Human-Robot Interaction Control for Multi-Mode Exosuit with Reinforcement Learning

Soft exoskeleton robots have promising potential in walking assistance with comfortable wearing experience. In this study, an exosuit equipped with a twisted string actuator (TSA) is developed to provide powerful driving force and diverse operating modes for hemiplegic patients in daily life. It is challenging to establish the human-robot coupling dynamic model due to the soft structure of the exosuit and tight coupling, precise control and effective assistance are difficult to guaranteed in current exosuits. Considering the impedance characteristics of human-robot interaction, an adaptive impedance control method based on reinforcement learning (RL) is proposed, where human motion intention is utilized to optimize impedance parameters and adjust the robot's operating mode. A nonlinear disturbance observer is proposed to compensate for the effects of model estimation errors, joint friction, and external disturbances. Experimental verification demonstrates the effectiveness and superiority of the robotic system.