Paper Info
Title
Rbfn-Based Adaptive Backstepping Sliding Mode Control Of An Upper-Limb Exoskeleton With Dynamic Uncertainties
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.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2941973
IEEE ACCESS
Keywords
DocType
Volume
Upper-limb exoskeleton, rehabilitation training, adaptive backstepping sliding mode control, neural uncertainty observer, Lyapunov stability theory
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Qingcong Wu1115.02
Bai Chen22014.41
Hong-tao Wu32214.32