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Page 134 Tong et al. Intell Robot 2024;4:125-45 I http://dx.doi.org/10.20517/ir.2024.08

17.5 12.5

15.0 10.0
7.5

10.0 7.5 5.0
5.0 2.5 0.0
12.5

0.0 2.5 −2.5
−5.0
−2.5
−7.5
0 2 4 6 8 10 0 2 4 6 8 10

15.0
4

12.5 3

10.0 7.5 5.0
2
2.5 1
0
0.0
0 2 4 6 8 10 0 2 4 6 8 10

Figure 6. Joint identification verification results.

proves the high accuracy of the identified parameter values and the good moment prediction effect when
applied to the robot dynamics.

3. ACTIVE TRAINING METHOD BASED ON OUTER LOOP PID
The ideal state for assistive control in exoskeleton robots is to enable the motion of an operator without inter-
ference from the robot, commonly referred to as ”transparent” control [38,39] . In this study, torque sensors are
integrated at the joints of the rehabilitative robot, allowing real-time monitoring of the torque exerted by the
robot. This facilitates the extraction of human-robot interaction forces, subsequently serving as input for the
controller to execute the assistive control of the exoskeleton robot.

In traditional zero-force control, precision dynamic models are required for position-based zero-force con-
trol, imposing high demands on sensor accuracy and exhibiting poor robustness. In torque-based zero-force
control, although external sensors are not needed, and only gravity and friction compensation are required,
uncertainties such as inertial forces and motor transmission losses prevent it from meeting the requirements
of active training.

To address the shortcomings of traditional zero-force control methods in active rehabilitation training, a strat-
egy based on outer-loop PID control is proposed, incorporating joint torque sensors at the robot joints. This
approach utilises external sensors to calculate interaction forces, obtaining compensation values for feedfor-
ward torque through outer-loop control. This not only provides compensation torque for external forces but
also enhances the robustness of robots to inaccuracies in the dynamic model. Through this control method,
the smoothness of the active rehabilitation training process can be improved.

Although traditional outer-loop PID algorithms address the issue of insufficient torque output from the dy-
namic model, they exhibit limitations in compensating for torque due to fixed parameters. This approach
may not be universally suitable for all patients, especially those in different stages of rehabilitation treatment.