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Ser
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m
e
asu
r
ed
o
b
ject,
wh
en
th
e
m
ea
s
u
r
ed
v
alu
e
is
r
an
d
o
m
er
r
o
r
u
n
ce
r
tain
ty
o
r
v
ib
r
atio
n
o
r
u
n
p
r
ed
icted
[
2
6
-
2
8
]
.
Fig
u
r
e
3
s
h
o
ws
th
e
b
lo
ck
d
ia
g
r
a
m
o
f
th
e
p
r
o
ce
s
s
o
f
Kalm
an
f
ilter
.
Her
e
,
th
r
ee
m
ai
n
eq
u
ati
o
n
s
n
ee
d
to
b
e
d
o
n
e
.
T
h
e
f
ir
s
t e
q
u
atio
n
is
to
f
in
d
th
e
g
ain
o
f
th
e
Kalm
an
f
ilter
.
T
h
e
s
ec
o
n
d
eq
u
atio
n
is
to
f
in
d
a
n
esti
m
ated
v
alu
e
o
f
cu
r
r
en
t.
T
h
e
th
ir
d
eq
u
atio
n
u
s
ed
to
f
in
d
t
h
e
last
esti
m
atio
n
er
r
o
r
.
T
h
ese
th
r
ee
c
alcu
latio
n
s
ar
e
r
e
p
ea
ted
c
o
n
tin
u
o
u
s
ly
to
im
p
r
o
v
e
t
h
e
cu
r
r
en
t
esti
m
ated
v
alu
e.
To
ca
lcu
late
th
e
g
ain
o
f
th
e
Kalm
an
f
ilter
,
two
p
ar
am
eter
s
m
u
s
t
b
e
av
ailab
le.
T
h
e
f
ir
s
t
o
n
e
is
th
e
er
r
o
r
o
f
esti
m
atio
n
.
At
f
ir
s
t,
th
e
er
r
o
r
esti
m
atio
n
v
alu
e
m
u
s
t
in
s
er
ted
an
d
th
en
co
m
e
f
r
o
m
th
ir
d
eq
u
atio
n
r
ep
ea
ted
ly
.
T
h
e
s
ec
o
n
d
p
ar
am
eter
is
th
e
e
r
r
o
r
in
m
ea
s
u
r
ed
d
ata.
T
h
e
Kalm
an
f
ilter
g
ain
is
r
ea
lly
p
u
t
th
e
er
r
o
r
in
th
e
d
ata
m
ea
s
u
r
ed
v
er
s
u
s
th
e
er
r
o
r
in
t
h
e
esti
m
ate.
I
f
th
e
er
r
o
r
in
th
e
esti
m
ate
is
s
m
all,
th
e
g
ain
p
u
t
th
e
m
o
s
t
im
p
o
r
tan
t
in
it
an
d
v
ice
v
er
s
a.
T
h
e
cu
r
r
en
t
esti
m
a
te
is
f
o
u
n
d
b
ased
o
n
in
p
u
t
d
at
a
in
ad
d
itio
n
to
p
r
ev
io
u
s
esti
m
ate.
T
h
e
n
ew
er
r
o
r
esti
m
ate
i
s
f
o
u
n
d
b
ased
o
n
th
e
g
ain
o
f
th
e
Kalm
an
f
ilter
an
d
th
e
cu
r
r
en
t
esti
m
ate
v
alu
e.
T
h
is
p
r
o
ce
s
s
r
ep
ea
ted
co
n
tin
u
o
u
s
ly
to
d
ec
r
ea
s
e
th
e
e
r
r
o
r
a
n
d
g
et
th
e
tr
u
e
v
alu
e.
Fig
u
r
e
3
e
x
p
lain
t
h
e
b
lo
c
k
d
iag
r
a
m
o
f
th
e
m
en
tio
n
ed
f
ilter
wh
ile
Fig
u
r
e
4
d
escr
ib
e
th
e
o
p
er
atio
n
o
f
Kalm
an
f
ilter
as
a
lo
o
p
.
T
h
e
d
ata
f
r
o
m
th
e
f
lex
s
en
s
o
r
s
in
th
is
wo
r
k
f
ilter
ed
u
s
in
g
Kalm
an
f
ilter
lo
o
p
o
f
o
p
er
atio
n
t
o
esti
m
ate
r
ea
l d
esire
d
s
tates.
Fig
u
r
e
3
.
B
lo
ck
d
iag
r
am
o
f
Ka
lm
an
f
ilter
Fig
u
r
e
4
.
L
o
o
p
o
p
er
atio
n
o
f
Kalm
an
f
ilter
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
Med
ica
l remo
tely
ca
r
in
g
w
ith
C
OV
I
D
-
1
9
viru
s
in
fecte
d
p
eo
p
le
u
s
in
g
… (
Mo
h
a
mme
d
A
z
h
er Th
erib
)
2889
4.
P
RO
P
O
SE
D
SYS
T
E
M
I
n
th
is
s
y
s
tem
,
we
m
ad
e
a
wir
e
less
co
n
tr
o
l
f
o
r
r
o
b
o
tic
ar
m
u
s
in
g
o
p
tim
ized
PID
co
n
tr
o
ller
an
d
Kalm
an
f
ilter
.
A
f
lex
s
en
s
o
r
is
co
n
n
ec
ted
with
ea
ch
lim
b
ac
co
r
d
in
g
to
Fig
u
r
e
5
.
Ar
d
u
in
o
is
u
s
ed
to
g
et
th
e
s
ig
n
al
o
f
ea
ch
f
lex
s
en
s
o
r
.
T
h
e
r
ec
eiv
ed
s
ig
n
al
was
f
ilter
ed
u
s
in
g
Kalm
an
f
ilter
in
o
r
d
er
to
g
et
b
etter
est
im
atio
n
o
f
cu
r
r
en
t
s
tate
in
s
tate
o
f
n
o
is
y
d
ata
g
ettin
g
b
ef
o
r
e.
T
h
e
f
ee
d
b
ac
k
d
ata
co
m
e
f
r
o
m
s
im
ilar
s
en
s
o
r
s
co
n
n
ec
ted
with
th
e
lim
b
o
f
r
o
b
o
tic
ar
m
as sh
o
wn
in
Fig
u
r
e
6
.
Fig
u
r
e
5
.
Flex
s
en
s
o
r
c
o
n
n
ec
te
d
to
ea
ch
lim
b
Fig
u
r
e
6
.
Flex
s
en
s
o
r
c
o
n
n
ec
te
d
to
th
e
r
o
b
o
t a
r
m
T
h
e
p
ar
am
eter
s
o
f
PID
co
n
tr
o
ller
s
o
p
tim
ized
u
s
in
g
PS
O
alg
o
r
ith
m
ac
c
o
r
d
in
g
to
th
e
b
lo
c
k
d
iag
r
am
s
h
o
wn
in
Fig
u
r
e
7
.
T
h
e
o
v
e
r
all
s
y
s
tem
ca
n
b
e
d
escr
ib
ed
in
th
e
f
o
llo
win
g
s
tep
s
:
Step
1
: I
n
s
tall th
e
s
y
s
tem
ac
co
r
d
in
g
to
t
h
e
cir
cu
it sh
o
wn
in
F
ig
u
r
e
8
.
Step
2
:
Get
th
e
d
esire
d
s
tates
f
r
o
m
f
le
x
s
en
s
o
r
s
th
at
co
n
n
ec
ted
to
th
e
h
u
m
an
lim
b
s
.
T
h
e
d
ata
r
ec
eiv
ed
f
r
o
m
an
alo
g
ter
m
i
n
als
o
f
Ar
d
u
in
o
.
C
h
an
g
e
th
e
f
lex
s
en
s
o
r
a
n
g
le
lead
s
to
ch
an
g
e
in
o
u
tp
u
t
v
o
ltag
e
o
f
th
e
v
o
ltag
e
d
iv
id
er
.
T
h
e
o
u
tp
u
t
v
o
ltag
e
c
h
an
g
es
f
r
o
m
0
-
5
V
co
n
v
er
ted
to
a
d
i
g
ital
f
o
r
m
with
r
an
g
e
0
-
1
0
2
4
.
T
h
e
ac
cu
r
ac
y
o
f
in
v
er
s
io
n
is
0
.
4
%.
Step
3
: T
h
e
d
ata
f
r
o
m
s
tep
2
f
il
ter
ed
u
s
in
g
Kalm
an
f
ilter
to
es
tim
ate
r
ea
l d
esire
d
s
tates.
Step
4
: Sen
d
f
ilter
ed
d
ata
g
o
tte
n
f
r
o
m
s
tep
3
to
t
h
e
r
o
b
o
t sid
e
wir
eless
ly
u
s
in
g
Z
ig
B
ee
p
r
o
to
co
l o
r
in
ter
n
et.
Step
5
: Rep
ea
t step
s
2
,
3
an
d
4
en
d
less
ly
with
1
s
ec
.
in
ter
v
al.
I
n
th
e
o
t
h
er
s
id
e
(
s
id
e
o
f
th
e
r
o
b
o
t)
,
t
h
e
s
y
s
tem
wo
r
k
s
as f
o
ll
o
win
g
s
tep
s
:
Step
1
: Rece
iv
e
th
e
d
esire
d
d
at
a
s
en
t f
r
o
m
th
e
h
u
m
a
n
s
id
e.
Step
2
:
Get
th
e
ac
tu
al
s
tat
es
o
f
th
e
r
o
b
o
t
u
s
in
g
f
lex
s
en
s
o
r
th
at
co
n
n
ec
te
d
to
th
e
lim
b
o
f
th
e
r
o
b
o
t
(
s
am
e
p
r
o
ce
d
u
r
e
o
f
s
tep
2
in
t
h
e
h
u
m
an
s
id
e
to
g
et
th
e
ac
t
u
al
s
tates).
Step
3
: Co
m
p
u
te
th
e
er
r
o
r
s
ig
n
al
b
ased
o
n
d
esire
d
an
d
ac
tu
al
v
alu
es.
Step
4
:
Use
PID
co
n
tr
o
ller
to
g
en
er
ate
o
u
tp
u
t
s
ig
n
al
u
s
ed
b
y
ac
tu
ato
r
s
(
s
er
v
o
m
o
to
r
s
)
t
o
ch
an
g
e
th
e
s
tates
o
f
lim
b
s
in
o
r
d
er
to
d
ec
r
ea
s
e
er
r
o
r
s
ig
n
al.
Step
5
: Rep
ea
t a
ll st
ep
s
b
ef
o
r
e
en
d
less
ly
with
in
1
s
ec
.
in
ter
v
a
l.
Fig
u
r
e
7
.
B
lo
ck
d
iag
r
am
o
f
PI
D
p
ar
am
eter
s
ettin
g
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
1
6
9
3
-
6
9
3
0
T
E
L
KOM
NI
KA
T
elec
o
m
m
u
n
C
o
m
p
u
t E
l Co
n
tr
o
l
,
Vo
l.
1
8
,
No
.
6
,
Dec
em
b
e
r
2
0
2
0
:
288
6
-
289
3
2890
Fig
u
r
e
8
.
B
lo
ck
d
iag
r
am
o
f
th
e
im
p
lem
en
ted
cir
c
u
it
5.
RE
SU
L
T
S
T
h
e
f
lex
s
en
s
o
r
s
wer
e
s
u
b
ject
ed
o
n
th
e
g
lo
v
e
th
at
t
h
e
d
o
ct
o
r
will
wea
r
it
b
y
h
an
d
an
d
th
e
y
will
co
p
y
th
e
m
o
v
em
e
n
t
o
f
h
is
f
in
g
e
r
s
.
T
h
e
an
g
les
o
f
th
ese
f
lex
s
en
s
o
r
s
will
b
e
tr
an
s
m
itted
u
s
ed
Z
ig
B
ee
p
r
o
to
co
l
u
s
in
g
XB
ee
m
o
d
u
le
s
h
ield
th
at
p
u
t
-
o
n
Ar
d
u
i
n
o
b
o
ar
d
.
T
h
is
h
u
m
a
n
s
id
e
is
s
h
o
wn
in
Fig
u
r
e
9
.
T
h
e
r
o
b
o
tic
a
r
m
s
h
o
w
n
in
Fig
u
r
e
1
0
was
u
s
ed
in
th
is
wo
r
k
to
r
ec
iv
e
th
e
a
n
g
les
d
ata
f
r
o
m
th
e
Xb
ee
m
o
d
u
le
wh
ich
ar
e
th
e
d
ata
g
et
f
r
o
m
f
lex
s
en
s
o
r
s
an
d
tr
ea
t t
h
e
in
f
ec
ted
-
p
eo
p
le
d
ir
ec
tly
.
T
h
e
im
p
le
m
en
ted
r
o
b
o
t sid
e
was
s
h
o
wn
in
Fig
u
r
e
1
0
.
Fig
u
r
e
9
.
Hu
m
an
s
id
e
Fig
u
r
e
1
0
.
R
o
b
o
t sid
e
T
h
is
tr
an
ciev
er
s
y
s
tem
(
h
u
m
an
an
d
r
o
b
o
t
s
id
es)
wa
s
test
ed
in
m
u
ltip
le
s
ce
n
a
r
io
s
an
d
g
iv
e
h
ig
h
p
er
f
o
r
m
an
e
to
tr
ea
t
p
eo
p
le
r
em
o
tely
u
s
in
g
Z
ig
B
ee
p
r
o
to
c
o
l
b
a
s
ed
o
n
XB
ee
m
o
d
u
le
wh
en
ex
a
m
in
ed
p
r
ac
tically
.
T
h
e
o
p
tim
ized
PID
co
n
tr
o
ller
is
an
ef
f
icien
t
co
n
t
r
o
ller
f
o
r
th
ese
ty
p
e
s
o
f
s
y
s
tem
s
.
T
h
e
s
tep
r
esp
o
n
s
e
with
o
u
t
o
p
tim
izatio
n
o
f
ea
ch
f
i
n
g
er
w
as
test
ed
u
s
in
g
Ma
tla
b
s
im
u
latio
n
an
d
is
s
h
o
wn
in
Fig
u
r
e
1
1
.
I
n
th
is
r
esp
o
n
s
e,
th
e
r
is
e
tim
e
an
d
s
ettlin
g
tim
e
is
v
er
y
h
ig
h
,
an
d
th
e
r
esp
o
n
s
e
d
id
n
o
t
r
ea
c
h
th
e
m
ax
im
u
m
o
v
er
s
h
o
o
t.
I
n
Fig
u
r
e
1
2
th
e
tu
n
in
g
o
r
im
p
r
o
v
ed
r
esp
o
n
s
e
to
th
e
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.
PS
O
an
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Kalm
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ak
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to
co
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l
th
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wir
eless
ly
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s
o
th
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ess
o
f
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ter
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et
in
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ter
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tem
.
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ad
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f
Kalm
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is
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ata
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s
m
all
in
ter
v
al.
T
h
e
s
y
s
tem
s
till
b
eh
av
es st
ab
le.
RE
F
E
R
E
NC
E
S
[1
]
A.
Bicc
h
i
a
n
d
G
.
To
n
ietti
,
“
F
a
st
a
n
d
‘so
ft
-
a
rm
’
tac
ti
c
s
[r
o
b
o
t
a
rm
d
e
sig
n
,
”
IE
EE
Ro
b
o
t
.
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u
to
m
.
M
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g
.
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l.
1
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,
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o
.
2
,
p
p
.
2
2
-
3
3
,
2
0
0
4
.
[2
]
R.
Ha
m
,
T.
G
.
S
u
g
a
r,
B.
Va
n
d
e
rb
o
rg
h
t,
K.
W.
Ho
l
lan
d
e
r,
a
n
d
D.
Le
fe
b
e
r,
“
Co
m
p
li
a
n
t
a
c
tu
a
to
r
d
e
sig
n
s
,
”
IEE
E
Ro
b
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t
.
Au
to
m.
M
a
g
.
,
v
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l.
1
6
,
n
o
.
3
,
p
p
.
8
1
-
9
4
,
S
e
p
.
2
0
0
9
.
[3
]
X.
Li
,
Y.
P
a
n
,
G
.
Ch
e
n
,
a
n
d
H.
Yu
,
“
M
u
lt
i
-
m
o
d
a
l
c
o
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tro
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sc
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e
m
e
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r
re
h
a
b
il
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a
ti
o
n
r
o
b
o
ti
c
e
x
o
sk
e
leto
n
s,”
In
t.
J
.
R
o
b
o
t.
Res
.
,
p
p
.
1
-
2
0
,
2
0
1
7
.
[4
]
Ib
ra
h
im
S
u
laim
a
n
,
En
g
r.
S
a
lam
M
.
O.
a
n
d
Ya
m
a
ji
n
Tan
imu
,
“
De
v
e
lo
p
m
e
n
t
o
f
A
R
o
b
o
t
Arm
:
A
Re
v
ie
w,”
T
h
e
Fed
e
ra
l
Po
lyte
c
h
n
ic B
id
a
,
S
c
h
o
o
l
o
f
E
n
g
i
n
e
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rin
g
T
e
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h
n
o
l
o
g
y
,
8
t
h
N
a
ti
o
n
a
l
En
g
in
e
e
rin
g
Co
n
fer
e
n
c
e
,
2
0
1
8
.
[
5
]
V
i
s
h
n
u
v
a
r
t
h
a
n
a
n
G
o
v
i
n
d
a
r
a
j
,
e
t
a
l
.
,
"
R
o
b
o
t
i
c
A
r
m
f
o
r
t
h
e
E
a
s
y
M
o
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l
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t
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m
p
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s
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n
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E
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G
S
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g
n
a
l
s
,
”
I
n
t
e
r
n
a
t
i
o
n
a
l
J
o
u
r
n
a
l
o
f
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n
n
o
v
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t
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T
e
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h
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y
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d
E
x
p
l
o
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n
g
E
n
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n
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r
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g
(
I
J
I
T
E
E
)
,
v
o
l
.
9
,
n
o
.
2
S
2
,
p
p
.
2
9
-
9
3
1
,
2
0
1
9
.
[6
]
Dish
a
n
t
K
h
o
sla
,
M
a
n
v
in
d
e
r
S
h
a
r
m
a
,
S
a
c
h
in
Krish
a
n
Kh
a
n
n
a
,
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ra
ti
b
h
a
K
h
a
n
n
a
,
G
u
rp
r
e
e
t
Ka
u
r,
“
S
m
a
rt
Ro
b
o
ti
c
Arm
Re
v
iew
,
”
J
o
u
rn
a
l
o
f
Arti
f
icia
l
I
n
t
e
ll
ig
e
n
c
e
Res
e
a
rc
h
&
Ad
v
a
n
c
e
s
,
v
o
l.
6
,
n
o
.
2
,
p
p
.
5
8
-
6
2
,
2
0
1
9
.
[7
]
Ra
g
h
u
l
B
.,
e
t
a
l
.
,
“
Io
t
E
n
a
b
led
Ro
b
o
ti
c
Arm
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Res
e
a
rc
h
in
El
e
c
tro
n
ics
,
v
o
l
.
0
5
,
n
o
.
0
1
,
p
p
.
1
1
-
1
3
,
2
0
1
8
.
[
8
]
M
o
h
a
m
m
e
d
A
z
h
e
r
T
h
e
r
i
b
e
t
a
l
.
,
"
S
m
a
r
t
D
i
g
i
t
a
l
B
i
-
D
i
r
e
c
t
i
o
n
a
l
V
i
s
i
t
o
r
s
C
o
u
n
t
e
r
B
a
s
e
d
o
n
I
o
T
,
”
J
o
u
r
n
a
l
o
f
P
h
y
s
i
c
s
C
o
n
f
e
r
e
n
c
e
S
e
r
i
e
s
,
M
a
y
,
2
0
2
0
.
[9
]
S
.
Bit
z
e
r,
P
.
v
a
n
d
e
r
S
m
a
g
t
,
“
Lea
rn
in
g
EM
G
c
o
n
tro
l
o
f
a
ro
b
o
ti
c
h
a
n
d
:
To
wa
rd
s
Ac
ti
v
e
P
r
o
sth
e
se
s,”
IEE
E
In
ter
n
a
t
io
n
a
l
C
o
n
fer
e
n
c
e
o
n
R
o
b
o
ti
c
s a
n
d
Au
t
o
ma
ti
o
n
,
p
p
.
2
8
1
9
-
2
8
2
3
,
2
0
0
6
.
[1
0
]
D.
Riv
a
s,
e
t
a
l.
,
"
c
o
n
tr
o
l
sy
ste
m
fo
r
a
ro
b
o
ti
c
a
rm
with
6
d
e
g
re
e
s
o
f
f
re
e
d
o
m
fo
r
e
d
u
c
a
ti
o
n
sy
ste
m
s,”
6
th
In
ter
n
a
ti
o
n
a
l
Co
n
fer
e
n
c
e
o
n
Au
to
ma
ti
o
n
,
R
o
b
o
ti
c
s a
n
d
A
p
p
li
c
a
ti
o
n
s
,
p
p
.
3
5
8
-
3
6
3
,
2
0
1
5
.
[1
1
]
Y.
P
a
n
,
e
t
a
l.
,
“
Ad
a
p
t
iv
e
C
o
m
m
a
n
d
-
F
i
lt
e
re
d
Ba
c
k
ste
p
p
in
g
C
o
n
tro
l
o
f
Ro
b
o
t
Arm
s
with
Co
m
p
li
a
n
t
Ac
tu
a
to
rs,”
IEE
E
tra
n
sa
c
ti
o
n
s
o
n
c
o
n
tr
o
l
sy
ste
ms
tec
h
n
o
l
o
g
y
,
v
o
l.
2
6
,
n
o
.
3
,
p
p
.
1
1
4
9
-
1
1
5
6
,
2
0
1
7
.
[1
2
]
J.
A.
F
a
rre
ll
a
n
d
M
.
M
.
P
o
ly
c
a
rp
o
u
,
“
Ad
a
p
ti
v
e
A
p
p
r
o
x
ima
ti
o
n
Ba
se
d
Co
n
tr
o
l:
U
n
ify
in
g
Ne
u
ra
l,
F
u
z
z
y
a
n
d
Tra
d
it
io
n
a
l
Ad
a
p
ti
v
e
A
p
p
r
o
x
ima
ti
o
n
A
p
p
r
o
a
c
h
e
s,”
Ho
b
o
k
en
,
NJ
,
USA:
W
il
e
y
,
2
0
0
6
.
[1
3
]
C.
D.
Be
ll
ico
so
,
L
.
R.
Bu
o
n
o
c
o
re
,
V.
Li
p
p
iello
,
B
.
S
icili
a
n
o
,
“
De
sig
n
,
M
o
d
e
li
n
g
a
n
d
Co
n
tro
l
o
f
a
5
-
D
o
F
Li
g
h
t
-
Wei
g
h
t
Ro
b
o
t
Arm
fo
r
Ae
rial
M
a
n
ip
u
lati
o
n
,
”
2
3
r
d
M
e
d
it
e
rr
a
n
e
a
n
C
o
n
fer
e
n
c
e
o
n
C
o
n
tro
l
a
n
d
A
u
to
m
a
ti
o
n
M
ED
,
2
0
1
5
.
[1
4
]
G
.
G
a
o
,
F
.
Li
u
,
H.
S
a
n
,
X.
Wu
,
W.
Wan
g
,
“
Hy
b
rid
Op
ti
m
a
l
Ki
n
e
m
a
ti
c
P
a
ra
m
e
ter
Id
e
n
ti
fica
ti
o
n
f
o
r
a
n
I
n
d
u
str
ial
Ro
b
o
t
Ba
se
d
o
n
BP
NN
-
P
S
O,”
Hin
d
a
wi
,
p
p
1
-
11
,
2
0
1
8
.
[1
5
]
A.
d
e
L
u
c
a
,
B.
S
icili
a
n
o
,
L.
Z
o
ll
o
,
“
P
D
c
o
n
tr
o
l
wi
th
o
n
-
li
n
e
g
ra
v
i
ty
c
o
m
p
e
n
sa
ti
o
n
f
o
r
r
o
b
o
ts
wit
h
e
las
ti
c
jo
i
n
ts:
T
h
e
o
r
y
a
n
d
e
x
p
e
rime
n
ts,”
Au
t
o
ma
t
ica
,
v
o
l.
4
1
,
n
o
.
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8
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[1
9
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2
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3
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sa
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c
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k
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4
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6
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T.
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Evaluation Warning : The document was created with Spire.PDF for Python.
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