An adaptive controller for human lower extremity exoskeleton robot

Abstract

Exoskeleton robot-assisted physical therapy has recently been studied extensively due to its proven effectiveness in providing different forms of physical therapy at any stage of physical recovery. The efficacy of robot-assisted physical therapy depends on the maneuverability of the robot during the rehabilitation application. Robot dynamics is inherently nonlinear. Often, robot control algorithms are developed based on an approximate robot dynamic model which leads to system instability and tracking errors. Accurately determining a rehabilitation robot's payload (human limb masses and inertial properties) is frequently impractical. An adaptive control scheme can handle modeling errors very efficiently. In this paper, a 7 degrees of freedom (DOF) human lower extremity dynamic model was developed using the Newton Euler’s method. To simulate joint friction, a realistic friction model is included. A direct adaptive controller is designed so that the robot can follow the prescribed trajectory with high speed and accuracy. A total of 31 model parameters were considered for adaption. To ensure system stability, the controller's adaptive gains are determined based on the Lyapunov stability approach. Simulation results show excellent tracking performance of the developed controller in the presence of joint friction.