Passivity and Stability of Human-Robot Interaction Control for Upper-Limb Rehabilitation Robots

Each year, stroke and traumatic brain injury leave millions of survivors with motion control loss, which results in great demand for recovery training. The great labor intensity in traditional human-based therapies has recently boosted the research on rehabilitation robotics. Existing controllers for rehabilitative robotics cannot solve the closed-loop system stability with uncertain nonlinear dynamics and conflicting human-robot interactions. This paper presents a theoretical framework that establishes the passivity of the closed-loop upper-limb rehabilitative robotic systems and allows rigorous stability analysis of human-robot interaction. Position-dependent stiffness and position-dependent desired trajectory are employed to resolve the possible conflicts in motions between patient and robot. The proposed method also realizes the "assist-as-needed" strategy. In addition, it 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. While guaranteeing these properties, the proposed controller allows parameter adjustment to provide flexibility for therapists to adjust and fine tune depending on the conditions of the patients and the progress of their recovery. Simulation and experiment results are presented to illustrate the performance of the method.