A
Adaptive methods,
Admittance, ,
Arbitration on message priority,
see AMP
Asymptotic stability, , , ,
13,
77,
84,
92,
99,
125,
127,
146,
147semiglobal,
13,
17,
33,
41,
44,
81,
90,
96,
106,
107,
139,
158
B
Baum–Welch algorithm,
184,
185
Boundary layer system,
59,
60
C
Carrier sense multiple access,
see CSMA
Closed-loop system, ,
15–18,
23,
25,
31–33,
37,
38,
44,
45,
68,
72,
84,
85,
88,
92,
93,
96,
97,
99–101,
128,
130,
131,
141,
143,
144,
147,
165–168
Collision detection,
see CD
Contact forces, ,
10,
159
Continuous interval,
61,
63
Control,
43,
195cooperative,
exoskeleton robots,
linear admittance PID,
143linear PID,
22,
28,
33,
35,
37,
44,
90,
92,
144,
146linear stable PID,
36,
143neural PD,
65,
72,
81,
84,
99,
104,
106,
125,
126,
129,
135,
138,
156,
158neural PID,
81,
85,
88,
89,
91,
92,
99,
100,
102–104,
106,
107,
145,
147neural PID tracking,
96,
105parallel neural sliding mode PD,
129,
135PD, ,
7–9,
15–17,
22,
26,
58,
67,
79,
80,
114,
123,
124,
127,
154,
167,
170,
173PID,
5–8,
10,
14,
17,
22,
25,
29,
33,
42,
144task space PD,
Controller area network,
see CAN
Controllers,
23,
77,
78,
90,
133,
137,
138,
154,
155,
157–159,
174nonlinear PD, , ,
D
Degrees-of-freedom,
see DoF
Dynamic time warping,
see DTW
Dynamics, , ,
13,
16,
35,
55,
78,
111,
125,
142,
159,
175,
195robot,
17,
22,
25,
55,
82,
150,
154,
155,
164,
167
E
End-effector, ,
35,
36,
43,
51,
52,
140,
141,
143,
145,
150,
152,
159
Equilibrium,
18,
28,
38,
44,
45,
85,
92,
93,
97,
100,
144
Error,
observer,
55,
57,
61,
64,
72,
73,
77,
112,
113,
115,
116regulation, , ,
15–17,
36,
83,
89,
104,
125,
143,
145steady-state, , ,
16,
22,
28,
104,
134,
135,
137,
138,
156tracking, , ,
15,
58,
61,
66,
68,
71,
81,
96,
101,
115,
116,
119,
125,
126,
129,
130,
132,
136–138
Exoskeleton, , ,
10,
30,
31,
36,
43,
49,
101,
143,
152,
195,
198,
200–202
F
Frequency domain methods,
Friction, , ,
16,
55,
66,
70–72,
75,
78–81,
98,
104,
106,
109,
111,
120,
121,
124,
135,
156
Functions,
Gaussian,
71,
78,
82,
83,
98,
103,
105,
116,
120,
145Lyapunov,
19,
38,
39,
42,
45,
61,
64,
73,
86,
89,
93,
97,
100,
115,
128,
131,
148,
155,
166,
167
G
Gains,
derivative, ,
17,
22,
33,
37,
83,
102,
104,
142,
143PID, , ,
25,
27,
28,
42,
48,
95,
103,
145
Gradient descent, ,
Gravity,
16,
17,
25,
55,
66,
70,
71,
75,
78–80,
98,
104,
106,
109,
111,
120,
121,
124,
135,
156
H
Hidden Markov model,
see HMM
Human machine interface,
see HMI
Human–machine integration, , , ,
10
I
Intelligent methods,
J
Joint space, , ,
35,
36,
43,
102,
144,
159,
160,
175,
180
K
Kinematics, ,
142,
159,
195inverse,
10,
11,
36,
139,
144,
159,
160,
174,
175,
186,
188,
192
L
Learning from demonstration,
see LfD
LfD, ,
Linear-in-the-parameter net,
82,
98,
145
M
Manipulators, ,
75,
83,
140,
141industrial,
robot,
23,
24,
33,
35,
37,
55,
57,
63,
81,
106,
125,
129robotic, ,
Matrix,
Coriolis,
10,
13,
14,
36,
55,
56,
82,
109,
125,
159,
164,
175inertia,
13,
14,
35,
36,
50,
55,
81,
82,
102,
125,
140,
159,
164,
175
Mechanical impedance,
140,
141
Model-based analytical tuning,
N
Neural compensation,
71,
127
Neural compensator, ,
55,
67,
71,
85,
96,
99,
104,
106,
147,
156,
158
Neural identification,
Neural networks, , ,
55,
65,
67,
82,
84,
92,
99,
105,
125,
126,
129,
130,
145,
146
Neural networks compensation,
70,
79
O
Observer,
55,
65,
72,
77,
78,
112,
113high-gain, ,
55,
57,
58,
61,
63–65,
67,
72,
76,
77,
80,
109,
111–113,
124model-based,
model-free,
Optimization methods,
Origin,
18,
21,
38,
41,
61,
85,
87,
88,
97,
101,
149
P
Parameters, , ,
26,
30,
35,
42,
120,
156,
158,
166,
170,
173
PbD,
PID tuning via neural net,
104
Positions, , , , ,
15,
35,
43,
77,
133,
135,
140,
150desired,
14,
36,
51,
58,
67,
71,
77,
109,
121,
125,
141,
143,
145
Probability distribution,
180,
181
Programming by demonstration,
see PbD
Proportional-derivative,
see PD
Proportional-integral-derivative,
see PID
R
Robot parameters,
75,
133
Robot trajectory generation, ,
180
Robots,
14,
22,
102,
109,
177,
187,
190humanoid,
surgical,
UCSC 7-DoF exoskeleton,
30upper limb exoskeleton,
29wearable, ,
S
Sliding mode control,
see SMC
Stability,
23,
37,
55,
80,
104,
106,
120,
126,
130,
174
Stability analysis, ,
14,
22,
71,
85,
88,
97,
99,
114,
138,
147,
170
T
Task space, , ,
35–37,
43,
53,
143–145,
158,
160,
161,
180,
187–191,
193neural PID control in,
145,
146
Time constant, derivative,
28,
31,
143
Torques,
10,
17,
51,
52,
105,
141,
150,
151,
159,
160,
162,
164,
171,
172
Trajectories,
10,
52,
139,
175,
177,
181,
182,
185,
190,
192
U
Upper limb exoskeleton,
13,
33,
48,
52,
101,
109,
120,
141,
189,
195,
197
V
Vectors, ,
35,
57,
81,
110,
140,
149,
154,
159,
164,
165
Velocities, , ,
25,
44,
58,
67,
71,
72,
77,
80,
105,
111,
112,
141,
177
W
Weights, , ,
31,
55,
70,
82,
85,
93,
98,
100,
126,
127,
130,
145,
147
Z
Ziegler–Nichols tuning,
27
Ziegler–Nichols methods, ,
26,
27,
31