Mirror Adaptive Impedance Control of Multi-Mode Soft Exoskeleton With Reinforcement Learning

Soft exoskeleton robots (exosuits) have exhibited promising potentials in walking assistance with comfortable wearing experience. In this paper, a twisted string actuator (TSA) is developed and equipped with the exosuit to provide powerful driving force and variable assistance intensity for hemiplegic patients, which provides human-domain and robot-domain training modes for subjects with different movement capabilities. Since the human-exosuit coupling dynamics is difficult to be modeled due to the soft structure of the exosuit and incomplete knowledge of the wearer's performance, accurate control and efficient assistance cannot be guaranteed in current exosuits. By taking advantage of the motion characteristic of hemiplegic patients, a mirror adaptive impedance control is proposed, where the robotic actuation is modulated based on the motion and physiological reference of the healthy limb (HL) as well as the performance of the impaired limb (IL). A linear quadratic regulation (LQR) is formulated to minimize the bilateral trajectory tracking errors and human effort, and the adaptation between the human-domain and robot-domain modes can be realized. A reinforcement learning (RL) algorithm is designed to solve the given LQR problem to optimize the impedance parameters with little information of the human or robot model. The proposed robotic system is validated through experiments to perform its effectiveness and superiority.