Impedance Identification and Adaptive Control of Rehabilitation Robot for Upper-Limb Passive Training

Abstract

Rehabilitation robot can assist post-stroke patients during rehabilitation therapy. The movement control of the robot plays an important role in the process of functional recovery training. Owing to the change of the arm impedance of the post-stroke patient in the passive recovery training, the conventional movement control based on PI controller is difficult to produce smooth movement to track the designed trajectory set by the rehabilitation therapist. In this paper, we model the dynamics of post-stroke patient arm as an impedance model, and an adaptive control scheme which consists of an adaptive PI control algorithm and a damp control algorithm is proposed to control the rehabilitation robot moving along predefined trajectories stably and smoothly. An equivalent 2-port circuit of the rehabilitation robot and human arm is built, and passivity theory of circuit is used to analyze the stability and smoothness performance of the robot. A slide least mean square with adaptive window (SLMS-AW) method is presented to online estimate the parameters of the arm impedance model, which is used for adjusting the gains of PI-damp controller. In this paper, the Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the post-stroke patient. Passive recovery training has been implemented on the WAM Arm. Experimental results demonstrate the effectiveness and potential of the proposed adaptive control strategies.