Integral Concurrent Learning for Admittance Control of a Hybrid Exoskeleton

Abstract

Hybrid exoskeletons are a technology used by people with neurological injuries (e.g., spinal cord injury) for rehabilitation. Hybrid exoskeletons are a hallmark example of physical human-robot interaction because they combine functional electrical stimulation (FES) with a powered exoskeleton. Due to the physical contact between the human and the robot, these exoskeletons must be well controlled to regulate the physical interaction that occurs. In this paper, the exoskeleton is actuated with an integral concurrent learning (ICL) controller to regulate an admittance error system and the human is actuated (via FES) with a robust controller to regulate a position error system. By separating the human dynamics from the hybrid exoskeleton dynamics via the interaction torque, ICL can address structured uncertainties in the exoskeleton dynamics. A Lyapunov-based stability analysis is conducted to prove that the admittance error system is globally exponentially stable under finite excitation. Moreover, the admittance controller is able to ensure bounded position errors, regardless of the FES (i.e., position) controller. A passivity analysis is leveraged to show that the position error system is output feedback passive with respect to the interaction torque, yet is bounded due to the admittance controller.