Control System Design for an Upper-Limb Rehabilitation Robot

Abstract

Control system implementation is one of the major difficulties in rehabilitation robot design. The purpose of our study is to present newly developed control strategies for an upper-limb rehabilitation robot. The Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the past-stroke patient. Passive and active recovery training have been implemented on the WAM Arm. A fuzzy-based PD position control strategy is proposed for the passive recovery exercise to control the WAM Arm stably and smoothly to stretch the impaired limb to move along predefined trajectories. An adaptive impedance force controller is employed in the active motion mode in which a fuzzy logic regulator is used to adjust the desired impedance between the robot and impaired limb to generate adaptive force in agreement with the change of the impaired limb's muscle strength. In order to evaluate the change of the impaired limb's muscle power, the impaired limb's mechanical impedance parameters as an objective evaluation index is estimated online by using a recursive least-squares algorithm with an adaptive forgetting factor. Experimental results demonstrate the effectiveness and potential of the proposed control strategies.