Sensorless Control with Friction and Human Intention Estimation of Exoskeleton Robot for Upperlimb Rehabilitation In this paper, for the control problem of upperlimb rehabilitation using exoskeleton robot, we propose a sensorless control scheme with human intention estimation. In order to implement the active mode rehabilitation therapy, an interactive torque observer using Kalman filter is utilized to obtain human intention on the humanrobot interaction control. Since the accurate friction model is crucial for constructing the observer, a deep neural network (DNN) is proposed in this study to obtain an accurate friction model. Furthermore, a variable admittance model is constructed to derive human intention to the desired motion trajectory. Various experiments have been conducted to verify the performance of the proposed control scheme based on the interactive torque observer.

Performance Analysis of Torquesensorless Assist Control of a Powered Exoskeleton Using Highly Backdrivable Actuators For human motion support, there is an active focus on research and development of powered exoskeletons. A powered exoskeleton is directly fitted to the human body; thus, it is necessary to consider the safety of the humanrobot interaction, including improving the backdrivability. Toward this problem, a powered exoskeleton that uses a highly backdrivable reduction gearbox even at a high reduction ratio is proposed, and a userxe2x80x99s external force can be estimated without using a torque sensor even though the system has very high total gear ratio of 408.57:1. This paper investigated the apparent mechanical impedance of an exoskeleton during assist control execution and examined the performance and operability of the assist control. Finally, a questionnaire of participants was conducted and the mechanical impedance that resulted in a high operability was examined by implementing admittance control.