Adaptive Admittance Control of an Upper Extremity Rehabilitation Robot With Neural-Network-Based Disturbance Observer

Qingcong Wu,Hongtao Wu,Bai Chen

doi:10.1109/access.2019.2938566

Abstract

Robot-assisted rehabilitation therapy has become an important technology applied to recover the motor functions of disabled individuals. In the present paper, an adaptive admittance control strategy combined with a neural-network-based disturbance observer (AACNDO) is developed for a therapeutic robot to provide upper extremity movement assistance. Firstly, a comprehensive overview of the robot hardware and real-time control system is introduced. Then, the dynamics-based adaptive admittance controller is designed to improve human-robot interaction compliance and induce the active participation of the patients during rehabilitation training. A disturbance observer with a radial basis function network is designed to guarantee the control performance with external uncertainties and dynamics error. Besides, an adaption law is integrated into the admittance model to adjust the interaction compliance in different working areas based on the motion intention and recovery phase of the patient. Further experimental investigations, including sinusoidal trajectory tracking experiments, circular trajectory tracking experiments with admittance adjustment, and intention-based resistive training experiments, are conducted by three volunteers. Finally, the experimental results validate the feasibility and effectiveness of the rehabilitation robot and AACNDO scheme in providing patient-passive and patient-cooperative rehabilitation training.

Highlights

With the acceleration of aging process in human society, more and more individuals have to suffer from the nervous and muscular diseases causing long-time extremity disability, such as stroke, spinal cord injury or orthopedic injury
The movement space of end-effector around the predefined trajectory can be divided into three position-error-based working areas with different compliance levels, i.e., large admittance model area (LAA), medium admittance model area (MAA), and small admittance model area (SAA), as shown in Fig
The state machine of AACNDO controller was switched to the patient-cooperative training mode, while the desired trajectory was fixed to the original location of end-effector

Summary

INTRODUCTION

With the acceleration of aging process in human society, more and more individuals have to suffer from the nervous and muscular diseases causing long-time extremity disability, such as stroke, spinal cord injury or orthopedic injury. Taking the above into consideration, the main innovation of this work is to develop a new adaptive admittance control scheme for an upper limb rehabilitation robot, which can assist the disabled patients in performing both passive and cooperative rehabilitation trainings. M ∈ R2×2 represents the Cartesian space inertia matrix; V ∈ R2×2 denotes the Cartesian space Coriolis/centripetal; Ff (t) ∈ R2 is the resultant friction vector from ball screw transmission mechanisms; Du(t) ∈ R2 represents the lumped effects of uncertainties including external disturbances and modeling errors; F(t) ∈ R2 represents the Cartesian space control force vector; (t) ∈ R2 denotes the human-robot interaction force vector along the horizontal plane; τ (t) ∈ R2 is the output driving torques of servo motors; η1, η2, σ1, and σ2 represent the transmission efficiencies and screw leads of the ball screw mechanisms, respectively. The system stability demonstration is completed, and the unknown disturbance and dynamics modeling error can by compensated by using the proposed controller

ADAPTION LAW OF ADMITTANCE MODEL

EXPERIMENTAL VERIFICATION

CONCLUSION