RBFN-Based Adaptive Backstepping Sliding Mode Control of an Upper-Limb Exoskeleton With Dynamic Uncertainties

Qingcong Wu,Bai Chen,Hongtao Wu

doi:10.1109/access.2019.2941973

Abstract

In recent decades, robot-assisted rehabilitation therapy has been widely researched and proven to be effective in the motor function recovery of disabled individuals. In this paper, an adaptive backstepping sliding mode control approach combined with neural uncertainty observer is developed for upper-limb exoskeleton, which can help the human operator perform repetitive rehabilitation training. Firstly, a comprehensive overview about the therapeutic exoskeleton hardware and real-time control system is introduced. Then, the neural adaptive backstepping sliding mode controller (NABSMC) is developed based on radial basis function network (RBFN) to improve the trajectory tracking accuracy with external disturbances and dynamics errors. Next, the closed-loop stability of the proposed controller is demonstrated according to the Lyapunov stability theory. Finally, further experimental investigation are conducted on three volunteers to compare the control performance of NABSMC strategy with an optimal backstepping sliding mode control (OBSMC) strategy. The comparison results show that the proposed NABSMC algorithm is capable of achieving higher trajectory tracking accuracy and better step response characteristic during repetitive passive rehabilitation training.

Highlights

Loss of motor ability in upper extremity is a serious problem faced by many individuals, such as stroke patients, elderly people, and the patients with spinal cord or orthopedic injury
Compared with the previous works, the novel contribution of this study focuses on developing a new neural adaptive backstepping sliding mode controller (NABSMC) for an upper extremity exoskeleton, which is capable of assisting the individuals with motor disorder in accurately performing passive repetitive training
In this paper, a novel neural adaptive backstepping sliding mode control strategy has been developed for the upperlimb exoskeleton performing rehabilitation training tasks

Summary

INTRODUCTION

Loss of motor ability in upper extremity is a serious problem faced by many individuals, such as stroke patients, elderly people, and the patients with spinal cord or orthopedic injury. Compared with the previous works, the novel contribution of this study focuses on developing a new neural adaptive backstepping sliding mode controller (NABSMC) for an upper extremity exoskeleton, which is capable of assisting the individuals with motor disorder in accurately performing passive repetitive training. To tackle the dynamic uncertainty and guarantee the control robustness, a feedforward neural adaptive uncertainty observer is developed based on a radial basis function network (RBFN) to estimate and compensate the lumped effects of disturbances and modeling errors. Since the lumped uncertainty of the exoskeleton system is unknown in practical application, it is difficult to determine the boundary of the term M (θ )−1Du. For simplification, we define β = M (θ )−1Du. a neural adaptive uncertainty observer is developed based on radial basis function network (RBFN) to adapt the estimated value of β.

EXPERIMENTAL VERIFICATION

Findings

CONCLUSION