I
A
E
S
I
n
t
e
r
n
at
io
n
al
Jou
r
n
al
of
A
r
t
if
ic
ia
l
I
n
t
e
ll
ig
e
n
c
e
(
I
J
-
A
I
)
V
ol
. 13, No. 4, D
e
c
e
m
be
r
2024, pp.
4206
~
4216
I
S
S
N
:
2252
-
8938
,
D
O
I
:
10.11591/
ij
a
i.
v13.i4.pp
4206
-
4216
4206
Jou
r
n
al
h
om
e
page
:
ht
tp
:
//
ij
ai
.
ia
e
s
c
or
e
.c
om
B
i
o i
n
s
p
i
r
e
d
t
e
c
h
n
i
q
u
e
f
or
c
on
t
r
ol
l
i
n
g an
gl
e
of
at
t
ac
k
of
ai
r
c
r
af
t
S
u
b
h
ak
an
t
a B
al
1
, S
r
in
ib
as
h
S
w
ai
n
2
, P
ar
t
h
a
S
ar
at
h
i
K
h
u
n
t
i
a
3
1
D
e
pa
r
t
m
e
nt
of
E
l
e
c
t
r
i
c
a
l
E
ngi
ne
e
r
i
ng, B
i
j
upa
t
t
na
i
k U
ni
ve
r
s
i
t
y of
T
e
c
hnol
ogy,
R
our
ke
l
a
, I
ndi
a
2
D
e
pa
r
t
m
e
nt
of
E
l
e
c
t
r
i
c
a
l
E
ngi
ne
e
r
i
ng, N
a
l
a
nda
I
ns
t
i
t
ut
e
of
T
e
c
hnol
ogy, B
huba
ne
s
w
a
r
, I
ndi
a
3
D
e
pa
r
t
m
e
nt
of
E
l
e
c
t
r
oni
c
s
a
nd
T
e
l
e
c
om
m
uni
c
a
t
i
on E
ngi
ne
e
r
i
ng, K
ona
r
k I
ns
t
i
t
ut
e
of
S
c
i
e
nc
e
a
nd
T
e
c
hnol
ogy, K
hur
da
, I
ndi
a
A
r
t
ic
le
I
n
f
o
A
B
S
T
R
A
C
T
A
r
ti
c
le
h
is
to
r
y
:
R
e
c
e
iv
e
d
J
a
n
23
,
2024
R
e
vi
s
e
d
J
un
11
,
2024
A
c
c
e
pt
e
d
J
un
14
,
2024
This paper deals with the design
of a proportional
–
integral (
PI)
control
ler for
controll
ing
the
angle
of
attack
of
flight
control
system.
For
the
firs
t
time
teaching
learning
-
based
optimization
(T
LBO)
algorithm
is
applied
in
th
is
area
to obtain the
paramete
rs of the
proposed
PI controlle
r. The de
sign
pro
blem is
formulated
as
an
optimizat
ion
problem
and
TLBO
is
employed
to
op
timize
the
parameters
of
the
PI
controller.
The
superi
ority
of
proposed
appr
oach
is
demonstrated by comparing the results with that of the conve
ntional methods
like
g
enetic
algorithm
(GA)
and
particle
swarm
optimization
(PSO
)
.
It
is
observed
that
TLBO
optimized
PI
controller
gives
better
d
ynamic
performance
i
n
terms
of
settling
time,
overshoot
,
and
undershoot
as
co
mpared
to
GA
and
PSO
based
PI
controllers.
The
various
performance
indic
es
like
mean
square
error
(MSE),
integral
absolute
error
(IAE),
and
integra
l
time
absolut
e
error
(ITAE)
are
improved
by
using
the
TLBO
soft
com
puting
techniques.
F
urther,
robustness
of
the
system
is
studied
by
varying
all
the
system paramete
rs from −
50% to +
50% in step of 25%.
Analysis also r
eveals
that
TLBO
optimized
PI
controller
gains
are
quite
robust
and
need
not
b
e
reset
for
wide variation in
system parameters.
K
e
y
w
o
r
ds
:
G
e
ne
ti
c
a
lg
or
it
hm
M
e
a
n s
qu
a
r
e
e
r
r
or
P
a
r
ti
c
le
s
w
a
r
m
opt
im
iz
a
ti
on
P
r
opor
ti
ona
l
–
in
te
gr
a
l
T
e
a
c
hi
ng l
e
a
r
ni
ng
-
ba
s
e
d
opt
im
iz
a
ti
on
This is an
open
acce
ss artic
le unde
r the
CC BY
-
SA
license.
C
or
r
e
s
pon
di
n
g A
u
th
or
:
S
ubha
ka
nt
a
B
a
l
D
e
pa
r
tm
e
nt
of
E
le
c
tr
ic
a
l
E
ngi
ne
e
r
in
g, B
ij
upa
tt
na
ik
U
ni
ve
r
s
it
y of
T
e
c
hnol
ogy
R
our
ke
la
,
O
di
s
ha
, I
ndi
a
E
m
a
il
:
ba
l.
s
ubha
ka
nt
a
@
gm
a
il
.c
om
1.
I
N
T
R
O
D
U
C
T
I
O
N
F
or
s
m
oot
h
f
ly
in
g
of
a
n
a
ir
c
r
a
f
t,
m
a
na
gi
ng
of
th
r
e
e
c
ont
r
ol
li
ng
s
ur
f
a
c
e
s
vi
z
r
udde
r
,
e
le
va
to
r
,
a
nd
a
il
e
r
on
be
c
om
e
s
i
ne
vi
ta
bl
e
.
T
he
m
ove
m
e
nt
of
a
f
li
ght
is
c
ont
r
ol
le
d
by
th
e
he
lp
of
a
bove
th
r
e
e
s
ur
f
a
c
e
s
a
bout
th
e
pi
tc
h,
r
ol
l
,
a
nd
ya
w
a
xe
s
.
F
or
t
he
or
ie
nt
a
ti
on
of
a
ir
c
r
a
f
t,
e
le
va
to
r
pe
r
f
or
m
s
a
n
e
s
s
e
nt
ia
l
pos
it
io
n
in
c
ha
ngi
ng
th
e
a
ngl
e
of
a
tt
a
c
k
a
lo
ng
w
it
h
pi
tc
h.
D
if
f
e
r
e
nt
s
of
t
c
om
put
in
g
te
c
hni
que
s
l
ik
e
f
uz
z
y
m
ode
l
r
e
f
e
r
e
nc
e
le
a
r
ni
ng
(
F
M
R
L
)
a
nd
r
a
di
a
l
ba
s
is
f
unc
ti
on
ne
ur
a
l
c
ont
r
o
ll
e
r
(
R
B
F
N
C
)
a
r
e
a
ppl
ie
d
pr
e
vi
ous
ly
f
or
obt
a
in
in
g
a
b
e
tt
e
r
r
e
s
ul
t
f
or
a
dyna
m
ic
s
ys
te
m
.
B
ut
a
ne
w
s
of
t
c
om
put
in
g
te
c
hni
que
na
m
e
d
te
a
c
hi
ng
le
a
r
ni
ng
-
ba
s
e
d
opt
im
iz
a
ti
on
(
T
L
B
O
)
is
i
nc
or
por
a
te
d
in
th
is
pa
pe
r
m
a
in
ly
f
or
a
dj
us
ti
ng
th
e
a
ngl
e
of
a
tt
a
c
k a
s
w
e
ll
a
s
upgr
a
di
ng
th
e
ove
r
a
ll
a
c
hi
e
ve
m
e
nt
of
th
e
pr
opos
e
d
s
ys
te
m
.
F
in
a
ll
y,
a
c
om
pa
r
is
on
is
m
a
de
be
twe
e
n
th
e
r
e
s
ul
ts
of
T
L
B
O
a
nd
ot
he
r
opt
im
iz
a
ti
on
m
e
th
ods
li
ke
ge
ne
ti
c
a
lg
or
it
hm
(
G
A
)
a
nd
pa
r
ti
c
le
s
w
a
r
m
opt
im
iz
a
ti
on
(
P
S
O
)
in
e
a
c
h
a
nd
e
ve
r
y
a
s
pe
c
t.
L
it
e
r
a
tu
r
e
s
ur
ve
y r
e
ve
a
ls
m
os
t
of
th
e
e
a
r
ly
w
or
ks
on f
li
ght
c
ont
r
ol
s
ys
te
m
. T
he
s
e
le
c
ti
on of
ga
in
of
a
pr
opor
ti
ona
l
–
in
te
gr
a
l
(
PI
)
c
ont
r
ol
le
r
f
or
nonli
ne
a
r
s
e
c
ond
or
de
r
pl
a
nt
s
w
a
s
s
ugge
s
te
d
by
K
um
a
r
e
t
al
.
[
1]
in
a
n
or
ga
ni
z
e
d
m
a
nne
r
.
T
he
r
e
gul
a
ti
ng
of
a
P
I
c
ont
r
ol
le
r
f
or
a
c
ont
r
ol
s
ys
te
m
w
a
s
ve
r
if
ie
d
by
X
ia
ng
e
t
al
.
[
2]
in
a
num
be
r
of
w
a
ys
.
A
be
tt
e
r
pr
opos
a
l
w
a
s
pr
opos
e
d
by
S
a
xe
na
a
nd
H
ot
e
[
3]
f
or
de
te
r
m
in
in
g
th
e
ga
in
of
a
P
I
c
ont
r
ol
le
r
.
A
n
e
a
s
y
a
nd
qui
c
k
m
e
th
od
f
or
tu
ni
ng
a
pr
opor
ti
ona
l
–
in
te
gr
a
l
–
de
r
iv
a
ti
ve
(
P
I
D
)
c
ont
r
ol
le
r
w
a
s
jo
in
tl
y
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
B
io
i
ns
pi
r
e
d t
e
c
hni
que
f
or
c
ont
r
ol
li
ng angle
of
at
ta
c
k
of
ai
r
c
r
af
t
(
Subhak
ant
a B
al
)
4207
a
na
ly
z
e
d
by
G
ha
ny
e
t
al
.
[
4]
in
a
pr
e
c
is
e
m
a
nne
r
.
T
he
va
r
io
us
ty
pe
s
of
m
e
th
ods
ne
e
de
d
f
or
e
s
ti
m
a
ti
ng
th
e
a
ngl
e
of
a
tt
a
c
k
of
a
f
li
ght
w
e
r
e
c
le
a
r
ly
de
s
c
r
ib
e
d
by
S
a
nka
r
a
li
nga
m
a
nd
R
a
m
pr
a
s
a
dh
[
5]
.
T
w
o
-
s
ta
ge
T
L
B
O
m
e
th
od
f
or
f
le
xi
bl
e
j
ob
-
s
hop s
c
he
dul
in
g
w
a
s
s
ugge
s
te
d
by
B
udda
la
a
nd M
a
ha
pa
tr
a
[
6]
. S
uga
nt
hi
e
t
al
.
[
7]
pr
opos
e
d
a
n
im
pr
ove
d
T
L
B
O
a
lg
or
it
hm
.
N
iu
e
t
al
.
[
8]
s
ugge
s
te
d
a
m
odi
f
ie
d
T
L
B
O
a
lg
or
it
hm
f
or
num
e
r
ic
a
l
f
unc
ti
on
opt
im
iz
a
ti
on. S
ha
hr
ouz
i
e
t
al
.
[
9]
s
ugge
s
te
d a
hybr
id
ba
t
a
lg
or
it
hm
a
nd
T
L
B
O
.
Z
ha
i
e
t
al
.
[
10]
pr
opos
e
d
a
nove
l
T
L
B
O
w
it
h
e
r
r
or
c
or
r
e
c
ti
on
f
or
pa
th
pl
a
nni
ng
of
unm
a
nne
d
a
ir
ve
hi
c
le
.
Z
ha
ng
e
t
al
.
[
11]
s
ugge
s
te
d
a
n
im
pr
ove
d
T
L
B
O
w
it
h
lo
ga
r
it
hm
ic
s
pi
r
a
l
a
nd
tr
ia
ngul
a
r
m
ut
a
ti
on
f
or
gl
oba
l
opt
im
iz
a
ti
on.
N
a
ya
k
e
t
al
.
[
12]
pr
opos
e
d
a
n
E
li
ti
s
t
te
a
c
hi
ng
–
le
a
r
ni
ng
-
ba
s
e
d
opt
im
iz
a
ti
on
(
E
T
L
B
O
)
w
it
h
hi
ghe
r
-
or
de
r
J
or
da
n
P
i
-
s
ig
m
a
ne
ur
a
l
ne
twor
k.
Y
a
ng
e
t
al
.
[
13]
pr
opos
e
d
a
m
ul
ti
obj
e
c
ti
ve
GA
on
a
n
a
c
c
e
le
r
a
to
r
la
tt
ic
e
.
I
n
a
ddi
ti
on,
G
a
in
g
[
14]
pr
opos
e
d
a
PSO
m
e
th
od
to
s
ol
ve
th
e
e
c
onomi
c
di
s
pa
tc
h.
E
vt
us
he
nko
a
nd
P
os
ypki
n
[
15]
s
ugge
s
te
d
a
ne
w
m
e
th
od
in
2013
f
or
gl
oba
l
box
-
c
ons
tr
a
in
e
d
opt
im
iz
a
ti
on.
Y
a
s
s
a
m
i
a
nd
A
s
ht
a
r
i
[
16]
pr
opos
e
d
a
nove
l
hybr
id
opt
im
iz
a
ti
on
a
lg
or
it
hm
.
S
to
r
n
a
nd
P
r
ic
e
[
17]
pr
opos
e
d
a
di
f
e
r
e
nt
ia
l
e
vol
ut
io
n f
or
gl
oba
l
opt
im
iz
a
ti
on ove
r
c
ont
in
uous
s
pa
c
e
s
.
A
f
uz
z
y
a
da
pt
iv
e
di
f
e
r
e
nt
ia
l
e
vol
ut
io
n a
lg
or
it
hm
. on
s
of
t
c
om
put
in
g
w
a
s
s
ugge
s
te
d
by.
L
iu
a
nd
L
a
m
pi
ne
n
[
18]
.
S
e
ve
r
a
l
r
e
s
e
a
r
c
he
r
s
pr
opos
e
d
on
a
nt
c
ol
ony
opt
im
iz
a
ti
on
[
19]
,
[
20]
.
P
la
c
e
m
e
nt
of
w
in
d
tu
r
bi
ne
s
us
in
g
GA
w
a
s
s
ugge
s
te
d
by
G
r
a
dy
e
t
al
.
[
21]
.
G
r
a
phi
c
pr
oc
e
s
s
in
g
uni
t
(
G
P
U
)
-
ba
s
e
d
pa
r
a
ll
e
l
PSO
w
a
s
pr
opos
e
d
by
Z
hou
a
nd
T
a
n
[
22]
.
A
s
ur
ve
y
on
ne
w
ge
ne
r
a
ti
on
m
e
ta
he
ur
is
ti
c
a
lg
or
it
hm
s
w
a
s
jo
nt
ly
s
ugge
s
te
d
in
2019
by
D
oke
r
ogl
u
e
t
al
.
[
23]
.
H
us
s
a
in
e
t
al
.
[
24]
di
d
a
c
om
pr
e
he
ns
iv
e
s
ur
ve
y
on
a
r
ti
f
ic
ia
l
in
te
ll
ig
e
nc
e
r
e
vi
e
w
.
V
a
r
io
us
w
or
ks
on
PSO
us
in
g
di
f
f
e
r
e
nt
te
c
hni
que
s
w
a
r
e
pr
opos
e
d
in
[
25]
‒
[
28]
.
P
S
O
-
ba
s
e
d
m
e
m
e
ti
c
a
lg
or
it
hm
f
or
f
ow
s
hop
s
c
he
dul
in
g
w
a
s
s
ugge
s
te
d
by
L
iu
e
t
al
.
[
29]
.
Y
a
ng
e
t
al
.
[
30]
s
ugge
s
te
d
a
n
im
pr
ove
d
P
S
O
-
ba
s
e
d
c
ha
r
gi
ng
s
tr
a
te
gy
of
e
le
c
tr
ic
ve
hi
c
le
s
in
e
le
c
tr
ic
a
l
di
s
tr
ib
ut
io
n gr
id
.
T
hi
s
pa
pe
r
s
how
s
a
be
tt
e
r
r
e
s
ul
t
by
a
ppl
yi
ng
T
L
B
O
m
e
th
od f
or
m
a
na
gi
ng
th
e
a
tt
a
c
ki
ng
a
ngl
e
of
a
n
a
ir
c
r
a
f
t
s
ys
te
m
.
A
f
te
r
c
om
pa
r
is
on
th
e
r
e
s
ul
ts
be
twe
e
n
T
L
B
O
, G
A
a
nd
P
S
O
m
e
th
ods
,
it
w
a
s
f
ound
th
a
t
T
L
B
O
pe
r
f
or
m
s
be
tt
e
r
i
n a
ll
a
s
pe
c
ts
t
ha
n G
A
a
nd P
S
O
m
e
th
ods
f
or
t
uni
ng t
he
P
I
D
c
ont
r
ol
le
r
.
2.
B
L
O
C
K
D
I
A
G
R
A
M
F
O
R
D
E
T
E
R
M
I
N
I
N
G
T
H
E
A
N
G
L
E
O
F
A
T
T
A
C
K
F
ig
ur
e
1
s
how
s
th
e
bl
oc
k
di
a
gr
a
m
f
or
c
ont
r
ol
li
ng
th
e
a
ngl
e
of
a
tt
a
c
k
of
a
n
a
ir
c
r
a
f
t
.
T
he
in
te
nde
d
a
ngl
e
of
a
tt
a
c
k (
α)
i
s
t
he
out
put
.
T
he
e
le
va
to
r
'
s
de
f
le
c
ti
on (
)
s
e
r
ve
s
a
s
th
e
i
nput
.
F
ig
ur
e
1.
S
c
he
m
a
ti
c
di
a
gr
a
m
of
a
ngl
e
of
a
tt
a
c
k f
or
a
n a
ir
c
r
a
f
t
s
ys
te
m
w
he
r
e
(
)
is
th
e
de
f
le
c
ti
on a
ngl
e
of
e
le
va
to
r
,
(
)
is
a
ngl
e
of
a
tt
a
c
k of
t
he
a
ir
c
r
a
f
t
,
G
(
s
)
is
th
e
f
or
w
a
r
d pa
th
ga
in
, a
nd
C
(
s
)
is
pr
opos
e
d P
I
c
ont
r
ol
le
r
.
3.
R
E
L
A
T
I
O
N
B
E
T
W
E
E
N
T
H
E
E
L
E
V
A
T
O
R
D
E
F
L
E
C
T
I
O
N
(
)
A
N
D
A
N
G
L
E
O
F
A
T
T
A
C
K
(
)
G
e
ne
r
a
ll
y,
a
ngl
e
of
a
tt
a
c
k
is
th
e
a
ngl
e
be
twe
e
n
r
e
la
ti
ve
w
in
d
a
nd
th
e
c
hor
d
li
ne
o
f
th
e
a
ir
c
r
a
f
t.
T
he
a
ngl
e
of
a
tt
a
c
k
is
obt
a
in
e
d
du
e
to
th
e
de
f
le
c
ti
on
in
c
ont
r
ol
s
ur
f
a
c
e
(
e
le
va
to
r
)
is
e
xhi
bi
te
d
in
F
ig
ur
e
2.
A
ir
c
r
a
f
t
s
pe
e
d
(
u)
,
is
c
ha
nge
d
due
to
th
e
de
f
le
c
ti
on
in
c
ont
r
ol
s
ur
f
a
c
e
s
a
nd
a
tm
os
phe
r
ic
tu
r
bul
e
nc
e
.
M
a
in
ly
th
e
a
ppr
oxi
m
a
ti
on
r
e
la
ti
ng
to
s
hor
t
pe
r
io
d
de
a
ls
w
it
h
va
r
yi
ng
f
l
ig
ht
s
pe
e
d
(
u)
a
nd
it
c
ons
is
ts
of
ve
r
y
s
hor
t
dur
a
ti
on.
T
he
s
pe
e
d
of
th
e
a
ir
c
r
a
f
t
0
a
lm
os
t
r
e
m
a
in
s
c
ons
t
a
nt
th
r
oughout
th
e
pr
oc
e
s
s
i.
e
.,
=
0
.
S
o
th
a
t
th
e
m
ot
io
n
r
e
la
te
d
e
qua
ti
on
in
vol
vi
ng
′
′
is
ge
ne
r
a
ll
y
ne
gl
e
c
te
d.
H
e
nc
e
th
e
e
qua
ti
ons
f
or
lo
ngi
tu
di
na
l
m
ot
io
n
m
a
y
be
di
c
ta
te
d a
s
:
̇
=
+
+
(
1)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
. 13, No. 4, D
e
c
e
m
be
r
2024:
4206
-
4216
4208
=
̇
+
̇
+
+
(
2)
=
(
̇
+
)
+
(
+
0
)
+
(
+
̇
)
F
ig
ur
e
2
. D
e
s
c
r
ip
ti
on of
a
ngl
e
of
a
tt
a
c
k
C
a
lc
ul
a
ti
on of
s
ta
te
ve
c
to
r
f
or
s
hor
t
pe
r
io
d m
ot
io
n m
a
y be
w
r
it
te
n a
s
:
=
[
]
w
he
r
e
a
nd
‘
’
a
r
e
th
e
a
ngl
e
of
de
f
le
c
ti
on
a
nd
c
ont
r
ol
ve
c
to
r
r
e
s
pe
c
ti
ve
ly
,
th
e
n
th
e
s
t
a
te
e
qua
ti
on
f
or
th
e
a
bove
t
w
o e
qua
ti
ons
c
a
n be
w
r
it
te
n a
s
:
̇
=
+
(
3)
w
he
r
e
a
s
:
=
[
0
(
+
̇
)
(
+
0
̇
)
]
,
=
[
+
̇
]
∴
[
−
]
=
[
1
0
0
1
]
−
[
0
(
+
̇
)
(
+
0
̇
)
]
=
[
0
0
]
−
[
0
(
+
̇
)
(
+
0
̇
)
]
=
[
−
−
0
−
(
+
̇
)
[
−
(
+
0
̇
)
]
]
(
)
=
[
−
]
=
2
+
[
−
(
+
+
0
̇
)
]
+
[
−
0
̇
]
=
2
+
2
+
2
(
4)
I
n (
4)
:
2
=
−
(
+
+
0
̇
)
,
=
[
−
0
̇
]
1
2
(
5)
(
)
(
)
=
(
0
+
)
{
1
+
0
−
}
(
)
=
(
1
+
1
)
(
)
w
he
r
e
:
=
(
0
+
)
a
nd
1
=
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
B
io
i
ns
pi
r
e
d t
e
c
hni
que
f
or
c
ont
r
ol
li
ng angle
of
at
ta
c
k
of
ai
r
c
r
af
t
(
Subhak
ant
a B
al
)
4209
A
ga
in
,
̇
=
̇
0
⇒
(
)
=
(
)
0
⇒
(
)
=
0
(
)
⇒
(
)
(
)
=
(
1
+
1
)
0
(
)
(
6)
3.1.
S
t
ab
il
it
y
d
e
r
iv
at
iv
e
s
of
ai
r
c
r
a
f
t
T
he
s
ta
nd
a
r
d va
lu
e
s
of
s
ta
bi
li
ty
de
r
iv
a
ti
ve
s
f
or
C
H
A
R
L
I
E
a
ir
c
r
a
f
t
in
t
hr
e
e
di
f
f
e
r
e
nt
c
ondi
ti
ons
a
r
e
de
pi
c
te
d
in
T
a
bl
e
1.
T
he
s
e
a
r
e
i
nt
e
nde
d f
or
t
he
a
ir
c
r
a
f
t'
s
l
ongi
tu
di
na
l
dyna
m
ic
s
. T
h
e
t
r
a
ns
f
e
r
f
unc
ti
on f
or
a
s
pe
c
if
ic
f
li
ght
c
ondi
ti
on
c
a
n be
f
ound us
in
g s
ta
bi
li
ty
de
r
iv
a
ti
ve
s
.
T
a
bl
e
1. S
ta
bi
li
ty
d
e
r
iv
a
ti
ve
s
of
a
ir
c
r
a
f
t
f
or
t
hr
e
e
di
s
ti
nc
t
f
li
ght
c
ondi
ti
ons
[
31]
F
l
i
ght
c
ondi
t
i
on
FC
-
1
FC
-
2
FC
-
3
0
(
−
1
)
67
158
250
-
0.021
0.003
-
0.00002
0.122
0.078
0.026
0.292
0.616
0.0
-
0.512
-
0.433
-
0.624
-
1.9
-
1.95
-
3.04
-
1.96
-
5.15
-
8.05
-
0.006
-
0.006
-
0.005
-
0.357
-
0.421
-
0.668
-
0.378
-
1.09
-
2.08
3.2.
T
r
an
s
f
e
r
f
u
n
c
t
io
n
s
of
d
if
f
e
r
e
n
t
f
li
gh
t
c
on
d
it
io
n
s
T
a
bl
e
2
s
ho
w
th
e
tr
a
n
s
f
e
r
f
un
c
ti
on
f
or
F
C
-
1,
F
C
-
2
,
a
n
d
FC
-
3
r
e
s
p
e
c
t
e
v
e
ly
.
F
C
s
ta
nd
s
f
or
f
li
ght
c
ond
it
i
on
.
FC
-
1
, F
C
-
2
,
a
n
d
FC
-
3 i
n
T
a
bl
e
2
c
a
n
be
ob
ta
in
e
d
by
p
ut
ti
ng
t
h
e
pa
r
a
m
e
tr
i
c
va
lu
e
s
f
r
om
T
a
b
le
1
in
(
6)
.
T
a
bl
e
2.
T
r
a
ns
f
e
r
f
unc
ti
ons
f
or
t
hr
e
e
di
f
f
e
r
e
nt
f
li
ght
c
ondi
ti
ons
F
l
i
ght
c
ondi
t
i
ons
G
(
S
)
FC
-
1
1
(
)
=
0
.
0
4
9
3
6
+
0
.
6
5
8
3
5
1
.
695
2
+
2
.
1546
+
1
FC
-
2
1
(
)
=
0
.
0128
+
0
.
978
0
.
8849
2
+
1
.
5
9
4
6
9
+
1
FC
-
3
3
(
)
=
0
.
0193
+
1
.
26
0
.
599
2
+
1
.
5
2
5
+
1
4.
P
R
O
P
O
S
E
D
O
P
T
I
M
I
Z
A
T
I
O
N
S
O
F
T
C
O
M
P
U
T
I
N
G
T
E
C
H
N
I
Q
U
E
S
T
he
r
e
a
r
e
s
o
m
a
ny
m
e
th
od
s
f
or
de
te
r
m
in
in
g
th
e
ga
in
of
P
I
c
ont
r
ol
le
r
.
A
m
ong
th
e
m
G
A
a
nd
P
S
O
m
e
th
ods
a
r
e
a
ppl
ie
d
he
r
e
f
or
tu
ni
ng
th
e
c
ont
r
ol
le
r
.
N
um
e
r
ou
s
opt
im
iz
a
ti
on
te
c
hni
que
s
ha
ve
be
e
n
us
e
d
to
a
ddr
e
s
s
th
e
di
f
f
e
r
e
nt
ki
nds
of
r
e
a
l
-
w
or
ld
is
s
ue
s
in
va
r
io
us
s
e
c
to
r
s
.
T
he
T
L
B
O
a
ppr
oa
c
h
is
th
ought
to
be
s
upe
r
io
r
to
t
he
r
e
s
t
a
m
ong the
m
.
4.1.
T
e
ac
h
in
g l
e
ar
n
in
g
-
b
as
e
d
op
t
im
iz
at
io
n
F
ol
lo
w
in
g i
ts
i
nt
r
oduc
ti
on by R
a
o
e
t
al
.
[
32]
,
T
L
B
O
ha
s
ga
in
e
d a
l
ot
of
popula
r
it
y
i
n t
he
e
ngi
ne
e
r
in
g
dom
a
in
.
I
ts
s
ta
bi
li
ty
a
na
ly
s
is
,
ti
m
e
c
ons
um
pt
io
n,
a
nd
s
ol
ut
io
n
q
ua
li
ty
a
r
e
s
upe
r
io
r
th
a
n
th
os
e
of
ot
he
r
m
e
th
ods
.
I
n ge
ne
r
a
l,
T
L
B
O
ope
r
a
te
s
i
n t
w
o s
ta
ge
s
:
I
n t
he
f
i
r
s
t
s
ta
ge
, known a
s
t
he
t
e
a
c
he
r
pha
s
e
, s
tu
de
nt
s
l
e
a
r
ne
d f
r
om
th
e
ir
in
di
vi
dua
l
te
a
c
he
r
s
;
in
th
e
s
e
c
ond
s
ta
ge
,
known
a
s
th
e
le
a
r
ne
r
pha
s
e
,
s
tu
de
nt
s
le
a
r
n
f
r
om
one
a
not
he
r
th
r
ough int
e
r
a
c
ti
on. T
he
f
ol
lo
w
in
g s
te
ps
a
r
e
pa
r
t
of
t
he
T
L
B
O
a
lg
or
it
hm
.
4.1.1. I
n
it
ia
li
z
a
t
io
n
T
he
popula
ti
on
s
iz
e
is
ta
ke
n
a
s
[
N
P
D
]
.
I
n
th
is
c
a
s
e
N
P
in
di
c
a
te
s
s
iz
e
of
popula
ti
on
i.
e
.
num
be
r
of
le
a
r
ne
r
s
a
nd
D
in
di
c
a
te
s
th
e
di
m
e
ns
io
n
of
th
e
pr
obl
e
m
i.
e
.
num
be
r
of
s
ubj
e
c
ts
of
f
e
r
e
d
.
T
he
i
th
c
ol
um
n
o
f
th
e
in
it
ia
l
popula
ti
on r
e
pr
e
s
e
nt
s
t
he
m
a
r
ks
s
e
c
ur
e
d by dif
f
e
r
e
nt
l
e
a
r
ne
r
s
i
n
i
th
s
ubj
e
c
t
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
. 13, No. 4, D
e
c
e
m
be
r
2024:
4206
-
4216
4210
I
ni
ti
a
l
p
opul
a
ti
on=
1
,
1
1
,
2
…
1
,
2
,
1
2
,
2
…
2
,
.
.
.
.
.
.
.
.
,
1
,
2
.
.
.
.
.
.
.
.
,
4.1.2. T
e
ac
h
e
r
p
h
as
e
D
ur
in
g
th
is
pha
s
e
,
th
e
d
e
s
ig
na
te
d
in
s
tr
uc
to
r
put
s
up
th
e
ir
be
s
t
e
f
f
or
t
to
r
a
is
e
th
e
c
la
s
s
m
e
a
n.
S
in
c
e
te
a
c
he
r
s
a
r
e
th
e
one
s
w
ho
tr
a
in
th
e
s
tu
de
nt
s
,
th
e
be
s
t
le
a
r
ne
r
'
s
s
o
lu
ti
on
a
lwa
ys
goe
s
to
th
a
t
s
pe
c
if
ic
te
a
c
he
r
.
T
he
a
ve
r
a
ge
gr
a
de
s
obt
a
in
e
d by va
r
io
us
pupil
s
i
n va
r
io
us
a
s
s
ig
nm
e
n
ts
a
r
e
c
om
put
e
d (
7)
.
=
[
1
,
2
,
…
,
]
(
7)
w
he
r
e
a
s
1
is
t
he
a
ggr
e
ga
te
m
a
r
ks
s
e
c
ur
e
d by the
s
tu
de
nt
s
i
n
ℎ
pa
pe
r
. T
he
di
s
s
im
il
a
r
it
y i
n m
e
a
n r
e
s
ul
t
s
of
a
pa
r
ti
c
ul
a
r
t
e
a
c
he
r
i
s
r
e
pr
e
s
e
nt
e
d a
s
=
(
0
,
1
)
[
−
]
.
I
n w
hi
c
h r
a
nd
(
0
,
1
)
is
c
hos
e
n
a
r
bi
tr
a
r
il
y a
s
0
or
1
a
nd
a
s
t
e
a
c
hi
ng f
a
c
to
r
.
is
t
a
ke
n a
r
bi
tr
a
r
il
y e
it
he
r
1 or
2.
=
[
1
+
(
0
,
1
)
]
(
8)
I
n (
9)
t
he
e
xi
ti
ng popula
ti
on i
s
r
e
ne
w
e
d a
s
(
9)
:
=
+
(
9)
is
a
c
c
e
pt
e
d i
f
(
)
<
(
)
, w
he
r
e
(
)
is
t
a
ke
n a
s
t
he
obj
e
c
ti
ve
f
unc
ti
on.
4.1.3. L
e
ar
n
e
r
p
h
as
e
I
n
th
is
c
a
s
e
,
th
e
te
a
c
he
r
c
hoos
e
s
a
s
tu
de
nt
a
t
r
a
ndom
th
r
ou
gh
c
ont
a
c
t
in
or
de
r
to
a
dva
nc
e
th
e
ir
knowle
dge
.
I
f
ot
he
r
pupi
ls
a
r
e
m
or
e
knowle
dge
a
bl
e
th
a
n
hi
m
,
th
e
n
he
c
a
n
e
f
f
e
c
ti
ve
ly
le
a
r
n
m
or
e
f
r
om
th
e
m
th
r
ough
in
te
r
a
c
ti
on.
T
he
s
te
ps
in
vol
ve
d
in
le
a
r
ni
ng
s
ta
ge
a
r
e
a
s
f
ol
lo
w
s
.
R
a
ndoml
y
s
e
le
c
t
two
le
a
r
ne
r
s
X
i
a
nd
X
j
s
uc
h t
ha
t
i
=
/
j
.
=
+
(
0
,
1
)
(
−
)
. I
f
(
)
<
(
)
(
10)
=
+
(
0
,
1
)
(
−
)
. T
a
ke
a
s
gr
a
nt
e
d i
f
be
tt
e
r
pe
r
f
or
m
a
nc
e
i
s
f
ound.
5.
S
I
M
U
L
A
T
I
O
N
R
E
S
U
L
T
I
n
th
is
pa
r
t,
T
L
B
O
te
c
hni
que
is
u
s
e
d
f
or
de
s
ig
ni
ng
th
e
be
s
t
va
r
ia
bl
e
s
of
a
P
I
s
ys
te
m
e
m
pl
oyi
ng
th
e
tr
a
ns
f
e
r
f
unc
ti
on
of
f
ir
s
t
f
li
ght
c
ondi
ti
on.
A
c
om
pa
r
is
on
is
m
a
de
be
twe
e
n
T
L
B
O
w
it
h
P
I
a
nd
c
onve
nt
io
na
l
m
e
th
ods
f
or
c
om
pa
r
in
g
th
e
a
dva
nt
a
ge
s
of
pr
opos
e
d
c
ont
r
ol
le
r
s
.
S
te
p
r
e
s
pons
e
s
of
th
e
f
li
ght
c
ont
r
ol
s
ys
te
m
e
m
pl
oyi
ng
T
L
B
O
–
P
I
,
G
A
a
nd
P
S
O
m
e
th
ods
a
r
e
obt
a
in
e
d
by
v
a
r
yi
ng
th
r
e
e
di
f
f
e
r
e
nt
pa
r
a
m
e
te
r
s
f
r
om
-
50%
to
+
50%
a
r
e
s
how
n
f
r
om
F
ig
ur
e
s
3
to
14.
S
i
m
il
a
r
f
ig
u
r
e
s
c
a
n
a
ls
o
be
dr
a
w
n
by
va
r
yi
ng
th
e
r
e
m
a
in
in
g
pa
r
a
m
e
te
r
s
.
I
t
is
e
vi
de
nt
f
r
om
th
e
s
e
f
ig
ur
e
s
th
a
t
s
e
tt
li
ng
ti
m
e
of
th
e
s
ugg
e
s
t
e
d
T
L
B
O
a
ppr
oa
c
h
is
lo
w
e
r
in
c
om
pa
r
is
on
to
P
S
O
a
nd G
A
pr
oc
e
dur
e
s
.
F
ig
ur
e
3. D
e
vi
a
ti
on of
by
-
50%
0
5
10
15
20
25
30
35
40
45
0
0
.
2
0
.
4
0
.
6
0
.
8
1
1
.
2
1
.
4
S
t
e
p
R
e
s
p
o
n
s
e
o
f
G
A
,
P
S
O
&
T
L
B
O
(
D
e
v
i
a
t
i
o
n
o
f
Z
w
b
y
-
5
0
%
)
T
i
m
e
(
s
e
c
o
n
d
s
)
A
m
p
l
i
t
u
d
e
GA
PSO
T
L
B
O
Evaluation Warning : The document was created with Spire.PDF for Python.
I
nt
J
A
r
ti
f
I
nt
e
ll
I
S
S
N
:
2252
-
8938
B
io
i
ns
pi
r
e
d t
e
c
hni
que
f
or
c
ont
r
ol
li
ng angle
of
at
ta
c
k
of
ai
r
c
r
af
t
(
Subhak
ant
a B
al
)
4211
F
ig
ur
e
4
.
D
e
vi
a
ti
on of
by
-
25%
F
ig
ur
e
5
.
D
e
vi
a
ti
on of
by +
25%
F
ig
ur
e
6. D
e
vi
a
ti
on of
by +
50%
F
ig
ur
e
7. D
e
vi
a
ti
on of
M
q by
-
50%
F
ig
ur
e
8. D
e
vi
a
ti
on of
M
q by
-
25%
F
ig
ur
e
9. D
e
vi
a
ti
on of
M
q by+
25%
F
ig
ur
e
10. De
vi
a
ti
ons
of
M
q by+
50%
0
5
10
15
20
25
30
35
40
45
0
0
.
2
0
.
4
0
.
6
0
.
8
1
1
.
2
1
.
4
S
t
e
p
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p
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A
,
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S
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&
T
L
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(
D
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v
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t
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o
n
o
f
Z
w
b
y
-
2
5
%
)
T
i
m
e
(
s
e
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d
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A
m
p
l
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t
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d
e
GA
PSO
T
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B
O
0
5
10
15
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25
0
0
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5
1
1
.
5
S
t
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p
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p
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f
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(
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v
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a
t
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n
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f
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w
b
y
+
2
5
%
)
T
i
m
e
(
s
e
c
o
n
d
s
)
A
m
p
l
i
t
u
d
e
GA
PSO
T
L
B
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0
5
10
15
20
25
0
0
.
5
1
1
.
5
S
t
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p
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p
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n
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s
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m
p
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PSO
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5
10
15
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0
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0
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1
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2
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4
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t
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PSO
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B
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0
5
10
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30
35
40
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0
0
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2
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4
0
.
6
0
.
8
1
1
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2
1
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4
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m
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d
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GA
PSO
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B
O
0
5
10
15
20
25
30
0
0
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5
1
1
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5
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t
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p
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p
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A
m
p
l
i
t
u
d
e
GA
PSO
T
L
B
O
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
. 13, No. 4, D
e
c
e
m
be
r
2024:
4206
-
4216
4212
F
ig
ur
e
11. De
vi
a
ti
on of
M
w
by
-
50%
F
ig
ur
e
12. De
vi
a
ti
on of
M
w
by
-
25%
F
ig
ur
e
13. De
vi
a
ti
on of
M
w
by +
25%
F
ig
ur
e
14. De
vi
a
ti
on of
M
w
by +
50%
6.
R
O
B
U
S
T
N
E
S
S
A
N
A
L
Y
S
I
S
F
or
t
e
s
ti
ng t
he
t
oughne
s
s
of
t
he
C
H
A
R
L
I
E
A
ir
c
r
a
f
t,
t
he
pa
r
a
m
e
te
r
s
a
r
e
c
ha
nge
d f
r
om
-
50%
t
o +
50%
.
T
he
n
r
obus
tn
e
s
s
is
m
e
a
s
ur
e
d
by
u
s
in
g
th
e
opt
im
um
va
lu
e
s
obt
a
in
e
d
f
r
om
T
L
B
O
opt
im
iz
e
d
P
I
c
ont
r
ol
le
r
.
A
c
om
pa
r
is
on
r
e
s
ul
ts
a
m
ong
G
A
,
P
S
O
a
nd
T
L
B
O
a
r
e
a
l
s
o
de
pi
c
te
d
in
T
a
bl
e
3
a
nd
4
r
e
s
p
e
c
ti
ve
ly
.
D
if
f
e
r
e
nt
a
na
ly
s
is
r
e
s
ul
t
s
r
e
la
te
d
to
in
te
gr
a
l
a
bs
ol
ut
e
e
r
r
or
(
I
A
E
)
,
in
te
gr
a
l
ti
m
e
a
bs
ol
ut
e
e
r
r
or
(
I
T
A
E
)
,
a
nd
m
e
a
n
s
qua
r
e
e
r
r
or
(
M
S
E
)
, s
e
tt
li
ng t
im
e
, pe
a
k unde
r
-
s
hoot
s
a
nd p
e
a
k ove
r
s
ho
ot
s
a
r
e
gi
ve
n i
n
t
he
s
e
t
a
bl
e
s
.
N
ow
i
t
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-
50
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25
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5
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T
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4.
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25
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T
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F
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
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J
A
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ti
f
I
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e
ll
I
S
S
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:
2252
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8938
B
io
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pi
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f
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Subhak
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4213
F
ig
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15. I
A
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a
m
ong T
L
B
O
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, a
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F
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16. M
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a
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17. T
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a
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F
ig
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18. I
T
A
E
a
m
ong T
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B
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, a
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GA
T
a
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5. V
a
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ti
on of
I
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, I
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nd M
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P
a
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a
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-
50
1.23
7.04
0.29
0.88
2.16
0.34
0.83
1.04
0.27
-
25
1.22
6.6
0.31
0.89
1.85
0.36
0.88
1.18
0.30
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1.23
5.56
0.36
1.05
1.82
0.44
1.04
1.68
0.33
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1.24
4.98
0.39
1.24
2.45
0.51
1.11
2.40
0.37
-
50
1.25
7.11
0.31
0.89
2.07
0.35
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1.14
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25
1.24
6.63
0.32
0.91
1.86
0.37
0.90
1.25
0.31
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1.21
5.62
0.35
1.02
1.74
0.43
1.01
1.62
0.34
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1.20
5.05
0.37
1.12
1.96
0.47
1.08
1.83
0.35
0
-
50
1.01
1.86
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2.12
7.18
0.83
0.82
1.04
0.34
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25
1.11
4.08
0.37
1.13
1.92
0.49
0.99
1.53
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0.41
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2.82
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0.44
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3.04
0.37
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50
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0.41
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0.94
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0.31
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25
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0.42
0.96
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0.93
1.37
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5.01
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1.70
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0.38
1.17
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0.41
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0.93
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0.42
0.95
1.78
0.38
0.94
1.15
0.32
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1.34
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0.41
0.97
1.68
0.42
0.96
1.40
0.36
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1.45
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0.42
0.99
1.58
0.47
0.97
1.45
0.35
-
50
1.15
4.73
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1.03
1.55
0.46
0.82
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0.36
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25
1.20
5.48
0.38
0.96
1.46
0.43
0.88
1.22
0.35
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1.21
6.36
0.36
1.17
2.51
0.37
0.99
2.07
0.32
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1.13
5.77
0.37
1.08
3.72
0.34
1.06
2.47
0.33
0
0
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0
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0
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0
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1
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5
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Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2252
-
8938
I
nt
J
A
r
ti
f
I
nt
e
ll
, V
ol
. 13, No. 4, D
e
c
e
m
be
r
2024:
4206
-
4216
4214
T
a
bl
e
6. V
a
r
ia
ti
on of
s
e
tt
li
ng t
im
e
, unde
r
s
hoot
, a
nd ove
r
s
hoot
P
a
r
a
m
e
t
e
r
s
%
D
e
vi
a
t
i
on
GA
PSO
T
L
B
O
Ts
U
s
h
O
s
h
Ts
U
s
h
O
s
h
Ts
U
s
h
O
s
h
-
50
23.9
16.67
24
6.16
16.88
24
4.32
20.10
23.9
-
25
21.1
17.54
26
6
18.3
27
5.62
21.26
25.6
+25
16
18.98
30.5
6.36
21.11
34.8
5.77
22.74
34.2
+50
13.5
19.86
33.4
6.71
22.27
40.4
6.28
20.89
33.1
-
50
23.5
17.23
25.2
6.07
17.73
25.6
5.58
20.67
25.1
-
25
20.9
17.61
26.6
6.04
18.19
27.8
5.69
21.48
26.2
+25
16.4
18.93
29.8
6.29
20.48
33.6
5.84
22.88
29.6
+50
14.1
19.18
31.7
6.52
21.72
37.2
5.82
23.17
31.5
0
-
50
7.11
17.79
25.6
12.6
23.84
52
4.39
22.52
25.1
-
25
11.3
17.34
25.7
6.98
22.02
35.7
5.69
23.76
25.5
+25
27
18.92
30.6
7.99
18.78
29.4
5.74
21.46
29.3
+50
36.3
19.95
32.8
11.8
18.63
29.9
8.69
23.23
29.4
-
50
27
15.97
23.5
8.03
16.1
23.3
5.66
23.47
23.2
-
25
22.8
17.05
25.7
6.15
17.78
26.6
5.66
22.36
25.5
+25
14.4
19.43
31
6.28
21.38
35.2
5.82
23.29
30.9
+50
10.4
20.98
34.2
6.57
23.49
40.8
6.22
21.81
33.8
M
δ
E
-
50
15.4
21.6
37.2
5.18
21.99
37.9
5.1
21.76
37.1
-
25
17.1
20.4
33.3
5.56
20.9
34.6
5.25
17.1
20.4
+25
19.9
14.93
21.4
6.75
17.47
25.4
6.33
20.02
21.3
+50
20.8
9.02
11.7
5.04
14.2
18,7
4.81
15.56
11.6
-
50
12.9
14.59
20.3
5.5
20.16
30.2
5.48
20.89
20.2
-
25
15.3
16.23
23.7
6.43
19.85
29.7
5.65
21.74
23.5
+25
22.7
20.23
34.1
7.56
20.62
33.6
6.34
23.03
33.1
+50
25.9
23.16
44
9.84
21.74
40.1
7.52
23.53
39.8
T
a
bl
e
7. C
ont
r
ol
le
r
pa
r
a
m
e
te
r
s
f
or
G
A
, P
S
O
, a
nd T
L
B
O
P
a
r
a
m
e
t
e
r
s
%
D
e
vi
a
t
i
on
GA
PSO
T
L
B
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Kp
Ki
Kp
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Kp
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-
50
23.5
1.2
16.31
3.12
14.5
5.5
-
25
21.5
1.25
14.94
3.3
13.8
5.7
+25
17.5
1.37
12.2
3.62
14.8
4.7
+50
15.6
1.45
10.8
3.84
13.2
2.7
-
50
22.3
1.2
15.5
3.2
14.6
5.2
-
25
20.9
1.25
14.5
3.3
13.5
4.5
+25
18.2
1.35
12.6
3.6
12.9
5.6
+50
16.8
1.42
11.7
3.74
14.8
4.7
0
-
50
8.2
1.9
5.72
5.26
13.5
5.8
-
25
13.5
1.56
9.4
4.1
14.8
7.4
+25
26.4
1.12
18.32
2.95
13.8
5.7
+50
33.9
0.98
23.6
2.6
12.2
10.6
-
50
22.6
1.21
15.7
3.2
15
10.8
-
25
21.1
1.25
14.65
3.3
14.6
7.4
+25
18
1.35
12.5
3.6
13.9
5.8
+50
16.5
1.42
11.43
3.74
13.2
2.72
-
50
19.5
1.06
13.6
2.8
13.6
2.74
-
25
19.5
1.16
13.6
3.06
0.82
0.01
+25
19.5
1.5
13.6
3.96
13.9
5.4
+50
19.5
1.8
13.6
4.9
14.5
5.6
-
50
13
1.6
9.05
4.2
13.6
4.9
-
25
15.6
1.45
10.9
3.84
14.5
5.4
+25
26
1.12
18.1
2.97
13.2
5.9
+50
39.1
0.9
27.2
2.4
12.2
5.8
7.
R
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F
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T
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how
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c
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A
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pe
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ly
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hus
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pr
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obs
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F
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to
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f
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if
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on.
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bove
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C
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w
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tt
in
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b
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in
of
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c
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on
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tw
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n
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A
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m
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A
be
tt
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r
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s
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is
a
c
hi
e
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d
in
T
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B
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m
a
na
g
e
d
P
I
c
ont
r
ol
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r
th
a
n
G
A
a
nd P
S
O
.
F
o
r
s
tu
dyi
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th
e
be
ha
vi
or
o
f
th
e
a
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c
r
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t
und
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va
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ha
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a
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d
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c
ondi
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,
it
s
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ol
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s
a
r
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c
ha
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d
f
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-
50%
to
+
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of
nom
in
a
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va
lu
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in
s
te
ps
of
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.
F
in
a
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s
ul
ts
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om
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in
f
a
vor
of
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B
O
a
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tu
ni
ng of
pa
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a
m
e
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c
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y ove
r
a
w
id
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a
nge
.
R
E
F
E
R
E
N
C
E
S
[
1]
R
.
K
um
a
r
,
I
.
K
a
s
i
r
e
ddy,
A
.
K
um
a
r
,
a
nd
A
.
K
.
S
i
ngh,
“
E
s
t
i
m
a
t
i
on
of
s
t
a
bi
l
i
t
y
r
e
gi
ons
of
f
r
a
c
t
i
ona
l
P
I
c
ont
r
ol
l
e
r
f
or
L
F
C
of
pow
e
r
s
ys
t
e
m
,”
i
n
2019
I
E
E
E
I
nt
e
r
nat
i
onal
C
onf
e
r
e
nc
e
on
Sus
t
ai
nabl
e
E
ne
r
gy
T
e
c
hnol
ogi
e
s
,
2019,
pp.
313
–
318
,
doi
:
10.1109/
I
C
S
E
T
S
.2019.8745072.
[
2]
Z
.
X
i
a
ng,
X
.
S
ha
o,
H
.
W
u,
D
. J
i
, F
.
Y
u,
a
nd
Y
.
L
i
,
“
A
n
a
da
pt
i
ve
i
nt
e
gr
a
l
s
e
pa
r
a
t
e
d
pr
opor
t
i
ona
l
–
i
nt
e
gr
a
l
c
ont
r
ol
l
e
r
ba
s
e
d
s
t
r
a
t
e
gy
f
or
pa
r
t
i
c
l
e
s
w
a
r
m
opt
i
m
i
z
a
t
i
on,”
K
now
l
e
dge
-
B
as
e
d Sy
s
t
e
m
s
, vol
. 195, M
a
y 20
20,
pp. 1
-
15,
doi
:
10.1016/
j
.knos
ys
.2020.105696.
[
3]
S
.
S
a
xe
na
a
nd
Y
.
V
H
ot
e
,
“
R
obus
t
l
y
s
t
a
bi
l
i
z
i
ng
pr
opor
t
i
ona
l
i
nt
e
gr
a
l
c
ont
r
ol
l
e
r
f
or
unc
e
r
t
a
i
n
s
ys
t
e
m
unde
r
c
om
put
a
t
i
ona
l
de
l
a
y,
”
J
our
nal
of
V
i
br
at
i
on and C
ont
r
ol
, vol
. 27, no. 19
–
20, pp. 2268
–
2278, O
c
t
. 202
1, doi
:
10.1177/
1077546320957921.
[
4]
M
. A
. A
. G
ha
ny, M
. E
. B
a
hga
t
, W
. M
. R
e
f
a
e
y, a
nd S
. S
ha
r
a
f
, “
T
ype
-
2 f
uz
z
y s
e
l
f
-
t
uni
ng of
m
odi
f
i
e
d f
r
a
c
t
i
ona
l
-
or
de
r
P
I
D
ba
s
e
d on
T
a
ka
gi
–
S
uge
no
m
e
t
hod,”
J
our
nal
of
E
l
e
c
t
r
i
c
al
Sy
s
t
e
m
s
and
I
nf
or
m
at
i
on
T
e
c
hnol
ogy
,
vol
.
7,
no.
1,
pp.
1
-
20,
D
e
c
.
2020,
doi
:
10.1186/
s
43067
-
019
-
0009
-
9.
[
5]
L
. S
a
nka
r
a
l
i
nga
m
a
nd C
. R
a
m
pr
a
s
a
dh, “
A
c
om
pr
e
he
ns
i
v
e
s
ur
ve
y on t
he
m
e
t
hods
of
a
ngl
e
of
a
t
t
a
c
k m
e
a
s
ur
e
m
e
nt
a
nd e
s
t
i
m
a
t
i
on i
n
U
A
V
s
,”
C
hi
ne
s
e
J
ou
r
nal
of
A
e
r
onaut
i
c
s
, vol
. 33, no. 3, pp. 749
–
770,
M
a
r
. 2020, doi
:
10.1016/
j
.c
j
a
.2019.11.003.
[
6]
R
.
B
udda
l
a
a
nd
S
.
S
.
M
a
ha
pa
t
r
a
,
“
T
w
o
-
s
t
a
ge
t
e
a
c
hi
ng
-
l
e
a
r
ni
ng
-
ba
s
e
d
opt
i
m
i
z
a
t
i
on
m
e
t
hod
f
o
r
f
l
e
xi
bl
e
j
ob
-
s
hop
s
c
he
dul
i
ng
unde
r
m
a
c
hi
ne
br
e
a
kdow
n,”
T
he
I
nt
e
r
nat
i
onal
J
our
nal
of
A
dv
anc
e
d
M
anuf
ac
t
ur
i
ng
T
e
c
hnol
ogy
,
vol
.
100,
no.
5
–
8,
2019,
doi
:
10.1007/
s
00170
-
018
-
2805
-
0.
[
7]
S
.T
.
S
uga
nt
hi
,
D
.
D
e
va
r
a
j
,
S
.H
.
T
hi
l
a
ga
r
,
a
nd
K
.
R
a
m
a
r
,
“
O
pt
i
m
a
l
ge
ne
r
a
t
or
r
e
s
c
he
dul
i
ng
w
i
t
h
di
s
t
r
i
but
e
d
s
l
a
c
k
bu
s
m
ode
l
f
or
c
onge
s
t
i
on
m
a
na
ge
m
e
nt
us
i
ng
i
m
pr
ove
d
t
e
a
c
hi
ng
l
e
a
r
ni
ng
ba
s
e
d
opt
i
m
i
z
a
t
i
on
a
l
gor
i
t
hm
,”
Sādhanā
,
vol
.
43,
n
o.
11,
2018,
doi
:
10.1007/
s
12046
-
018
-
0941
-
8.
[
8]
P
.
N
i
u,
Y
.
M
a
,
a
nd
S
.
Y
a
n,
“
A
m
odi
f
i
e
d
t
e
a
c
hi
ng
–
l
e
a
r
ni
ng
-
ba
s
e
d
opt
i
m
i
z
a
t
i
o
n
a
l
gor
i
t
hm
f
or
num
e
r
i
c
a
l
f
unc
t
i
on
opt
i
m
i
z
a
t
i
on,”
I
nt
e
r
nat
i
onal
J
our
nal
of
M
ac
hi
ne
L
e
a
r
ni
ng
and
C
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s
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s
t
r
uc
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s
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r
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m
i
c
c
ons
t
r
a
i
nt
s
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hybr
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d ba
t
a
l
gor
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t
hm
a
nd
t
e
a
c
hi
ng
–
l
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r
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a
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i
on w
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t
h e
r
r
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c
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e
c
t
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t
i
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(
E
T
L
B
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)
w
i
t
h
hi
ghe
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J
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Pi
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ne
t
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a
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a
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ve
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f
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t
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f
a
n
a
c
c
e
l
e
r
a
t
or
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a
t
t
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m
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i
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c
t
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ve
g
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ne
t
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N
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a
r
I
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M
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P
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c
s
R
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a
r
c
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Se
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on
A
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A
c
c
e
l
e
r
at
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s
,
Spe
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t
r
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e
t
e
r
s
,
D
e
t
e
c
t
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A
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r
t
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c
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i
m
i
z
a
t
i
on
t
o
s
ol
vi
ng
t
he
e
c
onom
i
c
di
s
pa
t
c
h
c
ons
i
de
r
i
ng
t
he
ge
ne
r
a
t
or
c
ons
t
r
a
i
nt
s
,”
I
E
E
E
T
r
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t
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r
Sy
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t
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r
m
i
ni
s
t
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c
a
ppr
oa
c
h t
o
gl
oba
l
box
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t
r
a
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ne
d opt
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m
i
z
a
t
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O
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opt
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m
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z
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t
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on
a
l
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t
hm
:
D
yna
m
i
c
hybr
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d
opt
i
m
i
z
a
t
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a
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gor
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t
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,”
M
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r
e
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i
a
l
e
vol
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s
i
m
pl
e
a
nd
e
f
c
i
e
nt
he
ur
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s
t
i
c
f
or
gl
oba
l
opt
i
m
i
z
a
t
i
on
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r
e
nt
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a
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f
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i
nuous
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ur
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