Research on rehabilitation robotics has been rising as a substitute to human practice to help neuro-damaged patients to restore impaired or lost functionalities. Most control methods for rehabilitative robotics do not consider the closed-loop system stability in presence of uncertainty of nonlinear dynamics, and conflicting movements between patient and robots. In this paper, we present a theoretical framework which allows rigorous stability analysis of human-robot interaction in rehabilitative robotic system. Position-dependant stiffness and desired trajectory are proposed to resolve the possible conflicts in motions between patient and robot. The proposed method also realizes the assist-as-needed policy and possesses the ability to be customized for operations during different stages of patient recovery. In addition, the proposed controller handles human-robot interactions in such a way that correct movements are encouraged and incorrect ones are suppressed to make the training process more effective. Experimental results are presented to illustrate the performance of the controller.
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