IEEE Robotics & Automation Magazine - March 2023 - 79

d
dt
Passive
Training
0s ≤ t < tf
Xd(t)
tf ≤ t
Active
Training
d
dt
e2,k(t)
+
-
Xd(t)
.
V(t)
FIGURE 4. Block diagram of the proposed control system for the RRDS.
controller parameters of the RRDS are ()x 02m=
04 ,rad
1
y 02m=
i () -
()
, ir=
()
x () =
y () =
v 00 rad/s 63R
{. ,,. }.
, ,{ ,, }, and R2 =
y==diag 64524
diag 0 150 1
The simulation results are as follows:
Figure 5 shows the human-robot uncertain environments
for the x-axis, y-axis, and rotation angle based on the SCN.
The neural network accurately estimates the human-robot
motion environments and lays a foundation for obtaining
accurate tracking training. The trajectory and path tracking
curves in the passive and active direct switching training
process are shown in Figure 6(a)-(d). The RRDS suppresses
the uncertain environments and achieves the switching
training from passive to active at
trajectory errors on each axis realize et 025m
et 025m and et 025rad
1y # () .,
t 8sf = whereas the
1x # () .,
,
1 #i () .. Figure 6(e)-(g)
shows the velocity tracking curves of the RRDS in the x-axis,
y-axis, and rotation angle directions in the passive and active
direct switching training process. It can be observed that the
RRDS velocity is within the specified range of
y #
vt 1 m/s,
x #
()
vt 1 m/s, and ()i #vt .025rad/s in the passive training
()
stage, and as the walking ability of the rehabilitee increases,
the system directly enters the active training stage, and the velocity
decision errors of each axis satisfy
D y #Vt 003 m/s
D x #Vt 003m/s
() ., and DVt #i
() ..
() .,
003rad/s Moreover,
the RRDS has the same safe movement velocity of
the rehabilitee.
To verify the effectiveness of the proposed method for passive
and active direct switching training, a simulation comTABLE
3. Trajectory tracking errors constraints.
| e1x ( t ) |
0.25 m
| e1y ( t ) |
0.25 m
| e1i ( t ) |
0.25 rad
TABLE 2. Velocity decision error constraints.
| Tvx ( t ) |
0.03 m/s
| Tvy ( t ) |
0.03 m/s
| Tvi ( t ) |
0.03 rad/s
,
v 00m/s, v 00m/s,
parison was performed with [18], which proposed a random
tracking control method for rehabilitation training robots.
Considering the offset characteristics of the system's center of
gravity and incomplete position output feedback information,
the controller in [18] was used to track the training trajectory
with passive and active direct switching training in this study.
The simulation results are as follows:
TABLE 1. Velocity constraints.
| vx ( t ) |
1 m/s
| vy ( t ) |
1 m/s
| vi ( t ) |
0.25 rad/s
+
Velocity Decision
Vt(t)
R1Sig(e1,k(t))ϖ
SCN
-
zk(t)
ϕ(e1,k(t))
d
dt
+
--
ϕ(zk(t))
R2Sig(zk(t))ϖ
d
dt
Direct
Switch
-
B(θ)Ma
+
RRDS
X(t)
Xd(t)
.
-
e2(t)
-
SCN
e1(t)
-
+
ν(t)
Velocity Constraints
V(t)
KG
d
dt
MARCH 2023 IEEE ROBOTICS & AUTOMATION MAGAZINE
79
"

IEEE Robotics & Automation Magazine - March 2023

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