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

Abstract

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. Note to Practitioners—To assist walking for hemiplegic patients, it is crucial to provide comfortable and compliant driving force that can minimize the patients’ voluntary effort. The development of soft exoskeleton is able to realize comfortable wearing experience, and the proposed TSA can output compliant driving force with different training modes and assistance intensities. To address the problem in accurately building the human-exosuit coupled model, this work achieves the adaptive control strategy for patients with various movement capabilities in two steps. Firstly, a mirror adaptive impedance controller is proposed to make the patient’s HL tightly follow the IL’s motion for high safety guarantee performance and training autonomy. Secondly, a reinforcement learning-based LQR framework is constructed to minimize the patient’s voluntary effort by optimizing the prescribed impedance model parameters, which can significantly facilitate assistance efficiency for different patients. The experiments demonstrate that the proposed robotic system can obtain appropriate training modes and efficient walking assistance for human subjects. In the future study, it will be investigated how to assist patients in walking on different terrains with highly stable and adaptive actuation, which will accelerate the application of the proposed exosuit into activities of daily living.