IEEE Robotics & Automation Magazine - March 2023 - 81

Figure 7(a) shows the path tracking curves of the RRDS
in the passive and active direct switching training system. It
can be observed that the RRDS can achieve stable path tracking
training. Moreover, the controller in [18] could suppress
the impact of the system offset on the tracking performance.
Figure 7(b)-(d) shows the velocity tracking curves of the
RRDS in the passive and active direct switching training process.
It can be observed that the robot in [18] could achieve
velocity tracking in the passive training stage. However, in
the active training stage, the velocity
exhibited a large deviation and could
not adapt to the velocity of the rehabilitee.
Thus, the velocity coordination
could not be realized between the
human and the robot, which threatens
the safety of rehabilitee. This shows
that the controller of [18] cannot
directly switch from passive to active
training and that it can only make the
rehabilitee walk in the passive mode
for rehabilitation exercises.
The controller design method was
10
12
14
16
2
4
6
8
applied to the model proposed in [19]
to illustrate the superiority of the proposed
method for passive and active
direct switching motions. The tracking
curve of the manipulator is the same
as that of the model proposed in this
study, and the simulation results are
as follows.
Figure 8(a) shows the path tracking
curves of the manipulator in the
passive and active direct switching
motions. It can be observed that the
manipulator achieved stable tracking
and that the controller could suppress
uncertain motion environments.
Figure 8(b) and (c) plots the angular
10
12
14
16
2
4
6
8
Passive
Training
1.2
1.4
0.2
0.4
0.6
0.8
1
05 81015
Time (s)
(c)
Response
velocity tracking curves of the joints and for the passive
and active direct switching motions, respectively. It can be
observed that the manipulator velocity was constrained within
the specified range for passive motion. Moreover, for active
motion, the manipulator had the capability of velocity decision.
This shows that the designed controller can contribute to
stable tracking in the manipulator for both passive and active
direct switching motions and that it can improve the safety
and intelligence of the system.
1.2
1.4
Active Training
Direct Switch
x-axis Position (m)
(a)
0.2
0.4
0.6
0.8
1
2 46 810121416 05 81015
Time (s)
(b)
0.2
Active Training
0.1
Passive Training
Direct Switch
-0.1
-0.2
0 58 10
Time (s)
(d)
Reference
FIGURE 7. Path tracking of RRDS and velocity tracking curves of each motion axis [18].
(a) Path tracking. (b) Velocity tracking of the x-axis. (c) Velocity tracking of the y-axis.
(d) Velocity tracking of the angle.
15
Active Training
Passive Training
Direct Switch
Active Training
Passive Training
Direct Switch
1.2
1.4
Active Training
Passive
Training
Active Training
Direct Switch
2 468 10 12 14 16
q1 Position (m)
(a)
0.2
0.4
0.6
0.8
1
58
Time (s)
(b)
Reference
Response
FIGURE 8. Path tracking of manipulator and velocity tracking of the double joint. (a) Path tracking. (b) Velocity tracking of q1.
(c) Velocity tracking of q2.
MARCH 2023 IEEE ROBOTICS & AUTOMATION MAGAZINE
81
10
15
1.2
1.4
Passive Training
Direct Switch
0.2
0.4
0.6
0.8
1
58
Time (s)
(c)
10
15
Active Training
Passive Training
Direct Switch
q2 Position (m)
q1 Velocity (m/s)
y-axis Velocity (m/s)
y-axis Position (m)
q2 Velocity (m/s)
Angular Velocity (rad/s)
x-axis Velocity (m/s)

IEEE Robotics & Automation Magazine - March 2023

Table of Contents for the Digital Edition of IEEE Robotics & Automation Magazine - March 2023

Contents
IEEE Robotics & Automation Magazine - March 2023 - Cover1
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