Compensation-corrective adaptive control strategy for upper-limb rehabilitation robots

Abstract

Trunk compensation is a common behavior observed in stroke patients during rehabilitation, and it can hinder their recovery outcomes. To address this issue, we developed a new upper-limb rehabilitation robot that takes advantage of both end-effector and exoskeleton robots. Moreover, we propose a compensation-corrective adaptive control (CCAC) strategy, which employs an admittance model and incorporates two estimators. Specifically, the first estimator is designed to assess human intention, allowing for compliant human-robot interaction. The second estimator calculates dynamic assistance that adjusts for trunk compensation, utilizing two virtual forces applied to the hand and shoulder. Based on this novel CCAC strategy, the newly designed robot is capable of assisting upper limb movements and correcting compensatory postures simultaneously. Results indicate a significant reduction in trunk compensation across three types of reaching tasks when the robot provides assistance. Moreover, the CCAC strategy enhances upper-limb motor performance, resulting in reduced position errors and increased shoulder and elbow joint angles. These findings underscore the potential of the proposed CCAC strategy, combined with upper-limb exoskeleton robots, as a promising approach for correcting compensatory postures and optimizing the advantages of robotic stroke rehabilitation.