Industrial Robotics Theory Modelling and Control Part 3 pot
Industrial Robotics Theory Modelling and Control Part 3 pot
... .
)0,0,0],M,M,M,M([MQM
34 333 231 433
><><==
(96)
where,
4 232
MM = ,
433 3
MM = and
4 434
MM = .
)cd,0,sd],M,M,M,M([MQM
232 324 232 22 132 2
>−<><==
(97)
where,
118 Industrial Robotics: Theory, Modelling ... +
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uuuuuu
13 1 1 2 3 2 1
rdu ae u de e u ae e e u
θαθα θαθαθ
++
31 1 1 32 12 31 1 1 32 12 33...
Industrial Robotics Theory Modelling and Control Part 7 pot
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Industrial Robotics Theory Modelling and Control Part 12 pot
... knowledge base.
696 Industrial Robotics: Theory, Modelling and Control
The fusion of the two parts, position control and the computation of the de-
sired path, yields control from image data.
This ... as joint displacements are sent through
662 Industrial Robotics: Theory, Modelling and Control
local CPU and can work on behalf of its remote counterpart showing r...
Industrial Robotics Theory Modelling and Control Part 1 ppt
... [Holland, 20 03] .
6 Industrial Robotics: Theory, Modelling and Control
Figure 5. EPIC architecture [Meyer & Kieras, 1997].
3. Multiagent Systems
3. 1 Multiagent Systems
In robotics, the term ... process sensory information and to control action pur-
28 Industrial Robotics: Theory, Modelling and Control
purple; toy type=Barney doll, Lego). ISAC was given tas...
Industrial Robotics Theory Modelling and Control Part 4 ppt
... already described in equation ( 13) , and the ex-
panded forms of the (15) and (16) are given respectively as follows;
()()
()( )
12 32 232 23 32 232 23
33 2 33 2 2 33 2 33 2 2
ˆˆ ˆ ˆ
ˆˆˆˆ
ˆˆˆ ˆ
ˆˆˆˆ
orr
rr
na ...
(
1o
n
,
1o
m
,
3r
n
,
3r
m
), (
1o
n
,
1o
m
,
3r
n
,
3r
m−
), (
1o
n−
,
1o
m
,
3r
n
,
3r
m
) and (
1o
n−
,
1o
m
,
3r
n ,
3r
m− ).
202 Industrial Robotics: Theory...
Industrial Robotics Theory Modelling and Control Part 5 ppt
...
KS
T
0
sph
1
()
()
»
»
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»
¼
º
«
«
«
«
¬
ª
−−
+−
+
1000
0
232 232 3
232 1 231 1 231
232 1 231 1 231
gcfscs
gsfcsssccs
gsfccscscc
2
()
()
»
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»
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¼
º
«
«
«
«
¬
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+
−+−−+
+++−−
1000
32 232 232
31 3212 131 3212 131 321
31 3212 131 3212 131 321
gssfsssccs
gccscsfcsccscsssscccs
gcssccfcccssccscssccc
3
()
()
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−+−−−+
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3...
Industrial Robotics Theory Modelling and Control Part 6 ppt
... Neuro-Fuzzy Adaptive Modelling and Control, Prentice-Hall,
UK.
31 4 Industrial Robotics: Theory, Modelling and Control
The output variable of this unit is an acceleration command
j
vΔ
, and can be
considered ... some recent results
33 2 Industrial Robotics: Theory, Modelling and Control
Kuipers, B. & Levitt, T. (1988). Navigation and mapping in large sc...
Industrial Robotics Theory Modelling and Control Part 8 pdf
... 15(b). Control signals for different error fuzzy set definitions.
454 Industrial Robotics: Theory, Modelling and Control
CʌıdtYʌ
ˆ
T1
+Γ+Γ=Γ
³
−
( 83)
In Equation ( 83) ,
π
ˆ
and DZ are unknown and ... control law in the known
parameter case such as , q
r
, and K. It is assumed that the parameter error is
unknown such that
(t)==
~
( 23)
432 Industrial Roboti...
Industrial Robotics Theory Modelling and Control Part 10 pdf
... 1990; Khalil & Dombre, 2002) and in (Grotjahn &
538 Industrial Robotics: Theory, Modelling and Control
neglected like it is almost always assumed in control application for such ro-
bots ... control.
542 Industrial Robotics: Theory, Modelling and Control
Figure 3. Accuracy validation of identified model of the KUKA KR15: Torques for ISO-
92 83 trajector...
Industrial Robotics Theory Modelling and Control Part 11 ppt
...
x
y
yx
zzyx
C
C
CCa
CDCCaa
3
3
1
2
3
2
33 1
2
33
2
3
2
3
2
32
2
31
1
2
tan
2
)(
cos
−−
+
+
−+++−
=
θ
(5)
Where,
PzC
aPyC
aPxC
z
y
x
=
++=
++=
3
133 3
133 3
)120sin(
)120cos(
θ
θ
Substitute point D1into Equation (1)
2
31
2
3
2
3
2
12
2
1 133 331 1
)()sincos(2
zzyxyx
CDCCaaCCa ... -1 ,36 E+00 -0,0 038 5 63
Z -2,98E-17 -1,12E- 03 -0,001124 931
afa 5 ,37 E- 03 5 ,3...
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