m » ¶ $ ¹ : t , n x y ³ ã Õ ç w ^ ° a qâ ^ u ° a t b z · y m » ¶ $ ¹ : t , n x y ³...
TRANSCRIPT
(A Study on Evaluations of the Behavior and Operational Skills
for an Excavator Based on the Control Engineering Approach)
D160613
2018 3
1 1
1.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6
2 8
2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8
2.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11
2.2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.1 . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.3.3 . . . . . . . 22
2.3.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28
2.4.1 PQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.4.2 PID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.4.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.5.1 PQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.5.2 PID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36
2.5.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.6 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
344
3.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
i
3.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
3.2.1 . . . . . . . . . . . . . . . . . . . . 46
3.2.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.3.1 . . . . . . . . . . . . . . . . . . . . . . 52
3.3.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.4.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56
3.4.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.4.3 L + T . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
463
4.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2 CMAC-PID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
4.2.2 CMAC-PID . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
4.2.3 CMAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.3.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.3.2 CMAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.4.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.4.2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
5 90
94
98
100
102
ii
1
1.1
2017 55
3 29 1 [1]
Fig.1.1
55 10
Fig. 1.1: Number of workers for constriction fields [1]
1
[2] [3] 2020
i-Construction
[4]
3D
ICT(Information and Communication Technolory
[5, 6]
i-Construction
3K
Society5.0
[7] ,
[8, 9]
2
3 ICT [10, 11]
ICT
[12]
[13]-[16]
[17]
[18] [19]
3
AI
(i)
(ii)
(iii)
1.2
James Watt
[20]-[22]
[23]
Fig.1.2
4
Fig. 1.2: Block diagram of block diagram
Fig. 1.3: Basic idea of study
[24]-[27]
Fig.1.3
Fig.1.4
5
Fig. 1.4: Hydraulic excavator
1.3
2
3
6
4
PID
5
7
2
2.1
[8, 9]
[28] PQ
PQ
8
PID
[29]
PID
[30] [31].
(1)
(2)
(3)
(4)
(5)
9
[32]
PID
(GMVC:Generalized Minimum Variance Control)
[33] [34]
PQ PID
10
Fig. 2.1: Typical hydraulic circuit for excavator
2.2
T (t)
Qin(t)
Fig.2.1
11
Fig. 2.2: Schematic of a hydraulic system for a swing motion
2.2.1
(1)
0
Fig.2.2
TI(t) ω(t)
TI(t) = IΔω(t)
Ts(2.1)
12
Δ Δ = 1− z−1 TI(t) I
Ts ω(t)
I TI(t) Δω(t)
TI(t)
TI(t) T (t)
2.2.2
T (t) Q(t)
P(t) N η
T (t) = ηP(t)Q(t)
2πN(2.2)
T (t) = CPQP(t)Q(t) (2.3)
CPQ Q(t) Qin(t)
AQ(z−1)Q(t) = z−(k+1)BQ(z−1)Qin(t) + ξ(t) (2.4)
13
⎧⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎩AQ(z−1) := 1 + a1Qz−1
BQ(z−1) := b0Q + b1Qz−1 + · · · + bmQz−m
(2.5)
k z−1 m BQ(z) ξ(t) 0
σ2 aiQ biQ
2.2.3
Pr
Pr
P(t) = Pr (2.6)
(2.6) (2.3)
T (t) = CPQ · Pr · Q(t) (2.7)
(2.4) (2.7)
T (t) = CPQ · Prz−(k+1)BQ(z−1)Qin(t) + ξ(t)
AQ(z−1)(2.8)
14
T (t) Qin(t)
AT (z−1)T (t) = z−(k+1)BT (z−1)Qin(t) + ξ(t) (2.9)
⎧⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎩AT (z−1) := 1 + a1T z−1
BT (z−1) := b0T + b1T z−1 + · · · + bmT z−m
(2.10)
aiT biT
2.2.4
Q(t) P(t) Q(t)
P(t)
Q(t) P(t)
ω(t) Qm(t)
Qm(t) =qm
2πω(t) (2.11)
qm P(t)
15
TI(t)
TI(t) =qm
2πP(t) (2.12)
Q(t)
Q(t) Qm(t)
P(t) =Ts
Δκ(Q(t) − Qm(t)) (2.13)
κ P(t)
Q(t) (2.1) (2.11) (2.12)
Qm(t) =1
Δ
(qm
2π
)2 Ts
IP(t) (2.14)
(2.14) (2.13)
P(t) =1
ΔTsκQ(t) − 1
Δ2κ(qm
2π
)2 T 2s
IP(t)) (2.15)
P(t) Q(t)
(K1 + Δ2)P(t) = K2ΔQ(t) (2.16)
K1 = κ(qm
2π
)2 Ts2
I(2.17)
K2 = κTs (2.18)
16
Q(t) P(t)
P(t) =K2
K1 + Δ2ΔQ(t) (2.19)
2.2.5
P(t) Pr
(2.19) P(t) Q(t)
(2.19) (2.4)
P(t) =K2
K1 + Δ2Δ
(z−(k+1)BQ(z−1)
AQ(z−1)Qin(t) +
ξ(t)AQ(z−1)
)(2.20)
(2.3) (2.4)
T (t) = CPQP(t)(z−(k+1)BQ(z−1)
AQ(z−1)Qin(t) +
ξ(t)AQ(z−1)
)(2.21)
(2.21) (2.20)
AT (z−1)T (t) = Δ(z−(k+1)BT (z−1)Qin(t) + ξ(t)
)2(2.22)
AT (z−1) = A2Q(z−1)(K1 + Δ
2) (2.23)
BT (z−1) = (CPQK2)12 BQ(z−1) (2.24)
Qin(t) Qin ΔQin(t)
17
AT (z−1)T (t) = Δz−(k+1)BT (z−1)ΔQin(t) + ξ(t) (2.25)
⎧⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎩AT (z−1) := 1 + a′1T z−1 + · · · + a′nT z−n
BT (z−1) := b′0T + b′1T z−1 + · · · + b′mT z−m
(2.26)
n AT (z) a′iT b′iT
Qmax
Qmax
T (t)
ΔQ(t) = ΔQin(t) = 0 (2.27)
P(t)
0 (2.6) (2.19)
18
Fig. 2.3: Block diagram of an event-driven controller
2.3
[30]
(2.9) (2.25)
[32]
Fig.2.3
PID
19
x(t)
P(t) u(t) Qin(t)
y(t) T (t)
2.3.1
(2.9)
PID
Δu(t) = KI(t)e(t) − KP(t)Δy(t) − KD(t)Δ2y(t) (2.28)
e(t) := r(t) − y(t) (2.29)
e(t) r(t)
(2.25)
PID
20
PID
Δ2u(t) = KI(t)Δe(t) + KII(t)e(t) − KP(t)Δ2y(t) − KD(t)Δ3y(t) (2.30)
KP(t),KI(t),KII(t),KD(t)
(2.27)
Δu(t) = 0 (2.31)
(2.30)
u(t)
(2.31)
21
2.3.2
ET
P(t)
ET1
ET2 P(t) 100% P1
ET3 P(t) 100% P2
ET4 P(t) 100% P3
P1 P2
(2.25) P3
P1
P(t) 100%
2.3.3
ET
PID ET P(t)
22
KP(t) =
⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
KP1(if ET1 ∼ ET2)
KP2− KP1
P2 − P1
(P(t) − P1) + KP1(if ET2 ∼ ET3)
KP2(if ET3 ∼ ET4)
0 (otherwise)
(2.32)
KI(t) =
⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
KI1(if ET1 ∼ ET2)
KI2− KI1
P2 − P1
(P(t) − P1) + KI1(if ET2 ∼ ET3)
KI2(if ET3 ∼ ET4)
0 (otherwise)
(2.33)
KII(t) =
⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
KII2
P2 − P1
(P(t) − P1) (if ET2 ∼ ET3)
KII2(if ET3 ∼ ET4)
0 (otherwise)
(2.34)
KD(t) =
⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
KD1(if ET1 ∼ ET2)
KD2− KD1
P2 − P1
(P(t) − P1) + KD1(if ET2 ∼ ET3)
KD2(if ET3 ∼ ET4)
0 (otherwise)
(2.35)
KP1,KI1,KD1
KP2,KI2,KII2,KD2
23
2.3.4
KP1KI1
KD1KP2
KI2KII2
KD2
KP1KI1
KD1KP2
KI2KII2
KD2
GMVC
PID [34]
GMVC PID
GMVC PID
A(z−1)y(t) = Δid z−(k+1)B(z−1)u(t) +ξ(t)Δ
(2.36)
A(z−1) := 1 + a1z−1 + a2z−2
B(z−1) := b0 + b1z−1 + · · · + bmz−m
⎫⎪⎪⎪⎪⎪⎪⎬⎪⎪⎪⎪⎪⎪⎭(2.37)
(2.37) ai bi id 0 1
id = 0 id = 1
J
J := E[φ2(t + k + 1)] (2.38)
24
φ2(t + k + 1)
φ(t + k + 1) := P(z−1)y(t + k + 1) + λΔ(id+1)u(t) − P(1)r(t) (2.39)
λ
Diophantine
P(z−1) = Δ(id+1)E(z−1)A(z−1) + z−(k+1)F(z−1) (2.40)
⎧⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎩E(z−1) := 1 + e1z−1 + · · · + ekz−k
F(z−1) := f0 + f1z−1 + f2z2 + f3z3 · id
(2.41)
E(z−1) F(z−1) Δ(id+1)A(z−1) P(z−1)
P(z−1)
[36]
Pl(z−1) = 1 + p1z−1 + p2z−2 (2.42)
p1 = −2 exp(− ρ
2μ
)cos
( √4μ−1
2μρ
)
p2 = exp(ρ
μ
)
ρ = Tsσ
μ = 0.25(1 − δ) + 0.51δ
⎫⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎬⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎭
(2.43)
σ δ
σ 60
δ 0 ≤ δ ≤ 2.0
25
δ = 0 Binomial δ = 1.0 Butterworth
(2.36) (2.39) (2.40) t k + 1
φ(t + k + 1|t) := F(z−1)y(t) +(E(z−1)B(z−1) + λ
)Δ(id+1)u(t) − P(1)r(t) + E(z−1)ξ(t + k + 1) (2.44)
t
φ̂(t + k + 1|t) := F(z−1)y(t) +(G(z−1) + λ
)Δ(id+1)u(t) − P(1)r(t) (2.45)
G(z−1)
G(z−1) := E(z−1)B(z−1) (2.46)
= g0 + g1z−1 + + · · · + gngz−ng (2.47)
(ng = m + k)
ng G(z−1) (2.44) (2.45)
φ(t + k + 1|t) = φ̂(t + k + 1|t) + E(z−1)ξ(t + k + 1) (2.48)
φ̂(t + k + 1|t) = 0 (2.49)
(2.38)
26
(2.45) (2.49) GMVC
Δ(id+1)u(t) =Pl(1)
G(z−1) + λr(t) − F(z−1)
G(z−1) + λy(t) (2.50)
u(t) y(t) (2.50)
fi gi
(2.50) id = 0 (2.28)
KP1= − f1 + 2 f2
G(1) + λ(2.51)
KI1=
f0 + f1 + f2
G(1) + λ(2.52)
KD1=
f2
G(1) + λ(2.53)
(2.50) id = 1 (2.30)
KP2=
f2 + 3 f3
G(1) + λ(2.54)
KI2= − f1 + 2 f2 + 3 f3
G(1) + λ(2.55)
KII2=
f0 + f1 + f2 + f3
G(1) + λ(2.56)
KD2= − f3
G(1) + λ(2.57)
27
2.4
2.4.1 PQ
100%
PQ Fig.2.4 Fig.2.5
2.4.2 PID
PID Fig.2.6
PID
KP = 3.00 × 10−6, KI = 1.10 × 10−7, KD = 1.00 × 10−7 (2.58)
PID
28
0 2 4 6 8 100
100
200
T(t)
[%] T(t)
r(t)
0 2 4 6 8 100
50
100
Q(t)
[%]
0 2 4 6 8 100
50
100
P(t)
[%]
Fig. 2.4: Control result of PQ controller with ordinary
29
0 2 4 6 8 100
100
200
T(t)
[%] T(t)
r(t)
0 2 4 6 8 100
50
100
Q(t)
[%]
0 2 4 6 8 100
50
100
P(t)
[%]
Fig. 2.5: Control result of PQ controller with cold condition
30
0 2 4 6 8 100
50
100
150
T(t)
[%] T(t)
r(t)
0 2 4 6 8 100
50
100
Q(t)
[%]
0 2 4 6 8 100
50
100
P(t)
[%]
Fig. 2.6: Control result of PID controller with cold condition
31
Table 2.1: PID gains for event-driven controller
Gain RV ON RV OFF
KP 1.97 × 10−6 1.95 × 10−6
KI 3.68 × 10−7 1.10 × 10−7
KII 0 3.89 × 10−10
KD 1.61 × 10−6 2.93 × 10−7
2.4.3
PID GMVC PID
(2.6) (2.9)
–(2.10) PID
2.2[s] 3.8[s]
0.1[s]
PID
Table 2.1
(2.19) ΔQ(t)
P(t) (2.25) –(2.26) 3.9[s] 4.3[s]
1[s]
PID Table 2.1
λ = 0.01
Pr 100% Fig.2.6 P1 P2 P3 97% 70% 40%
Fig.2.7 ET
32
Fig.2.8
PID
PID
33
0 2 4 6 8 100
50
100
150
T(t)
[%] T(t)
r(t)
0 2 4 6 8 100
50
100
Q(t)
[%]
0 2 4 6 8 100
50
100
150
P(t)
[%] P1
P3
P2
ET1 ET2
ET4ET3
Fig. 2.7: Control result of event-driven controller with cold condition
34
0 2 4 6 8 101.9
1.95
2
0 2 4 6 8 100
0.2
0.4
0 2 4 6 8 100
2
410-4
0 2 4 6 8 100
1
2
Fig. 2.8: Control parameter trajectories corresponding to Fig.2.7
35
2.5
2.5.1 PQ
Fig.1.4 20t SK200-9
PQ Fig.2.9
1
2.5.2 PID
PID Fig.2.10
PID
KP = 5.00 × 10−2, KI = 2.50 × 10−3, KD = 6.00 × 10−3 (2.59)
PID
36
0 1 2 3 4 5 6 7 8 9t [s]
0
100
200
T(t)
[%]
T(t)r(t)
0 1 2 3 4 5 6 7 8 9t [s]
0
50
100
Q(t)
[%]
0 1 2 3 4 5 6 7 8 9t [s]
0
50
100
150
P(t)
[%]
Fig. 2.9: Control result using a PQ controller
37
PID
Fig.2.6 PQ
2.5.3
PID
(2.6) (2.26)
P(t)
(2.6)
Pr
PID
0.8[s] 1.2[s]
0.1[s]
λ = 1
(2.19) ΔQ(t)
P(t) 3.9[s]
6.5[s]
1[s] λ = 0.01
PID
38
0 1 2 3 4 5 6 7 8 9t [s]
0
50
100
150
T(t)
[%]
T(t)r(t)
0 1 2 3 4 5 6 7 8 9t [s]
0
50
100
Q(t)
[%]
0 1 2 3 4 5 6 7 8 9t [s]
0
50
100
150
P(t)
[%]
Fig. 2.10: Control result using a PID controller
39
Table 2.2: PID gains included in the event-driven controller
Gain RV ON RV OFF
KP 5.10 × 10−2 0
KI 2.40 × 10−3 1.90 × 10−3
KII 0 2.00 × 10−6
KD 1.60 × 10−2 3.00 × 10−3
Table 2.2 KP2
Pr 100%
Fig.2.10 P1 P2 P3 83% 60% 40%
Fig.2.11 , Fig.2.12 Fig.2.11
ET
Qmax PID
0.5s
40
0 2 4 6 8t[s]
0
50
100
150
T(t)
[%]
T(t)r(t)
0 2 4 6 8t[s]
0
50
100
Q(t)
[%]
0 2 4 6 8t[s]
0
50
100
150
P(t)
[%]
ET1 ET2
ET3 ET4
P2P1
P3
Fig. 2.11: Control result using the event-driven torque controller
41
0 2 4 6 80
0.05
0.1
0 2 4 6 8
t[s]
0
2
4 10-3
0 2 4 6 80
2
4 10-6
0 2 4 6 80
0.01
0.02
Fig. 2.12: Control parameter trajectories corresponding to Fig.2.11
42
2.6
GMVC
PQ
PID
43
3
3.1
44
[37, 38, 39]
[40, 41]
[42]
[43]
(1)
(2)
(3)
(4)
[44, 45]
3 4 [46, 47]
45
+
+
[48]
+
3.2
3.2.1
(i)
Fig.1.4 20t SK200-9
360
46
3
3
(ii)
[51] PID
PID Ziegler and Nichols
47
[52] Chien, Hrones and Reswick [53]
T L K
G(s) =K
1 + T se−Ls (3.1)
L T
L/T 1
[54, 55]
Fig.3.1
[46]
[47]
48
Fig. 3.1: Scheme of operability evaluation
3.2.2
Fig. 3.2
Fig. 3.3
Fig. 3.4
(3.1) +
Fig. 3.5
Fig. 3.3
49
Fig. 3.2: View of Bucket Motion from Cabin
Fig. 3.3: Block diagram of evaluation for motion of hydraulic excavator
50
Fig. 3.4: Simulator of bucket motion
Fig. 3.5: Block diagram of evaluation for simulation
51
Fig. 3.6: Flow chart of GA
3.3
3.3.1
GA
GA (2) (6)
GA Fig.3.6
(1)
(2)
(3)
(4)
(5)
(6)
GA
••
52
Fig. 3.7: Block diagram of system parameter estimation by GA
Fig. 3.7
(1)
N
T K L
(2)
1 0
J :=1
1 +
n∑t=0
ω(t) {ym(t) − y(t)}2(3.2)
ym(t) y(t)
GA n
53
0 0.5 1 1.5 2 2.5 3 3.5t [s]
0
20
40
60
80
100
120y m
, y [%
]
ymy
63.2%
10%
Fig. 3.8: Weight coefficients of criteria for GA
ω(t)
ω(t) =
⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩
1 (ym(t) > 63.2%)
3 (63.2 ≥ ym(t) ≥ 10%)
5 (ym(t) < 10%)
(3.3)
GA
Fig. 3.8 ym(t)
63.2%
Fig. 3.8 100%
(3)
α%
54
(4)
(3) 2 β%
[49, 50]
Pc = Jmax(Pm, Pn) + |Pm−Pn |4
Pd = Jmax(Pm, Pn) − |Pm−Pn |4
⎫⎪⎪⎪⎪⎪⎪⎬⎪⎪⎪⎪⎪⎪⎭(3.4)
Pc Pd Pm Pn
Jmax(Pm, Pn) Pm, Pn
(5)
γ%
(6)
(2) (5) G
K,T, L
3.3.2
(i) L T
(ii) L T 10
10
55
L–T L + T
(iii) 3 1 5
(iv) GA
3.4
3.4.1
7 Table 3.1
7 T L
Fig. 3.9
3
L T L T
0.4 0.7
56
Table 3.1: Evaluation results of the simulation
No. L T L + T responsiveness
Criterion 0.2 0.25 0.45 3.0
1 0.2 2.0 2.2 2.0
2 0.2 0.1 0.3 4.1
3 0.4 0.5 0.9 2.6
4 0.1 0.5 0.6 3.1
5 0.4 2.0 2.4 1.4
6 0.4 0.2 0.6 3.1
7 0.1 0.125 0.225 4.6
8 0.3 0.15 0.45 3.4
9 0.1 1.0 1.1 2.1
10 0.3 1.5 1.8 1.8
0 0.5 1 1.5 2 2.5 3T [s]
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
L [s
]
2.0
2.6 1.4
4.6
3.4
2.1
1.8
3.0 4.1
3.1
3.1
Evaluation pointCriterionconstant L+T
Fig. 3.9: Simulation result of the responsiveness evaluation on the L-T map
57
3.4.2
5 GA
Table 3.2
5 T L
Fig. 3.10
3
3.4.3 L + T
L T
L T L + T
Fig. 3.11
L + T
S R
S = a log(R) + b (3.5)
S log(R) r Table 3.3
R S
L + T L
T
S
58
Table 3.2: Evaluation results of the excavator operation
No. Estimated Parameters responsiveness
L T L + T
1 0.13 0.14 0.27 4.6
2 0.03 2.60 2.63 1.6
3 0.17 0.58 0.75 2.4
4 0.20 0.27 0.47 3.8
5 0.21 0.18 0.39 4.0
6 0.69 0.27 0.96 1.4
7 0.23 0.32 0.55 3.2
8 0.21 0.27 0.48 3.4
9 0.17 0.21 0.38 4.0
10 0.15 0.17 0.32 4.2
0 0.5 1 1.5 2 2.5 3T [s]
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
L [s
]
4.6
1.6
2.4 3.8
4.0
1.4
3.2 3.4
4.2 3.0
Evaluation pointCriterionconstant L+T
Fig. 3.10: Excavator result of the responsiveness evaluation on the L-T map
59
R Fig. 3.11
[56] S R
60
0 0.5 1 1.5 2 2.5 3L+T [s]
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
5.5
Res
pons
ivin
ess
SimulationExcavatorCriterionSimulation WF
a = -2.72b = 2.51
Fig. 3.11: Result of the responsiveness evaluation on the L+T axis
Table 3.3: Correlation coefficients and P-values of evaluations
Simulation Excavator
r P[%] r P[%]
L 0.39 24.0 0.006 98.5
T -0.93 2.9 × 10−3 -0.72 0.80
L + T -0.97 9.1 × 10−5 -0.88 0.02
61
3.5
GA
62
4
4.1
i-construction [58]
ICT 3
[59] [10, 11]
[60]
63
[61, 62]
PID
CMAC [64, 65, 66]
[67, 68]
CMAC CMAC PID
PID
CMAC
PID
64
4.2 CMAC-PID
CMAC-PID
CMAC-PID
CMAC
CMAC-PID
4.2.1
[63]
Albus [64, 65, 66]
Cerebellar Model Articulation Controller
CMAC
(i) CMAC
CMAC Fig.4.1 CMAC
S →M→A→ P (4.1)
65
�
Fig. 4.1: Structure of cerebellum and CMAC
S M A P
S M
M A P
(4.2)
P = H(S) (4.2)
CMAC
(ii)
[Step1 ] CMAC P̂
[Step2 ] S1 CMAC P1 CMAC
[Step3 ] S1 P̂1
66
[Step4 ]
Wnewh ← Wold
h + gP̂1 − P1
K(4.3)
Woldh Wnew
h K S1
g
g = 1
[Step ] Step2 Step4
(iii) 2 CMAC
Fig.4.2 2 CMAC 2 CMAC CMAC
CMAC 3 4
S (7, 4) M {C,H,K} {b, g, j}
4 3 1 8
4 CMAC 4
8 3 −4/3
(iv) CMAC
CMAC
67
Fig. 4.2: Example of 2 dimensional CMAC model
(1)
(2)
(3)
(4)
CMAC
(1)
BP
BP
CMAC
68
(2)
CMAC
CMAC
CMAC
CMAC
Fig.4.2 S (7, 4) {C,H,K} {b, g, j}
(7, 4) (6, 4) {C,G,K} {b, g, j}
H G
(3)
CMAC
Fig.4.2 S (7, 4) {C,H,K} {b, g, j}
S (7, 4) (6, 4) {C,G,K} {b, g, j}
H G
S (4, 7)
69
Fig. 4.3: Block diagram of skill based CMAC-PID controller
(4)
CMAC
1
1 1
Fig.4.2 1 1
100
CMAC
48
4.2.2 CMAC-PID
CMAC-PID Fig.4.3
u∗(t)
y∗(t) PID CMAC
70
PID
Δu(t) = KI(t)e(t) − KP(t)Δy∗(t) − KD(t)Δ2y∗(t) (4.4)
e(t) := r(t) − y∗(t) (4.5)
KP(t) KI(t) KD(t) PID e∗(t) Δ
Δ = 1 − z−1
PID CMAC WPID,h(t)
KP(t) =K∑
h=1
WP,h(t)
KI(t) =K∑
h=1
WI,h(t) (4.6)
KD(t) =K∑
h=1
WD,h(t)
K CMAC CMAC 3
CMAC e(t), y∗(t) Δy∗(t) WP,h(t)
WI,h(t) WI,h(t)
4.2.3 CMAC
CMAC Fig.4.4
y∗(t) u(t)
71
Fig. 4.4: Human skill leaning by CMAC-PID
u∗(t) u(t) u∗(t)
WnewP,h (t) = Wold
P,h (t) − g(t) ∂J(t)∂KP(t)
1
K
WnewI,h (t) = Wold
I,h (t) − g(t) ∂J(t)∂KI(t)
1
K(4.7)
WnewD,h (t) = Wold
D,h(t) − g(t) ∂J(t)∂KD(t)
1
K
g(t) J(t)
g(t) =1
c + a · exp(−b|u∗(t) − u(t)|) (4.8)
J(t) :=1
2ε(t)2 (4.9)
ε(t) = u∗(t) − u(t) (4.10)
(4.8) a b c
a u∗(t) u(t) c u∗(t)
72
u(t) b u∗(t) u(t)
a (4.7)
∂J(t)∂KP(t)
=∂J(t)∂ε(t)
∂ε(t)∂u(t)
∂u(t)∂KP(t)
= ε(t)Δy∗(t)
∂J(t)∂KI(t)
=∂J(t)∂ε(t)
∂ε(t)∂u(t)
∂u(t)∂KI(t)
= −ε(t)e(t) (4.11)
∂J(t)∂KD(t)
=∂J(t)∂ε(t)
∂ε(t)∂u(t)
∂u(t)∂KD(t)
= ε(t)Δ2y∗(t)
73
Fig. 4.5: Experimental method
4.3
CAMC-PID
4.3.1
Fig.4.5
SK200-9
90◦ 10
0◦ 90◦
y∗(t) u∗(t)
Ts 200ms
74
0 20 40 60 80 100 120 140-1
0
1
u* (t)
0 20 40 60 80 100 120 140t[s]
0
0.5
1
y* (t)
y*
r
Fig. 4.6: Data of novice operation
0 20 40 60 80 100 120 140-1
-0.5
0
0.5
1
u* (t)
0 20 40 60 80 100 120 140t[s]
0
0.5
1
y* (t)
y*
r
Fig. 4.7: Data of professional operation
75
4.3.2 CMAC
y∗(t) u∗(t)
2 2
1 Fig.4.6 Fig.4.7
y∗(t) 1 u∗(t) 1
y∗(t) u∗(t) (4.4) PID
CMAC
(4.7) -(4.11) CMAC
10 10 CMAC Fig.4.8 Fig.4.9 Fig.4.8
Fig.4.9
76
0 20 40 60 80 100 120 140-1
0
1u(
t)u*(t)u(t)
0 20 40 60 80 100 120 1400
0.5
1
y(t)
y*(t)r(t)
0 20 40 60 80 100 120 1402
2.2
2.4
KP(t)
LearningInitial
0 20 40 60 80 100 120 1400
0.20.40.60.8
KI(t)
0 20 40 60 80 100 120 140t[s]
2.3
2.32
2.34
KD
(t)
Fig. 4.8: Learning result for novice operation
77
0 20 40 60 80 100 120 140-1
0
1u(
t)u*(t)u(t)
0 20 40 60 80 100 120 1400
0.5
1
y(t)
y*(t)r(t)
0 20 40 60 80 100 120 1402
2.2
2.4
KP(t)
LearningInitial
0 20 40 60 80 100 120 1400
0.20.40.60.8
KI(t)
0 20 40 60 80 100 120 140t[s]
4.36
4.38
4.4
KD
(t)
Fig. 4.9: Learning result for professional operation
78
4.4
4.4.1
PID Fig.4.8 Fig.4.9
Fig.4.10 Fig.4.11
71 77 52 58
6
PID
[69] PID
PID PID
u(t) = KPe(t) + KI
∫ t
0
e(τ)dτ + KDde(t)
dt(4.12)
(4.12) P e(t)
I
PID KP
KP
KI
KI
KP KD
KD
KD
PID
79
52 53 54 55 56 57 580
0.5
1
u(t)
u*(t)u(t)
52 53 54 55 56 57 58
0
0.5
1
y(t)
y*(t)r(t)
52 53 54 55 56 57 582
2.2
2.4
KP(t)
LearningInitial
52 53 54 55 56 57 580
0.20.40.60.8
KI(t)
52 53 54 55 56 57 58t[s]
2.3
2.32
2.34
KD
(t)
Fig. 4.10: Extracted data of Novice operation
80
71 72 73 74 75 76 770
0.5
1
u(t)
u*(t)u(t)
71 72 73 74 75 76 77
0
0.5
1
y(t)
y*(t)r(t)
71 72 73 74 75 76 772
2.2
2.4
KP(t)
LearningInitial
71 72 73 74 75 76 770
0.20.40.60.8
KI(t)
71 72 73 74 75 76 77t[s]
4.36
4.38
4.4
KD
(t)
Fig. 4.11: Extracted data of professional operation
81
Table 4.1: Behavior of each action of PID controller
(P) (I) (D)
PID
Table 4.1
Fig.4.11
Fig.4.10
PID
PID
56.5
0
0
0 ON/OFF
82
Fig.4.10
4.4.2
Fig.4.10 Fig.4.11 ΔKP(t) ΔKI(t) ΔKD(t)
Fig.4.12 Fig.4.13
Fig.4.14
Fig.4.15
Table 4.2
83
Table 4.2: Standard deviation of ΔKP, ΔKI & ΔKD
Operator Professional 1 Professional 2 Novice 1 Novice 2
ΔKP 0.017 0.019 0.032 0.041
ΔKI 0.051 0.060 0.063 0.075
ΔKD 0.003 0.006 0.009 0.009
84
-0.15 -0.1 -0.05 0 0.05 0.1 0.15
P
0
10
20
30
40
50
Num
ber P
-0.4 -0.2 0 0.2 0.4
I
0
10
20
30
40
Num
ber I
-0.05 0 0.05
D
0
10
20
30
Num
ber D
Fig. 4.12: Distribution chart of ΔKP, ΔKI & ΔKD for professional 1
85
-0.15 -0.1 -0.05 0 0.05 0.1 0.15
P
0
10
20
30
40
50
Num
ber P
-0.4 -0.2 0 0.2 0.4
I
0
10
20
30
40
Num
ber I
-0.05 0 0.05
D
0
10
20
30
Num
ber D
Fig. 4.13: Distribution chart of ΔKP, ΔKI & ΔKD for novice 1
86
-0.15 -0.1 -0.05 0 0.05 0.1 0.15
P
0
10
20
30
40
50
Num
ber P
-0.4 -0.2 0 0.2 0.4
I
0
10
20
30
40
Num
ber I
-0.05 0 0.05
D
0
10
20
30
Num
ber D
Fig. 4.14: Distribution chart of ΔKP, ΔKI & ΔKD for professional 2
87
-0.15 -0.1 -0.05 0 0.05 0.1 0.15
P
0
10
20
30
40
50
Num
ber P
-0.4 -0.2 0 0.2 0.4
I
0
10
20
30
40
Num
ber I
-0.05 0 0.05
D
0
10
20
30
Num
ber D
Fig. 4.15: Distribution chart of ΔKP, ΔKI & ΔKD for novice 2
88
4.5
CMAC-PID
PID
PID PID
PID
PID
89
5
90
2
PID
3
91
4
CMAC-PID
CMAC-PID CMAC
PID
CMAC
PID
ON/OFF
92
93
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Engineering Approach , Proceedings of the 2016 International Conference on Artificial Lifeand Robotics, pp.389-392, Okinawa, January 2016
[2] Takuya Kinoshita, Yasuhito Oshima, Kazushige Koiwai, Toru Yamamoto, Takao Nanjo, Yoichiro
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D.[1] Kazushige Koiwai, Toru Yamamoto, Takao Nanjo, Yoichiro Yamazaki, Yoshiaki Fujimoto:
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[3] Hiromu Imaji, Kazushige Koiwai, Toru Yamamoto, Koji Ueda, Yoichiro Yamazaki: Human
Skill Quantification for Excavator Operation using Random Forest , Proc. of the 2017 Inter-national Conference on Artificial Life and Robotics, pp.437- 440, Miyazaki, January 2017
[4] Yuntao Liao, Kazushige Koiwai, Toru Yamamoto: Design of a Hierarchical-Clustering CMAC-
PID Controller , Proc. of the 2017 International Joint Conference on Neural Networks,
pp.1333-1338, Anchorage, May 2017
[5] Qiuhao Fu, Kazushige Koiwai, Toru Yamamoto: Design of a Data-Oriented Evolutionary
Controller for a Nonlinear System , Proc. of the 6th International Symposium on AdvancedControl of Industrial Processes, pp.406-411, Taipei, May 2017
[6] Yuntao Liao, Takuya Kinoshita, Kazushige Koiwai, Toru Yamamoto: Design of a Performance-
Driven PID Controller for a Nonlinear System , Proc. of the 6th International Symposium onAdvanced Control of Industrial Processes, pp. 529-534, Taipei, May 2017
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Kurita: A Virtual Training System of a Hydraulic Excavator Using a Remote Controlled
Excavator with Augmented Reality , Proc. of the 2017 IEEE/SICE International Symposiumon System Integration, Taiwan, December 2017
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