Development of a Fuzzy-Based Adaptive Admittance Control Architecture for Upper Limb Robotic Rehabilitation

Recently, there has been an increasing number of human patients who suffer from upper limb disorders limiting their motor abilities. One of the possible solutions that gained extensive research interest is the use of end-effector-based robot-aided rehabilitation training setup which showed various advantages compared to the traditional manual rehabilitation therapy. One of the main challenges of these systems is to control the robot's motion to track a desirable trajectory while being affected by either assistive or resistive human forces depending on the patient's recovery state. In this regards, this study aims to develop a Robot Operating System (ROS)-based Fuzzy Logic adaptive control architecture composed of an upper-level admittance control and a lower-level inverse velocity-based joint control. The Fuzzy Logic is used to adapt the admittance control gains considering the trajectory tracking error, the external human applied force magnitude and its direction whether assisting or resisting the robot's motion. The architecture is evaluated in simulation and validated through real-life hardware experiments performing simple circular and other clinical-based trajectories. The results are satisfactory in terms of trajectory tracking accuracy while complying with the assistive forces or being stiff towards the resistive ones.