Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation Robot

Area of Science:

• Robotics
• Rehabilitation Engineering
• Control Systems

Background:

• Adaptive compliance control is essential for rehabilitation robots to adapt to user needs and ensure safety.
• Wearable lower limb rehabilitation robots (WLLRR) require sophisticated control for effective therapy.

Purpose of the Study:

• To present a trajectory deformation-based multi-modal adaptive compliance control strategy (TD-MACCS) for WLWRR.
• To enable smooth switching between human-dominant, robot-dominant, and soft-stop modes during rehabilitation.
• To evaluate the effectiveness of TD-MACCS in robot-assisted walking.

Main Methods:

• Integrated Dynamic Motion Primitives (DMPs) and a trajectory deformation algorithm (TDA) for trajectory planning.
• Developed a multi-modal adaptive regulator to adjust DMPs and TDA parameters.
• Implemented a linear active disturbance rejection controller for low-level position control.

Main Results:

• TD-MACCS demonstrated smooth switching between the three control modes.
• The strategy maintained high trajectory tracking accuracy during robot-assisted walking.
• Increasing the deformation factor's upper bound improved average walking speed (AWS) and reduced root mean square of trajectory deviation (RMSTD).

Conclusions:

• TD-MACCS offers a robust solution for adaptive compliance control in lower limb rehabilitation robots.
• The proposed control strategy enhances user safety and training effectiveness.
• Findings suggest potential for optimizing rehabilitation robot performance through parameter tuning.