
m
eth
o
d
ca
lled
th
e
HL
O
alg
o
r
i
th
m
u
s
es
m
ath
em
atics
to
m
o
d
el
v
ar
io
u
s
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e
s
tr
ateg
ies
s
u
ch
as
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y
p
s
is
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s
k
in
lig
h
ten
in
g
o
r
d
a
r
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en
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g
,
b
lo
o
d
s
p
r
ay
i
n
g
,
an
d
ce
llu
lar
d
ef
e
n
s
e
m
ec
h
an
is
m
s
.
T
h
e
im
p
r
o
v
ed
H
L
O
b
y
m
o
d
if
y
i
n
g
th
e
ad
d
itio
n
al
f
u
n
ctio
n
s
o
f
th
e
aq
u
ila
o
p
ti
m
izer
in
cr
ea
s
es
th
e
p
er
f
o
r
m
an
ce
o
f
th
e
HL
O.
T
h
i
s
h
y
b
r
i
d
m
et
h
o
d
is
ca
lled
HL
AO.
T
h
is
r
esear
ch
v
alid
ates
t
h
e
p
e
r
f
o
r
m
an
ce
o
f
HL
AO
u
s
in
g
p
er
f
o
r
m
an
ce
test
s
o
n
C
E
C
2
0
1
7
b
en
ch
m
ar
k
f
u
n
ctio
n
s
an
d
DC
m
o
to
r
s
.
Fro
m
th
e
s
im
u
latio
n
o
n
th
e
C
E
C
2
0
1
7
b
en
ch
m
ar
k
f
u
n
ctio
n
,
it
was
f
o
u
n
d
t
h
at
th
e
p
er
f
o
r
m
an
ce
o
f
HL
AO
h
a
s
m
o
r
e
p
r
o
m
is
in
g
ex
p
lo
r
atio
n
a
n
d
ex
p
lo
itatio
n
ca
p
ab
ilit
ies.
T
esti
n
g
o
n
DC
m
o
to
r
s
,
it
was
f
o
u
n
d
th
at
th
e
HL
AO
-
PID
m
eth
o
d
co
u
ld
r
e
d
u
ce
o
v
er
s
h
o
o
t.
I
n
a
d
d
itio
n
,
HL
AO
-
PID
h
as
th
e
b
est
I
T
SE
s
co
r
e.
T
h
e
I
T
SE
v
alu
e
o
f
HL
OA
is
8
9
.
2
5
%
a
n
d
5
.
7
1
4
3
%
b
etter
th
an
PID
an
d
HL
O
-
PID
.
T
h
i
s
r
esear
ch
ca
n
b
e
d
ev
elo
p
e
d
f
u
r
th
er
b
y
u
s
in
g
a
v
ar
iety
o
f
o
th
er
m
eth
o
d
s
a
n
d
u
s
in
g
m
o
r
e
c
o
m
p
lex
o
b
jects.
RE
F
E
R
E
NC
E
S
[
1
]
M
.
Z
g
h
a
i
b
e
h
e
t
a
l
.
,
“
O
p
t
i
m
i
z
a
t
i
o
n
o
f
g
r
e
e
n
h
y
d
r
o
g
e
n
p
r
o
d
u
c
t
i
o
n
i
n
h
y
d
r
o
e
l
e
c
t
r
i
c
-
p
h
o
t
o
v
o
l
t
a
i
c
g
r
i
d
c
o
n
n
e
c
t
e
d
p
o
w
e
r
st
a
t
i
o
n
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
H
y
d
r
o
g
e
n
E
n
e
rg
y
,
v
o
l
.
5
2
,
p
p
.
4
4
0
–
4
5
3
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
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6
/
j
.
i
j
h
y
d
e
n
e
.
2
0
2
3
.
0
6
.
0
2
0
.
[
2
]
J.
Z
h
e
n
g
,
Y
.
D
a
n
g
,
a
n
d
U
.
A
ss
a
d
,
“
H
o
u
s
e
h
o
l
d
e
n
e
r
g
y
c
o
n
s
u
m
p
t
i
o
n
,
e
n
e
r
g
y
e
f
f
i
c
i
e
n
c
y
,
a
n
d
h
o
u
se
h
o
l
d
i
n
c
o
m
e
–
E
v
i
d
e
n
c
e
f
r
o
m
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J Ar
tif
I
n
tell
I
SS
N:
2252
-
8
9
3
8
Hyb
r
id
h
o
r
n
ed
liz
a
r
d
o
p
timiz
a
tio
n
a
lg
o
r
ith
m
-
a
q
u
ila
o
p
timiz
er fo
r
DC
mo
to
r
(
Wid
i A
r
ib
o
w
o
)
1681
C
h
i
n
a
,
”
A
p
p
l
i
e
d
E
n
e
r
g
y
,
v
o
l
.
3
5
3
,
2
0
2
4
,
d
o
i
:
1
0
.
1
0
1
6
/
j
.
a
p
e
n
e
r
g
y
.
2
0
2
3
.
1
2
2
0
7
4
.
[
3
]
W
.
A
r
i
b
o
w
o
,
B
.
S
u
p
r
i
a
n
t
o
,
U
.
T
.
K
a
r
t
i
n
i
,
a
n
d
A
.
P
r
a
p
a
n
c
a
,
“
D
i
n
g
o
o
p
t
i
m
i
z
a
t
i
o
n
a
l
g
o
r
i
t
h
m
f
o
r
d
e
s
i
g
n
i
n
g
p
o
w
e
r
s
y
st
e
m
st
a
b
i
l
i
z
e
r
,
”
I
n
d
o
n
e
si
a
n
J
o
u
r
n
a
l
o
f
E
l
e
c
t
r
i
c
a
l
En
g
i
n
e
e
r
i
n
g
a
n
d
C
o
m
p
u
t
e
r
S
c
i
e
n
c
e
,
v
o
l
.
2
9
,
n
o
.
1
,
p
p
.
1
–
7
,
2
0
2
2
,
d
o
i
:
1
0
.
1
1
5
9
1
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i
j
e
e
c
s.
v
2
9
.
i
1
.
p
p
1
-
7.
[
4
]
R
.
R
a
ma
n
,
H
.
La
t
h
a
b
h
a
i
,
D
.
P
a
t
t
n
a
i
k
,
C
.
K
u
mar,
a
n
d
P
.
N
e
d
u
n
g
a
d
i
,
“
R
e
sea
r
c
h
c
o
n
t
r
i
b
u
t
i
o
n
o
f
b
i
b
l
i
o
m
e
t
r
i
c
st
u
d
i
e
s
r
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l
a
t
e
d
t
o
su
st
a
i
n
a
b
l
e
d
e
v
e
l
o
p
me
n
t
g
o
a
l
s
a
n
d
s
u
st
a
i
n
a
b
i
l
i
t
y
,
”
D
i
s
c
o
v
e
r
S
u
st
a
i
n
a
b
i
l
i
t
y
,
v
o
l
.
5
,
n
o
.
7
,
J
a
n
.
2
0
2
4
,
d
o
i
:
1
0
.
1
0
0
7
/
s4
3
6
2
1
-
024
-
0
0
1
8
2
-
w.
[
5
]
R
.
R
u
ss
e
l
l
-
B
e
n
n
e
t
t
,
M
.
S
.
R
o
se
n
b
a
u
m,
R
.
P
.
F
i
s
k
,
a
n
d
M
.
M
.
R
a
c
i
t
i
,
“
S
D
G
e
d
i
t
o
r
i
a
l
:
i
mp
r
o
v
i
n
g
l
i
f
e
o
n
p
l
a
n
e
t
e
a
r
t
h
–
a
c
a
l
l
t
o
a
c
t
i
o
n
f
o
r
s
e
r
v
i
c
e
r
e
se
a
r
c
h
t
o
a
c
h
i
e
v
e
t
h
e
s
u
s
t
a
i
n
a
b
l
e
d
e
v
e
l
o
p
me
n
t
g
o
a
l
s
(
S
D
G
s)
,
”
J
o
u
r
n
a
l
o
f
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9
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